Using Unix system auditing for detecting network intrusions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Christensen, M.J.

1993-03-01

Intrusion Detection Systems (IDSs) are designed to detect actions of individuals who use computer resources without authorization as well as legitimate users who exceed their privileges. This paper describes a novel approach to IDS research, namely a decision aiding approach to intrusion detection. The introduction of a decision tree represents the logical steps necessary to distinguish and identify different types of attacks. This tool, the Intrusion Decision Aiding Tool (IDAT), utilizes IDS-based attack models and standard Unix audit data. Since attacks have certain characteristics and are based on already developed signature attack models, experienced and knowledgeable Unix system administrators knowmore » what to look for in system audit logs to determine if a system has been attacked. Others, however, are usually less able to recognize common signatures of unauthorized access. Users can traverse the tree using available audit data displayed by IDAT and general knowledge they possess to reach a conclusion regarding suspicious activity. IDAT is an easy-to-use window based application that gathers, analyzes, and displays pertinent system data according to Unix attack characteristics. IDAT offers a more practical approach and allows the user to make an informed decision regarding suspicious activity.« less

A Study of User's Acceptance on Situational Mashups in Situational Language Teaching

ERIC Educational Resources Information Center

Huang, Angus F. M.; Yang, Stephen J. H.; Liaw, Shu-Sheng

2012-01-01

Situational awareness and mashups are two key factors influencing the success of situational language teaching. However, traditional situational language teaching cannot smoothly conduct relevant learning activities in changing learning context. This study developed a situational mashups system for detecting users' context and proposed a research…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Christoph, G.G; Jackson, K.A.; Neuman, M.C.

An effective method for detecting computer misuse is the automatic auditing and analysis of on-line user activity. This activity is reflected in the system audit record, by changes in the vulnerability posture of the system configuration, and in other evidence found through active testing of the system. In 1989 we started developing an automatic misuse detection system for the Integrated Computing Network (ICN) at Los Alamos National Laboratory. Since 1990 this system has been operational, monitoring a variety of network systems and services. We call it the Network Anomaly Detection and Intrusion Reporter, or NADIR. During the last year andmore » a half, we expanded NADIR to include processing of audit and activity records for the Cray UNICOS operating system. This new component is called the UNICOS Real-time NADIR, or UNICORN. UNICORN summarizes user activity and system configuration information in statistical profiles. In near real-time, it can compare current activity to historical profiles and test activity against expert rules that express our security policy and define improper or suspicious behavior. It reports suspicious behavior to security auditors and provides tools to aid in follow-up investigations. UNICORN is currently operational on four Crays in Los Alamos` main computing network, the ICN.« less

Physicality and Language Learning

ERIC Educational Resources Information Center

Park, Jaeuk; Seedhouse, Paul; Seedhouse, Rob; Kiaer, Jieun

2016-01-01

The study draws on the digital technology which allows users to be able to learn both linguistic and non-linguistic skills at the same time. Activity recognition as well as wireless sensor technology, similar to a Nintendo Wii, is embedded or attached to the equipment and ingredients, allowing users to detect and evaluate progress as they carry…

Detecting Rumors Through Modeling Information Propagation Networks in a Social Media Environment.

PubMed

Liu, Yang; Xu, Songhua; Tourassi, Georgia

2015-01-01

In the midst of today's pervasive influence of social media content and activities, information credibility has increasingly become a major issue. Accordingly, identifying false information, e.g. rumors circulated in social media environments, attracts expanding research attention and growing interests. Many previous studies have exploited user-independent features for rumor detection. These prior investigations uniformly treat all users relevant to the propagation of a social media message as instances of a generic entity. Such a modeling approach usually adopts a homogeneous network to represent all users, the practice of which ignores the variety across an entire user population in a social media environment. Recognizing this limitation of modeling methodologies, this study explores user-specific features in a social media environment for rumor detection. The new approach hypothesizes that whether a user tends to spread a rumor is dependent upon specific attributes of the user in addition to content characteristics of the message itself. Under this hypothesis, information propagation patterns of rumors versus those of credible messages in a social media environment are systematically differentiable. To explore and exploit this hypothesis, we develop a new information propagation model based on a heterogeneous user representation for rumor recognition. The new approach is capable of differentiating rumors from credible messages through observing distinctions in their respective propagation patterns in social media. Experimental results show that the new information propagation model based on heterogeneous user representation can effectively distinguish rumors from credible social media content.

Latent tuberculosis infections in hard-to-reach drug using population-detection, prevention and control.

PubMed

Hwang, Lu-Yu; Grimes, Carolyn Z; Beasley, R Palmer; Graviss, Edward A

2009-12-01

Interferon-gamma release assays (IGRAs) need be evaluated for effectiveness as screening tests for tuberculosis (TB) infection in drug users. These tests have demonstrated improved sensitivity and specificity, but have not been studied in drug users. These one step blood tests are intended to replace the tuberculin skin test (TST), which is difficult to use and requires 48 hour follow-up, so they are expected to be particularly suitable for risk groups, like drug users, in whom follow-up is problematic. Drug users have traditionally been identified as being at increased risk for acquiring TB disease. The results of our pilot study using the TST and simpler and more sensitive interferon-gamma release assays showed that about 45% of current drug users in Houston tested have at least one test positive for latent tuberculosis infection (LTBI). These preliminary data suggest that there is an important reservoir of LTBI in drug using populations, and the risk of progression to active TB disease with other infections is great. However, LTBI in drug using populations has not been studied in depth and deserves further investigation. We need to evaluate the validity of IGRAs for detection of latent TB infection, the factors associated with LTBI, the incidence and risk for developing active TB disease in drug users and the effectiveness of early treatment of LTBI. We believe that using better tuberculosis screening tools will allow us to more accurately measure the prevalence of latent TB infection and incidence of active TB disease in drug using populations and develop more effective TB prevention and treatment interventions in the community.

User-centric design of a personal assistance robot (FRASIER) for active aging.

PubMed

Padir, Taşkin; Skorinko, Jeanine; Dimitrov, Velin

2015-01-01

We present our preliminary results from the design process for developing the Worcester Polytechnic Institute's personal assistance robot, FRASIER, as an intelligent service robot for enabling active aging. The robot capabilities include vision-based object detection, tracking the user and help with carrying heavy items such as grocery bags or cafeteria trays. This work-in-progress report outlines our motivation and approach to developing the next generation of service robots for the elderly. Our main contribution in this paper is the development of a set of specifications based on the adopted user-centered design process, and realization of the prototype system designed to meet these specifications.

Detection of physical activities using a physical activity monitor system for wheelchair users.

PubMed

Hiremath, Shivayogi V; Intille, Stephen S; Kelleher, Annmarie; Cooper, Rory A; Ding, Dan

2015-01-01

Availability of physical activity monitors for wheelchair users can potentially assist these individuals to track regular physical activity (PA), which in turn could lead to a healthier and more active lifestyle. Therefore, the aim of this study was to develop and validate algorithms for a physical activity monitoring system (PAMS) to detect wheelchair based activities. The PAMS consists of a gyroscope based wheel rotation monitor (G-WRM) and an accelerometer device (wocket) worn on the upper arm or on the wrist. A total of 45 persons with spinal cord injury took part in the study, which was performed in a structured university-based laboratory environment, a semi-structured environment at the National Veterans Wheelchair Games, and in the participants' home environments. Participants performed at least ten PAs, other than resting, taken from a list of PAs. The classification performance for the best classifiers on the testing dataset for PAMS-Arm (G-WRM and wocket on upper arm) and PAMS-Wrist (G-WRM and wocket on wrist) was 89.26% and 88.47%, respectively. The outcomes of this study indicate that multi-modal information from the PAMS can help detect various types of wheelchair-based activities in structured laboratory, semi-structured organizational, and unstructured home environments. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Improving Understanding and Trust with Intelligibility in Context-Aware Applications

ERIC Educational Resources Information Center

Lim, Brian Y.

2012-01-01

To facilitate everyday activities, context-aware applications use sensors to detect what is happening and use increasingly complex mechanisms ( e.g., by using big rule-sets or machine learning) to infer the user's context and intent. For example, a mobile application can recognize that the user is in a conversation and suppress any incoming…

Action tagging in a multi-user indoor environment for behavioural analysis purposes.

PubMed

Guerra, Claudio; Bianchi, Valentina; De Munari, Ilaria; Ciampolini, Paolo

2015-01-01

EU population is getting older, so that ICT-based solutions are expected to provide support in the challenges implied by the demographic change. At the University of Parma an AAL (Ambient Assisted Living) system, named CARDEA, has been developed. In this paper a new feature of the system is introduced, in which environmental and personal (i.e., wearable) sensors coexist, providing an accurate picture of the user's activity and needs. Environmental devices may greatly help in performing activity recognition and behavioral analysis tasks. However, in a multi-user environment, this implies the need of attributing environmental sensors outcome to a specific user, i.e., identifying the user when he performs a task detected by an environmental device. We implemented such an "action tagging" feature, based on information fusion, within the CARDEA environment, as an inexpensive, alternative solution to the problematic issue of indoor locationing.

A Web Service-based framework model for people-centric sensing applications applied to social networking.

PubMed

Nunes, David; Tran, Thanh-Dien; Raposo, Duarte; Pinto, André; Gomes, André; Silva, Jorge Sá

2012-01-01

As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users' activities and locations, sharing this information amongst the user's friends within a social networking site. We also present some screenshot results of our experimental prototype.

Context-Aware Online Commercial Intention Detection

NASA Astrophysics Data System (ADS)

Hu, Derek Hao; Shen, Dou; Sun, Jian-Tao; Yang, Qiang; Chen, Zheng

With more and more commercial activities moving onto the Internet, people tend to purchase what they need through Internet or conduct some online research before the actual transactions happen. For many Web users, their online commercial activities start from submitting a search query to search engines. Just like the common Web search queries, the queries with commercial intention are usually very short. Recognizing the queries with commercial intention against the common queries will help search engines provide proper search results and advertisements, help Web users obtain the right information they desire and help the advertisers benefit from the potential transactions. However, the intentions behind a query vary a lot for users with different background and interest. The intentions can even be different for the same user, when the query is issued in different contexts. In this paper, we present a new algorithm framework based on skip-chain conditional random field (SCCRF) for automatically classifying Web queries according to context-based online commercial intention. We analyze our algorithm performance both theoretically and empirically. Extensive experiments on several real search engine log datasets show that our algorithm can improve more than 10% on F1 score than previous algorithms on commercial intention detection.

A multi-modal approach for activity classification and fall detection

NASA Astrophysics Data System (ADS)

Castillo, José Carlos; Carneiro, Davide; Serrano-Cuerda, Juan; Novais, Paulo; Fernández-Caballero, Antonio; Neves, José

2014-04-01

The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.

Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones.

PubMed

Guo, Hansong; Huang, He; Huang, Liusheng; Sun, Yu-E

2016-08-20

As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user's daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy.

Sensing Slow Mobility and Interesting Locations for Lombardy Region (italy): a Case Study Using Pointwise Geolocated Open Data

NASA Astrophysics Data System (ADS)

Brovelli, M. A.; Oxoli, D.; Zurbarán, M. A.

2016-06-01

During the past years Web 2.0 technologies have caused the emergence of platforms where users can share data related to their activities which in some cases are then publicly released with open licenses. Popular categories for this include community platforms where users can upload GPS tracks collected during slow travel activities (e.g. hiking, biking and horse riding) and platforms where users share their geolocated photos. However, due to the high heterogeneity of the information available on the Web, the sole use of these user-generated contents makes it an ambitious challenge to understand slow mobility flows as well as to detect the most visited locations in a region. Exploiting the available data on community sharing websites allows to collect near real-time open data streams and enables rigorous spatial-temporal analysis. This work presents an approach for collecting, unifying and analysing pointwise geolocated open data available from different sources with the aim of identifying the main locations and destinations of slow mobility activities. For this purpose, we collected pointwise open data from the Wikiloc platform, Twitter, Flickr and Foursquare. The analysis was confined to the data uploaded in Lombardy Region (Northern Italy) - corresponding to millions of pointwise data. Collected data was processed through the use of Free and Open Source Software (FOSS) in order to organize them into a suitable database. This allowed to run statistical analyses on data distribution in both time and space by enabling the detection of users' slow mobility preferences as well as places of interest at a regional scale.

Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective.

PubMed

Wang, Xi; Zhao, Kang; Street, Nick

2017-04-24

Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts. This study aimed to analyze OHC users' Web-based interactions, reveal which types of social support activities are related to users' participation, and predict whether and when a user will churn from the OHC. We collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users' continued participation. Using supervised machine learning methods, we developed a predictive model for user churn. Users' behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC. Detecting different types of social support activities via text mining contributes to better understanding and prediction of users' participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies. ©Xi Wang, Kang Zhao, Nick Street. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.04.2017.

Design and Implementation of Context-Aware Musuem Guide Agents

NASA Astrophysics Data System (ADS)

Satoh, Ichiro

This paper presents an agent-based system for building and operating context-aware services in public spaces, including museums. The system provides users with agents and detects the locations of users and deploys location-aware user-assistant agents at computers near the their current locations by using active RFID-tags. When a visitor moves between exhibits in a museum, this dynamically deploys his/her agent at the computers close to the exhibits by using mobile agent technology. It annotates the exhibits in his/her personalized form and navigate him/her user to the next exhibits along his/her routes. It also introduces user movement as a natural approach to interacting between users and agents. To demonstrate the utility and effectiveness of the system, we constructed location/user-aware visitor-guide services and experimented them for two weeks in a public museum.

Finding Influential Users in Social Media Using Association Rule Learning

NASA Astrophysics Data System (ADS)

Erlandsson, Fredrik; Bródka, Piotr; Borg, Anton; Johnson, Henric

2016-04-01

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.

Intelligent assistant carer for active aging

NASA Astrophysics Data System (ADS)

Bizjak, Jani; Gradišek, Anton; Stepančič, Luka; Gjoreski, Hristijan; Gams, Matjaž

2017-12-01

We present the concept of an Intelligent Assistant Carer system for the elderly, designed to help with active aging and to facilitate the interactions with carers. The system is modular, allowing the users to choose the appropriate functions according to their needs, and is built on an open platform in order to make it compatible with third-party products and services. Currently, the system consists of a wearable device (a smartwatch) and an internet portal that manages the data and takes care of the interactions between the user, the carers, and the support services. We present in detail one of the modules, i.e., fall detection, and the results of a pilot study for the system on 150 users over the course of 3 months.

Vision-Aided Context-Aware Framework for Personal Navigation Services

NASA Astrophysics Data System (ADS)

Saeedi, S.; Moussa, A.; El-Sheimy, N., , Dr.

2012-07-01

The ubiquity of mobile devices (such as smartphones and tablet-PCs) has encouraged the use of location-based services (LBS) that are relevant to the current location and context of a mobile user. The main challenge of LBS is to find a pervasive and accurate personal navigation system (PNS) in different situations of a mobile user. In this paper, we propose a method of personal navigation for pedestrians that allows a user to freely move in outdoor environments. This system aims at detection of the context information which is useful for improving personal navigation. The context information for a PNS consists of user activity modes (e.g. walking, stationary, driving, and etc.) and the mobile device orientation and placement with respect to the user. After detecting the context information, a low-cost integrated positioning algorithm has been employed to estimate pedestrian navigation parameters. The method is based on the integration of the relative user's motion (changes of velocity and heading angle) estimation based on the video image matching and absolute position information provided by GPS. A Kalman filter (KF) has been used to improve the navigation solution when the user is walking and the phone is in his/her hand. The Experimental results demonstrate the capabilities of this method for outdoor personal navigation systems.

Automatic identification of solid-phase medication intake using wireless wearable accelerometers.

PubMed

Rui Wang; Sitova, Zdenka; Xiaoqing Jia; Xiang He; Abramson, Tobi; Gasti, Paolo; Balagani, Kiran S; Farajidavar, Aydin

2014-01-01

We have proposed a novel solution to a fundamental problem encountered in implementing non-ingestion based medical adherence monitoring systems, namely, how to reliably identify pill medication intake. We show how wireless wearable devices with tri-axial accelerometer can be used to detect and classify hand gestures of users during solid-phase medication intake. Two devices were worn on the wrists of each user. Users were asked to perform two activities in the way that is natural and most comfortable to them: (1) taking empty gelatin capsules with water, and (2) drinking water and wiping mouth. 25 users participated in this study. The signals obtained from the devices were filtered and the patterns were identified using dynamic time warping algorithm. Using hand gesture signals, we achieved 84.17 percent true positive rate and 13.33 percent false alarm rate, thus demonstrating that the hand gestures could be used to effectively identify pill taking activity.

Analysis of an Anti-Phishing Lab Activity

ERIC Educational Resources Information Center

Werner, Laurie A.; Courte, Jill

2010-01-01

Despite advances in spam detection software, anti-spam laws, and increasingly sophisticated users, the number of successful phishing scams continues to grow. In addition to monetary losses attributable to phishing, there is also a loss of confidence that stifles use of online services. Using in-class activities in an introductory computer course…

Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones †

PubMed Central

Guo, Hansong; Huang, He; Huang, Liusheng; Sun, Yu-E

2016-01-01

As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user’s daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy. PMID:27556461

iFall: an Android application for fall monitoring and response.

PubMed

Sposaro, Frank; Tyson, Gary

2009-01-01

Injuries due to falls are among the leading causes of hospitalization in elderly persons, often resulting in a rapid decline in quality of life or death. Rapid response can improve the patients outcome, but this is often lacking when the injured person lives alone and the nature of the injury complicates calling for help. This paper presents an alert system for fall detection using common commercially available electronic devices to both detect the fall and alert authorities. We use an Android-based smart phone with an integrated tri-axial accelerometer. Data from the accelerometer is evaluated with several threshold based algorithms and position data to determine a fall. The threshold is adaptive based on user provided parameters such as: height, weight, and level of activity. The algorithm adapts to unique movements that a phone experiences as opposed to similar systems which require users to mount accelerometers to their chest or trunk. If a fall is suspected a notification is raised requiring the user's response. If the user does not respond, the system alerts pre-specified social contacts with an informational message via SMS. If a contact responds the system commits an audible notification, automatically connects, and enables the speakerphone. If a social contact confirms a fall, an appropriate emergency service is alerted. Our system provides a realizable, cost effective solution to fall detection using a simple graphical interface while not overwhelming the user with uncomfortable sensors.

Computerized controlled-substance surveillance: application involving automated storage and distribution cabinets.

PubMed

Wellman, G S; Hammond, R L; Talmage, R

2001-10-01

A secondary data-reporting system used to scan the archives of a hospital's automated storage and distribution cabinets (ASDCs) for indications of controlled-substance diversion is described. ASDCs, which allow access to multiple doses of the same medication at one time, use drug count verification to ensure complete audits and disposition tracking. Because an ASDC may interpret inappropriate removal of a medication as a normal transaction, users of ASDCs should have a comprehensive plan for detecting and investigating controlled-substance diversion. Monitoring for and detecting diversion can be difficult and time-consuming, given the limited report-generating features of many ASDCs. Managers at an 800-bed hospital used report-writing software to address these problems. This application interfaces with the hospital's computer system and generates customized reports. The monthly activity recapitulation report lists each user of the ASDCs and gives a summary of all the controlled-substance transactions for those users for the time period specified. The monthly summary report provides the backbone of the surveillance system and identifies situations that require further audit and review. This report provides a summary of each user's activity for a specific medication for the time period specified. The detailed summary report allows for efficient review of specific transactions before there is a decision to conduct a chart review. This report identifies all ASDC controlled-substance transactions associated with a user. A computerized report-generating system identifies instances of inappropriate removal of controlled substances from a hospital's ASDCs.

Utilizing Weak Indicators to Detect Anomalous Behaviors in Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Egid, Adin Ezra

We consider the use of a novel weak in- dicator alongside more commonly used weak indicators to help detect anomalous behavior in a large computer network. The data of the network which we are studying in this research paper concerns remote log-in information (Virtual Private Network, or VPN sessions) from the internal network of Los Alamos National Laboratory (LANL). The novel indicator we are utilizing is some- thing which, while novel in its application to data science/cyber security research, is a concept borrowed from the business world. The Her ndahl-Hirschman Index (HHI) is a computationally trivial index which provides amore » useful heuristic for regulatory agencies to ascertain the relative competitiveness of a particular industry. Using this index as a lagging indicator in the monthly format we have studied could help to detect anomalous behavior by a particular or small set of users on the network. Additionally, we study indicators related to the speed of movement of a user based on the physical location of their current and previous logins. This data can be ascertained from the IP addresses of the users, and is likely very similar to the fraud detection schemes regularly utilized by credit card networks to detect anomalous activity. In future work we would look to nd a way to combine these indicators for use as an internal fraud detection system.« less

Suomi NPP VIIRS active fire product status

NASA Astrophysics Data System (ADS)

Ellicott, E. A.; Csiszar, I. A.; Schroeder, W.; Giglio, L.; Wind, B.; Justice, C. O.

2012-12-01

We provide an overview of the evaluation and development of the Active Fires product derived from the Visible Infrared Imager Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (SNPP) satellite during the first year of on-orbit data. Results from the initial evaluation of the standard SNPP Active Fires product, generated by the SNPP Interface Data Processing System (IDPS), supported the stabilization of the VIIRS Sensor Data Record (SDR) product. This activity focused in particular on the processing of the dual-gain 4 micron VIIRS M13 radiometric measurements into 750m aggregated data, which are fundamental for active fire detection. Following the VIIRS SDR product's Beta maturity status in April 2012, correlative analysis between VIIRS and near-simultaneous fire detections from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System Aqua satellite confirmed the expected relative detection rates driven primarily by sensor differences. The VIIRS Active Fires Product Development and Validation Team also developed a science code that is based on the latest MODIS Collection 6 algorithm and provides a full spatially explicit fire mask to replace the sparse array output of fire locations from a MODIS Collection 4 equivalent algorithm in the current IDPS product. The Algorithm Development Library (ADL) was used to support the planning for the transition of the science code into IDPS operations in the future. Product evaluation and user outreach was facilitated by a product website that provided end user access to fire data in user-friendly format over North America as well as examples of VIIRS-MODIS comparisons. The VIIRS fire team also developed an experimental product based on 375m VIIRS Imagery band measurements and provided high quality imagery of major fire events in US. By August 2012 the IDPS product achieved Beta maturity, with some known and documented shortfalls related to the processing of incorrect SDR input data and to apparent algorithm deficiencies in select observing and environmental conditions.

A comparison of passive and active acoustic sampling for a bat community impacted by White-nose syndrome

USGS Publications Warehouse

Coleman, Laci S.; Ford, W. Mark; Dobony, Christopher A.; Britzke, Eric R.

2014-01-01

In the summers of 2011 and 2012, we compared passive and active acoustic sampling for bats at 31 sites at Fort Drum Military Installation, New York. We defined active sampling as acoustic sampling that occurred in 30-min intervals between the hours of sunset and 0200 with a user present to manipulate the directionality of the microphone. We defined passive sampling as acoustic sampling that occurred over a 12-h period (1900–0700 hours) without a user present and with the microphone set in a predetermined direction. We detected seven of the nine possible species at Fort Drum, including the federally endangered Indiana bat Myotis sodalis, the proposed-for-listing northern bat M. septentrionalis, the little brown bat M. lucifugus, and the big brown bat Eptesicus fuscus, which are impacted by white-nose syndrome (WNS); and the eastern red bat Lasiurus borealis, the hoary bat L. cinereus, and the silver-haired bat Lasionycteris noctivagans, which are not known to be impacted by WNS. We did not detect two additional WNS-impacted species known to historically occur in the area: the eastern small-footed bat Myotis leibii and the tri-colored bat Perimyotis subflavus. Single-season occupancy models revealed lower detection probabilities of all detected species using active sampling versus passive sampling. Additionally, overall detection probabilities declined in detected WNS-impacted species between years. A paired t-test of simultaneous sampling on 21 occasions revealed that overall recorded foraging activity per hour was greater using active than passive sampling for big brown bats and greater using passive than active sampling for little brown bats. There was no significant difference in recorded activity between methods for other WNS-impacted species, presumably because these species have been so reduced in number that their “apparency” on the landscape is lower. Finally, a cost analysis of standard passive and active sampling protocols revealed that passive sampling is substantially more cost-effective than active sampling per hour of data collection. We recommend passive sampling over active sampling methodologies as they are defined in our study for detection probability and/or occupancy studies focused on declining bat species in areas that have experienced severe WNS-associated impacts.

Towards the run and walk activity classification through step detection--an android application.

PubMed

Oner, Melis; Pulcifer-Stump, Jeffry A; Seeling, Patrick; Kaya, Tolga

2012-01-01

Falling is one of the most common accidents with potentially irreversible consequences, especially considering special groups, such as the elderly or disabled. One approach to solve this issue would be an early detection of the falling event. Towards reaching the goal of early fall detection, we have worked on distinguishing and monitoring some basic human activities such as walking and running. Since we plan to implement the system mostly for seniors and the disabled, simplicity of the usage becomes very important. We have successfully implemented an algorithm that would not require the acceleration sensor to be fixed in a specific position (the smart phone itself in our application), whereas most of the previous research dictates the sensor to be fixed in a certain direction. This algorithm reviews data from the accelerometer to determine if a user has taken a step or not and keeps track of the total amount of steps. After testing, the algorithm was more accurate than a commercial pedometer in terms of comparing outputs to the actual number of steps taken by the user.

Persistence of touch DNA on burglary-related tools.

PubMed

Pfeifer, Céline M; Wiegand, Peter

2017-07-01

Experts are increasingly concerned by issues regarding the activity level of DNA stains. A case from our burglary-related casework pointed out the need for experiments regarding the persistence of DNA when more than one person touched a tool handle. We performed short tandem repeat (STR) analyses for three groups of tools: (1) personal and mock owned tools; (2) tools, which were first "owned" by a first user and then handled in a burglary action by a second user; and (3) tools, which were first owned by a first user and then handled in a moderate action. At least three types of tool handles were included in each of the groups. Every second user handled the tool with and without gloves. In total, 234 samples were analyzed regarding profile completeness of first and second user as well as properties like detectable major profile or mixture attributes. When second users simulated a burglary by using a tool bare handed, we could not detect the first user as major component on their handles but attribute him to the stain in 1/40 cases. When second users broke up the burglary setup using gloves, the first user matched the DNA handle profile in 37% of the cases. Moderate use of mock borrowed tools demonstrated a material-dependent persistence. In total, we observed that the outcome depends mainly on the nature of contact, the handle material, and the user-specific characteristics. This study intends to supplement present knowledge about persistence of touch DNA with a special emphasis on burglary-related cases with two consecutive users and to act as experimental data for an evaluation of the relevance of alleged hypotheses, when such is needed in a court hearing.

A Study of Users with Suicidal Ideation on Sina Weibo.

PubMed

Wang, Zheng; Yu, Guang; Tian, Xianyun; Tang, Jingyun; Yan, Xiangbin

2018-01-02

Suicide is a leading cause of death in China, and so suicide intervention on social media is an important issue in the field of public health. Sina Weibo (Weibo) is an emerging surveillance tool that may provide online assistance for users at the risk of suicide. Keyword-based methods and supervised classifiers were employed to conduct this research. A control group was established to explore the differences between Weibo users with suicidal ideation (USI) and the general population. A total of 114 USI were detected from 1 million active Weibo users. By studying the negative postings of these USI, disclosure of the reasons for their bad moods was the most common theme. The emotions of USI tend to be particularly down between 05:00 pm and midnight. Use of the first-person pronoun by Weibo USI is significantly frequent. Our findings may help to identify individuals with suicidal ideation who are not identified by the traditional clinical approach. Consequently, detecting and helping individuals who may be at risk of committing suicide may become more efficient.

Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter.

PubMed

Benigni, Matthew C; Joseph, Kenneth; Carley, Kathleen M

2017-01-01

The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS' unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and "lone wolf" attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS' sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group's propaganda dissemination through retweets.

Applicability of satellite remote sensing for detection and monitoring of coal strip mining activities. [Kentucky

NASA Technical Reports Server (NTRS)

Brooks, R. L. (Principal Investigator); Parra, C. G.

1975-01-01

The author has identified the following significant results. Large areas covered by orbital photography allows the user to estimate the acreage of strip mining activity from a few frames. Infrared photography both in color and in black and white transparencies was found to be the best suited for this purpose.

Triggers and monitoring in intelligent personal health record.

PubMed

Luo, Gang

2012-10-01

Although Web-based personal health records (PHRs) have been widely deployed, the existing ones have limited intelligence. Previously, we introduced expert system technology and Web search technology into the PHR domain and proposed the concept of an intelligent PHR (iPHR). iPHR provides personalized healthcare information to facilitate users' daily activities of living. The current iPHR is passive and follows the pull model of information distribution. This paper introduces triggers and monitoring into iPHR to make iPHR become active. Our idea is to let medical professionals pre-compile triggers and store them in iPHR's knowledge base. Each trigger corresponds to an abnormal event that may have potential medical impact. iPHR keeps collecting, processing, and analyzing the user's medical data from various sources such as wearable sensors. Whenever an abnormal event is detected from the user's medical data, the corresponding trigger fires and the related personalized healthcare information is pushed to the user using natural language generation technology, expert system technology, and Web search technology.

Information Assurance Technology Analysis Center Information Assurance Tools Report Intrusion Detection

DTIC Science & Technology

1998-01-01

such as central processing unit (CPU) usage, disk input/output (I/O), memory usage, user activity, and number of logins attempted. The statistics... EMERALD Commercial anomaly detection, system monitoring SRI porras@csl.sri.com www.csl.sri.com/ emerald /index. html Gabriel Commercial system...sensors, it starts to protect the network with minimal configuration and maximum intelligence. T 11 EMERALD TITLE EMERALD (Event Monitoring

Detecting insider activity using enhanced directory virtualization.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shin, Dongwan; Claycomb, William R.

2010-07-01

Insider threats often target authentication and access control systems, which are frequently based on directory services. Detecting these threats is challenging, because malicious users with the technical ability to modify these structures often have sufficient knowledge and expertise to conceal unauthorized activity. The use of directory virtualization to monitor various systems across an enterprise can be a valuable tool for detecting insider activity. The addition of a policy engine to directory virtualization services enhances monitoring capabilities by allowing greater flexibility in analyzing changes for malicious intent. The resulting architecture is a system-based approach, where the relationships and dependencies between datamore » sources and directory services are used to detect an insider threat, rather than simply relying on point solutions. This paper presents such an architecture in detail, including a description of implementation results.« less

Retweets as a Predictor of Relationships among Users on Social Media.

PubMed

Tsugawa, Sho; Kito, Kosuke

2017-01-01

Link prediction is the problem of detecting missing links or predicting future link formation in a network. Application of link prediction to social media, such as Twitter and Facebook, is useful both for developing novel services and for sociological analyses. While most existing research on link prediction uses only the social network topology for the prediction, in social media, records of user activities such as posting, replying, and reposting are available. These records are expected to reflect user interest, and so incorporating them should improve link prediction. However, research into link prediction using the records of user activities is still in its infancy, and the effectiveness of such records for link prediction has not been fully explored. In this study, we focus in particular on records of reposting as a promising source that could be useful for link prediction, and investigate their effectiveness for link prediction on the popular social media platform Twitter. Our results show that (1) the prediction accuracy of techniques using reposting records is higher than that of popular topology-based techniques such as common neighbors and resource allocation for actively retweeting users, (2) the accuracy of link prediction techniques that use network topology alone can be improved by incorporating reposting records.

Retweets as a Predictor of Relationships among Users on Social Media

PubMed Central

Kito, Kosuke

2017-01-01

Link prediction is the problem of detecting missing links or predicting future link formation in a network. Application of link prediction to social media, such as Twitter and Facebook, is useful both for developing novel services and for sociological analyses. While most existing research on link prediction uses only the social network topology for the prediction, in social media, records of user activities such as posting, replying, and reposting are available. These records are expected to reflect user interest, and so incorporating them should improve link prediction. However, research into link prediction using the records of user activities is still in its infancy, and the effectiveness of such records for link prediction has not been fully explored. In this study, we focus in particular on records of reposting as a promising source that could be useful for link prediction, and investigate their effectiveness for link prediction on the popular social media platform Twitter. Our results show that (1) the prediction accuracy of techniques using reposting records is higher than that of popular topology-based techniques such as common neighbors and resource allocation for actively retweeting users, (2) the accuracy of link prediction techniques that use network topology alone can be improved by incorporating reposting records. PMID:28107489

Substance Abuse and the HIV Situation in Malaysia

PubMed Central

Singh, Darshan; Chawarski, Marek C.; Schottenfeld, Richard; Vicknasingam, Balasingam

2014-01-01

Heroin continues to be the main drug used in Malaysia, while amphetamine-type stimulants (ATS) have been recently identified as a growing problem. A cumulative total of 300,241 drug users were detected between 1988 and 2006. It is also estimated that Malaysia has 170,000 injecting drug users. HIV prevalence among drug users in the country ranges from 25% to 45%. Currently, there are approximately 380 general medical practice offices that offer agonist maintenance treatments for approximately 10,000 patients. There are 27,756 active patients in 333 general medical practice offices and government-run methadone maintenance treatment (MMT) centers. The Needle Syringe Exchange Program (NSEP) reached out to 34,244 injection drug users (IDUs) in 2011. In the last 2 years (2011 and 2012) the number of detected drug addicts decreased from 11,194 to 9015. The arrests made by the police related to opiate and cannabis use increased from 41,363 to 63,466 between the years 2008 and 2010, but decreased since 2010. An almost four-fold increase in the number of ATS and ketamine users was detected from 2006 (21,653 users) 2012 (76,812). Between 2004 and 2010, the yearly seizures for heroin ranged between 156 to 270 kg. However, in 2010 and 2011, heroin seizures showed a significant increase of 445kg and 410.02 kg, respectively. There has been a seizure of between 600 to 1000kg of syabu yearly from 2009 to 2012. Similar to heroin, increased seizures for Yaba have also been observed over the last 2 years. A significant increase has also been recorded for the seizures of ecstasy pills from 2011 (47,761 pills) to 2012 (634,573 pills). The cumulative number of reported HIV infections since 1986 is 94,841. In 2011, sexual activity superseded injection drug use as the main transmission factor for the epidemic. HIV in the country mainly involves males, as they constitute 90% of cumulative HIV cases and a majority of those individuals are IDUs. However, HIV infection trends are shifting from males to females. There are 37,306 people living with HIV (PLHIV) who are eligible for treatment, and 14,002 PLHIV were receiving antiretroviral treatment (ART) in 2011. The decreasing trend of heroin users who have been detected and arrested could be due to the introduction of medical treatments and harm reduction approaches for drug users, resulting in fewer drug users being arrested. However, we are unable to say with certainty why there has been an increase in heroin seizures in the country. There has been an increasing trend in both ATS users and seizures. A new trend of co-occurring opiate dependence and ATS underscores the need to develop and implement effective treatments for ATS, co-occurring opiate and ATS, and polysubstance abuse disorders. The low numbers of NSEP clients being tested for HIV underscores our caution in interpreting the decline of HIV infections among drug users and the importance of focusing on providing education, prevention, treatment, and outreach to those who are not in treatment. PMID:25278737

Substance Abuse and the HIV Situation in Malaysia.

PubMed

Singh, Darshan; Chawarski, Marek C; Schottenfeld, Richard; Vicknasingam, Balasingam

2013-12-01

Heroin continues to be the main drug used in Malaysia, while amphetamine-type stimulants (ATS) have been recently identified as a growing problem. A cumulative total of 300,241 drug users were detected between 1988 and 2006. It is also estimated that Malaysia has 170,000 injecting drug users. HIV prevalence among drug users in the country ranges from 25% to 45%. Currently, there are approximately 380 general medical practice offices that offer agonist maintenance treatments for approximately 10,000 patients. There are 27,756 active patients in 333 general medical practice offices and government-run methadone maintenance treatment (MMT) centers. The Needle Syringe Exchange Program (NSEP) reached out to 34,244 injection drug users (IDUs) in 2011. In the last 2 years (2011 and 2012) the number of detected drug addicts decreased from 11,194 to 9015. The arrests made by the police related to opiate and cannabis use increased from 41,363 to 63,466 between the years 2008 and 2010, but decreased since 2010. An almost four-fold increase in the number of ATS and ketamine users was detected from 2006 (21,653 users) 2012 (76,812). Between 2004 and 2010, the yearly seizures for heroin ranged between 156 to 270 kg. However, in 2010 and 2011, heroin seizures showed a significant increase of 445kg and 410.02 kg, respectively. There has been a seizure of between 600 to 1000kg of syabu yearly from 2009 to 2012. Similar to heroin, increased seizures for Yaba have also been observed over the last 2 years. A significant increase has also been recorded for the seizures of ecstasy pills from 2011 (47,761 pills) to 2012 (634,573 pills). The cumulative number of reported HIV infections since 1986 is 94,841. In 2011, sexual activity superseded injection drug use as the main transmission factor for the epidemic. HIV in the country mainly involves males, as they constitute 90% of cumulative HIV cases and a majority of those individuals are IDUs. However, HIV infection trends are shifting from males to females. There are 37,306 people living with HIV (PLHIV) who are eligible for treatment, and 14,002 PLHIV were receiving antiretroviral treatment (ART) in 2011. The decreasing trend of heroin users who have been detected and arrested could be due to the introduction of medical treatments and harm reduction approaches for drug users, resulting in fewer drug users being arrested. However, we are unable to say with certainty why there has been an increase in heroin seizures in the country. There has been an increasing trend in both ATS users and seizures. A new trend of co-occurring opiate dependence and ATS underscores the need to develop and implement effective treatments for ATS, co-occurring opiate and ATS, and polysubstance abuse disorders. The low numbers of NSEP clients being tested for HIV underscores our caution in interpreting the decline of HIV infections among drug users and the importance of focusing on providing education, prevention, treatment, and outreach to those who are not in treatment.

User-Wearable Devices that Monitor Exposure to Blue Light and Recommend Adjustments Thereto

NASA Technical Reports Server (NTRS)

Lee, Yong Jin (Inventor)

2017-01-01

Described herein are user-wearable devices that include an optical sensor, and methods for use therewith. In certain embodiments, an optical sensor of a user-wearable device (e.g., a wrist-worn device) is used to detect blue light that is incident on the optical sensor and to produce a blue light detection signal indicative thereof, and thus, indicative of the response of the user's intrinsically photosensitive Retinal Ganglion Cells (ipRGCs). In dependence on the blue light detection signal, there is a determination of a metric indicative of an amount of blue light detected by the optical sensor. The metric is compared to a corresponding threshold, and a user notification is triggered in dependence on results of the comparing, wherein the user notification informs a person wearing the user-wearable device to adjust their exposure to light.

Behavior Analysis Based on Coordinates of Body Tags

NASA Astrophysics Data System (ADS)

Luštrek, Mitja; Kaluža, Boštjan; Dovgan, Erik; Pogorelc, Bogdan; Gams, Matjaž

This paper describes fall detection, activity recognition and the detection of anomalous gait in the Confidence project. The project aims to prolong the independence of the elderly by detecting falls and other types of behavior indicating a health problem. The behavior will be analyzed based on the coordinates of tags worn on the body. The coordinates will be detected with radio sensors. We describe two Confidence modules. The first one classifies the user's activity into one of six classes, including falling. The second one detects walking anomalies, such as limping, dizziness and hemiplegia. The walking analysis can automatically adapt to each person by using only the examples of normal walking of that person. Both modules employ machine learning: the paper focuses on the features they use and the effect of tag placement and sensor noise on the classification accuracy. Four tags were enough for activity recognition accuracy of over 93% at moderate sensor noise, while six were needed to detect walking anomalies with the accuracy of over 90%.

Evaluation for the design of experience in virtual environments: modeling breakdown of interaction and illusion.

PubMed

Marsh, T; Wright, P; Smith, S

2001-04-01

New and emerging media technologies have the potential to induce a variety of experiences in users. In this paper, it is argued that the inducement of experience presupposes that users are absorbed in the illusion created by these media. Looking to another successful visual medium, film, this paper borrows from the techniques used in "shaping experience" to hold spectators' attention in the illusion of film, and identifies what breaks the illusion/experience for spectators. This paper focuses on one medium, virtual reality (VR), and advocates a transparent or "invisible style" of interaction. We argue that transparency keeps users in the "flow" of their activities and consequently enhances experience in users. Breakdown in activities breaks the experience and subsequently provides opportunities to identify and analyze potential causes of usability problems. Adopting activity theory, we devise a model of interaction with VR--through consciousness and activity--and introduce the concept of breakdown in illusion. From this, a model of effective interaction with VR is devised and the occurrence of breakdown in interaction and illusion is identified along a continuum of engagement. Evaluation guidelines for the design of experience are proposed and applied to usability problems detected in an empirical study of a head-mounted display (HMD) VR system. This study shows that the guidelines are effective in the evaluation of VR. Finally, we look at the potential experiences that may be induced in users and propose a way to evaluate user experience in virtual environments (VEs) and other new and emerging media.

Intentional Voice Command Detection for Trigger-Free Speech Interface

NASA Astrophysics Data System (ADS)

Obuchi, Yasunari; Sumiyoshi, Takashi

In this paper we introduce a new framework of audio processing, which is essential to achieve a trigger-free speech interface for home appliances. If the speech interface works continually in real environments, it must extract occasional voice commands and reject everything else. It is extremely important to reduce the number of false alarms because the number of irrelevant inputs is much larger than the number of voice commands even for heavy users of appliances. The framework, called Intentional Voice Command Detection, is based on voice activity detection, but enhanced by various speech/audio processing techniques such as emotion recognition. The effectiveness of the proposed framework is evaluated using a newly-collected large-scale corpus. The advantages of combining various features were tested and confirmed, and the simple LDA-based classifier demonstrated acceptable performance. The effectiveness of various methods of user adaptation is also discussed.

Neural mechanisms of working memory in ecstasy (MDMA) users who continue or discontinue ecstasy and amphetamine use: evidence from an 18-month longitudinal functional magnetic resonance imaging study.

PubMed

Daumann, Jörg; Fischermann, Thomas; Heekeren, Karsten; Thron, Armin; Gouzoulis-Mayfrank, Euphrosyne

2004-09-01

Working memory processing in ecstasy (3,4-methylenedioxymethamphetamine) users is associated with neural alterations as measured by functional magnetic resonance imaging. Here, we examined whether cortical activation patterns change after prolonged periods of continued use or abstinence from ecstasy and amphetamine. We used an n-back task and functional magnetic resonance imaging in 17 ecstasy users at baseline (t(1)) and after 18 months (t(2)). Based on the reported drug use at t(2) we separated subjects with continued ecstasy and amphetamine use from subjects reporting abstinence during the follow-up period (n = 9 and n = 8, respectively). At baseline both groups had similar task performance and similar cortical activation patterns. Task performance remained unchanged in both groups. Furthermore, there were no detectable functional magnetic resonance imaging signal changes from t(1) to t(2) in the follow-up abstinent group. However, the continuing users showed a dose-dependent increased parietal activation for the 2-back task after the follow-up period. Our data suggest that ecstasy use, particularly in high doses, is associated with greater parietal activation during working memory performance. An altered activation pattern might appear before changes in cognitive performance become apparent and, hence, may reflect an early stage of neuronal injury from the neurotoxic drug ecstasy.

Smart self management: assistive technology to support people with chronic disease.

PubMed

Zheng, Huiru; Nugent, Chris; McCullagh, Paul; Huang, Yan; Zhang, Shumei; Burns, William; Davies, Richard; Black, Norman; Wright, Peter; Mawson, Sue; Eccleston, Christopher; Hawley, Mark; Mountain, Gail

2010-01-01

We have developed a personalised self management system to support self management of chronic conditions with support from health-care professionals. Accelerometers are used to measure gross levels of activity, for example walking around the house, and used to infer higher level activity states, such as standing, sitting and lying. A smart phone containing an accelerometer and a global positioning system (GPS) module can be used to monitor outdoor activity, providing both activity and location based information. Heart rate, blood pressure and weight are recorded and input to the system by the user. A decision support system (DSS) detects abnormal activity and distinguishes life style patterns. The DSS is used to assess the self management process, and automates feedback to the user, consistent with the achievement of their life goals. We have found that telecare and assistive technology is feasible to support self management for chronic conditions within the home and local community environments.

Ubiquitous computing technology for just-in-time motivation of behavior change.

PubMed

Intille, Stephen S

2004-01-01

This paper describes a vision of health care where "just-in-time" user interfaces are used to transform people from passive to active consumers of health care. Systems that use computational pattern recognition to detect points of decision, behavior, or consequences automatically can present motivational messages to encourage healthy behavior at just the right time. Further, new ubiquitous computing and mobile computing devices permit information to be conveyed to users at just the right place. In combination, computer systems that present messages at the right time and place can be developed to motivate physical activity and healthy eating. Computational sensing technologies can also be used to measure the impact of the motivational technology on behavior.

Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective

PubMed Central

Wang, Xi; Street, Nick

2017-01-01

Background Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users’ participations and predict user churn for user retention efforts. Objective This study aimed to analyze OHC users’ Web-based interactions, reveal which types of social support activities are related to users’ participation, and predict whether and when a user will churn from the OHC. Methods We collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users’ continued participation. Using supervised machine learning methods, we developed a predictive model for user churn. Results Users’ behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC. Conclusions Detecting different types of social support activities via text mining contributes to better understanding and prediction of users’ participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies. PMID:28438725

Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter

PubMed Central

Joseph, Kenneth; Carley, Kathleen M.

2017-01-01

The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS’ unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and “lone wolf” attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS’ sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group’s propaganda dissemination through retweets. PMID:29194446

Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

PubMed Central

Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

2017-01-01

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware. PMID:28208736

Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition.

PubMed

Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

2017-02-08

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates ( F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware.

Profiler-2000: Attacking the Insider Threat

DTIC Science & Technology

2005-09-01

detection approach and its incorporation into a number of current automated intrusion-detection strategies (e.g., AT&T’s Com- puterWatch, SRI’s Emerald ...administrative privileges, to be activated upon his or her next login . The system calls required to implement this method are chmod and exit. These two calls...kinds of information that can be derived from these (and other) logs are: time of login , physical location of login , duration of user session

User Experience May be Producing Greater Heart Rate Variability than Motor Imagery Related Control Tasks during the User-System Adaptation in Brain-Computer Interfaces

PubMed Central

Alonso-Valerdi, Luz M.; Gutiérrez-Begovich, David A.; Argüello-García, Janet; Sepulveda, Francisco; Ramírez-Mendoza, Ricardo A.

2016-01-01

Brain-computer interface (BCI) is technology that is developing fast, but it remains inaccurate, unreliable and slow due to the difficulty to obtain precise information from the brain. Consequently, the involvement of other biosignals to decode the user control tasks has risen in importance. A traditional way to operate a BCI system is via motor imagery (MI) tasks. As imaginary movements activate similar cortical structures and vegetative mechanisms as a voluntary movement does, heart rate variability (HRV) has been proposed as a parameter to improve the detection of MI related control tasks. However, HR is very susceptible to body needs and environmental demands, and as BCI systems require high levels of attention, perceptual processing and mental workload, it is important to assess the practical effectiveness of HRV. The present study aimed to determine if brain and heart electrical signals (HRV) are modulated by MI activity used to control a BCI system, or if HRV is modulated by the user perceptions and responses that result from the operation of a BCI system (i.e., user experience). For this purpose, a database of 11 participants who were exposed to eight different situations was used. The sensory-cognitive load (intake and rejection tasks) was controlled in those situations. Two electrophysiological signals were utilized: electroencephalography and electrocardiography. From those biosignals, event-related (de-)synchronization maps and event-related HR changes were respectively estimated. The maps and the HR changes were cross-correlated in order to verify if both biosignals were modulated due to MI activity. The results suggest that HR varies according to the experience undergone by the user in a BCI working environment, and not because of the MI activity used to operate the system. PMID:27458384

Mobile user identity sensing using the motion sensor

NASA Astrophysics Data System (ADS)

Zhao, Xi; Feng, Tao; Xu, Lei; Shi, Weidong

2014-05-01

Employing mobile sensor data to recognize user behavioral activities has been well studied in recent years. However, to adopt the data as a biometric modality has rarely been explored. Existing methods either used the data to recognize gait, which is considered as a distinguished identity feature; or segmented a specific kind of motion for user recognition, such as phone picking-up motion. Since the identity and the motion gesture jointly affect motion data, to fix the gesture (walking or phone picking-up) definitively simplifies the identity sensing problem. However, it meanwhile introduces the complexity from gesture detection or requirement on a higher sample rate from motion sensor readings, which may draw the battery fast and affect the usability of the phone. In general, it is still under investigation that motion based user authentication in a large scale satisfies the accuracy requirement as a stand-alone biometrics modality. In this paper, we propose a novel approach to use the motion sensor readings for user identity sensing. Instead of decoupling the user identity from a gesture, we reasonably assume users have their own distinguishing phone usage habits and extract the identity from fuzzy activity patterns, represented by a combination of body movements whose signals in chains span in relative low frequency spectrum and hand movements whose signals span in relative high frequency spectrum. Then Bayesian Rules are applied to analyze the dependency of different frequency components in the signals. During testing, a posterior probability of user identity given the observed chains can be computed for authentication. Tested on an accelerometer dataset with 347 users, our approach has demonstrated the promising results.

Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones.

PubMed

Ge, Linfei; Zhang, Jin; Wei, Jing

2018-01-01

Respiration monitoring is helpful in disease prevention and diagnosis. Traditional respiration monitoring requires users to wear devices on their bodies, which is inconvenient for them. In this paper, we aim to design a noncontact respiration rate detection system utilizing off-the-shelf smartphones. We utilize the single-frequency ultrasound as the media to detect the respiration activity. By analyzing the ultrasound signals received by the built-in microphone sensor in a smartphone, our system can derive the respiration rate of the user. The advantage of our method is that the transmitted signal is easy to generate and the signal analysis is simple, which has lower power consumption and thus is suitable for long-term monitoring in daily life. The experimental result shows that our system can achieve accurate respiration rate estimation under various scenarios.

Detection rates of the MODIS active fire product in the United States

USGS Publications Warehouse

Hawbaker, T.J.; Radeloff, V.C.; Syphard, A.D.; Zhu, Z.; Stewart, S.I.

2008-01-01

MODIS active fire data offer new information about global fire patterns. However, uncertainties in detection rates can render satellite-derived fire statistics difficult to interpret. We evaluated the MODIS 1??km daily active fire product to quantify detection rates for both Terra and Aqua MODIS sensors, examined how cloud cover and fire size affected detection rates, and estimated how detection rates varied across the United States. MODIS active fire detections were compared to 361 reference fires (??? 18??ha) that had been delineated using pre- and post-fire Landsat imagery. Reference fires were considered detected if at least one MODIS active fire pixel occurred within 1??km of the edge of the fire. When active fire data from both Aqua and Terra were combined, 82% of all reference fires were found, but detection rates were less for Aqua and Terra individually (73% and 66% respectively). Fires not detected generally had more cloudy days, but not when the Aqua data were considered exclusively. MODIS detection rates decreased with fire size, and the size at which 50% of all fires were detected was 105??ha when combining Aqua and Terra (195??ha for Aqua and 334??ha for Terra alone). Across the United States, detection rates were greatest in the West, lower in the Great Plains, and lowest in the East. The MODIS active fire product captures large fires in the U.S. well, but may under-represent fires in areas with frequent cloud cover or rapidly burning, small, and low-intensity fires. We recommend that users of the MODIS active fire data perform individual validations to ensure that all relevant fires are included. ?? 2008 Elsevier Inc. All rights reserved.

A CZT-based blood counter for quantitative molecular imaging.

PubMed

Espagnet, Romain; Frezza, Andrea; Martin, Jean-Pierre; Hamel, Louis-André; Lechippey, Laëtitia; Beauregard, Jean-Mathieu; Després, Philippe

2017-12-01

Robust quantitative analysis in positron emission tomography (PET) and in single-photon emission computed tomography (SPECT) typically requires the time-activity curve as an input function for the pharmacokinetic modeling of tracer uptake. For this purpose, a new automated tool for the determination of blood activity as a function of time is presented. The device, compact enough to be used on the patient bed, relies on a peristaltic pump for continuous blood withdrawal at user-defined rates. Gamma detection is based on a 20 × 20 × 15 mm 3 cadmium zinc telluride (CZT) detector, read by custom-made electronics and a field-programmable gate array-based signal processing unit. A graphical user interface (GUI) allows users to select parameters and easily perform acquisitions. This paper presents the overall design of the device as well as the results related to the detector performance in terms of stability, sensitivity and energy resolution. Results from a patient study are also reported. The device achieved a sensitivity of 7.1 cps/(kBq/mL) and a minimum detectable activity of 2.5 kBq/ml for 18 F. The gamma counter also demonstrated an excellent stability with a deviation in count rates inferior to 0.05% over 6 h. An energy resolution of 8% was achieved at 662 keV. The patient study was conclusive and demonstrated that the compact gamma blood counter developed has the sensitivity and the stability required to conduct quantitative molecular imaging studies in PET and SPECT.

Image-based fall detection and classification of a user with a walking support system

NASA Astrophysics Data System (ADS)

Taghvaei, Sajjad; Kosuge, Kazuhiro

2017-10-01

The classification of visual human action is important in the development of systems that interact with humans. This study investigates an image-based classification of the human state while using a walking support system to improve the safety and dependability of these systems.We categorize the possible human behavior while utilizing a walker robot into eight states (i.e., sitting, standing, walking, and five falling types), and propose two different methods, namely, normal distribution and hidden Markov models (HMMs), to detect and recognize these states. The visual feature for the state classification is the centroid position of the upper body, which is extracted from the user's depth images. The first method shows that the centroid position follows a normal distribution while walking, which can be adopted to detect any non-walking state. The second method implements HMMs to detect and recognize these states. We then measure and compare the performance of both methods. The classification results are employed to control the motion of a passive-type walker (called "RT Walker") by activating its brakes in non-walking states. Thus, the system can be used for sit/stand support and fall prevention. The experiments are performed with four subjects, including an experienced physiotherapist. Results show that the algorithm can be adapted to the new user's motion pattern within 40 s, with a fall detection rate of 96.25% and state classification rate of 81.0%. The proposed method can be implemented to other abnormality detection/classification applications that employ depth image-sensing devices.

Posture Detection Based on Smart Cushion for Wheelchair Users

PubMed Central

Ma, Congcong; Li, Wenfeng; Gravina, Raffaele; Fortino, Giancarlo

2017-01-01

The postures of wheelchair users can reveal their sitting habit, mood, and even predict health risks such as pressure ulcers or lower back pain. Mining the hidden information of the postures can reveal their wellness and general health conditions. In this paper, a cushion-based posture recognition system is used to process pressure sensor signals for the detection of user’s posture in the wheelchair. The proposed posture detection method is composed of three main steps: data level classification for posture detection, backward selection of sensor configuration, and recognition results compared with previous literature. Five supervised classification techniques—Decision Tree (J48), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Naive Bayes, and k-Nearest Neighbor (k-NN)—are compared in terms of classification accuracy, precision, recall, and F-measure. Results indicate that the J48 classifier provides the highest accuracy compared to other techniques. The backward selection method was used to determine the best sensor deployment configuration of the wheelchair. Several kinds of pressure sensor deployments are compared and our new method of deployment is shown to better detect postures of the wheelchair users. Performance analysis also took into account the Body Mass Index (BMI), useful for evaluating the robustness of the method across individual physical differences. Results show that our proposed sensor deployment is effective, achieving 99.47% posture recognition accuracy. Our proposed method is very competitive for posture recognition and robust in comparison with other former research. Accurate posture detection represents a fundamental basic block to develop several applications, including fatigue estimation and activity level assessment. PMID:28353684

EULAR Sjögren's syndrome disease activity index (ESSDAI): a user guide

PubMed Central

Seror, Raphaèle; Bowman, Simon J; Brito-Zeron, Pilar; Theander, Elke; Bootsma, Hendrika; Tzioufas, Athanasios; Gottenberg, Jacques-Eric; Ramos-Casals, Manel; Dörner, Thomas; Ravaud, Philippe; Vitali, Claudio; Mariette, Xavier

2015-01-01

The EULAR Sjögren's syndrome (SS) disease activity index (ESSDAI) is a systemic disease activity index that was designed to measure disease activity in patients with primary SS. With the growing use of the ESSDAI, some domains appear to be more challenging to rate than others. The ESSDAI is now in use as a gold standard to measure disease activity in clinical studies, and as an outcome measure, even a primary outcome measure, in current randomised clinical trials. Therefore, ensuring an accurate and reproducible rating of each domain, by providing a more detailed definition of each domain, has emerged as an urgent need. The purpose of the present article is to provide a user guide for the ESSDAI. This guide provides definitions and precisions on the rating of each domain. It also includes some minor improvement of the score to integrate advance in knowledge of disease manifestations. This user guide may help clinicians to use the ESSDAI, and increase the reliability of rating and consequently of the ability to detect true changes over time. This better appraisal of ESSDAI items, along with the recent definition of disease activity levels and minimal clinically important change, will improve the assessment of patients with primary SS and facilitate the demonstration of effectiveness of treatment for patients with primary SS. PMID:26509054

Technical and operational users' opinions of a handheld device to detect directed energy.

PubMed

Boyd, Andrew D; Naiman, Melissa; Stevenson, Greer W; Preston, Richard; Valenta, Annette L

2013-05-01

Lasers, a form of directed energy (DE), are a threat to pilots and Air Force personnel. In light of this threat, a handheld medical device called the "Tricorder" is under development to improve situational awareness of DE. Current operational procedures do not include methods for recording or handling new information regarding DE. The purpose of this study was to understand Air Force personnel opinions and beliefs about desired features and operational use to enhance user acceptance of the Tricorder. Q-methodology was implemented to study opinions and beliefs related to DE. Two groups were approached, medical personnel in the Illinois Air National Guard and four active duty members of an Air Force Rescue Squadron. Both groups completed the same Q-sort of both operational and equipment concerns. Six opinion sets regarding operational concerns described 61% of the total variation in perceptions among participants. The factors were: concern over health effects, implications to individuals, combat/tactical concerns, force health protection, and theater/tactical concerns. Five opinion sets described 68% of the variation in the equipment functions perceived as most important. The participants indicated that ideally the device should measure exposure, enhance laser detection/response, support night vision and ease of use, detect threats, and enhance combat medicine. This survey revealed the complexity of equipment and the operational implications of detecting DE. Q-methodology is a unique strategy to both evaluate technology and explore users' concerns.

Heavy Cannabis Use Associated With Reduction in Activated and Inflammatory Immune Cell Frequencies in Antiretroviral Therapy-Treated Human Immunodeficiency Virus-Infected Individuals.

PubMed

Manuzak, Jennifer A; Gott, Toni M; Kirkwood, Jay S; Coronado, Ernesto; Hensley-McBain, Tiffany; Miller, Charlene; Cheu, Ryan K; Collier, Ann C; Funderburg, Nicholas T; Martin, Jeffery N; Wu, Michael C; Isoherranen, Nina; Hunt, Peter W; Klatt, Nichole R

2018-06-01

Cannabis is a widely used drug in the United States, and the frequency of cannabis use in the human immunodeficiency virus (HIV)-infected population is disproportionately high. Previous human and macaque studies suggest that cannabis may have an impact on plasma viral load; however, the relationship between cannabis use and HIV-associated systemic inflammation and immune activation has not been well defined. The impact of cannabis use on peripheral immune cell frequency, activation, and function was assessed in 198 HIV-infected, antiretroviral-treated individuals by flow cytometry. Individuals were categorized into heavy, medium, or occasional cannabis users or noncannabis users based on the amount of the cannabis metabolite 11-nor-carboxy-tetrahydrocannabinol (THC-COOH) detected in plasma by mass spectrometry. Heavy cannabis users had decreased frequencies of human leukocyte antigen (HLA)-DR+CD38+CD4+ and CD8+ T-cell frequencies, compared to frequencies of these cells in non-cannabis-using individuals. Heavy cannabis users had decreased frequencies of intermediate and nonclassical monocyte subsets, as well as decreased frequencies of interleukin 23- and tumor necrosis factor-α-producing antigen-presenting cells. While the clinical implications are unclear, our findings suggest that cannabis use is associated with a potentially beneficial reduction in systemic inflammation and immune activation in the context of antiretroviral-treated HIV infection.

Emergence of long-range correlations and bursty activity patterns in online communication

NASA Astrophysics Data System (ADS)

Panzarasa, Pietro; Bonaventura, Moreno

2015-12-01

Research has suggested that the activity occurring in a variety of social, economic, and technological systems exhibits long-range fluctuations in time. Pronounced levels of rapidly occurring events are typically observed over short periods of time, followed by long periods of inactivity. Relatively few studies, however, have shed light on the degree to which inhomogeneous temporal processes can be detected at, and emerge from, different levels of analysis. Here we investigate patterns of human activity within an online forum in which communication can be assessed at three intertwined levels: the micro level of the individual users; the meso level of discussion groups and continuous sessions; and the macro level of the whole system. To uncover the relation between different levels, we conduct a number of numerical simulations of a zero-crossing model in which users' behavior is constrained by progressively richer and more realistic rules of social interaction. Results indicate that, when users are solipsistic, their bursty behavior is not sufficient for generating heavy-tailed interevent time distributions at a higher level. However, when users are socially interdependent, the power spectra and interevent time distributions of the simulated and real forums are remarkably similar at all levels of analysis. Social interaction is responsible for the aggregation of multiple bursty activities at the micro level into an emergent bursty activity pattern at a higher level. We discuss the implications of the findings for an emergentist account of burstiness in complex systems.

Active glass-type human augmented cognition system considering attention and intention

NASA Astrophysics Data System (ADS)

Kim, Bumhwi; Ojha, Amitash; Lee, Minho

2015-10-01

Human cognition is the result of an interaction of several complex cognitive processes with limited capabilities. Therefore, the primary objective of human cognitive augmentation is to assist and expand these limited human cognitive capabilities independently or together. In this study, we propose a glass-type human augmented cognition system, which attempts to actively assist human memory functions by providing relevant, necessary and intended information by constantly assessing intention of the user. To achieve this, we exploit selective attention and intention processes. Although the system can be used in various real-life scenarios, we test the performance of the system in a person identity scenario. To detect the intended face, the system analyses the gaze points and change in pupil size to determine the intention of the user. An assessment of the gaze points and change in pupil size together indicates that the user intends to know the identity and information about the person in question. Then, the system retrieves several clues through speech recognition system and retrieves relevant information about the face, which is finally displayed through head-mounted display. We present the performance of several components of the system. Our results show that the active and relevant assistance based on users' intention significantly helps the enhancement of memory functions.

Multimodal audio guide for museums and exhibitions

NASA Astrophysics Data System (ADS)

Gebbensleben, Sandra; Dittmann, Jana; Vielhauer, Claus

2006-02-01

In our paper we introduce a new Audio Guide concept for exploring buildings, realms and exhibitions. Actual proposed solutions work in most cases with pre-defined devices, which users have to buy or borrow. These systems often go along with complex technical installations and require a great degree of user training for device handling. Furthermore, the activation of audio commentary related to the exhibition objects is typically based on additional components like infrared, radio frequency or GPS technology. Beside the necessity of installation of specific devices for user location, these approaches often only support automatic activation with no or limited user interaction. Therefore, elaboration of alternative concepts appears worthwhile. Motivated by these aspects, we introduce a new concept based on usage of the visitor's own mobile smart phone. The advantages in our approach are twofold: firstly the Audio Guide can be used in various places without any purchase and extensive installation of additional components in or around the exhibition object. Secondly, the visitors can experience the exhibition on individual tours only by uploading the Audio Guide at a single point of entry, the Audio Guide Service Counter, and keeping it on her or his personal device. Furthermore, since the user usually is quite familiar with the interface of her or his phone and can thus interact with the application device easily. Our technical concept makes use of two general ideas for location detection and activation. Firstly, we suggest an enhanced interactive number based activation by exploiting the visual capabilities of modern smart phones and secondly we outline an active digital audio watermarking approach, where information about objects are transmitted via an analog audio channel.

EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.

PubMed

Boubchir, Larbi; Touati, Youcef; Daachi, Boubaker; Chérif, Arab Ali

2015-08-01

In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.

Kinect-Based Virtual Game for the Elderly that Detects Incorrect Body Postures in Real Time

PubMed Central

Saenz-de-Urturi, Zelai; Garcia-Zapirain Soto, Begonya

2016-01-01

Poor posture can result in loss of physical function, which is necessary to preserving independence in later life. Its decline is often the determining factor for loss of independence in the elderly. To avoid this, a system to correct poor posture in the elderly, designed for Kinect-based indoor applications, is proposed in this paper. Due to the importance of maintaining a healthy life style in senior citizens, the system has been integrated into a game which focuses on their physical stimulation. The game encourages users to perform physical activities while the posture correction system helps them to adopt proper posture. The system captures limb node data received from the Kinect sensor in order to detect posture variations in real time. The DTW algorithm compares the original posture with the current one to detect any deviation from the original correct position. The system was tested and achieved a successful detection percentage of 95.20%. Experimental tests performed in a nursing home with different users show the effectiveness of the proposed solution. PMID:27196903

Breaking the color barrier - a multi-selective antibody reporter offers innovative strategies of fluorescence detection.

PubMed

Gallo, Eugenio; Jarvik, Jonathan W

2017-08-01

A novel bi-partite fluorescence platform exploits the high affinity and selectivity of antibody scaffolds to capture and activate small-molecule fluorogens. In this report, we investigated the property of multi-selectivity activation by a single antibody against diverse cyanine family fluorogens. Our fluorescence screen identified three cell-impermeant fluorogens, each with unique emission spectra (blue, green and red) and nanomolar affinities. Most importantly, as a protein fusion tag to G-protein-coupled receptors, the antibody biosensor retained full activity - displaying bright fluorogen signals with minimal background on live cells. Because fluorogen-activating antibodies interact with their target ligands via non-covalent interactions, we were able to perform advanced multi-color detection strategies on live cells, previously difficult or impossible with conventional reporters. We found that by fine-tuning the concentrations of the different color fluorogen molecules in solution, a user may interchange the fluorescence signal (onset versus offset), execute real-time signal exchange via fluorogen competition, measure multi-channel fluorescence via co-labeling, and assess real-time cell surface receptor traffic via pulse-chase experiments. Thus, here we inform of an innovative reporter technology based on tri-color signal that allows user-defined fluorescence tuning in live-cell applications. © 2017. Published by The Company of Biologists Ltd.

Pulmonary and latent tuberculosis screening in opiate drug users: an essential and neglected approach for harm-reduction facilities.

PubMed

Honarvar, Behnam; Lankarani, Kamran Bagheri; Odoomi, Neda; Roudgari, Amir; Moghadami, Mohsen; Kazerooni, Parvin Afsar; Abadi, Alireza Hassan

2013-01-01

Opiates drug users are at much higher risk of developing tuberculosis (TB) infection than general population. We conducted this study to determine the susceptibility for pulmonary and latent TB infection in opiates drug users. In this cross-sectional study, all opiates drug users referred to drop-in centers, methadone maintenance clinics, and harm-reduction facilities affiliated with Shiraz University of Medical Sciences in southern Iran were screened for pulmonary and latent TB infection. The participation rate of opiate drug users was 87.66% (263 of 300). Mean age was 37.37 ± 8.33 (range, 20-65) years. Two hundred twenty-six (85.93%) were male and 197 (74.90%) were injection drug users (IDUs). One hundred sixty-three (61.97%) had TB-related symptoms. Culture for TB was positive in 3 patients (1.14%) (2 non-IDUs and 1 IDU). Two patients (0.76%) showed acid-fast bacilli in the direct sputum smear. Eighty-five of 244 patients (34.83%) had a 5- to 10-mm induration in the skin TB test. Twenty-nine of 223 patients (13%) had abnormal findings from chest x-ray films. The prevalence of smear-positive pulmonary TB in opiate drug users is more than 100 times in the general population in Iran. Therefore, active and appropriate screening to detect pulmonary TB infection should be integrated into routine activities at all harm-reduction facilities for drug users, irrespective of their route of drug use or human immunodeficiency virus status, in this country.

A Vision-Based Wayfinding System for Visually Impaired People Using Situation Awareness and Activity-Based Instructions

PubMed Central

Kim, Eun Yi

2017-01-01

A significant challenge faced by visually impaired people is ‘wayfinding’, which is the ability to find one’s way to a destination in an unfamiliar environment. This study develops a novel wayfinding system for smartphones that can automatically recognize the situation and scene objects in real time. Through analyzing streaming images, the proposed system first classifies the current situation of a user in terms of their location. Next, based on the current situation, only the necessary context objects are found and interpreted using computer vision techniques. It estimates the motions of the user with two inertial sensors and records the trajectories of the user toward the destination, which are also used as a guide for the return route after reaching the destination. To efficiently convey the recognized results using an auditory interface, activity-based instructions are generated that guide the user in a series of movements along a route. To assess the effectiveness of the proposed system, experiments were conducted in several indoor environments: the sit in which the situation awareness accuracy was 90% and the object detection false alarm rate was 0.016. In addition, our field test results demonstrate that users can locate their paths with an accuracy of 97%. PMID:28813033

A DNA Logic Gate Automaton for Detection of Rabies and Other Lyssaviruses.

PubMed

Vijayakumar, Pavithra; Macdonald, Joanne

2017-07-05

Immediate activation of biosensors is not always desirable, particularly if activation is due to non-specific interactions. Here we demonstrate the use of deoxyribozyme-based logic gate networks arranged into visual displays to precisely control activation of biosensors, and demonstrate a prototype molecular automaton able to discriminate between seven different genotypes of Lyssaviruses, including Rabies virus. The device uses novel mixed-base logic gates to enable detection of the large diversity of Lyssavirus sequence populations, while an ANDNOT logic gate prevents non-specific activation across genotypes. The resultant device provides a user-friendly digital-like, but molecule-powered, dot-matrix text output for unequivocal results read-out that is highly relevant for point of care applications. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimizing Search and Ranking in Folksonomy Systems by Exploiting Context Information

NASA Astrophysics Data System (ADS)

Abel, Fabian; Henze, Nicola; Krause, Daniel

Tagging systems enable users to annotate resources with freely chosen keywords. The evolving bunch of tag assignments is called folksonomy and there exist already some approaches that exploit folksonomies to improve resource retrieval. In this paper, we analyze and compare graph-based ranking algorithms: FolkRank and SocialPageRank. We enhance these algorithms by exploiting the context of tags, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity itself is easy for users to perform. However, it delivers valuable semantic information about resources and their context. We present GRank that uses the context information to improve and optimize the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.

Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones

PubMed Central

Wei, Jing

2018-01-01

Respiration monitoring is helpful in disease prevention and diagnosis. Traditional respiration monitoring requires users to wear devices on their bodies, which is inconvenient for them. In this paper, we aim to design a noncontact respiration rate detection system utilizing off-the-shelf smartphones. We utilize the single-frequency ultrasound as the media to detect the respiration activity. By analyzing the ultrasound signals received by the built-in microphone sensor in a smartphone, our system can derive the respiration rate of the user. The advantage of our method is that the transmitted signal is easy to generate and the signal analysis is simple, which has lower power consumption and thus is suitable for long-term monitoring in daily life. The experimental result shows that our system can achieve accurate respiration rate estimation under various scenarios. PMID:29853985

Analysis of user characteristics related to drop-off detection with long cane

PubMed Central

Kim, Dae Shik; Emerson, Robert Wall; Curtis, Amy

2010-01-01

This study examined how user characteristics affect drop-off detection with the long cane. A mixed-measures design with block randomization was used for the study, in which 32 visually impaired adults attempted to detect the drop-offs using different cane techniques. Younger cane users detected drop-offs significantly more reliably (mean +/− standard deviation = 74.2% +/− 11.2% of the time) than older cane users (60.9% +/− 10.8%), p = 0.009. The drop-off detection threshold of the younger participants (5.2 +/− 2.1 cm) was also statistically significantly smaller than that of the older participants (7.9 +/− 2.2 cm), p = 0.007. Those with early-onset visual impairment (78.0% +/− 9.0%) also detected drop-offs significantly more reliably than those with later-onset visual impairment (67.3% +/− 12.4%), p = 0.01. No interaction occurred between examined user characteristics (age and age at onset of visual impairment) and the type of cane technique used in drop-off detection. The findings of the study may help orientation and mobility specialists select appropriate cane techniques in accordance with the cane user’s age and onset of visual impairment. PMID:20665349

An Abstract Model of Rogue Code Insertion into Radio Frequency Wireless Networks. The Effects of Computer Viruses on the Program Management Office

DTIC Science & Technology

1994-04-01

numerous articles on wireless LANs, only one by Lathrop discusses their vulnerabilities’. Lathrop’s paper provides an overview of wireless LANs and...to detect any action which deviates from the user’s observed recorded past behavior. These profiles list the operator’s commonly used commands, typing...current system activity audit records to rules describing past behavior patterns. W&S is especially effective in detecting rogue program penetrations. It

CRIM-TRACK: sensor system for detection of criminal chemical substances

NASA Astrophysics Data System (ADS)

Munk, Jens K.; Buus, Ole T.; Larsen, Jan; Dossi, Eleftheria; Tatlow, Sol; Lässig, Lina; Sandström, Lars; Jakobsen, Mogens H.

2015-10-01

Detection of illegal compounds requires a reliable, selective and sensitive detection device. The successful device features automated target acquisition, identification and signal processing. It is portable, fast, user friendly, sensitive, specific, and cost efficient. LEAs are in need of such technology. CRIM-TRACK is developing a sensing device based on these requirements. We engage highly skilled specialists from research institutions, industry, SMEs and LEAs and rely on a team of end users to benefit maximally from our prototypes. Currently we can detect minute quantities of drugs, explosives and precursors thereof in laboratory settings. Using colorimetric technology we have developed prototypes that employ disposable sensing chips. Ease of operation and intuitive sensor response are highly prioritized features that we implement as we gather data to feed into machine learning. With machine learning our ability to detect threat compounds amidst harmless substances improves. Different end users prefer their equipment optimized for their specific field. In an explosives-detecting scenario, the end user may prefer false positives over false negatives, while the opposite may be true in a drug-detecting scenario. Such decisions will be programmed to match user preference. Sensor output can be as detailed as the sensor allows. The user can be informed of the statistics behind the detection, identities of all detected substances, and quantities thereof. The response can also be simplified to "yes" vs. "no". The technology under development in CRIM-TRACK will provide custom officers, police and other authorities with an effective tool to control trafficking of illegal drugs and drug precursors.

Ant-Based Cyber Defense (also known as

DOE Office of Scientific and Technical Information (OSTI.GOV)

Glenn Fink, PNNL

2015-09-29

ABCD is a four-level hierarchy with human supervisors at the top, a top-level agent called a Sergeant controlling each enclave, Sentinel agents located at each monitored host, and mobile Sensor agents that swarm through the enclaves to detect cyber malice and misconfigurations. The code comprises four parts: (1) the core agent framework, (2) the user interface and visualization, (3) test-range software to create a network of virtual machines including a simulated Internet and user and host activity emulation scripts, and (4) a test harness to allow the safe running of adversarial code within the framework of monitored virtual machines.

Impact of incomplete correspondence between document titles and texts on users' representations: a cognitive and linguistic analysis based on 25 technical documents.

PubMed

Eyrolle, Hélène; Virbel, Jacques; Lemarié, Julie

2008-03-01

Based on previous research in the field of cognitive psychology, highlighting the facilitatory effects of titles on several text-related activities, this paper looks at the extent to which titles reflect text content. An exploratory study of real-life technical documents investigated the content of their Subject lines, which linguistic analyses had led us to regard as titles. The study showed that most of the titles supplied by the writers failed to represent the documents' contents and that most users failed to detect this lack of validity.

Trajectory Based Behavior Analysis for User Verification

NASA Astrophysics Data System (ADS)

Pao, Hsing-Kuo; Lin, Hong-Yi; Chen, Kuan-Ta; Fadlil, Junaidillah

Many of our activities on computer need a verification step for authorized access. The goal of verification is to tell apart the true account owner from intruders. We propose a general approach for user verification based on user trajectory inputs. The approach is labor-free for users and is likely to avoid the possible copy or simulation from other non-authorized users or even automatic programs like bots. Our study focuses on finding the hidden patterns embedded in the trajectories produced by account users. We employ a Markov chain model with Gaussian distribution in its transitions to describe the behavior in the trajectory. To distinguish between two trajectories, we propose a novel dissimilarity measure combined with a manifold learnt tuning for catching the pairwise relationship. Based on the pairwise relationship, we plug-in any effective classification or clustering methods for the detection of unauthorized access. The method can also be applied for the task of recognition, predicting the trajectory type without pre-defined identity. Given a trajectory input, the results show that the proposed method can accurately verify the user identity, or suggest whom owns the trajectory if the input identity is not provided.

A Novel Detection Model and Its Optimal Features to Classify Falls from Low- and High-Acceleration Activities of Daily Life Using an Insole Sensor System

PubMed Central

Cates, Benjamin; Sim, Taeyong; Heo, Hyun Mu; Kim, Bori; Kim, Hyunggun; Mun, Joung Hwan

2018-01-01

In order to overcome the current limitations in current threshold-based and machine learning-based fall detectors, an insole system and novel fall classification model were created. Because high-acceleration activities have a high risk for falls, and because of the potential damage that is associated with falls during high-acceleration activities, four low-acceleration activities, four high-acceleration activities, and eight types of high-acceleration falls were performed by twenty young male subjects. Encompassing a total of 800 falls and 320 min of activities of daily life (ADLs), the created Support Vector Machine model’s Leave-One-Out cross-validation provides a fall detection sensitivity (0.996), specificity (1.000), and accuracy (0.999). These classification results are similar or superior to other fall detection models in the literature, while also including high-acceleration ADLs to challenge the classification model, and simultaneously reducing the burden that is associated with wearable sensors and increasing user comfort by inserting the insole system into the shoe. PMID:29673165

Development of a smartphone application to measure physical activity using sensor-assisted self-report.

PubMed

Dunton, Genevieve Fridlund; Dzubur, Eldin; Kawabata, Keito; Yanez, Brenda; Bo, Bin; Intille, Stephen

2014-01-01

Despite the known advantages of objective physical activity monitors (e.g., accelerometers), these devices have high rates of non-wear, which leads to missing data. Objective activity monitors are also unable to capture valuable contextual information about behavior. Adolescents recruited into physical activity surveillance and intervention studies will increasingly have smartphones, which are miniature computers with built-in motion sensors. This paper describes the design and development of a smartphone application ("app") called Mobile Teen that combines objective and self-report assessment strategies through (1) sensor-informed context-sensitive ecological momentary assessment (CS-EMA) and (2) sensor-assisted end-of-day recall. The Mobile Teen app uses the mobile phone's built-in motion sensor to automatically detect likely bouts of phone non-wear, sedentary behavior, and physical activity. The app then uses transitions between these inferred states to trigger CS-EMA self-report surveys measuring the type, purpose, and context of activity in real-time. The end of the day recall component of the Mobile Teen app allows users to interactively review and label their own physical activity data each evening using visual cues from automatically detected major activity transitions from the phone's built-in motion sensors. Major activity transitions are identified by the app, which cues the user to label that "chunk," or period, of time using activity categories. Sensor-driven CS-EMA and end-of-day recall smartphone apps can be used to augment physical activity data collected by objective activity monitors, filling in gaps during non-wear bouts and providing additional real-time data on environmental, social, and emotional correlates of behavior. Smartphone apps such as these have potential for affordable deployment in large-scale epidemiological and intervention studies.

Development of a Smartphone Application to Measure Physical Activity Using Sensor-Assisted Self-Report

PubMed Central

Dunton, Genevieve Fridlund; Dzubur, Eldin; Kawabata, Keito; Yanez, Brenda; Bo, Bin; Intille, Stephen

2013-01-01

Introduction: Despite the known advantages of objective physical activity monitors (e.g., accelerometers), these devices have high rates of non-wear, which leads to missing data. Objective activity monitors are also unable to capture valuable contextual information about behavior. Adolescents recruited into physical activity surveillance and intervention studies will increasingly have smartphones, which are miniature computers with built-in motion sensors. Methods: This paper describes the design and development of a smartphone application (“app”) called Mobile Teen that combines objective and self-report assessment strategies through (1) sensor-informed context-sensitive ecological momentary assessment (CS-EMA) and (2) sensor-assisted end-of-day recall. Results: The Mobile Teen app uses the mobile phone’s built-in motion sensor to automatically detect likely bouts of phone non-wear, sedentary behavior, and physical activity. The app then uses transitions between these inferred states to trigger CS-EMA self-report surveys measuring the type, purpose, and context of activity in real-time. The end of the day recall component of the Mobile Teen app allows users to interactively review and label their own physical activity data each evening using visual cues from automatically detected major activity transitions from the phone’s built-in motion sensors. Major activity transitions are identified by the app, which cues the user to label that “chunk,” or period, of time using activity categories. Conclusion: Sensor-driven CS-EMA and end-of-day recall smartphone apps can be used to augment physical activity data collected by objective activity monitors, filling in gaps during non-wear bouts and providing additional real-time data on environmental, social, and emotional correlates of behavior. Smartphone apps such as these have potential for affordable deployment in large-scale epidemiological and intervention studies. PMID:24616888

Geographic Information Technologies as an outreach activity in geo-scientific education

NASA Astrophysics Data System (ADS)

Maman, Shimrit; Isaacson, Sivan; Blumberg, Dan G.

2016-04-01

In recent years, a decline in the rates of examinees in the academic track that were entitled to an enhanced matriculation certificate in scientific-technological education was reported in Israel. To confront this problem the Earth and Planetary Image Facility (EPIF) at Ben-Gurion University of the Negev fosters interdisciplinary exploration through educational programs that make use of the facility and its equipment and enable the empowerment of the community by understanding and appreciating science and technology. This is achieved by using Geographic Information Technologies (GIT) such as remote sensing and Geographical Information Systems (GIS) for geo-physical sciences in activities that combine theoretical background with hands-on activities. Monitoring Earth from space by satellites, digital atlases and virtual-based positioning applications are examples for fusion of spatial information (geographic) and technology that the activity is based on. GIT opens a new chapter and a recent history of Cartography starting from the collection of spatial data to its presentation and analysis. GIS have replaced the use of classical atlas books and offer a variety of Web-based applications that provide maps and display up-to-date imagery. The purpose of this workshop is to expose teachers and students to GITs which are applicable in every classroom. The activity imparts free geographic information systems that exist in cyberspace and accessible to single users as the Israeli national GIS and Google earth, which are based on a spatial data and long term local and global satellite imagery coverage. In this paper, our "Think global-Map Local" activity is presented. The activity uses GIS and change detection technologies as means to encourage students to explore environmental issues both around the globe and close to their surroundings. The students detect changes by comparing multi temporal images of a chosen site and learn how to map the alterations and produce change detection maps with simple and user friendly tools. The activity is offered both for students and supervised projects for teachers and youth.

How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?

PubMed Central

Gjoreski, Martin; Gjoreski, Hristijan; Luštrek, Mitja; Gams, Matjaž

2016-01-01

Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject’s daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data). PMID:27258282

A Two-Stage Composition Method for Danger-Aware Services Based on Context Similarity

NASA Astrophysics Data System (ADS)

Wang, Junbo; Cheng, Zixue; Jing, Lei; Ota, Kaoru; Kansen, Mizuo

Context-aware systems detect user's physical and social contexts based on sensor networks, and provide services that adapt to the user accordingly. Representing, detecting, and managing the contexts are important issues in context-aware systems. Composition of contexts is a useful method for these works, since it can detect a context by automatically composing small pieces of information to discover service. Danger-aware services are a kind of context-aware services which need description of relations between a user and his/her surrounding objects and between users. However when applying the existing composition methods to danger-aware services, they show the following shortcomings that (1) they have not provided an explicit method for representing composition of multi-user' contexts, (2) there is no flexible reasoning mechanism based on similarity of contexts, so that they can just provide services exactly following the predefined context reasoning rules. Therefore, in this paper, we propose a two-stage composition method based on context similarity to solve the above problems. The first stage is composition of the useful information to represent the context for a single user. The second stage is composition of multi-users' contexts to provide services by considering the relation of users. Finally the danger degree of the detected context is computed by using context similarity between the detected context and the predefined context. Context is dynamically represented based on two-stage composition rules and a Situation theory based Ontology, which combines the advantages of Ontology and Situation theory. We implement the system in an indoor ubiquitous environment, and evaluate the system through two experiments with the support of subjects. The experiment results show the method is effective, and the accuracy of danger detection is acceptable to a danger-aware system.

Detection of Anomalous Insiders in Collaborative Environments via Relational Analysis of Access Logs

PubMed Central

Chen, You; Malin, Bradley

2014-01-01

Collaborative information systems (CIS) are deployed within a diverse array of environments, ranging from the Internet to intelligence agencies to healthcare. It is increasingly the case that such systems are applied to manage sensitive information, making them targets for malicious insiders. While sophisticated security mechanisms have been developed to detect insider threats in various file systems, they are neither designed to model nor to monitor collaborative environments in which users function in dynamic teams with complex behavior. In this paper, we introduce a community-based anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on information recorded in the access logs of collaborative environments. CADS is based on the observation that typical users tend to form community structures, such that users with low a nity to such communities are indicative of anomalous and potentially illicit behavior. The model consists of two primary components: relational pattern extraction and anomaly detection. For relational pattern extraction, CADS infers community structures from CIS access logs, and subsequently derives communities, which serve as the CADS pattern core. CADS then uses a formal statistical model to measure the deviation of users from the inferred communities to predict which users are anomalies. To empirically evaluate the threat detection model, we perform an analysis with six months of access logs from a real electronic health record system in a large medical center, as well as a publicly-available dataset for replication purposes. The results illustrate that CADS can distinguish simulated anomalous users in the context of real user behavior with a high degree of certainty and with significant performance gains in comparison to several competing anomaly detection models. PMID:25485309

An Interative Grahical User Interface for Maritime Security Services

NASA Astrophysics Data System (ADS)

Reize, T.; Müller, R.; Kiefl, R.

2013-10-01

In order to analyse optical satellite images for maritime security issues in Near-Real-Time (NRT) an interactive graphical user interface (GUI) based on NASA World Wind was developed and is presented in this article. Targets or activities can be detected, measured and classified with this tool simply and quickly. The service uses optical satellite images, currently taken from 6 sensors: Worldview-1 and Worldview-2, Ikonos, Quickbird, GeoEye-1 and EROS-B. The GUI can also handle SAR-images, air-borne images or UAV images. Software configurations are provided in a job-order file and thus all preparation tasks, such as image installation are performed fully automatically. The imagery can be overlaid with vessels derived by an automatic detection processor. These potential vessel layers can be zoomed in by a single click and sorted with an adapted method. Further object properties, such as vessel type or confidence level of identification, can be added by the operator manually. The heading angle can be refined by dragging the vessel's head or switching it to 180° with a single click. Further vessels or other relevant objects can be added. The objects length, width, heading and position are calculated automatically from three clicks on top, bottom and an arbitrary point at one of the object's longer side. In case of an Activity Detection, the detected objects can be grouped in area of interests (AOI) and classified, according to the ordered activities. All relevant information is finally written to an exchange file, after quality control and necessary correction procedures are performed. If required, image thumbnails can be cut around objects or around whole areas of interest and saved as separated, geo-referenced images.

Voice Based City Panic Button System

NASA Astrophysics Data System (ADS)

Febriansyah; Zainuddin, Zahir; Bachtiar Nappu, M.

2018-03-01

The development of voice activated panic button application aims to design faster early notification of hazardous condition in community to the nearest police by using speech as the detector where the current application still applies touch-combination on screen and use coordination of orders from control center then the early notification still takes longer time. The method used in this research was by using voice recognition as the user voice detection and haversine formula for the comparison of closest distance between the user and the police. This research was equipped with auto sms, which sent notification to the victim’s relatives, that was also integrated with Google Maps application (GMaps) as the map to the victim’s location. The results show that voice registration on the application reaches 100%, incident detection using speech recognition while the application is running is 94.67% in average, and the auto sms to the victim relatives reaches 100%.

Identification of Program Signatures from Cloud Computing System Telemetry Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nichols, Nicole M.; Greaves, Mark T.; Smith, William P.

Malicious cloud computing activity can take many forms, including running unauthorized programs in a virtual environment. Detection of these malicious activities while preserving the privacy of the user is an important research challenge. Prior work has shown the potential viability of using cloud service billing metrics as a mechanism for proxy identification of malicious programs. Previously this novel detection method has been evaluated in a synthetic and isolated computational environment. In this paper we demonstrate the ability of billing metrics to identify programs, in an active cloud computing environment, including multiple virtual machines running on the same hypervisor. The openmore » source cloud computing platform OpenStack, is used for private cloud management at Pacific Northwest National Laboratory. OpenStack provides a billing tool (Ceilometer) to collect system telemetry measurements. We identify four different programs running on four virtual machines under the same cloud user account. Programs were identified with up to 95% accuracy. This accuracy is dependent on the distinctiveness of telemetry measurements for the specific programs we tested. Future work will examine the scalability of this approach for a larger selection of programs to better understand the uniqueness needed to identify a program. Additionally, future work should address the separation of signatures when multiple programs are running on the same virtual machine.« less

An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling

NASA Astrophysics Data System (ADS)

Moghadamfalahi, Mohammad; Akcakaya, Murat; Nezamfar, Hooman; Sourati, Jamshid; Erdogmus, Deniz

2017-10-01

A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm design in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset characters. However, the typing accuracy and also typing speed can potentially be enhanced with more informed subset selection and flash assignment. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to presentation in each iteration, rather than showing a subset of randomly selected characters, the developed framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed.

DESI-Detection of early-season invasives (software-installation manual and user's guide version 1.0)

USGS Publications Warehouse

Kokaly, Raymond F.

2011-01-01

This report describes a software system for detecting early-season invasive plant species, such as cheatgrass. The report includes instructions for installing the software and serves as a user's guide in processing Landsat satellite remote sensing data to map the distributions of cheatgrass and other early-season invasive plants. The software was developed for application to the semi-arid regions of southern Utah; however, the detection parameters can be altered by the user for application to other areas.

Effectively identifying user profiles in network and host metrics

NASA Astrophysics Data System (ADS)

Murphy, John P.; Berk, Vincent H.; Gregorio-de Souza, Ian

2010-04-01

This work presents a collection of methods that is used to effectively identify users of computers systems based on their particular usage of the software and the network. Not only are we able to identify individual computer users by their behavioral patterns, we are also able to detect significant deviations in their typical computer usage over time, or compared to a group of their peers. For instance, most people have a small, and relatively unique selection of regularly visited websites, certain email services, daily work hours, and typical preferred applications for mandated tasks. We argue that these habitual patterns are sufficiently specific to identify fully anonymized network users. We demonstrate that with only a modest data collection capability, profiles of individual computer users can be constructed so as to uniquely identify a profiled user from among their peers. As time progresses and habits or circumstances change, the methods presented update each profile so that changes in user behavior can be reliably detected over both abrupt and gradual time frames, without losing the ability to identify the profiled user. The primary benefit of our methodology allows one to efficiently detect deviant behaviors, such as subverted user accounts, or organizational policy violations. Thanks to the relative robustness, these techniques can be used in scenarios with very diverse data collection capabilities, and data privacy requirements. In addition to behavioral change detection, the generated profiles can also be compared against pre-defined examples of known adversarial patterns.

Prevention of sexually transmitted infections using mobile devices and ubiquitous computing.

PubMed

Besoain, Felipe; Perez-Navarro, Antoni; Caylà, Joan A; Aviñó, Constanza Jacques; de Olalla, Patricia García

2015-05-03

Advances in the development of information and communication technologies have facilitated social interrelationships, but also sexual contacts without appropriate preventive measures. In this paper, we will focus on situations in which people use applications to meet sexual partners nearby, which could increase their chance of exposure to sexually transmitted infections (STI). How can we encourage users to adopt preventive measures without violating their privacy or infringing on the character of the application? To achieve the goal of preventing STI, we have used the design and creation methodology and have developed a prototype software package. This prototype follows the RESTful services principles and has two parts: an Android OS application with emphasis on ubiquitous computing and designed according to General Responsibility Assignment Software Patterns (GRASP), and a server with a web page. To choose the preventive messages, we performed a test in 17 men who have sex with men (MSM). Our software sends preventive notifications to users when it detects situations such as the activation of particular applications on their smartphones, or their proximity to areas with a high probability of intercourse (hot zones). The underlying idea is the same as that for warning messages on cigarette packets, since users read the message just when they are going to smoke. The messages used have been selected from a list that has been rated by the users themselves. The most popular message is "Enjoy sex and enjoy life. Do not expose yourself to HIV". The user is unaware of the software, which runs in the background. Ubiquitous computing may be useful for alerting users with preventive and educational messages. The proposed application is non-intrusive because: 1) the users themselves decide to install it and, therefore, users' privacy rights are preserved; 2) it sends a message that helps users think about taking appropriate preventive measures; and 3) it works in the background without interfering with users unless a trigger situation is detected. Thus, this type of application could become an important tool in the complex task of STI prevention.

Assisting the visually impaired: obstacle detection and warning system by acoustic feedback.

PubMed

Rodríguez, Alberto; Yebes, J Javier; Alcantarilla, Pablo F; Bergasa, Luis M; Almazán, Javier; Cela, Andrés

2012-12-17

The aim of this article is focused on the design of an obstacle detection system for assisting visually impaired people. A dense disparity map is computed from the images of a stereo camera carried by the user. By using the dense disparity map, potential obstacles can be detected in 3D in indoor and outdoor scenarios. A ground plane estimation algorithm based on RANSAC plus filtering techniques allows the robust detection of the ground in every frame. A polar grid representation is proposed to account for the potential obstacles in the scene. The design is completed with acoustic feedback to assist visually impaired users while approaching obstacles. Beep sounds with different frequencies and repetitions inform the user about the presence of obstacles. Audio bone conducting technology is employed to play these sounds without interrupting the visually impaired user from hearing other important sounds from its local environment. A user study participated by four visually impaired volunteers supports the proposed system.

Assisting the Visually Impaired: Obstacle Detection and Warning System by Acoustic Feedback

PubMed Central

Rodríguez, Alberto; Yebes, J. Javier; Alcantarilla, Pablo F.; Bergasa, Luis M.; Almazán, Javier; Cela, Andrés

2012-01-01

The aim of this article is focused on the design of an obstacle detection system for assisting visually impaired people. A dense disparity map is computed from the images of a stereo camera carried by the user. By using the dense disparity map, potential obstacles can be detected in 3D in indoor and outdoor scenarios. A ground plane estimation algorithm based on RANSAC plus filtering techniques allows the robust detection of the ground in every frame. A polar grid representation is proposed to account for the potential obstacles in the scene. The design is completed with acoustic feedback to assist visually impaired users while approaching obstacles. Beep sounds with different frequencies and repetitions inform the user about the presence of obstacles. Audio bone conducting technology is employed to play these sounds without interrupting the visually impaired user from hearing other important sounds from its local environment. A user study participated by four visually impaired volunteers supports the proposed system. PMID:23247413

Understanding Active and Passive Users: The Effects of an Active User Using Normal, Hard and Unreliable Technologies on User Assessment of Trust in Technology and Co-User

PubMed Central

Montague, Enid; JieXu

2011-01-01

The aim of this study was to understand how passive users perceive the trustworthiness of active users and technologies under varying technological conditions. An experimental study was designed to vary the functioning of technologies that active users interacted with, while passive users observed these interactions. Active and passive user ratings of technology and partner were collected. Exploratory data analysis suggests that passive users developed perceptions of technologies based on the functioning of the technology and how the active user interacted with the technologies. Findings from this research have implications for the design of technologies in environments where active and passive users interact with technologies in different ways. Future work in this area should explore interventions that lead to enhanced affective engagement and trust calibration. PMID:22192788

On the Prototyping of an ICT-Enhanced Toilet System for Assisting Older Persons Living Independently and Safely at Home.

PubMed

Panek, Paul; Fazekas, Gabor; Lüftenegger, Theresa; Mayer, Peter; Pilissy, Tamas; Raffaelli, Matteo; Rist, Atilla; Rosenthal, Ramona; Savanovic, Arso; Sobjak, Anna; Sonntag, Franziska; Toth, Andras; Unger, Birgit

2017-01-01

Standard toilets often do not meet the needs of a significant number of older persons and persons with disabilities. The EU funded iToilet project aims at design and development of a new type of ICT enhanced modular toilet system which shall be able to support autonomy, dignity and safety of older persons living at home. Methodologically the project started with gathering user requirements by means of questionnaires, interviews and focus group discussion involving a total of 74 persons, thereof 41 subjects with movement disorders (primary users), 21 caregivers (secondary users) and 12 healthcare managers (tertiary users). Most important wishes were bilateral removable handrails, height and tilt adjustment, emergency detection, simplicity. In parallel to the ongoing technical development participatory design activities have been carried out at user test sites in order to continuously involve users into the design process and to allow quick feedback with regards to early prototype parts. The project currently is working on the finalization of the first prototype ready to enter the lab trial stage in spring 2017. The experiences will be used for redesigning a prototype 2 which is planned to be tested in real life settings early 2018.

Inferring transposons activity chronology by TRANScendence - TEs database and de-novo mining tool.

PubMed

Startek, Michał Piotr; Nogły, Jakub; Gromadka, Agnieszka; Grzebelus, Dariusz; Gambin, Anna

2017-10-16

The constant progress in sequencing technology leads to ever increasing amounts of genomic data. In the light of current evidence transposable elements (TEs for short) are becoming useful tools for learning about the evolution of host genome. Therefore the software for genome-wide detection and analysis of TEs is of great interest. Here we describe the computational tool for mining, classifying and storing TEs from newly sequenced genomes. This is an online, web-based, user-friendly service, enabling users to upload their own genomic data, and perform de-novo searches for TEs. The detected TEs are automatically analyzed, compared to reference databases, annotated, clustered into families, and stored in TEs repository. Also, the genome-wide nesting structure of found elements are detected and analyzed by new method for inferring evolutionary history of TEs. We illustrate the functionality of our tool by performing a full-scale analyses of TE landscape in Medicago truncatula genome. TRANScendence is an effective tool for the de-novo annotation and classification of transposable elements in newly-acquired genomes. Its streamlined interface makes it well-suited for evolutionary studies.

Shilling attack detection for recommender systems based on credibility of group users and rating time series.

PubMed

Zhou, Wei; Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian

2018-01-01

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user's credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.

Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use.

PubMed

Pineau, Joelle; Moghaddam, Athena K; Yuen, Hiu Kim; Archambault, Philippe S; Routhier, François; Michaud, François; Boissy, Patrick

2014-01-01

Using a powered wheelchair (PW) is a complex task requiring advanced perceptual and motor control skills. Unfortunately, PW incidents and accidents are not uncommon and their consequences can be serious. The objective of this paper is to develop technological tools that can be used to characterize a wheelchair user's driving behavior under various settings. In the experiments conducted, PWs are outfitted with a datalogging platform that records, in real-time, the 3-D acceleration of the PW. Data collection was conducted over 35 different activities, designed to capture a spectrum of PW driving events performed at different speeds (collisions with fixed or moving objects, rolling on incline plane, and rolling across multiple types obstacles). The data was processed using time-series analysis and data mining techniques, to automatically detect and identify the different events. We compared the classification accuracy using four different types of time-series features: 1) time-delay embeddings; 2) time-domain characterization; 3) frequency-domain features; and 4) wavelet transforms. In the analysis, we compared the classification accuracy obtained when distinguishing between safe and unsafe events during each of the 35 different activities. For the purposes of this study, unsafe events were defined as activities containing collisions against objects at different speed, and the remainder were defined as safe events. We were able to accurately detect 98% of unsafe events, with a low (12%) false positive rate, using only five examples of each activity. This proof-of-concept study shows that the proposed approach has the potential of capturing, based on limited input from embedded sensors, contextual information on PW use, and of automatically characterizing a user's PW driving behavior.

An automated processing chains for surface temperature monitoring on Earth's most active volcanoes by optical data from multiple satellites

NASA Astrophysics Data System (ADS)

Silvestri, Malvina; Musacchio, Massimo; Fabrizia Buongiorno, Maria

2017-04-01

The Geohazards Exploitation Platform, or GEP is one of six Thematic Exploitation Platforms developed by ESA to serve data user communities. As a new element of the ground segment delivering satellite results to users, these cloud-based platforms provide an online environment to access information, processing tools, computing resources for community collaboration. The aim is to enable the easy extraction of valuable knowledge from vast quantities of satellite-sensed data now being produced by Europe's Copernicus programme and other Earth observation satellites. In this context, the estimation of surface temperature on active volcanoes around the world is considered. E2E processing chains have been developed for different satellite data (ASTER, Landsat8 and Sentinel 3 missions) using thermal infrared (TIR) channels by applying specific algorithms. These chains have been implemented on the GEP platform enabling the use of EO missions and the generation of added value product such as surface temperature map, from not skilled users. This solution will enhance the use of satellite data and improve the dissemination of the results saving valuable time (no manual browsing, downloading or processing is needed) and producing time series data that can be speedily extracted from a single co-registered pixel, to highlight gradual trends within a narrow area. Moreover, thanks to the high-resolution optical imagery of Sentinel 2 (MSI), the detection of lava maps during an eruption can be automatically obtained. The proposed lava detection method is based on a contextual algorithm applied to Sentinel-2 NIR (band 8 - 0.8 micron) and SWIR (band 12 - 2.25 micron) data. Examples derived by last eruptions on active volcanoes are showed.

Unobstructive Body Area Networks (BAN) for efficient movement monitoring.

PubMed

Felisberto, Filipe; Costa, Nuno; Fdez-Riverola, Florentino; Pereira, António

2012-01-01

The technological advances in medical sensors, low-power microelectronics and miniaturization, wireless communications and networks have enabled the appearance of a new generation of wireless sensor networks: the so-called wireless body area networks (WBAN). These networks can be used for continuous monitoring of vital parameters, movement, and the surrounding environment. The data gathered by these networks contributes to improve users' quality of life and allows the creation of a knowledge database by using learning techniques, useful to infer abnormal behaviour. In this paper we present a wireless body area network architecture to recognize human movement, identify human postures and detect harmful activities in order to prevent risk situations. The WBAN was created using tiny, cheap and low-power nodes with inertial and physiological sensors, strategically placed on the human body. Doing so, in an as ubiquitous as possible way, ensures that its impact on the users' daily actions is minimum. The information collected by these sensors is transmitted to a central server capable of analysing and processing their data. The proposed system creates movement profiles based on the data sent by the WBAN's nodes, and is able to detect in real time any abnormal movement and allows for a monitored rehabilitation of the user.

Affective assessment of computer users based on processing the pupil diameter signal.

PubMed

Ren, Peng; Barreto, Armando; Gao, Ying; Adjouadi, Malek

2011-01-01

Detecting affective changes of computer users is a current challenge in human-computer interaction which is being addressed with the help of biomedical engineering concepts. This article presents a new approach to recognize the affective state ("relaxation" vs. "stress") of a computer user from analysis of his/her pupil diameter variations caused by sympathetic activation. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features are extracted from the preprocessed PD signal for the affective state classification. Finally, a random tree classifier is implemented, achieving an accuracy of 86.78%. In these experiments the Eye Blink Frequency (EBF), is also recorded and used for affective state classification, but the results show that the PD is a more promising physiological signal for affective assessment.

PEGASO: A Personalised and Motivational ICT System to Empower Adolescents Towards Healthy Lifestyles.

PubMed

Carrino, Stefano; Caon, Maurizio; Angelini, Leonardo; Mugellini, Elena; Abou Khaled, Omar; Orte, Silvia; Vargiu, Eloisa; Coulson, Neil; Serrano, José C E; Tabozzi, Sarah; Lafortuna, Claudio; Rizzo, Giovanna

2014-01-01

Unhealthy alimentary behaviours and physical inactivity habits are key risk factors for major non communicable diseases. Several researches demonstrate that juvenile obesity can lead to serious medical conditions, pathologies and have important psycho-social consequences. PEGASO is a multidisciplinary project aimed at promoting healthy lifestyles among teenagers through assistive technology. The core of this project is represented by the ICT system, which allows providing tailored interventions to the users through their smartphones in order to motivate them. The novelty of this approach consists of developing a Virtual Individual Model (VIM) for user characterization, which is based on physical, functional and behavioural parameters opportunely selected by experts. These parameters are digitised and updated thanks to the user monitoring through smartphone; data mining algorithms are applied for the detection of activity and nutrition habits and this information is used to provide personalised feedback. The user interface will be developed using gamified approaches and integrating serious games to effectively promote health literacy and facilitate behaviour change.

Learning to Detect Vandalism in Social Content Systems: A Study on Wikipedia

NASA Astrophysics Data System (ADS)

Javanmardi, Sara; McDonald, David W.; Caruana, Rich; Forouzan, Sholeh; Lopes, Cristina V.

A challenge facing user generated content systems is vandalism, i.e. edits that damage content quality. The high visibility and easy access to social networks makes them popular targets for vandals. Detecting and removing vandalism is critical for these user generated content systems. Because vandalism can take many forms, there are many different kinds of features that are potentially useful for detecting it. The complex nature of vandalism, and the large number of potential features, make vandalism detection difficult and time consuming for human editors. Machine learning techniques hold promise for developing accurate, tunable, and maintainable models that can be incorporated into vandalism detection tools. We describe a method for training classifiers for vandalism detection that yields classifiers that are more accurate on the PAN 2010 corpus than others previously developed. Because of the high turnaround in social network systems, it is important for vandalism detection tools to run in real-time. To this aim, we use feature selection to find the minimal set of features consistent with high accuracy. In addition, because some features are more costly to compute than others, we use cost-sensitive feature selection to reduce the total computational cost of executing our models. In addition to the features previously used for spam detection, we introduce new features based on user action histories. The user history features contribute significantly to classifier performance. The approach we use is general and can easily be applied to other user generated content systems.

Blind information-theoretic multiuser detection algorithms for DS-CDMA and WCDMA downlink systems.

PubMed

Waheed, Khuram; Salem, Fathi M

2005-07-01

Code division multiple access (CDMA) is based on the spread-spectrum technology and is a dominant air interface for 2.5G, 3G, and future wireless networks. For the CDMA downlink, the transmitted CDMA signals from the base station (BS) propagate through a noisy multipath fading communication channel before arriving at the receiver of the user equipment/mobile station (UE/MS). Classical CDMA single-user detection (SUD) algorithms implemented in the UE/MS receiver do not provide the required performance for modern high data-rate applications. In contrast, multi-user detection (MUD) approaches require a lot of a priori information not available to the UE/MS. In this paper, three promising adaptive Riemannian contra-variant (or natural) gradient based user detection approaches, capable of handling the highly dynamic wireless environments, are proposed. The first approach, blind multiuser detection (BMUD), is the process of simultaneously estimating multiple symbol sequences associated with all the users in the downlink of a CDMA communication system using only the received wireless data and without any knowledge of the user spreading codes. This approach is applicable to CDMA systems with relatively short spreading codes but becomes impractical for systems using long spreading codes. We also propose two other adaptive approaches, namely, RAKE -blind source recovery (RAKE-BSR) and RAKE-principal component analysis (RAKE-PCA) that fuse an adaptive stage into a standard RAKE receiver. This adaptation results in robust user detection algorithms with performance exceeding the linear minimum mean squared error (LMMSE) detectors for both Direct Sequence CDMA (DS-CDMA) and wide-band CDMA (WCDMA) systems under conditions of congestion, imprecise channel estimation and unmodeled multiple access interference (MAI).

Automated Coding Software: Development and Use to Enhance Anti-Fraud Activities*

PubMed Central

Garvin, Jennifer H.; Watzlaf, Valerie; Moeini, Sohrab

2006-01-01

This descriptive research project identified characteristics of automated coding systems that have the potential to detect improper coding and to minimize improper or fraudulent coding practices in the setting of automated coding used with the electronic health record (EHR). Recommendations were also developed for software developers and users of coding products to maximize anti-fraud practices. PMID:17238546

Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields.

PubMed

Cabezas, M; Corral, J F; Oliver, A; Díez, Y; Tintoré, M; Auger, C; Montalban, X; Lladó, M; Pareto, D; Rovira, À

2016-06-09

Detection of disease activity, defined as new/enlarging T2 lesions on brain MR imaging, has been proposed as a biomarker in MS. However, detection of new/enlarging T2 lesions can be hindered by several factors that can be overcome with image subtraction. The purpose of this study was to improve automated detection of new T2 lesions and reduce user interaction to eliminate inter- and intraobserver variability. Multiparametric brain MR imaging was performed at 2 time points in 36 patients with new T2 lesions. Images were registered by using an affine transformation and the Demons algorithm to obtain a deformation field. After affine registration, images were subtracted and a threshold was applied to obtain a lesion mask, which was then refined by using the deformation field, intensity, and local information. This pipeline was compared with only applying a threshold, and with a state-of-the-art approach relying only on image intensities. To assess improvements, we compared the results of the different pipelines with the expert visual detection. The multichannel pipeline based on the deformation field obtained a detection Dice similarity coefficient close to 0.70, with a false-positive detection of 17.8% and a true-positive detection of 70.9%. A statistically significant correlation (r = 0.81, P value = 2.2688e-09) was found between visual detection and automated detection by using our approach. The deformation field-based approach proposed in this study for detecting new/enlarging T2 lesions resulted in significantly fewer false-positives while maintaining most true-positives and showed a good correlation with visual detection annotations. This approach could reduce user interaction and inter- and intraobserver variability. © 2016 American Society of Neuroradiology.

Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis.

PubMed

Hur, Taeho; Bang, Jaehun; Kim, Dohyeong; Banos, Oresti; Lee, Sungyoung

2017-04-23

Activity recognition through smartphones has been proposed for a variety of applications. The orientation of the smartphone has a significant effect on the recognition accuracy; thus, researchers generally propose using features invariant to orientation or displacement to achieve this goal. However, those features reduce the capability of the recognition system to differentiate among some specific commuting activities (e.g., bus and subway) that normally involve similar postures. In this work, we recognize those activities by analyzing the vibrations of the vehicle in which the user is traveling. We extract natural vibration features of buses and subways to distinguish between them and address the confusion that can arise because the activities are both static in terms of user movement. We use the gyroscope to fix the accelerometer to the direction of gravity to achieve an orientation-free use of the sensor. We also propose a correction algorithm to increase the accuracy when used in free living conditions and a battery saving algorithm to consume less power without reducing performance. Our experimental results show that the proposed system can adequately recognize each activity, yielding better accuracy in the detection of bus and subway activities than existing methods.

Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis

PubMed Central

Hur, Taeho; Bang, Jaehun; Kim, Dohyeong; Banos, Oresti; Lee, Sungyoung

2017-01-01

Activity recognition through smartphones has been proposed for a variety of applications. The orientation of the smartphone has a significant effect on the recognition accuracy; thus, researchers generally propose using features invariant to orientation or displacement to achieve this goal. However, those features reduce the capability of the recognition system to differentiate among some specific commuting activities (e.g., bus and subway) that normally involve similar postures. In this work, we recognize those activities by analyzing the vibrations of the vehicle in which the user is traveling. We extract natural vibration features of buses and subways to distinguish between them and address the confusion that can arise because the activities are both static in terms of user movement. We use the gyroscope to fix the accelerometer to the direction of gravity to achieve an orientation-free use of the sensor. We also propose a correction algorithm to increase the accuracy when used in free living conditions and a battery saving algorithm to consume less power without reducing performance. Our experimental results show that the proposed system can adequately recognize each activity, yielding better accuracy in the detection of bus and subway activities than existing methods. PMID:28441743

Highly Portable, Sensor-Based System for Human Fall Monitoring.

PubMed

Mao, Aihua; Ma, Xuedong; He, Yinan; Luo, Jie

2017-09-13

Falls are a very dangerous situation especially among elderly people, because they may lead to fractures, concussion, and other injuries. Without timely rescue, falls may even endanger their lives. The existing optical sensor-based fall monitoring systems have some disadvantages, such as limited monitoring range and inconvenience to carry for users. Furthermore, the fall detection system based only on an accelerometer often mistakenly determines some activities of daily living (ADL) as falls, leading to low accuracy in fall detection. We propose a human fall monitoring system consisting of a highly portable sensor unit including a triaxis accelerometer, a triaxis gyroscope, and a triaxis magnetometer, and a mobile phone. With the data from these sensors, we obtain the acceleration and Euler angle (yaw, pitch, and roll), which represents the orientation of the user's body. Then, a proposed fall detection algorithm was used to detect falls based on the acceleration and Euler angle. With this monitoring system, we design a series of simulated falls and ADL and conduct the experiment by placing the sensors on the shoulder, waist, and foot of the subjects. Through the experiment, we re-identify the threshold of acceleration for accurate fall detection and verify the best body location to place the sensors by comparing the detection performance on different body segments. We also compared this monitoring system with other similar works and found that better fall detection accuracy and portability can be achieved by our system.

Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms

PubMed Central

Khan, Naveed; McClean, Sally; Zhang, Shuai; Nugent, Chris

2016-01-01

In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%. PMID:27792177

Securing Collaborative Spectrum Sensing against Untrustworthy Secondary Users in Cognitive Radio Networks

NASA Astrophysics Data System (ADS)

Wang, Wenkai; Li, Husheng; Sun, Yan(Lindsay); Han, Zhu

2009-12-01

Cognitive radio is a revolutionary paradigm to migrate the spectrum scarcity problem in wireless networks. In cognitive radio networks, collaborative spectrum sensing is considered as an effective method to improve the performance of primary user detection. For current collaborative spectrum sensing schemes, secondary users are usually assumed to report their sensing information honestly. However, compromised nodes can send false sensing information to mislead the system. In this paper, we study the detection of untrustworthy secondary users in cognitive radio networks. We first analyze the case when there is only one compromised node in collaborative spectrum sensing schemes. Then we investigate the scenario that there are multiple compromised nodes. Defense schemes are proposed to detect malicious nodes according to their reporting histories. We calculate the suspicious level of all nodes based on their reports. The reports from nodes with high suspicious levels will be excluded in decision-making. Compared with existing defense methods, the proposed scheme can effectively differentiate malicious nodes and honest nodes. As a result, it can significantly improve the performance of collaborative sensing. For example, when there are 10 secondary users, with the primary user detection rate being equal to 0.99, one malicious user can make the false alarm rate [InlineEquation not available: see fulltext.] increase to 72%. The proposed scheme can reduce it to 5%. Two malicious users can make [InlineEquation not available: see fulltext.] increase to 85% and the proposed scheme reduces it to 8%.

Spectrotemporal Modulation Detection and Speech Perception by Cochlear Implant Users

PubMed Central

Won, Jong Ho; Moon, Il Joon; Jin, Sunhwa; Park, Heesung; Woo, Jihwan; Cho, Yang-Sun; Chung, Won-Ho; Hong, Sung Hwa

2015-01-01

Spectrotemporal modulation (STM) detection performance was examined for cochlear implant (CI) users. The test involved discriminating between an unmodulated steady noise and a modulated stimulus. The modulated stimulus presents frequency modulation patterns that change in frequency over time. In order to examine STM detection performance for different modulation conditions, two different temporal modulation rates (5 and 10 Hz) and three different spectral modulation densities (0.5, 1.0, and 2.0 cycles/octave) were employed, producing a total 6 different STM stimulus conditions. In order to explore how electric hearing constrains STM sensitivity for CI users differently from acoustic hearing, normal-hearing (NH) and hearing-impaired (HI) listeners were also tested on the same tasks. STM detection performance was best in NH subjects, followed by HI subjects. On average, CI subjects showed poorest performance, but some CI subjects showed high levels of STM detection performance that was comparable to acoustic hearing. Significant correlations were found between STM detection performance and speech identification performance in quiet and in noise. In order to understand the relative contribution of spectral and temporal modulation cues to speech perception abilities for CI users, spectral and temporal modulation detection was performed separately and related to STM detection and speech perception performance. The results suggest that that slow spectral modulation rather than slow temporal modulation may be important for determining speech perception capabilities for CI users. Lastly, test–retest reliability for STM detection was good with no learning. The present study demonstrates that STM detection may be a useful tool to evaluate the ability of CI sound processing strategies to deliver clinically pertinent acoustic modulation information. PMID:26485715

CsSNP: A Web-Based Tool for the Detecting of Comparative Segments SNPs.

PubMed

Wang, Yi; Wang, Shuangshuang; Zhou, Dongjie; Yang, Shuai; Xu, Yongchao; Yang, Chao; Yang, Long

2016-07-01

SNP (single nucleotide polymorphism) is a popular tool for the study of genetic diversity, evolution, and other areas. Therefore, it is necessary to develop a convenient, utility, robust, rapid, and open source detecting-SNP tool for all researchers. Since the detection of SNPs needs special software and series steps including alignment, detection, analysis and present, the study of SNPs is limited for nonprofessional users. CsSNP (Comparative segments SNP, http://biodb.sdau.edu.cn/cssnp/ ) is a freely available web tool based on the Blat, Blast, and Perl programs to detect comparative segments SNPs and to show the detail information of SNPs. The results are filtered and presented in the statistics figure and a Gbrowse map. This platform contains the reference genomic sequences and coding sequences of 60 plant species, and also provides new opportunities for the users to detect SNPs easily. CsSNP is provided a convenient tool for nonprofessional users to find comparative segments SNPs in their own sequences, and give the users the information and the analysis of SNPs, and display these data in a dynamic map. It provides a new method to detect SNPs and may accelerate related studies.

Detection and Classification of Network Intrusions Using Hidden Markov Models

DTIC Science & Technology

2002-01-01

31 2.2.3 High-level state machines for misuse detection . . . . . . . 32 2.2.4 EMERALD ...Solaris host audit data to detect Solaris R2L (Remote-to-Local) and U2R (User-to-Root) attacks. 7 login as a legitimate user on a local system and use a...as suspicious rather than the entire login session and it can detect some anomalies that are difficult to detect with traditional approaches. It’s

Polarization of the vaccination debate on Facebook.

PubMed

Schmidt, Ana Lucía; Zollo, Fabiana; Scala, Antonio; Betsch, Cornelia; Quattrociocchi, Walter

2018-06-14

Vaccine hesitancy has been recognized as a major global health threat. Having access to any type of information in social media has been suggested as a potential influence on the growth of anti-vaccination groups. Recent studies w.r.t. other topics than vaccination show that access to a wide amount of content through the Internet without intermediaries resolved into major segregation of the users in polarized groups. Users select information adhering to theirs system of beliefs and tend to ignore dissenting information. The goal was to assess whether users' attitudes are polarized on the topic of vaccination on Facebook and how this polarization develops over time. We perform a thorough quantitative analysis by studying the interaction of 2.6 M users with 298,018 Facebook posts over a time span of seven years and 5 months. We applied community detection algorithms to automatically detect the emergence of communities accounting for the users' activity on the pages. Also, we quantified the cohesiveness of these communities over time. Our findings show that the consumption of content about vaccines is dominated by the echo chamber effect and that polarization increased over the years. Well-segregated communities emerge from the users' consumption habits i.e., the majority of users consume information in favor or against vaccines, not both. The existence of echo chambers may explain why social-media campaigns that provide accurate information have limited reach and be effective only in sub-groups, even fomenting further opinion polarization. The introduction of dissenting information into a sub-group is disregarded and can produce a backfire effect, thus reinforcing the pre-existing opinions within the sub-group. Public health professionals should try to understand the contents of these echo chambers, for example by getting passively involved in such groups. Only then it will be possible to find effective ways of countering anti-vaccination thinking. Copyright © 2018 Elsevier Ltd. All rights reserved.

Design of a Golf Swing Injury Detection and Evaluation open service platform with Ontology-oriented clustering case-based reasoning mechanism.

PubMed

Ku, Hao-Hsiang

2015-01-01

Nowadays, people can easily use a smartphone to get wanted information and requested services. Hence, this study designs and proposes a Golf Swing Injury Detection and Evaluation open service platform with Ontology-oritened clustering case-based reasoning mechanism, which is called GoSIDE, based on Arduino and Open Service Gateway initative (OSGi). GoSIDE is a three-tier architecture, which is composed of Mobile Users, Application Servers and a Cloud-based Digital Convergence Server. A mobile user is with a smartphone and Kinect sensors to detect the user's Golf swing actions and to interact with iDTV. An application server is with Intelligent Golf Swing Posture Analysis Model (iGoSPAM) to check a user's Golf swing actions and to alter this user when he is with error actions. Cloud-based Digital Convergence Server is with Ontology-oriented Clustering Case-based Reasoning (CBR) for Quality of Experiences (OCC4QoE), which is designed to provide QoE services by QoE-based Ontology strategies, rules and events for this user. Furthermore, GoSIDE will automatically trigger OCC4QoE and deliver popular rules for a new user. Experiment results illustrate that GoSIDE can provide appropriate detections for Golfers. Finally, GoSIDE can be a reference model for researchers and engineers.

An incremental community detection method for social tagging systems using locality-sensitive hashing.

PubMed

Wu, Zhenyu; Zou, Ming

2014-10-01

An increasing number of users interact, collaborate, and share information through social networks. Unprecedented growth in social networks is generating a significant amount of unstructured social data. From such data, distilling communities where users have common interests and tracking variations of users' interests over time are important research tracks in fields such as opinion mining, trend prediction, and personalized services. However, these tasks are extremely difficult considering the highly dynamic characteristics of the data. Existing community detection methods are time consuming, making it difficult to process data in real time. In this paper, dynamic unstructured data is modeled as a stream. Tag assignments stream clustering (TASC), an incremental scalable community detection method, is proposed based on locality-sensitive hashing. Both tags and latent interactions among users are incorporated in the method. In our experiments, the social dynamic behaviors of users are first analyzed. The proposed TASC method is then compared with state-of-the-art clustering methods such as StreamKmeans and incremental k-clique; results indicate that TASC can detect communities more efficiently and effectively. Copyright © 2014 Elsevier Ltd. All rights reserved.

Treatment of Parkinson's disease could be regulated by movement sensors: subcutaneous infusion of varying apomorphine doses according to the intensity of motor activity.

PubMed

Rodríguez-Molinero, Alejandro; Pérez-Martínez, David A; Català, Andreu; Cabestany, Joan; Yuste, Antonio

2009-04-01

Most recent therapeutic solutions to treat Parkinson's disease seek continuous administration of dopaminergic agonists, as for example rigotine patches or apomorphine infusion pumps. Such drug-delivery devices are aimed at preventing fluctuations in drug plasma levels, which could cause certain symptoms such as wearing-off periods or dyskinesia. However, we postulate that drug plasma levels should not keep constant, but rather adjust to the varying intensity of the different user's activities. The rationale behind this is that the drug amount appropriate to treat a patient at rest is lower than that required to treat the same patient when engaged in physical activity. We propose dynamic real-time dose adjustment, so that the doses increase as the patient starts performing physical activity, thus preventing off periods such as "freeze" phenomenon, and the doses reduce during the resting periods, thus preventing adverse effects. Small portable movement sensors are currently available, which detect the amount and type of activity in a continuous way. Combining such technology with infusion pumps to produce modified pumps capable of adjusting the infusion rate to the user's activity, seems to be feasible in the short-term.

A study on real-time low-quality content detection on Twitter from the users' perspective.

PubMed

Chen, Weiling; Yeo, Chai Kiat; Lau, Chiew Tong; Lee, Bu Sung

2017-01-01

Detection techniques of malicious content such as spam and phishing on Online Social Networks (OSN) are common with little attention paid to other types of low-quality content which actually impacts users' content browsing experience most. The aim of our work is to detect low-quality content from the users' perspective in real time. To define low-quality content comprehensibly, Expectation Maximization (EM) algorithm is first used to coarsely classify low-quality tweets into four categories. Based on this preliminary study, a survey is carefully designed to gather users' opinions on different categories of low-quality content. Both direct and indirect features including newly proposed features are identified to characterize all types of low-quality content. We then further combine word level analysis with the identified features and build a keyword blacklist dictionary to improve the detection performance. We manually label an extensive Twitter dataset of 100,000 tweets and perform low-quality content detection in real time based on the characterized significant features and word level analysis. The results of our research show that our method has a high accuracy of 0.9711 and a good F1 of 0.8379 based on a random forest classifier with real time performance in the detection of low-quality content in tweets. Our work therefore achieves a positive impact in improving user experience in browsing social media content.

Reinventing Image Detective: An Evidence-Based Approach to Citizen Science Online

NASA Astrophysics Data System (ADS)

Romano, C.; Graff, P. V.; Runco, S.

2017-12-01

Usability studies demonstrate that web users are notoriously impatient, spending as little as 15 seconds on a home page. How do you get users to stay long enough to understand a citizen science project? How do you get users to complete complex citizen science tasks online?Image Detective, a citizen science project originally developed by scientists and science engagement specialists at the NASA Johnson Space center to engage the public in the analysis of images taken from space by astronauts to help enhance NASA's online database of astronaut imagery, partnered with the CosmoQuest citizen science platform to modernize, offering new and improved options for participation in Image Detective. The challenge: to create a web interface that builds users' skills and knowledge, creating engagement while learning complex concepts essential to the accurate completion of tasks. The project team turned to usability testing for an objective understanding of how users perceived Image Detective and the steps required to complete required tasks. A group of six users was recruited online for unmoderated and initial testing. The users followed a think-aloud protocol while attempting tasks, and were recorded on video and audio. The usability test examined users' perception of four broad areas: the purpose of and context for Image Detective; the steps required to successfully complete the analysis (differentiating images of Earth's surface from those showing outer space and identifying common surface features); locating the image center point on a map of Earth; and finally, naming geographic locations or natural events seen in the image.Usability test findings demonstrated that the following best practices can increase participation in Image Detective and can be applied to the successful implementation of any citizen science project:• Concise explanation of the project, its context, and its purpose;• Including a mention of the funding agency (in this case, NASA);• A preview of the specific tasks required of participants;• A dedicated user interface for the actual citizen science interaction.In addition, testing revealed that users may require additional context when a task is complex, difficult, or unusual (locating a specific image and its center point on a map of Earth). Video evidence will be made available with this presentation.

Reinventing Image Detective: An Evidence-Based Approach to Citizen Science Online

NASA Technical Reports Server (NTRS)

Romano, Cia; Graff, Paige V.; Runco, Susan

2017-01-01

Usability studies demonstrate that web users are notoriously impatient, spending as little as 15 seconds on a home page. How do you get users to stay long enough to understand a citizen science project? How do you get users to complete complex citizen science tasks online? Image Detective, a citizen science project originally developed by scientists and science engagement specialists at the NASA Johnson Space center to engage the public in the analysis of images taken from space by astronauts to help enhance NASA's online database of astronaut imagery, partnered with the CosmoQuest citizen science platform to modernize, offering new and improved options for participation in Image Detective. The challenge: to create a web interface that builds users' skills and knowledge, creating engagement while learning complex concepts essential to the accurate completion of tasks. The project team turned to usability testing for an objective understanding of how users perceived Image Detective and the steps required to complete required tasks. A group of six users was recruited online for unmoderated and initial testing. The users followed a think-aloud protocol while attempting tasks, and were recorded on video and audio. The usability test examined users' perception of four broad areas: the purpose of and context for Image Detective; the steps required to successfully complete the analysis (differentiating images of Earth's surface from those showing outer space and identifying common surface features); locating the image center point on a map of Earth; and finally, naming geographic locations or natural events seen in the image. Usability test findings demonstrated that the following best practices can increase participation in Image Detective and can be applied to the successful implementation of any citizen science project: (1) Concise explanation of the project, its context, and its purpose; (2) Including a mention of the funding agency (in this case, NASA); (3) A preview of the specific tasks required of participants; (4) A dedicated user interface for the actual citizen science interaction. In addition, testing revealed that users may require additional context when a task is complex, difficult, or unusual (locating a specific image and its center point on a map of Earth). Video evidence will be made available with this presentation.

A Survey on Anomaly Based Host Intrusion Detection System

NASA Astrophysics Data System (ADS)

Jose, Shijoe; Malathi, D.; Reddy, Bharath; Jayaseeli, Dorathi

2018-04-01

An intrusion detection system (IDS) is hardware, software or a combination of two, for monitoring network or system activities to detect malicious signs. In computer security, designing a robust intrusion detection system is one of the most fundamental and important problems. The primary function of system is detecting intrusion and gives alerts when user tries to intrusion on timely manner. In these techniques when IDS find out intrusion it will send alert massage to the system administrator. Anomaly detection is an important problem that has been researched within diverse research areas and application domains. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. From the existing anomaly detection techniques, each technique has relative strengths and weaknesses. The current state of the experiment practice in the field of anomaly-based intrusion detection is reviewed and survey recent studies in this. This survey provides a study of existing anomaly detection techniques, and how the techniques used in one area can be applied in another application domain.

Benford’s Law Applies to Online Social Networks

PubMed Central

Golbeck, Jennifer

2015-01-01

Benford’s Law states that, in naturally occurring systems, the frequency of numbers’ first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford’s Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal), we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford’s Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual’s social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets. PMID:26308716

Benford's Law Applies to Online Social Networks.

PubMed

Golbeck, Jennifer

2015-01-01

Benford's Law states that, in naturally occurring systems, the frequency of numbers' first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal), we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.

Development of a wireless nonlinear wave modulation spectroscopy (NWMS) sensor node for fatigue crack detection

NASA Astrophysics Data System (ADS)

Liu, Peipei; Yang, Suyoung; Lim, Hyung Jin; Park, Hyung Chul; Ko, In Chang; Sohn, Hoon

2014-03-01

Fatigue crack is one of the main culprits for the failure of metallic structures. Recently, it has been shown that nonlinear wave modulation spectroscopy (NWMS) is effective in detecting nonlinear mechanisms produced by fatigue crack. In this study, an active wireless sensor node for fatigue crack detection is developed based on NWMS. Using PZT transducers attached to a target structure, ultrasonic waves at two distinctive frequencies are generated, and their modulation due to fatigue crack formation is detected using another PZT transducer. Furthermore, a reference-free NWMS algorithm is developed so that fatigue crack can be detected without relying on history data of the structure with minimal parameter adjustment by the end users. The algorithm is embedded into FPGA, and the diagnosis is transmitted to a base station using a commercial wireless communication system. The whole design of the sensor node is fulfilled in a low power working strategy. Finally, an experimental verification has been performed using aluminum plate specimens to show the feasibility of the developed active wireless NWMS sensor node.

Shilling attack detection for recommender systems based on credibility of group users and rating time series

PubMed Central

Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian

2018-01-01

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method. PMID:29742134

Ill Literates or Illiterates? Investigating the eHealth Literacy of Users of Online Health Communities

PubMed Central

Atanasova, Sara; Kamin, Tanja

2017-01-01

Background Electronic health (eHealth) literacy is an important skill that allows patients to navigate intelligibly through the vast, often misleading Web-based world. Although eHealth literacy has been investigated in general and specific demographic populations, it has not yet been analyzed on users of online health communities (OHCs). Evidence shows that OHCs are important Web 2.0 applications for patients for managing their health, but at the same time, warnings have been expressed regarding the quality and relevance of shared information. No studies exist that investigate levels of eHealth literacy among users of OHCs and differences in eHealth literacy between different types of users. Objective The study aimed to investigate eHealth literacy across different types of users of OHCs based on a revised and extended eHealth literacy scale (eHEALS). Methods The study was based on a cross-sectional Web survey on a simple random sample of 15,000 registered users of the most popular general OHC in Slovenia. The final sample comprised 644 users of the studied OHC. An extended eHEALS (eHEALS-E) was tested with factor analytical procedures, whereas user types were identified with a hierarchical clustering algorithm. The research question was analyzed with analysis of variance (ANOVA) procedure and pairwise comparison tests. Results Factor analysis of the revised and extended eHEALS revealed six dimensions: awareness of sources, recognizing quality and meaning, understanding information, perceived efficiency, validating information, and being smart on the Net. The factor solution demonstrates a good fit to the data (root mean square error of approximation [RMSEA]=.059). The most developed dimension of eHEALS-E is awareness of different Internet sources (mean=3.98, standard deviation [SD]=0.61), whereas the least developed is understanding information (mean=3.11, SD=0.75). Clustering resulted in four user types: active help-seekers (48.3%, 311/644), lurkers (31.8%, 205/644), core relational users (16.9%, 109/644), and low-engaged users (3%, 19/644). Analysis of the research question showed statistically significant differences among user types across all six dimensions of eHEALS-E. Most notably, core relational users performed worse than lurkers on the validating information dimension (P=.01) and worse than active help-seekers on the being smart on the Net dimension (P=.05). Active help-seekers have the highest scores in all dimensions of the eHEALS-E, whereas low-engaged users have statistically significantly lower scores on all dimensions of the eHEALS-E in comparison with the other groups. Conclusions Those who are looking for advice and support in OHCs by making queries are well equipped with eHealth literacy skills to filter potential misinformation and detect bad advice. However, core relational users (who produce the most content in OHCs) have less-developed skills for cross-validating the information obtained and navigating successfully through the perils of the online world. Site managers should monitor their activity to avoid the spread of misinformation that might lead to unhealthy practices. PMID:28978496

Ill Literates or Illiterates? Investigating the eHealth Literacy of Users of Online Health Communities.

PubMed

Petrič, Gregor; Atanasova, Sara; Kamin, Tanja

2017-10-04

Electronic health (eHealth) literacy is an important skill that allows patients to navigate intelligibly through the vast, often misleading Web-based world. Although eHealth literacy has been investigated in general and specific demographic populations, it has not yet been analyzed on users of online health communities (OHCs). Evidence shows that OHCs are important Web 2.0 applications for patients for managing their health, but at the same time, warnings have been expressed regarding the quality and relevance of shared information. No studies exist that investigate levels of eHealth literacy among users of OHCs and differences in eHealth literacy between different types of users. The study aimed to investigate eHealth literacy across different types of users of OHCs based on a revised and extended eHealth literacy scale (eHEALS). The study was based on a cross-sectional Web survey on a simple random sample of 15,000 registered users of the most popular general OHC in Slovenia. The final sample comprised 644 users of the studied OHC. An extended eHEALS (eHEALS-E) was tested with factor analytical procedures, whereas user types were identified with a hierarchical clustering algorithm. The research question was analyzed with analysis of variance (ANOVA) procedure and pairwise comparison tests. Factor analysis of the revised and extended eHEALS revealed six dimensions: awareness of sources, recognizing quality and meaning, understanding information, perceived efficiency, validating information, and being smart on the Net. The factor solution demonstrates a good fit to the data (root mean square error of approximation [RMSEA]=.059). The most developed dimension of eHEALS-E is awareness of different Internet sources (mean=3.98, standard deviation [SD]=0.61), whereas the least developed is understanding information (mean=3.11, SD=0.75). Clustering resulted in four user types: active help-seekers (48.3%, 311/644), lurkers (31.8%, 205/644), core relational users (16.9%, 109/644), and low-engaged users (3%, 19/644). Analysis of the research question showed statistically significant differences among user types across all six dimensions of eHEALS-E. Most notably, core relational users performed worse than lurkers on the validating information dimension (P=.01) and worse than active help-seekers on the being smart on the Net dimension (P=.05). Active help-seekers have the highest scores in all dimensions of the eHEALS-E, whereas low-engaged users have statistically significantly lower scores on all dimensions of the eHEALS-E in comparison with the other groups. Those who are looking for advice and support in OHCs by making queries are well equipped with eHealth literacy skills to filter potential misinformation and detect bad advice. However, core relational users (who produce the most content in OHCs) have less-developed skills for cross-validating the information obtained and navigating successfully through the perils of the online world. Site managers should monitor their activity to avoid the spread of misinformation that might lead to unhealthy practices. ©Gregor Petrič, Sara Atanasova, Tanja Kamin. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.10.2017.

A wearable device for emotional recognition using facial expression and physiological response.

PubMed

Jangho Kwon; Da-Hye Kim; Wanjoo Park; Laehyun Kim

2016-08-01

This paper introduces a glasses-typed wearable system to detect user's emotions using facial expression and physiological responses. The system is designed to acquire facial expression through a built-in camera and physiological responses such as photoplethysmogram (PPG) and electrodermal activity (EDA) in unobtrusive way. We used video clips for induced emotions to test the system suitability in the experiment. The results showed a few meaningful properties that associate emotions with facial expressions and physiological responses captured by the developed wearable device. We expect that this wearable system with a built-in camera and physiological sensors may be a good solution to monitor user's emotional state in daily life.

EEGgui: a program used to detect electroencephalogram anomalies after traumatic brain injury.

PubMed

Sick, Justin; Bray, Eric; Bregy, Amade; Dietrich, W Dalton; Bramlett, Helen M; Sick, Thomas

2013-05-21

Identifying and quantifying pathological changes in brain electrical activity is important for investigations of brain injury and neurological disease. An example is the development of epilepsy, a secondary consequence of traumatic brain injury. While certain epileptiform events can be identified visually from electroencephalographic (EEG) or electrocorticographic (ECoG) records, quantification of these pathological events has proved to be more difficult. In this study we developed MATLAB-based software that would assist detection of pathological brain electrical activity following traumatic brain injury (TBI) and present our MATLAB code used for the analysis of the ECoG. Software was developed using MATLAB(™) and features of the open access EEGLAB. EEGgui is a graphical user interface in the MATLAB programming platform that allows scientists who are not proficient in computer programming to perform a number of elaborate analyses on ECoG signals. The different analyses include Power Spectral Density (PSD), Short Time Fourier analysis and Spectral Entropy (SE). ECoG records used for demonstration of this software were derived from rats that had undergone traumatic brain injury one year earlier. The software provided in this report provides a graphical user interface for displaying ECoG activity and calculating normalized power density using fast fourier transform of the major brain wave frequencies (Delta, Theta, Alpha, Beta1, Beta2 and Gamma). The software further detects events in which power density for these frequency bands exceeds normal ECoG by more than 4 standard deviations. We found that epileptic events could be identified and distinguished from a variety of ECoG phenomena associated with normal changes in behavior. We further found that analysis of spectral entropy was less effective in distinguishing epileptic from normal changes in ECoG activity. The software presented here was a successful modification of EEGLAB in the Matlab environment that allows detection of epileptiform ECoG signals in animals after TBI. The code allows import of large EEG or ECoG data records as standard text files and uses fast fourier transform as a basis for detection of abnormal events. The software can also be used to monitor injury-induced changes in spectral entropy if required. We hope that the software will be useful for other investigators in the field of traumatic brain injury and will stimulate future advances of quantitative analysis of brain electrical activity after neurological injury or disease.

A Powerful, Cost Effective, Web Based Engineering Solution Supporting Conjunction Detection and Visual Analysis

NASA Astrophysics Data System (ADS)

Novak, Daniel M.; Biamonti, Davide; Gross, Jeremy; Milnes, Martin

2013-08-01

An innovative and visually appealing tool is presented for efficient all-vs-all conjunction analysis on a large catalogue of objects. The conjunction detection uses a nearest neighbour search algorithm, based on spatial binning and identification of pairs of objects in adjacent bins. This results in the fastest all vs all filtering the authors are aware of. The tool is constructed on a server-client architecture, where the server broadcasts to the client the conjunction data and ephemerides, while the client supports the user interface through a modern browser, without plug-in. In order to make the tool flexible and maintainable, Java software technologies were used on the server side, including Spring, Camel, ActiveMQ and CometD. The user interface and visualisation are based on the latest web technologies: HTML5, WebGL, THREE.js. Importance has been given on the ergonomics and visual appeal of the software. In fact certain design concepts have been borrowed from the gaming industry.

Monitoring and Indentification Packet in Wireless With Deep Packet Inspection Method

NASA Astrophysics Data System (ADS)

Fali Oklilas, Ahmad; Tasmi

2017-04-01

Layer 2 and Layer 3 are used to make a process of network monitoring, but with the development of applications on the network such as the p2p file sharing, VoIP, encrypted, and many applications that already use the same port, it would require a system that can classify network traffics, not only based on port number classification. This paper reports the implementation of the deep packet inspection method to analyse data packets based on the packet header and payload to be used in packet data classification. If each application can be grouped based on the application layer, then we can determine the pattern of internet users and also to perform network management of computer science department. In this study, a prototype wireless network and applications SSO were developed to detect the active user. The focus is on the ability of open DPI and nDPI in detecting the payload of an application and the results are elaborated in this paper.

Affective brain-computer music interfacing

NASA Astrophysics Data System (ADS)

Daly, Ian; Williams, Duncan; Kirke, Alexis; Weaver, James; Malik, Asad; Hwang, Faustina; Miranda, Eduardo; Nasuto, Slawomir J.

2016-08-01

Objective. We aim to develop and evaluate an affective brain-computer music interface (aBCMI) for modulating the affective states of its users. Approach. An aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions. Main results. The final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% (3 class, p\\lt 0.01) and modulate its user's affective states significantly above chance level (p\\lt 0.05). Significance. Our system represents one of the first demonstrations of an online aBCMI that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.

Development of an Electronic Kit for detecting asthma in Human Respiratory System

NASA Astrophysics Data System (ADS)

Shek Hong, Cheow; Ghani, Ahmad Shahrizan Abdul; Khairuddin, Ismail Mohd

2018-03-01

In this paper, a prototype of a carbon dioxide (CO2) measurement device is designed to detect and used to monitor asthma patients. Nowadays, capnogram device is widely used in monitoring asthma and asthma related medical services. However, capnogram is very costly and unaffordable for patient especially those who are in low income household. Thus, the proposed device is cost effective, affordable, and produced to detect and monitor the severity of asthma. Meanwhile, flow meter will cause patient to have chest pain as they needed maximum effort to blow in the device. To overcome these limitations, this prototype electronic kit is easy to use and suitable for all range patients. This prototype electronic kit consists of MH-Z14A carbon dioxide (CO2) sensor to detect the concentration of carbon dioxide from the user exhaled air. Arduino microcontroller is used to process the data while TFT Display shield is applied for data presentation. In addition, HC-06 Bluetooth module is used to communicate with PC for further analysis of the captured graph. This device was tested with 3 asthmatics and 3 normal users. The results showed that asthmatic user has a different graph pattern compared with normal user and these graphs are clearly differentiated on the device TFT screen. Asthmatic user produces “shark fin”-like pattern whereas normal user produces “square wave”-like pattern. This device has successfully produced distinguished-patterns difference between asthmatic and normal user; therefore, it is suitable for asthma monitoring.

Avoiding fraud risks associated with EHRs.

PubMed

Helton, Jeffrey R

2010-07-01

Fraud associated with electronic health records (EHRs) generally falls into two categories: inappropriate billing by healthcare providers and inappropriate access by a system's users. A provider's EHR system requires controls to be of any significant help in detecting such fraudulent activity, or in gathering transactional evidence should such activity be identified. To protect against potential EHR-related healthcare fraud, providers should follow the recommendations established in 2007 by RTI International for the Office of the National Coordinator for Health Information Technology of the U.S. Department of Health and Human Services.

Internet use and its impact on engagement in leisure activities in China.

PubMed

Zhou, Ronggang; Fong, Patrick S W; Tan, Peking

2014-01-01

Internet use has become an increasingly common leisure time activity among Chinese citizens. The association between Internet use and engagement in leisure activities is especially unclear among China population. This study aims to investigate Internet usage and to determine whether active Internet use is a marker for low or high levels of leisure time activities. With the use of a face-to-face structured questionnaire interview, a total of 2,400 respondents who met all screening requirements were surveyed to answer the questions in eight major cities in China. 66.2% (n = 1,589) of all respondents were identified as Internet users. Of these Internet users, 30.0%, 24.1%, 26.4%, and 19.6% were clustered as "informative or instrumental users," "entertainment users," "communication users," and "advanced users," respectively. Regarding time spent on Internet use in leisure time, more than 96% reported going online in non-work situations, and 26.2% (n = 416) were classified as "heavy Internet users." A logistic regression analysis revealed that there were significant differences in some leisure activities between non-Internet users and Internet users, with an observed one-unit increase in the leisure time dependence category increasing the probability of engaging in mental or social activities. In contrast, Internet users were less engaged in physical exercise-related activities. In addition, advanced Internet users were generally more active in leisure time activities than non-Internet users and other types of users. Internet use is one of very common leisure activities in Chinese citizens, and age, gender, income, and education are the key factors affecting Internet access. According to different types of leisure activities, Internet usage has different impacts on leisure activity engagement. High Internet dependence has no significant negative influence on engagement in mental or social leisure activities, but this group respondent tended to be less engaged in physical activities.

Internet Use and Its Impact on Engagement in Leisure Activities in China

PubMed Central

Zhou, Ronggang; Fong, Patrick S. W.; Tan, Peking

2014-01-01

Introduction Internet use has become an increasingly common leisure time activity among Chinese citizens. The association between Internet use and engagement in leisure activities is especially unclear among China population. This study aims to investigate Internet usage and to determine whether active Internet use is a marker for low or high levels of leisure time activities. Methods/Principal Findings With the use of a face-to-face structured questionnaire interview, a total of 2,400 respondents who met all screening requirements were surveyed to answer the questions in eight major cities in China. 66.2% (n = 1,589) of all respondents were identified as Internet users. Of these Internet users, 30.0%, 24.1%, 26.4%, and 19.6% were clustered as “informative or instrumental users,” “entertainment users,” “communication users,” and “advanced users,” respectively. Regarding time spent on Internet use in leisure time, more than 96% reported going online in non-work situations, and 26.2% (n = 416) were classified as “heavy Internet users.” A logistic regression analysis revealed that there were significant differences in some leisure activities between non-Internet users and Internet users, with an observed one-unit increase in the leisure time dependence category increasing the probability of engaging in mental or social activities. In contrast, Internet users were less engaged in physical exercise-related activities. In addition, advanced Internet users were generally more active in leisure time activities than non-Internet users and other types of users. Conclusion/Significance Internet use is one of very common leisure activities in Chinese citizens, and age, gender, income, and education are the key factors affecting Internet access. According to different types of leisure activities, Internet usage has different impacts on leisure activity engagement. High Internet dependence has no significant negative influence on engagement in mental or social leisure activities, but this group respondent tended to be less engaged in physical activities. PMID:24586902

Early warning and response system (EWARS) for dengue outbreaks: Recent advancements towards widespread applications in critical settings.

PubMed

Hussain-Alkhateeb, Laith; Kroeger, Axel; Olliaro, Piero; Rocklöv, Joacim; Sewe, Maquins Odhiambo; Tejeda, Gustavo; Benitez, David; Gill, Balvinder; Hakim, S Lokman; Gomes Carvalho, Roberta; Bowman, Leigh; Petzold, Max

2018-01-01

Dengue outbreaks are increasing in frequency over space and time, affecting people's health and burdening resource-constrained health systems. The ability to detect early emerging outbreaks is key to mounting an effective response. The early warning and response system (EWARS) is a toolkit that provides countries with early-warning systems for efficient and cost-effective local responses. EWARS uses outbreak and alarm indicators to derive prediction models that can be used prospectively to predict a forthcoming dengue outbreak at district level. We report on the development of the EWARS tool, based on users' recommendations into a convenient, user-friendly and reliable software aided by a user's workbook and its field testing in 30 health districts in Brazil, Malaysia and Mexico. 34 Health officers from the 30 study districts who had used the original EWARS for 7 to 10 months responded to a questionnaire with mainly open-ended questions. Qualitative content analysis showed that participants were generally satisfied with the tool but preferred open-access vs. commercial software. EWARS users also stated that the geographical unit should be the district, while access to meteorological information should be improved. These recommendations were incorporated into the second-generation EWARS-R, using the free R software, combined with recent surveillance data and resulted in higher sensitivities and positive predictive values of alarm signals compared to the first-generation EWARS. Currently the use of satellite data for meteorological information is being tested and a dashboard is being developed to increase user-friendliness of the tool. The inclusion of other Aedes borne viral diseases is under discussion. EWARS is a pragmatic and useful tool for detecting imminent dengue outbreaks to trigger early response activities.

Graphical user interface simplifies infusion pump programming and enhances the ability to detect pump-related faults.

PubMed

Syroid, Noah; Liu, David; Albert, Robert; Agutter, James; Egan, Talmage D; Pace, Nathan L; Johnson, Ken B; Dowdle, Michael R; Pulsipher, Daniel; Westenskow, Dwayne R

2012-11-01

Drug administration errors are frequent and are often associated with the misuse of IV infusion pumps. One source of these errors may be the infusion pump's user interface. We used failure modes-and-effects analyses to identify programming errors and to guide the design of a new syringe pump user interface. We designed the new user interface to clearly show the pump's operating state simultaneously in more than 1 monitoring location. We evaluated anesthesia residents in laboratory and simulated environments on programming accuracy and error detection between the new user interface and the user interface of a commercially available infusion pump. With the new user interface, we observed the number of programming errors reduced by 81%, the number of keystrokes per task reduced from 9.2 ± 5.0 to 7.5 ± 5.5 (mean ± SD), the time required per task reduced from 18.1 ± 14.1 seconds to 10.9 ± 9.5 seconds and significantly less perceived workload. Residents detected 38 of 70 (54%) of the events with the new user interface and 37 of 70 (53%) with the existing user interface, despite no experience with the new user interface and extensive experience with the existing interface. The number of programming errors and workload were reduced partly because it took less time and fewer keystrokes to program the pump when using the new user interface. Despite minimal training, residents quickly identified preexisting infusion pump problems with the new user interface. Intuitive and easy-to-program infusion pump interfaces may reduce drug administration errors and infusion pump-related adverse events.

An interactive VR system based on full-body tracking and gesture recognition

NASA Astrophysics Data System (ADS)

Zeng, Xia; Sang, Xinzhu; Chen, Duo; Wang, Peng; Guo, Nan; Yan, Binbin; Wang, Kuiru

2016-10-01

Most current virtual reality (VR) interactions are realized with the hand-held input device which leads to a low degree of presence. There is other solutions using sensors like Leap Motion to recognize the gestures of users in order to interact in a more natural way, but the navigation in these systems is still a problem, because they fail to map the actual walking to virtual walking only with a partial body of the user represented in the synthetic environment. Therefore, we propose a system in which users can walk around in the virtual environment as a humanoid model, selecting menu items and manipulating with the virtual objects using natural hand gestures. With a Kinect depth camera, the system tracks the joints of the user, mapping them to a full virtual body which follows the move of the tracked user. The movements of the feet can be detected to determine whether the user is in walking state, so that the walking of model in the virtual world can be activated and stopped by means of animation control in Unity engine. This method frees the hands of users comparing to traditional navigation way using hand-held device. We use the point cloud data getting from Kinect depth camera to recognize the gestures of users, such as swiping, pressing and manipulating virtual objects. Combining the full body tracking and gestures recognition using Kinect, we achieve our interactive VR system in Unity engine with a high degree of presence.

An eHealth Project on Invasive Pneumococcal Disease: Comprehensive Evaluation of a Promotional Campaign

PubMed Central

Gasparini, Roberto; Bonanni, Paolo; Icardi, Giancarlo; Amicizia, Daniela; Arata, Lucia; Carozzo, Stefano; Signori, Alessio; Bechini, Angela; Boccalini, Sara

2016-01-01

Background The recently launched Pneumo Rischio eHealth project, which consists of an app, a website, and social networking activity, is aimed at increasing public awareness of invasive pneumococcal disease (IPD). The launch of this project was prompted by the inadequate awareness of IPD among both laypeople and health care workers, the heavy socioeconomic burden of IPD, and the far from optimal vaccination coverage in Italy, despite the availability of safe and effective vaccines. Objective The objectives of our study were to analyze trends in Pneumo Rischio usage before and after a promotional campaign, to characterize its end users, and to assess its user-rated quality. Methods At 7 months after launching Pneumo Rischio, we established a 4-month marketing campaign to promote the project. This intervention used various approaches and channels, including both traditional and digital marketing strategies. To highlight usage trends, we used different techniques of time series analysis and modeling, including a modified Mann-Kendall test, change-point detection, and segmented negative binomial regression of interrupted time series. Users were characterized in terms of demographics and IPD risk categories. Customer-rated quality was evaluated by means of a standardized tool in a sample of app users. Results Over 1 year, the app was accessed by 9295 users and the website was accessed by 143,993 users, while the project’s Facebook page had 1216 fans. The promotional intervention was highly effective in increasing the daily number of users. In particular, the Mann-Kendall trend test revealed a significant (P ≤.01) increasing trend in both app and website users, while change-point detection analysis showed that the first significant change corresponded to the start of the promotional campaign. Regression analysis showed a significant immediate effect of the intervention, with a mean increase in daily numbers of users of 1562% (95% CI 456%-4870%) for the app and 620% (95% CI 176%-1777%) for the website. Similarly, the postintervention daily trend in the number of users was positive, with a relative increase of 0.9% (95% CI 0.0%-1.8%) for the app and 1.4% (95% CI 0.7%-2.1%) for the website. Demographics differed between app and website users and Facebook fans. A total of 69.15% (10,793/15,608) of users could be defined as being at risk of IPD, while 4729 users expressed intentions to ask their doctor for further information on IPD. The mean app quality score assigned by end users was approximately 79.5% (397/500). Conclusions Despite its specific topic, Pneumo Rischio was accessed by a considerable number of users, who ranked it as a high-quality project. In order to reach their target populations, however, such projects should be promoted. PMID:27913372

Unobstructive Body Area Networks (BAN) for Efficient Movement Monitoring

PubMed Central

Felisberto, Filipe; Costa, Nuno; Fdez-Riverola, Florentino; Pereira, António

2012-01-01

The technological advances in medical sensors, low-power microelectronics and miniaturization, wireless communications and networks have enabled the appearance of a new generation of wireless sensor networks: the so-called wireless body area networks (WBAN). These networks can be used for continuous monitoring of vital parameters, movement, and the surrounding environment. The data gathered by these networks contributes to improve users' quality of life and allows the creation of a knowledge database by using learning techniques, useful to infer abnormal behaviour. In this paper we present a wireless body area network architecture to recognize human movement, identify human postures and detect harmful activities in order to prevent risk situations. The WBAN was created using tiny, cheap and low-power nodes with inertial and physiological sensors, strategically placed on the human body. Doing so, in an as ubiquitous as possible way, ensures that its impact on the users' daily actions is minimum. The information collected by these sensors is transmitted to a central server capable of analysing and processing their data. The proposed system creates movement profiles based on the data sent by the WBAN's nodes, and is able to detect in real time any abnormal movement and allows for a monitored rehabilitation of the user. PMID:23112726

Scaling the PuNDIT project for wide area deployments

NASA Astrophysics Data System (ADS)

McKee, Shawn; Batista, Jorge; Carcassi, Gabriele; Dovrolis, Constantine; Lee, Danny

2017-10-01

In today’s world of distributed scientific collaborations, there are many challenges to providing reliable inter-domain network infrastructure. Network operators use a combination of active monitoring and trouble tickets to detect problems, but these are often ineffective at identifying issues that impact wide-area network users. Additionally, these approaches do not scale to wide area inter-domain networks due to unavailability of data from all the domains along typical network paths. The Pythia Network Diagnostic InfrasTructure (PuNDIT) project aims to create a scalable infrastructure for automating the detection and localization of problems across these networks. The project goal is to gather and analyze metrics from existing perfSONAR monitoring infrastructures to identify the signatures of possible problems, locate affected network links, and report them to the user in an intuitive fashion. Simply put, PuNDIT seeks to convert complex network metrics into easily understood diagnoses in an automated manner. We present our progress in creating the PuNDIT system and our status in developing, testing and deploying PuNDIT. We report on the project progress to-date, describe the current implementation architecture and demonstrate some of the various user interfaces it will support. We close by discussing the remaining challenges and next steps and where we see the project going in the future.

Sensor-Based Human Activity Recognition in a Multi-user Scenario

NASA Astrophysics Data System (ADS)

Wang, Liang; Gu, Tao; Tao, Xianping; Lu, Jian

Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.

Amaro-autonomous real-time detection of moving maritime objects: introducing a flight experiment for an on-board ship detection system

NASA Astrophysics Data System (ADS)

Schwenk, Kurt; Willburger, Katharina; Pless, Sebastian

2017-10-01

Motivated by politics and economy, the monitoring of the world wide ship traffic is a field of high topicality. To detect illegal activities like piracy, illegal fishery, ocean dumping and refugee transportation is of great value. The analysis of satellite images on the ground delivers a great contribution to situation awareness. However, for many applications the up-to-dateness of the data is crucial. With ground based processing, the time between image acquisition and delivery of the data to the end user is in the range of several hours. The highest influence to the duration of ground based processing is the delay caused by the transmission of the large amount of image data from the satellite to the processing centre on the ground. One expensive solution to this issue is the usage of data relay satellites systems like EDRS. Another approach is to analyse the image data directly on-board of the satellite. Since the product data (e.g. ship position, heading, velocity, characteristics) is very small compared to the input image data, real-time connections provided by satellite telecommunication services like Iridium or Orbcomm can be used to send small packets of information directly to the end user without significant delay. The AMARO (Autonomous real-time detection of moving maritime objects) project at DLR is a feasibility study of an on-board ship detection system involving a real-time low bandwidth communication. The operation of a prototype on-board ship detection system will be demonstrated on an airborne platform. In this article, the scope, aim and design of a flight experiment for an on-board ship detection system scheduled for mid of 2018 is presented. First, the scope and the constraints of the experiment are explained in detail. The main goal is to demonstrate the operability of an automatic ship detection system on board of an airplane. For data acquisition the optical high resolution DLR MACS-MARE camera (VIS/NIR) is used. The system will be able to send product data, like position, size and a small image of the ship directly to the user's smart-phone by email. The time between the acquisition of the image data and the delivery of the product data to the end-user is aimed to be less than three minutes. For communication, the SMS-like Iridium Short Burst Data (SBD) Service was chosen, providing a message size of around 300 Bytes. Under optimal sending/receiving conditions, messages can be transmitted bidirectional every 20 seconds. Due to the very small data bandwidth, not all product data may be transmittable at once, for instance, when flying over busy ships traffic zones. Therefore the system offers two services: a query and a push service. With the query service the end user can explicitly request data of a defined location and fixed time period by posting queries in an SQL-like language. With the push service, events can be predefined and messages are received automatically, if and when the event occurs. Finally, the hardware set-up, details of the ship detection algorithms and the current status of the experiment is presented.

Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks.

PubMed

Azcorra, A; Chiroque, L F; Cuevas, R; Fernández Anta, A; Laniado, H; Lillo, R E; Romo, J; Sguera, C

2018-05-03

Billions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity.

Pattern Activity Clustering and Evaluation (PACE)

NASA Astrophysics Data System (ADS)

Blasch, Erik; Banas, Christopher; Paul, Michael; Bussjager, Becky; Seetharaman, Guna

2012-06-01

With the vast amount of network information available on activities of people (i.e. motions, transportation routes, and site visits) there is a need to explore the salient properties of data that detect and discriminate the behavior of individuals. Recent machine learning approaches include methods of data mining, statistical analysis, clustering, and estimation that support activity-based intelligence. We seek to explore contemporary methods in activity analysis using machine learning techniques that discover and characterize behaviors that enable grouping, anomaly detection, and adversarial intent prediction. To evaluate these methods, we describe the mathematics and potential information theory metrics to characterize behavior. A scenario is presented to demonstrate the concept and metrics that could be useful for layered sensing behavior pattern learning and analysis. We leverage work on group tracking, learning and clustering approaches; as well as utilize information theoretical metrics for classification, behavioral and event pattern recognition, and activity and entity analysis. The performance evaluation of activity analysis supports high-level information fusion of user alerts, data queries and sensor management for data extraction, relations discovery, and situation analysis of existing data.

Plug&Play Brain-Computer Interfaces for effective Active and Assisted Living control.

PubMed

Mora, Niccolò; De Munari, Ilaria; Ciampolini, Paolo; Del R Millán, José

2017-08-01

Brain-Computer Interfaces (BCI) rely on the interpretation of brain activity to provide people with disabilities with an alternative/augmentative interaction path. In light of this, BCI could be considered as enabling technology in many fields, including Active and Assisted Living (AAL) systems control. Interaction barriers could be removed indeed, enabling user with severe motor impairments to gain control over a wide range of AAL features. In this paper, a cost-effective BCI solution, targeted (but not limited) to AAL system control is presented. A custom hardware module is briefly reviewed, while signal processing techniques are covered in more depth. Steady-state visual evoked potentials (SSVEP) are exploited in this work as operating BCI protocol. In contrast with most common SSVEP-BCI approaches, we propose the definition of a prediction confidence indicator, which is shown to improve overall classification accuracy. The confidence indicator is derived without any subject-specific approach and is stable across users: it can thus be defined once and then shared between different persons. This allows some kind of Plug&Play interaction. Furthermore, by modelling rest/idle periods with the confidence indicator, it is possible to detect active control periods and separate them from "background activity": this is capital for real-time, self-paced operation. Finally, the indicator also allows to dynamically choose the most appropriate observation window length, improving system's responsiveness and user's comfort. Good results are achieved under such operating conditions, achieving, for instance, a false positive rate of 0.16 min -1 , which outperform current literature findings.

Detecting cocaine use? The autobiographical implicit association test (aIAT) produces false positives in a real-world setting

PubMed Central

2013-01-01

Background The autobiographical Implicit Association Test (aIAT) is a novel application of the implicit association concept for detecting life events. It has been used to reveal concealed knowledge in clinical and forensic settings, including detecting drug use. In this study, we aimed to explore the functionality of the aIAT to identify drug use in real-world settings. Methods The study used mixed methodology with known groups of drug users and nonusers. Recreational cocaine users (n = 23) and non-users (n = 23) were recruited through ethnographic methodology and assessed using a bespoke brief aIAT for cocaine use. An identical aIAT test for heroin detection was also administered to a sub-sample of 10 cocaine users and 13 nonusers. The accuracy of the cocaine aIAT was measured through ROC analysis. Paradoxical aIAT results were explored by integrating craving, consumption measures and life-story interviews into the analysis. Results Whilst the two brief aIATs showed good concurrent validity for cocaine users by accurately detecting drug using status for 18 of the 23 users (78.3%), the test falsely reported 61% cocaine users in the non-user comparison group. The average D-scores were 0.257±0.246 for the cocaine users and 0.134±0.367 for the non-users, showing no discriminatory power (t(44) = 1.339, p = 0.187; AUC = 0.605, p = 0.223). Results were independent from craving and recent cocaine use. The comparison group’s cocaine and heroin aIAT scores correlated significantly (r(13) = 0.776, p = 0.002) whilst an accurate absence of such relationship was evidenced in the cocaine using sample (r(10) = 0.061, p = 0.866). Triangulation with life-story interviews suggests that in the absence of an autobiographical event, this test may measure an alternative cognitive construct linked to the Self-concept. Conclusion The aIAT is a variant of an attitude measure and can be better rationalized if propositional thinking is implied to explain outcomes. The Relational Frame and Social Knowledge Structure theories can perhaps provide a more plausible theoretical background. Further work is required to clarify which factors underlie this testing technique’s functioning. Reappraisal is advised before further forensic use of the instrument to ensure that general associations not related to autobiographical memory do not confound results. PMID:23767665

Design of indoor temperature and humidity detection system based on single chip microcomputer

NASA Astrophysics Data System (ADS)

Fu, Xiuwei; Fu, Li; Ma, Tianhui

2018-03-01

The indoor temperature and humidity detection system based on STC15F2K60S2 is designed in this paper. The temperature and humidity sensor DHT22 to monitor the indoor temperature and humidity are used, and the temperature and humidity data to the user's handheld device are wirelessly transmitted, when the temperature reaches or exceeds the user set the temperature alarm value, and the system sound and light alarm, to remind the user.

Important first encounter: Service user experience of pathways to care and early detection in first-episode psychosis.

PubMed

Jansen, Jens Einar; Pedersen, Marlene Buch; Hastrup, Lene Halling; Haahr, Ulrik Helt; Simonsen, Erik

2018-04-01

Long duration of untreated psychosis is associated with poor clinical and functional outcomes. However, few systematic attempts have been made to reduce this delay and little is known of service users' experience of early detection efforts. We explored service users' experience of an early detection service and transition to specialized treatment service, including pathway to care, understanding of illness and barriers to adequate assessment and treatment. In-depth interviews were conducted with 10 service users (median age 21, range 18-27, five males and five females) who were diagnosed with a first-episode non-affective psychosis and who were seen by an early detection team (TOP) and currently enrolled in a specialized early intervention service for this disorder (OPUS). Stigma and fear of the 'psychiatric system' were reported as significant barriers to help seeking, while family members were seen as a crucial support. Moreover, the impact of traumatic events on the experience and development of psychosis was highlighted. Finally, participants were relieved by the prospect of receiving help and the early detection team seemed to create a trusting relationship by offering a friendly, 'anti-stigmatized' space, where long-term symptomatology could be disclosed through accurate and validating questioning. Early detection services have two important functions. One is to make accurate assessments and referrals. The other is to instil hope and trust, and to facilitate further treatment by forming an early therapeutic alliance. The findings in this study provide important insights into the way in which early detection efforts and pathways to care are experienced by service users, with direct implications for improving psychiatric services. © 2015 Wiley Publishing Asia Pty Ltd.

Characterizing and modeling the dynamics of activity and popularity.

PubMed

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks.

Characterizing and Modeling the Dynamics of Activity and Popularity

PubMed Central

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks. PMID:24586586

Hyperspace geography: visualizing fitness landscapes beyond 4D.

PubMed

Wiles, Janet; Tonkes, Bradley

2006-01-01

Human perception is finely tuned to extract structure about the 4D world of time and space as well as properties such as color and texture. Developing intuitions about spatial structure beyond 4D requires exploiting other perceptual and cognitive abilities. One of the most natural ways to explore complex spaces is for a user to actively navigate through them, using local explorations and global summaries to develop intuitions about structure, and then testing the developing ideas by further exploration. This article provides a brief overview of a technique for visualizing surfaces defined over moderate-dimensional binary spaces, by recursively unfolding them onto a 2D hypergraph. We briefly summarize the uses of a freely available Web-based visualization tool, Hyperspace Graph Paper (HSGP), for exploring fitness landscapes and search algorithms in evolutionary computation. HSGP provides a way for a user to actively explore a landscape, from simple tasks such as mapping the neighborhood structure of different points, to seeing global properties such as the size and distribution of basins of attraction or how different search algorithms interact with landscape structure. It has been most useful for exploring recursive and repetitive landscapes, and its strength is that it allows intuitions to be developed through active navigation by the user, and exploits the visual system's ability to detect pattern and texture. The technique is most effective when applied to continuous functions over Boolean variables using 4 to 16 dimensions.

Safe Local Navigation for Visually Impaired Users With a Time-of-Flight and Haptic Feedback Device.

PubMed

Katzschmann, Robert K; Araki, Brandon; Rus, Daniela

2018-03-01

This paper presents ALVU (Array of Lidars and Vibrotactile Units), a contactless, intuitive, hands-free, and discreet wearable device that allows visually impaired users to detect low- and high-hanging obstacles, as well as physical boundaries in their immediate environment. The solution allows for safe local navigation in both confined and open spaces by enabling the user to distinguish free space from obstacles. The device presented is composed of two parts: a sensor belt and a haptic strap. The sensor belt is an array of time-of-flight distance sensors worn around the front of a user's waist, and the pulses of infrared light provide reliable and accurate measurements of the distances between the user and surrounding obstacles or surfaces. The haptic strap communicates the measured distances through an array of vibratory motors worn around the user's upper abdomen, providing haptic feedback. The linear vibration motors are combined with a point-loaded pretensioned applicator to transmit isolated vibrations to the user. We validated the device's capability in an extensive user study entailing 162 trials with 12 blind users. Users wearing the device successfully walked through hallways, avoided obstacles, and detected staircases.

MUNDUS project: MUltimodal neuroprosthesis for daily upper limb support.

PubMed

Pedrocchi, Alessandra; Ferrante, Simona; Ambrosini, Emilia; Gandolla, Marta; Casellato, Claudia; Schauer, Thomas; Klauer, Christian; Pascual, Javier; Vidaurre, Carmen; Gföhler, Margit; Reichenfelser, Werner; Karner, Jakob; Micera, Silvestro; Crema, Andrea; Molteni, Franco; Rossini, Mauro; Palumbo, Giovanna; Guanziroli, Eleonora; Jedlitschka, Andreas; Hack, Marco; Bulgheroni, Maria; d'Amico, Enrico; Schenk, Peter; Zwicker, Sven; Duschau-Wicke, Alexander; Miseikis, Justinas; Graber, Lina; Ferrigno, Giancarlo

2013-07-03

MUNDUS is an assistive framework for recovering direct interaction capability of severely motor impaired people based on arm reaching and hand functions. It aims at achieving personalization, modularity and maximization of the user's direct involvement in assistive systems. To this, MUNDUS exploits any residual control of the end-user and can be adapted to the level of severity or to the progression of the disease allowing the user to voluntarily interact with the environment. MUNDUS target pathologies are high-level spinal cord injury (SCI) and neurodegenerative and genetic neuromuscular diseases, such as amyotrophic lateral sclerosis, Friedreich ataxia, and multiple sclerosis (MS). The system can be alternatively driven by residual voluntary muscular activation, head/eye motion, and brain signals. MUNDUS modularly combines an antigravity lightweight and non-cumbersome exoskeleton, closed-loop controlled Neuromuscular Electrical Stimulation for arm and hand motion, and potentially a motorized hand orthosis, for grasping interactive objects. The definition of the requirements and of the interaction tasks were designed by a focus group with experts and a questionnaire with 36 potential end-users. The functionality of all modules has been successfully demonstrated. User's intention was detected with a 100% success. Averaging all subjects and tasks, the minimum evaluation score obtained was 1.13 ± 0.99 for the release of the handle during the drinking task, whilst all the other sub-actions achieved a mean value above 1.6. All users, but one, subjectively perceived the usefulness of the assistance and could easily control the system. Donning time ranged from 6 to 65 minutes, scaled on the configuration complexity. The MUNDUS platform provides functional assistance to daily life activities; the modules integration depends on the user's need, the functionality of the system have been demonstrated for all the possible configurations, and preliminary assessment of usability and acceptance is promising.

System to Detect Racial-Based Bullying through Gamification.

PubMed

Álvarez-Bermejo, José A; Belmonte-Ureña, Luis J; Martos-Martínez, Africa; Barragán-Martín, Ana B; Del Mar Simón-Marquez, María

2016-01-01

Prevention and detection of bullying due to racial stigma was studied in school contexts using a system designed following "gamification" principles and integrating less usual elements, such as social interaction, augmented reality and cell phones in educational scenarios. "Grounded Theory" and "User Centered Design" were employed to explore coexistence inside and outside the classroom in terms of preferences and distrust in several areas of action and social frameworks of activity, and to direct the development of a cell phone app for early detection of school bullying scenarios. One hundred and fifty-one interviews were given at five schools selected for their high multiracial percentage and conflict. The most outstanding results were structural, that is the distribution of the classroom group by type of activity and subject being dealt with. Furthermore, in groups over 12 years of age, the relational structures in the classroom in the digital settings in which they participated with their cell phones did not reoccur, because face-to-face and virtual interaction between students with the supervision and involvement of the teacher combined to detect bullying caused by racial discrimination.

System to Detect Racial-Based Bullying through Gamification

PubMed Central

Álvarez-Bermejo, José A.; Belmonte-Ureña, Luis J.; Martos-Martínez, Africa; Barragán-Martín, Ana B.; del Mar Simón-Marquez, María

2016-01-01

Prevention and detection of bullying due to racial stigma was studied in school contexts using a system designed following “gamification” principles and integrating less usual elements, such as social interaction, augmented reality and cell phones in educational scenarios. “Grounded Theory” and “User Centered Design” were employed to explore coexistence inside and outside the classroom in terms of preferences and distrust in several areas of action and social frameworks of activity, and to direct the development of a cell phone app for early detection of school bullying scenarios. One hundred and fifty-one interviews were given at five schools selected for their high multiracial percentage and conflict. The most outstanding results were structural, that is the distribution of the classroom group by type of activity and subject being dealt with. Furthermore, in groups over 12 years of age, the relational structures in the classroom in the digital settings in which they participated with their cell phones did not reoccur, because face-to-face and virtual interaction between students with the supervision and involvement of the teacher combined to detect bullying caused by racial discrimination. PMID:27933006

Securing the User's Work Environment

NASA Technical Reports Server (NTRS)

Cardo, Nicholas P.

2004-01-01

High performance computing at the Numerical Aerospace Simulation Facility at NASA Ames Research Center includes C90's, J90's and Origin 2000's. Not only is it necessary to protect these systems from outside attacks, but also to provide a safe working environment on the systems. With the right tools, security anomalies in the user s work environment can be deleted and corrected. Validating proper ownership of files against user s permissions, will reduce the risk of inadvertent data compromise. The detection of extraneous directories and files hidden amongst user home directories is important for identifying potential compromises. The first runs of these utilities detected over 350,000 files with problems. With periodic scans, automated correction of problems takes only minutes. Tools for detecting these types of problems as well as their development techniques will be discussed with emphasis on consistency, portability and efficiency for both UNICOS and IRIX.

Development of a computerized monitoring program to identify narcotic diversion in a pediatric anesthesia practice.

PubMed

Brenn, B Randall; Kim, Margaret A; Hilmas, Elora

2015-08-15

Development of an operational reporting dashboard designed to correlate data from multiple sources to help detect potential drug diversion by automated dispensing cabinet (ADC) users is described. A commercial business intelligence platform was used to create a dashboard tool for rapid detection of unusual patterns of ADC transactions by anesthesia service providers at a large pediatric hospital. By linking information from the hospital's pharmacy information management system (PIMS) and anesthesia information management system (AIMS) in an associative data model, the "narcotic reconciliation dashboard" can generate various reports to help spot outlier activity associated with ADC dispensing of controlled substances and documentation of medication waste processing. The dashboard's utility was evaluated by "back-testing" the program with historical data on an actual episode of diversion by an anesthesia provider that had not been detected through traditional methods of PIMS and AIMS data monitoring. Dashboard-generated reports on key metrics (e.g., ADC transaction counts, discrepancies in dispensed versus reconciled amounts of narcotics, PIMS-AIMS documentation mismatches) over various time frames during the period of known diversion clearly indicated the diverter's outlier status relative to other authorized ADC users. A dashboard program for correlating ADC transaction data with pharmacy and patient care data may be an effective tool for detecting patterns of ADC use that suggest drug diversion. Copyright © 2015 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Individual and Group-Based Engagement in an Online Physical Activity Monitoring Program in Georgia.

PubMed

Smith, Matthew Lee; Durrett, Nicholas K; Bowie, Maria; Berg, Alison; McCullick, Bryan A; LoPilato, Alexander C; Murray, Deborah

2018-06-07

Given the rising prevalence of obesity in the United States, innovative methods are needed to increase physical activity (PA) in community settings. Evidence suggests that individuals are more likely to engage in PA if they are given a choice of activities and have support from others (for encouragement, motivation, and accountability). The objective of this study was to describe the use of the online Walk Georgia PA tracking platform according to whether the user was an individual user or group user. Walk Georgia is a free, interactive online tracking platform that enables users to log PA by duration, activity, and perceived difficulty, and then converts these data into points based on metabolic equivalents. Users join individually or in groups and are encouraged to set weekly PA goals. Data were examined for 6,639 users (65.8% were group users) over 28 months. We used independent sample t tests and Mann-Whitney U tests to compare means between individual and group users. Two linear regression models were fitted to identify factors associated with activity logging. Users logged 218,766 activities (15,119,249 minutes of PA spanning 592,714 miles [41,858,446 points]). On average, group users had created accounts more recently than individual users (P < .001); however, group users logged more activities (P < .001). On average, group users logged more minutes of PA (P < .001) and earned more points (P < .001). Being in a group was associated with a larger proportion of weeks in which 150 minutes or more of weekly PA was logged (B = 20.47, P < .001). Use of Walk Georgia was significantly higher among group users than among individual users. To expand use and dissemination of online tracking of PA, programs should target naturally occurring groups (eg, workplaces, schools, faith-based groups).

Estimation and detection information trade-off for x-ray system optimization

NASA Astrophysics Data System (ADS)

Cushing, Johnathan B.; Clarkson, Eric W.; Mandava, Sagar; Bilgin, Ali

2016-05-01

X-ray Computed Tomography (CT) systems perform complex imaging tasks to detect and estimate system parameters, such as a baggage imaging system performing threat detection and generating reconstructions. This leads to a desire to optimize both the detection and estimation performance of a system, but most metrics only focus on one of these aspects. When making design choices there is a need for a concise metric which considers both detection and estimation information parameters, and then provides the user with the collection of possible optimal outcomes. In this paper a graphical analysis of Estimation and Detection Information Trade-off (EDIT) will be explored. EDIT produces curves which allow for a decision to be made for system optimization based on design constraints and costs associated with estimation and detection. EDIT analyzes the system in the estimation information and detection information space where the user is free to pick their own method of calculating these measures. The user of EDIT can choose any desired figure of merit for detection information and estimation information then the EDIT curves will provide the collection of optimal outcomes. The paper will first look at two methods of creating EDIT curves. These curves can be calculated using a wide variety of systems and finding the optimal system by maximizing a figure of merit. EDIT could also be found as an upper bound of the information from a collection of system. These two methods allow for the user to choose a method of calculation which best fits the constraints of their actual system.

National physical activity surveillance: Users of wearable activity monitors as a potential data source.

PubMed

Omura, John D; Carlson, Susan A; Paul, Prabasaj; Watson, Kathleen B; Fulton, Janet E

2017-03-01

The objective of this study was to assess usage patterns of wearable activity monitors among US adults and how user characteristics might influence physical activity estimates from this type of sample. We analyzed data on 3367 respondents to the 2015 HealthStyles survey, an annual consumer mail panel survey conducted on a nationwide sample. Approximately 1 in 8 respondents (12.5%) reported currently using a wearable activity monitor. Current use varied by sex, age, and education level. Use increased with physical activity level from 4.3% for inactive adults to 17.4% for active adults. Overall, 49.9% of all adults met the aerobic physical activity guideline, while this prevalence was 69.5% among current activity monitor users. Our findings suggest that current users of wearable activity monitors are not representative of the overall US population. Estimates of physical activity levels using data from wearable activity monitors users may be an overestimate and therefore data from users alone may have a limited role in physical activity surveillance.

ADVANTG An Automated Variance Reduction Parameter Generator, Rev. 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mosher, Scott W.; Johnson, Seth R.; Bevill, Aaron M.

2015-08-01

The primary objective of ADVANTG is to reduce both the user effort and the computational time required to obtain accurate and precise tally estimates across a broad range of challenging transport applications. ADVANTG has been applied to simulations of real-world radiation shielding, detection, and neutron activation problems. Examples of shielding applications include material damage and dose rate analyses of the Oak Ridge National Laboratory (ORNL) Spallation Neutron Source and High Flux Isotope Reactor (Risner and Blakeman 2013) and the ITER Tokamak (Ibrahim et al. 2011). ADVANTG has been applied to a suite of radiation detection, safeguards, and special nuclear materialmore » movement detection test problems (Shaver et al. 2011). ADVANTG has also been used in the prediction of activation rates within light water reactor facilities (Pantelias and Mosher 2013). In these projects, ADVANTG was demonstrated to significantly increase the tally figure of merit (FOM) relative to an analog MCNP simulation. The ADVANTG-generated parameters were also shown to be more effective than manually generated geometry splitting parameters.« less

Modeling long-term human activeness using recurrent neural networks for biometric data.

PubMed

Kim, Zae Myung; Oh, Hyungrai; Kim, Han-Gyu; Lim, Chae-Gyun; Oh, Kyo-Joong; Choi, Ho-Jin

2017-05-18

With the invention of fitness trackers, it has been possible to continuously monitor a user's biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user's "activeness", and investigates the feasibility in modeling and predicting the long-term activeness of the user. The dataset used in this study consisted of several months of biometric time-series data gathered by seven users independently. Four recurrent neural network (RNN) architectures-as well as a deep neural network and a simple regression model-were proposed to investigate the performance on predicting the activeness of the user under various length-related hyper-parameter settings. In addition, the learned model was tested to predict the time period when the user's activeness falls below a certain threshold. A preliminary experimental result shows that each type of activeness data exhibited a short-term autocorrelation; and among the three types of data, the consumed calories and the number of footsteps were positively correlated, while the heart rate data showed almost no correlation with neither of them. It is probably due to this characteristic of the dataset that although the RNN models produced the best results on modeling the user's activeness, the difference was marginal; and other baseline models, especially the linear regression model, performed quite admirably as well. Further experimental results show that it is feasible to predict a user's future activeness with precision, for example, a trained RNN model could predict-with the precision of 84%-when the user would be less active within the next hour given the latest 15 min of his activeness data. This paper defines and investigates the notion of a user's "activeness", and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively recommend suitable events or services to the user.

Implementing a combined polar-geostationary algorithm for smoke emissions estimation in near real time

NASA Astrophysics Data System (ADS)

Hyer, E. J.; Schmidt, C. C.; Hoffman, J.; Giglio, L.; Peterson, D. A.

2013-12-01

Polar and geostationary satellites are used operationally for fire detection and smoke source estimation by many near-real-time operational users, including operational forecast centers around the globe. The input satellite radiance data are processed by data providers to produce Level-2 and Level -3 fire detection products, but processing these data into spatially and temporally consistent estimates of fire activity requires a substantial amount of additional processing. The most significant processing steps are correction for variable coverage of the satellite observations, and correction for conditions that affect the detection efficiency of the satellite sensors. We describe a system developed by the Naval Research Laboratory (NRL) that uses the full raster information from the entire constellation to diagnose detection opportunities, calculate corrections for factors such as angular dependence of detection efficiency, and generate global estimates of fire activity at spatial and temporal scales suitable for atmospheric modeling. By incorporating these improved fire observations, smoke emissions products, such as NRL's FLAMBE, are able to produce improved estimates of global emissions. This talk provides an overview of the system, demonstrates the achievable improvement over older methods, and describes challenges for near-real-time implementation.

Design and fuzzy logic control of an active wrist orthosis.

PubMed

Kilic, Ergin; Dogan, Erdi

2017-08-01

People who perform excessive wrist movements throughout the day because of their professions have a higher risk of developing lateral and medial epicondylitis. If proper precautions are not taken against these diseases, serious consequences such as job loss and early retirement can occur. In this study, the design and control of an active wrist orthosis that is mobile, powerful and lightweight is presented as a means to avoid the occurrence and/or for the treatment of repetitive strain injuries in an effective manner. The device has an electromyography-based control strategy so that the user's intention always comes first. In fact, the device-user interaction is mainly activated by the electromyography signals measured from the forearm muscles that are responsible for the extension and flexion wrist movements. Contractions of the muscles are detected using surface electromyography sensors, and the desired quantity of the velocity value of the wrist is extracted from a fuzzy logic controller. Then, the actuator system of the device comes into play by conveying the necessary motion support to the wrist. Experimental studies show that the presented device actually reduces the demand on the muscles involved in repetitive strain injuries while performing challenging daily life activities including extension and flexion wrist motions.

Chronic Azithromycin Use in Cystic Fibrosis and Risk of Treatment-Emergent Respiratory Pathogens.

PubMed

Cogen, Jonathan D; Onchiri, Frankline; Emerson, Julia; Gibson, Ronald L; Hoffman, Lucas R; Nichols, David P; Rosenfeld, Margaret

2018-02-23

Azithromycin has been shown to improve lung function and reduce the number of pulmonary exacerbations in cystic fibrosis patients. Concerns remain, however, regarding the potential emergence of treatment-related respiratory pathogens. To determine if chronic azithromycin use (defined as thrice weekly administration) is associated with increased rates of detection of eight specific respiratory pathogens. We performed a new-user, propensity-score matched retrospective cohort study utilizing data from the Cystic Fibrosis Foundation Patient Registry. Incident azithromycin users were propensity-score matched 1:1 with contemporaneous non-users. Kaplan-Meier curves and Cox proportional hazards regression were used to evaluate the association between chronic azithromycin use and incident respiratory pathogen detection. Analyses were performed separately for each pathogen, limited to patients among whom that pathogen had not been isolated in the two years prior to cohort entry. After propensity score matching, mean age of the cohorts was ~12 years. Chronic azithromycin users had a significantly lower risk of detection of new methicillin-resistant Staphylococcus aureus, non-tuberculous mycobacteria, and Burkholderia cepacia complex compared to non-users. The risk of acquiring the remaining five pathogens was not significantly different between users and non-users. Using an innovative new-user, propensity-score matched study design to minimize indication and selection biases, we found in a predominantly pediatric cohort that chronic azithromycin users had a lower risk of acquiring several cystic fibrosis-related respiratory pathogens. These results may ease concerns that chronic azithromycin exposure increases the risk of acquiring new respiratory pathogens among pediatric cystic fibrosis patients.

You Spin my Head Right Round: Threshold of Limited Immersion for Rotation Gains in Redirected Walking.

PubMed

Schmitz, Patric; Hildebrandt, Julian; Valdez, Andre Calero; Kobbelt, Leif; Ziefle, Martina

2018-04-01

In virtual environments, the space that can be explored by real walking is limited by the size of the tracked area. To enable unimpeded walking through large virtual spaces in small real-world surroundings, redirection techniques are used. These unnoticeably manipulate the user's virtual walking trajectory. It is important to know how strongly such techniques can be applied without the user noticing the manipulation-or getting cybersick. Previously, this was estimated by measuring a detection threshold (DT) in highly-controlled psychophysical studies, which experimentally isolate the effect but do not aim for perceived immersion in the context of VR applications. While these studies suggest that only relatively low degrees of manipulation are tolerable, we claim that, besides establishing detection thresholds, it is important to know when the user's immersion breaks. We hypothesize that the degree of unnoticed manipulation is significantly different from the detection threshold when the user is immersed in a task. We conducted three studies: a) to devise an experimental paradigm to measure the threshold of limited immersion (TLI), b) to measure the TLI for slowly decreasing and increasing rotation gains, and c) to establish a baseline of cybersickness for our experimental setup. For rotation gains greater than 1.0, we found that immersion breaks quite late after the gain is detectable. However, for gains lesser than 1.0, some users reported a break of immersion even before established detection thresholds were reached. Apparently, the developed metric measures an additional quality of user experience. This article contributes to the development of effective spatial compression methods by utilizing the break of immersion as a benchmark for redirection techniques.

Improvement of emotional healthcare system with stress detection from ECG signal.

PubMed

Tivatansakul, S; Ohkura, M

2015-01-01

Our emotional healthcare system is designed to cope with users' negative emotions in daily life. To make the system more intelligent, we integrated emotion recognition by facial expression to provide appropriate services based on user's current emotional state. Our emotion recognition by facial expression has confusion issue to recognize some positive, neutral and negative emotions that make the emotional healthcare system provide a relaxation service even though users don't have negative emotions. Therefore, to increase the effectiveness of the system to provide the relaxation service, we integrate stress detection from ECG signal. The stress detection might be able to address the confusion issue of emotion recognition by facial expression to provide the service. Indeed, our results show that integration of stress detection increases the effectiveness and efficiency of the emotional healthcare system to provide services.

Naval Ship Database: Database Design, Implementation, and Schema

DTIC Science & Technology

2013-09-01

incoming data. The solution allows database users to store and analyze data collected by navy ships in the Royal Canadian Navy ( RCN ). The data...understanding RCN jargon and common practices on a typical RCN vessel. This experience led to the development of several error detection methods to...data to be stored in the database. Mr. Massel has also collected data pertaining to day to day activities on RCN vessels that has been imported into

Medically related activities of application team program

NASA Technical Reports Server (NTRS)

1971-01-01

Application team methodology identifies and specifies problems in technology transfer programs to biomedical areas through direct contact with users of aerospace technology. The availability of reengineering sources increases impact of the program on the medical community and results in broad scale application of some bioinstrumentation systems. Examples are given that include devices adapted to the rehabilitation of neuromuscular disorders, power sources for artificial organs, and automated monitoring and detection equipment in clinical medicine.

[Contamination and Cleaning of Touch Panels Used in Everyday Life and the Awareness of Persons in Charge and Users of Devices about Contamination].

PubMed

Morioka, Ikuharu; Uda, Kazu; Yamamoto, Mio

2015-01-01

The purpose of this study was to clarify the contamination and cleaning of touch panels used in everyday life and the awareness of persons in charge and users of devices about contamination. Samples from touch panels were cultured to detect viable bacteria (n=132), Staphylococcus aureus (n=66) and Escherichia coli (n=64). A questionnaire survey was conducted on persons in charge and users of the devices on the day of sampling. Viable bacterial cells were detected in more than 90% of the automatic teller machines (ATMs) at banks, the ticket machines at stations, and the copy machines at convenience stores. S. aureus and E. coli were detected in more than one-half of such devices examined. The detection rate of viable bacterial cells in smartphones was 57.5% and was lower than those in other devices. More than 65% of the ATMs, ticket machines, and copy machines were cleaned once or twice a day. More than one-half of the users of smartphones or button-type mobile phones did not clean their devices. Those who did not aware about the contamination of touch panels were 46.6% of the persons in charge and 38.2% of the users. It is necessary to examine the suitable number of times and methods of cleaning of touch panels and to raise the awareness of persons in charge or users of such devices about contamination.

Delivery dilemmas: How drug cryptomarket users identify and seek to reduce their risk of detection by law enforcement.

PubMed

Aldridge, Judith; Askew, Rebecca

2017-03-01

Cryptomarkets represent an important drug market innovation by bringing buyers and sellers of illegal drugs together in a 'hidden' yet public online marketplace. We ask: How do cryptomarket drug sellers and buyers perceive the risks of detection and arrest, and attempt to limit them? We analyse selected texts produced by vendors operating on the first major drug cryptomarket, Silk Road (N=600) alongside data extracted from the marketplace discussion forum that include buyer perspectives. We apply Fader's (2016) framework for understanding how drug dealers operating 'offline' attempt to reduce the risk of detection and arrest: visibility reduction, charge reduction and risk distribution. We characterize drug transactions on cryptomarkets as 'stretched' across time, virtual and physical space, and handlers, changing the location and nature of risks faced by cryptomarket users. The key locations of risk of detection and arrest by law enforcement were found in 'offline' activities of cryptomarket vendors (packaging and delivery drop-offs) and buyers (receiving deliveries). Strategies in response involved either creating or disrupting routine activities in line with a non-offending identity. Use of encrypted communication was seen as 'good practice' but often not employed. 'Drop shipping' allowed some Silk Road vendors to sell illegal drugs without the necessity of handling them. Silk Road participants neither viewed themselves as immune to, nor passively accepting of, the risk of detection and arrest. Rational choice theorists have viewed offending decisions as constrained by limited access to relevant information. Cryptomarkets as 'illicit capital' sharing communities provide expanded and low-cost access to information enabling drug market participants to make more accurate assessments of the risk of apprehension. The abundance of drug market intelligence available to those on both sides of the law may function to speed up innovation in illegal drug markets, as well as necessitate and facilitate the development of law enforcement responses. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Comparing the Trend of Physical Activity and Caloric Intake between Lipid-Lowering Drug Users and Nonusers among Adults with Dyslipidemia: Korean National Health and Nutrition Examination Surveys (2010-2013).

PubMed

Oh, Jin-Young; Chekal, Lan; Kim, Se-Won; Lee, Jee-Yon; Lee, Duk-Chul

2016-03-01

The purpose of this study was to compare the physical activity and caloric intake trends of lipid-lowering drug users with those of non-users among Korean adults with dyslipidemia. This study was a repeated cross-sectional study with a nationally representative sample of 2,635 Korean adults with dyslipidemia based on the 2010-2013 Korea National Health and Nutrition Examination Survey. Physical activity was assessed using the International Physical Activity Questionnaire, and caloric intake was estimated through 24-hour dietary recall. All statistical analyses were conducted using IBM SPSS ver. 21.0 (IBM Co., Armonk, NY, USA). The changes in physical activity and caloric intake were investigated for lipid-lowering drug users and non-users using generalized linear models. The proportion of lipid-lowering drug users in the 2010-2013 survey population increased from 3.5% to 5.0% (P<0.001). Among adults of dyslipidemia, total of 1,562 participants (56.6%) reported taking lipid-lowering drugs, and 1,073 (43.4%) reported not taking lipid-lowering drugs. Drug users were more likely to be older and less educated and to have a diagnosis of diabetes, higher body mass index, and lower low density lipoprotein cholesterol level. Physical activity trends were tested separately for the lipid-lowering drug users and non-users, and a significant decrease was found among the drug users during the study period. Physical activity among the drug users in 2013 was 38% lower (1,357.3±382.7 metabolic equivalent [MET]; P for trend=0.002) than in 2010 (2,201.4±442.6 MET). In contrast, there was no statistically significant difference between drug users and non-users in the trend of caloric intake during the same period. Physical activity significantly decreased among lipid-lowering drug users between 2010 and 2013, which was not observed among non-users. The importance of physical activity may need to be re-emphasized for lipid-lowering drug users.

A Single-Channel EOG-Based Speller.

PubMed

He, Shenghong; Li, Yuanqing

2017-11-01

Electrooculography (EOG) signals, which can be used to infer the intentions of a user based on eye movements, are widely used in human-computer interface (HCI) systems. Most existing EOG-based HCI systems incorporate a limited number of commands because they generally associate different commands with a few different types of eye movements, such as looking up, down, left, or right. This paper presents a novel single-channel EOG-based HCI that allows users to spell asynchronously by only blinking. Forty buttons corresponding to 40 characters displayed to the user via a graphical user interface are intensified in a random order. To select a button, the user must blink his/her eyes in synchrony as the target button is flashed. Two data processing procedures, specifically support vector machine (SVM) classification and waveform detection, are combined to detect eye blinks. During detection, we simultaneously feed the feature vectors extracted from the ongoing EOG signal into the SVM classification and waveform detection modules. Decisions are made based on the results of the SVM classification and waveform detection. Three online experiments were conducted with eight healthy subjects. We achieved an average accuracy of 94.4% and a response time of 4.14 s for selecting a character in synchronous mode, as well as an average accuracy of 93.43% and a false positive rate of 0.03/min in the idle state in asynchronous mode. The experimental results, therefore, demonstrated the effectiveness of this single-channel EOG-based speller.

Development of a handheld widefield hyperspectral imaging (HSI) sensor for standoff detection of explosive, chemical, and narcotic residues

NASA Astrophysics Data System (ADS)

Nelson, Matthew P.; Basta, Andrew; Patil, Raju; Klueva, Oksana; Treado, Patrick J.

2013-05-01

The utility of Hyper Spectral Imaging (HSI) passive chemical detection employing wide field, standoff imaging continues to be advanced in detection applications. With a drive for reduced SWaP (Size, Weight, and Power), increased speed of detection and sensitivity, developing a handheld platform that is robust and user-friendly increases the detection capabilities of the end user. In addition, easy to use handheld detectors could improve the effectiveness of locating and identifying threats while reducing risks to the individual. ChemImage Sensor Systems (CISS) has developed the HSI Aperio™ sensor for real time, wide area surveillance and standoff detection of explosives, chemical threats, and narcotics for use in both government and commercial contexts. Employing liquid crystal tunable filter technology, the HSI system has an intuitive user interface that produces automated detections and real-time display of threats with an end user created library of threat signatures that is easily updated allowing for new hazardous materials. Unlike existing detection technologies that often require close proximity for sensing and so endanger operators and costly equipment, the handheld sensor allows the individual operator to detect threats from a safe distance. Uses of the sensor include locating production facilities of illegal drugs or IEDs by identification of materials on surfaces such as walls, floors, doors, deposits on production tools and residue on individuals. In addition, the sensor can be used for longer-range standoff applications such as hasty checkpoint or vehicle inspection of residue materials on surfaces or bulk material identification. The CISS Aperio™ sensor has faster data collection, faster image processing, and increased detection capability compared to previous sensors.

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

SeeCoast: persistent surveillance and automated scene understanding for ports and coastal areas

NASA Astrophysics Data System (ADS)

Rhodes, Bradley J.; Bomberger, Neil A.; Freyman, Todd M.; Kreamer, William; Kirschner, Linda; L'Italien, Adam C.; Mungovan, Wendy; Stauffer, Chris; Stolzar, Lauren; Waxman, Allen M.; Seibert, Michael

2007-04-01

SeeCoast is a prototype US Coast Guard port and coastal area surveillance system that aims to reduce operator workload while maintaining optimal domain awareness by shifting their focus from having to detect events to being able to analyze and act upon the knowledge derived from automatically detected anomalous activities. The automated scene understanding capability provided by the baseline SeeCoast system (as currently installed at the Joint Harbor Operations Center at Hampton Roads, VA) results from the integration of several components. Machine vision technology processes the real-time video streams provided by USCG cameras to generate vessel track and classification (based on vessel length) information. A multi-INT fusion component generates a single, coherent track picture by combining information available from the video processor with that from surface surveillance radars and AIS reports. Based on this track picture, vessel activity is analyzed by SeeCoast to detect user-defined unsafe, illegal, and threatening vessel activities using a rule-based pattern recognizer and to detect anomalous vessel activities on the basis of automatically learned behavior normalcy models. Operators can optionally guide the learning system in the form of examples and counter-examples of activities of interest, and refine the performance of the learning system by confirming alerts or indicating examples of false alarms. The fused track picture also provides a basis for automated control and tasking of cameras to detect vessels in motion. Real-time visualization combining the products of all SeeCoast components in a common operating picture is provided by a thin web-based client.

Significant applications of ERTS-1 data to resource management activities at the state level in Ohio. [strip mining and land use mapping

NASA Technical Reports Server (NTRS)

Sweet, D. C.; Pincura, P. G.; Meier, C. J.; Garrett, G. B.; Herd, L.; Wukelic, G. E.; Stephan, J. G.; Smail, H. E.

1974-01-01

Described are techniques utilized and the progress made in applying ERTS-1 data to (1) detecting, inventorying, and monitoring surface mining activities, particularly in relation to recently passed strip mine legislation in Ohio; (2) updating current land use maps at various scales for multiagency usage, and (3) solving other real-time problems existing throughout the various Ohio governmental agencies. General conclusions regarding current user views as to the opportunities and limitations of operationally using ERTS-1 data at the state level are also noted.

An eHealth Project on Invasive Pneumococcal Disease: Comprehensive Evaluation of a Promotional Campaign.

PubMed

Panatto, Donatella; Domnich, Alexander; Gasparini, Roberto; Bonanni, Paolo; Icardi, Giancarlo; Amicizia, Daniela; Arata, Lucia; Carozzo, Stefano; Signori, Alessio; Bechini, Angela; Boccalini, Sara

2016-12-02

The recently launched Pneumo Rischio eHealth project, which consists of an app, a website, and social networking activity, is aimed at increasing public awareness of invasive pneumococcal disease (IPD). The launch of this project was prompted by the inadequate awareness of IPD among both laypeople and health care workers, the heavy socioeconomic burden of IPD, and the far from optimal vaccination coverage in Italy, despite the availability of safe and effective vaccines. The objectives of our study were to analyze trends in Pneumo Rischio usage before and after a promotional campaign, to characterize its end users, and to assess its user-rated quality. At 7 months after launching Pneumo Rischio, we established a 4-month marketing campaign to promote the project. This intervention used various approaches and channels, including both traditional and digital marketing strategies. To highlight usage trends, we used different techniques of time series analysis and modeling, including a modified Mann-Kendall test, change-point detection, and segmented negative binomial regression of interrupted time series. Users were characterized in terms of demographics and IPD risk categories. Customer-rated quality was evaluated by means of a standardized tool in a sample of app users. Over 1 year, the app was accessed by 9295 users and the website was accessed by 143,993 users, while the project's Facebook page had 1216 fans. The promotional intervention was highly effective in increasing the daily number of users. In particular, the Mann-Kendall trend test revealed a significant (P ≤.01) increasing trend in both app and website users, while change-point detection analysis showed that the first significant change corresponded to the start of the promotional campaign. Regression analysis showed a significant immediate effect of the intervention, with a mean increase in daily numbers of users of 1562% (95% CI 456%-4870%) for the app and 620% (95% CI 176%-1777%) for the website. Similarly, the postintervention daily trend in the number of users was positive, with a relative increase of 0.9% (95% CI 0.0%-1.8%) for the app and 1.4% (95% CI 0.7%-2.1%) for the website. Demographics differed between app and website users and Facebook fans. A total of 69.15% (10,793/15,608) of users could be defined as being at risk of IPD, while 4729 users expressed intentions to ask their doctor for further information on IPD. The mean app quality score assigned by end users was approximately 79.5% (397/500). Despite its specific topic, Pneumo Rischio was accessed by a considerable number of users, who ranked it as a high-quality project. In order to reach their target populations, however, such projects should be promoted. ©Donatella Panatto, Alexander Domnich, Roberto Gasparini, Paolo Bonanni, Giancarlo Icardi, Daniela Amicizia, Lucia Arata, Stefano Carozzo, Alessio Signori, Angela Bechini, Sara Boccalini. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 02.12.2016.

Autonomy and manual operation in a small robotic system for under-vehicle inspections at security checkpoints

NASA Astrophysics Data System (ADS)

Smuda, William; Muench, Paul L.; Gerhart, Grant R.; Moore, Kevin L.

2002-07-01

Unmanned ground vehicle (UGV) technology can be used in a number of ways to assist in counter-terrorism activities. In addition to the conventional uses of tele-operated robots for unexploded ordinance handling and disposal, water cannons and other crowd control devices, robots can also be employed for a host of terrorism deterrence and detection applications. In previous research USU developed a completely autonomous prototype robot for performing under- vehicle inspections in parking areas (ODIS). Testing of this prototype and discussions with the user community indicated that neither the technology nor the users are ready for complete autonomy. In this paper we present a robotic system based on ODIS that balances the users' desire/need for tele- operation with a limited level of autonomy that enhances the performance of the robot. The system can be used by both civilian law enforcement and military police to replace the traditional mirror on a stick system of looking under cars for bombs and contraband.

Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review.

PubMed

Kassianos, A P; Emery, J D; Murchie, P; Walter, F M

2015-06-01

Smartphone health applications ('apps') are widely available but experts remain cautious about their utility and safety. We reviewed currently available apps for the detection of melanoma (July 2014), aimed at general community, patient and generalist clinician users. A proforma was used to extract and assess each app that met the inclusion criteria, and we undertook content analysis to evaluate their content and the evidence applied in their development. Thirty-nine apps were identified with the majority available only for Apple users. Over half (n = 22) provided information or education about melanoma, ultraviolet radiation exposure prevention advice, and skin self-examination strategies, mainly using the ABCDE (A, Asymmetry; B, Border; C, Colour; D, Diameter; E, Evolving) method. Half (n = 19) helped users take and store images of their skin lesions either for review by a dermatologist or for self-monitoring to identify change, an important predictor of melanoma; a similar number (n = 18) used reminders to help users monitor their skin lesions. A few (n = 9) offered expert review of images. Four apps provided a risk assessment to patients about the probability that a lesion was malignant or benign, and one app calculated users' future risk of melanoma. None of the apps appeared to have been validated for diagnostic accuracy or utility using established research methods. Smartphone apps for detecting melanoma by nonspecialist users have a range of functions including information, education, classification, risk assessment and monitoring change. Despite their potential usefulness, and while clinicians may choose to use apps that provide information to educate their patients, apps for melanoma detection require further validation of their utility and safety. © 2015 The Authors. British Journal of Dermatology published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.

Ontology-Based High-Level Context Inference for Human Behavior Identification

PubMed Central

Villalonga, Claudia; Razzaq, Muhammad Asif; Khan, Wajahat Ali; Pomares, Hector; Rojas, Ignacio; Lee, Sungyoung; Banos, Oresti

2016-01-01

Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is required to scale to long-term scenarios with a large number of users. PMID:27690050

Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles

NASA Technical Reports Server (NTRS)

Eichler, Gabriel S.; Huang, Sui; Ingber, Donald E.

2003-01-01

Genome-wide expression profiles contain global patterns that evade visual detection in current gene clustering analysis. Here, a Gene Expression Dynamics Inspector (GEDI) is described that uses self-organizing maps to translate high-dimensional expression profiles of time courses or sample classes into animated, coherent and robust mosaics images. GEDI facilitates identification of interesting patterns of molecular activity simultaneously across gene, time and sample space without prior assumption of any structure in the data, and then permits the user to retrieve genes of interest. Important changes in genome-wide activities may be quickly identified based on 'Gestalt' recognition and hence, GEDI may be especially useful for non-specialist end users, such as physicians. AVAILABILITY: GEDI v1.0 is written in Matlab, and binary Matlab.dll files which require Matlab to run can be downloaded for free by academic institutions at http://www.chip.org/ge/gedihome.html Supplementary information: http://www.chip.org/ge/gedihome.html.

Biological activities caused by far-infrared radiation

NASA Astrophysics Data System (ADS)

Inoué, Shojiro; Kabaya, Morihiro

1989-09-01

Contrary to previous presumption, accumulated evidence indicates that far-infrared rays are biologically active. A small ceramic disk that emist far-infrared rays (4 16 μm) has commonly been applied to a local spot or a whole part of the body for exposure. Pioneering attempts to experimentally analyze an effect of acute and chronic radiation of far-infrared rays on living organisms have detected a growth-promoting effect in growing rats, a sleep-modulatory effect in freely behaving rats and an insomiac patient, and a blood circulation-enhancing effect in human skin. Question-paires to 542 users of far-infrared radiator disks embedded in bedelothes revealed that the majority of the users subjectively evaluated an improvement of their health. These effects on living organisms appear to be non-specifically triggered by an exposure to far-infrared rays, which eventually induce an increase in temperature of the body tissues or, more basically, an elevated motility of body fluids due to decrease in size of water clusters.

Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults

PubMed Central

Liu, Pei-Yang

2014-01-01

Metabolic syndrome (MetS) in young adults (age 20–39) is often undiagnosed. A simple screening tool using a surrogate measure might be invaluable in the early detection of MetS. Methods. A chi-squared automatic interaction detection (CHAID) decision tree analysis with waist circumference user-specified as the first level was used to detect MetS in young adults using data from the National Health and Nutrition Examination Survey (NHANES) 2009-2010 Cohort as a representative sample of the United States population (n = 745). Results. Twenty percent of the sample met the National Cholesterol Education Program Adult Treatment Panel III (NCEP) classification criteria for MetS. The user-specified CHAID model was compared to both CHAID model with no user-specified first level and logistic regression based model. This analysis identified waist circumference as a strong predictor in the MetS diagnosis. The accuracy of the final model with waist circumference user-specified as the first level was 92.3% with its ability to detect MetS at 71.8% which outperformed comparison models. Conclusions. Preliminary findings suggest that young adults at risk for MetS could be identified for further followup based on their waist circumference. Decision tree methods show promise for the development of a preliminary detection algorithm for MetS. PMID:24817904

Exploration of Metaphorical and Contextual Affect Sensing in a Virtual Improvisational Drama

NASA Astrophysics Data System (ADS)

Zhang, Li

Real-time affect detection from open-ended text-based dialogue is challenging but essential for the building of effective intelligent user interfaces. In this paper, we report updated developments of an affect detection model from text, including affect detection from one particular type of metaphorical affective expression (cooking metaphor) and affect detection based on context. The overall affect detection model has been embedded in an intelligent conversational AI agent interacting with human users under loose scenarios. Evaluation for the updated affect detection component is also provided. Our work contributes to the conference themes on engagement and emotion, interactions in games, storytelling and narrative in education, and virtual characters/agents development.

Activities on Facebook reveal the depressive state of users.

PubMed

Park, Sungkyu; Lee, Sang Won; Kwak, Jinah; Cha, Meeyoung; Jeong, Bumseok

2013-10-01

As online social media have become prominent, much effort has been spent on identifying users with depressive symptoms in order to aim at early diagnosis, treatment, and even prevention by using various online social media. In this paper, we focused on Facebook to discern any correlations between the platform's features and users' depressive symptoms. This work may be helpful in trying to reach and detect large numbers of depressed individuals more easily. Our goal was to develop a Web application and identify depressive symptom-related features from users of Facebook, a popular social networking platform. 55 Facebook users (male=40, female=15, mean age 24.43, SD 3.90) were recruited through advertisement fliers distributed to students in a large university in Korea. Using EmotionDiary, the Facebook application we developed, we evaluated depressive symptoms using the Center for Epidemiological Studies-Depression (CES-D) scale. We also provided tips and facts about depression to participants and measured their responses using EmotionDiary. To identify the Facebook features related to depression, correlation analyses were performed between CES-D and participants' responses to tips and facts or Facebook social features. Last, we interviewed depressed participants (CES-D≥25) to assess their depressive symptoms by a psychiatrist. Facebook activities had predictive power in distinguishing depressed and nondepressed individuals. Participants' response to tips and facts, which can be explained by the number of app tips viewed and app points, had a positive correlation (P=.04 for both cases), whereas the number of friends and location tags had a negative correlation with the CES-D scale (P=.08 and P=.045 respectively). Furthermore, in finding group differences in Facebook social activities, app tips viewed and app points resulted in significant differences (P=.01 and P=.03 respectively) between probably depressed and nondepressed individuals. Our results using EmotionDiary demonstrated that the more depressed one is, the more one will read tips and facts about depression. We also confirmed depressed individuals had significantly fewer interactions with others (eg, decreased number of friends and location tagging). Our app, EmotionDiary, can successfully evaluate depressive symptoms as well as provide useful tips and facts to users. These results open the door for examining Facebook activities to identify depressed individuals. We aim to conduct the experiment in multiple cultures as well.

Performance of a scanning laser line striper in outdoor lighting

NASA Astrophysics Data System (ADS)

Mertz, Christoph

2013-05-01

For search and rescue robots and reconnaissance robots it is important to detect objects in their vicinity. We have developed a scanning laser line striper that can produce dense 3D images using active illumination. The scanner consists of a camera and a MEMS-micro mirror based projector. It can also detect the presence of optically difficult material like glass and metal. The sensor can be used for autonomous operation or it can help a human operator to better remotely control the robot. In this paper we will evaluate the performance of the scanner under outdoor illumination, i.e. from operating in the shade to operating in full sunlight. We report the range, resolution and accuracy of the sensor and its ability to reconstruct objects like grass, wooden blocks, wires, metal objects, electronic devices like cell phones, blank RPG, and other inert explosive devices. Furthermore we evaluate its ability to detect the presence of glass and polished metal objects. Lastly we report on a user study that shows a significant improvement in a grasping task. The user is tasked with grasping a wire with the remotely controlled hand of a robot. We compare the time it takes to complete the task using the 3D scanner with using a traditional video camera.

AIDE - Advanced Intrusion Detection Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Cathy L.

2013-04-28

Would you like to know when someone has dropped an undesirable executable binary on our system? What about something less malicious such as a software installation by a user? What about the user who decides to install a newer version of mod_perl or PHP on your web server without letting you know beforehand? Or even something as simple as when an undocumented config file change is made by another member of the admin group? Do you even want to know about all the changes that happen on a daily basis on your server? The purpose of an intrusion detection systemmore » (IDS) is to detect unauthorized, possibly malicious activity. The purpose of a host-based IDS, or file integrity checker, is check for unauthorized changes to key system files, binaries, libraries, and directories on the system. AIDE is an Open Source file and directory integrity checker. AIDE will let you know when a file or directory has been added, deleted, modified. It is included with the Red Hat Enterprise 6. It is available for other Linux distros. This is a case study describing the process of configuring AIDE on an out of the box RHEL6 installation. Its goal is to illustrate the thinking and the process by which a useful AIDE configuration is built.« less

Design and implementation of modular home security system with short messaging system

NASA Astrophysics Data System (ADS)

Budijono, Santoso; Andrianto, Jeffri; Axis Novradin Noor, Muhammad

2014-03-01

Today we are living in 21st century where crime become increasing and everyone wants to secure they asset at their home. In that situation user must have system with advance technology so person do not worry when getting away from his home. It is therefore the purpose of this design to provide home security device, which send fast information to user GSM (Global System for Mobile) mobile device using SMS (Short Messaging System) and also activate - deactivate system by SMS. The Modular design of this Home Security System make expandable their capability by add more sensors on that system. Hardware of this system has been designed using microcontroller AT Mega 328, PIR (Passive Infra Red) motion sensor as the primary sensor for motion detection, camera for capturing images, GSM module for sending and receiving SMS and buzzer for alarm. For software this system using Arduino IDE for Arduino and Putty for testing connection programming in GSM module. This Home Security System can monitor home area that surrounding by PIR sensor and sending SMS, save images capture by camera, and make people panic by turn on the buzzer when trespassing surrounding area that detected by PIR sensor. The Modular Home Security System has been tested and succeed detect human movement.

LOSITAN: a workbench to detect molecular adaptation based on a Fst-outlier method.

PubMed

Antao, Tiago; Lopes, Ana; Lopes, Ricardo J; Beja-Pereira, Albano; Luikart, Gordon

2008-07-28

Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user. Here we present LOSITAN, a selection detection workbench based on a well evaluated Fst-outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral Fst), provides data import and export functions, iterative contour smoothing and generation of graphics in a easy to use graphical user interface. LOSITAN is able to use modern multi-core processor architectures by locally parallelizing fdist, reducing computation time by half in current dual core machines and with almost linear performance gains in machines with more cores. LOSITAN makes selection detection feasible to a much wider range of users, even for large population genomic datasets, by both providing an easy to use interface and essential functionality to complete the whole selection detection process.

Correcting Erroneous N+N Structures in the Productions of French Users of English

ERIC Educational Resources Information Center

Garnier, Marie

2012-01-01

This article presents the preliminary steps to the implementation of detection and correction strategies for the erroneous use of N+N structures in the written productions of French-speaking advanced users of English. This research is carried out as part of the grammar checking project "CorrecTools", in which errors are detected and corrected…

A Multimodal Emotion Detection System during Human-Robot Interaction

PubMed Central

Alonso-Martín, Fernando; Malfaz, María; Sequeira, João; Gorostiza, Javier F.; Salichs, Miguel A.

2013-01-01

In this paper, a multimodal user-emotion detection system for social robots is presented. This system is intended to be used during human–robot interaction, and it is integrated as part of the overall interaction system of the robot: the Robotics Dialog System (RDS). Two modes are used to detect emotions: the voice and face expression analysis. In order to analyze the voice of the user, a new component has been developed: Gender and Emotion Voice Analysis (GEVA), which is written using the Chuck language. For emotion detection in facial expressions, the system, Gender and Emotion Facial Analysis (GEFA), has been also developed. This last system integrates two third-party solutions: Sophisticated High-speed Object Recognition Engine (SHORE) and Computer Expression Recognition Toolbox (CERT). Once these new components (GEVA and GEFA) give their results, a decision rule is applied in order to combine the information given by both of them. The result of this rule, the detected emotion, is integrated into the dialog system through communicative acts. Hence, each communicative act gives, among other things, the detected emotion of the user to the RDS so it can adapt its strategy in order to get a greater satisfaction degree during the human–robot dialog. Each of the new components, GEVA and GEFA, can also be used individually. Moreover, they are integrated with the robotic control platform ROS (Robot Operating System). Several experiments with real users were performed to determine the accuracy of each component and to set the final decision rule. The results obtained from applying this decision rule in these experiments show a high success rate in automatic user emotion recognition, improving the results given by the two information channels (audio and visual) separately. PMID:24240598

Day, night and all-weather security surveillance automation synergy from combining two powerful technologies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morellas, Vassilios; Johnson, Andrew; Johnston, Chris

2006-07-01

Thermal imaging is rightfully a real-world technology proven to bring confidence to daytime, night-time and all weather security surveillance. Automatic image processing intrusion detection algorithms are also a real world technology proven to bring confidence to system surveillance security solutions. Together, day, night and all weather video imagery sensors and automated intrusion detection software systems create the real power to protect early against crime, providing real-time global homeland protection, rather than simply being able to monitor and record activities for post event analysis. These solutions, whether providing automatic security system surveillance at airports (to automatically detect unauthorized aircraft takeoff andmore » landing activities) or at high risk private, public or government facilities (to automatically detect unauthorized people or vehicle intrusion activities) are on the move to provide end users the power to protect people, capital equipment and intellectual property against acts of vandalism and terrorism. As with any technology, infrared sensors and automatic image intrusion detection systems for global homeland security protection have clear technological strengths and limitations compared to other more common day and night vision technologies or more traditional manual man-in-the-loop intrusion detection security systems. This paper addresses these strength and limitation capabilities. False Alarm (FAR) and False Positive Rate (FPR) is an example of some of the key customer system acceptability metrics and Noise Equivalent Temperature Difference (NETD) and Minimum Resolvable Temperature are examples of some of the sensor level performance acceptability metrics. (authors)« less

Double Threshold Energy Detection Based Cooperative Spectrum Sensing for Cognitive Radio Networks with QoS Guarantee

NASA Astrophysics Data System (ADS)

Hu, Hang; Yu, Hong; Zhang, Yongzhi

2013-03-01

Cooperative spectrum sensing, which can greatly improve the ability of discovering the spectrum opportunities, is regarded as an enabling mechanism for cognitive radio (CR) networks. In this paper, we employ a double threshold detection method in energy detector to perform spectrum sensing, only the CR users with reliable sensing information are allowed to transmit one bit local decision to the fusion center. Simulation results will show that our proposed double threshold detection method could not only improve the sensing performance but also save the bandwidth of the reporting channel compared with the conventional detection method with one threshold. By weighting the sensing performance and the consumption of system resources in a utility function that is maximized with respect to the number of CR users, it has been shown that the optimal number of CR users is related to the price of these Quality-of-Service (QoS) requirements.

Extraction and analysis of neuron firing signals from deep cortical video microscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kerekes, Ryan A; Blundon, Jay

We introduce a method for extracting and analyzing neuronal activity time signals from video of the cortex of a live animal. The signals correspond to the firing activity of individual cortical neurons. Activity signals are based on the changing fluorescence of calcium indicators in the cells over time. We propose a cell segmentation method that relies on a user-specified center point, from which the signal extraction method proceeds. A stabilization approach is used to reduce tissue motion in the video. The extracted signal is then processed to flatten the baseline and detect action potentials. We show results from applying themore » method to a cortical video of a live mouse.« less

Detection of S-Nitrosothiols

PubMed Central

Diers, Anne R.; Keszler, Agnes; Hogg, Neil

2015-01-01

BACKGROUND S-Nitrosothiols have been recognized as biologically-relevant products of nitric oxide that are involved in many of the diverse activities of this free radical. SCOPE OF REVIEW This review serves to discuss current methods for the detection and analysis of protein S-nitrosothiols. The major methods of S-nitrosothiol detection include chemiluminescence-based methods and switch-based methods, each of which comes in various flavors with advantages and caveats. MAJOR CONCLUSIONS The detection of S-nitrosothiols is challenging and prone to many artifacts. Accurate measurements require an understanding of the underlying chemistry of the methods involved and the use of appropriate controls. GENERAL SIGNIFICANCE Nothing is more important to a field of research than robust methodology that is generally trusted. The field of S-Nitrosation has developed such methods but, as S-nitrosothiols are easy to introduce as artifacts, it is vital that current users learn from the lessons of the past. PMID:23988402

Usability testing for the rest of us: the application of discount usability principles in the development of an online communications assessment application.

PubMed

Brock, Douglas; Kim, Sara; Palmer, Odawni; Gallagher, Thomas; Holmboe, Eric

2013-01-01

Usability evaluation provides developers and educators with the means to understand user needs, improve overall product utility, and increase user satisfaction. The application of "discount usability" principles developed to make usability testing more practical and useful may improve user experience at minimal cost and require little existing expertise to conduct. We describe an application of discount usability to a high-fidelity online communications assessment application developed by the University of Washington for the American Board of Internal Medicine. Eight internal medicine physicians completed a discount usability test. Sessions were recorded and the videos analyzed for significant usability concerns. Concerns were identified, summarized, discussed, and prioritized by the authors in collaboration with the software developers before implementing any changes to the interface. Thirty-eight significant usability issues were detected and four technical problems were identified. Each issue was responded to through modification of the software, by providing additional instruction, or delayed for a later version to be developed. Discount usability can be easily implemented in academic developmental activities. Our study resulted in the discovery and remediation of significant user problems, in addition to giving important insight into the novel methods built into the application.

Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision tree.

PubMed

Pärkkä, Juha; Cluitmans, Luc; Ermes, Miikka

2010-09-01

Inactive and sedentary lifestyle is a major problem in many industrialized countries today. Automatic recognition of type of physical activity can be used to show the user the distribution of his daily activities and to motivate him into more active lifestyle. In this study, an automatic activity-recognition system consisting of wireless motion bands and a PDA is evaluated. The system classifies raw sensor data into activity types online. It uses a decision tree classifier, which has low computational cost and low battery consumption. The classifier parameters can be personalized online by performing a short bout of an activity and by telling the system which activity is being performed. Data were collected with seven volunteers during five everyday activities: lying, sitting/standing, walking, running, and cycling. The online system can detect these activities with overall 86.6% accuracy and with 94.0% accuracy after classifier personalization.

siGnum: graphical user interface for EMG signal analysis.

PubMed

Kaur, Manvinder; Mathur, Shilpi; Bhatia, Dinesh; Verma, Suresh

2015-01-01

Electromyography (EMG) signals that represent the electrical activity of muscles can be used for various clinical and biomedical applications. These are complicated and highly varying signals that are dependent on anatomical location and physiological properties of the muscles. EMG signals acquired from the muscles require advanced methods for detection, decomposition and processing. This paper proposes a novel Graphical User Interface (GUI) siGnum developed in MATLAB that will apply efficient and effective techniques on processing of the raw EMG signals and decompose it in a simpler manner. It could be used independent of MATLAB software by employing a deploy tool. This would enable researcher's to gain good understanding of EMG signal and its analysis procedures that can be utilized for more powerful, flexible and efficient applications in near future.

Adaptive threshold determination for efficient channel sensing in cognitive radio network using mobile sensors

NASA Astrophysics Data System (ADS)

Morshed, M. N.; Khatun, S.; Kamarudin, L. M.; Aljunid, S. A.; Ahmad, R. B.; Zakaria, A.; Fakir, M. M.

2017-03-01

Spectrum saturation problem is a major issue in wireless communication systems all over the world. Huge number of users is joining each day to the existing fixed band frequency but the bandwidth is not increasing. These requirements demand for efficient and intelligent use of spectrum. To solve this issue, the Cognitive Radio (CR) is the best choice. Spectrum sensing of a wireless heterogeneous network is a fundamental issue to detect the presence of primary users' signals in CR networks. In order to protect primary users (PUs) from harmful interference, the spectrum sensing scheme is required to perform well even in low signal-to-noise ratio (SNR) environments. Meanwhile, the sensing period is usually required to be short enough so that secondary (unlicensed) users (SUs) can fully utilize the available spectrum. CR networks can be designed to manage the radio spectrum more efficiently by utilizing the spectrum holes in primary user's licensed frequency bands. In this paper, we have proposed an adaptive threshold detection method to detect presence of PU signal using free space path loss (FSPL) model in 2.4 GHz WLAN network. The model is designed for mobile sensors embedded in smartphones. The mobile sensors acts as SU while the existing WLAN network (channels) works as PU. The theoretical results show that the desired threshold range detection of mobile sensors mainly depends on the noise floor level of the location in consideration.

Toward achieving precision health

PubMed Central

Gambhir, Sanjiv Sam; Ge, T. Jessie; Vermesh, Ophir; Spitler, Ryan

2018-01-01

Health care systems primarily focus on patients after they present with disease, not before. The emerging field of precision health encourages disease prevention and earlier detection by monitoring health and disease based on an individual’s risk. Active participation in health care can be encouraged with continuous health-monitoring devices, providing a higher-resolution picture of human health and disease. However, the development of monitoring technologies must prioritize the collection of actionable data and long-term user engagement. PMID:29491186

Solving bezel reliability and CRT obsolescence

NASA Astrophysics Data System (ADS)

Schwartz, Richard J.; Bowen, Arlen R.; Knowles, Terry

2003-09-01

Scientific Research Corporation designed a Smart Multi-Function Color Display with Positive Pilot Feedback under the funding of an U. S. Navy Small Business Innovative Research program. The Smart Multi-Function Color Display can replace the obsolete monochrome Cathode Ray Tube display currently on the T-45C aircraft built by Boeing. The design utilizes a flat panel color Active Matrix Liquid Crystal Display and TexZec's patented Touch Thru Metal bezel technology providing both visual and biomechanical feedback to the pilot in a form, fit, and function replacement to the current T-45C display. Use of an existing color AMLCD, requires the least adaptation to fill the requirements of this application, thereby minimizing risk associated with developing a new display technology and maximizing the investment in improved user interface technology. The improved user interface uses TexZec's Touch Thru Metal technology to eliminate all of the moving parts that traditionally have limited Mean-Time-Between-Failure. The touch detection circuit consists of Commercial-Off-The-Shelf components, creating touch detection circuitry, which is simple and durable. This technology provides robust switch activation and a high level of environmental immunity, both mechanical and electrical. Replacement of all the T-45C multi-function displays with this design will improve the Mean-Time-Between-Failure and drastically reduce display life cycle costs. The design methodology described in this paper can be adapted to any new or replacement display.

Attention-level transitory response: a novel hybrid BCI approach

NASA Astrophysics Data System (ADS)

Diez, Pablo F.; Garcés Correa, Agustina; Orosco, Lorena; Laciar, Eric; Mut, Vicente

2015-10-01

Objective. People with disabilities may control devices such as a computer or a wheelchair by means of a brain-computer interface (BCI). BCI based on steady-state visual evoked potentials (SSVEP) requires visual stimulation of the user. However, this SSVEP-based BCI suffers from the ‘Midas touch effect’, i.e., the BCI can detect an SSVEP even when the user is not gazing at the stimulus. Then, these incorrect detections deteriorate the performance of the system, especially in asynchronous BCI because ongoing EEG is classified. In this paper, a novel transitory response of the attention-level of the user is reported. It was used to develop a hybrid BCI (hBCI). Approach. Three methods are proposed to detect the attention-level of the user. They are based on the alpha rhythm and theta/beta rate. The proposed hBCI scheme is presented along with these methods. Hence, the hBCI sends a command only when the user is at a high-level of attention, or in other words, when the user is really focused on the task being performed. The hBCI was tested over two different EEG datasets. Main results. The performance of the hybrid approach is superior to the standard one. Improvements of 20% in accuracy and 10 bits min-1 are reported. Moreover, the attention-level is extracted from the same EEG channels used in SSVEP detection and this way, no extra hardware is needed. Significance. A transitory response of EEG signal is used to develop the attention-SSVEP hBCI which is capable of reducing the Midas touch effect.

Attention-level transitory response: a novel hybrid BCI approach.

PubMed

Diez, Pablo F; Garcés Correa, Agustina; Orosco, Lorena; Laciar, Eric; Mut, Vicente

2015-10-01

People with disabilities may control devices such as a computer or a wheelchair by means of a brain-computer interface (BCI). BCI based on steady-state visual evoked potentials (SSVEP) requires visual stimulation of the user. However, this SSVEP-based BCI suffers from the 'Midas touch effect', i.e., the BCI can detect an SSVEP even when the user is not gazing at the stimulus. Then, these incorrect detections deteriorate the performance of the system, especially in asynchronous BCI because ongoing EEG is classified. In this paper, a novel transitory response of the attention-level of the user is reported. It was used to develop a hybrid BCI (hBCI). Three methods are proposed to detect the attention-level of the user. They are based on the alpha rhythm and theta/beta rate. The proposed hBCI scheme is presented along with these methods. Hence, the hBCI sends a command only when the user is at a high-level of attention, or in other words, when the user is really focused on the task being performed. The hBCI was tested over two different EEG datasets. The performance of the hybrid approach is superior to the standard one. Improvements of 20% in accuracy and 10 bits min(-1) are reported. Moreover, the attention-level is extracted from the same EEG channels used in SSVEP detection and this way, no extra hardware is needed. A transitory response of EEG signal is used to develop the attention-SSVEP hBCI which is capable of reducing the Midas touch effect.

GMDD: a database of GMO detection methods.

PubMed

Dong, Wei; Yang, Litao; Shen, Kailin; Kim, Banghyun; Kleter, Gijs A; Marvin, Hans J P; Guo, Rong; Liang, Wanqi; Zhang, Dabing

2008-06-04

Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed. GMO Detection method Database (GMDD) has collected almost all the previous developed and reported GMOs detection methods, which have been grouped by different strategies (screen-, gene-, construct-, and event-specific), and also provide a user-friendly search service of the detection methods by GMO event name, exogenous gene, or protein information, etc. In this database, users can obtain the sequences of exogenous integration, which will facilitate PCR primers and probes design. Also the information on endogenous genes, certified reference materials, reference molecules, and the validation status of developed methods is included in this database. Furthermore, registered users can also submit new detection methods and sequences to this database, and the newly submitted information will be released soon after being checked. GMDD contains comprehensive information of GMO detection methods. The database will make the GMOs analysis much easier.

Wireless Falling Detection System Based on Community.

PubMed

Xia, Yun; Wu, Yanqi; Zhang, Bobo; Li, Zhiyang; He, Nongyue; Li, Song

2015-06-01

The elderly are more likely to suffer the aches or pains from the accidental falls, and both the physiology and psychology of patients would subject to a long-term disturbance, especially when the emergency treatment was not given timely and properly. Although many methods and devices have been developed creatively and shown their efficiency in experiments, few of them are suitable for commercial applications routinely. Here, we design a wearable falling detector as a mobile terminal, and utilize the wireless technology to transfer and monitor the activity data of the host in a relatively small community. With the help of the accelerometer sensor and the Google Mapping service, information of the location and the activity data will be send to the remote server for the downstream processing. The experimental result has shown that SA (Sum-vector of all axes) value of 2.5 g is the threshold value to distinguish the falling from other activities. A three-stage detection algorithm was adopted to increase the accuracy of the real alarm, and the accuracy rate of our system was more than 95%. With the further improvement, the falling detecting device which is low-cost, accurate and user-friendly would become more and more common in everyday life.

A smart phone-based pocket fall accident detection, positioning, and rescue system.

PubMed

Kau, Lih-Jen; Chen, Chih-Sheng

2015-01-01

We propose in this paper a novel algorithm as well as architecture for the fall accident detection and corresponding wide area rescue system based on a smart phone and the third generation (3G) networks. To realize the fall detection algorithm, the angles acquired by the electronic compass (ecompass) and the waveform sequence of the triaxial accelerometer on the smart phone are used as the system inputs. The acquired signals are then used to generate an ordered feature sequence and then examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current state, it can proceed to next state; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. Once a fall accident event is detected, the user's position can be acquired by the global positioning system (GPS) or the assisted GPS, and sent to the rescue center via the 3G communication network so that the user can get medical help immediately. With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished fall accident detection accuracy up to 92% on the sensitivity and 99.75% on the specificity can be obtained when a set of 450 test actions in nine different kinds of activities are estimated by using the proposed cascaded classifier, which justifies the superiority of the proposed algorithm.

An autonomous fault detection, isolation, and recovery system for a 20-kHz electric power distribution test bed

NASA Technical Reports Server (NTRS)

Quinn, Todd M.; Walters, Jerry L.

1991-01-01

Future space explorations will require long term human presence in space. Space environments that provide working and living quarters for manned missions are becoming increasingly larger and more sophisticated. Monitor and control of the space environment subsystems by expert system software, which emulate human reasoning processes, could maintain the health of the subsystems and help reduce the human workload. The autonomous power expert (APEX) system was developed to emulate a human expert's reasoning processes used to diagnose fault conditions in the domain of space power distribution. APEX is a fault detection, isolation, and recovery (FDIR) system, capable of autonomous monitoring and control of the power distribution system. APEX consists of a knowledge base, a data base, an inference engine, and various support and interface software. APEX provides the user with an easy-to-use interactive interface. When a fault is detected, APEX will inform the user of the detection. The user can direct APEX to isolate the probable cause of the fault. Once a fault has been isolated, the user can ask APEX to justify its fault isolation and to recommend actions to correct the fault. APEX implementation and capabilities are discussed.

Acceptance Criteria Framework for Autonomous Biological Detectors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dzenitis, J M

2006-12-12

The purpose of this study was to examine a set of user acceptance criteria for autonomous biological detection systems for application in high-traffic, public facilities. The test case for the acceptance criteria was the Autonomous Pathogen Detection System (APDS) operating in high-traffic facilities in New York City (NYC). However, the acceptance criteria were designed to be generally applicable to other biological detection systems in other locations. For such detection systems, ''users'' will include local authorities (e.g., facility operators, public health officials, and law enforcement personnel) and national authorities [including personnel from the Department of Homeland Security (DHS), the BioWatch Program,more » the Centers for Disease Control and Prevention (CDC), and the Federal Bureau of Investigation (FBI)]. The panel members brought expertise from a broad range of backgrounds to complete this picture. The goals of this document are: (1) To serve as informal guidance for users in considering the benefits and costs of these systems. (2) To serve as informal guidance for developers in understanding the needs of users. In follow-up work, this framework will be used to systematically document the APDS for appropriateness and readiness for use in NYC.« less

Preliminary Measures of Instructor Learning in Teaching Junctional Tourniquet Users.

PubMed

Kragh, John F; Aden, James K; Shackelford, Stacy; Dubick, Michael A

2016-01-01

The objective of the present study was to assess the effect of instructor learning on student performance in use of junctional tourniquets. From a convenience sample of data available after another study, we used a manikin for assessment of control of bleeding from a right groin gunshot wound. Blood loss was measured by the instructor while training users. The data set represented a group of 30 persons taught one at a time. The first measure was a plot of mean blood loss volumes for the sequential users. The second measure was a plot of the cumulative sum (CUSUM) of mean blood loss (BL) volumes for users. Mean blood loss trended down as the instructor gained experience with each newly instructed user. User performance continually improved as the instructor gained more experience with teaching. No plateau effect was observed within the 30 users. The CUSUM plot illustrated a turning point or cusp at the seventh user. The prior portion of the plot (users 1-7) had the greatest improvement; performance did not improve as much thereafter. The improvement after the seventh user was the only change detected in the instructor's trend of performance. The instructor's teaching experience appeared to directly affect user performance; in a model of junctional hemorrhage, the volume of blood loss from the manikin during junctional tourniquet placement was a useful metric of instructor learning. The CUSUM technique detected a small but meaningful change in trend where the instructor learning curve was greatest while working with the first seven users. 2016.

Framework for power and activity factor allocation in a multiclass CDMA system

NASA Astrophysics Data System (ADS)

Wu, Xinzhou; Srikant, Rayadurgam

2001-07-01

We consider a multimedia CDMA uplink where there are multiple classes of users with different Quality-of-Service (QoS) requirements. Each user is modeled as an ON-OFF source, where in the ON state, the user transmits a fixed number of bits in each time slot and in the OFF state, the user is silent. The probability of being in the ON state, known as the activity factor, could be different for different users. Assuming a constant channel gain, we first characterize the set of transmit power levels, activity factors and number of users in each class that can be supported by a system with a given spreading gain under the constraint that each user's QoS requirement must be met. Using this characterization, we then present a utility function-based algorithm for choosing the activity factors of elastic users in the network.

14 CFR Appendix C to Part 1215 - Typical User Activity Timeline

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Typical User Activity Timeline C Appendix C... RELAY SATELLITE SYSTEM (TDRSS) Pt. 1215, App. C Appendix C to Part 1215—Typical User Activity Timeline... mission model. 3 years before launch (Ref. § 1215.109(c). Submit general user requirements to permit...

An immunity-based anomaly detection system with sensor agents.

PubMed

Okamoto, Takeshi; Ishida, Yoshiteru

2009-01-01

This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user's command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems.

Survey of Machine Learning Methods for Database Security

NASA Astrophysics Data System (ADS)

Kamra, Ashish; Ber, Elisa

Application of machine learning techniques to database security is an emerging area of research. In this chapter, we present a survey of various approaches that use machine learning/data mining techniques to enhance the traditional security mechanisms of databases. There are two key database security areas in which these techniques have found applications, namely, detection of SQL Injection attacks and anomaly detection for defending against insider threats. Apart from the research prototypes and tools, various third-party commercial products are also available that provide database activity monitoring solutions by profiling database users and applications. We present a survey of such products. We end the chapter with a primer on mechanisms for responding to database anomalies.

Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

NASA Technical Reports Server (NTRS)

Doran, G.

2013-01-01

In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short "transient" RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation.

Effects of Varying Gravity Levels on fNIRS Headgear Performance and Signal Recovery

NASA Technical Reports Server (NTRS)

Mackey, Jeffrey R.; Harrivel, Angela R.; Adamovsky, Grigory; Lewandowski, Beth E.; Gotti, Daniel J.; Tin, Padetha; Floyd, Bertram M.

2013-01-01

This paper reviews the effects of varying gravitational levels on functional Near-Infrared Spectroscopy (fNIRS) headgear. The fNIRS systems quantify neural activations in the cortex by measuring hemoglobin concentration changes via optical intensity. Such activation measurement allows for the detection of cognitive state, which can be important for emotional stability, human performance and vigilance optimization, and the detection of hazardous operator state. The technique depends on coupling between the fNIRS probe and users skin. Such coupling may be highly susceptible to motion if probe-containing headgear designs are not adequately tested. The lack of reliable and self-applicable headgear robust to the influence of motion artifact currently inhibits its operational use in aerospace environments. Both NASAs Aviation Safety and Human Research Programs are interested in this technology as a method of monitoring cognitive state of pilots and crew.

New reporting procedures based on long-term method detection levels and some considerations for interpretations of water-quality data provided by the U.S. Geological Survey National Water Quality Laboratory

USGS Publications Warehouse

Childress, Carolyn J. Oblinger; Foreman, William T.; Connor, Brooke F.; Maloney, Thomas J.

1999-01-01

This report describes the U.S. Geological Survey National Water Quality Laboratory?s approach for determining long-term method detection levels and establishing reporting levels, details relevant new reporting conventions, and provides preliminary guidance on interpreting data reported with the new conventions. At the long-term method detection level concentration, the risk of a false positive detection (analyte reported present at the long-term method detection level when not in sample) is no more than 1 percent. However, at the long-term method detection level, the risk of a false negative occurrence (analyte reported not present when present at the long-term method detection level concentration) is up to 50 percent. Because this false negative rate is too high for use as a default 'less than' reporting level, a more reliable laboratory reporting level is set at twice the determined long-term method detection level. For all methods, concentrations measured between the laboratory reporting level and the long-term method detection level will be reported as estimated concentrations. Non-detections will be censored to the laboratory reporting level. Adoption of the new reporting conventions requires a full understanding of how low-concentration data can be used and interpreted and places responsibility for using and presenting final data with the user rather than with the laboratory. Users must consider that (1) new laboratory reporting levels may differ from previously established minimum reporting levels, (2) long-term method detection levels and laboratory reporting levels may change over time, and (3) estimated concentrations are less certain than concentrations reported above the laboratory reporting level. The availability of uncensored but qualified low-concentration data for interpretation and statistical analysis is a substantial benefit to the user. A decision to censor data after they are reported from the laboratory may still be made by the user, if merited, on the basis of the intended use of the data.

Users' experiences of wearable activity trackers: a cross-sectional study.

PubMed

Maher, Carol; Ryan, Jillian; Ambrosi, Christina; Edney, Sarah

2017-11-15

Wearable activity trackers offer considerable promise for helping users to adopt healthier lifestyles. This study aimed to explore users' experience of activity trackers, including usage patterns, sharing of data to social media, perceived behaviour change (physical activity, diet and sleep), and technical issues/barriers to use. A cross-sectional online survey was developed and administered to Australian adults who were current or former activity tracker users. Results were analysed descriptively, with differences between current and former users and wearable brands explored using independent samples t-tests, Mann-Whitney, and chi square tests. Participants included 200 current and 37 former activity tracker users (total N = 237) with a mean age of 33.1 years (SD 12.4, range 18-74 years). Fitbit (67.5%) and Garmin devices (16.5%) were most commonly reported. Participants typically used their trackers for sustained periods (5-7 months) and most intended to continue usage. Participants reported they had improved their physical activity (51-81%) more commonly than they had their diet (14-40%) or sleep (11-24%), and slightly more participants reported to value the real time feedback (89%) compared to the long-term monitoring (78%). Most users (70%) reported they had experienced functionality issues with their devices, most commonly related to battery life and technical difficulties. Results suggest users find activity trackers appealing and useful tools for increasing perceived physical activity levels over a sustained period.

[Intelligent videosurveillance and falls detection: Perceptions of professionals and managers].

PubMed

Lapierre, Nolwenn; Carpentier, Isabelle; St-Arnaud, Alain; Ducharme, Francine; Meunier, Jean; Jobidon, Mireille; Rousseau, Jacqueline

2016-02-01

Gerontechnologies can be used to detect accidental falls. However, existing systems do not entirely meet users' expectations. Our team developed an intelligent video-monitoring systems to fill these gaps. Authors advocate consulting potential users at the early stages of the design of gerontechnologies and integrating their suggestions. This study aims to explore health care workers' opinion regarding the intelligent video monitoring to detect falls by older adults living at home. This qualitative study explored the opinions of 31 participants using focus groups. Transcripts were analyzed using predetermined codes based on the competence model. Participants reported several advantages for using the intelligent video monitoring and provided suggestions for improving its use. The participants' suggestions and comments will help to improve the system and match it to users' needs. © CAOT 2015.

Visualization techniques and graphical user interfaces in syndromic surveillance systems. Summary from the Disease Surveillance Workshop, Sept. 11-12, 2007; Bangkok, Thailand.

PubMed

Moore, Kieran M; Edge, Graham; Kurc, Andrew R

2008-11-14

Timeliness is a critical asset to the detection of public health threats when using syndromic surveillance systems. In order for epidemiologists to effectively distinguish which events are indicative of a true outbreak, the ability to utilize specific data streams from generalized data summaries is necessary. Taking advantage of graphical user interfaces and visualization capacities of current surveillance systems makes it easier for users to investigate detected anomalies by generating custom graphs, maps, plots, and temporal-spatial analysis of specific syndromes or data sources.

Visualization techniques and graphical user interfaces in syndromic surveillance systems. Summary from the Disease Surveillance Workshop, Sept. 11–12, 2007; Bangkok, Thailand

PubMed Central

Moore, Kieran M; Edge, Graham; Kurc, Andrew R

2008-01-01

Timeliness is a critical asset to the detection of public health threats when using syndromic surveillance systems. In order for epidemiologists to effectively distinguish which events are indicative of a true outbreak, the ability to utilize specific data streams from generalized data summaries is necessary. Taking advantage of graphical user interfaces and visualization capacities of current surveillance systems makes it easier for users to investigate detected anomalies by generating custom graphs, maps, plots, and temporal-spatial analysis of specific syndromes or data sources. PMID:19025683

Efficient detection of differentially methylated regions using DiMmeR.

PubMed

Almeida, Diogo; Skov, Ida; Silva, Artur; Vandin, Fabio; Tan, Qihua; Röttger, Richard; Baumbach, Jan

2017-02-15

Epigenome-wide association studies (EWAS) generate big epidemiological datasets. They aim for detecting differentially methylated DNA regions that are likely to influence transcriptional gene activity and, thus, the regulation of metabolic processes. The by far most widely used technology is the Illumina Methylation BeadChip, which measures the methylation levels of 450 (850) thousand cytosines, in the CpG dinucleotide context in a set of patients compared to a control group. Many bioinformatics tools exist for raw data analysis. However, most of them require some knowledge in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes. Here, we present DiMmeR (Discovery of Multiple Differentially Methylated Regions), the first free standalone software that interactively guides with a user-friendly graphical user interface (GUI) scientists the whole way through EWAS data analysis. It offers parallelized statistical methods for efficiently identifying DMRs in both Illumina 450K and 850K EPIC chip data. DiMmeR computes empirical P -values through randomization tests, even for big datasets of hundreds of patients and thousands of permutations within a few minutes on a standard desktop PC. It is independent of any third-party libraries, computes regression coefficients, P -values and empirical P -values, and it corrects for multiple testing. DiMmeR is publicly available at http://dimmer.compbio.sdu.dk . diogoma@bmb.sdu.dk. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Moderate and vigorous physical activity patterns among marijuana users: Results from the 2007-2014 National Health and Nutrition Examination Surveys.

PubMed

Vidot, Denise C; Bispo, Jordan B; Hlaing, WayWay M; Prado, Guillermo; Messiah, Sarah E

2017-09-01

The relationship between marijuana use and recreational physical activity has yet to be explored in the United States. Our aim was to examine this relationship in a population-based sample of 20-to-59-year olds (N=12,618) using 2007-2014 National Health and Nutrition Examination Surveys. Marijuana use was categorized as never (reference group), past (previously but not within the last 30-days), and current (>1day in the last 30-days) use. Current users were further categorized based on frequency of use (light, moderate, and heavy users). Physical activity was self-reported as moderate (small increase in heartrate/breathing for >10min; MPA) and vigorous (large increase in heartrate/breathing for >10min; VPA). Adjusted odds ratios (AOR) for the relationship between marijuana use and physical activity were estimated via logistic regression models. The majority of the overall sample reported either past (40.5%) or current (12.6%) marijuana use. Marijuana users had a lower prevalence of moderate physical activity than never users (current: 51.9%, past: 50.4%, never: 55.3%, p=0.001). Current (66.8%) and past (67.9%) marijuana users also had a lower prevalence of vigorous physical activity than never users (71.9%, p=0.001). Current and past users had lower odds of recreational MPA (current user AOR: 0.66, 95% CI: 0.50-0.87; past user AOR: 0.78, 95% CI: 0.62-0.98) than never users. As the frequency of marijuana use increased, time spent on MPA decreased. Results suggest that current and past marijuana users were less likely to report recreational MPA than never users. Future studies should examine the potential mechanisms and temporality of this relationship. Copyright © 2017 Elsevier B.V. All rights reserved.

Statistical data mining of streaming motion data for fall detection in assistive environments.

PubMed

Tasoulis, S K; Doukas, C N; Maglogiannis, I; Plagianakos, V P

2011-01-01

The analysis of human motion data is interesting for the purpose of activity recognition or emergency event detection, especially in the case of elderly or disabled people living independently in their homes. Several techniques have been proposed for identifying such distress situations using either motion, audio or video sensors on the monitored subject (wearable sensors) or the surrounding environment. The output of such sensors is data streams that require real time recognition, especially in emergency situations, thus traditional classification approaches may not be applicable for immediate alarm triggering or fall prevention. This paper presents a statistical mining methodology that may be used for the specific problem of real time fall detection. Visual data captured from the user's environment, using overhead cameras along with motion data are collected from accelerometers on the subject's body and are fed to the fall detection system. The paper includes the details of the stream data mining methodology incorporated in the system along with an initial evaluation of the achieved accuracy in detecting falls.

Use of an electronic patient portal among the chronically ill: an observational study.

PubMed

Riippa, Iiris; Linna, Miika; Rönkkö, Ilona; Kröger, Virpi

2014-12-08

Electronic patient portals may enhance effective interaction between the patient and the health care provider. To grasp the full potential of patient portals, health care providers need more knowledge on which patient groups prefer electronic services and how patients should be served through this channel. The objective of this study was to assess how chronically ill patients' state of health, comorbidities, and previous care are associated with their adoption and use of a patient portal. A total of 222 chronically ill patients, who were offered access to a patient portal with their health records and secure messaging with care professionals, were included in the study. Differences in the characteristics of non-users, viewers, and interactive users of the patient portal were analyzed before access to the portal. Patients' age, gender, diagnoses, levels of the relevant physiological measurements, health care contacts, and received physiological measurements were collected from the care provider's electronic health record. In addition, patient-reported health and patient activation were assessed by a survey. Despite the broad range of measures used to indicate the patients' state of health, the portal user groups differed only in their recorded diagnosis for hypertension, which was most common in the non-user group. However, there were significant differences in the amount of care received during the year before access to the portal. The non-user group had more nurse visits and more measurements of relevant physiological outcomes than viewers and interactive users. They also had fewer referrals to specialized care during the year before access to the portal than the two other groups. The viewers and the interactive users differed from each other significantly in the number of nurse calls received, the interactive users having more calls than the viewers. No significant differences in age, gender, or patient activation were detected between the user groups. Previous care received by the patient is an important predictor for the use of a patient portal. In a group of patients with a similar disease burden, demand for different types of health services and preferences related to the service channel seem to contribute to the choice to use the patient portal. Further research on patient portal functionalities and their potential to meet patient needs by complementing or substituting for traditional health care services is suggested.

SoM: a smart sensor for human activity monitoring and assisted healthy ageing.

PubMed

Naranjo-Hernández, David; Roa, Laura M; Reina-Tosina, Javier; Estudillo-Valderrama, Miguel Ángel

2012-11-01

This paper presents the hardware and software design and implementation of a low-cost, wearable, and unobstructive intelligent accelerometer sensor for the monitoring of human physical activities. In order to promote healthy lifestyles to elders for an active, independent, and healthy ageing, as well as for the early detection of psychomotor abnormalities, the activity monitoring is performed in a holistic manner in the same device through different approaches: 1) a classification of the level of activity that allows to establish patterns of behavior; 2) a daily activity living classifier that is able to distinguish activities such as climbing or descending stairs using a simple method to decouple the gravitational acceleration components of the motion components; and 3) an estimation of metabolic expenditure independent of the activity performed and the anthropometric characteristics of the user. Experimental results have demonstrated the feasibility of the prototype and the proposed algorithms.

I’ll Be Back: On the Multiple Lives of Users of a Mobile Activity Tracking Application

PubMed Central

Lin, Zhiyuan; Althoff, Tim; Leskovec, Jure

2018-01-01

Mobile health applications that track activities, such as exercise, sleep, and diet, are becoming widely used. While these activity tracking applications have the potential to improve our health, user engagement and retention are critical factors for their success. However, long-term user engagement patterns in real-world activity tracking applications are not yet well understood. Here we study user engagement patterns within a mobile physical activity tracking application consisting of 115 million logged activities taken by over a million users over 31 months. Specifically, we show that over 75% of users return and re-engage with the application after prolonged periods of inactivity, no matter the duration of the inactivity. We find a surprising result that the re-engagement usage patterns resemble those of the start of the initial engagement period, rather than being a simple continuation of the end of the initial engagement period. This evidence points to a conceptual model of multiple lives of user engagement, extending the prevalent single life view of user activity. We demonstrate that these multiple lives occur because the users have a variety of different primary intents or goals for using the app. These primary intents are associated with how long each life lasts and how likely the user is to re-engage for a new life. We find evidence for users being more likely to stop using the app once they achieved their primary intent or goal (e.g., weight loss). However, these users might return once their original intent resurfaces (e.g., wanting to lose newly gained weight). We discuss implications of the multiple life paradigm and propose a novel prediction task of predicting the number of lives of a user. Based on insights developed in this work, including a marker of improved primary intent performance, our prediction models achieve 71% ROC AUC. Overall, our research has implications for modeling user re-engagement in health activity tracking applications and has consequences for how notifications, recommendations as well as gamification can be used to increase engagement. PMID:29780978

The role of park conditions and features on park visitation and physical activity.

PubMed

Rung, Ariane L; Mowen, Andrew J; Broyles, Stephanie T; Gustat, Jeanette

2011-09-01

Neighborhood parks play an important role in promoting physical activity. We examined the effect of activity area, condition, and presence of supporting features on number of park users and park-based physical activity levels. 37 parks and 154 activity areas within parks were assessed during summer 2008 for their features and park-based physical activity. Outcomes included any park use, number of park users, mean and total energy expenditure. Independent variables included type and condition of activity area, supporting features, size of activity area, gender, and day of week. Multilevel models controlled for clustering of observations at activity area and park levels. Type of activity area was associated with number of park users, mean and total energy expenditure, with basketball courts having the highest number of users and total energy expenditure, and playgrounds having the highest mean energy expenditure. Condition of activity areas was positively associated with number of basketball court users and inversely associated with number of green space users and total green space energy expenditure. Various supporting features were both positively and negatively associated with each outcome. This study provides evidence regarding characteristics of parks that can contribute to achieving physical activity goals within recreational spaces.

Volcanic eruptions, hazardous ash clouds and visualization tools for accessing real-time infrared remote sensing data

NASA Astrophysics Data System (ADS)

Webley, P.; Dehn, J.; Dean, K. G.; Macfarlane, S.

2010-12-01

Volcanic eruptions are a global hazard, affecting local infrastructure, impacting airports and hindering the aviation community, as seen in Europe during Spring 2010 from the Eyjafjallajokull eruption in Iceland. Here, we show how remote sensing data is used through web-based interfaces for monitoring volcanic activity, both ground based thermal signals and airborne ash clouds. These ‘web tools’, http://avo.images.alaska.edu/, provide timely availability of polar orbiting and geostationary data from US National Aeronautics and Space Administration, National Oceanic and Atmosphere Administration and Japanese Meteorological Agency satellites for the North Pacific (NOPAC) region. This data is used operationally by the Alaska Volcano Observatory (AVO) for monitoring volcanic activity, especially at remote volcanoes and generates ‘alarms’ of any detected volcanic activity and ash clouds. The webtools allow the remote sensing team of AVO to easily perform their twice daily monitoring shifts. The web tools also assist the National Weather Service, Alaska and Kamchatkan Volcanic Emergency Response Team, Russia in their operational duties. Users are able to detect ash clouds, measure the distance from the source, area and signal strength. Within the web tools, there are 40 x 40 km datasets centered on each volcano and a searchable database of all acquired data from 1993 until present with the ability to produce time series data per volcano. Additionally, a data center illustrates the acquired data across the NOPAC within the last 48 hours, http://avo.images.alaska.edu/tools/datacenter/. We will illustrate new visualization tools allowing users to display the satellite imagery within Google Earth/Maps, and ArcGIS Explorer both as static maps and time-animated imagery. We will show these tools in real-time as well as examples of past large volcanic eruptions. In the future, we will develop the tools to produce real-time ash retrievals, run volcanic ash dispersion models from detected ash clouds and develop the browser interfaces to display other remote sensing datasets, such as volcanic sulfur dioxide detection.

Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way

PubMed Central

López-Gil, Juan-Miguel; Virgili-Gomá, Jordi; Gil, Rosa; Guilera, Teresa; Batalla, Iolanda; Soler-González, Jorge; García, Roberto

2016-01-01

Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does not allow properly determining whether a person in self-regulation its emotions while watching affective stimuli. However, adequately combining different data sources in a synchronous way to detect emotions makes it possible to overcome the limitations of single detection methods. PMID:27594831

Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way.

PubMed

López-Gil, Juan-Miguel; Virgili-Gomá, Jordi; Gil, Rosa; García, Roberto

2016-01-01

Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does not allow properly determining whether a person in self-regulation its emotions while watching affective stimuli. However, adequately combining different data sources in a synchronous way to detect emotions makes it possible to overcome the limitations of single detection methods.

Advanced image collection, information extraction, and change detection in support of NN-20 broad area search and analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Petrie, G.M.; Perry, E.M.; Kirkham, R.R.

1997-09-01

This report describes the work performed at the Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy`s Office of Nonproliferation and National Security, Office of Research and Development (NN-20). The work supports the NN-20 Broad Area Search and Analysis, a program initiated by NN-20 to improve the detection and classification of undeclared weapons facilities. Ongoing PNNL research activities are described in three main components: image collection, information processing, and change analysis. The Multispectral Airborne Imaging System, which was developed to collect georeferenced imagery in the visible through infrared regions of the spectrum, and flown on a light aircraftmore » platform, will supply current land use conditions. The image information extraction software (dynamic clustering and end-member extraction) uses imagery, like the multispectral data collected by the PNNL multispectral system, to efficiently generate landcover information. The advanced change detection uses a priori (benchmark) information, current landcover conditions, and user-supplied rules to rank suspect areas by probable risk of undeclared facilities or proliferation activities. These components, both separately and combined, provide important tools for improving the detection of undeclared facilities.« less

Comparison of cannabinoids in hair with self-reported cannabis consumption in heavy, light and non-cannabis users.

PubMed

Taylor, Michelle; Lees, Rosie; Henderson, Graeme; Lingford-Hughes, Anne; Macleod, John; Sullivan, John; Hickman, Matthew

2017-03-01

Biological tests of drug use can be used to inform clinical and legal decisions and hold potential to provide evidence for epidemiological studies where self-reported behaviour may be unavailable or unreliable. We test whether hair can be considered as a reliable marker of cannabis exposure. Hair samples were collected from 136 subjects who were self-reported heavy, light or non-users of cannabis and tested using GC-MS/MS. Sensitivity, specificity, positive predictive value and negative predictive value were calculated for five cannabinoids (tetrahydrocannabinol [THC], THC-OH, THC-COOH, cannabinol and cannabidiol). Samples also were segmented in 1 cm sections representing 1 month exposure and the correlation between amount of cannabinoid detected and self-reported cannabis consumption tested. All five cannabinoids were detected. Seventy-seven percent of heavy users, 39% of light users and 0% of non-users tested positive for THC. The sensitivity of detection of THC was 0.77 (0.56-0.91) comparing heavy cannabis smokers with light and non-users, whereas the sensitivity of other cannabinoids generally was considerably lower. The positive and negative predictive value of detection of THC were 0.57 (0.39-0.74) and 0.91 (0.82-0.97), respectively. A correlation of 0.52 (P < 0.001) was observed between self-reported monthly cannabis use and THC. Hair analysis can be used as a qualitative indicator of heavy (daily or near daily) cannabis consumption within the past 3 months. However, this approach is unable to reliably detect light cannabis consumption or determine the quantity of cannabis used by the individual. [Taylor M, Lees R, Henderson G, Lingford-Hughes A, Macleod J, Sullivan J, Hickman M. Comparison of cannabinoids in hair with self-reported cannabis consumption in heavy, light and non-cannabis users. Drug Alcohol Rev 2017;36:220-226]. © 2016 The Authors Drug and Alcohol Review published by John Wiley & Sons Australia, Ltd on behalf of Australasian Professional Society on Alcohol and other Drugs.

Can we develop an effective early warning system for volcanic eruptions using `off the shelf' webcams and low-light cameras?

NASA Astrophysics Data System (ADS)

Harrild, M.; Webley, P. W.; Dehn, J.

2016-12-01

An effective early warning system to detect volcanic activity is an invaluable tool, but often very expensive. Detecting and monitoring precursory events, thermal signatures, and ongoing eruptions in near real-time is essential, but conventional methods are often logistically challenging, expensive, and difficult to maintain. Our investigation explores the use of `off the shelf' webcams and low-light cameras, operating in the visible to near-infrared portions of the electromagnetic spectrum, to detect and monitor volcanic incandescent activity. Large databases of webcam imagery already exist at institutions around the world, but are often extremely underutilised and we aim to change this. We focus on the early detection of thermal signatures at volcanoes, using automated scripts to analyse individual images for changes in pixel brightness, allowing us to detect relative changes in thermally incandescent activity. Primarily, our work focuses on freely available streams of webcam images from around the world, which we can download and analyse in near real-time. When changes in activity are detected, an alert is sent to the users informing them of the changes in activity and a need for further investigation. Although relatively rudimentary, this technique provides constant monitoring for volcanoes in remote locations and developing nations, where it is not financially viable to deploy expensive equipment. We also purchased several of our own cameras, which were extensively tested in controlled laboratory settings with a black body source to determine their individual spectral response. Our aim is to deploy these cameras at active volcanoes knowing exactly how they will respond to varying levels of incandescence. They are ideal for field deployments as they are cheap (0-1,000), consume little power, are easily replaced, and can provide telemetered near real-time data. Data from Shiveluch volcano, Russia and our spectral response lab experiments are presented here.

panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics.

PubMed

Povysil, Gundula; Tzika, Antigoni; Vogt, Julia; Haunschmid, Verena; Messiaen, Ludwine; Zschocke, Johannes; Klambauer, Günter; Hochreiter, Sepp; Wimmer, Katharina

2017-07-01

Targeted next-generation-sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy-number variations (CNVs) in addition to single-nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user-friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state-of-the-art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user-selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user-friendliness rendering it highly suitable for routine clinical diagnostics. © 2017 The Authors. Human Mutation published by Wiley Periodicals, Inc.

An Analysis of Anxiety-Related Postings on Sina Weibo.

PubMed

Tian, Xianyun; He, Fang; Batterham, Philip; Wang, Zheng; Yu, Guang

2017-07-13

This study examines anxiety-related postings on Sina Weibo to gain insight into social networking about mental health. The themes of a random sample of anxiety-related postings ( n = 1000) were assessed. The disclosure of anxiety was the most common theme. The prevalence of anxiety was higher in certain areas where the economy is stronger than others, and the people living there suffered from more stress. Users who talked about feeling anxious tended to be more active on social media during leisure hours and less active during work hours. Our findings may be developed to detect and help individuals who may suffer from anxiety disorders at a low cost.

Active and realistic passive marijuana exposure tested by three immunoassays and GC/MS in urine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mule, S.J.; Lomax, P.; Gross, S.J.

Human urine samples obtained before and after active and passive exposure to marijuana were analyzed by immune kits (Roche, Amersham, and Syva) and gas chromatography/mass spectrometry (GC/MS). Seven of eight subjects were positive for the entire five-day test period with one immune kit. The latter correlated with GC/MS in 98% of the samples. Passive inhalation experiments under conditions likely to reflect realistic exposure resulted consistently in less than 10 ng/mL of cannabinoids. The 10-100-ng/mL cannabinoid concentration range essential for detection of occasional and moderate marijuana users is thus unaffected by realistic passive inhalation.

A Robust Crowdsourcing-Based Indoor Localization System.

PubMed

Zhou, Baoding; Li, Qingquan; Mao, Qingzhou; Tu, Wei

2017-04-14

WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS.

A Robust Crowdsourcing-Based Indoor Localization System

PubMed Central

Zhou, Baoding; Li, Qingquan; Mao, Qingzhou; Tu, Wei

2017-01-01

WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS. PMID:28420108

A software for managing after-hours activities in research user facilities

DOE PAGES

Camino, F. E.

2017-05-01

Here, we present an afterhours activity management program for shared facilities, which handles the processes required for afterhours access (request, approval, extension, etc.). It implements the concept of permitted afterhours activities, which consists of a list of well-defined activities that each user can perform afterhours. The program provides an easy and unambiguous way for users to know which activities they are allowed to perform afterhours. In addition, the program can enhance its safety efficacy by interacting with lab and instrument access control systems commonly present in user facilities.

A software for managing after-hours activities in research user facilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Camino, F. E.

Here, we present an afterhours activity management program for shared facilities, which handles the processes required for afterhours access (request, approval, extension, etc.). It implements the concept of permitted afterhours activities, which consists of a list of well-defined activities that each user can perform afterhours. The program provides an easy and unambiguous way for users to know which activities they are allowed to perform afterhours. In addition, the program can enhance its safety efficacy by interacting with lab and instrument access control systems commonly present in user facilities.

A software framework for real-time multi-modal detection of microsleeps.

PubMed

Knopp, Simon J; Bones, Philip J; Weddell, Stephen J; Jones, Richard D

2017-09-01

A software framework is described which was designed to process EEG, video of one eye, and head movement in real time, towards achieving early detection of microsleeps for prevention of fatal accidents, particularly in transport sectors. The framework is based around a pipeline structure with user-replaceable signal processing modules. This structure can encapsulate a wide variety of feature extraction and classification techniques and can be applied to detecting a variety of aspects of cognitive state. Users of the framework can implement signal processing plugins in C++ or Python. The framework also provides a graphical user interface and the ability to save and load data to and from arbitrary file formats. Two small studies are reported which demonstrate the capabilities of the framework in typical applications: monitoring eye closure and detecting simulated microsleeps. While specifically designed for microsleep detection/prediction, the software framework can be just as appropriately applied to (i) other measures of cognitive state and (ii) development of biomedical instruments for multi-modal real-time physiological monitoring and event detection in intensive care, anaesthesiology, cardiology, neurosurgery, etc. The software framework has been made freely available for researchers to use and modify under an open source licence.

Empirical analysis of online human dynamics

NASA Astrophysics Data System (ADS)

Zhao, Zhi-Dan; Zhou, Tao

2012-06-01

Patterns of human activities have attracted increasing academic interests, since the quantitative understanding of human behavior is helpful to uncover the origins of many socioeconomic phenomena. This paper focuses on behaviors of Internet users. Six large-scale systems are studied in our experiments, including the movie-watching in Netflix and MovieLens, the transaction in Ebay, the bookmark-collecting in Delicious, and the posting in FreindFeed and Twitter. Empirical analysis reveals some common statistical features of online human behavior: (1) The total number of user's actions, the user's activity, and the interevent time all follow heavy-tailed distributions. (2) There exists a strongly positive correlation between user's activity and the total number of user's actions, and a significantly negative correlation between the user's activity and the width of the interevent time distribution. We further study the rescaling method and show that this method could to some extent eliminate the different statistics among users caused by the different activities, yet the effectiveness depends on the data sets.

Chronic cannabis users show altered neurophysiological functioning on Stroop task conflict resolution.

PubMed

Battisti, Robert A; Roodenrys, Steven; Johnstone, Stuart J; Pesa, Nicole; Hermens, Daniel F; Solowij, Nadia

2010-12-01

Chronic cannabis use has been related to deficits in cognition (particularly memory) and the normal functioning of brain structures sensitive to cannabinoids. There is increasing evidence that conflict monitoring and resolution processes (i.e. the ability to detect and respond to change) may be affected. This study examined the ability to inhibit an automatic reading response in order to activate a more difficult naming response (i.e. conflict resolution) in a variant of the discrete trial Stroop colour-naming task. Event-related brain potentials to neutral, congruent and incongruent trials were compared between 21 cannabis users (mean 16.4 years of near daily use) in the unintoxicated state and 19 non-using controls. Cannabis users showed increased errors on colour-incongruent trials (e.g. "RED" printed in blue ink) but no performance differences from controls on colour congruent (e.g. "RED" printed in red ink) or neutral trials (e.g. "*****" printed in green ink). Poorer incongruent trial performance was predicted by an earlier age of onset of regular cannabis use. Users showed altered expression of a late sustained potential related to conflict resolution, evident by opposite patterns of activity between trial types at midline and central sites, and altered relationships between neurophysiological and behavioural outcome measures not evident in the control group. These findings indicate that chronic use of cannabis may impair the brain's ability to respond optimally in the presence of events that require conflict resolution and hold implications for the ability to refrain from substance misuse and/or maintain substance abstention behaviours.

DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response.

PubMed

Thapen, Nicholas; Simmie, Donal; Hankin, Chris; Gillard, Joseph

2016-01-01

In recent years social and news media have increasingly been used to explain patterns in disease activity and progression. Social media data, principally from the Twitter network, has been shown to correlate well with official disease case counts. This fact has been exploited to provide advance warning of outbreak detection, forecasting of disease levels and the ability to predict the likelihood of individuals developing symptoms. In this paper we introduce DEFENDER, a software system that integrates data from social and news media and incorporates algorithms for outbreak detection, situational awareness and forecasting. As part of this system we have developed a technique for creating a location network for any country or region based purely on Twitter data. We also present a disease nowcasting (forecasting the current but still unknown level) approach which leverages counts from multiple symptoms, which was found to improve the nowcasting accuracy by 37 percent over a model that used only previous case data. Finally we attempt to forecast future levels of symptom activity based on observed user movement on Twitter, finding a moderate gain of 5 percent over a time series forecasting model.

Effects of Chronic Active Cannabis Use on Visuomotor Integration, in Relation to Brain Activation and Cortisol Levels

PubMed Central

King, G.R.; Ernst, T.; Deng, W.; Stenger, A.; Gonzales, R.M.K; Nakama, H.; Chang, L.

2012-01-01

Cannabis is the most abused illegal substance in the United States. Alterations in brain function and motor behavior have been reported in chronic cannabis users, but the results have been variable. The current study aimed to determine whether chronic active cannabis use in humans may alter psychomotor function, brain activation, and hypothalamic-pituitary-axis (HPA) function in men and women. 30 cannabis users (16 men and 14 women, 18 to 45 years old) and 30 non-drug user controls (16 men and 14 women, 19 to 44 years old) were evaluated with neuropsychological tests designed to assess motor behavior and functional MRI (fMRI), using a 3 Tesla scanner, during a visually paced finger-sequencing task, cued by a flashing checkerboard (at 2 or 4 Hz). Salivary cortisol was measured to assess HPA function. Male, but not female, cannabis users had significantly slower performance on psychomotor speed tests. As a group, cannabis users had greater activation in BA 6 than controls, while controls had greater activation in the visual area BA 17 than cannabis users. Cannabis users also had higher salivary cortisol levels than controls (p = 0.002). Chronic active cannabis use is associated with slower and less efficient psychomotor function, especially in the male users, as indicated by a shift from regions involved with automated visually guided responses to more executive or attentional control areas. These brain activities may be attenuated by the higher cortisol levels in the cannabis users which in turn may lead to less efficient visual-motor function. PMID:22159107

GMDD: a database of GMO detection methods

PubMed Central

Dong, Wei; Yang, Litao; Shen, Kailin; Kim, Banghyun; Kleter, Gijs A; Marvin, Hans JP; Guo, Rong; Liang, Wanqi; Zhang, Dabing

2008-01-01

Background Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed. Results GMO Detection method Database (GMDD) has collected almost all the previous developed and reported GMOs detection methods, which have been grouped by different strategies (screen-, gene-, construct-, and event-specific), and also provide a user-friendly search service of the detection methods by GMO event name, exogenous gene, or protein information, etc. In this database, users can obtain the sequences of exogenous integration, which will facilitate PCR primers and probes design. Also the information on endogenous genes, certified reference materials, reference molecules, and the validation status of developed methods is included in this database. Furthermore, registered users can also submit new detection methods and sequences to this database, and the newly submitted information will be released soon after being checked. Conclusion GMDD contains comprehensive information of GMO detection methods. The database will make the GMOs analysis much easier. PMID:18522755

BALANCE: Towards a Usable Pervasive Wellness Application with Accurate Activity Inference

PubMed Central

Denning, Tamara; Andrew, Adrienne; Chaudhri, Rohit; Hartung, Carl; Lester, Jonathan; Borriello, Gaetano; Duncan, Glen

2010-01-01

Technology offers the potential to objectively monitor people’s eating and activity behaviors and encourage healthier lifestyles. BALANCE is a mobile phone-based system for long term wellness management. The BALANCE system automatically detects the user’s caloric expenditure via sensor data from a Mobile Sensing Platform unit worn on the hip. Users manually enter information on foods eaten via an interface on an N95 mobile phone. Initial validation experiments measuring oxygen consumption during treadmill walking and jogging show that the system’s estimate of caloric output is within 87% of the actual value. Future work will refine and continue to evaluate the system’s efficacy and develop more robust data input and activity inference methods. PMID:20445819

Assistive obstacle detection and navigation devices for vision-impaired users.

PubMed

Ong, S K; Zhang, J; Nee, A Y C

2013-09-01

Quality of life for the visually impaired is an urgent worldwide issue that needs to be addressed. Obstacle detection is one of the most important navigation tasks for the visually impaired. In this research, a novel range sensor placement scheme is proposed in this paper for the development of obstacle detection devices. Based on this scheme, two prototypes have been developed targeting at different user groups. This paper discusses the design issues, functional modules and the evaluation tests carried out for both prototypes. Implications for Rehabilitation Visual impairment problem is becoming more severe due to the worldwide ageing population. Individuals with visual impairment require assistance from assistive devices in daily navigation tasks. Traditional assistive devices that assist navigation may have certain drawbacks, such as the limited sensing range of a white cane. Obstacle detection devices applying the range sensor technology can identify road conditions with a higher sensing range to notify the users of potential dangers in advance.

Investigation of the applicability of a functional programming model to fault-tolerant parallel processing for knowledge-based systems

NASA Technical Reports Server (NTRS)

Harper, Richard

1989-01-01

In a fault-tolerant parallel computer, a functional programming model can facilitate distributed checkpointing, error recovery, load balancing, and graceful degradation. Such a model has been implemented on the Draper Fault-Tolerant Parallel Processor (FTPP). When used in conjunction with the FTPP's fault detection and masking capabilities, this implementation results in a graceful degradation of system performance after faults. Three graceful degradation algorithms have been implemented and are presented. A user interface has been implemented which requires minimal cognitive overhead by the application programmer, masking such complexities as the system's redundancy, distributed nature, variable complement of processing resources, load balancing, fault occurrence and recovery. This user interface is described and its use demonstrated. The applicability of the functional programming style to the Activation Framework, a paradigm for intelligent systems, is then briefly described.

CardioGuard: A Brassiere-Based Reliable ECG Monitoring Sensor System for Supporting Daily Smartphone Healthcare Applications

PubMed Central

Kwon, Sungjun; Kim, Jeehoon; Kang, Seungwoo; Lee, Youngki; Baek, Hyunjae

2014-01-01

Abstract We propose CardioGuard, a brassiere-based reliable electrocardiogram (ECG) monitoring sensor system, for supporting daily smartphone healthcare applications. It is designed to satisfy two key requirements for user-unobtrusive daily ECG monitoring: reliability of ECG sensing and usability of the sensor. The system is validated through extensive evaluations. The evaluation results showed that the CardioGuard sensor reliably measure the ECG during 12 representative daily activities including diverse movement levels; 89.53% of QRS peaks were detected on average. The questionnaire-based user study with 15 participants showed that the CardioGuard sensor was comfortable and unobtrusive. Additionally, the signal-to-noise ratio test and the washing durability test were conducted to show the high-quality sensing of the proposed sensor and its physical durability in practical use, respectively. PMID:25405527

Social relevance: toward understanding the impact of the individual in an information cascade

NASA Astrophysics Data System (ADS)

Hall, Robert T.; White, Joshua S.; Fields, Jeremy

2016-05-01

Information Cascades (IC) through a social network occur due to the decision of users to disseminate content. We define this decision process as User Diffusion (UD). IC models typically describe an information cascade by treating a user as a node within a social graph, where a node's reception of an idea is represented by some activation state. The probability of activation then becomes a function of a node's connectedness to other activated nodes as well as, potentially, the history of activation attempts. We enrich this Coarse-Grained User Diffusion (CGUD) model by applying actor type logics to the nodes of the graph. The resulting Fine-Grained User Diffusion (FGUD) model utilizes prior research in actor typing to generate a predictive model regarding the future influence a user will have on an Information Cascade. Furthermore, we introduce a measure of Information Resonance that is used to aid in predictions regarding user behavior.

Novel Virtual User Models of Mild Cognitive Impairment for Simulating Dementia

PubMed Central

Segkouli, Sofia; Tzovaras, Dimitrios; Tsakiris, Thanos; Tsolaki, Magda; Karagiannidis, Charalampos

2015-01-01

Virtual user modeling research has attempted to address critical issues of human-computer interaction (HCI) such as usability and utility through a large number of analytic, usability-oriented approaches as cognitive models in order to provide users with experiences fitting to their specific needs. However, there is demand for more specific modules embodied in cognitive architecture that will detect abnormal cognitive decline across new synthetic task environments. Also, accessibility evaluation of graphical user interfaces (GUIs) requires considerable effort for enhancing ICT products accessibility for older adults. The main aim of this study is to develop and test virtual user models (VUM) simulating mild cognitive impairment (MCI) through novel specific modules, embodied at cognitive models and defined by estimations of cognitive parameters. Well-established MCI detection tests assessed users' cognition, elaborated their ability to perform multitasks, and monitored the performance of infotainment related tasks to provide more accurate simulation results on existing conceptual frameworks and enhanced predictive validity in interfaces' design supported by increased tasks' complexity to capture a more detailed profile of users' capabilities and limitations. The final outcome is a more robust cognitive prediction model, accurately fitted to human data to be used for more reliable interfaces' evaluation through simulation on the basis of virtual models of MCI users. PMID:26339282

Analysis of an algorithm for distributed recognition and accountability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ko, C.; Frincke, D.A.; Goan, T. Jr.

1993-08-01

Computer and network systems are available to attacks. Abandoning the existing huge infrastructure of possibly-insecure computer and network systems is impossible, and replacing them by totally secure systems may not be feasible or cost effective. A common element in many attacks is that a single user will often attempt to intrude upon multiple resources throughout a network. Detecting the attack can become significantly easier by compiling and integrating evidence of such intrusion attempts across the network rather than attempting to assess the situation from the vantage point of only a single host. To solve this problem, we suggest an approachmore » for distributed recognition and accountability (DRA), which consists of algorithms which ``process,`` at a central location, distributed and asynchronous ``reports`` generated by computers (or a subset thereof) throughout the network. Our highest-priority objectives are to observe ways by which an individual moves around in a network of computers, including changing user names to possibly hide his/her true identity, and to associate all activities of multiple instance of the same individual to the same network-wide user. We present the DRA algorithm and a sketch of its proof under an initial set of simplifying albeit realistic assumptions. Later, we relax these assumptions to accommodate pragmatic aspects such as missing or delayed ``reports,`` clock slew, tampered ``reports,`` etc. We believe that such algorithms will have widespread applications in the future, particularly in intrusion-detection system.« less

A Novel Wearable Device for Food Intake and Physical Activity Recognition

PubMed Central

Farooq, Muhammad; Sazonov, Edward

2016-01-01

Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active and/or talking. The device consists of a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses. Data from 10 participants was collected while they performed activities including quiet sitting, talking, eating while sitting, eating while walking, and walking. Piezoelectric strain sensor and accelerometer signals were divided into non-overlapping epochs of 3 s; four features were computed for each signal. To differentiate between eating and not eating, as well as between sedentary postures and physical activity, two multiclass classification approaches are presented. The first approach used a single classifier with sensor fusion and the second approach used two-stage classification. The best results were achieved when two separate linear support vector machine (SVM) classifiers were trained for food intake and activity detection, and their results were combined using a decision tree (two-stage classification) to determine the final class. This approach resulted in an average F1-score of 99.85% and area under the curve (AUC) of 0.99 for multiclass classification. With its ability to differentiate between food intake and activity level, this device may potentially be used for tracking both energy intake and energy expenditure. PMID:27409622

A Novel Wearable Device for Food Intake and Physical Activity Recognition.

PubMed

Farooq, Muhammad; Sazonov, Edward

2016-07-11

Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active and/or talking. The device consists of a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses. Data from 10 participants was collected while they performed activities including quiet sitting, talking, eating while sitting, eating while walking, and walking. Piezoelectric strain sensor and accelerometer signals were divided into non-overlapping epochs of 3 s; four features were computed for each signal. To differentiate between eating and not eating, as well as between sedentary postures and physical activity, two multiclass classification approaches are presented. The first approach used a single classifier with sensor fusion and the second approach used two-stage classification. The best results were achieved when two separate linear support vector machine (SVM) classifiers were trained for food intake and activity detection, and their results were combined using a decision tree (two-stage classification) to determine the final class. This approach resulted in an average F1-score of 99.85% and area under the curve (AUC) of 0.99 for multiclass classification. With its ability to differentiate between food intake and activity level, this device may potentially be used for tracking both energy intake and energy expenditure.

Automatic Association of Chats and Video Tracks for Activity Learning and Recognition in Aerial Video Surveillance

PubMed Central

Hammoud, Riad I.; Sahin, Cem S.; Blasch, Erik P.; Rhodes, Bradley J.; Wang, Tao

2014-01-01

We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA) and multi-media indexing and explorer (MINER). VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1) a fusion of graphical track and text data using probabilistic methods; (2) an activity pattern learning framework to support querying an index of activities of interest (AOIs) and targets of interest (TOIs) by movement type and geolocation; and (3) a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV). VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports. PMID:25340453

Automatic association of chats and video tracks for activity learning and recognition in aerial video surveillance.

PubMed

Hammoud, Riad I; Sahin, Cem S; Blasch, Erik P; Rhodes, Bradley J; Wang, Tao

2014-10-22

We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA) and multi-media indexing and explorer (MINER). VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1) a fusion of graphical track and text data using probabilistic methods; (2) an activity pattern learning framework to support querying an index of activities of interest (AOIs) and targets of interest (TOIs) by movement type and geolocation; and (3) a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV). VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat Sensors 2014, 14 19844 messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports.

THREAT ENSEMBLE VULNERABILITY ASSESSMENT ...

EPA Pesticide Factsheets

software and manual TEVA-SPOT is used by water utilities to optimize the number and location of contamination detection sensors so that economic and/or public health consequences are minimized. TEVA-SPOT is interactive, allowing a user to specify the minimization objective (e.g., the number of people exposed, the time to detection, or the extent of pipe length contaminated). It also allows a user to specify constraints. For example, a TEVA-SPOT user can employ expert knowledge during the design process by identifying either existing or unfeasible sensor locations. Installation and maintenance costs for sensor placement can also be factored into the analysis. Python and Java are required to run TEVA-SPOT

Cartilage segmentation of 3D MRI scans of the osteoarthritic knee combining user knowledge and active contours

NASA Astrophysics Data System (ADS)

Lynch, John A.; Zaim, Souhil; Zhao, Jenny; Stork, Alexander; Peterfy, Charles G.; Genant, Harry K.

2000-06-01

A technique for segmentation of articular cartilage from 3D MRI scans of the knee has been developed. It overcomes the limitations of the conventionally used region growing techniques, which are prone to inter- and intra-observer variability, and which can require much manual intervention. We describe a hybrid segmentation method combining expert knowledge with directionally oriented Canny filters, cost functions and cubic splines. After manual initialization, the technique utilized 3 cost functions which aided automated detection of cartilage and its boundaries. Using the sign of the edge strength, and the local direction of the boundary, this technique is more reliable than conventional 'snakes,' and the user had little control over smoothness of boundaries. This means that the automatically detected boundary can conform to the true shape of the real boundary, also allowing reliable detection of subtle local lesions on the normally smooth cartilage surface. Manual corrections, with possible re-optimization were sometimes needed. When compared to the conventionally used region growing techniques, this newly described technique measured local cartilage volume with 3 times better reproducibility, and involved two thirds less human interaction. Combined with the use of 3D image registration, the new technique should also permit unbiased segmentation of followup scans by automated initialization from a baseline segmentation of an earlier scan of the same patient.

Harm reduction theory: Users culture, micro-social indigenous harm reduction, and the self-organization and outside-organizing of users’ groups

PubMed Central

Friedman, Samuel R.; de Jong, Wouter; Rossi, Diana; Touzé, Graciela; Rockwell, Russell; Jarlais, Don C Des; Elovich, Richard

2007-01-01

This paper discusses the user side of harm reduction, focusing to some extent on the early responses to the HIV/AIDS epidemic in each of four sets of localities—New York City, Rotterdam, Buenos Aires, and sites in Central Asia. Using available qualitative and quantitative information, we present a series of vignettes about user activities in four different localities in behalf of reducing drug-related harm. Some of these activities have been micro-social (small group) activities; others have been conducted by formal organizations of users that the users organised at their own initiative. In spite of the limitations of the methodology, the data suggest that users’ activities have helped limit HIV spread. These activities are shaped by broader social contexts, such as the extent to which drug scenes are integrated with broader social networks and the way the political and economic systems impinge on drug users’ lives. Drug users are active agents in their own individual and collective behalf, and in helping to protect wider communities. Harm reduction activities and research should take note of and draw upon both the micro-social and formal organizations of users. Finally, both researchers and policy makers should help develop ways to enable and support both micro-social and formally organized action by users PMID:17689353

Detecting Anomalous Insiders in Collaborative Information Systems

PubMed Central

Chen, You; Nyemba, Steve; Malin, Bradley

2012-01-01

Collaborative information systems (CISs) are deployed within a diverse array of environments that manage sensitive information. Current security mechanisms detect insider threats, but they are ill-suited to monitor systems in which users function in dynamic teams. In this paper, we introduce the community anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on the access logs of collaborative environments. The framework is based on the observation that typical CIS users tend to form community structures based on the subjects accessed (e.g., patients’ records viewed by healthcare providers). CADS consists of two components: 1) relational pattern extraction, which derives community structures and 2) anomaly prediction, which leverages a statistical model to determine when users have sufficiently deviated from communities. We further extend CADS into MetaCADS to account for the semantics of subjects (e.g., patients’ diagnoses). To empirically evaluate the framework, we perform an assessment with three months of access logs from a real electronic health record (EHR) system in a large medical center. The results illustrate our models exhibit significant performance gains over state-of-the-art competitors. When the number of illicit users is low, MetaCADS is the best model, but as the number grows, commonly accessed semantics lead to hiding in a crowd, such that CADS is more prudent. PMID:24489520

Supervised Semi-Automated Data Analysis Software for Gas Chromatography / Differential Mobility Spectrometry (GC/DMS) Metabolomics Applications.

PubMed

Peirano, Daniel J; Pasamontes, Alberto; Davis, Cristina E

2016-09-01

Modern differential mobility spectrometers (DMS) produce complex and multi-dimensional data streams that allow for near-real-time or post-hoc chemical detection for a variety of applications. An active area of interest for this technology is metabolite monitoring for biological applications, and these data sets regularly have unique technical and data analysis end user requirements. While there are initial publications on how investigators have individually processed and analyzed their DMS metabolomic data, there are no user-ready commercial or open source software packages that are easily used for this purpose. We have created custom software uniquely suited to analyze gas chromatograph / differential mobility spectrometry (GC/DMS) data from biological sources. Here we explain the implementation of the software, describe the user features that are available, and provide an example of how this software functions using a previously-published data set. The software is compatible with many commercial or home-made DMS systems. Because the software is versatile, it can also potentially be used for other similarly structured data sets, such as GC/GC and other IMS modalities.

Improved head direction command classification using an optimised Bayesian neural network.

PubMed

Nguyen, Son T; Nguyen, Hung T; Taylor, Philip B; Middleton, James

2006-01-01

Assistive technologies have recently emerged to improve the quality of life of severely disabled people by enhancing their independence in daily activities. Since many of those individuals have limited or non-existing control from the neck downward, alternative hands-free input modalities have become very important for these people to access assistive devices. In hands-free control, head movement has been proved to be a very effective user interface as it can provide a comfortable, reliable and natural way to access the device. Recently, neural networks have been shown to be useful not only for real-time pattern recognition but also for creating user-adaptive models. Since multi-layer perceptron neural networks trained using standard back-propagation may cause poor generalisation, the Bayesian technique has been proposed to improve the generalisation and robustness of these networks. This paper describes the use of Bayesian neural networks in developing a hands-free wheelchair control system. The experimental results show that with the optimised architecture, classification Bayesian neural networks can detect head commands of wheelchair users accurately irrespective to their levels of injuries.

Classification of unconscious like/dislike decisions: First results towards a novel application for BCI technology.

PubMed

Wriessnegger, S C; Hackhofer, D; Muller-Putz, G R

2015-01-01

More and more applications for BCI technology emerge that are not restricted to communication or control, like gaming, rehabilitation, Neuro-IS research, neuro-economics or security. In this context a so called passive BCI, a system that derives its outputs from arbitrary brain activity for enriching a human-machine interaction with implicit information on the actual user state will be used. Concretely EEG-based BCI technology enables the use of signals related to attention, intentions and mental state, without relying on indirect measures based on overt behavior or other physiological signals which is an important point e.g. in Neuromarketing research. The scope of this pilot EEG-study was to detect like/dislike decisions on car stimuli just by means of ERP analysis. Concretely to define user preferences concerning different car designs by implementing an offline BCI based on shrinkage LDA classification. Although classification failed in the majority of participants the elicited early (sub) conscious ERP components reflect user preferences for cars. In a broader sense this study should pave the way towards a "product design BCI" suitable for neuromarketing research.

Objectifying user critique. A means of continuous quality assurance for physician discharge letter composition.

PubMed

Oschem, M; Mahler, V; Prokosch, H U

2011-01-01

The aim of this study is to objectify user critique rendering it usable for quality assurance. Based on formative and summative evaluation results we strive to promote software improvements; in our case, the physician discharge letter composition process at the Department of Dermatology, University Hospital Erlangen, Germany. We developed a novel six-step approach to objectify user critique: 1) acquisition of user critique using subjectivist methods, 2) creation of a workflow model, 3) definition of hypothesis and indicators, 4) measuring of indicators, 5) analyzing results, 6) optimization of the system regarding both subjectivist and objectivist evaluation results. In particular, we derived indicators and workflows directly from user critique/narratives. The identified indicators were mapped onto workflow activities, creating a link between user critique and the evaluated system. Users criticized a new discharge letter system as "too slow" and "too labor-intensive" in comparison with the previously used system. In a stepwise approach we collected subjective user critique, derived a comprehensive process model including deviations and deduced a set of five indicators for objectivist evaluation: processing time, system-related waiting time, number of mouse clicks, number of keyboard inputs, and throughput time. About 3500 measurements have been performed to compare the workflow-steps of both systems, regarding 20 discharge letters. Although the difference of the mean total processing time between both systems was statistically insignificant (2011.7 s vs. 1971.5 s; p = 0.457), we detected a significant difference in waiting times (101.8 s vs. 37.2 s; p <0.001) and number of user interactions (77 vs. 69; p <0.001) in favor of the old system, thus objectifying user critique. Our six-step approach enables objectification of user critique, resulting in objective values for continuous quality assurance. To our knowledge no previous study in medical informatics mapped user critique onto workflow steps. Subjectivist analysis prompted us to use the indicator system-related waiting time for the objectivist study, which was rarely done before. We consider combining subjectivist and objectivist methods as a key point of our approach. Future work will concentrate on automated measurement of indicators.

Fitness levels and physical activity among class A drug users entering prison.

PubMed

Fischer, Jan; Butt, Christine; Dawes, Helen; Foster, Charlie; Neale, Joanne; Plugge, Emma; Wheeler, Carly; Wright, Nat

2012-12-01

Physical activity could benefit drug users' physiological and mental health. Previous research has suggested that physical activity levels change when drug users enter prison. Twenty-five class A drug users who were new to prison answered physical activity and drug use cross-sectional questionnaires, took a submaximal fitness test and wore a pedometer for 1 week. Participants' mean aerobic capacity was estimated as 49 mls O2/kg/min (±12 SD). Their mean self-reported walking distance outside of prison was 4.67 miles on an average day (±4.14 SD). Pedometer data suggest they walked a mean of 1.8 miles/day in prison. Many class A drug users entering prison had high levels of fitness and physical activity before admission, often gained from walking. Walking activity reduced when they entered prison, posing a challenge to maintaining healthy activity levels.

A pilot study on nicotine residues in houses of electronic cigarette users, tobacco smokers, and non-users of nicotine-containing products.

PubMed

Bush, Derek; Goniewicz, Maciej L

2015-06-01

Nicotine deposited on the surfaces has been shown to react with airborne chemicals leading to formation of carcinogens and contributing to thirdhand exposure. While prior studies revealed nicotine residues in tobacco smokers' homes, none have examined the nicotine residue in electronic cigarette (e-cigarette) users' homes. We measured nicotine on the surfaces in households of 8 e-cigarette users, 6 cigarette smokers, and 8 non-users of nicotine-containing products in Western New York, USA. Three surface wipe samples were taken from the floor, wall and window. Nicotine was extracted from the wipes and analyzed using gas chromatography. Half of the e-cigarette users' homes had detectable levels of nicotine on surfaces whereas nicotine was found in all of the tobacco cigarette smokers' homes. Trace amounts of nicotine were also detected in half of the homes of non-users of nicotine-containing products. Nicotine levels in e-cigarette users homes was significantly lower than that found in cigarette smokers homes (average concentration 7.7±17.2 vs. 1303±2676 μg/m2; p<0.05). There was no significant difference in the amount of nicotine in homes of e-cigarette users and non-users (p>0.05). Nicotine is a common contaminant found on indoor surfaces. Using e-cigarettes indoors leads to significantly less thirdhand exposure to nicotine compared to smoking tobacco cigarettes. Copyright © 2015 Elsevier B.V. All rights reserved.

The role of the salience network in processing lexical and nonlexical stimuli in cochlear implant users: an ALE meta-analysis of PET studies.

PubMed

Song, Jae-Jin; Vanneste, Sven; Lazard, Diane S; Van de Heyning, Paul; Park, Joo Hyun; Oh, Seung Ha; De Ridder, Dirk

2015-05-01

Previous positron emission tomography (PET) studies have shown that various cortical areas are activated to process speech signal in cochlear implant (CI) users. Nonetheless, differences in task dimension among studies and low statistical power preclude from understanding sound processing mechanism in CI users. Hence, we performed activation likelihood estimation meta-analysis of PET studies in CI users and normal hearing (NH) controls to compare the two groups. Eight studies (58 CI subjects/92 peak coordinates; 45 NH subjects/40 peak coordinates) were included and analyzed, retrieving areas significantly activated by lexical and nonlexical stimuli. For lexical and nonlexical stimuli, both groups showed activations in the components of the dual-stream model such as bilateral superior temporal gyrus/sulcus, middle temporal gyrus, left posterior inferior frontal gyrus, and left insula. However, CI users displayed additional unique activation patterns by lexical and nonlexical stimuli. That is, for the lexical stimuli, significant activations were observed in areas comprising salience network (SN), also known as the intrinsic alertness network, such as the left dorsal anterior cingulate cortex (dACC), left insula, and right supplementary motor area in the CI user group. Also, for the nonlexical stimuli, CI users activated areas comprising SN such as the right insula and left dACC. Previous episodic observations on lexical stimuli processing using the dual auditory stream in CI users were reconfirmed in this study. However, this study also suggests that dual-stream auditory processing in CI users may need supports from the SN. In other words, CI users need to pay extra attention to cope with degraded auditory signal provided by the implant. © 2015 Wiley Periodicals, Inc.

77 FR 35992 - Agency Information Collection Activities: User Fees

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-15

... Activities: User Fees AGENCY: U.S. Customs and Border Protection (CBP), Department of Homeland Security... User Fees. This request for comment is being made pursuant to the Paperwork Reduction Act of 1995 (Pub... soliciting comments concerning the following information collection: Title: User Fees. OMB Number: 1651-0052...

Glycemic Allostasis during Mental Activities on Fasting in Non-alcohol Users and Alcohol Users with Different Durations of Abstinence.

PubMed

Welcome, Mo; Pereverzev, Va

2014-09-01

Glycemic allostasis is the process by which blood glucose stabilization is achieved through the balancing of glucose consumption rate and release into the blood stream under a variety of stressors. This paper reviews findings on the dynamics of glycemic levels during mental activities on fasting in non-alcohol users and alcohol users with different periods of abstinence. Referred articles for this review were searched in the databases of PubMed, Scopus, DOAJ and AJOL. The search was conducted in 2013 between January 20 and July 31. The following keywords were used in the search: alcohol action on glycemia OR brain glucose OR cognitive functions; dynamics of glycemia, dynamics of glycemia during mental activities; dynamics of glycemia on fasting; dynamics of glycemia in non-alcohol users OR alcohol users; glycemic regulation during sobriety. Analysis of the selected articles showed that glycemic allostasis during mental activities on fasting is poorly regulated in alcohol users even after a long duration of sobriety (1-4 weeks after alcohol consumption), compared to non-alcohol users. The major contributor to the maintenance of euglycemia during mental activities after the night's rest (during continuing fast) is gluconeogenesis.

Glycemic Allostasis during Mental Activities on Fasting in Non-alcohol Users and Alcohol Users with Different Durations of Abstinence

PubMed Central

Welcome, MO; Pereverzev, VA

2014-01-01

Glycemic allostasis is the process by which blood glucose stabilization is achieved through the balancing of glucose consumption rate and release into the blood stream under a variety of stressors. This paper reviews findings on the dynamics of glycemic levels during mental activities on fasting in non-alcohol users and alcohol users with different periods of abstinence. Referred articles for this review were searched in the databases of PubMed, Scopus, DOAJ and AJOL. The search was conducted in 2013 between January 20 and July 31. The following keywords were used in the search: alcohol action on glycemia OR brain glucose OR cognitive functions; dynamics of glycemia, dynamics of glycemia during mental activities; dynamics of glycemia on fasting; dynamics of glycemia in non-alcohol users OR alcohol users; glycemic regulation during sobriety. Analysis of the selected articles showed that glycemic allostasis during mental activities on fasting is poorly regulated in alcohol users even after a long duration of sobriety (1-4 weeks after alcohol consumption), compared to non-alcohol users. The major contributor to the maintenance of euglycemia during mental activities after the night's rest (during continuing fast) is gluconeogenesis. PMID:25364589

Wearable sensor glove based on conducting fabric using electrodermal activity and pulse-wave sensors for e-health application.

PubMed

Lee, Youngbum; Lee, Byungwoo; Lee, Myoungho

2010-03-01

Improvement of the quality and efficiency of health in medicine, both at home and the hospital, calls for improved sensors that might be included in a common carrier such as a wearable sensor device to measure various biosignals and provide healthcare services that use e-health technology. Designed to be user-friendly, smart clothes and gloves respond well to the end users for health monitoring. This study describes a wearable sensor glove that is equipped with an electrodermal activity (EDA) sensor, pulse-wave sensor, conducting fabric, and an embedded system. The EDA sensor utilizes the relationship between drowsiness and the EDA signal. The EDA sensors were made using a conducting fabric instead of silver chloride electrodes, as a more practical and practically wearable device. The pulse-wave sensor measurement system, which is widely applied in oriental medicinal practices, is also a strong element in e-health monitoring systems. The EDA and pulse-wave signal acquisition module was constructed by connecting the sensor to the glove via a conductive fabric. The signal acquisition module is then connected to a personal computer that displays the results of the EDA and pulse-wave signal processing analysis and gives accurate feedback to the user. This system is designed for a number of applications for the e-health services, including drowsiness detection and oriental medicine.

Anabolic steroids detected in bodybuilding dietary supplements - a significant risk to public health.

PubMed

Abbate, V; Kicman, A T; Evans-Brown, M; McVeigh, J; Cowan, D A; Wilson, C; Coles, S J; Walker, C J

2015-07-01

Twenty-four products suspected of containing anabolic steroids and sold in fitness equipment shops in the United Kingdom (UK) were analyzed for their qualitative and semi-quantitative content using full scan gas chromatography-mass spectrometry (GC-MS), accurate mass liquid chromatography-mass spectrometry (LC-MS), high pressure liquid chromatography with diode array detection (HPLC-DAD), UV-Vis, and nuclear magnetic resonance (NMR) spectroscopy. In addition, X-ray crystallography enabled the identification of one of the compounds, where reference standard was not available. Of the 24 products tested, 23 contained steroids including known anabolic agents; 16 of these contained steroids that were different to those indicated on the packaging and one product contained no steroid at all. Overall, 13 different steroids were identified; 12 of these are controlled in the UK under the Misuse of Drugs Act 1971. Several of the products contained steroids that may be considered to have considerable pharmacological activity, based on their chemical structures and the amounts present. This could unwittingly expose users to a significant risk to their health, which is of particular concern for naïve users. Copyright © 2014 John Wiley & Sons, Ltd.

Cross-modal perception of rhythm in music and dance by cochlear implant users.

PubMed

Vongpaisal, Tara; Monaghan, Melanie

2014-05-01

Two studies examined adult cochlear implant (CI) users' ability to match auditory rhythms occurring in music to visual rhythms occurring in dance (Cha Cha, Slow Swing, Tango and Jive). In Experiment 1, adults CI users (n = 10) and hearing controls matched a music excerpt to choreographed dance sequences presented as silent videos. In Experiment 2, participants matched a silent video of a dance sequence to music excerpts. CI users were successful in detecting timing congruencies across music and dance at well above-chance levels suggesting that they were able to process distinctive auditory and visual rhythm patterns that characterized each style. However, they were better able to detect cross-modal timing congruencies when the reference was an auditory rhythm than when the reference was a visual rhythm. Learning strategies that encourage cross-modal learning of musical rhythms may have applications in developing novel rehabilitative strategies to enhance music perception and appreciation outcomes of child implant users.

Identifying cases of heroin toxicity where 6-acetylmorphine (6-AM) is not detected by toxicological analyses.

PubMed

Ellis, Ashley D; McGwin, Gerald; Davis, Gregory G; Dye, Daniel W

2016-09-01

Heroin has a half-life of 2-6 min and is metabolized too quickly to be detected in autopsy samples. The presence of 6-acetylmophine (6-AM) in urine, blood, or other samples is convincing evidence of heroin use by a decedent, but 6-AM itself has a half-life of 6-25 min before it is hydrolyzed to morphine, so 6-AM may not be present in sufficient concentration to detect in postmortem samples. Codeine is often present in heroin preparations as an impurity and is not a metabolite of heroin. Studies report that a ratio of morphine to codeine greater than one indicates heroin use. We hypothesize that the ratio of morphine to codeine in our decedents abusing drugs intravenously will be no different in individuals with 6-AM present than in individuals where no 6-AM is detected, and we report our study of this hypothesis. All accidental deaths investigated by the Jefferson County Coroner/Medical Examiner Office from 2010 to 2013 with morphine detected in blood samples collected at autopsy were reviewed. Five deaths where trauma caused or contributed to death were excluded from the review. The presence or absence of 6-AM and the concentrations of morphine and codeine were recorded for each case. The ratio of morphine to codeine was calculated for all decedents. Any individual in whom no morphine or codeine was detected in a postmortem sample was excluded from further study. Absence or presence of drug paraphernalia or evidence of intravascular (IV) drug use was documented in each case to identify IV drug users. The proportion of the IV drug users with and without 6-AM present in a postmortem sample was compared to the M/C ratio for the individuals. Of the 230 deaths included in the analysis, 103 IV drug users with quantifiable morphine and codeine in a postmortem sample were identified allowing for calculation of an M/C ratio. In these IV drug users, the M/C ratio was greater than 1 in 98 % of decedents. When controlling for the absence or presence of 6-AM there was no statistically significant difference in the proportion of IV drug users when compared to non IV drug users with an M/C ratio of greater than 1 (p = 1.000). The M/C ratio in IV drug users, if greater than 1, is seen in deaths due to heroin toxicity where 6-AM is detected in a postmortem sample. This study provides evidence that a M/C ratio greater than one in an IV drug user is evidence of a death due to heroin toxicity even if 6-AM is not detected in the blood. Using the M/C ratio, in addition to scene and autopsy findings, provides sufficient evidence to show heroin is the source of the morphine and codeine. Listing heroin as a cause or contributing factor in deaths with evidence of IV drug abuse and where the M/C ratio exceeds 1 will improve identification of heroin fatalities, which will allow better allocation of resources for public health initiatives.

Detection and display of acoustic window for guiding and training cardiac ultrasound users

NASA Astrophysics Data System (ADS)

Huang, Sheng-Wen; Radulescu, Emil; Wang, Shougang; Thiele, Karl; Prater, David; Maxwell, Douglas; Rafter, Patrick; Dupuy, Clement; Drysdale, Jeremy; Erkamp, Ramon

2014-03-01

Successful ultrasound data collection strongly relies on the skills of the operator. Among different scans, echocardiography is especially challenging as the heart is surrounded by ribs and lung tissue. Less experienced users might acquire compromised images because of suboptimal hand-eye coordination and less awareness of artifacts. Clearly, there is a need for a tool that can guide and train less experienced users to position the probe optimally. We propose to help users with hand-eye coordination by displaying lines overlaid on B-mode images. The lines indicate the edges of blockages (e.g., ribs) and are updated in real time according to movement of the probe relative to the blockages. They provide information about how probe positioning can be improved. To distinguish between blockage and acoustic window, we use coherence, an indicator of channel data similarity after applying focusing delays. Specialized beamforming was developed to estimate coherence. Image processing is applied to coherence maps to detect unblocked beams and the angle of the lines for display. We built a demonstrator based on a Philips iE33 scanner, from which beamsummed RF data and video output are transferred to a workstation for processing. The detected lines are overlaid on B-mode images and fed back to the scanner display to provide users real-time guidance. Using such information in addition to B-mode images, users will be able to quickly find a suitable acoustic window for optimal image quality, and improve their skill.

Malaria rapid diagnostic tests in endemic settings.

PubMed

Maltha, J; Gillet, P; Jacobs, J

2013-05-01

Malaria rapid diagnostic tests (RDTs) are instrument-free tests that provide results within 20 min and can be used by community health workers. RDTs detect antigens produced by the Plasmodium parasite such as Plasmodium falciparum histidine-rich protein-2 (PfHPR2) and Plasmodium lactate dehydrogenase (pLDH). The accuracy of RDTs for the diagnosis of uncomplicated P. falciparum infection is equal or superior to routine microscopy (but inferior to expert microscopy). Sensitivity for Plasmodium vivax is 75-100%; for Plasmodium ovale and Plasmodium malariae, diagnostic performance is poor. Design limitations of RDTs include poor sensitivity at low parasite densities, susceptibility to the prozone effect (PfHRP2-detecting RDTs), false-negative results due to PfHRP2 deficiency in the case of pfhrp2 gene deletions (PfHRP2-detecting RDTs), cross-reactions between Plasmodium antigens and detection antibodies, false-positive results by other infections and susceptibility to heat and humidity. End-user's errors relate to safety, procedure (delayed reading, incorrect sample and buffer volumes) and interpretation (not recognizing invalid test results, disregarding faint test lines). Withholding antimalarial treatment in the case of negative RDT results tends to be infrequent and tendencies towards over-prescription of antibiotics have been noted. Numerous shortcomings in RDT kits' labelling, instructions for use (correctness and readability) and contents have been observed. The World Health Organization and partners actively address quality assurance of RDTs by comparative testing of RDTs, inspections of manufacturing sites, lot testing and training tools but no formal external quality assessment programme of end-user performance exists. Elimination of malaria requires RDTs with lower detection limits, for which nucleic acid amplification tests are under development. © 2013 The Authors Clinical Microbiology and Infection © 2013 European Society of Clinical Microbiology and Infectious Diseases.

Examining the association between physical activity, sedentary behaviour and sport participation with e-cigarette use and smoking status in a large sample of Canadian Youth.

PubMed

Milicic, Sandra; Piérard, Emma; DeCicca, Philip; Leatherdale, Scott T

2017-11-01

Youth e-cigarette use is common worldwide, but the profile of e-cigarette users compared with tobacco users is unclear. This study examines how sport participation and activity levels among youth differ between e-cigarette users and smokers. Using Canadian data from 38,977 grade 9 to 12 students who participated in Year 3 (2014-15) of the COMPASS study, logistic regression models were used to examine the likelihood of sport participation and activity level based on e-cigarette use and smoking status. Pearson's chi-square tests were used to examine subgroup differences by gender. E-cigarette users are more likely to participate in intramural, competitive, and team sports compared to non-users. Current and former smokers are less likely to participate in those sports than non-smokers. Youth e-cigarette users are more likely than non-users to meet the physical activity guidelines. Current smokers are more likely than non-smokers to undertake physical activity at least 60 minutes daily but less likely than non-smokers to tone at least 3 times per week. Youth e-cigarette users are less likely than non-users to be sedentary less than 2 hours daily. Gender differences among males and females show that male e-cigarettes users drive the general relationship. Results suggest that e-cigarette users are more likely to engage in physical activity compared to non e-cigarette users. Youth e-cigarette users are more likely to be physically active while the opposite is true for smokers. Although e-cigarettes may be less harmful to health compared to cigarette smoking, the increased uptake among youth of differing profiles should be considered in prevention efforts. These results highlight the importance of addressing e-cigarette use in youth who undertake health promoting behaviours. Prevention efforts should not focus only on youth who may undertake riskier health habits; e-cigarette prevention programs should go beyond the domain of tobacco control. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Involving service users in trials: developing a standard operating procedure

PubMed Central

2013-01-01

Background Many funding bodies require researchers to actively involve service users in research to improve relevance, accountability and quality. Current guidance to researchers mainly discusses general principles. Formal guidance about how to involve service users operationally in the conduct of trials is lacking. We aimed to develop a standard operating procedure (SOP) to support researchers to involve service users in trials and rigorous studies. Methods Researchers with experience of involving service users and service users who were contributing to trials collaborated with the West Wales Organisation for Rigorous Trials in Health, a registered clinical trials unit, to develop the SOP. Drafts were prepared in a Task and Finish Group, reviewed by all co-authors and amendments made. Results We articulated core principles, which defined equality of service users with all other research team members and collaborative processes underpinning the SOP, plus guidance on how to achieve these. We developed a framework for involving service users in research that defined minimum levels of collaboration plus additional consultation and decision-making opportunities. We recommended service users be involved throughout the life of a trial, including planning and development, data collection, analysis and dissemination, and listed tasks for collaboration. We listed people responsible for involving service users in studies and promoting an inclusive culture. We advocate actively involving service users as early as possible in the research process, with a minimum of two on all formal trial groups and committees. We propose that researchers protect at least 1% of their total research budget as a minimum resource to involve service users and allow enough time to facilitate active involvement. Conclusions This SOP provides guidance to researchers to involve service users successfully in developing and conducting clinical trials and creating a culture of actively involving service users in research at all stages. The UK Clinical Research Collaboration should encourage clinical trials units actively to involve service users and research funders should provide sufficient funds and time for this in research grants. PMID:23866730

Analysis of geostationary satellite-derived cloud parameters associated with environments with high ice water content

NASA Astrophysics Data System (ADS)

de Laat, Adrianus; Defer, Eric; Delanoë, Julien; Dezitter, Fabien; Gounou, Amanda; Grandin, Alice; Guignard, Anthony; Fokke Meirink, Jan; Moisselin, Jean-Marc; Parol, Frédéric

2017-04-01

We present an evaluation of the ability of passive broadband geostationary satellite measurements to detect high ice water content (IWC > 1 g m-3) as part of the European High Altitude Ice Crystals (HAIC) project for detection of upper-atmospheric high IWC, which can be a hazard for aviation. We developed a high IWC mask based on measurements of cloud properties using the Cloud Physical Properties (CPP) algorithm applied to the geostationary Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Evaluation of the high IWC mask with satellite measurements of active remote sensors of cloud properties (CLOUDSAT/CALIPSO combined in the DARDAR (raDAR-liDAR) product) reveals that the high IWC mask is capable of detecting high IWC values > 1 g m-3 in the DARDAR profiles with a probability of detection of 60-80 %. The best CPP predictors of high IWC were the condensed water path, cloud optical thickness, cloud phase, and cloud top height. The evaluation of the high IWC mask against DARDAR provided indications that the MSG-CPP high IWC mask is more sensitive to cloud ice or cloud water in the upper part of the cloud, which is relevant for aviation purposes. Biases in the CPP results were also identified, in particular a solar zenith angle (SZA) dependence that reduces the performance of the high IWC mask for SZAs > 60°. Verification statistics show that for the detection of high IWC a trade-off has to be made between better detection of high IWC scenes and more false detections, i.e., scenes identified by the high IWC mask that do not contain IWC > 1 g m-3. However, the large majority of these detections still contain IWC values between 0.1 and 1 g m-3. Comparison of the high IWC mask against results from the Rapidly Developing Thunderstorm (RDT) algorithm applied to the same geostationary SEVIRI data showed that there are similarities and differences with the high IWC mask: the RDT algorithm is very capable of detecting young/new convective cells and areas, whereas the high IWC mask appears to be better capable of detecting more mature and ageing convection as well as cirrus remnants. The lack of detailed understanding of what causes aviation hazards related to high IWC, as well as the lack of clearly defined user requirements, hampers further tuning of the high IWC mask. Future evaluation of the high IWC mask against field campaign data, as well as obtaining user feedback and user requirements from the aviation industry, should provide more information on the performance of the MSG-CPP high IWC mask and contribute to improving the practical use of the high IWC mask.

High Frequency Active Auroral Research Program (HAARP) imager. Final report, 29 August 1991-29 August 1993

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lance, C.; Eather, R.

1993-09-30

A low-light-level monochromatic imaging system was designed and fabricated which was optimized to detect and record optical emissions associated with high-power rf heating of the ionosphere. The instrument is capable of detecting very low intensities, of the order of 1 Rayleigh, from typical ionospheric atomic and molecular emissions. This is achieved through co-adding of ON images during heater pulses and subtraction of OFF (background) images between pulses. Images can be displayed and analyzed in real time and stored in optical disc for later analysis. Full image processing software is provided which was customized for this application and uses menu ormore » mouse user interaction.« less

UTILIZING SOCIAL MEDIA AND PROTECTING MILITARY MEMBERS AND THEIR FAMILIES

DTIC Science & Technology

2016-02-01

is the property of the United States Government. iii Table of Contents Disclaimer...ii Table of Contents ...According to Facebook, it had 12 million active users in 2006, 500 million active users in 2010, and 1.65 billion active users in 2014.7 The DoD

Human ecstasy (MDMA) polydrug users have altered brain activation during semantic processing.

PubMed

Watkins, Tristan J; Raj, Vidya; Lee, Junghee; Dietrich, Mary S; Cao, Aize; Blackford, Jennifer U; Salomon, Ronald M; Park, Sohee; Benningfield, Margaret M; Di Iorio, Christina R; Cowan, Ronald L

2013-05-01

Ecstasy (3,4-methylenedioxymethamphetamine [MDMA]) polydrug users have verbal memory performance that is statistically significantly lower than that of control subjects. Studies have correlated long-term MDMA use with altered brain activation in regions that play a role in verbal memory. The aim of our study was to examine the association of lifetime ecstasy use with semantic memory performance and brain activation in ecstasy polydrug users. A total of 23 abstinent ecstasy polydrug users (age = 24.57 years) and 11 controls (age = 22.36 years) performed a two-part functional magnetic resonance imaging (fMRI) semantic encoding and recognition task. To isolate brain regions activated during each semantic task, we created statistical activation maps in which brain activation was greater for word stimuli than for non-word stimuli (corrected p < 0.05). During the encoding phase, ecstasy polydrug users had greater activation during semantic encoding bilaterally in language processing regions, including Brodmann areas 7, 39, and 40. Of this bilateral activation, signal intensity with a peak T in the right superior parietal lobe was correlated with lifetime ecstasy use (r s = 0.43, p = 0.042). Behavioral performance did not differ between groups. These findings demonstrate that ecstasy polydrug users have increased brain activation during semantic processing. This increase in brain activation in the absence of behavioral deficits suggests that ecstasy polydrug users have reduced cortical efficiency during semantic encoding, possibly secondary to MDMA-induced 5-HT neurotoxicity. Although pre-existing differences cannot be ruled out, this suggests the possibility of a compensatory mechanism allowing ecstasy polydrug users to perform equivalently to controls, providing additional support for an association of altered cerebral neurophysiology with MDMA exposure.

Human ecstasy (MDMA) polydrug users have altered brain activation during semantic processing

PubMed Central

Watkins, Tristan J.; Raj, Vidya; Lee, Junghee; Dietrich, Mary S.; Cao, Aize; Blackford, Jennifer U.; Salomon, Ronald M.; Park, Sohee; Benningfield, Margaret M.; Di Iorio, Christina R.; Cowan, Ronald L.

2012-01-01

Rationale Ecstasy (MDMA) polydrug users have verbal memory performance that is statistically significantly lower than comparison control subjects. Studies have correlated long-term MDMA use with altered brain activation in regions that play a role in verbal memory. Objectives The aim of our study was to examine the association of lifetime ecstasy use with semantic memory performance and brain activation in ecstasy polydrug users. Methods 23 abstinent ecstasy polydrug users (age=24.57) and 11 controls (age=22.36) performed a two-part fMRI semantic encoding and recognition task. To isolate brain regions activated during each semantic task, we created statistical activation maps in which brain activation was greater for word stimuli than for non-word stimuli (corrected p<0.05). Results During the encoding phase, ecstasy polydrug users had greater activation during semantic encoding bilaterally in language processing regions, including Brodmann Areas 7, 39, and 40. Of this bilateral activation, signal intensity with a peak T in the right superior parietal lobe was correlated with lifetime ecstasy use (rs=0.43, p=0.042). Behavioral performance did not differ between groups. Conclusions These findings demonstrate that ecstasy polydrug users have increased brain activation during semantic processing. This increase in brain activation in the absence of behavioral deficits suggests that ecstasy polydrug users have reduced cortical efficiency during semantic encoding, possibly secondary to MDMA-induced 5-HT neurotoxicity. Although pre-existing differences cannot be ruled out, this suggests the possibility of a compensatory mechanism allowing ecstasy polydrug users to perform equivalently to controls, providing additional support for an association of altered cerebral neurophysiology with MDMA exposure. PMID:23241648

User experience of lower-limb orthosis.

PubMed

Yang, Bing-Shiang; Chen, Yen-Wan; Tong, Ji-Rou

2017-06-09

If an assistive device is not acceptable to the user, it will not achieve efficacy and would be resource-wasting. This study employed in-depth interviews to understand what users' individual activities of daily living, problems of using orthoses, and considerations for selecting orthoses are. We conducted qualitative interviews with 35 lower-limb orthosis users, and semi-structured interviews were applied in this study. We analyzed the interview data from transcripts, through coding and concepts, to theories based on grounded theory. The results showed that problems of using orthoses are mostly related to activities of daily living of the user and user's expectation. Therefore, in order to enhance its efficacy and use intention, the design and prescribing process of orthoses need to address the problems in the light of activities of daily living and user education.

Estimating endogenous changes in task performance from EEG

PubMed Central

Touryan, Jon; Apker, Gregory; Lance, Brent J.; Kerick, Scott E.; Ries, Anthony J.; McDowell, Kaleb

2014-01-01

Brain wave activity is known to correlate with decrements in behavioral performance as individuals enter states of fatigue, boredom, or low alertness.Many BCI technologies are adversely affected by these changes in user state, limiting their application and constraining their use to relatively short temporal epochs where behavioral performance is likely to be stable. Incorporating a passive BCI that detects when the user is performing poorly at a primary task, and adapts accordingly may prove to increase overall user performance. Here, we explore the potential for extending an established method to generate continuous estimates of behavioral performance from ongoing neural activity; evaluating the extended method by applying it to the original task domain, simulated driving; and generalizing the method by applying it to a BCI-relevant perceptual discrimination task. Specifically, we used EEG log power spectra and sequential forward floating selection (SFFS) to estimate endogenous changes in behavior in both a simulated driving task and a perceptual discrimination task. For the driving task the average correlation coefficient between the actual and estimated lane deviation was 0.37 ± 0.22 (μ ± σ). For the perceptual discrimination task we generated estimates of accuracy, reaction time, and button press duration for each participant. The correlation coefficients between the actual and estimated behavior were similar for these three metrics (accuracy = 0.25 ± 0.37, reaction time = 0.33 ± 0.23, button press duration = 0.36 ± 0.30). These findings illustrate the potential for modeling time-on-task decrements in performance from concurrent measures of neural activity. PMID:24994968

Abnormal medial prefrontal cortex activity in heavy cannabis users during conscious emotional evaluation.

PubMed

Wesley, Michael J; Lile, Joshua A; Hanlon, Colleen A; Porrino, Linda J

2016-03-01

Long-term heavy cannabis users (cannabis users) who are not acutely intoxicated have diminished subconscious neural responsiveness to affective stimuli. This study sought to determine if abnormal processing extends to the conscious evaluation of emotional stimuli. Functional magnetic resonance imaging (fMRI) was used to examine brain activity as cannabis users (N = 16) and non-cannabis-using controls (N = 17) evaluated and categorized standardized International Affective Picture System (IAPS) stimuli. Individual judgments were used to isolate activity during the evaluation of emotional (i.e., emotional evaluation) or neutral (i.e., neutral evaluation) stimuli. Within- and between-group analyses were performed. Both groups judged the same stimuli as emotional and had activations in visual, midbrain, and middle cingulate cortices during emotional evaluation, relative to neutral. Within-group analyses also revealed amygdalar and inferior frontal gyrus activations in controls, but not cannabis users, and medial prefrontal cortex (mPFC) deactivations in cannabis users, but not controls, during emotional evaluation, relative to neutral. Between-group comparisons found that mPFC activity during positive and negative evaluation was significantly hypoactive in cannabis users, relative to controls. Abnormal neural processing of affective content extends to the level of consciousness in cannabis users. The hypoactive mPFC responses observed resembles the attenuated mPFC responses found during increased non-affective cognitive load in prior research. These findings suggest that abnormal mPFC singling in cannabis users during emotional evaluation might be associated with increased non-affective cognitive load.

Abnormal Medial Prefrontal Cortex Activity in Heavy Cannabis Users During Conscious Emotional Evaluation

PubMed Central

Lile, Joshua A.; Hanlon, Colleen A.; Porrino, Linda J.

2015-01-01

Rationale Long-term heavy cannabis users (cannabis users) who are not acutely intoxicated have diminished subconscious neural responsiveness to affective stimuli. Objective This study sought to determine if abnormal processing extends to the conscious evaluation of emotional stimuli. Methods Functional Magnetic Resonance Imaging (fMRI) was used to examine brain activity as cannabis users (N=16) and non-cannabis using controls (N=17) evaluated and categorized standardized International Affective Picture System (IAPS) stimuli. Individual judgments were used to isolate activity during the evaluation of emotional (i.e., emotional evaluation) or neutral (i.e., neutral evaluation) stimuli. Within- and between-group analyses were performed. Results Both groups judged the same stimuli as emotional and had activations in visual, midbrain, and middle cingulate cortices during emotional evaluation, relative to neutral. Within-group analyses also revealed amygdalar and inferior frontal gyrus activations in controls, but not cannabis users, and medial prefrontal cortex (mPFC) deactivations in cannabis users, but not controls, during emotional evaluation, relative to neutral. Between-group comparisons found that mPFC activity during positive and negative evaluation was significantly hypoactive in cannabis users, relative to controls. Conclusions Abnormal neural processing of affective content extends to the level of consciousness in cannabis users. The hypoactive mPFC responses observed resembles the attenuated mPFC responses found during increased non-affective cognitive load in prior research. These findings suggest that abnormal mPFC singling in cannabis users during emotional evaluation might be associated with increased non-affective cognitive load. PMID:26690589

Activities on Facebook Reveal the Depressive State of Users

PubMed Central

Kwak, Jinah

2013-01-01

Background As online social media have become prominent, much effort has been spent on identifying users with depressive symptoms in order to aim at early diagnosis, treatment, and even prevention by using various online social media. In this paper, we focused on Facebook to discern any correlations between the platform’s features and users’ depressive symptoms. This work may be helpful in trying to reach and detect large numbers of depressed individuals more easily. Objective Our goal was to develop a Web application and identify depressive symptom–related features from users of Facebook, a popular social networking platform. Methods 55 Facebook users (male=40, female=15, mean age 24.43, SD 3.90) were recruited through advertisement fliers distributed to students in a large university in Korea. Using EmotionDiary, the Facebook application we developed, we evaluated depressive symptoms using the Center for Epidemiological Studies-Depression (CES-D) scale. We also provided tips and facts about depression to participants and measured their responses using EmotionDiary. To identify the Facebook features related to depression, correlation analyses were performed between CES-D and participants’ responses to tips and facts or Facebook social features. Last, we interviewed depressed participants (CES-D≥25) to assess their depressive symptoms by a psychiatrist. Results Facebook activities had predictive power in distinguishing depressed and nondepressed individuals. Participants’ response to tips and facts, which can be explained by the number of app tips viewed and app points, had a positive correlation (P=.04 for both cases), whereas the number of friends and location tags had a negative correlation with the CES-D scale (P=.08 and P=.045 respectively). Furthermore, in finding group differences in Facebook social activities, app tips viewed and app points resulted in significant differences (P=.01 and P=.03 respectively) between probably depressed and nondepressed individuals. Conclusions Our results using EmotionDiary demonstrated that the more depressed one is, the more one will read tips and facts about depression. We also confirmed depressed individuals had significantly fewer interactions with others (eg, decreased number of friends and location tagging). Our app, EmotionDiary, can successfully evaluate depressive symptoms as well as provide useful tips and facts to users. These results open the door for examining Facebook activities to identify depressed individuals. We aim to conduct the experiment in multiple cultures as well. PMID:24084314

77 FR 51818 - Agency Information Collection Activities; User Fees

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-27

... Activities; User Fees AGENCY: U.S. Customs and Border Protection, Department of Homeland Security. ACTION: 30... review and approval in accordance with the Paperwork Reduction Act: User Fees. This is a proposed...: User Fees. OMB Number: 1651-0052. Form Number: CBP Forms 339A, 339C and 339V. Abstract: The...

Heavy-tailed distribution of the SSH Brute-force attack duration in a multi-user environment

NASA Astrophysics Data System (ADS)

Lee, Jae-Kook; Kim, Sung-Jun; Park, Chan Yeol; Hong, Taeyoung; Chae, Huiseung

2016-07-01

Quite a number of cyber-attacks to be place against supercomputers that provide highperformance computing (HPC) services to public researcher. Particularly, although the secure shell protocol (SSH) brute-force attack is one of the traditional attack methods, it is still being used. Because stealth attacks that feign regular access may occur, they are even harder to detect. In this paper, we introduce methods to detect SSH brute-force attacks by analyzing the server's unsuccessful access logs and the firewall's drop events in a multi-user environment. Then, we analyze the durations of the SSH brute-force attacks that are detected by applying these methods. The results of an analysis of about 10 thousands attack source IP addresses show that the behaviors of abnormal users using SSH brute-force attacks are based on human dynamic characteristics of a typical heavy-tailed distribution.

User acceptance of intelligent avionics: A study of automatic-aided target recognition

NASA Technical Reports Server (NTRS)

Becker, Curtis A.; Hayes, Brian C.; Gorman, Patrick C.

1991-01-01

User acceptance of new support systems typically was evaluated after the systems were specified, designed, and built. The current study attempts to assess user acceptance of an Automatic-Aided Target Recognition (ATR) system using an emulation of such a proposed system. The detection accuracy and false alarm level of the ATR system were varied systematically, and subjects rated the tactical value of systems exhibiting different performance levels. Both detection accuracy and false alarm level affected the subjects' ratings. The data from two experiments suggest a cut-off point in ATR performance below which the subjects saw little tactical value in the system. An ATR system seems to have obvious tactical value only if it functions at a correct detection rate of 0.7 or better with a false alarm level of 0.167 false alarms per square degree or fewer.

An evaluation of an employment pilot to support forensic mental health service users into work and vocational activities.

PubMed

Samele, Chiara; Forrester, Andrew; Bertram, Mark

2018-02-01

Few employment programmes exist to support forensic service users with severe mental health problems and a criminal history. Little is known about how best to achieve this. The Employment and Social Inclusion Project (ESIP) was developed and piloted to support forensic service users into employment and vocational activities. This pilot service evaluation aimed to assess the number of service users who secured employment/vocational activities and explored services users' and staff experiences. Quantitative data were collected to record the characteristics of participating service users and how many secured employment and engaged in vocational activities. Eighteen qualitative interviews were conducted with service users and staff. Fifty-seven service users engaged with the project, most were men (93.0%) and previously employed (82.5%). Four service users (7.0%) secured paid competitive employment. Eight (14.0%) gained other paid employment. Tailored one-to-one support to increase skills and build confidence was an important feature of the project. Creation of a painting and decorating programme offered training and paid/flexible work. This exploratory project achieved some success in assisting forensic service users into paid employment. Further research to identify what works well for this important group will be of great value.

ActivityAware: An App for Real-Time Daily Activity Level Monitoring on the Amulet Wrist-Worn Device.

PubMed

Boateng, George; Batsis, John A; Halter, Ryan; Kotz, David

2017-03-01

Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person's daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present ActivityAware , an application on the Amulet wrist-worn device that measures daily activity levels (sedentary, moderate and vigorous) of individuals, continuously and in real-time. The app implements an activity-level detection model, continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day's accumulated time spent at that activity level, logs the data for later analysis, and displays the results on the screen. We developed an activity-level detection model using a Support Vector Machine (SVM). We trained our classifiers using data from a user study, where subjects performed the following physical activities: sit, stand, lay down, walk and run. With 10-fold cross validation and leave-one-subject-out (LOSO) cross validation, we obtained preliminary results that suggest accuracies up to 98%, for n=14 subjects. Testing the ActivityAware app revealed a projected battery life of up to 4 weeks before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring, and eventually for the development of interventions that could improve the health of individuals.

A two-stage flow-based intrusion detection model for next-generation networks.

PubMed

Umer, Muhammad Fahad; Sher, Muhammad; Bi, Yaxin

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.

A two-stage flow-based intrusion detection model for next-generation networks

PubMed Central

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results. PMID:29329294

Ultrasonic sensor system to detect solids in a milk pasteurization process

NASA Astrophysics Data System (ADS)

Barroeta Z., Carlos; Sanchez M., Fernando L.; Fernando R., G. Moreno; Montes P., Laura

2002-11-01

In the food industry, many products require a specific process. In the milk industry, the raw milk passes through several process stages before reaching the end user in a very qualitative and healthy way. One of the problems of the milk is that it can contain solids in suspension, result of contamination of the milk, or inherent to the pasteurization process itself. In order to control these solids, a solid detection system is being developed, which will detect the solids by the reflection and refraction of ultrasonic waves. The sensor must be set in the upper part of the milk containers, and with a grid array to allow the control system to prevent these solids from entering into the pipes of the processing plant. The sensing system may activate an acoustic alarm to indicate that a solid has been detected, and a visual one to indicate the affected part of the process. (To be presented in Spanish.)

Cryptosporidium (Crypto) Disease: Diagnosis & Detection

MedlinePlus

... Camps Boil Water Advisories Public Users of Public Water Supplies Commercial Establishments Commercial Ice Maker Users Childcare Facilities Dental Offices Hospitals, Healthcare Facilities, & Nursing Homes Dialysis A Guide to Water Filters A Guide to Commercially-Bottled Water and ...

Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior.

PubMed

Althoff, Tim; Jindal, Pranav; Leskovec, Jure

2017-02-01

Many of today's most widely used computing applications utilize social networking features and allow users to connect, follow each other, share content, and comment on others' posts. However, despite the widespread adoption of these features, there is little understanding of the consequences that social networking has on user retention, engagement, and online as well as offline behavior. Here, we study how social networks influence user behavior in a physical activity tracking application. We analyze 791 million online and offline actions of 6 million users over the course of 5 years, and show that social networking leads to a significant increase in users' online as well as offline activities. Specifically, we establish a causal effect of how social networks influence user behavior. We show that the creation of new social connections increases user online in-application activity by 30%, user retention by 17%, and user offline real-world physical activity by 7% (about 400 steps per day). By exploiting a natural experiment we distinguish the effect of social influence of new social connections from the simultaneous increase in user's motivation to use the app and take more steps. We show that social influence accounts for 55% of the observed changes in user behavior, while the remaining 45% can be explained by the user's increased motivation to use the app. Further, we show that subsequent, individual edge formations in the social network lead to significant increases in daily steps. These effects diminish with each additional edge and vary based on edge attributes and user demographics. Finally, we utilize these insights to develop a model that accurately predicts which users will be most influenced by the creation of new social network connections.

Technologies for physical activity self-monitoring: a study of differences between users and non-users

PubMed Central

Åkerberg, Anna; Söderlund, Anne; Lindén, Maria

2017-01-01

Background Different kinds of physical activity (PA) self-monitoring technologies are used today to monitor and motivate PA behavior change. The user focus is essential in the development process of this technology, including potential future users such as representatives from the group of non-users. There is also a need to study whether there are differences between the groups of users and non-users. The aims of this study were to investigate possible differences between users and non-users regarding their opinions about PA self-monitoring technologies and to investigate differences in demographic variables between the groups. Materials and methods Participants were randomly selected from seven municipalities in central Sweden. In total, 107 adults responded to the Physical Activity Products Questionnaire, which consisted of 22 questions. Results Significant differences between the users and non-users were shown for six of the 20 measurement-related items: measures accurately (p=0.007), measures with high precision (p=0.024), measures distance (p=0.020), measures speed (p=0.003), shows minutes of activity (p=0.004), and shows geographical position (p=0.000). Significant differences between the users and non-users were also found for two of the 29 encouragement items: measures accurately (p=0.001) and has long-term memory (p=0.019). Significant differences between the groups were also shown for level of education (p=0.030) and level of physical exercise (p=0.037). Conclusion With a few exceptions, the users and the non-users in this study had similar opinions about PA self-monitoring technologies. Because this study showed significant differences regarding level of education and level of physical exercise, these demographic variables seemed more relevant to investigate than differences in opinions about the PA self-monitoring technologies. PMID:28280399

Portable nuclear material detector and process

DOEpatents

Hofstetter, Kenneth J [Aiken, SC; Fulghum, Charles K [Aiken, SC; Harpring, Lawrence J [North Augusta, SC; Huffman, Russell K [Augusta, GA; Varble, Donald L [Evans, GA

2008-04-01

A portable, hand held, multi-sensor radiation detector is disclosed. The detection apparatus has a plurality of spaced sensor locations which are contained within a flexible housing. The detection apparatus, when suspended from an elevation, will readily assume a substantially straight, vertical orientation and may be used to monitor radiation levels from shipping containers. The flexible detection array can also assume a variety of other orientations to facilitate any unique container shapes or to conform to various physical requirements with respect to deployment of the detection array. The output of each sensor within the array is processed by at least one CPU which provides information in a usable form to a user interface. The user interface is used to provide the power requirements and operating instructions to the operational components within the detection array.

Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data

PubMed Central

Sloan, Luke; Morgan, Jeffrey; Burnap, Pete; Williams, Matthew

2015-01-01

This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: http://www.cosmosproject.net/) relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect “signatures” of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed. PMID:25729900

Spatial working memory in heavy cannabis users: a functional magnetic resonance imaging study.

PubMed

Kanayama, Gen; Rogowska, Jadwiga; Pope, Harrison G; Gruber, Staci A; Yurgelun-Todd, Deborah A

2004-11-01

Many neuropsychological studies have documented deficits in working memory among recent heavy cannabis users. However, little is known about the effects of cannabis on brain activity. We assessed brain function among recent heavy cannabis users while they performed a working memory task. Functional magnetic resonance imaging was used to examine brain activity in 12 long-term heavy cannabis users, 6-36 h after last use, and in 10 control subjects while they performed a spatial working memory task. Regional brain activation was analyzed and compared using statistical parametric mapping techniques. Compared with controls, cannabis users exhibited increased activation of brain regions typically used for spatial working memory tasks (such as prefrontal cortex and anterior cingulate). Users also recruited additional regions not typically used for spatial working memory (such as regions in the basal ganglia). These findings remained essentially unchanged when re-analyzed using subjects' ages as a covariate. Brain activation showed little or no significant correlation with subjects' years of education, verbal IQ, lifetime episodes of cannabis use, or urinary cannabinoid levels at the time of scanning. Recent cannabis users displayed greater and more widespread brain activation than normal subjects when attempting to perform a spatial working memory task. This observation suggests that recent cannabis users may experience subtle neurophysiological deficits, and that they compensate for these deficits by "working harder"-calling upon additional brain regions to meet the demands of the task.

Can we detect, monitor, and characterize volcanic activity using 'off the shelf' webcams and low-light cameras?

NASA Astrophysics Data System (ADS)

Harrild, M.; Webley, P. W.; Dehn, J.

2015-12-01

The ability to detect and monitor precursory events, thermal signatures, and ongoing volcanic activity in near-realtime is an invaluable tool. Volcanic hazards often range from low level lava effusion to large explosive eruptions, easily capable of ejecting ash to aircraft cruise altitudes. Using ground based remote sensing to detect and monitor this activity is essential, but the required equipment is often expensive and difficult to maintain, which increases the risk to public safety and the likelihood of financial impact. Our investigation explores the use of 'off the shelf' cameras, ranging from computer webcams to low-light security cameras, to monitor volcanic incandescent activity in near-realtime. These cameras are ideal as they operate in the visible and near-infrared (NIR) portions of the electromagnetic spectrum, are relatively cheap to purchase, consume little power, are easily replaced, and can provide telemetered, near-realtime data. We focus on the early detection of volcanic activity, using automated scripts that capture streaming online webcam imagery and evaluate each image according to pixel brightness, in order to automatically detect and identify increases in potentially hazardous activity. The cameras used here range in price from 0 to 1,000 and the script is written in Python, an open source programming language, to reduce the overall cost to potential users and increase the accessibility of these tools, particularly in developing nations. In addition, by performing laboratory tests to determine the spectral response of these cameras, a direct comparison of collocated low-light and thermal infrared cameras has allowed approximate eruption temperatures to be correlated to pixel brightness. Data collected from several volcanoes; (1) Stromboli, Italy (2) Shiveluch, Russia (3) Fuego, Guatemala (4) Popcatépetl, México, along with campaign data from Stromboli (June, 2013), and laboratory tests are presented here.

Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media.

PubMed

Hswen, Yulin; Naslund, John A; Brownstein, John S; Hawkins, Jared B

2018-01-12

Digital technologies hold promise for supporting the detection and management of schizophrenia. This exploratory study aimed to generate an initial understanding of whether patterns of communication about depression and anxiety on popular social media among individuals with schizophrenia are consistent with offline representations of the illness. From January to July 2016, posts on Twitter were collected from a sample of Twitter users who self-identify as having a schizophrenia spectrum disorder (n = 203) and a randomly selected sample of control users (n = 173). Frequency and timing of communication about depression and anxiety were compared between groups. In total, the groups posted n = 1,544,122 tweets and users had similar characteristics. Twitter users with schizophrenia showed significantly greater odds of tweeting about depression compared with control users (OR = 2.69; 95% CI 1.76-4.10), and significantly greater odds of tweeting about anxiety compared with control users (OR = 1.81; 95% CI 1.20-2.73). This study offers preliminary insights that Twitter users with schizophrenia may express elevated symptoms of depression and anxiety in their online posts, which is consistent with clinical characteristics of schizophrenia observed in offline settings. Social media platforms could further our understanding of schizophrenia by informing a digital phenotype and may afford new opportunities to support early illness detection.

Solar active region display system

NASA Astrophysics Data System (ADS)

Golightly, M.; Raben, V.; Weyland, M.

2003-04-01

The Solar Active Region Display System (SARDS) is a client-server application that automatically collects a wide range of solar data and displays it in a format easy for users to assimilate and interpret. Users can rapidly identify active regions of interest or concern from color-coded indicators that visually summarize each region's size, magnetic configuration, recent growth history, and recent flare and CME production. The active region information can be overlaid onto solar maps, multiple solar images, and solar difference images in orthographic, Mercator or cylindrical equidistant projections. Near real-time graphs display the GOES soft and hard x-ray flux, flare events, and daily F10.7 value as a function of time; color-coded indicators show current trends in soft x-ray flux, flare temperature, daily F10.7 flux, and x-ray flare occurrence. Through a separate window up to 4 real-time or static graphs can simultaneously display values of KP, AP, daily F10.7 flux, GOES soft and hard x-ray flux, GOES >10 and >100 MeV proton flux, and Thule neutron monitor count rate. Climatologic displays use color-valued cells to show F10.7 and AP values as a function of Carrington/Bartel's rotation sequences - this format allows users to detect recurrent patterns in solar and geomagnetic activity as well as variations in activity levels over multiple solar cycles. Users can customize many of the display and graph features; all displays can be printed or copied to the system's clipboard for "pasting" into other applications. The system obtains and stores space weather data and images from sources such as the NOAA Space Environment Center, NOAA National Geophysical Data Center, the joint ESA/NASA SOHO spacecraft, and the Kitt Peak National Solar Observatory, and can be extended to include other data series and image sources. Data and images retrieved from the system's database are converted to XML and transported from a central server using HTTP and SOAP protocols, allowing operation through network firewalls; data is compressed to enhance performance over limited bandwidth connections. All applications and services are written in the JAVA program language for platform independence. Several versions of SARDS have been in operational use by the NASA Space Radiation Analysis Group, NOAA Space Weather Operations, and U.S. Air Force Weather Agency since 1999.

GlobVolcano pre-operational services for global monitoring active volcanoes

NASA Astrophysics Data System (ADS)

Tampellini, Lucia; Ratti, Raffaella; Borgström, Sven; Seifert, Frank Martin; Peltier, Aline; Kaminski, Edouard; Bianchi, Marco; Branson, Wendy; Ferrucci, Fabrizio; Hirn, Barbara; van der Voet, Paul; van Geffen, J.

2010-05-01

The GlobVolcano project (2007-2010) is part of the Data User Element programme of the European Space Agency (ESA). The project aims at demonstrating Earth Observation (EO) based integrated services to support the Volcano Observatories and other mandate users (e.g. Civil Protection) in their monitoring activities. The information services are assessed in close cooperation with the user organizations for different types of volcano, from various geographical areas in various climatic zones. In a first phase, a complete information system has been designed, implemented and validated, involving a limited number of test areas and respective user organizations. In the currently on-going second phase, GlobVolcano is delivering pre-operational services over 15 volcanic sites located in three continents and as many user organizations are involved and cooperating with the project team. The set of GlobVolcano offered EO based information products is composed as follows: Deformation Mapping DInSAR (Differential Synthetic Aperture Radar Interferometry) has been used to study a wide range of surface displacements related to different phenomena (e.g. seismic faults, volcanoes, landslides) at a spatial resolution of less than 100 m and cm-level precision. Permanent Scatterers SAR Interferometry method (PSInSARTM) has been introduced by Politecnico of Milano as an advanced InSAR technique capable of measuring millimetre scale displacements of individual radar targets on the ground by using multi-temporal data-sets, estimating and removing the atmospheric components. Other techniques (e.g. CTM) have followed similar strategies and have shown promising results in different scenarios. Different processing approaches have been adopted, according to data availability, characteristic of the area and dynamic characteristics of the volcano. Conventional DInSAR: Colima (Mexico), Nyiragongo (Congo), Pico (Azores), Areanal (Costa Rica) PSInSARTM: Piton de la Fournaise (La Reunion Island), Stromboli and Volcano (Italy), Hilo (Hawai), Mt. St. Helens (United States), CTM (Coherent Target Monitoring): Cumbre Vieja (La Palma) To generate products either Envisat ASAR, Radarsat 1or ALOS PALSAR data have been used. Surface Thermal Anomalies Volcanic hot-spots detection, radiant flux and effusion rate (where applicable) calculation of high temperature surface thermal anomalies such as active lava flow, strombolian activity, lava dome, pyroclastic flow and lava lake can be performed through MODIS (Terra / Aqua) MIR and TIR channels, or ASTER (Terra), HRVIR/HRGT (SPOT4/5) and Landsat family SWIR channels analysis. ASTER and Landsat TIR channels allow relative radiant flux calculation of low temperature anomalies such as lava and pyroclastic flow cooling, crater lake and low temperature fumarolic fields. MODIS, ASTER and SPOT data are processed to detect and measure the following volcanic surface phenomena: Effusive activity Piton de la Fournaise (Reunion Island); Mt Etna (Italy). Lava dome growths, collapses and related pyroclastic flows Soufrière Hills (Montserrat); Arenal - (Costa Rica). Permanent crater lake and ephemeral lava lake Karthala (Comores Islands). Strombolian activity Stromboli (Italy). Low temperature fumarolic fields Nisyros (Greece), Vulcano (Italy), Mauna Loa (Hawaii). Volcanic Emission The Volcanic Emission Service is provided to the users by a link to GSE-PROMOTE - Support to Aviation Control Service (SACS). The aim of the service is to deliver in near-real-time data derived from satellite measurements regarding SO2 emissions (SO2 vertical column density - Dobson Unit [DU]) possibly related to volcanic eruptions and to track the ash injected into the atmosphere during a volcanic eruption. SO2 measurements are derived from different satellite instruments, such as SCIAMACHY, OMI and GOME-2. The tracking of volcanic ash is accomplished by using SEVIRI-MSG data and, in particular, the following channels VIS 0.6 and IR 3.9, and along with IR8.7, IR 10.8 and IR 12.0. The GlobVolcano information system and its current experimentation represent a significant step ahead towards the implementation of an operational, global observatory of volcanoes by the synergetic use of data from available Earth Observation satellites.

AFRL Commander's Challenge 2015: stopping the active shooter

NASA Astrophysics Data System (ADS)

McIntire, John P.; Boston, Jonathan; Smith, Brandon; Swartz, Pete; Whitney-Rawls, Amy; Martinez Calderon, Julian; Magin, Jonathan

2017-05-01

In this work, we describe a rapid-innovation challenge to combat and deal with the problem of internal, insider physical threats (e.g., active shooters) and associated first-responder situation awareness on military installations. Our team's research and development effort described within focused on several key tech development areas: (1) indoor acoustical gunshot detection, (2) indoor spatial tracking of first responders, (3) bystander safety and protection, (4) two-way mass alerting capability, and (5) spatial information displays for command and control. The technological solutions were specifically designed to be innovative, low-cost, and (relatively) easy-to-implement, and to provide support across the spectrum of possible users including potential victims/bystanders, first responders, dispatch, and incident command.

An Algorithm Enabling Blind Users to Find and Read Barcodes

PubMed Central

Tekin, Ender; Coughlan, James M.

2010-01-01

Most camera-based systems for finding and reading barcodes are designed to be used by sighted users (e.g. the Red Laser iPhone app), and assume the user carefully centers the barcode in the image before the barcode is read. Blind individuals could benefit greatly from such systems to identify packaged goods (such as canned goods in a supermarket), but unfortunately in their current form these systems are completely inaccessible because of their reliance on visual feedback from the user. To remedy this problem, we propose a computer vision algorithm that processes several frames of video per second to detect barcodes from a distance of several inches; the algorithm issues directional information with audio feedback (e.g. “left,” “right”) and thereby guides a blind user holding a webcam or other portable camera to locate and home in on a barcode. Once the barcode is detected at sufficiently close range, a barcode reading algorithm previously developed by the authors scans and reads aloud the barcode and the corresponding product information. We demonstrate encouraging experimental results of our proposed system implemented on a desktop computer with a webcam held by a blindfolded user; ultimately the system will be ported to a camera phone for use by visually impaired users. PMID:20617114

Robust changes in reward circuitry during reward loss in current and former cocaine users during performance of a monetary incentive delay task.

PubMed

Patel, Krishna T; Stevens, Michael C; Meda, Shashwath A; Muska, Christine; Thomas, Andre D; Potenza, Marc N; Pearlson, Godfrey D

2013-10-01

Abnormal function in reward circuitry in cocaine addiction could predate drug use as a risk factor, follow drug use as a consequence of substance-induced alterations, or both. We used a functional magnetic resonance imaging monetary incentive delay task (MIDT) to investigate reward-loss neural response differences among 42 current cocaine users, 35 former cocaine users, and 47 healthy subjects who also completed psychological measures and tasks related to impulsivity and reward. We found various reward processing-related group differences in several MIDT phases. Across task phases we found a control > current user > former user activation pattern, except for loss outcome, where former compared with current cocaine users activated ventral tegmental area more robustly. We also found regional prefrontal activation differences during loss anticipation between cocaine-using groups. Both groups of cocaine users scored higher than control subjects on impulsivity, compulsivity and reward-punishment sensitivity factors. In addition, impulsivity-related factors correlated positively with activation in amygdala and negatively with anterior cingulate activation during loss anticipation. Compared with healthy subjects, both former and current users displayed abnormal brain activation patterns during MIDT performance. Both cocaine groups differed similarly from healthy subjects, but differences between former and current users were localized to the ventral tegmental area during loss outcome and to prefrontal regions during loss anticipation, suggesting that long-term cocaine abstinence does not normalize most reward circuit abnormalities. Elevated impulsivity-related factors that relate to loss processing in current and former users suggest that these tendencies and relationships may pre-exist cocaine addiction. © 2013 Society of Biological Psychiatry.

A Socio-Technical Approach to Preventing, Mitigating, and Recovering from Ransomware Attacks.

PubMed

Sittig, Dean F; Singh, Hardeep

2016-01-01

Recently there have been several high-profile ransomware attacks involving hospitals around the world. Ransomware is intended to damage or disable a user's computer unless the user makes a payment. Once the attack has been launched, users have three options: 1) try to restore their data from backup; 2) pay the ransom; or 3) lose their data. In this manuscript, we discuss a socio-technical approach to address ransomware and outline four overarching steps that organizations can undertake to secure an electronic health record (EHR) system and the underlying computing infrastructure. First, health IT professionals need to ensure adequate system protection by correctly installing and configuring computers and networks that connect them. Next, the health care organizations need to ensure more reliable system defense by implementing user-focused strategies, including simulation and training on correct and complete use of computers and network applications. Concomitantly, the organization needs to monitor computer and application use continuously in an effort to detect suspicious activities and identify and address security problems before they cause harm. Finally, organizations need to respond adequately to and recover quickly from ransomware attacks and take actions to prevent them in future. We also elaborate on recommendations from other authoritative sources, including the National Institute of Standards and Technology (NIST). Similar to approaches to address other complex socio-technical health IT challenges, the responsibility of preventing, mitigating, and recovering from these attacks is shared between health IT professionals and end-users.

Fusion of radar and ultrasound sensors for concealed weapons detection

NASA Astrophysics Data System (ADS)

Felber, Franklin S.; Davis, Herbert T., III; Mallon, Charles E.; Wild, Norbert C.

1996-06-01

An integrated radar and ultrasound sensor, capable of remotely detecting and imaging concealed weapons, is being developed. A modified frequency-agile, mine-detection radar is intended to specify with high probability of detection at ranges of 1 to 10 m which individuals in a moving crowd may be concealing metallic or nonmetallic weapons. Within about 1 to 5 m, the active ultrasound sensor is intended to enable a user to identify a concealed weapon on a moving person with low false-detection rate, achieved through a real-time centimeter-resolution image of the weapon. The goal for sensor fusion is to have the radar acquire concealed weapons at long ranges and seamlessly hand over tracking data to the ultrasound sensor for high-resolution imaging on a video monitor. We have demonstrated centimeter-resolution ultrasound images of metallic and non-metallic weapons concealed on a human at ranges over 1 m. Processing of the ultrasound images includes filters for noise, frequency, brightness, and contrast. A frequency-agile radar has been developed by JAYCOR under the U.S. Army Advanced Mine Detection Radar Program. The signature of an armed person, detected by this radar, differs appreciably from that of the same person unarmed.

Requirements UML Tool (RUT) Expanded for Extreme Programming (CI02)

NASA Technical Reports Server (NTRS)

McCoy, James R.

2003-01-01

A procedure for capturing and managing system requirements that incorporates XP user stories. Because costs associated with identifying problems in requirements increase dramatically over the lifecycle of a project, a method for identifying sources of software risks in user stories is urgently needed. This initiative aims to determine a set of guide-lines for user stories that will result in high-quality requirement. To further this initiative, a tool is needed to analyze user stories that can assess the quality of individual user stories, detect sources cf software risk's, produce software metrics, and identify areas in user stories that can be improved.

Detecting Disease Outbreaks in Mass Gatherings Using Internet Data

PubMed Central

Yom-Tov, Elad; Cox, Ingemar J; McKendry, Rachel A

2014-01-01

Background Mass gatherings, such as music festivals and religious events, pose a health care challenge because of the risk of transmission of communicable diseases. This is exacerbated by the fact that participants disperse soon after the gathering, potentially spreading disease within their communities. The dispersion of participants also poses a challenge for traditional surveillance methods. The ubiquitous use of the Internet may enable the detection of disease outbreaks through analysis of data generated by users during events and shortly thereafter. Objective The intent of the study was to develop algorithms that can alert to possible outbreaks of communicable diseases from Internet data, specifically Twitter and search engine queries. Methods We extracted all Twitter postings and queries made to the Bing search engine by users who repeatedly mentioned one of nine major music festivals held in the United Kingdom and one religious event (the Hajj in Mecca) during 2012, for a period of 30 days and after each festival. We analyzed these data using three methods, two of which compared words associated with disease symptoms before and after the time of the festival, and one that compared the frequency of these words with those of other users in the United Kingdom in the days following the festivals. Results The data comprised, on average, 7.5 million tweets made by 12,163 users, and 32,143 queries made by 1756 users from each festival. Our methods indicated the statistically significant appearance of a disease symptom in two of the nine festivals. For example, cough was detected at higher than expected levels following the Wakestock festival. Statistically significant agreement (chi-square test, P<.01) between methods and across data sources was found where a statistically significant symptom was detected. Anecdotal evidence suggests that symptoms detected are indeed indicative of a disease that some users attributed to being at the festival. Conclusions Our work shows the feasibility of creating a public health surveillance system for mass gatherings based on Internet data. The use of multiple data sources and analysis methods was found to be advantageous for rejecting false positives. Further studies are required in order to validate our findings with data from public health authorities. PMID:24943128

Detecting disease outbreaks in mass gatherings using Internet data.

PubMed

Yom-Tov, Elad; Borsa, Diana; Cox, Ingemar J; McKendry, Rachel A

2014-06-18

Mass gatherings, such as music festivals and religious events, pose a health care challenge because of the risk of transmission of communicable diseases. This is exacerbated by the fact that participants disperse soon after the gathering, potentially spreading disease within their communities. The dispersion of participants also poses a challenge for traditional surveillance methods. The ubiquitous use of the Internet may enable the detection of disease outbreaks through analysis of data generated by users during events and shortly thereafter. The intent of the study was to develop algorithms that can alert to possible outbreaks of communicable diseases from Internet data, specifically Twitter and search engine queries. We extracted all Twitter postings and queries made to the Bing search engine by users who repeatedly mentioned one of nine major music festivals held in the United Kingdom and one religious event (the Hajj in Mecca) during 2012, for a period of 30 days and after each festival. We analyzed these data using three methods, two of which compared words associated with disease symptoms before and after the time of the festival, and one that compared the frequency of these words with those of other users in the United Kingdom in the days following the festivals. The data comprised, on average, 7.5 million tweets made by 12,163 users, and 32,143 queries made by 1756 users from each festival. Our methods indicated the statistically significant appearance of a disease symptom in two of the nine festivals. For example, cough was detected at higher than expected levels following the Wakestock festival. Statistically significant agreement (chi-square test, P<.01) between methods and across data sources was found where a statistically significant symptom was detected. Anecdotal evidence suggests that symptoms detected are indeed indicative of a disease that some users attributed to being at the festival. Our work shows the feasibility of creating a public health surveillance system for mass gatherings based on Internet data. The use of multiple data sources and analysis methods was found to be advantageous for rejecting false positives. Further studies are required in order to validate our findings with data from public health authorities.

A streamlined Python framework for AT-TPC data analysis

NASA Astrophysics Data System (ADS)

Taylor, J. Z.; Bradt, J.; Bazin, D.; Kuchera, M. P.

2017-09-01

User-friendly data analysis software has been developed for the Active-Target Time Projection Chamber (AT-TPC) experiment at the National Superconducting Cyclotron Laboratory at Michigan State University. The AT-TPC, commissioned in 2014, is a gas-filled detector that acts as both the detector and target for high-efficiency detection of low-intensity, exotic nuclear reactions. The pytpc framework is a Python package for analyzing AT-TPC data. The package was developed for the analysis of 46Ar(p, p) data. The existing software was used to analyze data produced by the 40Ar(p, p) experiment that ran in August, 2015. Usage of the package was documented in an analysis manual both to improve analysis steps and aid in the work of future AT-TPC users. Software features and analysis methods in the pytpc framework will be presented along with the 40Ar results.

DS-CDMA satellite diversity reception for personal satellite communication: Downlink performance analysis

NASA Technical Reports Server (NTRS)

DeGaudenzi, Riccardo; Giannetti, Filippo

1995-01-01

The downlink of a satellite-mobile personal communication system employing power-controlled Direct Sequence Code Division Multiple Access (DS-CDMA) and exploiting satellite-diversity is analyzed and its performance compared with a more traditional communication system utilizing single satellite reception. The analytical model developed has been thoroughly validated by means of extensive Monte Carlo computer simulations. It is shown how the capacity gain provided by diversity reception shrinks considerably in the presence of increasing traffic or in the case of light shadowing conditions. Moreover, the quantitative results tend to indicate that to combat system capacity reduction due to intra-system interference, no more than two satellites shall be active over the same region. To achieve higher system capacity, differently from terrestrial cellular systems, Multi-User Detection (MUD) techniques are likely to be required in the mobile user terminal, thus considerably increasing its complexity.

Enhancing Long-Term Motivation of Cardiac Patients by Applying Exergaming in Rehabilitation Training.

PubMed

Volmer, Joe; Burkert, Malte; Krumm, Heiko; Abodahab, Abdurrahman; Dinklage, Patrick; Feltmann, Marius; Kröger, Chris; Panta, Pernes; Schäfer, Felix; Scheidt, David; Sellung, Marcel; Singerhoff, Hauke; Steingrefer, Christofer; Schmidt, Thomas; Hoffmann, Jan-Dirk; Willemsen, Detlev; Reiss, Nils

2017-01-01

Although regular physical activities reduce mortality and increase quality of life many cardiac patients discontinue training due to lack of motivation, lack of time or having health concerns because of a too high training intensity. Therefore, we developed an exergaming based system to enhance long-term motivation in the context of rehabilitation training. We combined different hardware components such as vital sensors, a virtual reality headset, a motion detecting camera, a bicycle ergometer and a motion platform to create an immersive and fun experience for the training user without having to worry about any negative health impact. Our evaluation shows that the system is well accepted by the users and is capable of tackling the aforementioned reasons for an inactive lifestyle. The system is designed to be easily extensible, safe to use and enables professionals to adjust and to telemonitor the training at any time.

Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

NASA Astrophysics Data System (ADS)

Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

A Real-Time Lift Detection Strategy for a Hip Exoskeleton

PubMed Central

Chen, Baojun; Grazi, Lorenzo; Lanotte, Francesco; Vitiello, Nicola; Crea, Simona

2018-01-01

Repetitive lifting of heavy loads increases the risk of back pain and even lumbar vertebral injuries to workers. Active exoskeletons can help workers lift loads by providing power assistance, and therefore reduce the moment and force applied on L5/S1 joint of human body when performing lifting tasks. However, most existing active exoskeletons for lifting assistance are unable to automatically detect user's lift movement, which limits the wide application of active exoskeletons in factories. In this paper, we propose a simple but effective lift detection strategy for exoskeleton control. This strategy uses only exoskeleton integrated sensors, without any extra sensors to capture human motion intentions. This makes the lift detection system more practical for applications in manufacturing environments. Seven healthy subjects participated in this research. Three different sessions were carried out, two for training and one for testing the algorithm. In the two training sessions, subjects were asked to wear a hip exoskeleton, controlled in transparent mode, and perform repetitive lifting and a locomotion circuit; lifting was executed with different techniques. The collected data were used to train the lift detection model. In the testing session, the exoskeleton was controlled in order to deliver torque to assist the lifting action, based on the lift detection made by the trained algorithm. The across-subject average accuracy of lift detection during online test was 97.97 ± 1.39% with subject-dependent model. Offline, the algorithm was trained with data acquired from all subjects to verify its performance for subject-independent detection, and an accuracy of 97.48 ± 1.53% was achieved. In addition, timeliness of the algorithm was quantitatively evaluated and the time delay was <160 ms across different lifting speeds. Surface electromyography was also measured to assess the efficacy of the exoskeleton in assisting subjects in performing load lifting tasks. These results validate the promise of applying the proposed lift detection strategy for exoskeleton control aiming at lift assistance. PMID:29706881

A Real-Time Lift Detection Strategy for a Hip Exoskeleton.

PubMed

Chen, Baojun; Grazi, Lorenzo; Lanotte, Francesco; Vitiello, Nicola; Crea, Simona

2018-01-01

Repetitive lifting of heavy loads increases the risk of back pain and even lumbar vertebral injuries to workers. Active exoskeletons can help workers lift loads by providing power assistance, and therefore reduce the moment and force applied on L5/S1 joint of human body when performing lifting tasks. However, most existing active exoskeletons for lifting assistance are unable to automatically detect user's lift movement, which limits the wide application of active exoskeletons in factories. In this paper, we propose a simple but effective lift detection strategy for exoskeleton control. This strategy uses only exoskeleton integrated sensors, without any extra sensors to capture human motion intentions. This makes the lift detection system more practical for applications in manufacturing environments. Seven healthy subjects participated in this research. Three different sessions were carried out, two for training and one for testing the algorithm. In the two training sessions, subjects were asked to wear a hip exoskeleton, controlled in transparent mode, and perform repetitive lifting and a locomotion circuit; lifting was executed with different techniques. The collected data were used to train the lift detection model. In the testing session, the exoskeleton was controlled in order to deliver torque to assist the lifting action, based on the lift detection made by the trained algorithm. The across-subject average accuracy of lift detection during online test was 97.97 ± 1.39% with subject-dependent model. Offline, the algorithm was trained with data acquired from all subjects to verify its performance for subject-independent detection, and an accuracy of 97.48 ± 1.53% was achieved. In addition, timeliness of the algorithm was quantitatively evaluated and the time delay was <160 ms across different lifting speeds. Surface electromyography was also measured to assess the efficacy of the exoskeleton in assisting subjects in performing load lifting tasks. These results validate the promise of applying the proposed lift detection strategy for exoskeleton control aiming at lift assistance.

Autonomous power expert system advanced development

NASA Technical Reports Server (NTRS)

Quinn, Todd M.; Walters, Jerry L.

1991-01-01

The autonomous power expert (APEX) system is being developed at Lewis Research Center to function as a fault diagnosis advisor for a space power distribution test bed. APEX is a rule-based system capable of detecting faults and isolating the probable causes. APEX also has a justification facility to provide natural language explanations about conclusions reached during fault isolation. To help maintain the health of the power distribution system, additional capabilities were added to APEX. These capabilities allow detection and isolation of incipient faults and enable the expert system to recommend actions/procedure to correct the suspected fault conditions. New capabilities for incipient fault detection consist of storage and analysis of historical data and new user interface displays. After the cause of a fault is determined, appropriate recommended actions are selected by rule-based inferencing which provides corrective/extended test procedures. Color graphics displays and improved mouse-selectable menus were also added to provide a friendlier user interface. A discussion of APEX in general and a more detailed description of the incipient detection, recommended actions, and user interface developments during the last year are presented.

Design and Evaluation of Virtual Reality-Based Therapy Games with Dual Focus on Therapeutic Relevance and User Experience for Children with Cerebral Palsy.

PubMed

Ni, Lian Ting; Fehlings, Darcy; Biddiss, Elaine

2014-06-01

Virtual reality (VR)-based therapy for motor rehabilitation of children with cerebral palsy (CP) is growing in prevalence. Although mainstream active videogames typically offer children an appealing user experience, they are not designed for therapeutic relevance. Conversely, rehabilitation-specific games often struggle to provide an immersive experience that sustains interest. This study aims to design and evaluate two VR-based therapy games for upper and lower limb rehabilitation and to evaluate their efficacy with dual focus on therapeutic relevance and user experience. Three occupational therapists, three physiotherapists, and eight children (8-12 years old), with CP Level I-III on the Gross Motor Function Classification System, evaluated two games for the Microsoft(®) (Redmond, WA) Kinect™ for Windows and completed the System Usability Scale (SUS), Physical Activity Enjoyment Scale (PACES), and custom feedback questionnaires. Children and therapists unanimously agreed on the enjoyment and therapeutic value of the games. Median scores on the PACES were high (6.24±0.95 on the 7-point scale). Therapists considered the system to be of average usability (50th percentile on the SUS). The most prevalent usability issue was detection errors distinguishing the child's movements from the supporting therapist's. The ability to adjust difficulty settings and to focus on targeted goals (e.g., elbow/shoulder extension, weight shifting) was highly valued by therapists. Engaging both therapists and children in a user-centered design approach enabled the development of two VR-based therapy games for upper and lower limb rehabilitation that are dually (a) engaging to the child and (b) therapeutically relevant.

Programmable and highly resolved in vitro detection of 5-methylcytosine by TALEs.

PubMed

Kubik, Grzegorz; Schmidt, Moritz J; Penner, Johanna E; Summerer, Daniel

2014-06-02

Gene expression is extensively regulated by specific patterns of genomic 5-methylcytosine (mC), but the ability to directly detect this modification at user-defined genomic loci is limited. One reason is the lack of molecules that discriminate between mC and cytosine (C) and at the same time provide inherent, programmable sequence-selectivity. Programmable transcription-activator-like effectors (TALEs) have been observed to exhibit mC-sensitivity in vivo, but to only a limited extent in vitro. We report an mC-detection assay based on TALE control of DNA replication that displays unexpectedly strong mC-discrimination ability in vitro. The status and level of mC modification at single positions in oligonucleotides can be determined unambiguously by this assay, independently of the overall target sequence. Moreover, discrimination is reliably observed for positions bound by N-terminal and central regions of TALEs. This indicates the wide scope and robustness of the approach for highly resolved mC detection and enabled the detection of a single mC in a large, eukaryotic genome. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Activity Monitors as Support for Older Persons' Physical Activity in Daily Life: Qualitative Study of the Users' Experiences.

PubMed

Ehn, Maria; Eriksson, Lennie Carlén; Åkerberg, Nina; Johansson, Ann-Christin

2018-02-01

Falls are a major threat to the health and independence of seniors. Regular physical activity (PA) can prevent 40% of all fall injuries. The challenge is to motivate and support seniors to be physically active. Persuasive systems can constitute valuable support for persons aiming at establishing and maintaining healthy habits. However, these systems need to support effective behavior change techniques (BCTs) for increasing older adults' PA and meet the senior users' requirements and preferences. Therefore, involving users as codesigners of new systems can be fruitful. Prestudies of the user's experience with similar solutions can facilitate future user-centered design of novel persuasive systems. The aim of this study was to investigate how seniors experience using activity monitors (AMs) as support for PA in daily life. The addressed research questions are as follows: (1) What are the overall experiences of senior persons, of different age and balance function, in using wearable AMs in daily life?; (2) Which aspects did the users perceive relevant to make the measurements as meaningful and useful in the long-term perspective?; and (3) What needs and requirements did the users perceive as more relevant for the activity monitors to be useful in a long-term perspective? This qualitative interview study included 8 community-dwelling older adults (median age: 83 years). The participants' experiences in using two commercial AMs together with tablet-based apps for 9 days were investigated. Activity diaries during the usage and interviews after the usage were exploited to gather user experience. Comments in diaries were summarized, and interviews were analyzed by inductive content analysis. The users (n=8) perceived that, by using the AMs, their awareness of own PA had increased. However, the AMs' impact on the users' motivation for PA and activity behavior varied between participants. The diaries showed that self-estimated physical effort varied between participants and varied for each individual over time. Additionally, participants reported different types of accomplished activities; talking walks was most frequently reported. To be meaningful, measurements need to provide the user with a reliable receipt of whether his or her current activity behavior is sufficient for reaching an activity goal. Moreover, praise when reaching a goal was described as motivating feedback. To be useful, the devices must be easy to handle. In this study, the users perceived wearables as easy to handle, whereas tablets were perceived difficult to maneuver. Users reported in the diaries that the devices had been functional 78% (58/74) of the total test days. Activity monitors can be valuable for supporting seniors' PA. However, the potential of the solutions for a broader group of seniors can significantly be increased. Areas of improvement include reliability, usability, and content supporting effective BCTs with respect to increasing older adults' PA. ©Maria Ehn, Lennie Carlén Eriksson, Nina Åkerberg, Ann-Christin Johansson. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 01.02.2018.

Image based book cover recognition and retrieval

NASA Astrophysics Data System (ADS)

Sukhadan, Kalyani; Vijayarajan, V.; Krishnamoorthi, A.; Bessie Amali, D. Geraldine

2017-11-01

In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.

Presence of human papilloma virus, herpes simplex virus and Epstein-Barr virus DNA in oral biopsies from Sudanese patients with regard to toombak use.

PubMed

Jalouli, Jamshid; Ibrahim, Salah O; Sapkota, Dipak; Jalouli, Miranda M; Vasstrand, Endre N; Hirsch, Jan M; Larsson, Per-Anders

2010-09-01

Using PCR/DNA sequencing, we investigated the prevalence of human papillomavirus (HPV), herpes simplex virus (HSV) and Epstein-Barr virus (EBV) DNA in brush biopsies obtained from 150 users of Sudanese snuff (toombak) and 25 non-users of toombak in formalin-fixed paraffin-embedded tissue samples obtained from 31 patients with oral dysplasias (25 toombak users and 6 non-users), and from 217 patients with oral cancers (145 toombak users and 72 non-users). In the brush tissue samples from toombak users, HPV was detected in 60 (40%), HSV in 44 (29%) and EBV in 97 (65%) of the samples. The corresponding figures for the 25 samples from non-users were 17 (68%) positive for HPV, 6 (24%) positive for HSV and 21 (84%) for EBV. The formalin-fixed samples with oral dysplasias were all negative for HPV. In the 145 oral cancer samples from toombak users, HPV was detected in 39 (27%), HSV in 15 (10%) and EBV in 53 (37%) of the samples. The corresponding figures for the samples from non-users were 15 (21%) positive for HPV, 5 (7%) for HSV and 16 (22%) for EBV. These findings illustrate that prevalence of HSV, HPV and EBV infections are common and may influence oral health and cancer development. It is not obvious that cancer risk is increased in infected toombak users. These observations warrant further studies involving toombak-associated oral lesions, to uncover the possible mechanisms of these viral infections in the development of oral cancer, and the influence of toombak on these viruses. © 2010 John Wiley & Sons A/S.

Active and passive computed tomography mixed waste focus area final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roberson, G P

1998-08-19

The Mixed Waste Focus Area (MWFA) Characterization Development Strategy delineates an approach to resolve technology deficiencies associated with the characterization of mixed wastes. The intent of this strategy is to ensure the availability of technologies to support the Department of Energy's (DOE) mixed waste low-level or transuranic (TRU) contaminated waste characterization management needs. To this end the MWFA has defined and coordinated characterization development programs to ensure that data and test results necessary to evaluate the utility of non-destructive assay technologies are available to meet site contact handled waste management schedules. Requirements used as technology development project benchmarks are basedmore » in the National TRU Program Quality Assurance Program Plan. These requirements include the ability to determine total bias and total measurement uncertainty. These parameters must be completely evaluated for waste types to be processed through a given nondestructive waste assay system constituting the foundation of activities undertaken in technology development projects. Once development and testing activities have been completed, Innovative Technology Summary Reports are generated to provide results and conclusions to support EM-30, -40, or -60 end user/customer technology selection. The Active and Passive Computed Tomography non-destructive assay system is one of the technologies selected for development by the MWFA. Lawrence Livermore National Laboratory's (LLNL) is developing the Active and Passive Computed Tomography (A&PCT) nondestructive assay (NDA) technology to identify and accurately quantify all detectable radioisotopes in closed containers of waste. This technology will be applicable to all types of waste regardless of .their classification; low level, transuranic or provide results and conclusions to support EM-30, -40, or -60 end user/customer technology selection. The Active and Passive Computed Tomography non-destructive assay system is one of the technologies selected for development by the MWFA. Lawrence Livermore National Laboratory's (LLNL) is developing the Active and Passive Computed Tomography (A&PCT) nondestructive assay (NDA) technology to identify and accurately quantify all detectable radioisotopes in closed containers of waste. This technology will be applicable to all types of waste regardless of .their classification; low level, transuranic or mixed, which contains radioactivity and hazardous organic species. The scope of our technology is to develop a non-invasive waste-drum scanner that employs the principles of computed tomography and gamma-ray spectral analysis to identify and quantify all of the detectable radioisotopes. Once this and other applicable technologies are developed, waste drums can be non- destructively and accurately characterized to satisfy repository and regulatory guidelines prior to disposal.« less

Application of the remote microphone method to active noise control in a mobile phone.

PubMed

Cheer, Jordan; Elliott, Stephen J; Oh, Eunmi; Jeong, Jonghoon

2018-04-01

Mobile phones are used in a variety of situations where environmental noise may interfere with the ability of the near-end user to communicate with the far-end user. To overcome this problem, it might be possible to use active noise control technology to reduce the noise experienced by the near-end user. This paper initially demonstrates that when an active noise control system is used in a practical mobile phone configuration to minimise the noise measured by an error microphone mounted on the mobile phone, the attenuation achieved at the user's ear depends strongly on the position of the source generating the acoustic interference. To help overcome this problem, a remote microphone processing strategy is investigated that estimates the pressure at the user's ear from the pressure measured by the microphone on the mobile phone. Through an experimental implementation, it is demonstrated that this arrangement achieves a significant improvement in the attenuation measured at the ear of the user, compared to the standard active control strategy. The robustness of the active control system to changes in both the interfering sound field and the position of the mobile device relative to the ear of the user is also investigated experimentally.

Patient Portals

PubMed Central

Skinner, Asheley; Thornhill, Jonathan; Weinberger, Morris

2016-01-01

Summary Background Patient portals have demonstrated numerous benefits including improved patient-provider communication, patient satisfaction with care, and patient engagement. Recent literature has begun to illustrate how patients use selected portal features and an association between portal usage and improved clinical outcomes. Objectives This study sought to: (1) identify patient characteristics associated with the use of a patient portal; (2) determine the frequency with which common patient portal features are used; and (3) examine whether the level of patient portal use (non-users, light users, active users) is associated with 30-day hospital readmission. Methods My UNC Chart is the patient portal for the UNC Health Care System. We identified adults discharged from three UNC Health Care hospitals with acute myocardial infarction, congestive heart failure, or pneumonia and classified them as active, light, or non-users of My UNC Chart. Multivariable analyses were conducted to compare across user groups; logistic regression was used to predict whether patient portal use was associated with 30-day readmission. Results Of 2,975 eligible patients, 83.4% were non-users; 8.6% were light users; and 8.0% were active users of My UNC Chart. The messaging feature was used most often. For patients who were active users, the odds of being readmitted within 30 days was 66% greater than patients who were non-users (p<0.05). There was no difference in 30-day readmission between non-users and light users. Conclusions The vast majority of patients who were given an access code for My UNC Chart did not use it within 30 days of discharge. Of those who used the portal, active users had a higher odds of being readmitted within 30 days. Health care systems should consider strategies to: (1) increase overall use of patient portals and (2) target patients with the highest comorbidity scores to reduce hospital readmissions. PMID:27437056

Participant characteristics of users of holistic movement practices in Australia.

PubMed

Vergeer, Ineke; Bennie, Jason A; Charity, Melanie J; van Uffelen, Jannique G Z; Harvey, Jack T; Biddle, Stuart J H; Eime, Rochelle M

2018-05-01

The aim of this study was to compare the characteristics of users of holistic movement practices in Australia to people who were physically active but not using holistic movement practices. A second aim was to compare characteristics of users of specific holistic movement practices (yoga/Pilates and t'ai chi/qigong). We performed a secondary data analysis on pooled data of a nationally-representative physical activity survey conducted yearly 2001-2010 (n = 195,926). Australia-wide Exercise, Recreation, and Sport Survey (ERASS). A range of socio-demographic and participation characteristics were documented and compared between users and non-users of holistic movement practices and between yoga/Pilates and t'ai chi/qigong users, employing descriptive statistics, chi square, and multiple logistic regression analyses. Users of holistic movement practices (n = 6826) were significantly more likely than non-users to be female, older, have fewer children at home, and have higher levels of education, socio-economic background, and physical activity involvement (p < 0.001). Yoga/Pilates (n = 5733) and t'ai chi/qigong (n = 947) users were also found to differ on a number of characteristics, including age, sex, socioeconomic background, and marital status. As a group, Australian users of holistic movement practices differ on a range of characteristics from those Australians active in other types of physical activities. However, differences between yoga/Pilates and t'ai chi/qigong users suggest these practices attract somewhat different sub-populations. To what extent these differences are due to characteristics inherent to the practices themselves or to differences in delivery-related parameters needs to be examined in future research. Copyright © 2018 Elsevier Ltd. All rights reserved.

A novel algorithm for detecting active propulsion in wheelchair users following spinal cord injury.

PubMed

Popp, Werner L; Brogioli, Michael; Leuenberger, Kaspar; Albisser, Urs; Frotzler, Angela; Curt, Armin; Gassert, Roger; Starkey, Michelle L

2016-03-01

Physical activity in wheelchair-bound individuals can be assessed by monitoring their mobility as this is one of the most intense upper extremity activities they perform. Current accelerometer-based approaches for describing wheelchair mobility do not distinguish between self- and attendant-propulsion and hence may overestimate total physical activity. The aim of this study was to develop and validate an inertial measurement unit based algorithm to monitor wheel kinematics and the type of wheelchair propulsion (self- or attendant-) within a "real-world" situation. Different sensor set-ups were investigated, ranging from a high precision set-up including four sensor modules with a relatively short measurement duration of 24 h, to a less precise set-up with only one module attached at the wheel exceeding one week of measurement because the gyroscope of the sensor was turned off. The "high-precision" algorithm distinguished self- and attendant-propulsion with accuracy greater than 93% whilst the long-term measurement set-up showed an accuracy of 82%. The estimation accuracy of kinematic parameters was greater than 97% for both set-ups. The possibility of having different sensor set-ups allows the use of the inertial measurement units as high precision tools for researchers as well as unobtrusive and simple tools for manual wheelchair users. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Association of Proton Pump Inhibitor (PPI) Use with Energy Intake, Physical Activity, and Weight Gain

PubMed Central

Czwornog, Jennifer L.; Austin, Gregory L.

2015-01-01

Studies suggest proton pump inhibitor (PPI) use impacts body weight regulation, though the effect of PPIs on energy intake, energy extraction, and energy expenditure is unknown. We used data on 3073 eligible adults from the National Health and Nutrition Examination Survey (NHANES). Medication use, energy intake, diet composition, and physical activity were extracted from NHANES. Multivariate regression models included confounding variables. Daily energy intake was similar between PPI users and non-users (p = 0.41). Diet composition was similar between the two groups, except that PPI users consumed a slightly greater proportion of calories from fat (34.5% vs. 33.2%; p = 0.02). PPI users rated themselves as being as physically active as their age/gender-matched peers and reported similar frequencies of walking or biking. However, PPI users were less likely to have participated in muscle-strengthening activities (OR: 0.53; 95% CI: 0.30–0.95). PPI users reported similar sedentary behaviors to non-users. Male PPI users had an increase in weight (of 1.52 ± 0.59 kg; p = 0.021) over the previous year compared to non-users, while female PPI users had a non-significant increase in weight. The potential mechanisms for PPI-associated weight gain are unclear as we did not find evidence for significant differences in energy intake or markers of energy expenditure. PMID:26492268

FUNCTIONAL ASSESSMENT OF A CAMERA PHONE-BASED WAYFINDING SYSTEM OPERATED BY BLIND AND VISUALLY IMPAIRED USERS

PubMed Central

COUGHLAN, JAMES; MANDUCHI, ROBERTO

2009-01-01

We describe a wayfinding system for blind and visually impaired persons that uses a camera phone to determine the user's location with respect to color markers, posted at locations of interest (such as offices), which are automatically detected by the phone. The color marker signs are specially designed to be detected in real time in cluttered environments using computer vision software running on the phone; a novel segmentation algorithm quickly locates the borders of the color marker in each image, which allows the system to calculate how far the marker is from the phone. We present a model of how the user's scanning strategy (i.e. how he/she pans the phone left and right to find color markers) affects the system's ability to detect color markers given the limitations imposed by motion blur, which is always a possibility whenever a camera is in motion. Finally, we describe experiments with our system tested by blind and visually impaired volunteers, demonstrating their ability to reliably use the system to find locations designated by color markers in a variety of indoor and outdoor environments, and elucidating which search strategies were most effective for users. PMID:19960101

Safe trajectory estimation at a pedestrian crossing to assist visually impaired people.

PubMed

Alghamdi, Saleh; van Schyndel, Ron; Khalil, Ibrahim

2012-01-01

The aim of this paper is to present a service for blind and people with low vision to assist them to cross the street independently. The presented approach provides the user with significant information such as detection of pedestrian crossing signal from any point of view, when the pedestrian crossing signal light is green, the detection of dynamic and fixed obstacles, predictions of the movement of fellow pedestrians and information on objects which may intersect his path. Our approach is based on capturing multiple frames using a depth camera which is attached to a user's headgear. Currently a testbed system is built on a helmet and is connected to a laptop in the user's backpack. In this paper, we discussed efficiency of using Speeded-Up Robust Features (SURF) algorithm for object recognition for purposes of blind people assistance. The system predicts the movement of objects of interest to provide the user with information on the safest path to navigate and information on the surrounding area. Evaluation of this approach on real sequence video frames provides 90% of human detection and more than 80% for recognition of other related objects.

Consensus-based methodology for detection communities in multilayered networks

NASA Astrophysics Data System (ADS)

Karimi-Majd, Amir-Mohsen; Fathian, Mohammad; Makrehchi, Masoud

2018-03-01

Finding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users' behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.

FUNCTIONAL ASSESSMENT OF A CAMERA PHONE-BASED WAYFINDING SYSTEM OPERATED BY BLIND AND VISUALLY IMPAIRED USERS.

PubMed

Coughlan, James; Manduchi, Roberto

2009-06-01

We describe a wayfinding system for blind and visually impaired persons that uses a camera phone to determine the user's location with respect to color markers, posted at locations of interest (such as offices), which are automatically detected by the phone. The color marker signs are specially designed to be detected in real time in cluttered environments using computer vision software running on the phone; a novel segmentation algorithm quickly locates the borders of the color marker in each image, which allows the system to calculate how far the marker is from the phone. We present a model of how the user's scanning strategy (i.e. how he/she pans the phone left and right to find color markers) affects the system's ability to detect color markers given the limitations imposed by motion blur, which is always a possibility whenever a camera is in motion. Finally, we describe experiments with our system tested by blind and visually impaired volunteers, demonstrating their ability to reliably use the system to find locations designated by color markers in a variety of indoor and outdoor environments, and elucidating which search strategies were most effective for users.

Classifying and profiling Social Networking Site users: a latent segmentation approach.

PubMed

Alarcón-del-Amo, María-del-Carmen; Lorenzo-Romero, Carlota; Gómez-Borja, Miguel-Ángel

2011-09-01

Social Networking Sites (SNSs) have showed an exponential growth in the last years. The first step for an efficient use of SNSs stems from an understanding of the individuals' behaviors within these sites. In this research, we have obtained a typology of SNS users through a latent segmentation approach, based on the frequency by which users perform different activities within the SNSs, sociodemographic variables, experience in SNSs, and dimensions related to their interaction patterns. Four different segments have been obtained. The "introvert" and "novel" users are the more occasional. They utilize SNSs mainly to communicate with friends, although "introverts" are more passive users. The "versatile" user performs different activities, although occasionally. Finally, the "expert-communicator" performs a greater variety of activities with a higher frequency. They tend to perform some marketing-related activities such as commenting on ads or gathering information about products and brands as well as commenting ads. The companies can take advantage of these segmentation schemes in different ways: first, by tracking and monitoring information interchange between users regarding their products and brands. Second, they should match the SNS users' profiles with their market targets to use SNSs as marketing tools. Finally, for most business, the expert users could be interesting opinion leaders and potential brand influencers.

A natural approach to convey numerical digits using hand activity recognition based on hand shape features

NASA Astrophysics Data System (ADS)

Chidananda, H.; Reddy, T. Hanumantha

2017-06-01

This paper presents a natural representation of numerical digit(s) using hand activity analysis based on number of fingers out stretched for each numerical digit in sequence extracted from a video. The analysis is based on determining a set of six features from a hand image. The most important features used from each frame in a video are the first fingertip from top, palm-line, palm-center, valley points between the fingers exists above the palm-line. Using this work user can convey any number of numerical digits using right or left or both the hands naturally in a video. Each numerical digit ranges from 0 to9. Hands (right/left/both) used to convey digits can be recognized accurately using the valley points and with this recognition whether the user is a right / left handed person in practice can be analyzed. In this work, first the hand(s) and face parts are detected by using YCbCr color space and face part is removed by using ellipse based method. Then, the hand(s) are analyzed to recognize the activity that represents a series of numerical digits in a video. This work uses pixel continuity algorithm using 2D coordinate geometry system and does not use regular use of calculus, contours, convex hull and datasets.

Brain metabolism of children with profound deafness: a visual language activation study by 18F-fluorodeoxyglucose positron emission tomography.

PubMed

Fujiwara, Keizo; Naito, Yasushi; Senda, Michio; Mori, Toshiko; Manabe, Tomoko; Shinohara, Shogo; Kikuchi, Masahiro; Hori, Shin-Ya; Tona, Yosuke; Yamazaki, Hiroshi

2008-04-01

The use of fluorodeoxyglucose positron emission tomography (FDG-PET) with a visual language task provided objective information on the development and plasticity of cortical language networks. This approach could help individuals involved in the habilitation and education of prelingually deafened children to decide upon the appropriate mode of communication. To investigate the cortical processing of the visual component of language and the effect of deafness upon this activity. Six prelingually deafened children participated in this study. The subjects were numbered 1-6 in the order of their spoken communication skills. In the time period between an intravenous injection of 370 MBq 18F-FDG and PET scanning of the brain, each subject was instructed to watch a video of the face of a speaking person. The cortical radioactivity of each deaf child was compared with that of a group of normal- hearing adults using a t test in a basic SPM2 model. The widest bilaterally activated cortical area was detected in subject 1, who was the worst user of spoken language. By contrast, there was no significant difference between subject 6, who was the best user of spoken language with a hearing aid, and the normal hearing group.

A Web Service-Based Framework Model for People-Centric Sensing Applications Applied to Social Networking

PubMed Central

Nunes, David; Tran, Thanh-Dien; Raposo, Duarte; Pinto, André; Gomes, André; Silva, Jorge Sá

2012-01-01

As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users’ activities and locations, sharing this information amongst the user’s friends within a social networking site. We also present some screenshot results of our experimental prototype. PMID:22438732

Development and user validation of driving tasks for a power wheelchair simulator.

PubMed

Archambault, Philippe S; Blackburn, Émilie; Reid, Denise; Routhier, François; Miller, William C

2017-07-01

Mobility is important for participation in daily activities and a power wheelchair (PW) can improve quality of life of individuals with mobility impairments. A virtual reality simulator may be helpful in complementing PW skills training, which is generally seen as insufficient by both clinicians and PW users. To this end, specific, ecologically valid activities, such as entering an elevator and navigating through a shopping mall crowd, have been added to the McGill wheelchair (miWe) simulator through a user-centred approach. The objective of this study was to validate the choice of simulated activities in a group of newly trained PW users. We recruited 17 new PW users, who practiced with the miWe simulator at home for two weeks. They then related their experience through the Short Feedback Questionnaire, the perceived Ease of Use Questionnaire, and semi-structured interviews. Participants in general greatly appreciated their experience with the simulator. During the interviews, this group made similar comments about the activities as our previous group of expert PW users had done. They also insisted on the importance of realism in the miWe activities, for their use in training. A PW simulator may be helpful if it supports the practice of activities in specific contexts (such as a bathroom or supermarket), to complement the basic skills training received in the clinic (such as driving forward, backward, turning, and avoiding obstacles). Implications for Rehabilitation New power wheelchair users appreciate practicing on a virtual reality simulator and find the experience useful when the simulated diving activities are realistic and ecologically valid. User-centred development can lead to simulated power wheelchair activities that adequately capture everyday driving challenges experienced in various environmental contexts.

Enhancing Users' Participation in Business Process Modeling through Ontology-Based Training

NASA Astrophysics Data System (ADS)

Macris, A.; Malamateniou, F.; Vassilacopoulos, G.

Successful business process design requires active participation of users who are familiar with organizational activities and business process modelling concepts. Hence, there is a need to provide users with reusable, flexible, agile and adaptable training material in order to enable them instil their knowledge and expertise in business process design and automation activities. Knowledge reusability is of paramount importance in designing training material on process modelling since it enables users participate actively in process design/redesign activities stimulated by the changing business environment. This paper presents a prototype approach for the design and use of training material that provides significant advantages to both the designer (knowledge - content reusability and semantic web enabling) and the user (semantic search, knowledge navigation and knowledge dissemination). The approach is based on externalizing domain knowledge in the form of ontology-based knowledge networks (i.e. training scenarios serving specific training needs) so that it is made reusable.

Distributed user interfaces for clinical ubiquitous computing applications.

PubMed

Bång, Magnus; Larsson, Anders; Berglund, Erik; Eriksson, Henrik

2005-08-01

Ubiquitous computing with multiple interaction devices requires new interface models that support user-specific modifications to applications and facilitate the fast development of active workspaces. We have developed NOSTOS, a computer-augmented work environment for clinical personnel to explore new user interface paradigms for ubiquitous computing. NOSTOS uses several devices such as digital pens, an active desk, and walk-up displays that allow the system to track documents and activities in the workplace. We present the distributed user interface (DUI) model that allows standalone applications to distribute their user interface components to several devices dynamically at run-time. This mechanism permit clinicians to develop their own user interfaces and forms to clinical information systems to match their specific needs. We discuss the underlying technical concepts of DUIs and show how service discovery, component distribution, events and layout management are dealt with in the NOSTOS system. Our results suggest that DUIs--and similar network-based user interfaces--will be a prerequisite of future mobile user interfaces and essential to develop clinical multi-device environments.

A Smartphone App to Screen for HIV-Related Neurocognitive Impairment.

PubMed

Robbins, Reuben N; Brown, Henry; Ehlers, Andries; Joska, John A; Thomas, Kevin G F; Burgess, Rhonda; Byrd, Desiree; Morgello, Susan

2014-02-01

Neurocognitive Impairment (NCI) is one of the most common complications of HIV-infection, and has serious medical and functional consequences. However, screening for it is not routine and NCI often goes undiagnosed. Screening for NCI in HIV disease faces numerous challenges, such as limited screening tests, the need for specialized equipment and apparatuses, and highly trained personnel to administer, score and interpret screening tests. To address these challenges, we developed a novel smartphone-based screening tool, NeuroScreen , to detect HIV-related NCI that includes an easy-to-use graphical user interface with ten highly automated neuropsychological tests. To examine NeuroScreen's : 1) acceptability among patients and different potential users; 2) test construct and criterion validity; and 3) sensitivity and specificity to detect NCI. Fifty HIV+ individuals were administered a gold-standard neuropsychological test battery, designed to detect HIV-related NCI, and NeuroScreen . HIV+ test participants and eight potential provider-users of NeuroScreen were asked about its acceptability. There was a high level of acceptability of NeuroScreen by patients and potential provider-users. Moderate to high correlations between individual NeuroScreen tests and paper-and-pencil tests assessing the same cognitive domains were observed. NeuroScreen also demonstrated high sensitivity to detect NCI. NeuroScreen, a highly automated, easy-to-use smartphone-based screening test to detect NCI among HIV patients and usable by a range of healthcare personnel could help make routine screening for HIV-related NCI feasible. While NeuroScreen demonstrated robust psychometric properties and acceptability, further testing with larger and less neurocognitively impaired samples is warranted.

The research on a novel type of the solar-blind UV head-mounted displays

NASA Astrophysics Data System (ADS)

Zhao, Shun-long

2011-08-01

Ultraviolet technology of detecting is playing a more and more important role in the field of civil application, especially in the corona discharge detection, in modern society. Now the UV imaging detector is one of the most important equipments in power equipment flaws detection. And the modern head-mounted displays (HMDs) have shown the applications in the fields of military, industry production, medical treatment, entertainment, 3D visualization, education and training. We applied the system of head-mounted displays to the UV image detection, and a novel type of head-mounted displays is presented: the solar-blind UV head-mounted displays. And the structure is given. By the solar-blind UV head-mounted displays, a real-time, isometric and visible image of the corona discharge is correctly displayed upon the background scene where it exists. The user will see the visible image of the corona discharge on the real scene rather than on a small screen. Then the user can easily find out the power equipment flaws and repair them. Compared with the traditional UV imaging detector, the introducing of the HMDs simplifies the structure of the whole system. The original visible spectrum optical system is replaced by the eye in the solar-blind UV head-mounted displays. And the optical image fusion technology would be used rather than the digital image fusion system which is necessary in traditional UV imaging detector. That means the visible spectrum optical system and digital image fusion system are not necessary. This makes the whole system cheaper than the traditional UV imaging detector. Another advantage of the solar-blind UV head-mounted displays is that the two hands of user will be free. So while observing the corona discharge the user can do some things about it. Therefore the solar-blind UV head-mounted displays can make the corona discharge expose itself to the user in a better way, and it will play an important role in corona detection in the future.

Who Is Still Playing Pokémon Go? A Web-Based Survey.

PubMed

Rasche, Peter; Schlomann, Anna; Mertens, Alexander

2017-04-05

Poor physical activity is one of the major health care problems in Western civilizations. Various digital gadgets aiming to increase physical activity, such as activity trackers or fitness apps, have been introduced over recent years. The newest products are serious games that incorporate real-life physical activity into their game concept. Recent studies have shown that such games increase the physical activity of their users over the short term. In this study, we investigated the motivational effects of the digital game "Pokémon Go" leading to continued use or abandonment of the game. The aim of the study was to determine aspects that motivate individuals to play augmented reality exergames and how this motivation can be used to strengthen the initial interest in physical activity. A total of 199 participants completed an open self-selected Web-based survey. On the basis of their self-indicated assignment to one of three predefined user groups (active, former, and nonuser of Pokémon Go), participants answered various questions regarding game experience, physical activity, motivation, and personality as measured by the Big Five Inventory. In total, 81 active, 56 former, and 62 nonusers of Pokémon Go were recruited. When asked about the times they perform physical activity, active users stated that they were less physically active in general than former and nonusers. However, based on a subjective rating, active users were more motivated to be physically active due to playing Pokémon Go. Motivational aspects differed for active and former users, whereas fan status was the same within both groups. Active users are more motivated by features directly related to Pokémon, such as catching all possible Pokémon and reaching higher levels, whereas former users stress the importance of general game quality, such as better augmented reality and more challenges in the game. Personality did not affect whether a person started to play Pokémon Go nor their abandonment of the game. The results show various motivating elements that should be incorporated into augmented reality exergames based on the game Pokémon Go. We identified different user types for whom different features of the game contribute to maintained motivation or abandonment. Our results show aspects that augmented reality exergame designers should keep in mind to encourage individuals to start playing their game and facilitate long-term user engagement, resulting in a greater interest in physical activity. ©Peter Rasche, Anna Schlomann, Alexander Mertens. Originally published in JMIR Serious Games (http://games.jmir.org), 05.04.2017.

Who Is Still Playing Pokémon Go? A Web-Based Survey

PubMed Central

Mertens, Alexander

2017-01-01

Background Poor physical activity is one of the major health care problems in Western civilizations. Various digital gadgets aiming to increase physical activity, such as activity trackers or fitness apps, have been introduced over recent years. The newest products are serious games that incorporate real-life physical activity into their game concept. Recent studies have shown that such games increase the physical activity of their users over the short term. Objective In this study, we investigated the motivational effects of the digital game “Pokémon Go” leading to continued use or abandonment of the game. The aim of the study was to determine aspects that motivate individuals to play augmented reality exergames and how this motivation can be used to strengthen the initial interest in physical activity. Methods A total of 199 participants completed an open self-selected Web-based survey. On the basis of their self-indicated assignment to one of three predefined user groups (active, former, and nonuser of Pokémon Go), participants answered various questions regarding game experience, physical activity, motivation, and personality as measured by the Big Five Inventory. Results In total, 81 active, 56 former, and 62 nonusers of Pokémon Go were recruited. When asked about the times they perform physical activity, active users stated that they were less physically active in general than former and nonusers. However, based on a subjective rating, active users were more motivated to be physically active due to playing Pokémon Go. Motivational aspects differed for active and former users, whereas fan status was the same within both groups. Active users are more motivated by features directly related to Pokémon, such as catching all possible Pokémon and reaching higher levels, whereas former users stress the importance of general game quality, such as better augmented reality and more challenges in the game. Personality did not affect whether a person started to play Pokémon Go nor their abandonment of the game. Conclusions The results show various motivating elements that should be incorporated into augmented reality exergames based on the game Pokémon Go. We identified different user types for whom different features of the game contribute to maintained motivation or abandonment. Our results show aspects that augmented reality exergame designers should keep in mind to encourage individuals to start playing their game and facilitate long-term user engagement, resulting in a greater interest in physical activity. PMID:28381393

Exploring Association between Morphology of Tree Planting and User Activities in Urban Public Space; An opportunity of Urban Public Space Revitalisation

NASA Astrophysics Data System (ADS)

Shen, Qi; Liu, Yan

2018-03-01

This paper discusses the association between the morphology of tree planting in urban riverside brown field and user activities. With the growth of popularity, the revitalisation of urban public space is also promising. This research used drone photography and mapping to systematically surveys sample sites. An original observation study of user activities proceed in four sample public spaces in Sheffield. The study results found there are huge popularity and duration difference of user activities between various tree planting morphologies and typologies. The public space with lawn and rounded by mature trees attracted most users with the most activity types; the neat and silent public space is the favourite choice of lunch and reading, meanwhile it got the longest activity duration; but the space with sparse morphology and small trees are more likely be forgotten and abandoned. This finding offered a great opportunity for urban public space revitalisation in post-industrial cities.

On the enhanced detectability of GPS anomalous behavior with relative entropy

NASA Astrophysics Data System (ADS)

Cho, Jeongho

2016-10-01

A standard receiver autonomous integrity monitoring (RAIM) technique for the global positioning system (GPS) has been dedicated to provide an integrity monitoring capability for safety-critical GPS applications, such as in civil aviation for the en-route (ER) through non-precision approach (NPA) or lateral navigation (LNAV). The performance of the existing RAIM method, however, may not meet more stringent aviation requirements for availability and integrity during the precision approach and landing phases of flight due to insufficient observables and/or untimely warning to the user beyond a specified time-to-alert in the event of a significant GPS failure. This has led to an enhanced RAIM architecture ensuring stricter integrity requirement by greatly decreasing the detection time when a satellite failure or a measurement error has occurred. We thus attempted to devise a user integrity monitor which is capable of identifying the GPS failure more rapidly than a standard RAIM scheme by incorporating the RAIM with the relative entropy, which is a likelihood ratio approach to assess the inconsistence between two data streams, quite different from a Euclidean distance. In addition, the delay-coordinate embedding technique needs to be considered and preprocessed to associate the discriminant measure obtained from the RAIM with the relative entropy in the new RAIM design. In simulation results, we demonstrate that the proposed user integrity monitor outperforms the standard RAIM with a higher level of detection rate of anomalies which could be hazardous to the users in the approach or landing phase and is a very promising alternative for the detection of deviations in GPS signal. The comparison also shows that it enables to catch even small anomalous gradients more rapidly than a typical user integrity monitor.

A cardiorespiratory classifier of voluntary and involuntary electrodermal activity

PubMed Central

2010-01-01

Background Electrodermal reactions (EDRs) can be attributed to many origins, including spontaneous fluctuations of electrodermal activity (EDA) and stimuli such as deep inspirations, voluntary mental activity and startling events. In fields that use EDA as a measure of psychophysiological state, the fact that EDRs may be elicited from many different stimuli is often ignored. This study attempts to classify observed EDRs as voluntary (i.e., generated from intentional respiratory or mental activity) or involuntary (i.e., generated from startling events or spontaneous electrodermal fluctuations). Methods Eight able-bodied participants were subjected to conditions that would cause a change in EDA: music imagery, startling noises, and deep inspirations. A user-centered cardiorespiratory classifier consisting of 1) an EDR detector, 2) a respiratory filter and 3) a cardiorespiratory filter was developed to automatically detect a participant's EDRs and to classify the origin of their stimulation as voluntary or involuntary. Results Detected EDRs were classified with a positive predictive value of 78%, a negative predictive value of 81% and an overall accuracy of 78%. Without the classifier, EDRs could only be correctly attributed as voluntary or involuntary with an accuracy of 50%. Conclusions The proposed classifier may enable investigators to form more accurate interpretations of electrodermal activity as a measure of an individual's psychophysiological state. PMID:20184746

Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity.

PubMed

Zander, Thorsten O; Krol, Laurens R; Birbaumer, Niels P; Gramann, Klaus

2016-12-27

The effectiveness of today's human-machine interaction is limited by a communication bottleneck as operators are required to translate high-level concepts into a machine-mandated sequence of instructions. In contrast, we demonstrate effective, goal-oriented control of a computer system without any form of explicit communication from the human operator. Instead, the system generated the necessary input itself, based on real-time analysis of brain activity. Specific brain responses were evoked by violating the operators' expectations to varying degrees. The evoked brain activity demonstrated detectable differences reflecting congruency with or deviations from the operators' expectations. Real-time analysis of this activity was used to build a user model of those expectations, thus representing the optimal (expected) state as perceived by the operator. Based on this model, which was continuously updated, the computer automatically adapted itself to the expectations of its operator. Further analyses showed this evoked activity to originate from the medial prefrontal cortex and to exhibit a linear correspondence to the degree of expectation violation. These findings extend our understanding of human predictive coding and provide evidence that the information used to generate the user model is task-specific and reflects goal congruency. This paper demonstrates a form of interaction without any explicit input by the operator, enabling computer systems to become neuroadaptive, that is, to automatically adapt to specific aspects of their operator's mindset. Neuroadaptive technology significantly widens the communication bottleneck and has the potential to fundamentally change the way we interact with technology.

Physical and leisure activity in older community-dwelling canadians who use wheelchairs: a population study.

PubMed

Best, Krista L; Miller, William C

2011-04-13

Background. Physical and leisure activities are proven health promotion modalities and have not been examined in older wheelchair users. Main Objectives. Examine physical and leisure activity in older wheelchair users and explore associations between wheelchair use and participation in physical and leisure activity, and wheelchair use, physical and leisure activity, and perceived health. Methods. 8301 Canadians ≥60 years of age were selected from the Canadian Community Health Survey. Sociodemographic, health-related, mobility-related, and physical and leisure activity variables were analysed using logistic regression to determine, the likelihood of participation in physical and leisure activity, and whether participation in physical and leisure activities mediates the relationship between wheelchair use and perceived health. Results. 8.3% and 41.3% older wheelchair users were physically and leisurely active. Wheelchair use was a risk factor for reduced participation in physical (OR = 44.71) and leisure activity (OR = 10.83). Wheelchair use was a risk factor for poor perceived health (OR = 10.56) and physical and leisure activity negatively mediated the relationship between wheelchair user and perceived health. Conclusion. There is a need for the development of suitable physical and leisure activity interventions for older wheelchair users. Participation in such interventions may have associations with health benefits.

Physical and Leisure Activity in Older Community-Dwelling Canadians Who Use Wheelchairs: A Population Study

PubMed Central

Best, Krista L.; Miller, William C.

2011-01-01

Background. Physical and leisure activities are proven health promotion modalities and have not been examined in older wheelchair users. Main Objectives. Examine physical and leisure activity in older wheelchair users and explore associations between wheelchair use and participation in physical and leisure activity, and wheelchair use, physical and leisure activity, and perceived health. Methods. 8301 Canadians ≥60 years of age were selected from the Canadian Community Health Survey. Sociodemographic, health-related, mobility-related, and physical and leisure activity variables were analysed using logistic regression to determine, the likelihood of participation in physical and leisure activity, and whether participation in physical and leisure activities mediates the relationship between wheelchair use and perceived health. Results. 8.3% and 41.3% older wheelchair users were physically and leisurely active. Wheelchair use was a risk factor for reduced participation in physical (OR = 44.71) and leisure activity (OR = 10.83). Wheelchair use was a risk factor for poor perceived health (OR = 10.56) and physical and leisure activity negatively mediated the relationship between wheelchair user and perceived health. Conclusion. There is a need for the development of suitable physical and leisure activity interventions for older wheelchair users. Participation in such interventions may have associations with health benefits. PMID:21584226

The Stratway Program for Strategic Conflict Resolution: User's Guide

NASA Technical Reports Server (NTRS)

Hagen, George E.; Butler, Ricky W.; Maddalon, Jeffrey M.

2016-01-01

Stratway is a strategic conflict detection and resolution program. It provides both intent-based conflict detection and conflict resolution for a single ownship in the presence of multiple traffic aircraft and weather cells defined by moving polygons. It relies on a set of heuristic search strategies to solve conflicts. These strategies are user configurable through multiple parameters. The program can be called from other programs through an application program interface (API) and can also be executed from a command line.

DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response

PubMed Central

Simmie, Donal; Hankin, Chris; Gillard, Joseph

2016-01-01

In recent years social and news media have increasingly been used to explain patterns in disease activity and progression. Social media data, principally from the Twitter network, has been shown to correlate well with official disease case counts. This fact has been exploited to provide advance warning of outbreak detection, forecasting of disease levels and the ability to predict the likelihood of individuals developing symptoms. In this paper we introduce DEFENDER, a software system that integrates data from social and news media and incorporates algorithms for outbreak detection, situational awareness and forecasting. As part of this system we have developed a technique for creating a location network for any country or region based purely on Twitter data. We also present a disease nowcasting (forecasting the current but still unknown level) approach which leverages counts from multiple symptoms, which was found to improve the nowcasting accuracy by 37 percent over a model that used only previous case data. Finally we attempt to forecast future levels of symptom activity based on observed user movement on Twitter, finding a moderate gain of 5 percent over a time series forecasting model. PMID:27192059

Fluorogen-Activating-Proteins as Universal Affinity Biosensors for Immunodetection

PubMed Central

Gallo, Eugenio; Vasilev, Kalin V.; Jarvik, Jonathan

2014-01-01

Fluorogen-activating-proteins (FAPs) are a novel platform of fluorescence biosensors utilized for protein discovery. The technology currently demands molecular manipulation methods that limit its application and adaptability. Here, we highlight an alternative approach based on universal affinity reagents for protein detection. The affinity reagents were engineered as bi-partite fusion proteins, where the specificity moiety is derived from IgG-binding proteins –Protein-A or Protein-G – and the signaling element is a FAP. In this manner, primary antibodies provide the antigenic selectivity against a desired protein in biological samples, while FAP affinity reagents target the constant region (Fc) of antibodies and provide the biosensor component of detection. Fluorescence results using various techniques indicate minimal background and high target specificity for exogenous and endogenous proteins in mammalian cells. Additionally, FAP-based affinity reagents provide enhanced properties of detection previously absent using conventional affinity systems. Distinct features explored in this report include: (1) unfixed signal wavelengths (excitation and emission) determined by the particular fluorogen chosen, (2) real-time user controlled fluorescence on-set and off-set, (3) signal wavelength substitution while performing live analysis, and (4) enhanced resistance to photobleaching. PMID:24122476

Harnessing Scientific Literature Reports for Pharmacovigilance

PubMed Central

Ripple, Anna; Tonning, Joseph; Munoz, Monica; Hasan, Rashedul; Ly, Thomas; Francis, Henry; Bodenreider, Olivier

2017-01-01

Summary Objectives We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers’ capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. Methods A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We leveraged Medical Subject Heading (MeSH) indexing terms assigned to published citations in PubMed/MEDLINE to generate candidate drug-adverse event pairs for quantitative data mining. Six FDA regulatory reviewers participated in usability testing by employing the tool as part of their ongoing real-life pharmacovigilance activities to provide subjective feedback on its practical impact, added value, and fitness for use. Results All usability test participants cited the tool’s ease of learning, ease of use, and generation of quantitative ADE safety signals, some of which corresponded to known established adverse drug reactions. Potential concerns included the comparability of the tool’s automated literature search relative to a manual ‘all fields’ PubMed search, missing drugs and adverse event terms, interpretation of signal scores, and integration with existing computer-based analytical tools. Conclusions Usability testing demonstrated that this novel tool can automate the detection of ADE safety signals from published literature reports. Various mitigation strategies are described to foster improvements in design, productivity, and end user satisfaction. PMID:28326432

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

PubMed

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

Harnessing scientific literature reports for pharmacovigilance. Prototype software analytical tool development and usability testing.

PubMed

Sorbello, Alfred; Ripple, Anna; Tonning, Joseph; Munoz, Monica; Hasan, Rashedul; Ly, Thomas; Francis, Henry; Bodenreider, Olivier

2017-03-22

We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers' capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We leveraged Medical Subject Heading (MeSH) indexing terms assigned to published citations in PubMed/MEDLINE to generate candidate drug-adverse event pairs for quantitative data mining. Six FDA regulatory reviewers participated in usability testing by employing the tool as part of their ongoing real-life pharmacovigilance activities to provide subjective feedback on its practical impact, added value, and fitness for use. All usability test participants cited the tool's ease of learning, ease of use, and generation of quantitative ADE safety signals, some of which corresponded to known established adverse drug reactions. Potential concerns included the comparability of the tool's automated literature search relative to a manual 'all fields' PubMed search, missing drugs and adverse event terms, interpretation of signal scores, and integration with existing computer-based analytical tools. Usability testing demonstrated that this novel tool can automate the detection of ADE safety signals from published literature reports. Various mitigation strategies are described to foster improvements in design, productivity, and end user satisfaction.

Fast WEP-Key Recovery Attack Using Only Encrypted IP Packets

NASA Astrophysics Data System (ADS)

Teramura, Ryoichi; Asakura, Yasuo; Ohigashi, Toshihiro; Kuwakado, Hidenori; Morii, Masakatu

Conventional efficient key recovery attacks against Wired Equivalent Privacy (WEP) require specific initialization vectors or specific packets. Since it takes much time to collect the packets sufficiently, any active attack should be performed. An Intrusion Detection System (IDS), however, will be able to prevent the attack. Since the attack logs are stored at the servers, it is possible to prevent such an attack. This paper proposes an algorithm for recovering a 104-bit WEP key from any IP packets in a realistic environment. This attack needs about 36, 500 packets with a success probability 0.5, and the complexity of our attack is equivalent to about 220 computations of the RC4 key setups. Since our attack is passive, it is difficult for both WEP users and administrators to detect our attack.

Structural monitoring for rare events in remote locations

NASA Astrophysics Data System (ADS)

Hale, J. M.

2005-01-01

A structural monitoring system has been developed for use on high value engineering structures, which is particularly suitable for use in remote locations where rare events such as accidental impacts, seismic activity or terrorist attack might otherwise go undetected. The system comprises a low power intelligent on-site data logger and a remote analysis computer that communicate with one another using the internet and mobile telephone technology. The analysis computer also generates e-mail alarms and maintains a web page that displays detected events in near real-time to authorised users. The application of the prototype system to pipeline monitoring is described in which the analysis of detected events is used to differentiate between impacts and pressure surges. The system has been demonstrated successfully and is ready for deployment.

Cry-Bt identifier: a biological database for PCR detection of Cry genes present in transgenic plants.

PubMed

Singh, Vinay Kumar; Ambwani, Sonu; Marla, Soma; Kumar, Anil

2009-10-23

We describe the development of a user friendly tool that would assist in the retrieval of information relating to Cry genes in transgenic crops. The tool also helps in detection of transformed Cry genes from Bacillus thuringiensis present in transgenic plants by providing suitable designed primers for PCR identification of these genes. The tool designed based on relational database model enables easy retrieval of information from the database with simple user queries. The tool also enables users to access related information about Cry genes present in various databases by interacting with different sources (nucleotide sequences, protein sequence, sequence comparison tools, published literature, conserved domains, evolutionary and structural data). http://insilicogenomics.in/Cry-btIdentifier/welcome.html.

Statins and physical activity in older men: the osteoporotic fractures in men study.

PubMed

Lee, David S H; Markwardt, Sheila; Goeres, Leah; Lee, Christine G; Eckstrom, Elizabeth; Williams, Craig; Fu, Rongwei; Orwoll, Eric; Cawthon, Peggy M; Stefanick, Marcia L; Mackey, Dawn; Bauer, Douglas C; Nielson, Carrie M

2014-08-01

Muscle pain, fatigue, and weakness are common adverse effects of statin medications and may decrease physical activity in older men. To determine whether statin use is associated with physical activity, longitudinally and cross-sectionally. Men participating in the Osteoporotic Fractures in Men Study (N = 5994), a multicenter prospective cohort study of community-living men 65 years and older, enrolled between March 2000 and April 2002. Follow-up was conducted through 2009. Statin use as determined by an inventory of medications (taken within the last 30 days). In cross-sectional analyses (n = 4137), statin use categories were users and nonusers. In longitudinal analyses (n = 3039), categories were prevalent users (baseline use and throughout the study), new users (initiated use during the study), and nonusers (never used). Self-reported physical activity at baseline and 2 follow-up visits using the Physical Activity Scale for the Elderly (PASE). At the third visit, an accelerometer measured metabolic equivalents (METs [kilocalories per kilogram per hour]) and minutes of moderate activity (METs ≥3.0), vigorous activity (METs ≥6.0), and sedentary behavior (METs ≤1.5). At baseline, 989 men (24%) were users and 3148 (76%) were nonusers. The adjusted difference in baseline PASE between users and nonusers was -5.8 points (95% CI, -10.9 to -0.7 points). A total of 3039 men met the inclusion criteria for longitudinal analysis: 727 (24%) prevalent users, 845 (28%) new users, and 1467 (48%) nonusers. PASE score declined by a mean (95% CI) of 2.5 (2.0 to 3.0) points per year for nonusers and 2.8 (2.1 to 3.5) points per year for prevalent users, a nonstatistical difference (0.3 [-0.5 to 1.0] points). For new users, annual PASE score declined at a faster rate than nonusers (difference of 0.9 [95% CI, 0.1 to 1.7] points). A total of 3071 men had adequate accelerometry data, 1542 (50%) were statin users. Statin users expended less METs (0.03 [95% CI, 0.02-0.04] METs less) and engaged in less moderate physical activity (5.4 [95% CI, 1.9-8.8] fewer minutes per day), less vigorous activity (0.6 [95% CI, 0.1-1.1] fewer minutes per day), and more sedentary behavior (7.6 [95% CI, 2.6-12.4] greater minutes per day). Statin use was associated with modestly lower physical activity among community-living men, even after accounting for medical history and other potentially confounding factors. The clinical significance of these findings deserves further investigation.

Towards a Semantic-Based Approach for Affect and Metaphor Detection

ERIC Educational Resources Information Center

Zhang, Li; Barnden, John

2013-01-01

Affect detection from open-ended virtual improvisational contexts is a challenging task. To achieve this research goal, the authors developed an intelligent agent which was able to engage in virtual improvisation and perform sentence-level affect detection from user inputs. This affect detection development was efficient for the improvisational…

Active Involvement of End Users When Developing Web-Based Mental Health Interventions.

PubMed

de Beurs, Derek; van Bruinessen, Inge; Noordman, Janneke; Friele, Roland; van Dulmen, Sandra

2017-01-01

Although many web-based mental health interventions are being released, the actual uptake by end users is limited. The marginal level of engagement of end users when developing these interventions is recognized as an important cause for uptake problems. In this paper, we offer our perceptive on how to improve user engagement. By doing so, we aim to stimulate a discourse on user involvement within the field of online mental health interventions. We shortly describe three different methods (the expert-driven method, intervention mapping, and scrum) that were currently used to develop web-based health interventions. We will focus to what extent the end user was involved in the developmental phase, and what the additional challenges were. In the final paragraph, lessons learned are summarized, and recommendations provided. Every method seems to have its trade-off: if end users are highly involved, availability of end users and means become problematic. If end users are less actively involved, the product may be less appropriate for the end user. Other challenges to consider are the funding of the more active role of technological companies, and the time it takes to process the results of shorter development cycles. Thinking about user-centered design and carefully planning, the involvement of end users should become standard in the field of web-based (mental) health. When deciding on the level of user involvement, one should balance the need for input from users with the availability of resources such as time and funding.

The Relationship Between Spectral Modulation Detection and Speech Recognition: Adult Versus Pediatric Cochlear Implant Recipients

PubMed Central

Noble, Jack H.; Camarata, Stephen M.; Sunderhaus, Linsey W.; Dwyer, Robert T.; Dawant, Benoit M.; Dietrich, Mary S.; Labadie, Robert F.

2018-01-01

Adult cochlear implant (CI) recipients demonstrate a reliable relationship between spectral modulation detection and speech understanding. Prior studies documenting this relationship have focused on postlingually deafened adult CI recipients—leaving an open question regarding the relationship between spectral resolution and speech understanding for adults and children with prelingual onset of deafness. Here, we report CI performance on the measures of speech recognition and spectral modulation detection for 578 CI recipients including 477 postlingual adults, 65 prelingual adults, and 36 prelingual pediatric CI users. The results demonstrated a significant correlation between spectral modulation detection and various measures of speech understanding for 542 adult CI recipients. For 36 pediatric CI recipients, however, there was no significant correlation between spectral modulation detection and speech understanding in quiet or in noise nor was spectral modulation detection significantly correlated with listener age or age at implantation. These findings suggest that pediatric CI recipients might not depend upon spectral resolution for speech understanding in the same manner as adult CI recipients. It is possible that pediatric CI users are making use of different cues, such as those contained within the temporal envelope, to achieve high levels of speech understanding. Further investigation is warranted to investigate the relationship between spectral and temporal resolution and speech recognition to describe the underlying mechanisms driving peripheral auditory processing in pediatric CI users. PMID:29716437

Setting Priorities in Behavioral Interventions: An Application to Reducing Phishing Risk.

PubMed

Canfield, Casey Inez; Fischhoff, Baruch

2018-04-01

Phishing risk is a growing area of concern for corporations, governments, and individuals. Given the evidence that users vary widely in their vulnerability to phishing attacks, we demonstrate an approach for assessing the benefits and costs of interventions that target the most vulnerable users. Our approach uses Monte Carlo simulation to (1) identify which users were most vulnerable, in signal detection theory terms; (2) assess the proportion of system-level risk attributable to the most vulnerable users; (3) estimate the monetary benefit and cost of behavioral interventions targeting different vulnerability levels; and (4) evaluate the sensitivity of these results to whether the attacks involve random or spear phishing. Using parameter estimates from previous research, we find that the most vulnerable users were less cautious and less able to distinguish between phishing and legitimate emails (positive response bias and low sensitivity, in signal detection theory terms). They also accounted for a large share of phishing risk for both random and spear phishing attacks. Under these conditions, our analysis estimates much greater net benefit for behavioral interventions that target these vulnerable users. Within the range of the model's assumptions, there was generally net benefit even for the least vulnerable users. However, the differences in the return on investment for interventions with users with different degrees of vulnerability indicate the importance of measuring that performance, and letting it guide interventions. This study suggests that interventions to reduce response bias, rather than to increase sensitivity, have greater net benefit. © 2017 Society for Risk Analysis.

The closer the relationship, the more the interaction on facebook? Investigating the case of Taiwan users.

PubMed

Hsu, Chiung-Wen Julia; Wang, Ching-Chan; Tai, Yi-Ting

2011-01-01

This study argues for the necessity of applying offline contexts to social networking site research and the importance of distinguishing the relationship types of users' counterparts when studying Facebook users' behaviors. In an attempt to examine the relationship among users' behaviors, their counterparts' relationship types, and the users' perceived acquaintanceships after using Facebook, this study first investigated users' frequently used tools when interacting with different types of friends. Users tended to use less time- and effort-consuming and less privacy-concerned tools with newly acquired friends. This study further examined users' behaviors in terms of their closeness and intimacy and their perceived acquaintanceships toward four different types of friends. The study found that users gained more perceived acquaintanceships from less close friends with whom users have more frequent interaction but less intimate behaviors. As for closer friends, users tended to use more intimate activities to interact with them. However, these activities did not necessarily occur more frequently than the activities they employed with their less close friends. It was found that perceived acquaintanceships with closer friends were significantly lower than those with less close friends. This implies that Facebook is a mechanism for new friends, rather than close friends, to become more acquainted.

Evaluation of the efficacy of a portable LIBS system for detection of CWA on surfaces.

PubMed

L'Hermite, D; Vors, E; Vercouter, T; Moutiers, G

2016-05-01

Laser-induced breakdown spectroscopy (LIBS) is a laser-based optical technique particularly suited for in situ surface analysis. A portable LIBS instrument was tested to detect surface chemical contamination by chemical warfare agents (CWAs). Test of detection of surface contamination was carried out in a toxlab facility with four CWAs, sarin (GB), lewisite (L1), mustard gas (HD), and VX, which were deposited on different substrates, wood, concrete, military green paint, gloves, and ceramic. The CWAs were detected by means of the detection of atomic markers (As, P, F, Cl, and S). The LIBS instrument can give a direct response in terms of detection thanks to an integrated interface for non-expert users or so called end-users. We have evaluated the capability of automatic detection of the selected CWAs. The sensitivity of our portable LIBS instrument was confirmed for the detection of a CWA at surface concentrations above 15 μg/cm(2). The simultaneous detection of two markers may lead to a decrease of the number of false positive.

Biocompatible enzymatic roller pens for direct writing of biocatalytic materials: "do-it-yourself" electrochemical biosensors.

PubMed

Bandodkar, Amay J; Jia, Wenzhao; Ramírez, Julian; Wang, Joseph

2015-06-03

The development of enzymatic-ink-based roller pens for direct drawing of biocatalytic sensors, in general, and for realizing renewable glucose sensor strips, in particular, is described. The resulting enzymatic-ink pen allows facile fabrication of high-quality inexpensive electrochemical biosensors of any design by the user on a wide variety of surfaces having complex textures with minimal user training. Unlike prefabricated sensors, this approach empowers the end user with the ability of "on-demand" and "on-site" designing and fabricating of biocatalytic sensors to suit their specific requirement. The resulting devices are thus referred to as "do-it-yourself" sensors. The bio-active pens produce highly reproducible biocatalytic traces with minimal edge roughness. The composition of the new enzymatic inks has been optimized for ensuring good biocatalytic activity, electrical conductivity, biocompati-bility, reproducible writing, and surface adherence. The resulting inks are characterized using spectroscopic, viscometric, electrochemical, thermal and microscopic techniques. Applicability to renewable blood glucose testing, epidermal glucose monitoring, and on-leaf phenol detection are demonstrated in connection to glucose oxidase and tyrosinase-based carbon inks. The "do-it-yourself" renewable glucose sensor strips offer a "fresh," reproducible, low-cost biocatalytic sensor surface for each blood test. The ability to directly draw biocatalytic conducting traces even on unconventional surfaces opens up new avenues in various sensing applications in low-resource settings and holds great promise for diverse healthcare, environmental, and defense domains. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Similarity between community structures of different online social networks and its impact on underlying community detection

NASA Astrophysics Data System (ADS)

Fan, W.; Yeung, K. H.

2015-03-01

As social networking services are popular, many people may register in more than one online social network. In this paper we study a set of users who have accounts of three online social networks: namely Foursquare, Facebook and Twitter. Community structure of this set of users may be reflected in these three online social networks. Therefore, high correlation between these reflections and the underlying community structure may be observed. In this work, community structures are detected in all three online social networks. Also, we investigate the similarity level of community structures across different networks. It is found that they show strong correlation with each other. The similarity between different networks may be helpful to find a community structure close to the underlying one. To verify this, we propose a method to increase the weights of some connections in networks. With this method, new networks are generated to assist community detection. By doing this, value of modularity can be improved and the new community structure match network's natural structure better. In this paper we also show that the detected community structures of online social networks are correlated with users' locations which are identified on Foursquare. This information may also be useful for underlying community detection.

Bidirectional Active Learning: A Two-Way Exploration Into Unlabeled and Labeled Data Set.

PubMed

Zhang, Xiao-Yu; Wang, Shupeng; Yun, Xiaochun

2015-12-01

In practical machine learning applications, human instruction is indispensable for model construction. To utilize the precious labeling effort effectively, active learning queries the user with selective sampling in an interactive way. Traditional active learning techniques merely focus on the unlabeled data set under a unidirectional exploration framework and suffer from model deterioration in the presence of noise. To address this problem, this paper proposes a novel bidirectional active learning algorithm that explores into both unlabeled and labeled data sets simultaneously in a two-way process. For the acquisition of new knowledge, forward learning queries the most informative instances from unlabeled data set. For the introspection of learned knowledge, backward learning detects the most suspiciously unreliable instances within the labeled data set. Under the two-way exploration framework, the generalization ability of the learning model can be greatly improved, which is demonstrated by the encouraging experimental results.

A user friendly system for ultrasound carotid intima-media thickness image interpretation

NASA Astrophysics Data System (ADS)

Zhu, Xiangjun; Kendall, Christopher B.; Hurst, R. Todd; Liang, Jianming

2011-03-01

Assessment of Carotid Intima-Media Thickness (CIMT) by B-mode ultrasound is a technically mature and reproducible technology. Given the high morbidity, mortality and the large societal burden associated with CV diseases, as a safe yet inexpensive tool, CIMT is increasingly utilized for cardiovascular (CV) risk stratification. However, CIMT requires a precise measure of the thickness of the intima and media layers of the carotid artery that can be tedious, time consuming, and demand specialized expertise and experience. To this end, we have developed a highly user-friendly system for semiautomatic CIMT image interpretation. Our contribution is the application of active contour models (snake models) with hard constraints, leading to an accurate, adaptive and user-friendly border detection algorithm. A comparison study with the CIMT measurement software in Siemens Syngo® Arterial Health Package shows that our system gives a small bias in mean (0.049 +/-0.051mm) and maximum (0.010 +/- 0.083 mm) CIMT measures and offers a higher reproducibility (average correlation coefficients were 0.948 and 0.844 in mean and maximum CIMT respectively (P <0.001)). This superior performance is attributed to our novel interface design for hard constraints in the snake models.

Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior

PubMed Central

Althoff, Tim; Jindal, Pranav; Leskovec, Jure

2017-01-01

Many of today’s most widely used computing applications utilize social networking features and allow users to connect, follow each other, share content, and comment on others’ posts. However, despite the widespread adoption of these features, there is little understanding of the consequences that social networking has on user retention, engagement, and online as well as offline behavior. Here, we study how social networks influence user behavior in a physical activity tracking application. We analyze 791 million online and offline actions of 6 million users over the course of 5 years, and show that social networking leads to a significant increase in users’ online as well as offline activities. Specifically, we establish a causal effect of how social networks influence user behavior. We show that the creation of new social connections increases user online in-application activity by 30%, user retention by 17%, and user offline real-world physical activity by 7% (about 400 steps per day). By exploiting a natural experiment we distinguish the effect of social influence of new social connections from the simultaneous increase in user’s motivation to use the app and take more steps. We show that social influence accounts for 55% of the observed changes in user behavior, while the remaining 45% can be explained by the user’s increased motivation to use the app. Further, we show that subsequent, individual edge formations in the social network lead to significant increases in daily steps. These effects diminish with each additional edge and vary based on edge attributes and user demographics. Finally, we utilize these insights to develop a model that accurately predicts which users will be most influenced by the creation of new social network connections. PMID:28345078

Posting behaviour patterns in an online smoking cessation social network: implications for intervention design and development.

PubMed

Healey, Benjamin; Hoek, Janet; Edwards, Richard

2014-01-01

Online Cessation Support Networks (OCSNs) are associated with increased quit success rates, but few studies have examined their use over time. We identified usage patterns in New Zealand's largest OCSN over two years and explored implications for OCSN intervention design and evaluation. We analysed metadata relating to 133,096 OCSN interactions during 2011 and 2012. Metrics covered aggregate network activity, user posting activity and longevity, and between-user commenting. Binary logistic regression models were estimated to investigate the feasibility of predicting low user engagement using early interaction data. Repeating periodic peaks and troughs in aggregate activity related not only to seasonality (e.g., New Year), but also to day of the week. Out of 2,062 unique users, 69 Highly Engaged Users (180+ interactions each) contributed 69% of all OCSN interactions in 2012 compared to 1.3% contributed by 864 Minimally Engaged Users (< = 2 items each). The proportion of Highly Engaged Users increased with network growth between 2011 and 2012 (with marginal significance), but the proportion of Minimally Engaged Users did not decline substantively. First week interaction data enabled identification of Minimally Engaged Users with high specificity and sensitivity (AUROC= 0.94). Results suggest future research should develop and test interventions that promote activity, and hence cessation support, amongst specific user groups or at key time points. For example, early usage information could help identify Minimally Engaged Users for tests of targeted messaging designed to improve their integration into, or re-engagement with, the OCSN. Furthermore, although we observed strong growth over time on varied metrics including posts and comments, this change did not coincide with large gains in first-time user persistence. Researchers assessing intervention effects should therefore examine multiple measures when evaluating changes in network dynamics over time.

A Method for Automated Detection of Usability Problems from Client User Interface Events

PubMed Central

Saadawi, Gilan M.; Legowski, Elizabeth; Medvedeva, Olga; Chavan, Girish; Crowley, Rebecca S.

2005-01-01

Think-aloud usability analysis provides extremely useful data but is very time-consuming and expensive to perform because of the extensive manual video analysis that is required. We describe a simple method for automated detection of usability problems from client user interface events for a developing medical intelligent tutoring system. The method incorporates (1) an agent-based method for communication that funnels all interface events and system responses to a centralized database, (2) a simple schema for representing interface events and higher order subgoals, and (3) an algorithm that reproduces the criteria used for manual coding of usability problems. A correction factor was empirically determining to account for the slower task performance of users when thinking aloud. We tested the validity of the method by simultaneously identifying usability problems using TAU and manually computing them from stored interface event data using the proposed algorithm. All usability problems that did not rely on verbal utterances were detectable with the proposed method. PMID:16779121

Ergonomic factors related to drop-off detection with the long cane: effects of cane tips and techniques.

PubMed

Kim, Dae Shik; Emerson, Robert S Wall; Curtis, Amy B

2010-06-01

This study examined the effect of cane tips and cane techniques on drop-off detection with the long cane. Blind pedestrians depend on a long cane to detect drop-offs. Missing a drop-off may result in falls or collision with moving vehicles in the street. Although cane tips appear to affect a cane user's ability to detect drop-offs, few experimental studies have examined such effect. A repeated-measures design with block randomization was used for the study. Participants were 17 adults who were legally blind and had no other disabilities. Participants attempted to detect the drop-offs of varied depths using different cane tips and cane techniques. Drop-off detection rates were similar between the marshmallow tip (77.0%) and the marshmallow roller tip (79.4%) when both tips were used with the constant contact technique, p = .294. However, participants detected drop-offs at a significantly higher percentage when they used the constant contact technique with the marshmallow roller tip (79.4%) than when they used the two-point touch technique with the marshmallow tip (63.2%), p < .001. The constant contact technique used with a marshmallow roller tip (perceived as a less advantageous tip) was more effective than the two-point touch technique used with a marshmallow tip (perceived as a more advantageous tip) in detecting drop-offs. The findings of the study may help cane users and orientation and mobility specialists select appropriate cane techniques and cane tips in accordance with the cane user's characteristics and the nature of the travel environment.

A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system

NASA Astrophysics Data System (ADS)

Hu, Fengye; Du, Dakun; Zhang, Peng; Wang, Zhijun

2014-11-01

In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.

User Facilities

Science.gov Websites

Los Alamos National Laboratory Search Site submit About Mission Business Newsroom Publications Los Innovation in New Mexico Los Alamos Collaboration for Explosives Detection (LACED) SensorNexus Exascale Computing Project (ECP) User Facilities Center for Integrated Nanotechnologies (CINT) Los Alamos Neutron

Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

PubMed Central

Barboza, Philippe; Vaillant, Laetitia; Mawudeku, Abla; Nelson, Noele P.; Hartley, David M.; Madoff, Lawrence C.; Linge, Jens P.; Collier, Nigel; Brownstein, John S.; Yangarber, Roman; Astagneau, Pascal; on behalf of the Early Alerting, Reporting Project of the Global Health Security Initiative

2013-01-01

The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7–13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users. PMID:23472077

Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events.

PubMed

Barboza, Philippe; Vaillant, Laetitia; Mawudeku, Abla; Nelson, Noele P; Hartley, David M; Madoff, Lawrence C; Linge, Jens P; Collier, Nigel; Brownstein, John S; Yangarber, Roman; Astagneau, Pascal

2013-01-01

The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7-13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.

[Early detection of breast and cervical cancer among indigenous communities in Morelos, Mexico].

PubMed

Campero, Lourdes; Atienzo, Erika E; Marín, Eréndira; de la Vara-Salazar, Elvia; Pelcastre-Villafuerte, Blanca; González, Guillermo

2014-01-01

To analyze the perception in relation to when and how to perform actions for the early detection of breast and cervical cancer among women and health care providers in communities with a high percentage of indigenous population in Morelos, Mexico. Ten health providers and 58 women users of health services were interviewed which have a first level of attention in five communities. The analysis was developed under the approach of the Grounded Theory. Providers are poorly informed about current regulations and specific clinical indications for the detection of cervical and breast cancer. Few practice health literacy under intercultural sensitization. The users have imprecise or wrong notions of the early detection. The need for training in adherence to norms is evident. It is urgent to assume a culturally relevant approach to enable efficient communication and promote health literacy for early detection of these two cancers.

Detection of adverse drug reactions among ordinary users of liraglutide on the occasion of drug dispensing in the community pharmacy setting.

PubMed

Christensen, Søren Troels; Bjerrum, Ole Jannik

2013-12-01

Postmarketing studies of drugs forms an essential part of safety surveillance. In particular, this concerns new drugs as safety information of these by large rests on randomized clinical studies conducted on a limited number of subjects before licensing. Pharmacists in community pharmacies are in a unique position for detection of user experienced adverse drug reactions (ADRs) in relation to drug dispensing. The study reports from a research initiative exploring prompt and proactive ADR detection of liraglutide and reporting facilitated by pharmacy students undertaking internship in a community pharmacy in Denmark. Nineteen pharmacy students undertaking regular 6 months' internship--eighth semester--in a Danish community pharmacy participated in the data collection. Before the data collection, students attended an interactive training seminar addressing ADRs in general, organ symptoms, diagnostic classification, and pharmacovigilance systems. Pharmacy students approached recurrent drug users purchasing liraglutide. Participating users were asked about experienced ADRs linked to liraglutide use. Reported ADRs were collected and analyzed. Sixty-two liraglutide users participated in the study, of whom, 38 reported 84 ADRs possibly linked to liraglutide usage. Nausea was by far the most reported ADR followed by decreased appetite, diarrhea, fatigue, and abdominal pain (upper). The reported ADRs are in accordance with previously reported ADRs. The study has demonstrated the feasibility of community pharmacy driven pharmacovigilance. The study supports the thesis that community pharmacists in the future may play a proactive and prominent role in patient-centered pharmacovigilance.

Viewer: a User Interface for Failure Region Analysis

DTIC Science & Technology

1990-12-01

another possible area of continued research. The program could detect whether the user is a beginner , intermediate, or expert and provide different...interfaces for each level. The beginner level would provide detailed help functions, and prompt the user with detailed explanations of what the program...June 1990. Brooke, J.B. and Duncan, K.D., "Experimental Studies of Flowchart Use at Different Stages of Program Debugging" (Ergonomics, Vol 23, No

Evaluation of the user seal check on gross leakage detection of 3 different designs of N95 filtering facepiece respirators.

PubMed

Lam, Simon C; Lui, Andrew K F; Lee, Linda Y K; Lee, Joseph K L; Wong, K F; Lee, Cathy N Y

2016-05-01

The use of N95 respirators prevents spread of respiratory infectious agents, but leakage hampers its protection. Manufacturers recommend a user seal check to identify on-site gross leakage. However, no empirical evidence is provided. Therefore, this study aims to examine validity of a user seal check on gross leakage detection in commonly used types of N95 respirators. A convenience sample of 638 nursing students was recruited. On the wearing of 3 different designs of N95 respirators, namely 3M-1860s, 3M-1862, and Kimberly-Clark 46827, the standardized user seal check procedure was carried out to identify gross leakage. Repeated testing of leakage was followed by the use of a quantitative fit testing (QNFT) device in performing normal breathing and deep breathing exercises. Sensitivity, specificity, predictive values, and likelihood ratios were calculated accordingly. As indicated by QNFT, prevalence of actual gross leakage was 31.0%-39.2% with the 3M respirators and 65.4%-65.8% with the Kimberly-Clark respirator. Sensitivity and specificity of the user seal check for identifying actual gross leakage were approximately 27.7% and 75.5% for 3M-1860s, 22.1% and 80.5% for 3M-1862, and 26.9% and 80.2% for Kimberly-Clark 46827, respectively. Likelihood ratios were close to 1 (range, 0.89-1.51) for all types of respirators. The results did not support user seal checks in detecting any actual gross leakage in the donning of N95 respirators. However, such a check might alert health care workers that donning a tight-fitting respirator should be performed carefully. Copyright © 2016 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Novel centrifugal technology for measuring sperm concentration in the home.

PubMed

Schaff, Ulrich Y; Fredriksen, Laura L; Epperson, Jon G; Quebral, Tiffany R; Naab, Sara; Sarno, Mark J; Eisenberg, Michael L; Sommer, Greg J

2017-02-01

To evaluate the analytical performance and usability of the Trak Male Fertility Testing System, a semiquantitative (categorical) device recently US Food and Drug Administration (FDA)-cleared for measuring sperm concentration in the home by untrained users. A three-site clinical trial comparing self-reported lay user results versus reference results obtained by computer-aided semen analysis (CASA). Simulated home use environments at fertility centers and urologist offices. A total of 239 untrained users. None. Sperm concentration results reported from self-testing lay users and laboratory reference method by CASA were evaluated semiquantitatively against the device's clinical cutoffs of 15 M/mL (current World Health Organization cutoff) and 55 M/mL (associated with faster time to pregnancy). Additional reported metrics include assay linearity, precision, limit of detection, and ease-of-use ratings from lay users. Lay users achieved an accuracy (versus the reference) of 93.3% (95% confidence interval [CI] 84.1%-97.4%) for results categorized as ≤15 M/mL, 82.4% (95% CI 73.3%-88.9%) for results categorized as 15-55 M/mL, and 95.5% (95% CI 88.9%-98.2%) for results categorized as >55 M/mL. When measured quantitatively, Trak results had a strong linear correlation with CASA measurements (r = 0.99). The precision and limit of detection studies show that the device has adequate reproducibility and detection range for home use. Subjects generally rated the device as easy to use. The Trak System is an accurate tool for semiquantitatively measuring sperm concentration in the home. The system may enable screening and longitudinal assessment of sperm concentration at home. ClinicalTrials.gov identifier: NCT02475395. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Cue-reactivity in experienced electronic cigarette users: Novel stimulus videos and a pilot fMRI study

PubMed Central

Nichols, Travis T.; Foulds, Jonathan; Yingst, Jessica; Veldheer, Susan; Hrabovsky, Shari; Richie, John; Eissenberg, Thomas; Wilson, Stephen J.

2015-01-01

Some individuals who try electronic cigarettes (e-cigarettes) continue to use long-term. Previous research has investigated the safety of e-cigarettes and their potential for use in smoking cessation, but comparatively little research has explored chronic or habitual e-cigarette use. In particular, the relationship between e-cigarette cues and craving is unknown. We sought to bridge this gap by developing a novel set of e-cigarette (salient) and electronic toothbrush (neutral) videos for use in cue-reactivity paradigms. Additionally, we demonstrate the utility of this approach in a pilot fMRI study of 7 experienced e-cigarette users. Participants were scanned while viewing the cue videos before and after 10 minute use of their own e-cigarettes (producing an 11.7 ng/ml increase in plasma nicotine concentration). A significant session (pre- and post-use) by video type (salient and neutral) interaction was exhibited in many sensorimotor areas commonly activated in other cue-reactivity paradigms. We did not detect significant cue-related activity in other brain regions notable in the craving literature. Possible reasons for this discrepancy are discussed, including the importance of matching cue stimuli to participants’ experiences. PMID:26478134

A Study of a Handrim-Activated Power-Assist Wheelchair Based on a Non-Contact Torque Sensor

PubMed Central

Nam, Ki-Tae; Jang, Dae-Jin; Kim, Yong Chol; Heo, Yoon; Hong, Eung-Pyo

2016-01-01

Demand for wheelchairs is increasing with growing numbers of aged and disabled persons. Manual wheelchairs are the most commonly used assistive device for mobility because they are convenient to transport. Manual wheelchairs have several advantages but are not easy to use for the elderly or those who lack muscular strength. Therefore, handrim-activated power-assist wheelchairs (HAPAW) that can aid driving power with a motor by detecting user driving intentions through the handrim are being researched. This research will be on HAPAW that judge user driving intentions by using non-contact torque sensors. To deliver the desired motion, which is sensed from handrim rotation relative to a fixed controller, a new driving wheel mechanism is designed by applying a non-contact torque sensor, and corresponding torques are simulated. Torques are measured by a driving wheel prototype and compared with simulation results. The HAPAW prototype was developed using the wheels and a driving control algorithm that uses left and right input torques and time differences are used to check if the non-contact torque sensor can distinguish users’ driving intentions. Through this procedure, it was confirmed that the proposed sensor can be used effectively in HAPAW. PMID:27509508

Cue-reactivity in experienced electronic cigarette users: Novel stimulus videos and a pilot fMRI study.

PubMed

Nichols, Travis T; Foulds, Jonathan; Yingst, Jessica M; Veldheer, Susan; Hrabovsky, Shari; Richie, John; Eissenberg, Thomas; Wilson, Stephen J

2016-05-01

Some individuals who try electronic cigarettes (e-cigarettes) continue to use long-term. Previous research has investigated the safety of e-cigarettes and their potential for use in smoking cessation, but comparatively little research has explored chronic or habitual e-cigarette use. In particular, the relationship between e-cigarette cues and craving is unknown. We sought to bridge this gap by developing a novel set of e-cigarette (salient) and electronic toothbrush (neutral) videos for use in cue-reactivity paradigms. Additionally, we demonstrate the utility of this approach in a pilot fMRI study of 7 experienced e-cigarette users. Participants were scanned while viewing the cue videos before and after 10min use of their own e-cigarettes (producing an 11.7ng/ml increase in plasma nicotine concentration). A significant session (pre- and post-use) by video type (salient and neutral) interaction was exhibited in many sensorimotor areas commonly activated in other cue-reactivity paradigms. We did not detect significant cue-related activity in other brain regions notable in the craving literature. Possible reasons for this discrepancy are discussed, including the importance of matching cue stimuli to participants' experiences. Copyright © 2015 Elsevier Inc. All rights reserved.

Haptic device for colonoscopy training simulator.

PubMed

Kwon, Jun Yong; Woo, Hyun Soo; Lee, Doo Yong

2005-01-01

A new 2-DOF haptic device for colonoscopy training simulator employing flexible endoscopes, is developed. The user operates the device in translational and roll directions. The developed folding guides of the device keep the endoscope tube straight. This helps transmit large decoupled forces of the colonoscopy simulation to the user. The device also includes a mechanism to detect jiggling motion of the scopes to allow users to practice this important skill of the colonoscopy. The device includes PD controller to compensate the inertia and friction effects. This provides the users with better transparent sensation of the simulation.

Air-Sense: indoor environment monitoring evaluation system based on ZigBee network

NASA Astrophysics Data System (ADS)

Huang, Yang; Hu, Liang; Yang, Disheng; Liu, Hengchang

2017-08-01

In the modern life, people spend most of their time indoors. However, indoor environmental quality problems have always been affecting people’s social activities. In general, indoor environmental quality is also related to our indoor activities. Since most of the organic irritants and volatile gases are colorless, odorless and too tiny to be seen, because we have been unconsciously overlooked indoor environment quality. Consequently, our body suffer a great health problem. In this work, we propose Air-Sense system which utilizes the platform of ZigBee Network to collect and detect the real-time indoor environment quality. What’s more, Air-Sense system can also provide data analysis, and visualizing the results of the indoor environment to the user.

How different types of users develop trust in technology: A qualitative analysis of the antecedents of active and passive user trust in a shared technology

PubMed Central

Xu, Jie; Le, Kim; Deitermann, Annika; Montague, Enid

2014-01-01

The aim of this study was to investigate the antecedents of trust in technology for active users and passive users working with a shared technology. According to the prominence-interpretation theory, to assess the trustworthiness of a technology, a person must first perceive and evaluate elements of the system that includes the technology. An experimental study was conducted with 54 participants who worked in two-person teams in a multi-task environment with a shared technology. Trust in technology was measured using a trust in technology questionnaire and antecedents of trust were elicited using an open-ended question. A list of antecedents of trust in technology was derived using qualitative analysis techniques. The following categories emerged from the antecedent: technology factors, user factors, and task factors. Similarities and differences between active users and passive user responses, in terms of trust in technology were discussed. PMID:24882059

How different types of users develop trust in technology: a qualitative analysis of the antecedents of active and passive user trust in a shared technology.

PubMed

Xu, Jie; Le, Kim; Deitermann, Annika; Montague, Enid

2014-11-01

The aim of this study was to investigate the antecedents of trust in technology for active users and passive users working with a shared technology. According to the prominence-interpretation theory, to assess the trustworthiness of a technology, a person must first perceive and evaluate elements of the system that includes the technology. An experimental study was conducted with 54 participants who worked in two-person teams in a multi-task environment with a shared technology. Trust in technology was measured using a trust in technology questionnaire and antecedents of trust were elicited using an open-ended question. A list of antecedents of trust in technology was derived using qualitative analysis techniques. The following categories emerged from the antecedent: technology factors, user factors, and task factors. Similarities and differences between active users and passive user responses, in terms of trust in technology were discussed. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Semantic-gap-oriented active learning for multilabel image annotation.

PubMed

Tang, Jinhui; Zha, Zheng-Jun; Tao, Dacheng; Chua, Tat-Seng

2012-04-01

User interaction is an effective way to handle the semantic gap problem in image annotation. To minimize user effort in the interactions, many active learning methods were proposed. These methods treat the semantic concepts individually or correlatively. However, they still neglect the key motivation of user feedback: to tackle the semantic gap. The size of the semantic gap of each concept is an important factor that affects the performance of user feedback. User should pay more efforts to the concepts with large semantic gaps, and vice versa. In this paper, we propose a semantic-gap-oriented active learning method, which incorporates the semantic gap measure into the information-minimization-based sample selection strategy. The basic learning model used in the active learning framework is an extended multilabel version of the sparse-graph-based semisupervised learning method that incorporates the semantic correlation. Extensive experiments conducted on two benchmark image data sets demonstrated the importance of bringing the semantic gap measure into the active learning process.

Dynamic sample size detection in learning command line sequence for continuous authentication.

PubMed

Traore, Issa; Woungang, Isaac; Nakkabi, Youssef; Obaidat, Mohammad S; Ahmed, Ahmed Awad E; Khalilian, Bijan

2012-10-01

Continuous authentication (CA) consists of authenticating the user repetitively throughout a session with the goal of detecting and protecting against session hijacking attacks. While the accuracy of the detector is central to the success of CA, the detection delay or length of an individual authentication period is important as well since it is a measure of the window of vulnerability of the system. However, high accuracy and small detection delay are conflicting requirements that need to be balanced for optimum detection. In this paper, we propose the use of sequential sampling technique to achieve optimum detection by trading off adequately between detection delay and accuracy in the CA process. We illustrate our approach through CA based on user command line sequence and naïve Bayes classification scheme. Experimental evaluation using the Greenberg data set yields encouraging results consisting of a false acceptance rate (FAR) of 11.78% and a false rejection rate (FRR) of 1.33%, with an average command sequence length (i.e., detection delay) of 37 commands. When using the Schonlau (SEA) data set, we obtain FAR = 4.28% and FRR = 12%.

Event Detection Using Mobile Phone Mass GPS Data and Their Reliavility Verification by Dmsp/ols Night Light Image

NASA Astrophysics Data System (ADS)

Yuki, Akiyama; Satoshi, Ueyama; Ryosuke, Shibasaki; Adachi, Ryuichiro

2016-06-01

In this study, we developed a method to detect sudden population concentration on a certain day and area, that is, an "Event," all over Japan in 2012 using mass GPS data provided from mobile phone users. First, stay locations of all phone users were detected using existing methods. Second, areas and days where Events occurred were detected by aggregation of mass stay locations into 1-km-square grid polygons. Finally, the proposed method could detect Events with an especially large number of visitors in the year by removing the influences of Events that occurred continuously throughout the year. In addition, we demonstrated reasonable reliability of the proposed Event detection method by comparing the results of Event detection with light intensities obtained from the night light images from the DMSP/OLS night light images. Our method can detect not only positive events such as festivals but also negative events such as natural disasters and road accidents. These results are expected to support policy development of urban planning, disaster prevention, and transportation management.

Chromothripsis Detection and Characterization Using the CTLPScanner Web Server.

PubMed

Yang, Jian; Liu, Bo; Cai, Haoyang

2018-01-01

Accurate detection of chromothripsis event is important to study the mechanisms underlying this phenomenon. CTLPScanner ( http://cgma.scu.edu.cn/CTLPScanner/ ) is a web-based tool for identification and annotation of chromothripsis-like pattern (CTLP) in genomic array data. In this chapter, we illustrate the utility of CTLPScanner for screening chromosome pulverization regions and give interpretation of the results. The web interface offers a set of parameters and thresholds for customized screening. We also provide practical recommendations for effective chromothripsis detection. In addition to the user data processing module, CTLPScanner contains more than 50,000 preprocessed oncogenomic arrays, which allow users to explore the presence of chromothripsis signatures from public data resources.

Loss of laterality in chronic cocaine users: an fMRI investigation of sensorimotor control.

PubMed

Hanlon, Colleen A; Wesley, Michael J; Roth, Alicia J; Miller, Mack D; Porrino, Linda J

2010-01-30

Movement disturbances are often overlooked consequences of chronic cocaine abuse. The purpose of this study was to systematically investigate sensorimotor performance in chronic cocaine users and characterize changes in brain activity among movement-related regions of interest (ROIs) in these users. Functional magnetic resonance imaging data were collected from 14 chronic cocaine users and 15 age- and gender-matched controls. All participants performed a sequential finger-tapping task with their dominant, right hand interleaved with blocks of rest. For each participant, percent signal change from rest was calculated for seven movement-related ROIs in both the left and right hemisphere. Cocaine users had significantly longer reaction times and higher error rates than controls. Whereas the controls used a left-sided network of motor-related brain areas to perform the task, cocaine users activated a less lateralized pattern of brain activity. Users had significantly more activity in the ipsilateral (right) motor and premotor cortical areas, anterior cingulate cortex and the putamen than controls. These data demonstrate that, in addition to the cognitive and affective consequences of chronic cocaine abuse, there are also pronounced alterations in sensorimotor control in these individuals, which are associated with functional alterations throughout movement-related neural networks.

Experiences of User Involvement in Mental Health Settings: User Motivations and Benefits.

PubMed

Neech, Sophie G B; Scott, Helen; Priest, Helena M; Bradley, Eleanor J; Tweed, Alison E

2018-05-12

Despite guidance promoting user involvement, meaningful involvement continues to be debated within services. To effectively implement involvement, it is important to acknowledge why users devote time to such activities. This study explores user representatives' experiences of involvement, including motivations and personal benefits. Thirteen user representatives involved in activities such as staff training and interviews were recruited from a UK National Health Service mental health Trust during 2015. Themes within semi-structured interviews were developed using constructivist grounded theory analysis. Memo-writing, process and focused coding, and core categories supported development of the conceptual framework of being a user representative. Being a user representative was inextricably linked to wellness, yet staff governed opportunities. Making a difference to others and giving back were initial motivating factors. Experiences depended on feeling valued, and the theme of transition captured shifts in identity. User representatives reported increased confidence and wellbeing when supported by staff. However, involvement triggered mental health difficulties, and identified need for regular monitoring and reflection of involvement activities and practice. Services should consider coproduction, where users and staff agree together on involvement definitions. Dedicated involvement workers are crucial to supporting individual wellbeing and monitoring involvement. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

A Smartphone App to Screen for HIV-Related Neurocognitive Impairment

PubMed Central

Robbins, Reuben N.; Brown, Henry; Ehlers, Andries; Joska, John A.; Thomas, Kevin G.F.; Burgess, Rhonda; Byrd, Desiree; Morgello, Susan

2014-01-01

Background Neurocognitive Impairment (NCI) is one of the most common complications of HIV-infection, and has serious medical and functional consequences. However, screening for it is not routine and NCI often goes undiagnosed. Screening for NCI in HIV disease faces numerous challenges, such as limited screening tests, the need for specialized equipment and apparatuses, and highly trained personnel to administer, score and interpret screening tests. To address these challenges, we developed a novel smartphone-based screening tool, NeuroScreen, to detect HIV-related NCI that includes an easy-to-use graphical user interface with ten highly automated neuropsychological tests. Aims To examine NeuroScreen’s: 1) acceptability among patients and different potential users; 2) test construct and criterion validity; and 3) sensitivity and specificity to detect NCI. Methods Fifty HIV+ individuals were administered a gold-standard neuropsychological test battery, designed to detect HIV-related NCI, and NeuroScreen. HIV+ test participants and eight potential provider-users of NeuroScreen were asked about its acceptability. Results There was a high level of acceptability of NeuroScreen by patients and potential provider-users. Moderate to high correlations between individual NeuroScreen tests and paper-and-pencil tests assessing the same cognitive domains were observed. NeuroScreen also demonstrated high sensitivity to detect NCI. Conclusion NeuroScreen, a highly automated, easy-to-use smartphone-based screening test to detect NCI among HIV patients and usable by a range of healthcare personnel could help make routine screening for HIV-related NCI feasible. While NeuroScreen demonstrated robust psychometric properties and acceptability, further testing with larger and less neurocognitively impaired samples is warranted. PMID:24860624

InfoSec-MobCop - Framework for Theft Detection and Data Security on Mobile Computing Devices

NASA Astrophysics Data System (ADS)

Gupta, Anand; Gupta, Deepank; Gupta, Nidhi

People steal mobile devices with the intention of making money either by selling the mobile or by taking the sensitive information stored inside it. Mobile thefts are rising even with existing deterrents in place. This is because; they are ineffective, as they generate unnecessary alerts and might require expensive hardware equipments. In this paper a novel framework termed as InfoSec-MobCop is proposed which secures a mobile user’s data and discovers theft by detecting any anomaly in the user behavior. The anomaly of the user is computed by extracting and monitoring user specific details (typing pattern and usage history). The result of any intrusion attempt by a masquerader is intimated to the service provider through an SMS. Effectiveness of the used approach is discussed using FAR and FRR graphs. The experimental system uses both real users and simulated studies to quantify the effectiveness of the InfoSec-MobCop (Information Security Mobile Cop).

Users guide for the hydroacoustic coverage assessment model (HydroCAM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farrell, T., LLNL

1997-12-01

A model for predicting the detection and localization performance of hydroacoustic monitoring networks has been developed. The model accounts for major factors affecting global-scale acoustic propagation in the ocean. including horizontal refraction, travel time variability due to spatial and temporal fluctuations in the ocean, and detailed characteristics of the source. Graphical user interfaces are provided to setup the models and visualize the results. The model produces maps of network detection coverage and localization area of uncertainty, as well as intermediate results such as predicted path amplitudes, travel time and travel time variance. This Users Guide for the model is organizedmore » into three sections. First a summary of functionality available in the model is presented, including example output products. The second section provides detailed descriptions of each of models contained in the system. The last section describes how to run the model, including a summary of each data input form in the user interface.« less

Betel nut chewing behaviour and its association with oral mucosal lesions and conditions in Ghaziabad, India.

PubMed

Prasad, Sumanth; Anand, Richa; Dhingra, Chandan

2014-01-01

To assess the practices and behaviour among Betel nut users in Ghaziabad and to detect the clinically associated oral mucosal lesions and conditions. A community-based survey was conducted in Ghaziabad among 332 betel nut users. Data on betel nut use was obtained through a self-administered questionnaire. Oral mucosal lesions and conditions were recorded using WHO criteria. Out of 332 betel nut users, 32.8% consumed Gutkha. 62.3% users used betel nut with tobacco. Most of the study population started chewing betel nut because of peer pressure and the habit started at the workplace or school. A majority found that there was no physical discomfort due to the habit. The significant oral diseases detected were oral leukoplakia in 11.7% and oral submucous fibrosis in 6.1% of individuals. The findings of the present study revealed that 74.7% of the participants were current chewers. 30.4% of all participants had oral mucosal lesions and conditions.

Portable water quality monitoring system

NASA Astrophysics Data System (ADS)

Nizar, N. B.; Ong, N. R.; Aziz, M. H. A.; Alcain, J. B.; Haimi, W. M. W. N.; Sauli, Z.

2017-09-01

Portable water quality monitoring system was a developed system that tested varied samples of water by using different sensors and provided the specific readings to the user via short message service (SMS) based on the conditions of the water itself. In this water quality monitoring system, the processing part was based on a microcontroller instead of Lead and Copper Rule (LCR) machines to receive the results. By using four main sensors, this system obtained the readings based on the detection of the sensors, respectively. Therefore, users can receive the readings through SMS because there was a connection between Arduino Uno and GSM Module. This system was designed to be portable so that it would be convenient for users to carry it anywhere and everywhere they wanted to since the processor used is smaller in size compared to the LCR machines. It was also developed to ease the user to monitor and control the water quality. However, the ranges of the sensors' detection still a limitation in this study.

Automatic removal of cosmic ray signatures in Deep Impact images

NASA Astrophysics Data System (ADS)

Ipatov, S. I.; A'Hearn, M. F.; Klaasen, K. P.

The results of recognition of cosmic ray (CR) signatures on single images made during the Deep Impact mission were analyzed for several codes written by several authors. For automatic removal of CR signatures on many images, we suggest using the code imgclean ( http://pdssbn.astro.umd.edu/volume/didoc_0001/document/calibration_software/dical_v5/) written by E. Deutsch as other codes considered do not work properly automatically with a large number of images and do not run to completion for some images; however, other codes can be better for analysis of certain specific images. Sometimes imgclean detects false CR signatures near the edge of a comet nucleus, and it often does not recognize all pixels of long CR signatures. Our code rmcr is the only code among those considered that allows one to work with raw images. For most visual images made during low solar activity at exposure time t > 4 s, the number of clusters of bright pixels on an image per second per sq. cm of CCD was about 2-4, both for dark and normal sky images. At high solar activity, it sometimes exceeded 10. The ratio of the number of CR signatures consisting of n pixels obtained at high solar activity to that at low solar activity was greater for greater n. The number of clusters detected as CR signatures on a single infrared image is by at least a factor of several greater than the actual number of CR signatures; the number of clusters based on analysis of two successive dark infrared frames is in agreement with an expected number of CR signatures. Some glitches of false CR signatures include bright pixels repeatedly present on different infrared images. Our interactive code imr allows a user to choose the regions on a considered image where glitches detected by imgclean as CR signatures are ignored. In other regions chosen by the user, the brightness of some pixels is replaced by the local median brightness if the brightness of these pixels is greater by some factor than the median brightness. The interactive code allows one to delete long CR signatures and prevents removal of false CR signatures near the edge of the nucleus of the comet. The interactive code can be applied to editing any digital images. Results obtained can be used for other missions to comets.

Detecting errors and anomalies in computerized materials control and accountability databases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whiteson, R.; Hench, K.; Yarbro, T.

The Automated MC and A Database Assessment project is aimed at improving anomaly and error detection in materials control and accountability (MC and A) databases and increasing confidence in the data that they contain. Anomalous data resulting in poor categorization of nuclear material inventories greatly reduces the value of the database information to users. Therefore it is essential that MC and A data be assessed periodically for anomalies or errors. Anomaly detection can identify errors in databases and thus provide assurance of the integrity of data. An expert system has been developed at Los Alamos National Laboratory that examines thesemore » large databases for anomalous or erroneous data. For several years, MC and A subject matter experts at Los Alamos have been using this automated system to examine the large amounts of accountability data that the Los Alamos Plutonium Facility generates. These data are collected and managed by the Material Accountability and Safeguards System, a near-real-time computerized nuclear material accountability and safeguards system. This year they have expanded the user base, customizing the anomaly detector for the varying requirements of different groups of users. This paper describes the progress in customizing the expert systems to the needs of the users of the data and reports on their results.« less

Emotional persistence in online chatting communities

NASA Astrophysics Data System (ADS)

Garas, Antonios; Garcia, David; Skowron, Marcin; Schweitzer, Frank

2012-05-01

How do users behave in online chatrooms, where they instantaneously read and write posts? We analyzed about 2.5 million posts covering various topics in Internet relay channels, and found that user activity patterns follow known power-law and stretched exponential distributions, indicating that online chat activity is not different from other forms of communication. Analysing the emotional expressions (positive, negative, neutral) of users, we revealed a remarkable persistence both for individual users and channels. I.e. despite their anonymity, users tend to follow social norms in repeated interactions in online chats, which results in a specific emotional ``tone'' of the channels. We provide an agent-based model of emotional interaction, which recovers qualitatively both the activity patterns in chatrooms and the emotional persistence of users and channels. While our assumptions about agent's emotional expressions are rooted in psychology, the model allows to test different hypothesis regarding their emotional impact in online communication.

Emotional persistence in online chatting communities

PubMed Central

Garas, Antonios; Garcia, David; Skowron, Marcin; Schweitzer, Frank

2012-01-01

How do users behave in online chatrooms, where they instantaneously read and write posts? We analyzed about 2.5 million posts covering various topics in Internet relay channels, and found that user activity patterns follow known power-law and stretched exponential distributions, indicating that online chat activity is not different from other forms of communication. Analysing the emotional expressions (positive, negative, neutral) of users, we revealed a remarkable persistence both for individual users and channels. I.e. despite their anonymity, users tend to follow social norms in repeated interactions in online chats, which results in a specific emotional “tone” of the channels. We provide an agent-based model of emotional interaction, which recovers qualitatively both the activity patterns in chatrooms and the emotional persistence of users and channels. While our assumptions about agent's emotional expressions are rooted in psychology, the model allows to test different hypothesis regarding their emotional impact in online communication. PMID:22577512

Effects of congruent and incongruent visual cues on speech perception and brain activity in cochlear implant users.

PubMed

Song, Jae-Jin; Lee, Hyo-Jeong; Kang, Hyejin; Lee, Dong Soo; Chang, Sun O; Oh, Seung Ha

2015-03-01

While deafness-induced plasticity has been investigated in the visual and auditory domains, not much is known about language processing in audiovisual multimodal environments for patients with restored hearing via cochlear implant (CI) devices. Here, we examined the effect of agreeing or conflicting visual inputs on auditory processing in deaf patients equipped with degraded artificial hearing. Ten post-lingually deafened CI users with good performance, along with matched control subjects, underwent H 2 (15) O-positron emission tomography scans while carrying out a behavioral task requiring the extraction of speech information from unimodal auditory stimuli, bimodal audiovisual congruent stimuli, and incongruent stimuli. Regardless of congruency, the control subjects demonstrated activation of the auditory and visual sensory cortices, as well as the superior temporal sulcus, the classical multisensory integration area, indicating a bottom-up multisensory processing strategy. Compared to CI users, the control subjects exhibited activation of the right ventral premotor-supramarginal pathway. In contrast, CI users activated primarily the visual cortices more in the congruent audiovisual condition than in the null condition. In addition, compared to controls, CI users displayed an activation focus in the right amygdala for congruent audiovisual stimuli. The most notable difference between the two groups was an activation focus in the left inferior frontal gyrus in CI users confronted with incongruent audiovisual stimuli, suggesting top-down cognitive modulation for audiovisual conflict. Correlation analysis revealed that good speech performance was positively correlated with right amygdala activity for the congruent condition, but negatively correlated with bilateral visual cortices regardless of congruency. Taken together these results suggest that for multimodal inputs, cochlear implant users are more vision-reliant when processing congruent stimuli and are disturbed more by visual distractors when confronted with incongruent audiovisual stimuli. To cope with this multimodal conflict, CI users activate the left inferior frontal gyrus to adopt a top-down cognitive modulation pathway, whereas normal hearing individuals primarily adopt a bottom-up strategy.

Experiences about HIV-AIDS preventive-control activities. Discourses from non-governmental organizations professionals and users.

PubMed

Berenguera, Anna; Pujol-Ribera, Enriqueta; Violan, Concepció; Romaguera, Amparo; Mansilla, Rosa; Giménez, Albert; Almeda, Jesús

2011-01-01

The main aim of this study was to identify the experiences of professionals in nongovernmental organizations (NGO) in Catalonia (Spain) working in HIV/AIDS prevention and control activities and potential areas of improvement of these activities and their evaluation. A further aim was to characterize the experiences, knowledge and practices of users of these organizations with regard to HIV infection and its prevention. A phenomenological qualitative study was conducted with the participation of both professionals and users of Catalan nongovernmental organizations (NGO) working in HIV/AIDS. Theoretical sampling (professional) and opportunistic sampling (users) were performed. To collect information, the following techniques were used: four focus groups and one triangular group (professionals), 22 semi-structured interviews, and two observations (users). A thematic interpretive content analysis was conducted by three analysts. The professionals of nongovernmental organizations working in HIV/AIDS adopted a holistic approach in their activities, maintained confidentiality, had cultural and professional competence and followed the principles of equality and empathy. The users of these organizations had knowledge of HIV/AIDS and understood the risk of infection. However, a gap was found between knowledge, attitudes and behavior. NGO offer distinct activities adapted to users' needs. Professionals emphasize the need for support and improvement of planning and implementation of current assessment. The preventive activities of these HIV/AIDS organizations are based on a participatory health education model adjusted to people's needs and focused on empowerment. Copyright © 2010 SESPAS. Published by Elsevier Espana. All rights reserved.

SEROLOGICAL RESPONSES TO CRYPTOSPORIDIUM ANTIGENS AMONG USERS OF SURFACE VERSUS UNDERGROUND DRINKING WATER SOURCES

EPA Science Inventory

Cryptosporidium oocysts have been detected in source and treated drinking waters in the United States and elsewhere. Enhanced enteric disease surveillance, initiated following detection of oocysts, has not often detected elevated rates of infection or of symptoms compatible with...

A Support System for Mouse Operations Using Eye-Gaze Input

NASA Astrophysics Data System (ADS)

Abe, Kiyohiko; Nakayama, Yasuhiro; Ohi, Shoichi; Ohyama, Minoru

We have developed an eye-gaze input system for people with severe physical disabilities, such as amyotrophic lateral sclerosis (ALS) patients. This system utilizes a personal computer and a home video camera to detect eye-gaze under natural light. The system detects both vertical and horizontal eye-gaze by simple image analysis, and does not require special image processing units or sensors. Our conventional eye-gaze input system can detect horizontal eye-gaze with a high degree of accuracy. However, it can only classify vertical eye-gaze into 3 directions (up, middle and down). In this paper, we propose a new method for vertical eye-gaze detection. This method utilizes the limbus tracking method for vertical eye-gaze detection. Therefore our new eye-gaze input system can detect the two-dimension coordinates of user's gazing point. By using this method, we develop a new support system for mouse operation. This system can move the mouse cursor to user's gazing point.

A Human-Centered Design Methodology to Enhance the Usability, Human Factors, and User Experience of Connected Health Systems: A Three-Phase Methodology

PubMed Central

Harte, Richard; Glynn, Liam; Rodríguez-Molinero, Alejandro; Baker, Paul MA; Scharf, Thomas; ÓLaighin, Gearóid

2017-01-01

Background Design processes such as human-centered design, which involve the end user throughout the product development and testing process, can be crucial in ensuring that the product meets the needs and capabilities of the user, particularly in terms of safety and user experience. The structured and iterative nature of human-centered design can often present a challenge when design teams are faced with the necessary, rapid, product development life cycles associated with the competitive connected health industry. Objective We wanted to derive a structured methodology that followed the principles of human-centered design that would allow designers and developers to ensure that the needs of the user are taken into account throughout the design process, while maintaining a rapid pace of development. In this paper, we present the methodology and its rationale before outlining how it was applied to assess and enhance the usability, human factors, and user experience of a connected health system known as the Wireless Insole for Independent and Safe Elderly Living (WIISEL) system, a system designed to continuously assess fall risk by measuring gait and balance parameters associated with fall risk. Methods We derived a three-phase methodology. In Phase 1 we emphasized the construction of a use case document. This document can be used to detail the context of use of the system by utilizing storyboarding, paper prototypes, and mock-ups in conjunction with user interviews to gather insightful user feedback on different proposed concepts. In Phase 2 we emphasized the use of expert usability inspections such as heuristic evaluations and cognitive walkthroughs with small multidisciplinary groups to review the prototypes born out of the Phase 1 feedback. Finally, in Phase 3 we emphasized classical user testing with target end users, using various metrics to measure the user experience and improve the final prototypes. Results We report a successful implementation of the methodology for the design and development of a system for detecting and predicting falls in older adults. We describe in detail what testing and evaluation activities we carried out to effectively test the system and overcome usability and human factors problems. Conclusions We feel this methodology can be applied to a wide variety of connected health devices and systems. We consider this a methodology that can be scaled to different-sized projects accordingly. PMID:28302594

PMU Data Event Detection: A User Guide for Power Engineers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allen, A.; Singh, M.; Muljadi, E.

2014-10-01

This user guide is intended to accompany a software package containing a Matrix Laboratory (MATLAB) script and related functions for processing phasor measurement unit (PMU) data. This package and guide have been developed by the National Renewable Energy Laboratory and the University of Texas at Austin. The objective of this data processing exercise is to discover events in the vast quantities of data collected by PMUs. This document attempts to cover some of the theory behind processing the data to isolate events as well as the functioning of the MATLAB scripts. The report describes (1) the algorithms and mathematical backgroundmore » that the accompanying MATLAB codes use to detect events in PMU data and (2) the inputs required from the user and the outputs generated by the scripts.« less

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection.

PubMed

Ergeneci, Mert; Gokcesu, Kaan; Ertan, Erhan; Kosmas, Panagiotis

2018-02-01

Wearable technology has gained increasing popularity in the applications of healthcare, sports science, and biomedical engineering in recent years. Because of its convenient nature, the wearable technology is particularly useful in the acquisition of the physiological signals. Specifically, the (surface electromyography) sEMG systems, which measure the muscle activation potentials, greatly benefit from this technology in both clinical and industrial applications. However, the current wearable sEMG systems have several drawbacks including inefficient noise cancellation, insufficient measurement quality, and difficult integration to customized applications. Additionally, none of these sEMG data acquisition systems can detect sEMG signals (i.e., contractions), which provides a valuable environment for further studies such as human machine interaction, gesture recognition, and fatigue tracking. To this end, we introduce an embedded, eight channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection. Our design consists of two stages, which are the sEMG sensors and the multichannel data acquisition unit. For the first stage, we propose a low cost, dry, and active sEMG sensor that captures the muscle activation potentials, a data acquisition unit that evaluates these captured multichannel sEMG signals and transmits them to a user interface. In the data acquisition unit, the sEMG signals are processed through embedded, adaptive methods in order to reject the power line noise and detect the muscle contractions. Through extensive experiments, we demonstrate that our sEMG sensor outperforms a widely used commercially available product and our data acquisition system achieves 4.583 dB SNR gain with accuracy in the detection of the contractions.

Usability evaluation of a medication reconciliation tool: Embedding safety probes to assess users' detection of medication discrepancies.

PubMed

Russ, Alissa L; Jahn, Michelle A; Patel, Himalaya; Porter, Brian W; Nguyen, Khoa A; Zillich, Alan J; Linsky, Amy; Simon, Steven R

2018-06-01

An electronic medication reconciliation tool was previously developed by another research team to aid provider-patient communication for medication reconciliation. To evaluate the usability of this tool, we integrated artificial safety probes into standard usability methods. The objective of this article is to describe this method of using safety probes, which enabled us to evaluate how well the tool supports users' detection of medication discrepancies. We completed a mixed-method usability evaluation in a simulated setting with 30 participants: 20 healthcare professionals (HCPs) and 10 patients. We used factual scenarios but embedded three artificial safety probes: (1) a missing medication (i.e., omission); (2) an extraneous medication (i.e., commission); and (3) an inaccurate dose (i.e., dose discrepancy). We measured users' detection of each probe to estimate the probability that a HCP or patient would detect these discrepancies. Additionally, we recorded participants' detection of naturally occurring discrepancies. Each safety probe was detected by ≤50% of HCPs. Patients' detection rates were generally higher. Estimates indicate that a HCP and patient, together, would detect 44.8% of these medication discrepancies. Additionally, HCPs and patients detected 25 and 45 naturally-occurring discrepancies, respectively. Overall, detection of medication discrepancies was low. Findings indicate that more advanced interface designs are warranted. Future research is needed on how technologies can be designed to better aid HCPs' and patients' detection of medication discrepancies. This is one of the first studies to evaluate the usability of a collaborative medication reconciliation tool and assess HCPs' and patients' detection of medication discrepancies. Results demonstrate that embedded safety probes can enhance standard usability methods by measuring additional, clinically-focused usability outcomes. The novel safety probes we used may serve as an initial, standard set for future medication reconciliation research. More prevalent use of safety probes could strengthen usability research for a variety of health information technologies. Published by Elsevier Inc.

Evolution properties of online user preference diversity

NASA Astrophysics Data System (ADS)

Guo, Qiang; Ji, Lei; Liu, Jian-Guo; Han, Jingti

2017-02-01

Detecting the evolution properties of online user preference diversity is of significance for deeply understanding online collective behaviors. In this paper, we empirically explore the evolution patterns of online user rating preference, where the preference diversity is measured by the variation coefficient of the user rating sequence. The statistical results for four real systems show that, for movies and reviews, the user rating preference would become diverse and then get centralized finally. By introducing the empirical variation coefficient, we present a Markov model, which could regenerate the evolution properties of two online systems regarding to the stable variation coefficients. In addition, we investigate the evolution of the correlation between the user ratings and the object qualities, and find that the correlation would keep increasing as the user degree increases. This work could be helpful for understanding the anchoring bias and memory effects of the online user collective behaviors.

Data Randomization and Cluster-Based Partitioning for Botnet Intrusion Detection.

PubMed

Al-Jarrah, Omar Y; Alhussein, Omar; Yoo, Paul D; Muhaidat, Sami; Taha, Kamal; Kim, Kwangjo

2016-08-01

Botnets, which consist of remotely controlled compromised machines called bots, provide a distributed platform for several threats against cyber world entities and enterprises. Intrusion detection system (IDS) provides an efficient countermeasure against botnets. It continually monitors and analyzes network traffic for potential vulnerabilities and possible existence of active attacks. A payload-inspection-based IDS (PI-IDS) identifies active intrusion attempts by inspecting transmission control protocol and user datagram protocol packet's payload and comparing it with previously seen attacks signatures. However, the PI-IDS abilities to detect intrusions might be incapacitated by packet encryption. Traffic-based IDS (T-IDS) alleviates the shortcomings of PI-IDS, as it does not inspect packet payload; however, it analyzes packet header to identify intrusions. As the network's traffic grows rapidly, not only the detection-rate is critical, but also the efficiency and the scalability of IDS become more significant. In this paper, we propose a state-of-the-art T-IDS built on a novel randomized data partitioned learning model (RDPLM), relying on a compact network feature set and feature selection techniques, simplified subspacing and a multiple randomized meta-learning technique. The proposed model has achieved 99.984% accuracy and 21.38 s training time on a well-known benchmark botnet dataset. Experiment results demonstrate that the proposed methodology outperforms other well-known machine-learning models used in the same detection task, namely, sequential minimal optimization, deep neural network, C4.5, reduced error pruning tree, and randomTree.

Detection and clustering of features in aerial images by neuron network-based algorithm

NASA Astrophysics Data System (ADS)

Vozenilek, Vit

2015-12-01

The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.

Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection.

PubMed

Son, Junggab; Park, Juyoung; Oh, Heekuck; Bhuiyan, Md Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

2017-06-12

Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan-Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities.

Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection †

PubMed Central

Son, Junggab; Park, Juyoung; Oh, Heekuck; Bhuiyan, Md Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

2017-01-01

Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan–Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities. PMID:28604628

The first AGILE low-energy (< 30 MeV) Terrestrial Gamma-ray Flashes catalog

NASA Astrophysics Data System (ADS)

Marisaldi, Martino; Fuschino, Fabio; Pittori, Carlotta; Verrecchia, Francesco; Giommi, Paolo; Tavani, Marco; Dietrich, Stefano; Price, Colin; Argan, Andrea; Labanti, Claudio; Galli, Marcello; Longo, Francesco; Del Monte, Ettore; Barbiellini, Guido; Giuliani, Andrea; Bulgarelli, Andrea; Gianotti, Fulvio; Trifoglio, Massimo; Trois, Alessio

2014-05-01

We present the first catalog of Terrestrial Gamma-ray Flashes (TGFs) detected by the Minicalorimeter (MCAL) instrument on-board the AGILE satellite. The catalog includes 308 TGFs detected during the period March 2009 - July 2012 in the +/- 2.5° latitude band and selected to have the maximum photon energy up to 30 MeV. The characteristics of the AGILE events are analysed and compared to the observational framework established by the two other currently active missions capable of detecting TGFs from space, RHESSI and Fermi. A detailed model of the MCAL dead time is presented, which is fundamental to properly interpret our observations, particularly concerning duration, intensity and correlation with lightning sferics detected by the World Wide Lightning Location Network. The TGFs cumulative spectrum supports a low production altitude, in agreement with previous measurements. The AGILE TGF catalog below 30 MeV is publicly accessible online at the website of the ASI Science Data Center (ASDC) http://www.asdc.asi.it/mcaltgfcat/ In addition to the TGF sample properties we also present the catalog website functionalities available to users.

Design of smart home sensor visualizations for older adults.

PubMed

Le, Thai; Reeder, Blaine; Chung, Jane; Thompson, Hilaire; Demiris, George

2014-01-01

Smart home sensor systems provide a valuable opportunity to continuously and unobtrusively monitor older adult wellness. However, the density of sensor data can be challenging to visualize, especially for an older adult consumer with distinct user needs. We describe the design of sensor visualizations informed by interviews with older adults. The goal of the visualizations is to present sensor activity data to an older adult consumer audience that supports both longitudinal detection of trends and on-demand display of activity details for any chosen day. The design process is grounded through participatory design with older adult interviews during a six-month pilot sensor study. Through a secondary analysis of interviews, we identified the visualization needs of older adults. We incorporated these needs with cognitive perceptual visualization guidelines and the emotional design principles of Norman to develop sensor visualizations. We present a design of sensor visualization that integrate both temporal and spatial components of information. The visualization supports longitudinal detection of trends while allowing the viewer to view activity within a specific date. Appropriately designed visualizations for older adults not only provide insight into health and wellness, but also are a valuable resource to promote engagement within care.

Design of smart home sensor visualizations for older adults.

PubMed

Le, Thai; Reeder, Blaine; Chung, Jane; Thompson, Hilaire; Demiris, George

2014-07-24

Smart home sensor systems provide a valuable opportunity to continuously and unobtrusively monitor older adult wellness. However, the density of sensor data can be challenging to visualize, especially for an older adult consumer with distinct user needs. We describe the design of sensor visualizations informed by interviews with older adults. The goal of the visualizations is to present sensor activity data to an older adult consumer audience that supports both longitudinal detection of trends and on-demand display of activity details for any chosen day. The design process is grounded through participatory design with older adult interviews during a six-month pilot sensor study. Through a secondary analysis of interviews, we identified the visualization needs of older adults. We incorporated these needs with cognitive perceptual visualization guidelines and the emotional design principles of Norman to develop sensor visualizations. We present a design of sensor visualization that integrate both temporal and spatial components of information. The visualization supports longitudinal detection of trends while allowing the viewer to view activity within a specific date.CONCLUSIONS: Appropriately designed visualizations for older adults not only provide insight into health and wellness, but also are a valuable resource to promote engagement within care.

Automated videography for residential communications

NASA Astrophysics Data System (ADS)

Kurtz, Andrew F.; Neustaedter, Carman; Blose, Andrew C.

2010-02-01

The current widespread use of webcams for personal video communication over the Internet suggests that opportunities exist to develop video communications systems optimized for domestic use. We discuss both prior and existing technologies, and the results of user studies that indicate potential needs and expectations for people relative to personal video communications. In particular, users anticipate an easily used, high image quality video system, which enables multitasking communications during the course of real-world activities and provides appropriate privacy controls. To address these needs, we propose a potential approach premised on automated capture of user activity. We then describe a method that adapts cinematography principles, with a dual-camera videography system, to automatically control image capture relative to user activity, using semantic or activity-based cues to determine user position and motion. In particular, we discuss an approach to automatically manage shot framing, shot selection, and shot transitions, with respect to one or more local users engaged in real-time, unscripted events, while transmitting the resulting video to a remote viewer. The goal is to tightly frame subjects (to provide more detail), while minimizing subject loss and repeated abrupt shot framing changes in the images as perceived by a remote viewer. We also discuss some aspects of the system and related technologies that we have experimented with thus far. In summary, the method enables users to participate in interactive video-mediated communications while engaged in other activities.

Mitigating the Insider Threat with High-Dimensional Anomaly Detection

DTIC Science & Technology

2004-12-01

a more serious attack. Various systems such as NSM [56], GrIDS [57], snort [58], Emerald [59], and Spice [60] generate alerts for portscan...reboot etc. The user measurements include the user profiles such as time of login , duration of user session, cumulative CPU time, names of files...already been implemented in a real-time system for information retrieval [3]. A technique developed at SRI in the Emerald system [22] uses historical

Optimizing Targeting of Intrusion Detection Systems in Social Networks

NASA Astrophysics Data System (ADS)

Puzis, Rami; Tubi, Meytal; Elovici, Yuval

Internet users communicate with each other in various ways: by Emails, instant messaging, social networking, accessing Web sites, etc. In the course of communicating, users may unintentionally copy files contaminated with computer viruses and worms [1, 2] to their computers and spread them to other users [3]. (Hereafter we will use the term "threats", rather than computer viruses and computer worms). The Internet is the chief source of these threats [4].

Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical usea)

PubMed Central

Huo, Zhimin; Summers, Ronald M.; Paquerault, Sophie; Lo, Joseph; Hoffmeister, Jeffrey; Armato, Samuel G.; Freedman, Matthew T.; Lin, Jesse; Ben Lo, Shih-Chung; Petrick, Nicholas; Sahiner, Berkman; Fryd, David; Yoshida, Hiroyuki; Chan, Heang-Ping

2013-01-01

Computer-aided detection/diagnosis (CAD) is increasingly used for decision support by clinicians for detection and interpretation of diseases. However, there are no quality assurance (QA) requirements for CAD in clinical use at present. QA of CAD is important so that end users can be made aware of changes in CAD performance both due to intentional or unintentional causes. In addition, end-user training is critical to prevent improper use of CAD, which could potentially result in lower overall clinical performance. Research on QA of CAD and user training are limited to date. The purpose of this paper is to bring attention to these issues, inform the readers of the opinions of the members of the American Association of Physicists in Medicine (AAPM) CAD subcommittee, and thus stimulate further discussion in the CAD community on these topics. The recommendations in this paper are intended to be work items for AAPM task groups that will be formed to address QA and user training issues on CAD in the future. The work items may serve as a framework for the discussion and eventual design of detailed QA and training procedures for physicists and users of CAD. Some of the recommendations are considered by the subcommittee to be reasonably easy and practical and can be implemented immediately by the end users; others are considered to be “best practice” approaches, which may require significant effort, additional tools, and proper training to implement. The eventual standardization of the requirements of QA procedures for CAD will have to be determined through consensus from members of the CAD community, and user training may require support of professional societies. It is expected that high-quality CAD and proper use of CAD could allow these systems to achieve their true potential, thus benefiting both the patients and the clinicians, and may bring about more widespread clinical use of CAD for many other diseases and applications. It is hoped that the awareness of the need for appropriate CAD QA and user training will stimulate new ideas and approaches for implementing such procedures efficiently and effectively as well as funding opportunities to fulfill such critical efforts. PMID:23822459

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar

PubMed Central

Shin, Young Hoon; Seo, Jiwon

2016-01-01

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. PMID:27801867

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

PubMed

Shin, Young Hoon; Seo, Jiwon

2016-10-29

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

Computer systems and methods for the query and visualization of multidimensional databases

DOEpatents

Stolte, Chris; Tang, Diane L; Hanrahan, Patrick

2014-04-29

In response to a user request, a computer generates a graphical user interface on a computer display. A schema information region of the graphical user interface includes multiple operand names, each operand name associated with one or more fields of a multi-dimensional database. A data visualization region of the graphical user interface includes multiple shelves. Upon detecting a user selection of the operand names and a user request to associate each user-selected operand name with a respective shelf in the data visualization region, the computer generates a visual table in the data visualization region in accordance with the associations between the operand names and the corresponding shelves. The visual table includes a plurality of panes, each pane having at least one axis defined based on data for the fields associated with a respective operand name.

Computer systems and methods for the query and visualization of multidimensional databases

DOEpatents

Stolte, Chris [Palo Alto, CA; Tang, Diane L [Palo Alto, CA; Hanrahan, Patrick [Portola Valley, CA

2011-02-01

In response to a user request, a computer generates a graphical user interface on a computer display. A schema information region of the graphical user interface includes multiple operand names, each operand name associated with one or more fields of a multi-dimensional database. A data visualization region of the graphical user interface includes multiple shelves. Upon detecting a user selection of the operand names and a user request to associate each user-selected operand name with a respective shelf in the data visualization region, the computer generates a visual table in the data visualization region in accordance with the associations between the operand names and the corresponding shelves. The visual table includes a plurality of panes, each pane having at least one axis defined based on data for the fields associated with a respective operand name.

Computer systems and methods for the query and visualization of multidimensional databases

DOEpatents

Stolte, Chris [Palo Alto, CA; Tang, Diane L [Palo Alto, CA; Hanrahan, Patrick [Portola Valley, CA

2012-03-20

In response to a user request, a computer generates a graphical user interface on a computer display. A schema information region of the graphical user interface includes multiple operand names, each operand name associated with one or more fields of a multi-dimensional database. A data visualization region of the graphical user interface includes multiple shelves. Upon detecting a user selection of the operand names and a user request to associate each user-selected operand name with a respective shelf in the data visualization region, the computer generates a visual table in the data visualization region in accordance with the associations between the operand names and the corresponding shelves. The visual table includes a plurality of panes, each pane having at least one axis defined based on data for the fields associated with a respective operand name.

Affinity+: Semi-Structured Brainstorming on Large Displays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burtner, Edwin R.; May, Richard A.; Scarberry, Randall E.

2013-04-27

Affinity diagraming is a powerful method for encouraging and capturing lateral thinking in a group environment. The Affinity+ Concept was designed to improve the collaborative brainstorm process through the use of large display surfaces in conjunction with mobile devices like smart phones and tablets. The system works by capturing the ideas digitally and allowing users to sort and group them on a large touch screen manually. Additionally, Affinity+ incorporates theme detection, topic clustering, and other processing algorithms that help bring structured analytic techniques to the process without requiring explicit leadership roles and other overhead typically involved in these activities.

Paper analytical devices for detection of low-quality pharmaceuticals

NASA Astrophysics Data System (ADS)

Weaver, A.; Lieberman, M.

2014-03-01

There is currently no global screening system to detect low quality pharmaceuticals, despite widespread recognition of the public health problems caused by substandard and falsified medicines. In order to fill this void, we designed a rapid field screening test that is interfaced with the mobile phone network. The user scrapes a pill over several reaction areas on a paper test card, and then dips one edge of the card into water to activate dried reagents stored on the paper. These reagents carry out multiple color tests and result in a pattern of colored stripes that give information about the chemical content of the pill. The test cards are inexpensive and instrument-free, and we think they will be a scalable testing option in low resource settings. Studies on falsified drugs archived at the FDA show that the test cards are effective at detecting a wide variety of low-quality formulations of many classes of pharmaceuticals, and field tests are currently under way in Kenya.

Thermal Expert System (TEXSYS): Systems autonomy demonstration project, volume 2. Results

NASA Technical Reports Server (NTRS)

Glass, B. J. (Editor)

1992-01-01

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS testbed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

Thermal Expert System (TEXSYS): Systems automony demonstration project, volume 1. Overview

NASA Technical Reports Server (NTRS)

Glass, B. J. (Editor)

1992-01-01

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS test bed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

ElemeNT: a computational tool for detecting core promoter elements.

PubMed

Sloutskin, Anna; Danino, Yehuda M; Orenstein, Yaron; Zehavi, Yonathan; Doniger, Tirza; Shamir, Ron; Juven-Gershon, Tamar

2015-01-01

Core promoter elements play a pivotal role in the transcriptional output, yet they are often detected manually within sequences of interest. Here, we present 2 contributions to the detection and curation of core promoter elements within given sequences. First, the Elements Navigation Tool (ElemeNT) is a user-friendly web-based, interactive tool for prediction and display of putative core promoter elements and their biologically-relevant combinations. Second, the CORE database summarizes ElemeNT-predicted core promoter elements near CAGE and RNA-seq-defined Drosophila melanogaster transcription start sites (TSSs). ElemeNT's predictions are based on biologically-functional core promoter elements, and can be used to infer core promoter compositions. ElemeNT does not assume prior knowledge of the actual TSS position, and can therefore assist in annotation of any given sequence. These resources, freely accessible at http://lifefaculty.biu.ac.il/gershon-tamar/index.php/resources, facilitate the identification of core promoter elements as active contributors to gene expression.

Thermal Expert System (TEXSYS): Systems autonomy demonstration project, volume 2. Results

NASA Astrophysics Data System (ADS)

Glass, B. J.

1992-10-01

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS testbed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

Deficits in learning and memory: parahippocampal hyperactivity and frontocortical hypoactivity in cannabis users.

PubMed

Nestor, Liam; Roberts, Gloria; Garavan, Hugh; Hester, Robert

2008-04-15

The consumption of cannabis has been linked to impairments in human learning and memory, as well as aspects of executive functioning. Cannabis-related impairments in learning and memory in chronic cannabis users, it has been argued, are caused by the effects of cannabis on hippocampal functioning. The current study involved two experiments. Experiment 1 compared 35 current users of cannabis and 38 well-matched controls on a face-name task, previously shown to activate the hippocampal region. Based on the results of experiment 1, experiment 2 used fMRI and a modified version of the face-name task, to examine cortical and (para)hippocampal activity during learning and recall in 14 current users of cannabis and 14 controls. Results of experiment 1 showed that cannabis users were significantly worse with respect to learning, short and long-term memory performance. Experiment 2 showed that despite non-significant differences in learning and memory performance, cannabis users had significantly lower levels of BOLD activity in the right superior temporal gyrus, right superior frontal gyrus, right middle frontal gyrus and left superior frontal gyrus compared to controls during learning. Results also showed that cannabis users had significantly higher BOLD activity in the right parahippocampal gyrus during learning. Hypoactivity in frontal and temporal cortices, and relative hyperactivity in the parahippocampus identify functional deficits and compensatory processes in cannabis users.

Analysis of environmental sounds

NASA Astrophysics Data System (ADS)

Lee, Keansub

Environmental sound archives - casual recordings of people's daily life - are easily collected by MPS players or camcorders with low cost and high reliability, and shared in the web-sites. There are two kinds of user generated recordings we would like to be able to handle in this thesis: Continuous long-duration personal audio and Soundtracks of short consumer video clips. These environmental recordings contain a lot of useful information (semantic concepts) related with activity, location, occasion and content. As a consequence, the environment archives present many new opportunities for the automatic extraction of information that can be used in intelligent browsing systems. This thesis proposes systems for detecting these interesting concepts on a collection of these real-world recordings. The first system is to segment and label personal audio archives - continuous recordings of an individual's everyday experiences - into 'episodes' (relatively consistent acoustic situations lasting a few minutes or more) using the Bayesian Information Criterion and spectral clustering. The second system is for identifying regions of speech or music in the kinds of energetic and highly-variable noise present in this real-world sound. Motivated by psychoacoustic evidence that pitch is crucial in the perception and organization of sound, we develop a noise-robust pitch detection algorithm to locate speech or music-like regions. To avoid false alarms resulting from background noise with strong periodic components (such as air-conditioning), a new scheme is added in order to suppress these noises in the domain of autocorrelogram. In addition, the third system is to automatically detect a large set of interesting semantic concepts; which we chose for being both informative and useful to users, as well as being technically feasible. These 25 concepts are associated with people's activities, locations, occasions, objects, scenes and sounds, and are based on a large collection of consumer videos in conjunction with user studies. We model the soundtrack of each video, regardless of its original duration, as a fixed-sized clip-level summary feature. For each concept, an SVM-based classifier is trained according to three distance measures (Kullback-Leibler, Bhattacharyya, and Mahalanobis distance). Detecting the time of occurrence of a local object (for instance, a cheering sound) embedded in a longer soundtrack is useful and important for applications such as search and retrieval in consumer video archives. We finally present a Markov-model based clustering algorithm able to identify and segment consistent sets of temporal frames into regions associated with different ground-truth labels, and at the same time to exclude a set of uninformative frames shared in common from all clips. The labels are provided at the clip level, so this refinement of the time axis represents a variant of Multiple-Instance Learning (MIL). Quantitative evaluation shows that the performance of our proposed approaches tested on the 60h personal audio archives or 1900 YouTube video clips is significantly better than existing algorithms for detecting these useful concepts in real-world personal audio recordings.

Trauma and PTSD rates in an irish psychiatric population

PubMed Central

Wilson, Fiona E; Hennessy, Eilis; Dooley, Barbara; Kelly, Brendan D; Ryan, Dermot A

2013-01-01

Although Western mental health services are increasingly finding themselves concerned with assisting traumatized individuals migrating from other countries, trauma and posttraumatic stress disorder (PTSD) are under-detected and undiagnosed in psychiatric populations. This study examined and compared rates of traumatic experiences, frequency of traumatic events, trauma symptomatology levels, rates of torture, rates of PTSD and chart documentation of trauma and PTSD between (a) Irish and migrant service-users and (b) forced migrant and voluntary migrant service-users in Dublin, Ireland. Data were gathered from 178 psychiatric outpatients attending using a sociodemographic questionnaire, the Harvard Trauma Questionnaire-Revised Cambodian Version and the SCID-I/P. A substantial number of service-users had experienced at least one lifetime trauma (71.3%), and a high percentage of both the Irish (47.4%) and migrant groups (70.3%) of service-users had experienced two or more events. Overall, analyses comparing rates between Irish, forced migrant and voluntary migrant service-users found that forced migrants displayed more traumatic life events, posttraumatic symptoms, and higher levels of PTSD than their voluntary migrant and Irish counterparts, with over 50% experiencing torture prior to arrival in Ireland. The lifetime rate of PTSD in the overall sample was 15.7% but only 53.57% of cases were documented in patient charts. The results of this study are informative about the nature and extent of the problem of trauma and PTSD among migrant mental health service users as well as highlighting the under-detected levels of trauma among native-born service users. PMID:28228990

Landsat Based Woody Vegetation Loss Detection in Queensland, Australia Using the Google Earth Engine

NASA Astrophysics Data System (ADS)

Johansen, K.; Phinn, S. R.; Taylor, M.

2014-12-01

Land clearing detection and woody Foliage Projective Cover (FPC) monitoring at the state and national level in Australia has mainly been undertaken by state governments and the Terrestrial Ecosystem Research Network (TERN) because of the considerable expense, expertise, sustained duration of activities and staffing levels needed. Only recently have services become available, providing low budget, generalized access to change detection tools suited to this task. The objective of this research was to examine if a globally available service, Google Earth Engine Beta, could be used to predict woody vegetation loss with accuracies approaching the methods used by TERN and the government of the state of Queensland, Australia. Two change detection approaches were investigated using Landsat Thematic Mapper time series and the Google Earth Engine Application Programming Interface: (1) CART and Random Forest classifiers; and (2) a normalized time series of Foliage Projective Cover (FPC) and NDVI combined with a spectral index. The CART and Random Forest classifiers produced high user's and producer's mapping accuracies of clearing (77-92% and 54-77%, respectively) when detecting change within epochs for which training data were available, but extrapolation to epochs without training data reduced the mapping accuracies. The use of FPC and NDVI time series provided a more robust approach for calculation of a clearing probability, as it did not rely on training data but instead on the difference of the normalized FPC / NDVI mean and standard deviation of a single year at the change point in relation to the remaining time series. However, the FPC and NDVI time series approach represented a trade-off between user's and producer's accuracies. Both change detection approaches explored in this research were sensitive to ephemeral greening and drying of the landscape. However, the developed normalized FPC and NDVI time series approach can be tuned to provide automated alerts for large woody vegetation clearing events by selecting suitable thresholds to identify very likely clearing. This research provides a comprehensive foundation to build further capacity to use globally accessible, free, online image datasets and processing tools to accurately detect woody vegetation clearing in an automated and rapid manner.

HIV Infection, Tenofovir, and Urine α1-Microglobulin: A Cross-sectional Analysis in the Multicenter AIDS Cohort Study.

PubMed

Jotwani, Vasantha; Scherzer, Rebecca; Estrella, Michelle M; Jacobson, Lisa P; Witt, Mallory D; Palella, Frank J; Macatangay, Bernard; Bennett, Michael; Parikh, Chirag R; Ix, Joachim H; Shlipak, Michael G

2016-10-01

Tenofovir disoproxil fumarate (TDF) can cause proximal tubular damage and chronic kidney disease in human immunodeficiency virus (HIV)-infected individuals. Urine α1-microglobulin (A1M), a low-molecular-weight protein indicative of proximal tubular dysfunction, may enable earlier detection of TDF-associated tubular toxicity. Cross-sectional. 883 HIV-infected and 350 -uninfected men enrolled in the Multicenter AIDS Cohort Study. HIV infection and TDF exposure. Urine A1M level. Urine A1M was detectable in 737 (83%) HIV-infected and 202 (58%) -uninfected men (P<0.001). Among HIV-infected participants, 573 (65%) were current TDF users and 112 (13%) were past TDF users. After multivariable adjustment including demographics, traditional kidney disease risk factors, and estimated glomerular filtration rate, HIV infection was associated with 136% (95% CI, 104%-173%) higher urine A1M levels and 1.5-fold (95% CI, 1.3- to 1.6-fold) prevalence of detectable A1M. When participants were stratified by TDF exposure, HIV infection was associated with higher adjusted A1M levels, by 164% (95% CI, 127%-208%) among current users, 124% (95% CI, 78%-183%) among past users, and 76% (95% CI, 45%-115%) among never users. Among HIV-infected participants, each year of cumulative TDF exposure was associated with 7.6% (95% CI, 5.4%-9.9%) higher A1M levels in fully adjusted models, a 4-fold effect size relative to advancing age (1.8% [95% CI, 0.9%-2.7%] per year). Each year since TDF treatment discontinuation was associated with 4.9% (95% CI, -9.4%--0.2%) lower A1M levels among past users. Results may not be generalizable to women. HIV-infected men had higher urine A1M levels compared with HIV-uninfected men. Among HIV-infected men, cumulative TDF exposure was associated with incrementally higher A1M levels, whereas time since TDF treatment discontinuation was associated with progressively lower A1M levels. Urine A1M appears to be a promising biomarker for detecting and monitoring TDF-associated tubular toxicity. Copyright © 2016 National Kidney Foundation, Inc. All rights reserved.

Visual and somatic sensory feedback of brain activity for intuitive surgical robot manipulation.

PubMed

Miura, Satoshi; Matsumoto, Yuya; Kobayashi, Yo; Kawamura, Kazuya; Nakashima, Yasutaka; Fujie, Masakatsu G

2015-01-01

This paper presents a method to evaluate the hand-eye coordination of the master-slave surgical robot by measuring the activation of the intraparietal sulcus in users brain activity during controlling virtual manipulation. The objective is to examine the changes in activity of the intraparietal sulcus when the user's visual or somatic feedback is passed through or intercepted. The hypothesis is that the intraparietal sulcus activates significantly when both the visual and somatic sense pass feedback, but deactivates when either visual or somatic is intercepted. The brain activity of three subjects was measured by the functional near-infrared spectroscopic-topography brain imaging while they used a hand controller to move a virtual arm of a surgical simulator. The experiment was performed several times with three conditions: (i) the user controlled the virtual arm naturally under both visual and somatic feedback passed, (ii) the user moved with closed eyes under only somatic feedback passed, (iii) the user only gazed at the screen under only visual feedback passed. Brain activity showed significantly better control of the virtual arm naturally (p<;0.05) when compared with moving with closed eyes or only gazing among all participants. In conclusion, the brain can activate according to visual and somatic sensory feedback agreement.

Early warning and response system (EWARS) for dengue outbreaks: Recent advancements towards widespread applications in critical settings

PubMed Central

Kroeger, Axel; Olliaro, Piero; Rocklöv, Joacim; Sewe, Maquins Odhiambo; Tejeda, Gustavo; Benitez, David; Gill, Balvinder; Hakim, S. Lokman; Gomes Carvalho, Roberta; Bowman, Leigh; Petzold, Max

2018-01-01

Background Dengue outbreaks are increasing in frequency over space and time, affecting people’s health and burdening resource-constrained health systems. The ability to detect early emerging outbreaks is key to mounting an effective response. The early warning and response system (EWARS) is a toolkit that provides countries with early-warning systems for efficient and cost-effective local responses. EWARS uses outbreak and alarm indicators to derive prediction models that can be used prospectively to predict a forthcoming dengue outbreak at district level. Methods We report on the development of the EWARS tool, based on users’ recommendations into a convenient, user-friendly and reliable software aided by a user’s workbook and its field testing in 30 health districts in Brazil, Malaysia and Mexico. Findings 34 Health officers from the 30 study districts who had used the original EWARS for 7 to 10 months responded to a questionnaire with mainly open-ended questions. Qualitative content analysis showed that participants were generally satisfied with the tool but preferred open-access vs. commercial software. EWARS users also stated that the geographical unit should be the district, while access to meteorological information should be improved. These recommendations were incorporated into the second-generation EWARS-R, using the free R software, combined with recent surveillance data and resulted in higher sensitivities and positive predictive values of alarm signals compared to the first-generation EWARS. Currently the use of satellite data for meteorological information is being tested and a dashboard is being developed to increase user-friendliness of the tool. The inclusion of other Aedes borne viral diseases is under discussion. Conclusion EWARS is a pragmatic and useful tool for detecting imminent dengue outbreaks to trigger early response activities. PMID:29727447

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

PubMed

Guvensan, M Amac; Kansiz, A Oguz; Camgoz, N Cihan; Turkmen, H Irem; Yavuz, A Gokhan; Karsligil, M Elif

2017-06-23

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

Soft Smart Garments for Lower Limb Joint Position Analysis.

PubMed

Totaro, Massimo; Poliero, Tommaso; Mondini, Alessio; Lucarotti, Chiara; Cairoli, Giovanni; Ortiz, Jesùs; Beccai, Lucia

2017-10-12

Revealing human movement requires lightweight, flexible systems capable of detecting mechanical parameters (like strain and pressure) while being worn comfortably by the user, and not interfering with his/her activity. In this work we address such multifaceted challenge with the development of smart garments for lower limb motion detection, like a textile kneepad and anklet in which soft sensors and readout electronics are embedded for retrieving movement of the specific joint. Stretchable capacitive sensors with a three-electrode configuration are built combining conductive textiles and elastomeric layers, and distributed around knee and ankle. Results show an excellent behavior in the ~30% strain range, hence the correlation between sensors' responses and the optically tracked Euler angles is allowed for basic lower limb movements. Bending during knee flexion/extension is detected, and it is discriminated from any external contact by implementing in real time a low computational algorithm. The smart anklet is designed to address joint motion detection in and off the sagittal plane. Ankle dorsi/plantar flexion, adduction/abduction, and rotation are retrieved. Both knee and ankle smart garments show a high accuracy in movement detection, with a RMSE less than 4° in the worst case.

Soft Smart Garments for Lower Limb Joint Position Analysis

PubMed Central

Totaro, Massimo; Poliero, Tommaso; Mondini, Alessio; Lucarotti, Chiara; Cairoli, Giovanni; Ortiz, Jesùs; Beccai, Lucia

2017-01-01

Revealing human movement requires lightweight, flexible systems capable of detecting mechanical parameters (like strain and pressure) while being worn comfortably by the user, and not interfering with his/her activity. In this work we address such multifaceted challenge with the development of smart garments for lower limb motion detection, like a textile kneepad and anklet in which soft sensors and readout electronics are embedded for retrieving movement of the specific joint. Stretchable capacitive sensors with a three-electrode configuration are built combining conductive textiles and elastomeric layers, and distributed around knee and ankle. Results show an excellent behavior in the ~30% strain range, hence the correlation between sensors’ responses and the optically tracked Euler angles is allowed for basic lower limb movements. Bending during knee flexion/extension is detected, and it is discriminated from any external contact by implementing in real time a low computational algorithm. The smart anklet is designed to address joint motion detection in and off the sagittal plane. Ankle dorsi/plantar flexion, adduction/abduction, and rotation are retrieved. Both knee and ankle smart garments show a high accuracy in movement detection, with a RMSE less than 4° in the worst case. PMID:29023365

Designing for privacy management in hospitals: Understanding the gap between user activities and IT staff's understandings.

PubMed

Eikey, Elizabeth V; Murphy, Alison R; Reddy, Madhu C; Xu, Heng

2015-12-01

We examined the role of privacy in collaborative clinical work and how it is understood by hospital IT staff. The purpose of our study was to identify the gaps between hospital IT staff members' perceptions of how electronic health record (EHR) users' protect the privacy of patient information and how users actually protect patients' private information in their daily collaborative activities. Since the IT staff play an important role in implementing and maintaining the EHR, any gaps that exist between the IT staff's perceptions of user work practices and the users' actual work practices can result in a number of problems in the configuration, implementation, or customization of the EHR, which can lead to collaboration challenges, interrupted workflow, and privacy breaches. We used qualitative data collection methods for this study. We conducted semi-structured interviews with 20 hospital IT staff members. We also conducted observations of EHR users in the in-patient units of the same hospital. We identified gaps in IT staff's understandings of users' work activities, especially in regards to privacy-compromising workarounds that are used by users and why they are used. We discuss the reasons why this gap may exist between IT staff and users and ways to improve IT staff's understanding of why users perform certain privacy-compromising workarounds. A hospital's IT staff face a daunting task in ensuring users' collaborative work practices are supported by the system while providing effective privacy mechanisms. In order to achieve both goals, the IT staff must have a clear understanding of their users' practices. However, as this study highlights, there may be a mismatch between the IT staff's understandings of how users protect patient privacy and how users actually protect privacy. Copyright © 2015. Published by Elsevier Ireland Ltd.

Integrated multisensor perimeter detection systems

NASA Astrophysics Data System (ADS)

Kent, P. J.; Fretwell, P.; Barrett, D. J.; Faulkner, D. A.

2007-10-01

The report describes the results of a multi-year programme of research aimed at the development of an integrated multi-sensor perimeter detection system capable of being deployed at an operational site. The research was driven by end user requirements in protective security, particularly in threat detection and assessment, where effective capability was either not available or prohibitively expensive. Novel video analytics have been designed to provide robust detection of pedestrians in clutter while new radar detection and tracking algorithms provide wide area day/night surveillance. A modular integrated architecture based on commercially available components has been developed. A graphical user interface allows intuitive interaction and visualisation with the sensors. The fusion of video, radar and other sensor data provides the basis of a threat detection capability for real life conditions. The system was designed to be modular and extendable in order to accommodate future and legacy surveillance sensors. The current sensor mix includes stereoscopic video cameras, mmWave ground movement radar, CCTV and a commercially available perimeter detection cable. The paper outlines the development of the system and describes the lessons learnt after deployment in a pilot trial.

Detection of levamisole exposure in cocaine users by liquid chromatography-tandem mass spectrometry.

PubMed

Lynch, Kara L; Dominy, Stephen S; Graf, Jonathan; Kral, Alexander H

2011-04-01

Levamisole, a veterinary antihelminthic, was recently recognized as an adulterant in cocaine and is known to cause severe adverse reactions in some cocaine users. Because of the health concerns involving levamisole-adulterated cocaine, we developed a liquid chromatography-tandem mass spectrometry (LC-MS-MS) method for the detection of levamisole in urine. This method was used to determine the prevalence of levamisole in cocaine-positive patient samples. All cocaine-positive urine samples that were sent to the San Francisco General Hospital Clinical Laboratory were tested for levamisole for one month. For LC, an Agilent 1200 series was used with a C(18) column and a gradient of mobile phase A (0.05% formic acid) and B (acetonitrile/methanol). Detection was carried out with an Applied Biosystems QTRAP(®) LC-MS-MS. The levamisole LC-MS-MS method was linear over the range of 5-2500 ng/mL (r > 0.996). Interassay and intraassay CVs were < 6%. The lower limit of detection for levamisole was 0.5 ng/mL. Out of 949 total urine drug screens, 20% were positive for benzoylecgonine, and of those, 88% were positive for levamisole. The high prevalence of levamisole-adulterated cocaine and potential toxicity in cocaine users is a serious public health concern. These findings validate the utility of an LC-MS-MS method for the detection of levamisole.

Involvement of the end user: exploration of older people’s needs and preferences for a wearable fall detection device – a qualitative descriptive study

PubMed Central

Thilo, Friederike JS; Bilger, Selina; Halfens, Ruud JG; Schols, Jos MGA; Hahn, Sabine

2017-01-01

Purpose To explore the needs and preferences of community-dwelling older people, by involving them in the device design and mock-up development stage of a fall detection device, consisting of a body-worn sensor linked to a smartphone application. Patients and methods A total of 22 community-dwelling persons 75 years of age and older were involved in the development of a fall detection device. Three semistructured focus group interviews were conducted. The interview data were analyzed using qualitative descriptive analysis with deductive coding. Results The mock-up of a waterproof, body-worn, automatic and manual alerting device, which served both as a day-time wearable sensor and a night-time wearable sensor, was welcomed. Changes should be considered regarding shape, color and size along with alternate ways of integrating the sensor with items already in use in daily life, such as jewelry and personal watches. The reliability of the sensor is key for the participants. Issues important to the alerting process were discussed, for instance, who should be contacted and why. Several participants were concerned with the mandatory use of the smartphone and assumed that it would be difficult to use. They criticized the limited distance between the sensor and the smartphone for reliable fall detection, as it might restrict activity and negatively influence their degree of independence in daily life. Conclusion This study supports that involving end users in the design and mock-up development stage is welcomed by older people and allows their needs and preferences concerning the fall detection device to be explored. Based on these findings, the development of a “need-driven” prototype is possible. As participants are doubtful regarding smartphone usage, careful training and support of community-dwelling older people during real field testing will be crucial. PMID:28053509

Saúde na Copa: The World’s First Application of Participatory Surveillance for a Mass Gathering at FIFA World Cup 2014, Brazil

PubMed Central

Dimech, George Santiago; Libel, Marlo; de Souza, Wayner Vieira; Cesse, Eduarda; Smolinski, Mark; Oliveira, Wanderson; Albuquerque, Jones

2017-01-01

Background The 2005 International Health Regulations (IHRs) established parameters for event assessments and notifications that may constitute public health emergencies of international concern. These requirements and parameters opened up space for the use of nonofficial mechanisms (such as websites, blogs, and social networks) and technological improvements of communication that can streamline the detection, monitoring, and response to health problems, and thus reduce damage caused by these problems. Specifically, the revised IHR created space for participatory surveillance to function, in addition to the traditional surveillance mechanisms of detection, monitoring, and response. Participatory surveillance is based on crowdsourcing methods that collect information from society and then return the collective knowledge gained from that information back to society. The spread of digital social networks and wiki-style knowledge platforms has created a very favorable environment for this model of production and social control of information. Objective The aim of this study was to describe the use of a participatory surveillance app, Healthy Cup, for the early detection of acute disease outbreaks during the Fédération Internationale de Football Association (FIFA) World Cup 2014. Our focus was on three specific syndromes (respiratory, diarrheal, and rash) related to six diseases that were considered important in a mass gathering context (influenza, measles, rubella, cholera, acute diarrhea, and dengue fever). Methods From May 12 to July 13, 2014, users from anywhere in the world were able to download the Healthy Cup app and record their health condition, reporting whether they were good, very good, ill, or very ill. For users that reported being ill or very ill, a screen with a list of 10 symptoms was displayed. Participatory surveillance allows for the real-time identification of aggregates of symptoms that indicate possible cases of infectious diseases. Results From May 12 through July 13, 2014, there were 9434 downloads of the Healthy Cup app and 7155 (75.84%) registered users. Among the registered users, 4706 (4706/7155, 65.77%) were active users who posted a total of 47,879 times during the study period. The maximum number of users that signed up in one day occurred on May 30, 2014, the day that the app was officially launched by the Minister of Health during a press conference. During this event, the Minister of Health announced the special government program Health in the World Cup on national television media. On that date, 3633 logins were recorded, which accounted for more than half of all sign-ups across the entire duration of the study (50.78%, 3633/7155). Conclusions Participatory surveillance through community engagement is an innovative way to conduct epidemiological surveillance. Compared to traditional epidemiological surveillance, advantages include lower costs of data acquisition, timeliness of information collected and shared, platform scalability, and capacity for integration between the population being served and public health services. PMID:28473308

Poor decision-making by chronic marijuana users is associated with decreased functional responsiveness to negative consequences.

PubMed

Wesley, Michael J; Hanlon, Colleen A; Porrino, Linda J

2011-01-30

Chronic marijuana users (MJ Users) perform poorly on the Iowa Gambling Task (IGT), a complex decision-making task in which monetary wins and losses guide strategy development. This functional magnetic resonance imaging (MRI) study sought to determine if the poor performance of MJ Users was related to differences in brain activity while evaluating wins and losses during the strategy development phase of the IGT. MJ Users (16) and Controls (16) performed a modified IGT in an MRI scanner. Performance was tracked and functional activity in response to early wins and losses was examined. While the MJ Users continued to perform poorly at the end of the task, there was no difference in group performance during the initial strategy development phase. During this phase, before the emergence of behavioral differences, Controls exhibited significantly greater activity in response to losses in the anterior cingulate cortex, medial frontal cortex, precuneus, superior parietal lobe, occipital lobe and cerebellum as compared to MJ Users. Furthermore, in Controls, but not MJ Users, the functional response to losses in the anterior cingulate cortex, ventral medial prefrontal cortex and rostral prefrontal cortex positively correlated with performance over time. These data suggest MJ Users are less sensitive to negative feedback during strategy development. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Residual effects of cannabis use in adolescent and adult brains - A meta-analysis of fMRI studies.

PubMed

Blest-Hopley, Grace; Giampietro, Vincent; Bhattacharyya, Sagnik

2018-05-01

While numerous studies have investigated the residual effects of cannabis use on human brain function, results of these studies have been inconsistent. Using meta-analytic approaches we summarize the effects of prolonged cannabis exposure on human brain function as measured using task-based functional MRI (fMRI) across studies employing a range of cognitive activation tasks comparing regular cannabis users with non-users. Separate meta-analyses were carried out for studies investigating adult and adolescent cannabis users. Systematic literature search identified 20 manuscripts (13 adult and 7 adolescent studies) meeting study inclusion criteria. Adult analyses compared 530 cannabis users to 580 healthy controls while adolescent analyses compared 219 cannabis users to 224 healthy controls. In adult cannabis users brain activation was increased in the superior and posterior transverse temporal and inferior frontal gyri and decreased in the striate area, insula and middle temporal gyrus. In adolescent cannabis users, activation was increased in the inferior parietal gyrus and putamen compared to healthy controls. Functional alteration in these areas may reflect compensatory neuroadaptive changes in cannabis users. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

PubMed Central

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-01-01

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches. PMID:27792136

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-10-25

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

An Ambient Intelligence Framework for the Provision of Geographically Distributed Multimedia Content to Mobility Impaired Users

NASA Astrophysics Data System (ADS)

Kehagias, Dionysios D.; Giakoumis, Dimitris; Tzovaras, Dimitrios; Bekiaris, Evangelos; Wiethoff, Marion

This chapter presents an ambient intelligence framework whose goal is to facilitate the information needs of mobility impaired users on the move. This framework couples users with geographically distributed services and the corresponding multimedia content, enabling access to context-sensitive information based on user geographic location and the use case under consideration. It provides a multi-modal facility that is realized through a set of mobile devices and user interfaces that address the needs of ten different types of user impairments. The overall ambient intelligence framework enables users who are equipped with mobile devices to access multimedia content in order to undertake activities relevant to one or more of the following domains: transportation, tourism and leisure, personal support services, work, business, education, social relations and community building. User experience is being explored against those activities through a specific usage scenario.

KAPEAN: Understanding Affective States of Children with ADHD

ERIC Educational Resources Information Center

Martínez, Fernando; Barraza, Claudia; González, Nimrod; González, Juan

2016-01-01

Affective computing seeks to create computational systems that adapt content and resources according to the affective states of the users. However, the detection of the user's affection such as motivation and emotion is challenging especially when an attention problem is present. An approach to convey learning resources to children with learning…

Posting Behaviour Patterns in an Online Smoking Cessation Social Network: Implications for Intervention Design and Development

PubMed Central

Healey, Benjamin; Hoek, Janet; Edwards, Richard

2014-01-01

Objectives Online Cessation Support Networks (OCSNs) are associated with increased quit success rates, but few studies have examined their use over time. We identified usage patterns in New Zealand's largest OCSN over two years and explored implications for OCSN intervention design and evaluation. Methods We analysed metadata relating to 133,096 OCSN interactions during 2011 and 2012. Metrics covered aggregate network activity, user posting activity and longevity, and between-user commenting. Binary logistic regression models were estimated to investigate the feasibility of predicting low user engagement using early interaction data. Results Repeating periodic peaks and troughs in aggregate activity related not only to seasonality (e.g., New Year), but also to day of the week. Out of 2,062 unique users, 69 Highly Engaged Users (180+ interactions each) contributed 69% of all OCSN interactions in 2012 compared to 1.3% contributed by 864 Minimally Engaged Users (< = 2 items each). The proportion of Highly Engaged Users increased with network growth between 2011 and 2012 (with marginal significance), but the proportion of Minimally Engaged Users did not decline substantively. First week interaction data enabled identification of Minimally Engaged Users with high specificity and sensitivity (AUROC  = 0.94). Implications Results suggest future research should develop and test interventions that promote activity, and hence cessation support, amongst specific user groups or at key time points. For example, early usage information could help identify Minimally Engaged Users for tests of targeted messaging designed to improve their integration into, or re-engagement with, the OCSN. Furthermore, although we observed strong growth over time on varied metrics including posts and comments, this change did not coincide with large gains in first-time user persistence. Researchers assessing intervention effects should therefore examine multiple measures when evaluating changes in network dynamics over time. PMID:25192174

User Centered System Design: Papers for the CHI '83 Conference on Human Factors in Computer Systems.

ERIC Educational Resources Information Center

California Univ., San Diego. Center for Human Information Processing.

Four papers from the University of California at San Diego (UCSD) Project on Human-Computer Interfaces are presented in this report. "Evaluation and Analysis of User's Activity Organization," by Liam Bannon, Allen Cypher, Steven Greenspan, and Melissa Monty, analyzes the activities performed by users of computer systems, develops a…

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

PubMed

Bourobou, Serge Thomas Mickala; Yoo, Younghwan

2015-05-21

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

An interactive ontology-driven information system for simulating background radiation and generating scenarios for testing special nuclear materials detection algorithms

DOE PAGES

Sorokine, Alexandre; Schlicher, Bob G.; Ward, Richard C.; ...

2015-05-22

This paper describes an original approach to generating scenarios for the purpose of testing the algorithms used to detect special nuclear materials (SNM) that incorporates the use of ontologies. Separating the signal of SNM from the background requires sophisticated algorithms. To assist in developing such algorithms, there is a need for scenarios that capture a very wide range of variables affecting the detection process, depending on the type of detector being used. To provide such a cpability, we developed an ontology-driven information system (ODIS) for generating scenarios that can be used in creating scenarios for testing of algorithms for SNMmore » detection. The ontology-driven scenario generator (ODSG) is an ODIS based on information supplied by subject matter experts and other documentation. The details of the creation of the ontology, the development of the ontology-driven information system, and the design of the web user interface (UI) are presented along with specific examples of scenarios generated using the ODSG. We demonstrate that the paradigm behind the ODSG is capable of addressing the problem of semantic complexity at both the user and developer levels. Compared to traditional approaches, an ODIS provides benefits such as faithful representation of the users' domain conceptualization, simplified management of very large and semantically diverse datasets, and the ability to handle frequent changes to the application and the UI. Furthermore, the approach makes possible the generation of a much larger number of specific scenarios based on limited user-supplied information« less

Hand-held survey probe

DOEpatents

Young, Kevin L [Idaho Falls, ID; Hungate, Kevin E [Idaho Falls, ID

2010-02-23

A system for providing operational feedback to a user of a detection probe may include an optical sensor to generate data corresponding to a position of the detection probe with respect to a surface; a microprocessor to receive the data; a software medium having code to process the data with the microprocessor and pre-programmed parameters, and making a comparison of the data to the parameters; and an indicator device to indicate results of the comparison. A method of providing operational feedback to a user of a detection probe may include generating output data with an optical sensor corresponding to the relative position with respect to a surface; processing the output data, including comparing the output data to pre-programmed parameters; and indicating results of the comparison.

Incorporating profile information in community detection for online social networks

NASA Astrophysics Data System (ADS)

Fan, W.; Yeung, K. H.

2014-07-01

Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

A MATLAB-based graphical user interface for the identification of muscular activations from surface electromyography signals.

PubMed

Mengarelli, Alessandro; Cardarelli, Stefano; Verdini, Federica; Burattini, Laura; Fioretti, Sandro; Di Nardo, Francesco

2016-08-01

In this paper a graphical user interface (GUI) built in MATLAB® environment is presented. This interactive tool has been developed for the analysis of superficial electromyography (sEMG) signals and in particular for the assessment of the muscle activation time intervals. After the signal import, the tool performs a first analysis in a totally user independent way, providing a reliable computation of the muscular activation sequences. Furthermore, the user has the opportunity to modify each parameter of the on/off identification algorithm implemented in the presented tool. The presence of an user-friendly GUI allows the immediate evaluation of the effects that the modification of every single parameter has on the activation intervals recognition, through the real-time updating and visualization of the muscular activation/deactivation sequences. The possibility to accept the initial signal analysis or to modify the on/off identification with respect to each considered signal, with a real-time visual feedback, makes this GUI-based tool a valuable instrument in clinical, research applications and also in an educational perspective.

A feasibility study of stateful automaton packet inspection for streaming application detection systems

NASA Astrophysics Data System (ADS)

Tseng, Kuo-Kun; Lo, Jiao; Liu, Yiming; Chang, Shih-Hao; Merabti, Madjid; Ng, Felix, C. K.; Wu, C. H.

2017-10-01

The rapid development of the internet has brought huge benefits and social impacts; however, internet security has also become a great problem for users, since traditional approaches to packet classification cannot achieve satisfactory detection performance due to their low accuracy and efficiency. In this paper, a new stateful packet inspection method is introduced, which can be embedded in the network gateway and used by a streaming application detection system. This new detection method leverages the inexact automaton approach, using part of the header field and part of the application layer data of a packet. Based on this approach, an advanced detection system is proposed for streaming applications. The workflow of the system involves two stages: the training stage and the detection stage. In the training stage, the system initially captures characteristic patterns from a set of application packet flows. After this training is completed, the detection stage allows the user to detect the target application by capturing new application flows. This new detection approach is also evaluated using experimental analysis; the results of this analysis show that this new approach not only simplifies the management of the state detection system, but also improves the accuracy of data flow detection, making it feasible for real-world network applications.

Active in-database processing to support ambient assisted living systems.

PubMed

de Morais, Wagner O; Lundström, Jens; Wickström, Nicholas

2014-08-12

As an alternative to the existing software architectures that underpin the development of smart homes and ambient assisted living (AAL) systems, this work presents a database-centric architecture that takes advantage of active databases and in-database processing. Current platforms supporting AAL systems use database management systems (DBMSs) exclusively for data storage. Active databases employ database triggers to detect and react to events taking place inside or outside of the database. DBMSs can be extended with stored procedures and functions that enable in-database processing. This means that the data processing is integrated and performed within the DBMS. The feasibility and flexibility of the proposed approach were demonstrated with the implementation of three distinct AAL services. The active database was used to detect bed-exits and to discover common room transitions and deviations during the night. In-database machine learning methods were used to model early night behaviors. Consequently, active in-database processing avoids transferring sensitive data outside the database, and this improves performance, security and privacy. Furthermore, centralizing the computation into the DBMS facilitates code reuse, adaptation and maintenance. These are important system properties that take into account the evolving heterogeneity of users, their needs and the devices that are characteristic of smart homes and AAL systems. Therefore, DBMSs can provide capabilities to address requirements for scalability, security, privacy, dependability and personalization in applications of smart environments in healthcare.

Active In-Database Processing to Support Ambient Assisted Living Systems

PubMed Central

de Morais, Wagner O.; Lundström, Jens; Wickström, Nicholas

2014-01-01

As an alternative to the existing software architectures that underpin the development of smart homes and ambient assisted living (AAL) systems, this work presents a database-centric architecture that takes advantage of active databases and in-database processing. Current platforms supporting AAL systems use database management systems (DBMSs) exclusively for data storage. Active databases employ database triggers to detect and react to events taking place inside or outside of the database. DBMSs can be extended with stored procedures and functions that enable in-database processing. This means that the data processing is integrated and performed within the DBMS. The feasibility and flexibility of the proposed approach were demonstrated with the implementation of three distinct AAL services. The active database was used to detect bed-exits and to discover common room transitions and deviations during the night. In-database machine learning methods were used to model early night behaviors. Consequently, active in-database processing avoids transferring sensitive data outside the database, and this improves performance, security and privacy. Furthermore, centralizing the computation into the DBMS facilitates code reuse, adaptation and maintenance. These are important system properties that take into account the evolving heterogeneity of users, their needs and the devices that are characteristic of smart homes and AAL systems. Therefore, DBMSs can provide capabilities to address requirements for scalability, security, privacy, dependability and personalization in applications of smart environments in healthcare. PMID:25120164

Implementing an Intrusion Detection System in the Mysea Architecture

DTIC Science & Technology

2008-06-01

password for each user passwd <username> then follow the prompts 2. PostgreSQL 7.4.18 Installation Perform the following steps as root: 1. Copy...password changed Repeat for user snort. exit After making the groups and users the group and passwd file needs to be updated. Set security and...untrusted/bin/xtsmkgroup > /etc/group chmod 644 /etc/group /xts/untrusted/bin/xtsmkpasswd > /etc/ passwd chmod 644 /etc/ passwd 3. PostgreSQL 7.4.18

Augmenting intracortical brain-machine interface with neurally driven error detectors

NASA Astrophysics Data System (ADS)

Even-Chen, Nir; Stavisky, Sergey D.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.

2017-12-01

Objective. Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. Approach. We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. Main results. We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error ‘detect-and-act’ system that attempts to automatically ‘undo’ or ‘prevent’ mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). Significance. Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.

Developing a music programme for preschool children with cochlear implants.

PubMed

Koşaner, Julie; Kilinc, Aynur; Deniz, Murat

2012-11-01

Although music perception is especially challenging for cochlear implant (CI) users, young CI users' musical perception abilities are improved by participation in structured musical activities. To design, implement, evaluate, and publish a music training programme with a monitoring tool for preschool CI users, for use in family-centred habilitation programmes. We devised a programme of musical activities, Musical EARS®, and a curriculum-related hierarchical Evaluation Form to represent performance. The programme included sections on singing; recognizing songs, tunes, and timbre; and responding appropriately to music and rhythm. It was implemented over 18 months at Ilkses Rehabilitation Centre, with 25 paediatric MED-EL CI users split into three groups of varying age, duration of CI use, and ability. Mean total scores increased significantly for all groups. Scores increased unevenly across subscales. Participation in and enjoyment of musical activities increased for both children and parents. Significant correlations were found between scores and length of CI use. The training programme effectively enriches child CI users' musical experience. To varying degrees, children learned to perform the Musical EARS® activities. The study allowed us to validate the lesson content and the hierarchical nature of the Evaluation Form. We conclude that prelingually deafened CI users should be systematically involved in musical activities to help them acquire skills acquired more easily by hearing peers.