Selection of an Online Public Access Catalog: A Checklist Approach.

ERIC Educational Resources Information Center

O'Rourke, Victoria

1987-01-01

The development, field testing, and evaluation of a checklist approach to selecting an integrated library automation system are described, and recommendations for using this approach are outlined. The checklist, which is divided into five main sections of catalog features and functions, is appended. (Author/CLB)

On-line Tool Wear Detection on DCMT070204 Carbide Tool Tip Based on Noise Cutting Audio Signal using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Prasetyo, T.; Amar, S.; Arendra, A.; Zam Zami, M. K.

2018-01-01

This study develops an on-line detection system to predict the wear of DCMT070204 tool tip during the cutting process of the workpiece. The machine used in this research is CNC ProTurn 9000 to cut ST42 steel cylinder. The audio signal has been captured using the microphone placed in the tool post and recorded in Matlab. The signal is recorded at the sampling rate of 44.1 kHz, and the sampling size of 1024. The recorded signal is 110 data derived from the audio signal while cutting using a normal chisel and a worn chisel. And then perform signal feature extraction in the frequency domain using Fast Fourier Transform. Feature selection is done based on correlation analysis. And tool wear classification was performed using artificial neural networks with 33 input features selected. This artificial neural network is trained with back propagation method. Classification performance testing yields an accuracy of 74%.

PopGen Fishbowl: A Free Online Simulation Model of Microevolutionary Processes

ERIC Educational Resources Information Center

Jones, Thomas C.; Laughlin, Thomas F.

2010-01-01

Natural selection and other components of evolutionary theory are known to be particularly challenging concepts for students to understand. To help illustrate these concepts, we developed a simulation model of microevolutionary processes. The model features all the components of Hardy-Weinberg theory, with population size, selection, gene flow,…

Robust online tracking via adaptive samples selection with saliency detection

NASA Astrophysics Data System (ADS)

Yan, Jia; Chen, Xi; Zhu, QiuPing

2013-12-01

Online tracking has shown to be successful in tracking of previously unknown objects. However, there are two important factors which lead to drift problem of online tracking, the one is how to select the exact labeled samples even when the target locations are inaccurate, and the other is how to handle the confusors which have similar features with the target. In this article, we propose a robust online tracking algorithm with adaptive samples selection based on saliency detection to overcome the drift problem. To deal with the problem of degrading the classifiers using mis-aligned samples, we introduce the saliency detection method to our tracking problem. Saliency maps and the strong classifiers are combined to extract the most correct positive samples. Our approach employs a simple yet saliency detection algorithm based on image spectral residual analysis. Furthermore, instead of using the random patches as the negative samples, we propose a reasonable selection criterion, in which both the saliency confidence and similarity are considered with the benefits that confusors in the surrounding background are incorporated into the classifiers update process before the drift occurs. The tracking task is formulated as a binary classification via online boosting framework. Experiment results in several challenging video sequences demonstrate the accuracy and stability of our tracker.

Predicting Key Events in the Popularity Evolution of Online Information.

PubMed

Hu, Ying; Hu, Changjun; Fu, Shushen; Fang, Mingzhe; Xu, Wenwen

2017-01-01

The popularity of online information generally experiences a rising and falling evolution. This paper considers the "burst", "peak", and "fade" key events together as a representative summary of popularity evolution. We propose a novel prediction task-predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify "burst", "peak", and "fade" in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution.

Predicting Key Events in the Popularity Evolution of Online Information

PubMed Central

Fu, Shushen; Fang, Mingzhe; Xu, Wenwen

2017-01-01

The popularity of online information generally experiences a rising and falling evolution. This paper considers the “burst”, “peak”, and “fade” key events together as a representative summary of popularity evolution. We propose a novel prediction task—predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify “burst”, “peak”, and “fade” in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution. PMID:28046121

User interface to administrative DRMS within a distributed environment

NASA Technical Reports Server (NTRS)

Martin, L. D.; Kirk, R. D.

1983-01-01

The implementation of a data base management system (DBMS) into a communications office to control and report on communication leased service contracts is discussed. The system user executes online programs to update five files residing on a UNIVAC 1100/82, through the forms mode features of the Tektronix 4025 terminal and IMSAI 8080 microcomputer. This user can select the appropriate form to the Tektronix 4025 screen, and enter new data, update existing data, or discontinue service. Selective online printing of 40 reports is accomplished by the system user to satisfy management, budget, and bill payment reporting requirements.

A novel missense-mutation-related feature extraction scheme for 'driver' mutation identification.

PubMed

Tan, Hua; Bao, Jiguang; Zhou, Xiaobo

2012-11-15

It becomes widely accepted that human cancer is a disease involving dynamic changes in the genome and that the missense mutations constitute the bulk of human genetic variations. A multitude of computational algorithms, especially the machine learning-based ones, has consequently been proposed to distinguish missense changes that contribute to the cancer progression ('driver' mutation) from those that do not ('passenger' mutation). However, the existing methods have multifaceted shortcomings, in the sense that they either adopt incomplete feature space or depend on protein structural databases which are usually far from integrated. In this article, we investigated multiple aspects of a missense mutation and identified a novel feature space that well distinguishes cancer-associated driver mutations from passenger ones. An index (DX score) was proposed to evaluate the discriminating capability of each feature, and a subset of these features which ranks top was selected to build the SVM classifier. Cross-validation showed that the classifier trained on our selected features significantly outperforms the existing ones both in precision and robustness. We applied our method to several datasets of missense mutations culled from published database and literature and obtained more reasonable results than previous studies. The software is available online at http://www.methodisthealth.com/software and https://sites.google.com/site/drivermutationidentification/. xzhou@tmhs.org. Supplementary data are available at Bioinformatics online.

Advances in feature selection methods for hyperspectral image processing in food industry applications: a review.

PubMed

Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zeng, Xin-An

2015-01-01

There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.

An Evaluation of Online Help for the NOTIS OPAC.

ERIC Educational Resources Information Center

White, Frank

1994-01-01

Discussion of online help systems in online public access catalogs (OPACs) focuses on a study that evaluated the online help system for the NOTIS (Northwestern Online Total Integrated System) OPAC. Features of the system reviewed include online functions; training features; general interface features; access points; and message content and display…

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

PubMed

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

m-BIRCH: an online clustering approach for computer vision applications

NASA Astrophysics Data System (ADS)

Madan, Siddharth K.; Dana, Kristin J.

2015-03-01

We adapt a classic online clustering algorithm called Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), to incrementally cluster large datasets of features commonly used in multimedia and computer vision. We call the adapted version modified-BIRCH (m-BIRCH). The algorithm uses only a fraction of the dataset memory to perform clustering, and updates the clustering decisions when new data comes in. Modifications made in m-BIRCH enable data driven parameter selection and effectively handle varying density regions in the feature space. Data driven parameter selection automatically controls the level of coarseness of the data summarization. Effective handling of varying density regions is necessary to well represent the different density regions in data summarization. We use m-BIRCH to cluster 840K color SIFT descriptors, and 60K outlier corrupted grayscale patches. We use the algorithm to cluster datasets consisting of challenging non-convex clustering patterns. Our implementation of the algorithm provides an useful clustering tool and is made publicly available.

A visual tracking method based on deep learning without online model updating

NASA Astrophysics Data System (ADS)

Tang, Cong; Wang, Yicheng; Feng, Yunsong; Zheng, Chao; Jin, Wei

2018-02-01

The paper proposes a visual tracking method based on deep learning without online model updating. In consideration of the advantages of deep learning in feature representation, deep model SSD (Single Shot Multibox Detector) is used as the object extractor in the tracking model. Simultaneously, the color histogram feature and HOG (Histogram of Oriented Gradient) feature are combined to select the tracking object. In the process of tracking, multi-scale object searching map is built to improve the detection performance of deep detection model and the tracking efficiency. In the experiment of eight respective tracking video sequences in the baseline dataset, compared with six state-of-the-art methods, the method in the paper has better robustness in the tracking challenging factors, such as deformation, scale variation, rotation variation, illumination variation, and background clutters, moreover, its general performance is better than other six tracking methods.

An RNAi-Enhanced Logic Circuit for Cancer Specific Detection and Destruction

DTIC Science & Technology

2013-02-01

monomeric protein secreted by Corynebacterium diphtheriae, and pro-apoptotic members of Bcl-2 family: mBax (Mus musculus), hBax ( Homo sapiens ), and its...Gata3 mStaple. Intron- feature sequences – donor site, branch point, poly- pyrimidine tract, and acceptor site – were selected based on previously...sequences found in literature our intron features were chosen according SplicePort [4], an online analyzer that detects the likelihood of splicing to

On-Line Pattern Analysis and Recognition System. OLPARS VI. Software Reference Manual,

DTIC Science & Technology

1982-06-18

Discriminant Analysis Data Transformation, Feature Extraction, Feature Evaluation Cluster Analysis, Classification Computer Software 20Z. ABSTRACT... cluster /scatter cut-off value, (2) change the one-space bin factor, (3) change from long prompts to short prompts or vice versa, (4) change the...value, a cluster plot is displayed, otherwise a scatter plot is shown. if option 1 is selected, the program requests that a new value be input

Video PATSEARCH: A Mixed-Media System.

ERIC Educational Resources Information Center

Schulman, Jacque-Lynne

1982-01-01

Describes a videodisc-based information display system in which a computer terminal is used to search the online PATSEARCH database from a remote host with local microcomputer control to select and display drawings from the retrieved records. System features and system components are discussed and criteria for system evaluation are presented.…

Grant Writing

ERIC Educational Resources Information Center

Mallon, Melissa

2017-01-01

The Internet Resources column is designed to be a clearinghouse for free, online websites or apps; each column focuses on a theme relevant to current issues and trends in academic libraries and features resources selected to make the lives of public services librarians easier. While academic library budget woes may not be quite as drastic as they…

Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest

NASA Astrophysics Data System (ADS)

Schudlo, Larissa C.; Chau, Tom

2014-02-01

Objective. Near-infrared spectroscopy (NIRS) has recently gained attention as a modality for brain-computer interfaces (BCIs), which may serve as an alternative access pathway for individuals with severe motor impairments. For NIRS-BCIs to be used as a real communication pathway, reliable online operation must be achieved. Yet, only a limited number of studies have been conducted online to date. These few studies were carried out under a synchronous paradigm and did not accommodate an unconstrained resting state, precluding their practical clinical implication. Furthermore, the potentially discriminative power of spatiotemporal characteristics of activation has yet to be considered in an online NIRS system. Approach. In this study, we developed and evaluated an online system-paced NIRS-BCI which was driven by a mental arithmetic activation task and accommodated an unconstrained rest state. With a dual-wavelength, frequency domain near-infrared spectrometer, measurements were acquired over nine sites of the prefrontal cortex, while ten able-bodied participants selected letters from an on-screen scanning keyboard via intentionally controlled brain activity (using mental arithmetic). Participants were provided dynamic NIR topograms as continuous visual feedback of their brain activity as well as binary feedback of the BCI's decision (i.e. if the letter was selected or not). To classify the hemodynamic activity, temporal features extracted from the NIRS signals and spatiotemporal features extracted from the dynamic NIR topograms were used in a majority vote combination of multiple linear classifiers. Main results. An overall online classification accuracy of 77.4 ± 10.5% was achieved across all participants. The binary feedback was found to be very useful during BCI use, while not all participants found value in the continuous feedback provided. Significance. These results demonstrate that mental arithmetic is a potent mental task for driving an online system-paced NIRS-BCI. BCI feedback that reflects the classifier's decision has the potential to improve user performance. The proposed system can provide a framework for future online NIRS-BCI development and testing.

Research on Signature Verification Method Based on Discrete Fréchet Distance

NASA Astrophysics Data System (ADS)

Fang, J. L.; Wu, W.

2018-05-01

This paper proposes a multi-feature signature template based on discrete Fréchet distance, which breaks through the limitation of traditional signature authentication using a single signature feature. It solves the online handwritten signature authentication signature global feature template extraction calculation workload, signature feature selection unreasonable problem. In this experiment, the false recognition rate (FAR) and false rejection rate (FRR) of the statistical signature are calculated and the average equal error rate (AEER) is calculated. The feasibility of the combined template scheme is verified by comparing the average equal error rate of the combination template and the original template.

Geology of National Parks

USGS Publications Warehouse

Stoffer, Philip W.

2008-01-01

This is a set of two sheets of 3D images showing geologic features of many National Parks. Red-and-cyan viewing glasses are need to see the three-dimensional effect. A search on the World Wide Web will yield many sites about anaglyphs and where to get 3D glasses. Red-blue glasses will do but red-cyan glasses are a little better. This publication features a photo quiz game: Name that park! where you can explore, interpret, and identify selected park landscapes. Can you identify landscape features in the images? Can you explain processes that may have helped form the landscape features? You can get the answers online.

Attracting Views and Going Viral: How Message Features and News-Sharing Channels Affect Health News Diffusion

PubMed Central

Kim, Hyun Suk

2015-01-01

This study examined how intrinsic as well as perceived message features affect the extent to which online health news stories prompt audience selections and social retransmissions, and how news-sharing channels (e-mail vs. social media) shape what goes viral. The study analyzed actual behavioral data on audience viewing and sharing of New York Times health news articles, and associated article content and context data. News articles with high informational utility and positive sentiment invited more frequent selections and retransmissions. Articles were also more frequently selected when they presented controversial, emotionally evocative, and familiar content. Informational utility and novelty had stronger positive associations with e-mail-specific virality, while emotional evocativeness, content familiarity, and exemplification played a larger role in triggering social media-based retransmissions. PMID:26441472

Simulating Phase Variation: A Practical Approach to Teaching Mutation and Diversity

ERIC Educational Resources Information Center

Wanford, Joe; Aidley, Jack; Bayliss, Chris; Ketley, Julian; Goodwin, Mark

2018-01-01

Mutation, diversity, natural selection and the biology of human pathogens (including antibiotic resistance) are key features of the biosciences curriculum at A Level and undergraduate study. Few resources exist to allow students to engage with these topics in an interactive manner. This paper describes an interactive, online simulation of mutation…

Health and performance monitoring of the online computer cluster of CMS

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

Bauer, G.; et al.

2012-01-01

The CMS experiment at the LHC features over 2'500 devices that need constant monitoring in order to ensure proper data taking. The monitoring solution has been migrated from Nagios to Icinga, with several useful plugins. The motivations behind the migration and the selection of the plugins are discussed.

The Gamma-Ray Burst ToolSHED is Open for Business

NASA Astrophysics Data System (ADS)

Giblin, Timothy W.; Hakkila, Jon; Haglin, David J.; Roiger, Richard J.

2004-09-01

The GRB ToolSHED, a Gamma-Ray Burst SHell for Expeditions in Data-Mining, is now online and available via a web browser to all in the scientific community. The ToolSHED is an online web utility that contains pre-processed burst attributes of the BATSE catalog and a suite of induction-based machine learning and statistical tools for classification and cluster analysis. Users create their own login account and study burst properties within user-defined multi-dimensional parameter spaces. Although new GRB attributes are periodically added to the database for user selection, the ToolSHED has a feature that allows users to upload their own burst attributes (e.g. spectral parameters, etc.) so that additional parameter spaces can be explored. A data visualization feature using GNUplot and web-based IDL has also been implemented to provide interactive plotting of user-selected session output. In an era in which GRB observations and attributes are becoming increasingly more complex, a utility such as the GRB ToolSHED may play an important role in deciphering GRB classes and understanding intrinsic burst properties.

Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.

PubMed

Khan, Adil Mehmood; Siddiqi, Muhammad Hameed; Lee, Seok-Won

2013-09-27

Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.

Fibre Optic Sensors for Selected Wastewater Characteristics

PubMed Central

Chong, Su Sin; Abdul Aziz, A. R.; Harun, Sulaiman W.

2013-01-01

Demand for online and real-time measurements techniques to meet environmental regulation and treatment compliance are increasing. However the conventional techniques, which involve scheduled sampling and chemical analysis can be expensive and time consuming. Therefore cheaper and faster alternatives to monitor wastewater characteristics are required as alternatives to conventional methods. This paper reviews existing conventional techniques and optical and fibre optic sensors to determine selected wastewater characteristics which are colour, Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD). The review confirms that with appropriate configuration, calibration and fibre features the parameters can be determined with accuracy comparable to conventional method. With more research in this area, the potential for using FOS for online and real-time measurement of more wastewater parameters for various types of industrial effluent are promising. PMID:23881131

Databank Software for the 1990s and Beyond--Part 1: The User's Wish List.

ERIC Educational Resources Information Center

Basch, Reva

1990-01-01

Describes desired software enhancements identified by the Southern California Online Users Group in the areas of search language, database selection, document retrieval and display, user interface, customer support, and cost and economic issues. The need to prioritize these wishes and to determine whether features should reside in the mainframe or…

Unsupervised Online Classifier in Sleep Scoring for Sleep Deprivation Studies

PubMed Central

Libourel, Paul-Antoine; Corneyllie, Alexandra; Luppi, Pierre-Hervé; Chouvet, Guy; Gervasoni, Damien

2015-01-01

Study Objective: This study was designed to evaluate an unsupervised adaptive algorithm for real-time detection of sleep and wake states in rodents. Design: We designed a Bayesian classifier that automatically extracts electroencephalogram (EEG) and electromyogram (EMG) features and categorizes non-overlapping 5-s epochs into one of the three major sleep and wake states without any human supervision. This sleep-scoring algorithm is coupled online with a new device to perform selective paradoxical sleep deprivation (PSD). Settings: Controlled laboratory settings for chronic polygraphic sleep recordings and selective PSD. Participants: Ten adult Sprague-Dawley rats instrumented for chronic polysomnographic recordings Measurements: The performance of the algorithm is evaluated by comparison with the score obtained by a human expert reader. Online detection of PS is then validated with a PSD protocol with duration of 72 hours. Results: Our algorithm gave a high concordance with human scoring with an average κ coefficient > 70%. Notably, the specificity to detect PS reached 92%. Selective PSD using real-time detection of PS strongly reduced PS amounts, leaving only brief PS bouts necessary for the detection of PS in EEG and EMG signals (4.7 ± 0.7% over 72 h, versus 8.9 ± 0.5% in baseline), and was followed by a significant PS rebound (23.3 ± 3.3% over 150 minutes). Conclusions: Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method. Citation: Libourel PA, Corneyllie A, Luppi PH, Chouvet G, Gervasoni D. Unsupervised online classifier in sleep scoring for sleep deprivation studies. SLEEP 2015;38(5):815–828. PMID:25325478

Exploring 3D Human Action Recognition: from Offline to Online.

PubMed

Liu, Zhenyu; Li, Rui; Tan, Jianrong

2018-02-20

With the introduction of cost-effective depth sensors, a tremendous amount of research has been devoted to studying human action recognition using 3D motion data. However, most existing methods work in an offline fashion, i.e., they operate on a segmented sequence. There are a few methods specifically designed for online action recognition, which continually predicts action labels as a stream sequence proceeds. In view of this fact, we propose a question: can we draw inspirations and borrow techniques or descriptors from existing offline methods, and then apply these to online action recognition? Note that extending offline techniques or descriptors to online applications is not straightforward, since at least two problems-including real-time performance and sequence segmentation-are usually not considered in offline action recognition. In this paper, we give a positive answer to the question. To develop applicable online action recognition methods, we carefully explore feature extraction, sequence segmentation, computational costs, and classifier selection. The effectiveness of the developed methods is validated on the MSR 3D Online Action dataset and the MSR Daily Activity 3D dataset.

Exploring 3D Human Action Recognition: from Offline to Online

PubMed Central

Li, Rui; Liu, Zhenyu; Tan, Jianrong

2018-01-01

With the introduction of cost-effective depth sensors, a tremendous amount of research has been devoted to studying human action recognition using 3D motion data. However, most existing methods work in an offline fashion, i.e., they operate on a segmented sequence. There are a few methods specifically designed for online action recognition, which continually predicts action labels as a stream sequence proceeds. In view of this fact, we propose a question: can we draw inspirations and borrow techniques or descriptors from existing offline methods, and then apply these to online action recognition? Note that extending offline techniques or descriptors to online applications is not straightforward, since at least two problems—including real-time performance and sequence segmentation—are usually not considered in offline action recognition. In this paper, we give a positive answer to the question. To develop applicable online action recognition methods, we carefully explore feature extraction, sequence segmentation, computational costs, and classifier selection. The effectiveness of the developed methods is validated on the MSR 3D Online Action dataset and the MSR Daily Activity 3D dataset. PMID:29461502

Unsupervised online classifier in sleep scoring for sleep deprivation studies.

PubMed

Libourel, Paul-Antoine; Corneyllie, Alexandra; Luppi, Pierre-Hervé; Chouvet, Guy; Gervasoni, Damien

2015-05-01

This study was designed to evaluate an unsupervised adaptive algorithm for real-time detection of sleep and wake states in rodents. We designed a Bayesian classifier that automatically extracts electroencephalogram (EEG) and electromyogram (EMG) features and categorizes non-overlapping 5-s epochs into one of the three major sleep and wake states without any human supervision. This sleep-scoring algorithm is coupled online with a new device to perform selective paradoxical sleep deprivation (PSD). Controlled laboratory settings for chronic polygraphic sleep recordings and selective PSD. Ten adult Sprague-Dawley rats instrumented for chronic polysomnographic recordings. The performance of the algorithm is evaluated by comparison with the score obtained by a human expert reader. Online detection of PS is then validated with a PSD protocol with duration of 72 hours. Our algorithm gave a high concordance with human scoring with an average κ coefficient > 70%. Notably, the specificity to detect PS reached 92%. Selective PSD using real-time detection of PS strongly reduced PS amounts, leaving only brief PS bouts necessary for the detection of PS in EEG and EMG signals (4.7 ± 0.7% over 72 h, versus 8.9 ± 0.5% in baseline), and was followed by a significant PS rebound (23.3 ± 3.3% over 150 minutes). Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method. © 2015 Associated Professional Sleep Societies, LLC.

Measuring the Interestingness of Articles in a Limited User Environment

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

Pon, Raymond K.

Search engines, such as Google, assign scores to news articles based on their relevancy to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevancy scores do not take into account what makes an article interesting, which varies from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation systems, in a limited user environment, there are not enough users that would make collaborative filtering effective. A general framework,more » called iScore, is presented for defining and measuring the 'interestingness' of articles, incorporating user-feedback. iScore addresses various aspects of what makes an article interesting, such as topic relevancy, uniqueness, freshness, source reputation, and writing style. It employs various methods to measure these features and uses a classifier operating on these features to recommend articles. The basic iScore configuration is shown to improve recommendation results by as much as 20%. In addition to the basic iScore features, additional features are presented to address the deficiencies of existing feature extractors, such as one that tracks multiple topics, called MTT, and a version of the Rocchio algorithm that learns its parameters online as it processes documents, called eRocchio. The inclusion of both MTT and eRocchio into iScore is shown to improve iScore recommendation results by as much as 3.1% and 5.6%, respectively. Additionally, in TREC11 Adaptive Filter Task, eRocchio is shown to be 10% better than the best filter in the last run of the task. In addition to these two major topic relevancy measures, other features are also introduced that employ language models, phrases, clustering, and changes in topics to improve recommendation results. These additional features are shown to improve recommendation results by iScore by up to 14%. Due to varying reasons that users hold regarding why an article is interesting, an online feature selection method in naive Bayes is also introduced. Online feature selection can improve recommendation results in iScore by up to 18.9%. In summary, iScore in its best configuration can outperform traditional IR techniques by as much as 50.7%. iScore and its components are evaluated in the news recommendation task using three datasets from Yahoo! News, actual users, and Digg. iScore and its components are also evaluated in the TREC Adaptive Filter task using the Reuters RCV1 corpus.« less

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

NASA Astrophysics Data System (ADS)

Jegadeeshwaran, R.; Sugumaran, V.

2015-02-01

Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

New service of Earth Interactions offers sneak peek at work in progress

NASA Astrophysics Data System (ADS)

A new service of the all-electronic journal Earth Interactions (Web site http://EarthInter-actions.org) now provides online access to abstracts or preprints of selected papers being presented at various Earth system science conferences. The new service, “Earth Abstractions,” is separate from the peer-reviewed articles in Earth Interactions. The editors select the sessions that will be featured. AGU Spring Meeting abstracts are now highlighted on the site.The abstract titles in Earth Abstractions will link directly to online extended abstracts or preprints located on the authors' home servers if such abstracts are made available. As the author updates the preprint and posts it to the same URL, Earth Abstractions will continue to feature the most recent information from the author related to that work. Readers can preview an author's work as it evolves prior to the meeting as well as refer to it for a year after the meeting has ended. This exchange also provides a means for authors to receive positive feedback on their papers independent of the conference session, which may help those who plan to submit papers about their work to a peer-reviewed journal.

An online sleep apnea detection method based on recurrence quantification analysis.

PubMed

Nguyen, Hoa Dinh; Wilkins, Brek A; Cheng, Qi; Benjamin, Bruce Allen

2014-07-01

This paper introduces an online sleep apnea detection method based on heart rate complexity as measured by recurrence quantification analysis (RQA) statistics of heart rate variability (HRV) data. RQA statistics can capture nonlinear dynamics of a complex cardiorespiratory system during obstructive sleep apnea. In order to obtain a more robust measurement of the nonstationarity of the cardiorespiratory system, we use different fixed amount of neighbor thresholdings for recurrence plot calculation. We integrate a feature selection algorithm based on conditional mutual information to select the most informative RQA features for classification, and hence, to speed up the real-time classification process without degrading the performance of the system. Two types of binary classifiers, i.e., support vector machine and neural network, are used to differentiate apnea from normal sleep. A soft decision fusion rule is developed to combine the results of these classifiers in order to improve the classification performance of the whole system. Experimental results show that our proposed method achieves better classification results compared with the previous recurrence analysis-based approach. We also show that our method is flexible and a strong candidate for a real efficient sleep apnea detection system.

Features of Mobile Diabetes Applications: Review of the Literature and Analysis of Current Applications Compared Against Evidence-Based Guidelines

PubMed Central

Fernandez-Luque, Luis; Årsand, Eirik; Hartvigsen, Gunnar

2011-01-01

Background Interest in mobile health (mHealth) applications for self-management of diabetes is growing. In July 2009, we found 60 diabetes applications on iTunes for iPhone; by February 2011 the number had increased by more than 400% to 260. Other mobile platforms reflect a similar trend. Despite the growth, research on both the design and the use of diabetes mHealth applications is scarce. Furthermore, the potential influence of social media on diabetes mHealth applications is largely unexplored. Objective Our objective was to study the salient features of mobile applications for diabetes care, in contrast to clinical guideline recommendations for diabetes self-management. These clinical guidelines are published by health authorities or associations such as the National Institute for Health and Clinical Excellence in the United Kingdom and the American Diabetes Association. Methods We searched online vendor markets (online stores for Apple iPhone, Google Android, BlackBerry, and Nokia Symbian), journal databases, and gray literature related to diabetes mobile applications. We included applications that featured a component for self-monitoring of blood glucose and excluded applications without English-language user interfaces, as well as those intended exclusively for health care professionals. We surveyed the following features: (1) self-monitoring: (1.1) blood glucose, (1.2) weight, (1.3) physical activity, (1.4) diet, (1.5) insulin and medication, and (1.6) blood pressure, (2) education, (3) disease-related alerts and reminders, (4) integration of social media functions, (5) disease-related data export and communication, and (6) synchronization with personal health record (PHR) systems or patient portals. We then contrasted the prevalence of these features with guideline recommendations. Results The search resulted in 973 matches, of which 137 met the selection criteria. The four most prevalent features of the applications available on the online markets (n = 101) were (1) insulin and medication recording, 63 (62%), (2) data export and communication, 61 (60%), (3) diet recording, 47 (47%), and (4) weight management, 43 (43%). From the literature search (n = 26), the most prevalent features were (1) PHR or Web server synchronization, 18 (69%), (2) insulin and medication recording, 17 (65%), (3) diet recording, 17 (65%), and (4) data export and communication, 16 (62%). Interestingly, although clinical guidelines widely refer to the importance of education, this is missing from the top functionalities in both cases. Conclusions While a wide selection of mobile applications seems to be available for people with diabetes, this study shows there are obvious gaps between the evidence-based recommendations and the functionality used in study interventions or found in online markets. Current results confirm personalized education as an underrepresented feature in diabetes mobile applications. We found no studies evaluating social media concepts in diabetes self-management on mobile devices, and its potential remains largely unexplored. PMID:21979293

Classification of clinically useful sentences in clinical evidence resources.

PubMed

Morid, Mohammad Amin; Fiszman, Marcelo; Raja, Kalpana; Jonnalagadda, Siddhartha R; Del Fiol, Guilherme

2016-04-01

Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care. To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians' information needs. We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient population identified by a pattern-based natural language processing (NLP) algorithm, and cue words extracted by a feature selection technique. Algorithm performance was measured in terms of precision, recall, and F-measure. The feature-rich approach yielded an F-measure of 74% versus 37% for a feature co-occurrence method (p<0.001). Excluding predication, population, semantic concept or text-based features reduced the F-measure to 62%, 66%, 58% and 69% respectively (p<0.01). The classifier applied to Medline sentences reached an F-measure of 73%, which is equivalent to the performance of the classifier on UpToDate sentences (p=0.62). The feature-rich approach significantly outperformed general baseline methods. This approach significantly outperformed classifiers based on a single type of feature. Different types of semantic features provided a unique contribution to overall classification performance. The classifier's model and features used for UpToDate generalized well to Medline abstracts. Copyright © 2016 Elsevier Inc. All rights reserved.

A Procedure for Extending Input Selection Algorithms to Low Quality Data in Modelling Problems with Application to the Automatic Grading of Uploaded Assignments

PubMed Central

Otero, José; Palacios, Ana; Suárez, Rosario; Junco, Luis

2014-01-01

When selecting relevant inputs in modeling problems with low quality data, the ranking of the most informative inputs is also uncertain. In this paper, this issue is addressed through a new procedure that allows the extending of different crisp feature selection algorithms to vague data. The partial knowledge about the ordinal of each feature is modelled by means of a possibility distribution, and a ranking is hereby applied to sort these distributions. It will be shown that this technique makes the most use of the available information in some vague datasets. The approach is demonstrated in a real-world application. In the context of massive online computer science courses, methods are sought for automatically providing the student with a qualification through code metrics. Feature selection methods are used to find the metrics involved in the most meaningful predictions. In this study, 800 source code files, collected and revised by the authors in classroom Computer Science lectures taught between 2013 and 2014, are analyzed with the proposed technique, and the most relevant metrics for the automatic grading task are discussed. PMID:25114967

Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle.

PubMed

Cheng, Jun-Hu; Sun, Da-Wen; Pu, Hongbin

2016-04-15

The potential use of feature wavelengths for predicting drip loss in grass carp fish, as affected by being frozen at -20°C for 24 h and thawed at 4°C for 1, 2, 4, and 6 days, was investigated. Hyperspectral images of frozen-thawed fish were obtained and their corresponding spectra were extracted. Least-squares support vector machine and multiple linear regression (MLR) models were established using five key wavelengths, selected by combining a genetic algorithm and successive projections algorithm, and this showed satisfactory performance in drip loss prediction. The MLR model with a determination coefficient of prediction (R(2)P) of 0.9258, and lower root mean square error estimated by a prediction (RMSEP) of 1.12%, was applied to transfer each pixel of the image and generate the distribution maps of exudation changes. The results confirmed that it is feasible to identify the feature wavelengths using variable selection methods and chemometric analysis for developing on-line multispectral imaging. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prominent feature extraction for review analysis: an empirical study

NASA Astrophysics Data System (ADS)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

The effects of pre-processing strategies in sentiment analysis of online movie reviews

NASA Astrophysics Data System (ADS)

Zin, Harnani Mat; Mustapha, Norwati; Murad, Masrah Azrifah Azmi; Sharef, Nurfadhlina Mohd

2017-10-01

With the ever increasing of internet applications and social networking sites, people nowadays can easily express their feelings towards any products and services. These online reviews act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like sentiment analysis and classification to provide a meaningful information for future uses. In text analysis tasks, the appropriate selection of words/features will have a huge impact on the effectiveness of the classifier. Thus, this paper explores the effect of the pre-processing strategies in the sentiment analysis of online movie reviews. In this paper, supervised machine learning method was used to classify the reviews. The support vector machine (SVM) with linear and non-linear kernel has been considered as classifier for the classification of the reviews. The performance of the classifier is critically examined based on the results of precision, recall, f-measure, and accuracy. Two different features representations were used which are term frequency and term frequency-inverse document frequency. Results show that the pre-processing strategies give a significant impact on the classification process.

Constructing an Online Test Framework, Using the Example of a Sign Language Receptive Skills Test

ERIC Educational Resources Information Center

Haug, Tobias; Herman, Rosalind; Woll, Bencie

2015-01-01

This paper presents the features of an online test framework for a receptive skills test that has been adapted, based on a British template, into different sign languages. The online test includes features that meet the needs of the different sign language versions. Features such as usability of the test, automatic saving of scores, and score…

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

PubMed Central

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM. PMID:27143958

Online Sequential Projection Vector Machine with Adaptive Data Mean Update.

PubMed

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis.

PubMed

Regalia, Giulia; Coelli, Stefania; Biffi, Emilia; Ferrigno, Giancarlo; Pedrocchi, Alessandra

2016-01-01

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting "building blocks" into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis

PubMed Central

Pedrocchi, Alessandra

2016-01-01

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting “building blocks” into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis. PMID:27239191

Automatic topic identification of health-related messages in online health community using text classification.

PubMed

Lu, Yingjie

2013-01-01

To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.

Patient seeking behaviors and online personas: social media's role in cosmetic dermatology.

PubMed

Ross, Nicholas A; Todd, Quintin; Saedi, Nazanin

2015-02-01

Social media sites, composed of providers, patients, and their social circles, facilitate health and healthcare delivery. To examine patients' perspective on social media as an information source, communication tool, and referral service through an anonymous survey. In addition, influences on patient Internet personas, an actively constructed online identity, around the time of cosmetic procedures are examined. Patients completed an anonymous institutional review board-approved survey during their initial cosmetic visit. Patients are highly active on social media using it as a multipurpose tool for physician referral services, support groups, and disease education. Patients gathered dermatology information from multiple sources, including friends, family, social media pages, and other online sources, often sharing their own experiences through social media platforms. Patients indicated a desire for provider educational materials on interactive media pages. Most preferred material written by a physician, but some indicated a preference for both physician and lay material. Online images highlighting dissatisfying skin features were influential to select patients, prompting manipulation of online personas and evaluation for aesthetic procedures. Although the study examines cosmetic patient perspectives, data highlight valuable trends for all dermatologists. Social media can improve patient education, collaboration, recruitment, and online professional image, leading to healthier patient-centered care.

A Granular Self-Organizing Map for Clustering and Gene Selection in Microarray Data.

PubMed

Ray, Shubhra Sankar; Ganivada, Avatharam; Pal, Sankar K

2016-09-01

A new granular self-organizing map (GSOM) is developed by integrating the concept of a fuzzy rough set with the SOM. While training the GSOM, the weights of a winning neuron and the neighborhood neurons are updated through a modified learning procedure. The neighborhood is newly defined using the fuzzy rough sets. The clusters (granules) evolved by the GSOM are presented to a decision table as its decision classes. Based on the decision table, a method of gene selection is developed. The effectiveness of the GSOM is shown in both clustering samples and developing an unsupervised fuzzy rough feature selection (UFRFS) method for gene selection in microarray data. While the superior results of the GSOM, as compared with the related clustering methods, are provided in terms of β -index, DB-index, Dunn-index, and fuzzy rough entropy, the genes selected by the UFRFS are not only better in terms of classification accuracy and a feature evaluation index, but also statistically more significant than the related unsupervised methods. The C-codes of the GSOM and UFRFS are available online at http://avatharamg.webs.com/software-code.

Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

PubMed

Le, Trang T; Simmons, W Kyle; Misaki, Masaya; Bodurka, Jerzy; White, Bill C; Savitz, Jonathan; McKinney, Brett A

2017-09-15

Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting. We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection. On simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder. Code available at http://insilico.utulsa.edu/software/privateEC . brett-mckinney@utulsa.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Does self-selection affect samples' representativeness in online surveys? An investigation in online video game research.

PubMed

Khazaal, Yasser; van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

2014-07-07

The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Our objective was to explore the representativeness of a self-selected sample of online gamers using online players' virtual characters (avatars). All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars' characteristics were defined using various games' scores, reported on the WoW's official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted.

Active fantasy sports: rationale and feasibility of leveraging online fantasy sports to promote physical activity.

PubMed

Moller, Arlen C; Majewski, Sara; Standish, Melanie; Agarwal, Pooja; Podowski, Aleksandra; Carson, Rebecca; Eyesus, Biruk; Shah, Aakash; Schneider, Kristin L

2014-11-25

The popularity of active video games (AVGs) has skyrocketed over the last decade. However, research suggests that the most popular AVGs, which rely on synchronous integration between players' activity and game features, fail to promote physical activity outside of the game or for extended periods of engagement. This limitation has led researchers to consider AVGs that involve asynchronous integration of players' ongoing physical activity with game features. Rather than build an AVG de novo, we selected an established sedentary video game uniquely well suited for the incorporation of asynchronous activity: online fantasy sports. The primary aim of this study was to explore the feasibility of a new asynchronous AVG-active fantasy sports-designed to promote physical activity. We conducted two pilot studies of an active fantasy sports game designed to promote physical activity. Participants wore a low cost triaxial accelerometer and participated in an online fantasy baseball (Study 1, n=9, 13-weeks) or fantasy basketball (Study 2, n=10, 17-weeks) league. Privileges within the game were made contingent on meeting weekly physical activity goals (eg, averaging 10,000 steps/day). Across the two studies, the feasibility of integrating physical activity contingent features and privileges into online fantasy sports games was supported. Participants found the active fantasy sports game enjoyable, as or more enjoyable than traditional (sedentary) online fantasy sports (Study 1: t8=4.43, P<.01; Study 2: t9=2.09, P=.07). Participants in Study 1 increased their average steps/day, t8=2.63, P<.05, while participants in Study 2 maintained (ie, did not change) their activity, t9=1.57, P=.15). In postassessment interviews, social support within the game was cited as a key motivating factor for increasing physical activity. Preliminary evidence supports potential for the active fantasy sports system as a sustainable and scalable intervention for promoting adult physical activity.

Active Fantasy Sports: Rationale and Feasibility of Leveraging Online Fantasy Sports to Promote Physical Activity

PubMed Central

Majewski, Sara; Standish, Melanie; Agarwal, Pooja; Podowski, Aleksandra; Carson, Rebecca; Eyesus, Biruk; Shah, Aakash; Schneider, Kristin L

2014-01-01

Background The popularity of active video games (AVGs) has skyrocketed over the last decade. However, research suggests that the most popular AVGs, which rely on synchronous integration between players’ activity and game features, fail to promote physical activity outside of the game or for extended periods of engagement. This limitation has led researchers to consider AVGs that involve asynchronous integration of players’ ongoing physical activity with game features. Rather than build an AVG de novo, we selected an established sedentary video game uniquely well suited for the incorporation of asynchronous activity: online fantasy sports. Objective The primary aim of this study was to explore the feasibility of a new asynchronous AVG—active fantasy sports—designed to promote physical activity. Methods We conducted two pilot studies of an active fantasy sports game designed to promote physical activity. Participants wore a low cost triaxial accelerometer and participated in an online fantasy baseball (Study 1, n=9, 13-weeks) or fantasy basketball (Study 2, n=10, 17-weeks) league. Privileges within the game were made contingent on meeting weekly physical activity goals (eg, averaging 10,000 steps/day). Results Across the two studies, the feasibility of integrating physical activity contingent features and privileges into online fantasy sports games was supported. Participants found the active fantasy sports game enjoyable, as or more enjoyable than traditional (sedentary) online fantasy sports (Study 1: t 8=4.43, P<.01; Study 2: t 9=2.09, P=.07). Participants in Study 1 increased their average steps/day, t 8=2.63, P<.05, while participants in Study 2 maintained (ie, did not change) their activity, t 9=1.57, P=.15). In postassessment interviews, social support within the game was cited as a key motivating factor for increasing physical activity. Conclusions Preliminary evidence supports potential for the active fantasy sports system as a sustainable and scalable intervention for promoting adult physical activity. PMID:25654304

UGTA Photograph Database

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

NSTec Environmental Restoration

One of the advantages of the Nevada Test Site (NTS) is that most of the geologic and hydrologic features such as hydrogeologic units (HGUs), hydrostratigraphic units (HSUs), and faults, which are important aspects of flow and transport modeling, are exposed at the surface somewhere in the vicinity of the NTS and thus are available for direct observation. However, due to access restrictions and the remote locations of many of the features, most Underground Test Area (UGTA) participants cannot observe these features directly in the field. Fortunately, National Security Technologies, LLC, geologists and their predecessors have photographed many of these featuresmore » through the years. During fiscal year 2009, work was done to develop an online photograph database for use by the UGTA community. Photographs were organized, compiled, and imported into Adobe® Photoshop® Elements 7. The photographs were then assigned keyword tags such as alteration type, HGU, HSU, location, rock feature, rock type, and stratigraphic unit. Some fully tagged photographs were then selected and uploaded to the UGTA website. This online photograph database provides easy access for all UGTA participants and can help “ground truth” their analytical and modeling tasks. It also provides new participants a resource to more quickly learn the geology and hydrogeology of the NTS.« less

Content Analysis of Select YouTube Postings: Comparisons of Reactions to the Sandy Hook and Aurora Shootings and Hurricane Sandy.

PubMed

Miller, Eric D

2015-11-01

This study details an innovative and methodical content analysis of 2,207 YouTube comments from four different YouTube videos (e.g., breaking news or memorials) related to the 2012 Sandy Hook Elementary School and Aurora theater mass shootings and the catastrophic Hurricane Sandy. As expected, YouTube comments associated with the Sandy Hook shootings (particularly those from a memorial video) were especially likely to feature compassion and grief with lessened hostility. This study highlights differing online contexts by which individuals show grief and related emotions following man-made and natural calamities and how-even in an online environment-powerful situational contexts greatly guide behavior.

NASA Releases 'NASA App HD' for iPad

NASA Image and Video Library

2012-07-06

The NASA App HD invites you to discover a wealth of NASA information right on your iPad. The application collects, customizes and delivers an extensive selection of dynamically updated mission information, images, videos and Twitter feeds from various online NASA sources in a convenient mobile package. Come explore with NASA, now on your iPad. 2012 Updated Version - HD Resolution and new features. Original version published on Sept. 1, 2010.

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis.

PubMed

Li, Bo; Tang, Jing; Yang, Qingxia; Cui, Xuejiao; Li, Shuang; Chen, Sijie; Cao, Quanxing; Xue, Weiwei; Chen, Na; Zhu, Feng

2016-12-13

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental &biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

PubMed Central

Li, Bo; Tang, Jing; Yang, Qingxia; Cui, Xuejiao; Li, Shuang; Chen, Sijie; Cao, Quanxing; Xue, Weiwei; Chen, Na; Zhu, Feng

2016-01-01

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data. PMID:27958387

Does Self-Selection Affect Samples’ Representativeness in Online Surveys? An Investigation in Online Video Game Research

PubMed Central

van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

2014-01-01

Background The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Objective Our objective was to explore the representativeness of a self-selected sample of online gamers using online players’ virtual characters (avatars). Methods All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars’ characteristics were defined using various games’ scores, reported on the WoW’s official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. Results We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Conclusions Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted. PMID:25001007

Comparative analysis of data base management systems

NASA Technical Reports Server (NTRS)

Smith, R.

1983-01-01

A study to determine if the Remote File Inquiry (RFI) system would handle the future requirements of the user community is discussed. RFI is a locally written and locally maintained on-line query/update package. The current and future on-line requirements of the user community were studied. Additional consideration was given to the types of data structuring the users required. The survey indicated the features of greatest benefit were: sort, subtotals, totals, record selection, storage of queries, global updating and the ability to page break. The major deficiencies were: one level of hierarchy, excessive response time, software unreliability, difficult to add, delete and modify records, complicated error messages and the lack of ability to perform interfield comparisons. Missing features users required were: formatted screens, interfield comparions, interfield arithmetic, multiple file access, security and data integrity. The survey team recommended Kennedy Space Center move forward to state-of-the-art software, a Data Base Management System which is thoroughly tested and easy to implement and use.

Online tracking of outdoor lighting variations for augmented reality with moving cameras.

PubMed

Liu, Yanli; Granier, Xavier

2012-04-01

In augmented reality, one of key tasks to achieve a convincing visual appearance consistency between virtual objects and video scenes is to have a coherent illumination along the whole sequence. As outdoor illumination is largely dependent on the weather, the lighting condition may change from frame to frame. In this paper, we propose a full image-based approach for online tracking of outdoor illumination variations from videos captured with moving cameras. Our key idea is to estimate the relative intensities of sunlight and skylight via a sparse set of planar feature-points extracted from each frame. To address the inevitable feature misalignments, a set of constraints are introduced to select the most reliable ones. Exploiting the spatial and temporal coherence of illumination, the relative intensities of sunlight and skylight are finally estimated by using an optimization process. We validate our technique on a set of real-life videos and show that the results with our estimations are visually coherent along the video sequences.

Online Mendelian Inheritance in Man (OMIM).

PubMed

Hamosh, A; Scott, A F; Amberger, J; Valle, D; McKusick, V A

2000-01-01

Online Mendelian Inheritance In Man (OMIM) is a public database of bibliographic information about human genes and genetic disorders. Begun by Dr. Victor McKusick as the authoritative reference Mendelian Inheritance in Man, it is now distributed electronically by the National Center for Biotechnology Information (NCBI). Material in OMIM is derived from the biomedical literature and is written by Dr. McKusick and his colleagues at Johns Hopkins University and elsewhere. Each OMIM entry has a full text summary of a genetic phenotype and/or gene and has copious links to other genetic resources such as DNA and protein sequence, PubMed references, mutation databases, approved gene nomenclature, and more. In addition, NCBI's neighboring feature allows users to identify related articles from PubMed selected on the basis of key words in the OMIM entry. Through its many features, OMIM is increasingly becoming a major gateway for clinicians, students, and basic researchers to the ever-growing literature and resources of human genetics. Copyright 2000 Wiley-Liss, Inc.

Development of online NIR urine analyzing system based on AOTF

NASA Astrophysics Data System (ADS)

Wan, Feng; Sun, Zhendong; Li, Xiaoxia

2006-09-01

In this paper, some key techniques on development of on-line MR urine analyzing system based on AOTF (Acousto - Optics Tunable Filter) are introduced. Problems about designing the optical system including collimation of incident light and working distance (the shortest distance for separating incident light and diffracted light) are analyzed and researched. DDS (Direct Digital Synthesizer) controlled by microprocessor is used to realize the wavelength scan. The experiment results show that this MR urine analyzing system based on. AOTF has 10000 - 4000cm -1 wavelength range and O.3ms wavelength transfer rate. Compare with the conventional Fourier Transform NIP. spectrophotometer for analyzing multi-components in urine, this system features low cost, small volume and on-line measurement function. Unscrambler software (multivariate statistical software by CAMO Inc. Norway) is selected as the software for processing the data. This system can realize on line quantitative analysis of protein, urea and creatinine in urine.

Automatic Learning of Fine Operating Rules for Online Power System Security Control.

PubMed

Sun, Hongbin; Zhao, Feng; Wang, Hao; Wang, Kang; Jiang, Weiyong; Guo, Qinglai; Zhang, Boming; Wehenkel, Louis

2016-08-01

Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state to determine critical flowgates, and then a continuation power flow-based security analysis is used to compute the initial transfer capability of critical flowgates. Next, the system applies the Monte Carlo simulations to expected short-term operating condition changes, feature selection, and a linear least squares fitting of the fine operating rules. The proposed system was validated both on an academic test system and on a provincial power system in China. The results indicated that the derived rules provide accuracy and good interpretability and are suitable for real-time power system security control. The use of high-performance computing systems enables these fine operating rules to be refreshed online every 15 min.

A novel aptamer-based online magnetic solid phase extraction method for the selective determination of 8-hydroxy-2'-deoxyguanosine in human urine.

PubMed

Gan, Haijiao; Xu, Hui

2018-05-30

In this work, an innovative magnetic aptamer adsorbent (Fe 3 O 4 -aptamer MNPs) was synthesized for the selective extraction of 8-hydroxy-2'-deoxyguanosine (8-OHdG). Amino-functionalized-Fe 3 O 4 was crosslinked with 8-OHdG aptamer by glutaraldehyde and fixed into a steel stainless tube as the sorbent of magnetic solid phase extraction (MSPE). After selective extraction by the aptamer adsorbent, the adsorbed 8-OHdG was desorbed dynamically and online analyzed by high performance liquid chromatography-mass spectrometry (HPLC-MS). The synthesized sorbent presented outstanding features, including specific selectivity, high enrichment capacity, stability and biocompatibility. Moreover, this proposed MSPE-HPLC-MS can achieve adsorption and desorption operation integration, greatly simplify the analysis process and reduce human errors. When compared with offline MSPE, a sensitivity enhancement of 800 times was obtained for the online method. Some experimental parameters such as the amount of the sorbent, sample flow rate and sample volume, were optimized systematically. Under the optimal conditions, low limit of detection (0.01 ng mL -1 , S/N = 3), limit of quantity (0.03 ng mL -1 , S/N = 10) and wide linear range with a satisfactory correlation coefficient (R 2  ≥ 0.9992) were obtained. And the recoveries of 8-OHdG in the urine samples varied from 82% to 116%. All these results revealed that the method is simple, rapid, selective, sensitive and automated, and it could be expected to become a potential approach for the selective determination of trace 8-OHdG in complex urinary samples. Copyright © 2017 Elsevier B.V. All rights reserved.

Expanding the Possibilities of Discussion: A Strategic Approach to Using Online Discussion Boards in the Middle and High School English Classroom

ERIC Educational Resources Information Center

Ruday, Sean

2011-01-01

This paper focused on whether the use of online discussion boards can enhance the quality of interaction in the middle and high school English classroom, covering both the characteristics of online discussion boards and potential negative effects of their features. The features of online discussion boards, their effects, and how these boards…

Multiresolution analysis over graphs for a motor imagery based online BCI game.

PubMed

Asensio-Cubero, Javier; Gan, John Q; Palaniappan, Ramaswamy

2016-01-01

Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain-computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human-machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Self-Expression, Social Roles, and Faculty Members' Attitudes towards Online Teaching

ERIC Educational Resources Information Center

Glass, Chris R.

2017-01-01

There is a widening gap between administrators' and faculty members' attitudes towards online education. This post-positivist grounded theory study explored features of the experiences that shaped sixteen faculty members' attitudes towards online education. Two features are identified: (a) they strived to express subject matter of personal…

Wire bonding quality monitoring via refining process of electrical signal from ultrasonic generator

NASA Astrophysics Data System (ADS)

Feng, Wuwei; Meng, Qingfeng; Xie, Youbo; Fan, Hong

2011-04-01

In this paper, a technique for on-line quality detection of ultrasonic wire bonding is developed. The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system. A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality. The method includes three steps. First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic. Second, each subband envelope is obtained using the Hilbert transform for further feature extraction. Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes. The different waveform features are extracted from each phase of these subband envelopes. The principal components analysis (PCA) method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables. Using the selected features as inputs, an artificial neural network (ANN) is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality.

MySchoolDayOnline: Applying Universal Design Principles to the Development of a Fully Accessible Online Scheduling Tool for Students with Visual Impairments

ERIC Educational Resources Information Center

Sapp, Wendy

2007-01-01

This article presents the universal design features that were identified during the alpha development of a scheduler software program, known as MySchoolDayOnline, for use in schools, and provides preliminary research on the usability of these features. The study presented here investigated the accessibility and usability of MySchoolDayOnline for…

Effects of Website Interactivity on Online Retail Shopping Behavior

NASA Astrophysics Data System (ADS)

Islam, Hafizul

Motivations to engage in retail online shopping can include both utilitarian and hedonic shopping dimensions. To cater to these consumers, online retailers can create a cognitively and esthetically rich shopping environment, through sophisticated levels of interactive web utilities and features, offering not only utilitarian benefits and attributes but also providing hedonic benefits of enjoyment. Since the effect of interactive websites has proven to stimulate online consumer’s perceptions, this study presumes that websites with multimedia rich interactive utilities and features can influence online consumers’ shopping motivations and entice them to modify or even transform their original shopping predispositions by providing them with attractive and enhanced interactive features and controls, thus generating a positive attitude towards products and services offered by the retailer. This study seeks to explore the effects of Web interactivity on online consumer behavior through an attitudinal model of technology acceptance.

Harmonic wavelet packet transform for on-line system health diagnosis

NASA Astrophysics Data System (ADS)

Yan, Ruqiang; Gao, Robert X.

2004-07-01

This paper presents a new approach to on-line health diagnosis of mechanical systems, based on the wavelet packet transform. Specifically, signals acquired from vibration sensors are decomposed into sub-bands by means of the discrete harmonic wavelet packet transform (DHWPT). Based on the Fisher linear discriminant criterion, features in the selected sub-bands are then used as inputs to three classifiers (Nearest Neighbor rule-based and two Neural Network-based), for system health condition assessment. Experimental results have confirmed that, comparing to the conventional approach where statistical parameters from raw signals are used, the presented approach enabled higher signal-to-noise ratio for more effective and intelligent use of the sensory information, thus leading to more accurate system health diagnosis.

Robust Indoor Human Activity Recognition Using Wireless Signals.

PubMed

Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

2015-07-15

Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Front End Software for Online Database Searching Part 1: Definitions, System Features, and Evaluation.

ERIC Educational Resources Information Center

Hawkins, Donald T.; Levy, Louise R.

1985-01-01

This initial article in series of three discusses barriers inhibiting use of current online retrieval systems by novice users and notes reasons for front end and gateway online retrieval systems. Definitions, front end features, user interface, location (personal computer, host mainframe), evaluation, and strengths and weaknesses are covered. (16…

Semantic memory: a feature-based analysis and new norms for Italian.

PubMed

Montefinese, Maria; Ambrosini, Ettore; Fairfield, Beth; Mammarella, Nicola

2013-06-01

Semantic norms for properties produced by native speakers are valuable tools for researchers interested in the structure of semantic memory and in category-specific semantic deficits in individuals following brain damage. The aims of this study were threefold. First, we sought to extend existing semantic norms by adopting an empirical approach to category (Exp. 1) and concept (Exp. 2) selection, in order to obtain a more representative set of semantic memory features. Second, we extensively outlined a new set of semantic production norms collected from Italian native speakers for 120 artifactual and natural basic-level concepts, using numerous measures and statistics following a feature-listing task (Exp. 3b). Finally, we aimed to create a new publicly accessible database, since only a few existing databases are publicly available online.

TSAPA: identification of tissue-specific alternative polyadenylation sites in plants.

PubMed

Ji, Guoli; Chen, Moliang; Ye, Wenbin; Zhu, Sheng; Ye, Congting; Su, Yaru; Peng, Haonan; Wu, Xiaohui

2018-06-15

Alternative polyadenylation (APA) is now emerging as a widespread mechanism modulated tissue-specifically, which highlights the need to define tissue-specific poly(A) sites for profiling APA dynamics across tissues. We have developed an R package called TSAPA based on the machine learning model for identifying tissue-specific poly(A) sites in plants. A feature space including more than 200 features was assembled to specifically characterize poly(A) sites in plants. The classification model in TSAPA can be customized by selecting desirable features or classifiers. TSAPA is also capable of predicting tissue-specific poly(A) sites in unannotated intergenic regions. TSAPA will be a valuable addition to the community for studying dynamics of APA in plants. https://github.com/BMILAB/TSAPA. Supplementary data are available at Bioinformatics online.

Sexually explicit racialised media targeting men who have sex with men online: a content analysis of high-risk behaviour depicted in online advertisements.

PubMed

White, Jaclyn M; Dunham, Emilia; Rowley, Blake; Reisner, Sari L; Mimiaga, Matthew J

2015-01-01

Sexually explicit media may perpetuate racial and sexual norms among men who have sex with men. While men may be exposed to sexually explicit media in the online settings where they seek sex with other men, no studies to our knowledge have explored the relationship between the racial and sexual content of advertisements appearing in these spaces. In 2011, using a detailed codebook, 217 sexually explicit advertisements on a male sex-seeking website were coded for themes, actor characteristics and sexual acts depicted. Multivariable logistic regression models examined the association between skin colour, theme, sexual acts and condomless sex acts. Nearly half (45%) featured a 'thug' theme (a style emphasising Black masculinity/hip-hop culture), 21% featured a college theme and 44% featured condomless sex. Advertisements featuring only Black men, advertisements featuring Black men with men of other skin tones and advertisements depicting a thug theme were positively associated with depictions of condomless sex. Online sexually explicit advertisements featuring Black themes and actors more frequently depicted condomless sex than advertisements with White men alone. Future research should examine whether depictions of Black men engaging in condomless sex in online advertisements influence the sexual norms and cognitions of Black men who have sex with men and their partners.

Sexually explicit racialised media targeting men who have sex with men online: A content analysis of high-risk behaviour depicted in online advertisements

PubMed Central

White, Jaclyn M.; Dunham, Emilia; Rowley, Blake; Reisner, Sari L.; Mimiaga, Matthew J.

2015-01-01

Sexually explicit media may perpetuate racial and sexual norms among men who have sex with men. While men may be exposed to sexually explicit media in the online settings where they seek sex with other men, no studies to our knowledge have explored the relationship between the racial and sexual content of advertisements appearing in these spaces. In 2011, 217 sexually explicit advertisements on a male sex-seeking website were coded for themes, actor characteristics, and sexual acts depicted using a detailed codebook. Multivariable logistic regression models examined the association between skin colour, theme, sexual acts, and condomless sex acts. Nearly half (45%) featured a ‘thug’ theme (style emphasising Black masculinity/hip-hop culture), 21% featured a college theme, and 44% featured condomless sex. Ads featuring only Black men, ads featuring Black men with men of other skin tones, and ads depicting a thug theme were positively associated with depictions of condomless sex. Online sexually explicit ads featuring Black themes and actors more frequently depicted risky sex than ads with White men alone. Future research should examine whether risky depictions of Black men in online ads influence the sexual norms and cognitions of Black men who have sex with men and their partners. PMID:25891135

Quantifying the role of online news in linking conservation research to Facebook and Twitter.

PubMed

Papworth, S K; Nghiem, T P L; Chimalakonda, D; Posa, M R C; Wijedasa, L S; Bickford, D; Carrasco, L R

2015-06-01

Conservation science needs to engage the general public to ensure successful conservation interventions. Although online technologies such as Twitter and Facebook offer new opportunities to accelerate communication between conservation scientists and the online public, factors influencing the spread of conservation news in online media are not well understood. We explored transmission of conservation research through online news articles with generalized linear mixed-effects models and an information theoretic approach. In particular, we assessed differences in the frequency conservation research is featured on online news sites and the impact of online conservation news content and delivery on Facebook likes and shares and Twitter tweets. Five percent of articles in conservation journals are reported in online news, and the probability of reporting depended on the journal. There was weak evidence that articles on climate change and mammals were more likely to be featured. Online news articles about charismatic mammals with illustrations were more likely to be shared or liked on Facebook and Twitter, but the effect of news sites was much larger. These results suggest journals have the greatest impact on which conservation research is featured and that news site has the greatest impact on how popular an online article will be on Facebook and Twitter. © 2015 Society for Conservation Biology.

Feasibility of feature-based indexing, clustering, and search of clinical trials: A case study of breast cancer trials from ClinicalTrials.gov

PubMed Central

Boland, Mary Regina; Miotto, Riccardo; Gao, Junfeng; Weng, Chunhua

2013-01-01

Summary Background When standard therapies fail, clinical trials provide experimental treatment opportunities for patients with drug-resistant illnesses or terminal diseases. Clinical Trials can also provide free treatment and education for individuals who otherwise may not have access to such care. To find relevant clinical trials, patients often search online; however, they often encounter a significant barrier due to the large number of trials and in-effective indexing methods for reducing the trial search space. Objectives This study explores the feasibility of feature-based indexing, clustering, and search of clinical trials and informs designs to automate these processes. Methods We decomposed 80 randomly selected stage III breast cancer clinical trials into a vector of eligibility features, which were organized into a hierarchy. We clustered trials based on their eligibility feature similarities. In a simulated search process, manually selected features were used to generate specific eligibility questions to filter trials iteratively. Results We extracted 1,437 distinct eligibility features and achieved an inter-rater agreement of 0.73 for feature extraction for 37 frequent features occurring in more than 20 trials. Using all the 1,437 features we stratified the 80 trials into six clusters containing trials recruiting similar patients by patient-characteristic features, five clusters by disease-characteristic features, and two clusters by mixed features. Most of the features were mapped to one or more Unified Medical Language System (UMLS) concepts, demonstrating the utility of named entity recognition prior to mapping with the UMLS for automatic feature extraction. Conclusions It is feasible to develop feature-based indexing and clustering methods for clinical trials to identify trials with similar target populations and to improve trial search efficiency. PMID:23666475

Feasibility of feature-based indexing, clustering, and search of clinical trials. A case study of breast cancer trials from ClinicalTrials.gov.

PubMed

Boland, M R; Miotto, R; Gao, J; Weng, C

2013-01-01

When standard therapies fail, clinical trials provide experimental treatment opportunities for patients with drug-resistant illnesses or terminal diseases. Clinical Trials can also provide free treatment and education for individuals who otherwise may not have access to such care. To find relevant clinical trials, patients often search online; however, they often encounter a significant barrier due to the large number of trials and in-effective indexing methods for reducing the trial search space. This study explores the feasibility of feature-based indexing, clustering, and search of clinical trials and informs designs to automate these processes. We decomposed 80 randomly selected stage III breast cancer clinical trials into a vector of eligibility features, which were organized into a hierarchy. We clustered trials based on their eligibility feature similarities. In a simulated search process, manually selected features were used to generate specific eligibility questions to filter trials iteratively. We extracted 1,437 distinct eligibility features and achieved an inter-rater agreement of 0.73 for feature extraction for 37 frequent features occurring in more than 20 trials. Using all the 1,437 features we stratified the 80 trials into six clusters containing trials recruiting similar patients by patient-characteristic features, five clusters by disease-characteristic features, and two clusters by mixed features. Most of the features were mapped to one or more Unified Medical Language System (UMLS) concepts, demonstrating the utility of named entity recognition prior to mapping with the UMLS for automatic feature extraction. It is feasible to develop feature-based indexing and clustering methods for clinical trials to identify trials with similar target populations and to improve trial search efficiency.

Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.

PubMed

Liu, Zhenqiu; Hsiao, William; Cantarel, Brandi L; Drábek, Elliott Franco; Fraser-Liggett, Claire

2011-12-01

Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota. By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data. The MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/. zliu@umm.edu Supplementary data are available at Bioinformatics online.

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.

PubMed

Manavalan, Balachandran; Shin, Tae Hwan; Lee, Gwang

2018-01-05

DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html.

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

PubMed Central

Manavalan, Balachandran; Shin, Tae Hwan; Lee, Gwang

2018-01-01

DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html PMID:29416743

Measuring the Interestingness of Articles in a Limited User Environment

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

Pon, R; Cardenas, A; Buttler, David

Search engines, such as Google, assign scores to news articles based on their relevance to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevance scores do not take into account what makes an article interesting, which would vary from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation systems, in a limited user environment, there are not enough users that would make collaborative filtering effective. A generalmore » framework, called iScore, is presented for defining and measuring the ‘‘interestingness of articles, incorporating user-feedback. iScore addresses the various aspects of what makes an article interesting, such as topic relevance, uniqueness, freshness, source reputation, and writing style. It employs various methods, such as multiple topic tracking, online parameter selection, language models, clustering, sentiment analysis, and phrase extraction to measure these features. Due to varying reasons that users hold about why an article is interesting, an online feature selection method in naι¨ve Bayes is also used to improve recommendation results. iScore can outperform traditional IR techniques by as much as 50.7%. iScore and its components are evaluated in the news recommendation task using three datasets from Yahoo! News, actual users, and Digg.« less

A GIS-based 3D online information system for underground energy storage in northern Germany

NASA Astrophysics Data System (ADS)

Nolde, Michael; Malte, Schwanebeck; Ehsan, Biniyaz; Rainer, Duttmann

2015-04-01

We would like to present the concept and current state of development of a GIS-based 3D online information system for underground energy storage. Its aim is to support the local authorities through pre-selection of possible sites for thermal, electrical and substantial underground energy storages. Since the extension of renewable energies has become legal requirement in Germany, the underground storing of superfluously produced green energy (such as during a heavy wind event) in the form of compressed air, gas or heated water has become increasingly important. However, the selection of suitable sites is a complex task. The presented information system uses data of geological features such as rock layers, salt domes and faults enriched with attribute data such as rock porosity and permeability. This information is combined with surface data of the existing energy infrastructure, such as locations of wind and biogas stations, powerline arrangement and cable capacity, and energy distribution stations. Furthermore, legal obligations such as protected areas on the surface and current underground mining permissions are used for the process of pre-selecting sites suitable for energy storage. Not only the current situation but also prospective scenarios, such as expected growth in produced amount of energy are incorporated in the system. While the process of pre-selection itself is completely automated, the user has full control of the weighting of the different factors via the web interface. The system is implemented as an online 3D server GIS environment, so that it can easily be utilized in any web browser. The results are visualized online as interactive 3d graphics. The information system is implemented in the Python programming language in combination with current Web standards, and is build using only free and open source software. It is being developed at Kiel University as part of the ANGUS+ project (lead by Prof. Sebastian Bauer) for the federal state of Schleswig-Holstein in northern Germany.

Numerical image manipulation and display in solar astronomy

NASA Technical Reports Server (NTRS)

Levine, R. H.; Flagg, J. C.

1977-01-01

The paper describes the system configuration and data manipulation capabilities of a solar image display system which allows interactive analysis of visual images and on-line manipulation of digital data. Image processing features include smoothing or filtering of images stored in the display, contrast enhancement, and blinking or flickering images. A computer with a core memory of 28,672 words provides the capacity to perform complex calculations based on stored images, including computing histograms, selecting subsets of images for further analysis, combining portions of images to produce images with physical meaning, and constructing mathematical models of features in an image. Some of the processing modes are illustrated by some image sequences from solar observations.

BioModels Database: a repository of mathematical models of biological processes.

PubMed

Chelliah, Vijayalakshmi; Laibe, Camille; Le Novère, Nicolas

2013-01-01

BioModels Database is a public online resource that allows storing and sharing of published, peer-reviewed quantitative, dynamic models of biological processes. The model components and behaviour are thoroughly checked to correspond the original publication and manually curated to ensure reliability. Furthermore, the model elements are annotated with terms from controlled vocabularies as well as linked to relevant external data resources. This greatly helps in model interpretation and reuse. Models are stored in SBML format, accepted in SBML and CellML formats, and are available for download in various other common formats such as BioPAX, Octave, SciLab, VCML, XPP and PDF, in addition to SBML. The reaction network diagram of the models is also available in several formats. BioModels Database features a search engine, which provides simple and more advanced searches. Features such as online simulation and creation of smaller models (submodels) from the selected model elements of a larger one are provided. BioModels Database can be accessed both via a web interface and programmatically via web services. New models are available in BioModels Database at regular releases, about every 4 months.

A Smartphone Indoor Localization Algorithm Based on WLAN Location Fingerprinting with Feature Extraction and Clustering.

PubMed

Luo, Junhai; Fu, Liang

2017-06-09

With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS), which is collected from Access Points (APs). The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA) is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC) algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML) estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update.

PubMed

Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

2016-04-15

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the "good" models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm.

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

PubMed Central

Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

2016-01-01

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm. PMID:27092505

Electronic Gaming Machine (EGM) Environments: Market Segments and Risk.

PubMed

Rockloff, Matthew; Moskovsky, Neda; Thorne, Hannah; Browne, Matthew; Bryden, Gabrielle

2017-12-01

This study used a marketing-research paradigm to explore gamblers' attraction to EGMs based on different elements of the environment. A select set of environmental features was sourced from a prior study (Thorne et al. in J Gambl Issues 2016b), and a discrete choice experiment was conducted through an online survey. Using the same dataset first described by Rockloff et al. (EGM Environments that contribute to excess consumption and harm, 2015), a sample of 245 EGM gamblers were sourced from clubs in Victoria, Australia, and 7516 gamblers from an Australian national online survey-panel. Participants' choices amongst sets of hypothetical gambling environments allowed for an estimation of the implied individual-level utilities for each feature (e.g., general sounds, location, etc.). K-means clustering on these utilities identified four unique market segments for EGM gambling, representing four different types of consumers. The segments were named according to their dominant features: Social, Value, High Roller and Internet. We found that the environments orientated towards the Social and Value segments were most conducive to attracting players with relatively few gambling problems, while the High Roller and Internet-focused environments had greater appeal for players with problems and vulnerabilities. This study has generated new insights into the kinds of gambling environments that are most consistent with safe play.

A temperature-based feedback control system for electromagnetic phased-array hyperthermia: theory and simulation.

PubMed

Kowalski, M E; Jin, J M

2003-03-07

A hybrid proportional-integral-in-time and cost-minimizing-in-space feedback control system for electromagnetic, deep regional hyperthermia is proposed. The unique features of this controller are that (1) it uses temperature, not specific absorption rate, as the criterion for selecting the relative phases and amplitudes with which to drive the electromagnetic phased-array used for hyperthermia and (2) it requires on-line computations that are all deterministic in duration. The former feature, in addition to optimizing the treatment directly on the basis of a clinically relevant quantity, also allows the controller to sense and react to time- and temperature-dependent changes in local blood perfusion rates and other factors that can significantly impact the temperature distribution quality of the delivered treatment. The latter feature makes it feasible to implement the scheme on-line in a real-time feedback control loop. This is in sharp contrast to other temperature optimization techniques proposed in the literature that generally involve an iterative approximation that cannot be guaranteed to terminate in a fixed amount of computational time. An example of its application is presented to illustrate the properties and demonstrate the capability of the controller to sense and compensate for local, time-dependent changes in blood perfusion rates.

Why people continue to play online games: in search of critical design factors to increase customer loyalty to online contents.

PubMed

Choi, Dongseong; Kim, Jinwoo

2004-02-01

As people increasingly play online games, numerous new features have been proposed to increase players' log-on time at online gaming sites. However, few studies have investigated why people continue to play certain online games or which design features are most closely related to the amount of time spent by players at particular online gaming sites. This study proposes a theoretical model using the concepts of customer loyalty, flow, personal interaction, and social interaction to explain why people continue to play online network games. The study then conducts a large-scale survey to validate the model. Finally, it analyzes current online games to identify design features that are closely related to the theoretical concepts. The results indicate that people continue to play online games if they have optimal experiences while playing the games. This optimal experience can be attained if the player has effective personal interaction with the system or pleasant social interactions with other people connected to the Internet. Personal interaction can be facilitated by providing appropriate goals, operators and feedback; social interaction can be facilitated through appropriate communication places and tools. This paper ends with the implications of applying the study results to other domains such as e-commerce and cyber communities.

Online Patient Education for Chronic Disease Management: Consumer Perspectives.

PubMed

Win, Khin Than; Hassan, Naffisah Mohd; Oinas-Kukkonen, Harri; Probst, Yasmine

2016-04-01

Patient education plays an important role in chronic disease management. The aim of this study is to identify patients' preferences in regard to the design features of effective online patient education (OPE) and the benefits. A review of the existing literature was conducted in order to identify the benefits of OPE and its essential design features. These design features were empirically tested by conducting survey with patients and caregivers. Reliability analysis, construct validity and regression analysis were performed for data analysis. The results identified patient-tailored information, interactivity, content credibility, clear presentation of content, use of multimedia and interpretability as the essential design features of online patient education websites for chronic disease management.

EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed Privacy-Preserving Online Model Learning

PubMed Central

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-01-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection etc.) as the traditional frequentist Logistic Regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. PMID:23562651

Improved semi-supervised online boosting for object tracking

NASA Astrophysics Data System (ADS)

Li, Yicui; Qi, Lin; Tan, Shukun

2016-10-01

The advantage of an online semi-supervised boosting method which takes object tracking problem as a classification problem, is training a binary classifier from labeled and unlabeled examples. Appropriate object features are selected based on real time changes in the object. However, the online semi-supervised boosting method faces one key problem: The traditional self-training using the classification results to update the classifier itself, often leads to drifting or tracking failure, due to the accumulated error during each update of the tracker. To overcome the disadvantages of semi-supervised online boosting based on object tracking methods, the contribution of this paper is an improved online semi-supervised boosting method, in which the learning process is guided by positive (P) and negative (N) constraints, termed P-N constraints, which restrict the labeling of the unlabeled samples. First, we train the classification by an online semi-supervised boosting. Then, this classification is used to process the next frame. Finally, the classification is analyzed by the P-N constraints, which are used to verify if the labels of unlabeled data assigned by the classifier are in line with the assumptions made about positive and negative samples. The proposed algorithm can effectively improve the discriminative ability of the classifier and significantly alleviate the drifting problem in tracking applications. In the experiments, we demonstrate real-time tracking of our tracker on several challenging test sequences where our tracker outperforms other related on-line tracking methods and achieves promising tracking performance.

Information presentation features and comprehensibility of hospital report cards: design analysis and online survey among users.

PubMed

Sander, Uwe; Emmert, Martin; Dickel, Jochen; Meszmer, Nina; Kolb, Benjamin

2015-03-16

Improving the transparency of information about the quality of health care providers is one way to improve health care quality. It is assumed that Internet information steers patients toward better-performing health care providers and will motivate providers to improve quality. However, the effect of public reporting on hospital quality is still small. One of the reasons is that users find it difficult to understand the formats in which information is presented. We analyzed the presentation of risk-adjusted mortality rate (RAMR) for coronary angiography in the 10 most commonly used German public report cards to analyze the impact of information presentation features on their comprehensibility. We wanted to determine which information presentation features were utilized, were preferred by users, led to better comprehension, and had similar effects to those reported in evidence-based recommendations described in the literature. The study consisted of 5 steps: (1) identification of best-practice evidence about the presentation of information on hospital report cards; (2) selection of a single risk-adjusted quality indicator; (3) selection of a sample of designs adopted by German public report cards; (4) identification of the information presentation elements used in public reporting initiatives in Germany; and (5) an online panel completed an online questionnaire that was conducted to determine if respondents were able to identify the hospital with the lowest RAMR and if respondents' hospital choices were associated with particular information design elements. Evidence-based recommendations were made relating to the following information presentation features relevant to report cards: evaluative table with symbols, tables without symbols, bar charts, bar charts without symbols, bar charts with symbols, symbols, evaluative word labels, highlighting, order of providers, high values to indicate good performance, explicit statements of whether high or low values indicate good performance, and incomplete data ("N/A" as a value). When investigating the RAMR in a sample of 10 hospitals' report cards, 7 of these information presentation features were identified. Of these, 5 information presentation features improved comprehensibility in a manner reported previously in literature. To our knowledge, this is the first study to systematically analyze the most commonly used public reporting card designs used in Germany. Best-practice evidence identified in international literature was in agreement with 5 findings about German report card designs: (1) avoid tables without symbols, (2) include bar charts with symbols, (3) state explicitly whether high or low values indicate good performance or provide a "good quality" range, (4) avoid incomplete data (N/A given as a value), and (5) rank hospitals by performance. However, these findings are preliminary and should be subject of further evaluation. The implementation of 4 of these recommendations should not present insurmountable obstacles. However, ranking hospitals by performance may present substantial difficulties.

Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization.

PubMed

Wen, Ping-Ping; Shi, Shao-Ping; Xu, Hao-Dong; Wang, Li-Na; Qiu, Jian-Ding

2016-10-15

As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: 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.

Online signature recognition using principal component analysis and artificial neural network

NASA Astrophysics Data System (ADS)

Hwang, Seung-Jun; Park, Seung-Je; Baek, Joong-Hwan

2016-12-01

In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space. Artificial neural network is adopted to solve the complex signature classification problem. 30 dimensional features are converted into 10 principal components using principal component analysis, which is 99.02% of total variances. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows 98.47% of recognition rate when using only 10 feature vectors.

Designing Online Conferences to Promote Professional Development in Africa

ERIC Educational Resources Information Center

Carr, Tony

2016-01-01

This article considers how online conferences can support professional development across Africa and reviews elements of the literatures of social learning, online professional development and online conferences. The e/merge online conference is then described in terms of design features and participation metrics. This sets context for discussion…

A real-time method to predict social media popularity

NASA Astrophysics Data System (ADS)

Chen, Xiao; Lu, Zhe-Ming

How to predict the future popularity of a message or video on online social media (OSM) has long been an attractive problem for researchers. Although many difficulties are still ahead, recent studies suggest that temporal and topological features of early adopters generally play a very important role. However, with the increase of the adopters, the feature space will grow explosively. How to select the most effective features is still an open issue. In this work, we investigate several feature extraction methods over the Twitter platform and find that most predictive power concentrates on the second half of the propagation period, and that not only a model trained on one platform generalizes well to others as previous works observed, but also a model trained on one dataset performs well on predicting the popularity for other datasets with different number of observed early adopters. According to these findings, at least for the best features by far, the data used to extract features can be halved without loss of evident accuracy and we provide a way to roughly predict the growth trend of a social-media item in real-time.

An online condition monitoring system implemented an internet connectivity and FTP for low speed slew bearing

NASA Astrophysics Data System (ADS)

Caesarendra, W.; Kosasih, B.; Tjahjowidodo, T.; Ariyanto, M.; Daryl, LWQ; Pamungkas, D.

2018-04-01

Rapid and reliable information in slew bearing maintenance is not trivial issue. This paper presents the online monitoring system to assist maintenance engineer in order to monitor the bearing condition of low speed slew bearing in sheet metal company. The system is able to pass the vibration information from the place where the bearing and accelerometer sensors are attached to the data center; and from the data center it can be access by opening the online monitoring website from any place and by any person. The online monitoring system is built using some programming languages such as C language, MATLAB, PHP, HTML and CSS. Generally, the flow process is start with the automatic vibration data acquisition; then features are calculated from the acquired vibration data. These features are then sent to the data center; and form the data center, the vibration features can be seen through the online monitoring website. This online monitoring system has been successfully applied in School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong.

ADAM: An Accident Diagnostic,Analysis and Management System - Applications to Severe Accident Simulation and Management

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

Zavisca, M.J.; Khatib-Rahbar, M.; Esmaili, H.

2002-07-01

The Accident Diagnostic, Analysis and Management (ADAM) computer code has been developed as a tool for on-line applications to accident diagnostics, simulation, management and training. ADAM's severe accident simulation capabilities incorporate a balance of mechanistic, phenomenologically based models with simple parametric approaches for elements including (but not limited to) thermal hydraulics; heat transfer; fuel heatup, meltdown, and relocation; fission product release and transport; combustible gas generation and combustion; and core-concrete interaction. The overall model is defined by a relatively coarse spatial nodalization of the reactor coolant and containment systems and is advanced explicitly in time. The result is to enablemore » much faster than real time (i.e., 100 to 1000 times faster than real time on a personal computer) applications to on-line investigations and/or accident management training. Other features of the simulation module include provision for activation of water injection, including the Engineered Safety Features, as well as other mechanisms for the assessment of accident management and recovery strategies and the evaluation of PSA success criteria. The accident diagnostics module of ADAM uses on-line access to selected plant parameters (as measured by plant sensors) to compute the thermodynamic state of the plant, and to predict various margins to safety (e.g., times to pressure vessel saturation and steam generator dryout). Rule-based logic is employed to classify the measured data as belonging to one of a number of likely scenarios based on symptoms, and a number of 'alarms' are generated to signal the state of the reactor and containment. This paper will address the features and limitations of ADAM with particular focus on accident simulation and management. (authors)« less

An FPGA-based trigger for the phase II of the MEG experiment

NASA Astrophysics Data System (ADS)

Baldini, A.; Bemporad, C.; Cei, F.; Galli, L.; Grassi, M.; Morsani, F.; Nicolò, D.; Ritt, S.; Venturini, M.

2016-07-01

For the phase II of MEG, we are going to develop a combined trigger and DAQ system. Here we focus on the former side, which operates an on-line reconstruction of detector signals and event selection within 450 μs from event occurrence. Trigger concentrator boards (TCB) are under development to gather data from different crates, each connected to a set of detector channels, to accomplish higher-level algorithms to issue a trigger in the case of a candidate signal event. We describe the major features of the new system, in comparison with phase I, as well as its performances in terms of selection efficiency and background rejection.

Shopping on the Public and Private Health Insurance Marketplaces: Consumer Decision Aids and Plan Presentation.

PubMed

Wong, Charlene A; Kulhari, Sajal; McGeoch, Ellen J; Jones, Arthur T; Weiner, Janet; Polsky, Daniel; Baker, Tom

2018-05-29

The design of the Affordable Care Act's (ACA) health insurance marketplaces influences complex health plan choices. To compare the choice environments of the public health insurance exchanges in the fourth (OEP4) versus third (OEP3) open enrollment period and to examine online marketplace run by private companies, including a total cost estimate comparison. In November-December 2016, we examined the public and private online health insurance exchanges. We navigated each site for "real-shopping" (personal information required) and "window-shopping" (no required personal information). Public (n = 13; 12 state-based marketplaces and HealthCare.gov ) and private (n = 23) online health insurance exchanges. Features included consumer decision aids (e.g., total cost estimators, provider lookups) and plan display (e.g., order of plans). We examined private health insurance exchanges for notable features (i.e., those not found on public exchanges) and compared the total cost estimates on public versus private exchanges for a standardized consumer. Nearly all studied consumer decision aids saw increased deployment in the public marketplaces in OEP4 compared to OEP3. Over half of the public exchanges (n = 7 of 13) had total cost estimators (versus 5 of 14 in OEP3) in window-shopping and integrated provider lookups (window-shopping: 7; real-shopping: 8). The most common default plan orders were by premium or total cost estimate. Notable features on private health insurance exchanges were unique data presentation (e.g., infographics) and further personalized shopping (e.g., recommended plan flags). Health plan total cost estimates varied substantially between the public and private exchanges (average difference $1526). The ACA's public health insurance exchanges offered more tools in OEP4 to help consumers select a plan. While private health insurance exchanges presented notable features, the total cost estimates for a standardized consumer varied widely on public versus private exchanges.

SU-F-J-199: Predictive Models for Cone Beam CT-Based Online Verification of Pencil Beam Scanning Proton Therapy

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

Yin, L; Lin, A; Ahn, P

Purpose: To utilize online CBCT scans to develop models for predicting DVH metrics in proton therapy of head and neck tumors. Methods: Nine patients with locally advanced oropharyngeal cancer were retrospectively selected in this study. Deformable image registration was applied to the simulation CT, target volumes, and organs at risk (OARs) contours onto each weekly CBCT scan. Intensity modulated proton therapy (IMPT) treatment plans were created on the simulation CT and forward calculated onto each corrected CBCT scan. Thirty six potentially predictive metrics were extracted from each corrected CBCT. These features include minimum/maximum/mean over and under-ranges at the proximal andmore » distal surface of PTV volumes, and geometrical and water equivalent distance between PTV and each OARs. Principal component analysis (PCA) was used to reduce the dimension of the extracted features. Three principal components were found to account for over 90% of variances in those features. Datasets from eight patients were used to train a machine learning model to fit these principal components with DVH metrics (dose to 95% and 5% of PTV, mean dose or max dose to OARs) from the forward calculated dose on each corrected CBCT. The accuracy of this model was verified on the datasets from the 9th patient. Results: The predicted changes of DVH metrics from the model were in good agreement with actual values calculated on corrected CBCT images. Median differences were within 1 Gy for most DVH metrics except for larynx and constrictor mean dose. However, a large spread of the differences was observed, indicating additional training datasets and predictive features are needed to improve the model. Conclusion: Intensity corrected CBCT scans hold the potential to be used for online verification of proton therapy and prediction of delivered dose distributions.« less

Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors

PubMed Central

ROSE, SUSAN; DHANDAYUDHAM, ARUN

2014-01-01

Background: Compulsive and addictive forms of consumption and buying behaviour have been researched in both business and medical literature. Shopping enabled via the Internet now introduces new features to the shopping experience that translate to positive benefits for the shopper. Evidence now suggests that this new shopping experience may lead to problematic online shopping behaviour. This paper provides a theoretical review of the literature relevant to online shopping addiction (OSA). Based on this selective review, a conceptual model of OSA is presented. Method: The selective review of the literature draws on searches within databases relevant to both clinical and consumer behaviour literature including EBSCO, ABI Pro-Quest, Web of Science – Social Citations Index, Medline, PsycINFO and Pubmed. The article reviews current thinking on problematic, and specifically addictive, behaviour in relation to online shopping. Results: The review of the literature enables the extension of existing knowledge into the Internet-context. A conceptual model of OSA is developed with theoretical support provided for the inclusion of 7 predictor variables: low self-esteem, low self-regulation; negative emotional state; enjoyment; female gender; social anonymity and cognitive overload. The construct of OSA is defined and six component criteria of OSA are proposed based on established technological addiction criteria. Conclusions: Current Internet-based shopping experiences may trigger problematic behaviours which can be classified on a spectrum which at the extreme end incorporates OSA. The development of a conceptual model provides a basis for the future measurement and testing of proposed predictor variables and the outcome variable OSA. PMID:25215218

Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors.

PubMed

Rose, Susan; Dhandayudham, Arun

2014-06-01

Compulsive and addictive forms of consumption and buying behaviour have been researched in both business and medical literature. Shopping enabled via the Internet now introduces new features to the shopping experience that translate to positive benefits for the shopper. Evidence now suggests that this new shopping experience may lead to problematic online shopping behaviour. This paper provides a theoretical review of the literature relevant to online shopping addiction (OSA). Based on this selective review, a conceptual model of OSA is presented. The selective review of the literature draws on searches within databases relevant to both clinical and consumer behaviour literature including EBSCO, ABI Pro-Quest, Web of Science - Social Citations Index, Medline, PsycINFO and Pubmed. The article reviews current thinking on problematic, and specifically addictive, behaviour in relation to online shopping. The review of the literature enables the extension of existing knowledge into the Internet-context. A conceptual model of OSA is developed with theoretical support provided for the inclusion of 7 predictor variables: low self-esteem, low self-regulation; negative emotional state; enjoyment; female gender; social anonymity and cognitive overload. The construct of OSA is defined and six component criteria of OSA are proposed based on established technological addiction criteria. Current Internet-based shopping experiences may trigger problematic behaviours which can be classified on a spectrum which at the extreme end incorporates OSA. The development of a conceptual model provides a basis for the future measurement and testing of proposed predictor variables and the outcome variable OSA.

Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

PubMed

Liu, Jingfang; Zhang, Pengzhu; Lu, Yingjie

2014-11-01

User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure. In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier. By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

Aesthetic quality inference for online fashion shopping

NASA Astrophysics Data System (ADS)

Chen, Ming; Allebach, Jan

2014-03-01

On-line fashion communities in which participants post photos of personal fashion items for viewing and possible purchase by others are becoming increasingly popular. Generally, these photos are taken by individuals who have no training in photography with low-cost mobile phone cameras. It is desired that photos of the products have high aesthetic quality to improve the users' online shopping experience. In this work, we design features for aesthetic quality inference in the context of online fashion shopping. Psychophysical experiments are conducted to construct a database of the photos' aesthetic evaluation, specifically for photos from an online fashion shopping website. We then extract both generic low-level features and high-level image attributes to represent the aesthetic quality. Using a support vector machine framework, we train a predictor of the aesthetic quality rating based on the feature vector. Experimental results validate the efficacy of our approach. Metadata such as the product type are also used to further improve the result.

On-line object feature extraction for multispectral scene representation

NASA Technical Reports Server (NTRS)

Ghassemian, Hassan; Landgrebe, David

1988-01-01

A new on-line unsupervised object-feature extraction method is presented that reduces the complexity and costs associated with the analysis of the multispectral image data and data transmission, storage, archival and distribution. The ambiguity in the object detection process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the decision making process. The unity relation was defined that must exist among the pixels of an object. Automatic Multispectral Image Compaction Algorithm (AMICA) uses the within object pixel-feature gradient vector as a valuable contextual information to construct the object's features, which preserve the class separability information within the data. For on-line object extraction the path-hypothesis and the basic mathematical tools for its realization are introduced in terms of a specific similarity measure and adjacency relation. AMICA is applied to several sets of real image data, and the performance and reliability of features is evaluated.

Mapping of medical acronyms and initialisms to Medical Subject Headings (MeSH) across selected systems

PubMed Central

Shultz, Mary

2006-01-01

Introduction: Given the common use of acronyms and initialisms in the health sciences, searchers may be entering these abbreviated terms rather than full phrases when searching online systems. The purpose of this study is to evaluate how various MEDLINE Medical Subject Headings (MeSH) interfaces map acronyms and initialisms to the MeSH vocabulary. Methods: The interfaces used in this study were: the PubMed MeSH database, the PubMed Automatic Term Mapping feature, the NLM Gateway Term Finder, and Ovid MEDLINE. Acronyms and initialisms were randomly selected from 2 print sources. The test data set included 415 randomly selected acronyms and initialisms whose related meanings were found to be MeSH terms. Each acronym and initialism was entered into each MEDLINE MeSH interface to determine if it mapped to the corresponding MeSH term. Separately, 46 commonly used acronyms and initialisms were tested. Results: While performance differed widely, the success rates were low across all interfaces for the randomly selected terms. The common acronyms and initialisms tested at higher success rates across the interfaces, but the differences between the interfaces remained. Conclusion: Online interfaces do not always map medical acronyms and initialisms to their corresponding MeSH phrases. This may lead to inaccurate results and missed information if acronyms and initialisms are used in search strategies. PMID:17082832

EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning.

PubMed

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-06-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. Copyright © 2013 Elsevier Inc. All rights reserved.

"Developing a Perspective", "Inter-Connecting", and "Bringing It Together": Who Chooses to Use a Labelling Feature in Online Conversations in a Graduate Course?

ERIC Educational Resources Information Center

Bures, Eva Mary; Schmid, Richard F.; Abrami, Philip C.

2009-01-01

This study explores a labelling feature that allows students to tag parts of their online messages. Data comes from four sequentially offered sessions of a graduate education course. Students engaged in two to three online activities in groups of three or four. Students (n = 53) contributed from 0 to 56 labels (M = 12.42, SD = 13.50) and 18 to 114…

Family physicians' interests in special features of electronic publication

PubMed Central

Torre, Dario M.; Wright, Scott M.; Wilson, Renee F.; Diener-West, Marie; Bass, Eric B.

2003-01-01

Objective: Because many of the medical journals read by family physicians now have an electronic version, the authors conducted a survey to determine the interest of family physicians in specific features of electronic journal publications. Setting and Participants: We surveyed 175 family physicians randomly selected from the American Academy of Family Physicians. Results: The response rate was 63%. About half of family physicians reported good to excellent computer proficiency, and about one quarter used online journals sometimes or often. Many respondents reported high interest in having links to: an electronic medical text (48% for original articles, 56% for review articles), articles' list of references (52% for original articles, 56% for review articles), and health-related Websites (48% for original and review articles). Conclusion: Primary care–oriented journals should consider the interests of family physicians when developing and offering electronic features for their readers. PMID:12883561

Flight control system design factors for applying automated testing techniques

NASA Technical Reports Server (NTRS)

Sitz, Joel R.; Vernon, Todd H.

1990-01-01

The principal design features and operational experiences of the X-29 forward-swept-wing aircraft and F-18 high alpha research vehicle (HARV) automated test systems are discussed. It is noted that operational experiences in developing and using these automated testing techniques have highlighted the need for incorporating target system features to improve testability. Improved target system testability can be accomplished with the addition of nonreal-time and real-time features. Online access to target system implementation details, unobtrusive real-time access to internal user-selectable variables, and proper software instrumentation are all desirable features of the target system. Also, test system and target system design issues must be addressed during the early stages of the target system development. Processing speeds of up to 20 million instructions/s and the development of high-bandwidth reflective memory systems have improved the ability to integrate the target system and test system for the application of automated testing techniques. It is concluded that new methods of designing testability into the target systems are required.

Gaze-independent brain-computer interfaces based on covert attention and feature attention

NASA Astrophysics Data System (ADS)

Treder, M. S.; Schmidt, N. M.; Blankertz, B.

2011-10-01

There is evidence that conventional visual brain-computer interfaces (BCIs) based on event-related potentials cannot be operated efficiently when eye movements are not allowed. To overcome this limitation, the aim of this study was to develop a visual speller that does not require eye movements. Three different variants of a two-stage visual speller based on covert spatial attention and non-spatial feature attention (i.e. attention to colour and form) were tested in an online experiment with 13 healthy participants. All participants achieved highly accurate BCI control. They could select one out of thirty symbols (chance level 3.3%) with mean accuracies of 88%-97% for the different spellers. The best results were obtained for a speller that was operated using non-spatial feature attention only. These results show that, using feature attention, it is possible to realize high-accuracy, fast-paced visual spellers that have a large vocabulary and are independent of eye gaze.

Weaving Contexts of Participation Online: The Digital Tapestry of Secondary English Teachers

ERIC Educational Resources Information Center

Rodesiler, Luke

2014-01-01

This article presents research from a qualitative study exploring five secondary English teachers' professionally oriented participation online. Drawing upon Cole's (1996) "surround" and "weaving" views of context, the specific line of research featured here was guided by the following question: What are the features of the…

MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

PubMed

Mitra, Ramkrishna; Bandyopadhyay, Sanghamitra

2011-01-01

Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.

Mining online e-liquid reviews for opinion polarities about e-liquid features.

PubMed

Chen, Zhipeng; Zeng, Daniel D

2017-07-07

In recent years, the emerging electronic cigarette (e-cigarette) marketplace has developed prosperously all over the world. By analyzing online e-liquid reviews, we seek to identify the features attracting users. We collected e-liquid reviews from one of the largest online e-liquid review websites and extracted the e-liquid features by keywords. Then we used sentiment analysis to classify the features into two polarities: positive and negative. The positive sentiment ratio of a feature reflects the e-cigarette users' preference on this feature. The popularity and preference of e-liquid features are not correlated. Nuts and cream are the favorite flavor categories, while fruit and cream are the most popular categories. The top mixed flavors are preferable to single flavors. Fruit and cream categories are most frequently mixed with other flavors. E-cigarette users are satisfied with cloud production, but not satisfied with the ingredients and throat hit. We identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users.

Genetic algorithm and graph theory based matrix factorization method for online friend recommendation.

PubMed

Li, Qu; Yao, Min; Yang, Jianhua; Xu, Ning

2014-01-01

Online friend recommendation is a fast developing topic in web mining. In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy. To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector. At the same time, we used graph theory to partition communities with fairly low time and space complexity. What is more, matrix factorization can combine online and offline recommendation. Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy.

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification.

PubMed

Huang, Chuen-Der; Lin, Chin-Teng; Pal, Nikhil Ranjan

2003-12-01

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.

Visualizing Human Migration Trhough Space and Time

NASA Astrophysics Data System (ADS)

Zambotti, G.; Guan, W.; Gest, J.

2015-07-01

Human migration has been an important activity in human societies since antiquity. Since 1890, approximately three percent of the world's population has lived outside of their country of origin. As globalization intensifies in the modern era, human migration persists even as governments seek to more stringently regulate flows. Understanding this phenomenon, its causes, processes and impacts often starts from measuring and visualizing its spatiotemporal patterns. This study builds a generic online platform for users to interactively visualize human migration through space and time. This entails quickly ingesting human migration data in plain text or tabular format; matching the records with pre-established geographic features such as administrative polygons; symbolizing the migration flow by circular arcs of varying color and weight based on the flow attributes; connecting the centroids of the origin and destination polygons; and allowing the user to select either an origin or a destination feature to display all flows in or out of that feature through time. The method was first developed using ArcGIS Server for world-wide cross-country migration, and later applied to visualizing domestic migration patterns within China between provinces, and between states in the United States, all through multiple years. The technical challenges of this study include simplifying the shapes of features to enhance user interaction, rendering performance and application scalability; enabling the temporal renderers to provide time-based rendering of features and the flow among them; and developing a responsive web design (RWD) application to provide an optimal viewing experience. The platform is available online for the public to use, and the methodology is easily adoptable to visualizing any flow, not only human migration but also the flow of goods, capital, disease, ideology, etc., between multiple origins and destinations across space and time.

Off-lexicon online Arabic handwriting recognition using neural network

NASA Astrophysics Data System (ADS)

Yahia, Hamdi; Chaabouni, Aymen; Boubaker, Houcine; Alimi, Adel M.

2017-03-01

This paper highlights a new method for online Arabic handwriting recognition based on graphemes segmentation. The main contribution of our work is to explore the utility of Beta-elliptic model in segmentation and features extraction for online handwriting recognition. Indeed, our method consists in decomposing the input signal into continuous part called graphemes based on Beta-Elliptical model, and classify them according to their position in the pseudo-word. The segmented graphemes are then described by the combination of geometric features and trajectory shape modeling. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results using the benchmarking ADAB Database show the performance of the proposed method.

Personalised news filtering and recommendation system using Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model

NASA Astrophysics Data System (ADS)

Adeniyi, D. A.; Wei, Z.; Yang, Y.

2017-10-01

Recommendation problem has been extensively studied by researchers in the field of data mining, database and information retrieval. This study presents the design and realisation of an automated, personalised news recommendations system based on Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model. The proposed χ2SB-KNN model has the potential to overcome computational complexity and information overloading problems, reduces runtime and speeds up execution process through the use of critical value of χ2 distribution. The proposed recommendation engine can alleviate scalability challenges through combined online pattern discovery and pattern matching for real-time recommendations. This work also showcases the development of a novel method of feature selection referred to as Data Discretisation-Based feature selection method. This is used for selecting the best features for the proposed χ2SB-KNN algorithm at the preprocessing stage of the classification procedures. The implementation of the proposed χ2SB-KNN model is achieved through the use of a developed in-house Java program on an experimental website called OUC newsreaders' website. Finally, we compared the performance of our system with two baseline methods which are traditional Euclidean distance K-nearest neighbour and Naive Bayesian techniques. The result shows a significant improvement of our method over the baseline methods studied.

Location and Modality Effects in Online Dating: Rich Modality Profile and Location-Based Information Cues Increase Social Presence, While Moderating the Impact of Uncertainty Reduction Strategy.

PubMed

Jung, Soyoung; Roh, Soojin; Yang, Hyun; Biocca, Frank

2017-09-01

This study investigates how different interface modality features of online dating sites, such as location awareness cues and modality of profiles, affect the sense of social presence of a prospective date. We also examined how various user behaviors aimed at reducing uncertainty about online interactions affect social presence perceptions and are affected by the user interface features. Male users felt a greater sense of social presence when exposed to both location and accessibility cues (geographical proximity) and a richer medium (video profiles). Viewing a richer medium significantly increased the sense of social presence among female participants whereas location-based information sharing features did not directly affect their social presence perception. Augmented social presence, as a mediator, contributed to users' greater intention to meet potential dating partners in a face-to-face setting and to buy paid memberships on online dating sites.

Online & Offline data storage and data processing at the European XFEL facility

NASA Astrophysics Data System (ADS)

Gasthuber, Martin; Dietrich, Stefan; Malka, Janusz; Kuhn, Manuela; Ensslin, Uwe; Wrona, Krzysztof; Szuba, Janusz

2017-10-01

For the upcoming experiments at the European XFEL light source facility, a new online and offline data processing and storage infrastructure is currently being built and verified. Based on the experience of the system being developed for the Petra III light source at DESY, presented at the last CHEP conference, we further develop the system to cope with the much higher volumes and rates ( 50GB/sec) together with a more complex data analysis and infrastructure conditions (i.e. long range InfiniBand connections). This work will be carried out in collaboration of DESY/IT, European XFEL and technology support from IBM/Research. This presentation will shortly wrap up the experience of 1 year runtime of the PetraIII ([3]) system, continue with a short description of the challenges for the European XFEL ([2]) experiments and the main section, showing the proposed system for online and offline with initial result from real implementation (HW & SW). This will cover the selected cluster filesystem GPFS ([5]) including Quality of Service (QOS), extensive use of flash based subsystems and other new and unique features this architecture will benefit from.

A Cognitive Framework for the Analysis of Online Chemistry Courses

ERIC Educational Resources Information Center

Evans, Karen L.; Leinhardt, Gaea

2008-01-01

Many students now are receiving instruction in online environments created by universities, museums, corporations, and even students. What features of a given online course contribute to its effectiveness? This paper addresses that query by proposing and applying an analytic framework to five online introductory chemistry courses. Introductory…

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering

PubMed Central

2012-01-01

Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike sorting algorithms. Conclusions This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity. This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces. PMID:22871125

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.

PubMed

Oliynyk, Andriy; Bonifazzi, Claudio; Montani, Fernando; Fadiga, Luciano

2012-08-08

Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike sorting algorithms. This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity. This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

Review of "The Policy Framework for Online Charter Schools"

ERIC Educational Resources Information Center

Miron, Gary

2016-01-01

Relative to earlier research, this study from the Center for Reinventing Public Education provides a more in-depth analysis of policy features across the 27 states that allow online charter schools. It presents a well-organized description of policy features and includes a set of policy recommendations that generally, but not always, follow well…

Extending the Online Public Access Catalog into the Microcomputer Environment.

ERIC Educational Resources Information Center

Sutton, Brett

1990-01-01

Describes PCBIS, a database program for MS-DOS microcomputers that features a utility for automatically converting online public access catalog search results stored as text files into structured database files that can be searched, sorted, edited, and printed. Topics covered include the general features of the program, record structure, record…

Scalable Online Network Modeling and Simulation

DTIC Science & Technology

2005-08-01

ONLINE NETWORK MODELING AND SIMULATION 6. AUTHOR(S) Boleslaw Szymanski , Shivkumar Kalyanaraman, Biplab Sikdar and Christopher Carothers 5...performance for a wide range of parameter values (parameter sensitivity), understanding of protocol stability and dynamics, and studying feature ...a wide range of parameter values (parameter sensitivity), understanding of protocol stability and dynamics, and studying feature interactions

Arc-Welding Spectroscopic Monitoring based on Feature Selection and Neural Networks.

PubMed

Garcia-Allende, P Beatriz; Mirapeix, Jesus; Conde, Olga M; Cobo, Adolfo; Lopez-Higuera, Jose M

2008-10-21

A new spectral processing technique designed for application in the on-line detection and classification of arc-welding defects is presented in this paper. A noninvasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed in two consecutive stages. A compression algorithm is first applied to the data, allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in previous works, giving rise to an improvement in the performance of the monitoring system.

Computer Security Issues in Online Banking: An Assessment from the Context of Usable Security

NASA Astrophysics Data System (ADS)

Mahmadi, FN; Zaaba, ZF; Osman, A.

2016-11-01

Today's online banking is a convenient mode of finance management. Despite the ease of doing online banking, there are people that still sceptical in utilizing it due to perception and its security. This paper highlights the subject of online banking security in Malaysia, especially from the perspective of the end-users. The study is done by assessing human computer interaction, usability and security. An online survey utilising 137 participants was previously conducted to gain preliminary insights on security issues of online banking in Malaysia. Following from those results, 37 participants were interviewed to gauge deeper understanding about end-users perception on online banking within the context of usable security. The results suggested that most of the end-users are continuingly experiencing significant difficulties especially in relation to the technical terminologies, security features and other technical issues. Although the security features are provided to provide a shield or protection, users are still incapable to cope with the technical aspects of such implementation.

Confidentiality, anonymity and amnesty for midwives in distress seeking online support - Ethical?

PubMed

Pezaro, Sally; Clyne, Wendy; Gerada, Clare

2018-06-01

Midwife health is intrinsically linked to the quality of safe patient care. To ensure safe patient care, there is a need to deliver emotional support to midwives. One option that midwives may turn to may be a confidential online intervention, instead of localised, face-to-face support. Following the Realist And MEta-narrative Evidence Syntheses: Evolving Standards publication standards, this realist synthesis approach explores the ethical considerations in permitting confidentiality, anonymity and amnesty in online interventions to support midwives in work-related psychological distress. An iterative search methodology was used to select nine papers for review. To assimilate information, papers were examined for ideas relating to ethical dimensions of online interventions to support midwives in work-related psychological distress. This review takes a narrative approach. Online interventions can support the development of insight, help seeking and open discussion. Additionally, Internet support groups can become morally persuasive in nature. Anonymity and confidentiality are both effective and therapeutic features of online interventions when used in collaboration with effective online moderation. Yet, ethical dilemmas remain where users cannot be identified. Confidentiality and anonymity remain key components of successful online interventions. However, sanctioning the corollary component of amnesty may provoke moral discomfort for those seeking immediate accountability. For others, amnesty is seen as essential for open disclosure and help seeking. Ultimately, the needs of midwives must be balanced with the requirement to protect the public and the professional reputation of midwifery. In supporting midwives online, the principles of anonymity, confidentiality and amnesty may evoke some resistance on ethical grounds. However, without offering identity protection, it may not be possible to create effective online support services for midwives. The authors of this article argue that the principles of confidentiality, anonymity and amnesty should be upheld in the pursuit of the greatest benefit for the greatest number of people.

An online network tool for quality information to answer questions about occupational safety and health: usability and applicability.

PubMed

Rhebergen, Martijn D F; Hulshof, Carel T J; Lenderink, Annet F; van Dijk, Frank J H

2010-10-22

Common information facilities do not always provide the quality information needed to answer questions on health or health-related issues, such as Occupational Safety and Health (OSH) matters. Barriers may be the accessibility, quantity and readability of information. Online Question & Answer (Q&A) network tools, which link questioners directly to experts can overcome some of these barriers. When designing and testing online tools, assessing the usability and applicability is essential. Therefore, the purpose of this study is to assess the usability and applicability of a new online Q&A network tool for answers on OSH questions. We applied a cross-sectional usability test design. Eight occupational health experts and twelve potential questioners from the working population (workers) were purposively selected to include a variety of computer- and internet-experiences. During the test, participants were first observed while executing eight tasks that entailed important features of the tool. In addition, they were interviewed. Through task observations and interviews we assessed applicability, usability (effectiveness, efficiency and satisfaction) and facilitators and barriers in use. Most features were usable, though several could be improved. Most tasks were executed effectively. Some tasks, for example searching stored questions in categories, were not executed efficiently and participants were less satisfied with the corresponding features. Participants' recommendations led to improvements. The tool was found mostly applicable for additional information, to observe new OSH trends and to improve contact between OSH experts and workers. Hosting and support by a trustworthy professional organization, effective implementation campaigns, timely answering and anonymity were seen as important use requirements. This network tool is a promising new strategy for offering company workers high quality information to answer OSH questions. Q&A network tools can be an addition to existing information facilities in the field of OSH, but also to other healthcare fields struggling with how to answer questions from people in practice with high quality information. In the near future, we will focus on the use of the tool and its effects on information and knowledge dissemination.

VHDL implementation of feature-extraction algorithm for the PANDA electromagnetic calorimeter

NASA Astrophysics Data System (ADS)

Guliyev, E.; Kavatsyuk, M.; Lemmens, P. J. J.; Tambave, G.; Löhner, H.; Panda Collaboration

2012-02-01

A simple, efficient, and robust feature-extraction algorithm, developed for the digital front-end electronics of the electromagnetic calorimeter of the PANDA spectrometer at FAIR, Darmstadt, is implemented in VHDL for a commercial 16 bit 100 MHz sampling ADC. The source-code is available as an open-source project and is adaptable for other projects and sampling ADCs. Best performance with different types of signal sources can be achieved through flexible parameter selection. The on-line data-processing in FPGA enables to construct an almost dead-time free data acquisition system which is successfully evaluated as a first step towards building a complete trigger-less readout chain. Prototype setups are studied to determine the dead-time of the implemented algorithm, the rate of false triggering, timing performance, and event correlations.

ArachnoServer 3.0: an online resource for automated discovery, analysis and annotation of spider toxins.

PubMed

Pineda, Sandy S; Chaumeil, Pierre-Alain; Kunert, Anne; Kaas, Quentin; Thang, Mike W C; Le, Lien; Nuhn, Michael; Herzig, Volker; Saez, Natalie J; Cristofori-Armstrong, Ben; Anangi, Raveendra; Senff, Sebastian; Gorse, Dominique; King, Glenn F

2018-03-15

ArachnoServer is a manually curated database that consolidates information on the sequence, structure, function and pharmacology of spider-venom toxins. Although spider venoms are complex chemical arsenals, the primary constituents are small disulfide-bridged peptides that target neuronal ion channels and receptors. Due to their high potency and selectivity, these peptides have been developed as pharmacological tools, bioinsecticides and drug leads. A new version of ArachnoServer (v3.0) has been developed that includes a bioinformatics pipeline for automated detection and analysis of peptide toxin transcripts in assembled venom-gland transcriptomes. ArachnoServer v3.0 was updated with the latest sequence, structure and functional data, the search-by-mass feature has been enhanced, and toxin cards provide additional information about each mature toxin. http://arachnoserver.org. support@arachnoserver.org. Supplementary data are available at Bioinformatics online.

Rugged large volume injection for sensitive capillary LC-MS environmental monitoring

NASA Astrophysics Data System (ADS)

Roberg-Larsen, Hanne; Abele, Silvija; Demir, Deniz; Dzabijeva, Diana; Amundsen, Sunniva F.; Wilson, Steven R.; Bartkevics, Vadims; Lundanes, Elsa

2017-08-01

A rugged and high throughput capillary column (cLC) LC-MS switching platform using large volume injection and on-line automatic filtration and filter back-flush (AFFL) solid phase extraction (SPE) for analysis of environmental water samples with minimal sample preparation is presented. Although narrow columns and on-line sample preparation are used in the platform, high ruggedness is achieved e.g. injection of 100 non-filtrated water samples would did not result in a pressure rise/clogging of the SPE/capillary columns (inner diameter 300 µm). In addition, satisfactory retention time stability and chromatographic resolution were also features of the system. The potential of the platform for environmental water samples was demonstrated with various pharmaceutical products, which had detection limits (LOD) in the 0.05 - 12.5 ng/L range. Between-day and within-day repeatability of selected analytes were < 20% RSD.

Why reread? Evidence from garden-path and local coherence structures.

PubMed

Christianson, Kiel; Luke, Steven G; Hussey, Erika K; Wochna, Kacey L

2017-07-01

Two eye-tracking experiments were conducted to compare the online reading and offline comprehension of main verb/reduced relative garden-path sentences and local coherence sentences. Rereading of early material in garden-path reduced relatives should be revisionary, aimed at reanalysing an earlier misparse; however, rereading of early material in a local coherence reduced relative need only be confirmatory, as the original parse of the earlier portion of these sentences is ultimately correct. Results of online and offline measures showed that local coherence structures elicited signals of reading disruption that arose earlier and lasted longer, and local coherence comprehension was also better than garden path comprehension. Few rereading measures in either sentence type were predicted by structural features of these sentences, nor was rereading related to comprehension accuracy, which was extremely low overall. Results are discussed with respect to selective reanalysis and good-enough processing.

When the Medium Illustrates the Content: Exploiting the Unique Features of Online Communication

ERIC Educational Resources Information Center

Foertsch, Julie; Gernsbacher, Morton Ann

2008-01-01

Julie Foertsch and Morton Ann Gernsbacher present the results of an evaluation of an online undergraduate course in psychology that adheres to the seven widely accepted principles of effective online teaching and suggests an eighth principle: using the unique benefits and constraints of online communication to prompt critical thinking about…

Catalog Use Studies--Since the Introduction of Online Interactive Catalogs: Impact on Design for Subject Access.

ERIC Educational Resources Information Center

Cochrane, Pauline A.; Markey, Karen

1983-01-01

This review of the transition from library card catalogs to online public access catalogs (OPAC) (1981-1982) discusses methods employed by online catalog use studies (self-administered questionnaires, OPAC transaction logs, focused-group interviews, feature analysis, online search and retrieval experiments) and new directions for OPAC research…

The Features of Effective Online Professional Development for Early Childhood Educators

ERIC Educational Resources Information Center

Ascetta, Kate Elisabeth

2017-01-01

The purpose of this current study was to examine the effect of a preschool teacher intervention around the use self-monitoring and the online learning modules. The interventions were delivered online using: online learning modules that provided exemplars of the operationally defined instructional language supports. The study included 12 Head Start…

Designing an Online Writing System: Learning with Support

ERIC Educational Resources Information Center

Kuo, Chih-Hua

2008-01-01

The potential of online language learning has received much attention recently. This paper reports the design of an online writing system featuring learning support for non-native students during their writing process. The central premise is that in the online writing situation, students are in great need of writing aids. The proposed system…

The Role of Auditory Features Within Slot-Themed Social Casino Games and Online Slot Machine Games.

PubMed

Bramley, Stephanie; Gainsbury, Sally M

2015-12-01

Over the last few years playing social casino games has become a popular entertainment activity. Social casino games are offered via social media platforms and mobile apps and resemble gambling activities. However, social casino games are not classified as gambling as they can be played for free, outcomes may not be determined by chance, and players receive no monetary payouts. Social casino games appear to be somewhat similar to online gambling activities in terms of their visual and auditory features, but to date little research has investigated the cross over between these games. This study examines the auditory features of slot-themed social casino games and online slot machine games using a case study design. An example of each game type was played on three separate occasions during which, the auditory features (i.e., music, speech, sound effects, and the absence of sound) within the games were logged. The online slot-themed game was played in demo mode. This is the first study to provide a qualitative account of the role of auditory features within a slot-themed social casino game and an online slot machine game. Our results found many similarities between how sound is utilised within the two games. Therefore the sounds within these games may serve functions including: setting the scene for gaming, creating an image, demarcating space, interacting with visual features, prompting players to act, communicating achievements to players, providing reinforcement, heightening player emotions and the gaming experience. As a result this may reduce the ability of players to make a clear distinction between these two activities, which may facilitate migration between games.

Designing Online Courses: A Taxonomy to Guide Strategic Use of Features Available in Course Management Systems (CMS) in Distance Education

ERIC Educational Resources Information Center

Koszalka, Tiffany A.; Ganesan, Radha

2004-01-01

Course developers can be distracted from applying sound instructional design principles by the amount of flexibility offered through online course development resources (Kidney & Puckett, "Quarterly Review of Distance Education," 4 (2003), 203-212). Distance education course management systems (CMS) provide multiple features that can be easily…

Barriers to Innovation in Online Pedagogy

ERIC Educational Resources Information Center

Christie, M.; Garrote Jurado, R.

2009-01-01

In this article, the authors report on a study that was carried out at the University College of Boras. Teachers using an online learning platform (WebCT) were surveyed to see to what extent they made use of the various features available to them on the learning platform. The extent to which teachers employed all the features was low. The article…

Investigating Relationships between Features of Learning Designs and Student Learning Outcomes

ERIC Educational Resources Information Center

McNaught, Carmel; Lam, Paul; Cheng, Kin Fai

2012-01-01

This article reports a study of eLearning in 21 courses in Hong Kong universities that had a blended design of face-to-face classes combined with online learning. The main focus of the study was to examine possible relationships between features of online learning designs and student learning outcomes. Data-collection strategies included expert…

Automatic identification of abstract online groups

DOEpatents

Engel, David W; Gregory, Michelle L; Bell, Eric B; Cowell, Andrew J; Piatt, Andrew W

2014-04-15

Online abstract groups, in which members aren't explicitly connected, can be automatically identified by computer-implemented methods. The methods involve harvesting records from social media and extracting content-based and structure-based features from each record. Each record includes a social-media posting and is associated with one or more entities. Each feature is stored on a data storage device and includes a computer-readable representation of an attribute of one or more records. The methods further involve grouping records into record groups according to the features of each record. Further still the methods involve calculating an n-dimensional surface representing each record group and defining an outlier as a record having feature-based distances measured from every n-dimensional surface that exceed a threshold value. Each of the n-dimensional surfaces is described by a footprint that characterizes the respective record group as an online abstract group.

Feature extraction for ultrasonic sensor based defect detection in ceramic components

NASA Astrophysics Data System (ADS)

Kesharaju, Manasa; Nagarajah, Romesh

2014-02-01

High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

Postprocessing for character recognition using pattern features and linguistic information

NASA Astrophysics Data System (ADS)

Yoshikawa, Takatoshi; Okamoto, Masayosi; Horii, Hiroshi

1993-04-01

We propose a new method of post-processing for character recognition using pattern features and linguistic information. This method corrects errors in the recognition of handwritten Japanese sentences containing Kanji characters. This post-process method is characterized by having two types of character recognition. Improving the accuracy of the character recognition rate of Japanese characters is made difficult by the large number of characters, and the existence of characters with similar patterns. Therefore, it is not practical for a character recognition system to recognize all characters in detail. First, this post-processing method generates a candidate character table by recognizing the simplest features of characters. Then, it selects words corresponding to the character from the candidate character table by referring to a word and grammar dictionary before selecting suitable words. If the correct character is included in the candidate character table, this process can correct an error, however, if the character is not included, it cannot correct an error. Therefore, if this method can presume a character does not exist in a candidate character table by using linguistic information (word and grammar dictionary). It then can verify a presumed character by character recognition using complex features. When this method is applied to an online character recognition system, the accuracy of character recognition improves 93.5% to 94.7%. This proved to be the case when it was used for the editorials of a Japanese newspaper (Asahi Shinbun).

The Nature of Selected English Teachers' Online Participation

ERIC Educational Resources Information Center

Rodesiler, Luke

2015-01-01

This article documents an investigation into the nature of selected secondary English teachers' online participation across platforms (i.e., blogs, microblogs, social networking sites) as they explored issues related to teaching, learning, and literacy. Ethnographic content analysis of online artifacts generated over approximately 10 months…

Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing

PubMed Central

Villa-Parra, Ana Cecilia; Bastos-Filho, Teodiano; López-Delis, Alberto; Frizera-Neto, Anselmo; Krishnan, Sridhar

2017-01-01

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly (p<0.01) improved for most of the subjects (ACC≥74.79%), when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry. PMID:29186848

Level-2 perspectives computed quickly and spontaneously: Evidence from eight- to 9.5-year-old children.

PubMed

Elekes, Fruzsina; Varga, Máté; Király, Ildikó

2017-11-01

It has been widely assumed that computing how a scene looks from another perspective (level-2 perspective taking, PT) is an effortful process, as opposed to the automatic capacity of tracking visual access to objects (level-1 PT). Recently, adults have been found to compute both forms of visual perspectives in a quick but context-sensitive way, indicating that the two functions share more features than previously assumed. However, the developmental literature still shows the dissociation between automatic level-1 and effortful level-2 PT. In the current paper, we report an experiment showing that in a minimally social situation, participating in a number verification task with an adult confederate, eight- to 9.5-year-old children demonstrate similar online level-2 PT capacities as adults. Future studies need to address whether online PT shows selectivity in children as well and develop paradigms that are adequate to test preschoolers' online level-2 PT abilities. Statement of Contribution What is already known on this subject? Adults can access how objects appear to others (level-2 perspective) spontaneously and online Online level-1, but not level-2 perspective taking (PT) has been documented in school-aged children What the present study adds? Eight- to 9.5-year-olds performed a number verification task with a confederate who had the same task Children showed similar perspective interference as adults, indicating spontaneous level-2 PT Not only agent-object relations but also object appearances are computed online by eight- to 9.5-year-olds. © 2017 The British Psychological Society.

Online selective kernel-based temporal difference learning.

PubMed

Chen, Xingguo; Gao, Yang; Wang, Ruili

2013-12-01

In this paper, an online selective kernel-based temporal difference (OSKTD) learning algorithm is proposed to deal with large scale and/or continuous reinforcement learning problems. OSKTD includes two online procedures: online sparsification and parameter updating for the selective kernel-based value function. A new sparsification method (i.e., a kernel distance-based online sparsification method) is proposed based on selective ensemble learning, which is computationally less complex compared with other sparsification methods. With the proposed sparsification method, the sparsified dictionary of samples is constructed online by checking if a sample needs to be added to the sparsified dictionary. In addition, based on local validity, a selective kernel-based value function is proposed to select the best samples from the sample dictionary for the selective kernel-based value function approximator. The parameters of the selective kernel-based value function are iteratively updated by using the temporal difference (TD) learning algorithm combined with the gradient descent technique. The complexity of the online sparsification procedure in the OSKTD algorithm is O(n). In addition, two typical experiments (Maze and Mountain Car) are used to compare with both traditional and up-to-date O(n) algorithms (GTD, GTD2, and TDC using the kernel-based value function), and the results demonstrate the effectiveness of our proposed algorithm. In the Maze problem, OSKTD converges to an optimal policy and converges faster than both traditional and up-to-date algorithms. In the Mountain Car problem, OSKTD converges, requires less computation time compared with other sparsification methods, gets a better local optima than the traditional algorithms, and converges much faster than the up-to-date algorithms. In addition, OSKTD can reach a competitive ultimate optima compared with the up-to-date algorithms.

Toward a Real-Time (Day) Dreamcatcher: Sensor-Free Detection of Mind Wandering during Online Reading

ERIC Educational Resources Information Center

Mills, Caitlin; D'Mello, Sidney

2015-01-01

This paper reports the results from a sensor-free detector of mind wandering during an online reading task. Features consisted of reading behaviors (e.g., reading time) and textual features (e.g., level of difficulty) extracted from self-paced reading log files. Supervised machine learning was applied to two datasets in order to predict if…

Web-Based Online Public Access Catalogues of IIT Libraries in India: An Evaluative Study

ERIC Educational Resources Information Center

Madhusudhan, Margam; Aggarwal, Shalini

2011-01-01

Purpose: The purpose of the paper is to examine the various features and components of web-based online public access catalogues (OPACs) of IIT libraries in India with the help of a specially designed evaluation checklist. Design/methodology/approach: The various features of the web-based OPACs in six IIT libraries (IIT Delhi, IIT Bombay, IIT…

Effect of Incentives and Mailing Features on Online Health Program Enrollment

PubMed Central

Alexander, Gwen L.; Divine, George W.; Couper, Mick P.; McClure, Jennifer B.; Stopponi, Melanie A.; Fortman, Kristine K.; Tolsma, Dennis D.; Strecher, Victor J.; Johnson, Christine Cole

2008-01-01

Background With the growing use of Internet-based interventions, strategies are needed to encourage broader participation. This study examined the effects of combinations of monetary incentives and mailing characteristics on enrollment, retention, and cost effectiveness for an online health program. Methods In 2004, a recruitment letter was mailed to randomly selected Midwestern integrated health system members aged 21–65 and stratified by gender and race/ethnicity; recipients were randomly pre-assigned to one of 24 combinations of incentives and various mailing characteristics. Enrollment and 3-month retention rates were measured by completion of online surveys. Analysis, completed in 2005, compared enrollment and retention factors using t tests and chi-square tests. Multivariate logistic regression modeling assessed the probability of enrollment and retention. Results Of 12,289 subjects, 531 (4.3%) enrolled online, ranging from 1% to 11% by incentive combination. Highest enrollment occurred with unconditional incentives, and responses varied by gender. Retention rates ranged from 0% to 100%, with highest retention linked to higher-value incentives. The combination of a $2 bill prepaid incentive and the promise of $20 for retention (10% enrollment and 71% retention) was optimal, considering per-subject recruitment costs ($32 enrollment, $70 retention) and equivalent enrollment by gender and race/ethnicity. Conclusions Cash incentives improved enrollment in an online health program. Men and women responded differently to mailing characteristics and incentives. Including a small prepaid monetary incentive ($2 or $5) and revealing the higher promised-retention incentive was cost effective and boosted enrollment. PMID:18407004

Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions.

PubMed

Yan, Yuguang; Wu, Qingyao; Tan, Mingkui; Ng, Michael K; Min, Huaqing; Tsang, Ivor W

2017-10-10

In this paper, we study the online heterogeneous transfer (OHT) learning problem, where the target data of interest arrive in an online manner, while the source data and auxiliary co-occurrence data are from offline sources and can be easily annotated. OHT is very challenging, since the feature spaces of the source and target domains are different. To address this, we propose a novel technique called OHT by hedge ensemble by exploiting both offline knowledge and online knowledge of different domains. To this end, we build an offline decision function based on a heterogeneous similarity that is constructed using labeled source data and unlabeled auxiliary co-occurrence data. After that, an online decision function is learned from the target data. Last, we employ a hedge weighting strategy to combine the offline and online decision functions to exploit knowledge from the source and target domains of different feature spaces. We also provide a theoretical analysis regarding the mistake bounds of the proposed approach. Comprehensive experiments on three real-world data sets demonstrate the effectiveness of the proposed technique.

Protein classification using modified n-grams and skip-grams.

PubMed

Islam, S M Ashiqul; Heil, Benjamin J; Kearney, Christopher Michel; Baker, Erich J

2018-05-01

Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG). A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists. m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg. erich_baker@baylor.edu. Supplementary data are available at Bioinformatics online.

Evaluating Online Dictionaries From Faculty Prospective: A Case Study Performed On English Faculty Members At King Saud University--Wadi Aldawaser Branch

ERIC Educational Resources Information Center

Abouserie, Hossam Eldin Mohamed Refaat

2010-01-01

The purpose of this study was to evaluate online dictionaries from faculty prospective. The study tried to obtain in depth information about various forms of dictionaries the faculty used; degree of awareness and accessing online dictionaries; types of online dictionaries accessed; basic features of information provided; major benefits gained…

The IVTANTHERMO-Online database for thermodynamic properties of individual substances with web interface

NASA Astrophysics Data System (ADS)

Belov, G. V.; Dyachkov, S. A.; Levashov, P. R.; Lomonosov, I. V.; Minakov, D. V.; Morozov, I. V.; Sineva, M. A.; Smirnov, V. N.

2018-01-01

The database structure, main features and user interface of an IVTANTHERMO-Online system are reviewed. This system continues the series of the IVTANTHERMO packages developed in JIHT RAS. It includes the database for thermodynamic properties of individual substances and related software for analysis of experimental results, data fitting, calculation and estimation of thermodynamical functions and thermochemistry quantities. In contrast to the previous IVTANTHERMO versions it has a new extensible database design, the client-server architecture, a user-friendly web interface with a number of new features for online and offline data processing.

Process analysis of recycled thermoplasts from consumer electronics by laser-induced plasma spectroscopy.

PubMed

Fink, Herbert; Panne, Ulrich; Niessner, Reinhard

2002-09-01

An experimental setup for direct elemental analysis of recycled thermoplasts from consumer electronics by laser-induced plasma spectroscopy (LIPS, or laser-induced breakdown spectroscopy, LIBS) was realized. The combination of a echelle spectrograph, featuring a high resolution with a broad spectral coverage, with multivariate methods, such as PLS, PCR, and variable subset selection via a genetic algorithm, resulted in considerable improvements in selectivity and sensitivity for this complex matrix. With a normalization to carbon as internal standard, the limits of detection were in the ppm range. A preliminary pattern recognition study points to the possibility of polymer recognition via the line-rich echelle spectra. Several experiments at an extruder within a recycling plant demonstrated successfully the capability of LIPS for different kinds of routine on-line process analysis.

Construction of In-house Databases in a Corporation

NASA Astrophysics Data System (ADS)

Sano, Hikomaro

This report outlines “Repoir” (Report information retrieval) system of Toyota Central R & D Laboratories, Inc. as an example of in-house information retrieval system. The online system was designed to process in-house technical reports with the aid of a mainframe computer and has been in operation since 1979. Its features are multiple use of the information for technical and managerial purposes and simplicity in indexing and data input. The total number of descriptors, specially selected for the system, was minimized for ease of indexing. The report also describes the input items, processing flow and typical outputs in kanji letters.

An online handwriting recognition system for Turkish

NASA Astrophysics Data System (ADS)

Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

2004-12-01

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

An online handwriting recognition system for Turkish

NASA Astrophysics Data System (ADS)

Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

2005-01-01

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

Online 3D Ear Recognition by Combining Global and Local Features.

PubMed

Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

2016-01-01

The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%.

Online 3D Ear Recognition by Combining Global and Local Features

PubMed Central

Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

2016-01-01

The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%. PMID:27935955

Business Magazines Online: The Big Three on the Three--and More.

ERIC Educational Resources Information Center

Marcus, John

1995-01-01

Describes features of the three major general business magazines and the online services they are currently associated with: "Business Week," which is available through America Online; "Fortune," available through CompuServe; and "Forbes," which has just become available through CompuServe. Discusses search features…

Integrating Subject Pathfinders into Online Catalogs.

ERIC Educational Resources Information Center

Jarvis, William E.

1985-01-01

Discusses the integration of subject pathfinders into online public access catalogs (OPAC) through following features: within the OPAC, offline user guide manuals, remotely printed upon user request, or online as saved searches displayed in help screen format. Excerpts of a pathfinder display for biotechnology are presented. Four sources are…

Edesign: Primer and Enhanced Internal Probe Design Tool for Quantitative PCR Experiments and Genotyping Assays.

PubMed

Kimura, Yasumasa; Soma, Takahiro; Kasahara, Naoko; Delobel, Diane; Hanami, Takeshi; Tanaka, Yuki; de Hoon, Michiel J L; Hayashizaki, Yoshihide; Usui, Kengo; Harbers, Matthias

2016-01-01

Analytical PCR experiments preferably use internal probes for monitoring the amplification reaction and specific detection of the amplicon. Such internal probes have to be designed in close context with the amplification primers, and may require additional considerations for the detection of genetic variations. Here we describe Edesign, a new online and stand-alone tool for designing sets of PCR primers together with an internal probe for conducting quantitative real-time PCR (qPCR) and genotypic experiments. Edesign can be used for selecting standard DNA oligonucleotides like for instance TaqMan probes, but has been further extended with new functions and enhanced design features for Eprobes. Eprobes, with their single thiazole orange-labelled nucleotide, allow for highly sensitive genotypic assays because of their higher DNA binding affinity as compared to standard DNA oligonucleotides. Using new thermodynamic parameters, Edesign considers unique features of Eprobes during primer and probe design for establishing qPCR experiments and genotyping by melting curve analysis. Additional functions in Edesign allow probe design for effective discrimination between wild-type sequences and genetic variations either using standard DNA oligonucleotides or Eprobes. Edesign can be freely accessed online at http://www.dnaform.com/edesign2/, and the source code is available for download.

Edesign: Primer and Enhanced Internal Probe Design Tool for Quantitative PCR Experiments and Genotyping Assays

PubMed Central

Kasahara, Naoko; Delobel, Diane; Hanami, Takeshi; Tanaka, Yuki; de Hoon, Michiel J. L.; Hayashizaki, Yoshihide; Usui, Kengo; Harbers, Matthias

2016-01-01

Analytical PCR experiments preferably use internal probes for monitoring the amplification reaction and specific detection of the amplicon. Such internal probes have to be designed in close context with the amplification primers, and may require additional considerations for the detection of genetic variations. Here we describe Edesign, a new online and stand-alone tool for designing sets of PCR primers together with an internal probe for conducting quantitative real-time PCR (qPCR) and genotypic experiments. Edesign can be used for selecting standard DNA oligonucleotides like for instance TaqMan probes, but has been further extended with new functions and enhanced design features for Eprobes. Eprobes, with their single thiazole orange-labelled nucleotide, allow for highly sensitive genotypic assays because of their higher DNA binding affinity as compared to standard DNA oligonucleotides. Using new thermodynamic parameters, Edesign considers unique features of Eprobes during primer and probe design for establishing qPCR experiments and genotyping by melting curve analysis. Additional functions in Edesign allow probe design for effective discrimination between wild-type sequences and genetic variations either using standard DNA oligonucleotides or Eprobes. Edesign can be freely accessed online at http://www.dnaform.com/edesign2/, and the source code is available for download. PMID:26863543

Hidden Markov Model and Support Vector Machine based decoding of finger movements using Electrocorticography

PubMed Central

Wissel, Tobias; Pfeiffer, Tim; Frysch, Robert; Knight, Robert T.; Chang, Edward F.; Hinrichs, Hermann; Rieger, Jochem W.; Rose, Georg

2013-01-01

Objective Support Vector Machines (SVM) have developed into a gold standard for accurate classification in Brain-Computer-Interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of Hidden Markov Models (HMM)for online BCIs and discuss strategies to improve their performance. Approach We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from the Electrocorticograms of four subjects doing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. Main results We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. Significance We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online brain-computer interfaces. PMID:24045504

Using EPIC to search the OCLC Online Union Catalog in a health sciences library.

PubMed

Richwine, P W

1991-01-01

EPIC is a service that provides keyword or subject access to the OCLC Online Union Catalog (OLUC). This capability increases the success rate for title location as well as the potential uses of the OLUC. The features of the EPIC system, application of these features to the OLUC, and specific uses in health sciences libraries are described in this article.

Online social networks that connect users to physical activity partners: a review and descriptive analysis.

PubMed

Nakhasi, Atul; Shen, Album Xiaotian; Passarella, Ralph Joseph; Appel, Lawrence J; Anderson, Cheryl Am

2014-06-16

The US Centers for Disease Control and Prevention have identified a lack of encouragement, support, or companionship from family and friends as a major barrier to physical activity. To overcome this barrier, online social networks are now actively leveraging principles of companion social support in novel ways. The aim was to evaluate the functionality, features, and usability of existing online social networks which seek to increase physical activity and fitness among users by connecting them to physical activity partners, not just online, but also face-to-face. In September 2012, we used 3 major databases to identify the website addresses for relevant online social networks. We conducted a Google search using 8 unique keyword combinations: the common keyword "find" coupled with 1 of 4 prefix terms "health," "fitness," "workout," or "physical" coupled with 1 of 2 stem terms "activity partners" or "activity buddies." We also searched 2 prominent technology start-up news sites, TechCrunch and Y Combinator, using 2 unique keyword combinations: the common keyword "find" coupled with 1 of 2 stem terms "activity partners" and "activity buddies." Sites were defined as online social health activity networks if they had the ability to (1) actively find physical activity partners or activities for the user, (2) offer dynamic, real-time tracking or sharing of social activities, and (3) provide virtual profiles to users. We excluded from our analysis sites that were not Web-based, publicly available, in English, or free. Of the 360 initial search results, we identified 13 websites that met our complete criteria of an online social health activity network. Features such as physical activity creation (13/13, 100%) and private messaging (12/13, 92%) appeared almost universally among these websites. However, integration with Web 2.0 technologies such as Facebook and Twitter (9/13, 69%) and the option of direct event joining (8/13, 62%) were not as universally present. Largely absent were more sophisticated features that would enable greater usability, such as interactive engagement prompts (3/13, 23%) and system-created best fit activities (3/13, 23%). Several major online social networks that connect users to physical activity partners currently exist and use standardized features to achieve their goals. Future research is needed to better understand how users utilize these features and how helpful they truly are.

Web design and development for centralize area radiation monitoring system in Malaysian Nuclear Agency

NASA Astrophysics Data System (ADS)

Ibrahim, Maslina Mohd; Yussup, Nolida; Haris, Mohd Fauzi; Soh @ Shaari, Syirrazie Che; Azman, Azraf; Razalim, Faizal Azrin B. Abdul; Yapp, Raymond; Hasim, Harzawardi; Aslan, Mohd Dzul Aiman

2017-01-01

One of the applications for radiation detector is area monitoring which is crucial for safety especially at a place where radiation source is involved. An environmental radiation monitoring system is a professional system that combines flexibility and ease of use for data collection and monitoring. Nowadays, with the growth of technology, devices and equipment can be connected to the network and Internet to enable online data acquisition. This technology enables data from the area monitoring devices to be transmitted to any place and location directly and faster. In Nuclear Malaysia, area radiation monitor devices are located at several selective locations such as laboratories and radiation facility. This system utilizes an Ethernet as a communication media for data acquisition of the area radiation levels from radiation detectors and stores the data at a server for recording and analysis. This paper discusses on the design and development of website that enable all user in Nuclear Malaysia to access and monitor the radiation level for each radiation detectors at real time online. The web design also included a query feature for history data from various locations online. The communication between the server's software and web server is discussed in detail in this paper.

Critical product features' identification using an opinion analyzer.

PubMed

Shamim, Azra; Balakrishnan, Vimala; Tahir, Muhammad; Shiraz, Muhammad

2014-01-01

The increasing use and ubiquity of the Internet facilitate dissemination of word-of-mouth through blogs, online forums, newsgroups, and consumer's reviews. Online consumer's reviews present tremendous opportunities and challenges for consumers and marketers. One of the challenges is to develop interactive marketing practices for making connections with target consumers that capitalize consumer-to-consumer communications for generating product adoption. Opinion mining is employed in marketing to help consumers and enterprises in the analysis of online consumers' reviews by highlighting the strengths and weaknesses of the products. This paper describes an opinion mining system based on novel review and feature ranking methods to empower consumers and enterprises for identifying critical product features from enormous consumers' reviews. Consumers and business analysts are the main target group for the proposed system who want to explore consumers' feedback for determining purchase decisions and enterprise strategies. We evaluate the proposed system on real dataset. Results show that integration of review and feature-ranking methods improves the decision making processes significantly.

Critical Product Features' Identification Using an Opinion Analyzer

PubMed Central

Shamim, Azra; Balakrishnan, Vimala

2014-01-01

The increasing use and ubiquity of the Internet facilitate dissemination of word-of-mouth through blogs, online forums, newsgroups, and consumer's reviews. Online consumer's reviews present tremendous opportunities and challenges for consumers and marketers. One of the challenges is to develop interactive marketing practices for making connections with target consumers that capitalize consumer-to-consumer communications for generating product adoption. Opinion mining is employed in marketing to help consumers and enterprises in the analysis of online consumers' reviews by highlighting the strengths and weaknesses of the products. This paper describes an opinion mining system based on novel review and feature ranking methods to empower consumers and enterprises for identifying critical product features from enormous consumers' reviews. Consumers and business analysts are the main target group for the proposed system who want to explore consumers' feedback for determining purchase decisions and enterprise strategies. We evaluate the proposed system on real dataset. Results show that integration of review and feature-ranking methods improves the decision making processes significantly. PMID:25506612

Cross-Domain Shoe Retrieval with a Semantic Hierarchy of Attribute Classification Network.

PubMed

Zhan, Huijing; Shi, Boxin; Kot, Alex C

2017-08-04

Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the Semantic Hierarchy Of attributE Convolutional Neural Network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe dataset composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.

NREL: A Year in Clean Energy Innovations; A Review of NREL's 2011 Feature Stories

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

Not Available

2012-04-01

This document is a compilation of articles featuring NREL research and development, deployment, commercialization, and outreach activities in 2011. The feature stories can be found online at http:www.nrel.gov/features/.

Retirement | Alaska Division of Retirement and Benefits

Science.gov Websites

Comp All Other Programs Features Empower Retirement Account Info Online myRnB Member Services Seminars Benefits > Retirement Online Counselor Scheduler Empower Retirement Account Info Online myRnB Member Welcome to the Retirement Section News Empower Increases Participant Account Security Help What retirement

Understanding Online Knowledge Sharing: An Interpersonal Relationship Perspective

ERIC Educational Resources Information Center

Ma, Will W. K.; Yuen, Allan H. K.

2011-01-01

The unique features and capabilities of online learning are built on the ability to connect to a wider range of learning resources and peer learners that benefit individual learners, such as through discussion forums, collaborative learning, and community building. The success of online learning thus depends on the participation, engagement, and…

Visual object tracking by correlation filters and online learning

NASA Astrophysics Data System (ADS)

Zhang, Xin; Xia, Gui-Song; Lu, Qikai; Shen, Weiming; Zhang, Liangpei

2018-06-01

Due to the complexity of background scenarios and the variation of target appearance, it is difficult to achieve high accuracy and fast speed for object tracking. Currently, correlation filters based trackers (CFTs) show promising performance in object tracking. The CFTs estimate the target's position by correlation filters with different kinds of features. However, most of CFTs can hardly re-detect the target in the case of long-term tracking drifts. In this paper, a feature integration object tracker named correlation filters and online learning (CFOL) is proposed. CFOL estimates the target's position and its corresponding correlation score using the same discriminative correlation filter with multi-features. To reduce tracking drifts, a new sampling and updating strategy for online learning is proposed. Experiments conducted on 51 image sequences demonstrate that the proposed algorithm is superior to the state-of-the-art approaches.

Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm.

PubMed

Saba, Luca; Jain, Pankaj K; Suri, Harman S; Ikeda, Nobutaka; Araki, Tadashi; Singh, Bikesh K; Nicolaides, Andrew; Shafique, Shoaib; Gupta, Ajay; Laird, John R; Suri, Jasjit S

2017-06-01

Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings.

Selecting Media for Effective Learning in Online and Blended Courses: A Review Study

ERIC Educational Resources Information Center

Amaka, Ifewulu Henrietta; Goeman, Katie

2017-01-01

As the number of online and blended learning courses offered by higher education institutions increase, a predominant issue for instructors is their design. This study focuses on the selection of appropriate media to support online and blended learning (OBL) activities. To this end, we mapped and synthesized in two consecutive systematic review…

Evaluating and Selecting Online Magazines for Children. ERIC Digest.

ERIC Educational Resources Information Center

Lu, Mei-Yu

This Digest provides an overview of children's online magazines, also known as e-zines. It begins with a brief review of factors that contribute to the popularity of these publications, followed by a list of criteria for selecting high-quality online magazines for children. Samples of high-quality children's e-zines are also included in this…

What Characteristics of College Students Influence Their Decisions to Select Online Courses?

ERIC Educational Resources Information Center

Mann, John T.; Henneberry, Shida R.

2012-01-01

The primary goal of this study was to identify a wide range of characteristics of college students that may influence their decisions to select online courses. The motivation underlying this study is the realization that online courses are no longer exclusively being taken by non-traditional students (for undergraduates, that would be students age…

Sorting Through Vendors.

ERIC Educational Resources Information Center

Hirsch, Jim

2001-01-01

Offers advice on selecting online vendors, such as determining quality, graduation credit, course articulation, delivery equipment, teacher support, handicap accessibility, pricing methods, and grading functions. Includes a selected list of vendors of online courses. (PKP)

The Role of Semantics in Next-Generation Online Virtual World-Based Retail Store

NASA Astrophysics Data System (ADS)

Sharma, Geetika; Anantaram, C.; Ghosh, Hiranmay

Online virtual environments are increasingly becoming popular for entrepreneurship. While interactions are primarily between avatars, some interactions could occur through intelligent chatbots. Such interactions require connecting to backend business applications to obtain information, carry out real-world transactions etc. In this paper, we focus on integrating business application systems with virtual worlds. We discuss the probable features of a next-generation online virtual world-based retail store and the technologies involved in realizing the features of such a store. In particular, we examine the role of semantics in integrating popular virtual worlds with business applications to provide natural language based interactions.

Toward automated consumer question answering: automatically separating consumer questions from professional questions in the healthcare domain.

PubMed

Liu, Feifan; Antieau, Lamont D; Yu, Hong

2011-12-01

Both healthcare professionals and healthcare consumers have information needs that can be met through the use of computers, specifically via medical question answering systems. However, the information needs of both groups are different in terms of literacy levels and technical expertise, and an effective question answering system must be able to account for these differences if it is to formulate the most relevant responses for users from each group. In this paper, we propose that a first step toward answering the queries of different users is automatically classifying questions according to whether they were asked by healthcare professionals or consumers. We obtained two sets of consumer questions (~10,000 questions in total) from Yahoo answers. The professional questions consist of two question collections: 4654 point-of-care questions (denoted as PointCare) obtained from interviews of a group of family doctors following patient visits and 5378 questions from physician practices through professional online services (denoted as OnlinePractice). With more than 20,000 questions combined, we developed supervised machine-learning models for automatic classification between consumer questions and professional questions. To evaluate the robustness of our models, we tested the model that was trained on the Consumer-PointCare dataset on the Consumer-OnlinePractice dataset. We evaluated both linguistic features and statistical features and examined how the characteristics in two different types of professional questions (PointCare vs. OnlinePractice) may affect the classification performance. We explored information gain for feature reduction and the back-off linguistic category features. The 10-fold cross-validation results showed the best F1-measure of 0.936 and 0.946 on Consumer-PointCare and Consumer-OnlinePractice respectively, and the best F1-measure of 0.891 when testing the Consumer-PointCare model on the Consumer-OnlinePractice dataset. Healthcare consumer questions posted at Yahoo online communities can be reliably classified from professional questions posted by point-of-care clinicians and online physicians. The supervised machine-learning models are robust for this task. Our study will significantly benefit further development in automated consumer question answering. Copyright © 2011 Elsevier Inc. All rights reserved.

Baccalaureate Nursing Students' Abilities in Critically Identifying and Evaluating the Quality of Online Health Information.

PubMed

Theron, Maggie; Redmond, Anne; Borycki, Elizabeth M

2017-01-01

Both the Internet and social media have become important tools that patients and health professionals, including health professional students, use to obtain information and support their decision-making surrounding health care. Students in the health sciences require increased competence to select, appraise, and use online sources to adequately educate and support patients and advocate for patient needs and best practices. The purpose of this study was to ascertain if second year nursing students have the ability to critically identify and evaluate the quality of online health information through comparisons between student and expert assessments of selected online health information postings using an adapted Trust in Online Health Information scale. Interviews with experts provided understanding of how experts applied the selected criteria and what experts recommend for implementing nursing informatics literacy in curriculums. The difference between student and expert assessments of the quality of the online information is on average close to 40%. Themes from the interviews highlighted several possible factors that may influence informatics competency levels in students, specifically regarding the critical appraisal of the quality of online health information.

Dewey Decimal Classification Online Project: Evaluation of a Library Schedule and Index Integrated into the Subject Searching Capabilities of an Online Catalog. Final Report.

ERIC Educational Resources Information Center

Markey, Karen; Demeyer, Anh N.

In this research project, subject terms from the Dewey Decimal Classification (DDC) Schedules and Relative Index were incorporated into an online catalog as searcher's tools for subject access, browsing, and display. Four features of the DDC were employed to help searchers browse for and match their own subject terms with the online catalog's…

Facilitating Interactivity in an Online Business Writing Course.

ERIC Educational Resources Information Center

Mabrito, Mark

2001-01-01

Suggests ways of developing an online business writing course that uses technology to simulate features of the face-to-face classroom and that achieves an interactive learning experience for students. Uses the author's online business writing class as an example of one which manages to simulate, through the judicious use of software, the…

University Business Models and Online Practices: A Third Way

ERIC Educational Resources Information Center

Rubin, Beth

2013-01-01

Higher Education is in a state of change, and the existing business models do not meet the needs of stakeholders. This article contrasts the current dominant business models of universities, comparing the traditional non-profit against the for-profit online model, examining the structural features and online teaching practices that underlie each.…

Online Course Design in Higher Education: A Review of National and Statewide Evaluation Instruments

ERIC Educational Resources Information Center

Baldwin, Sally; Ching, Yu-Hui; Hsu, Yu-Chang

2018-01-01

This research identifies six online course evaluation instruments used nationally or in statewide systems. We examined the characteristics (i.e., number of standards and criteria) and coded the criteria that guide the design of online courses. We discussed the focus of the instruments and their unique features.

Technology Transience and Learner Data: Shifting Notions of Privacy in Online Learning

ERIC Educational Resources Information Center

Dennen, Vanessa P.

2015-01-01

The technologies that support online learning are continuously evolving, providing instructors and students with a continuous stream of new tools, features, and functionalities for existing tools. During an online course, instructors and students generate and share a tremendous amount of data using these tools. These data are often created in…

Satisfaction with Online Teaching Videos: A Quantitative Approach

ERIC Educational Resources Information Center

Meseguer-Martinez, Angel; Ros-Galvez, Alejandro; Rosa-Garcia, Alfonso

2017-01-01

We analyse the factors that determine the number of clicks on the "Like" button in online teaching videos, with a sample of teaching videos in the area of Microeconomics across Spanish-speaking countries. The results show that users prefer short online teaching videos. Moreover, some features of the videos have a significant impact on…

COLUG: Chicago Online Users Introductory Guide.

ERIC Educational Resources Information Center

Moore, Alexandra L., Ed.; Pyrce, Sharon R., Ed.

Intended to serve as an introduction to online searching in the Chicago area, the guide answers these basic questions for those considering going online for the first time: what is online searching, starting out online, local training for online searching, how to choose a terminal, 1200 baud equipment selection, how to prepare for and evaluate a…

Find a Diabetes Prevention Program Near You

MedlinePlus

... throughout the country. Find an In-person Class Select From List Find a class near you by ... some locations. Search by ZIP ZIP Code: Distance: Select Location Location: Find an Online Program Online programs ...

Finding Reliable Health Information Online

MedlinePlus

Skip to main content Finding Reliable Health Information Online Enter Search Term(s): Español Research Funding An Overview Bioinformatics Current Grants Education and Training Funding Extramural Research News Features Funding Divisions Funding ...

Disseminating Innovations in Teaching Value-Based Care Through an Online Learning Network.

PubMed

Gupta, Reshma; Shah, Neel T; Moriates, Christopher; Wallingford, September; Arora, Vineet M

2017-08-01

A national imperative to provide value-based care requires new strategies to teach clinicians about high-value care. We developed a virtual online learning network aimed at disseminating emerging strategies in teaching value-based care. The online Teaching Value in Health Care Learning Network includes monthly webinars that feature selected innovators, online discussion forums, and a repository for sharing tools. The learning network comprises clinician-educators and health system leaders across North America. We conducted a cross-sectional online survey of all webinar presenters and the active members of the network, and we assessed program feasibility. Six months after the program launched, there were 277 learning community members in 22 US states. Of the 74 active members, 50 (68%) completed the evaluation. Active members represented independently practicing physicians and trainees in 7 specialties, nurses, educators, and health system leaders. Nearly all speakers reported that the learning network provided them with a unique opportunity to connect with a different audience and achieve greater recognition for their work. Of the members who were active in the learning network, most reported that strategies gleaned from the network were helpful, and some adopted or adapted these innovations at their home institutions. One year after the program launched, the learning network had grown to 364 total members. The learning network helped participants share and implement innovations to promote high-value care. The model can help disseminate innovations in emerging areas of health care transformation, and is sustainable without ongoing support after a period of start-up funding.

Examining trust factors in online food risk information: The case of unpasteurized or 'raw' milk.

PubMed

Sillence, Elizabeth; Hardy, Claire; Medeiros, Lydia C; LeJeune, Jeffrey T

2016-04-01

The internet has become an increasingly important way of communicating with consumers about food risk information. However, relatively little is known about how consumers evaluate and come to trust the information they encounter online. Using the example of unpasteurized or raw milk this paper presents two studies exploring the trust factors associated with online information about the risks and benefits of raw milk consumption. In the first study, eye-tracking data was collected from 33 pasteurised milk consumers whilst they viewed six different milk related websites. A descriptive analysis of the eye-tracking data was conducted to explore viewing patterns. Reports revealed the importance of images as a way of capturing initial attention and foregrounding other features and highlighted the significance of introductory text within a homepage. In the second, qualitative study, 41 consumers, some of whom drank raw milk, viewed a selection of milk related websites before participating in either a group discussion or interview. Seventeen of the participants also took part in a follow up telephone interview 2 weeks later. The qualitative data supports the importance of good design whilst noting that balance, authorship agenda, the nature of evidence and personal relevance were also key factors affecting consumers trust judgements. The results of both studies provide support for a staged approach to online trust in which consumers engage in a more rapid, heuristic assessment of a site before moving on to a more in-depth evaluation of the information available. Findings are discussed in relation to the development of trustworthy online food safety resources. Copyright © 2016 Elsevier Ltd. All rights reserved.

Online dynamical downscaling of temperature and precipitation within the iLOVECLIM model (version 1.1)

NASA Astrophysics Data System (ADS)

Quiquet, Aurélien; Roche, Didier M.; Dumas, Christophe; Paillard, Didier

2018-02-01

This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km × 40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.

Online aptitude automatic surface quality inspection system for hot rolled strips steel

NASA Astrophysics Data System (ADS)

Lin, Jin; Xie, Zhi-jiang; Wang, Xue; Sun, Nan-Nan

2005-12-01

Defects on the surface of hot rolled steel strips are main factors to evaluate quality of steel strips, an improved image recognition algorithm are used to extract the feature of Defects on the surface of steel strips. Base on the Machine vision and Artificial Neural Networks, establish a defect recognition method to select defect on the surface of steel strips. Base on these research. A surface inspection system and advanced algorithms for image processing to hot rolled strips is developed. Preparing two different fashion to lighting, adopting line blast vidicon of CCD on the surface steel strips on-line. Opening up capacity-diagnose-system with level the surface of steel strips on line, toward the above and undersurface of steel strips with ferric oxide, injure, stamp etc of defects on the surface to analyze and estimate. Miscarriage of justice and alternate of justice rate not preponderate over 5%.Geting hold of applications on some big enterprises of steel at home. Experiment proved that this measure is feasible and effective.

A Predictive Model of Anesthesia Depth Based on SVM in the Primary Visual Cortex

PubMed Central

Shi, Li; Li, Xiaoyuan; Wan, Hong

2013-01-01

In this paper, a novel model for predicting anesthesia depth is put forward based on local field potentials (LFPs) in the primary visual cortex (V1 area) of rats. The model is constructed using a Support Vector Machine (SVM) to realize anesthesia depth online prediction and classification. The raw LFP signal was first decomposed into some special scaling components. Among these components, those containing higher frequency information were well suited for more precise analysis of the performance of the anesthetic depth by wavelet transform. Secondly, the characteristics of anesthetized states were extracted by complexity analysis. In addition, two frequency domain parameters were selected. The above extracted features were used as the input vector of the predicting model. Finally, we collected the anesthesia samples from the LFP recordings under the visual stimulus experiments of Long Evans rats. Our results indicate that the predictive model is accurate and computationally fast, and that it is also well suited for online predicting. PMID:24044024

Arc-welding quality assurance by means of embedded fiber sensor and spectral processing combining feature selection and neural networks

NASA Astrophysics Data System (ADS)

Mirapeix, J.; García-Allende, P. B.; Cobo, A.; Conde, O.; López-Higuera, J. M.

2007-07-01

A new spectral processing technique designed for its application in the on-line detection and classification of arc-welding defects is presented in this paper. A non-invasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed by means of two consecutive stages. A compression algorithm is first applied to the data allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in a previous paper, giving rise to an improvement in the performance of the monitoring system.

Perceptually Guided Photo Retargeting.

PubMed

Xia, Yingjie; Zhang, Luming; Hong, Richang; Nie, Liqiang; Yan, Yan; Shao, Ling

2016-04-22

We propose perceptually guided photo retargeting, which shrinks a photo by simulating a human's process of sequentially perceiving visually/semantically important regions in a photo. In particular, we first project the local features (graphlets in this paper) onto a semantic space, wherein visual cues such as global spatial layout and rough geometric context are exploited. Thereafter, a sparsity-constrained learning algorithm is derived to select semantically representative graphlets of a photo, and the selecting process can be interpreted by a path which simulates how a human actively perceives semantics in a photo. Furthermore, we learn the prior distribution of such active graphlet paths (AGPs) from training photos that are marked as esthetically pleasing by multiple users. The learned priors enforce the corresponding AGP of a retargeted photo to be maximally similar to those from the training photos. On top of the retargeting model, we further design an online learning scheme to incrementally update the model with new photos that are esthetically pleasing. The online update module makes the algorithm less dependent on the number and contents of the initial training data. Experimental results show that: 1) the proposed AGP is over 90% consistent with human gaze shifting path, as verified by the eye-tracking data, and 2) the retargeting algorithm outperforms its competitors significantly, as AGP is more indicative of photo esthetics than conventional saliency maps.

Understanding Digital Learning and Its Variable Effects

NASA Astrophysics Data System (ADS)

Means, B.

2016-12-01

An increasing proportion of undergraduate courses use an online or blended learning format. This trend signals major changes in the kind of instruction students receive in their STEM courses, yet evidence about the effectiveness of these new approaches is sparse. Existing syntheses and meta-analyses summarize outcomes from experimental or quasi-experimental studies of online and blended courses and document how few studies incorporate proper controls for differences in student characteristics, instructor behaviors, and other course conditions. The evidence that is available suggests that on average blended courses are equal to or better than traditional face-to-face courses and that online courses are equivalent in terms of learning outcomes. But these averages conceal a tremendous underlying variability. Results vary markedly from course to course, even when the same technology is used in both. Some research suggests that online instruction puts lower-achieving students at a disadvantage. It is clear that introducing digital learning per se is no guarantee that student engagement and learning will be enhanced. Getting more consistently positive impacts out of learning technologies is going to require systematic characterization of the features of learning technologies and associated instructional practices as well as attention to context and student characteristics. This presentation will present a framework for characterizing essential features of digital learning resources, implementation practices, and conditions. It will also summarize the research evidence with respect to the learning impacts of specific technology features including spaced practice, immediate feedback, mastery learning based pacing, visualizations and simulations, gaming features, prompts for explanations and reflection, and tools for online collaboration.

An offline-online Web-GIS Android application for fast data acquisition of landslide hazard and risk

NASA Astrophysics Data System (ADS)

Olyazadeh, Roya; Sudmeier-Rieux, Karen; Jaboyedoff, Michel; Derron, Marc-Henri; Devkota, Sanjaya

2017-04-01

Regional landslide assessments and mapping have been effectively pursued by research institutions, national and local governments, non-governmental organizations (NGOs), and different stakeholders for some time, and a wide range of methodologies and technologies have consequently been proposed. Land-use mapping and hazard event inventories are mostly created by remote-sensing data, subject to difficulties, such as accessibility and terrain, which need to be overcome. Likewise, landslide data acquisition for the field navigation can magnify the accuracy of databases and analysis. Open-source Web and mobile GIS tools can be used for improved ground-truthing of critical areas to improve the analysis of hazard patterns and triggering factors. This paper reviews the implementation and selected results of a secure mobile-map application called ROOMA (Rapid Offline-Online Mapping Application) for the rapid data collection of landslide hazard and risk. This prototype assists the quick creation of landslide inventory maps (LIMs) by collecting information on the type, feature, volume, date, and patterns of landslides using open-source Web-GIS technologies such as Leaflet maps, Cordova, GeoServer, PostgreSQL as the real DBMS (database management system), and PostGIS as its plug-in for spatial database management. This application comprises Leaflet maps coupled with satellite images as a base layer, drawing tools, geolocation (using GPS and the Internet), photo mapping, and event clustering. All the features and information are recorded into a GeoJSON text file in an offline version (Android) and subsequently uploaded to the online mode (using all browsers) with the availability of Internet. Finally, the events can be accessed and edited after approval by an administrator and then be visualized by the general public.

Time drawings: Spatial representation of temporal concepts.

PubMed

Leone, María Juliana; Salles, Alejo; Pulver, Alejandro; Golombek, Diego Andrés; Sigman, Mariano

2018-03-01

Time representation is a fundamental property of human cognition. Ample evidence shows that time (and numbers) are represented in space. However, how the conceptual mapping varies across individuals, scales, and temporal structures remains largely unknown. To investigate this issue, we conducted a large online study consisting in five experiments that addressed different time scales and topology: Zones of time, Seasons, Days of the week, Parts of the day and Timeline. Participants were asked to map different kinds of time events to a location in space and to determine their size and color. Results showed that time is organized in space in a hierarchical progression: some features appear to be universal (i.e. selection order), others are shaped by how time is organized in distinct cultures (i.e. location order) and, finally, some aspects vary depending on individual features such as age, gender, and chronotype (i.e. size and color). Copyright © 2018 Elsevier Inc. All rights reserved.

Computing Prediction and Functional Analysis of Prokaryotic Propionylation.

PubMed

Wang, Li-Na; Shi, Shao-Ping; Wen, Ping-Ping; Zhou, Zhi-You; Qiu, Jian-Ding

2017-11-27

Identification and systematic analysis of candidates for protein propionylation are crucial steps for understanding its molecular mechanisms and biological functions. Although several proteome-scale methods have been performed to delineate potential propionylated proteins, the majority of lysine-propionylated substrates and their role in pathological physiology still remain largely unknown. By gathering various databases and literatures, experimental prokaryotic propionylation data were collated to be trained in a support vector machine with various features via a three-step feature selection method. A novel online tool for seeking potential lysine-propionylated sites (PropSeek) ( http://bioinfo.ncu.edu.cn/PropSeek.aspx ) was built. Independent test results of leave-one-out and n-fold cross-validation were similar to each other, showing that PropSeek is a stable and robust predictor with satisfying performance. Meanwhile, analyses of Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathways, and protein-protein interactions implied a potential role of prokaryotic propionylation in protein synthesis and metabolism.

Features of Online Health Communities for Adolescents With Type 1 Diabetes

PubMed Central

Ho, Yun-Xian; O’Connor, Brendan H.; Mulvaney, Shelagh A.

2014-01-01

The aim of this exploratory study was to examine diabetes online health communities (OHCs) available to adolescents with type 1 diabetes (T1D). We sought to identify and classify site features and relate them to evidence-based processes for improving self-management. We reviewed 18 OHCs and identified the following five feature categories: social learning and networking, information, guidance, engagement, and personal health data sharing. While features that have been associated with improved self-management were present, such as social learning, results suggest that more guidance or structure would be helpful to ensure that those processes were focused on promoting positive beliefs and behaviors. Enhancing guidance-related features and structure to existing OHCs could provide greater opportunity for effective diabetes self-management support. To support clinical recommendations, more research is needed to quantitatively relate features and participation in OHCs to patient outcomes. PMID:24473058

Can Online Learning Reproduce the Full College Experience? Center for Policy Innovation Discussion Paper. Number 3

ERIC Educational Resources Information Center

McKeown, Karen D.

2012-01-01

With the tuition cost of traditional colleges and universities soaring and education technology advancing, online courses and degree programs are becoming more common. Some critics argue that an online degree cannot provide all the important features of a traditional college education, from extracurricular activities to new professional networks,…

In This Online University, Students Do the Teaching as Well as the Learning

ERIC Educational Resources Information Center

Mangan, Katherine

2012-01-01

As free online courses draw students to star professors at prestigious colleges, Peer 2 Peer University asks whether instructors are needed at all. This article features Peer 2 Peer University, a three-year-old online institution where students learn together, at no charge, using materials found on the Web. The unusual institution, where anyone…

Online K-12 Teachers' Perceptions and Practices of Supporting Self-Regulated Learning

ERIC Educational Resources Information Center

Huh, Yeol; Reigeluth, Charles M.

2018-01-01

With growing interest in and popularity of online learning and lifelong learners, students' ability to be engaged in self-regulated learning (SRL) has become more important. Moreover, online learning is becoming an important feature of K-12 education. Although SRL is known to be important and teachable, little research has been conducted on…

Supporting Online Faculty Holistically: Developing a Support Website Resource

ERIC Educational Resources Information Center

Nordin, Eric; Anthony, Peter John

2014-01-01

Current trends in post-secondary education enrollment indicate that colleges and universities are likely to experience an increase in the number of online students. The purpose of this study was to ascertain the type of resources and support features online faculty need, desire, and expect in a support website. The method used to collect research…

A Comparative Study of Directly Selected, In-Service Promoted and Online Selected Subject Specialists Regarding Teaching Effectiveness in Kohat Division, Pakistan

ERIC Educational Resources Information Center

Suleman, Qaiser; Gul, Rizwana

2015-01-01

The main objective of the study was to compare the teaching effectiveness of directly selected, in-service promoted and online selected subject specialists teaching at higher secondary school level in Kohat Division, Pakistan. The target population of the study was the higher secondary school students in Kohat Division, Pakistan. A sample of 600…

A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating.

PubMed

Knips, Guido; Zibner, Stephan K U; Reimann, Hendrik; Schöner, Gregor

2017-01-01

Reaching for objects and grasping them is a fundamental skill for any autonomous robot that interacts with its environment. Although this skill seems trivial to adults, who effortlessly pick up even objects they have never seen before, it is hard for other animals, for human infants, and for most autonomous robots. Any time during movement preparation and execution, human reaching movement are updated if the visual scene changes (with a delay of about 100 ms). The capability for online updating highlights how tightly perception, movement planning, and movement generation are integrated in humans. Here, we report on an effort to reproduce this tight integration in a neural dynamic process model of reaching and grasping that covers the complete path from visual perception to movement generation within a unified modeling framework, Dynamic Field Theory. All requisite processes are realized as time-continuous dynamical systems that model the evolution in time of neural population activation. Population level neural processes bring about the attentional selection of objects, the estimation of object shape and pose, and the mapping of pose parameters to suitable movement parameters. Once a target object has been selected, its pose parameters couple into the neural dynamics of movement generation so that changes of pose are propagated through the architecture to update the performed movement online. Implementing the neural architecture on an anthropomorphic robot arm equipped with a Kinect sensor, we evaluate the model by grasping wooden objects. Their size, shape, and pose are estimated from a neural model of scene perception that is based on feature fields. The sequential organization of a reach and grasp act emerges from a sequence of dynamic instabilities within a neural dynamics of behavioral organization, that effectively switches the neural controllers from one phase of the action to the next. Trajectory formation itself is driven by a dynamical systems version of the potential field approach. We highlight the emergent capacity for online updating by showing that a shift or rotation of the object during the reaching phase leads to the online adaptation of the movement plan and successful completion of the grasp.

A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating

PubMed Central

Knips, Guido; Zibner, Stephan K. U.; Reimann, Hendrik; Schöner, Gregor

2017-01-01

Reaching for objects and grasping them is a fundamental skill for any autonomous robot that interacts with its environment. Although this skill seems trivial to adults, who effortlessly pick up even objects they have never seen before, it is hard for other animals, for human infants, and for most autonomous robots. Any time during movement preparation and execution, human reaching movement are updated if the visual scene changes (with a delay of about 100 ms). The capability for online updating highlights how tightly perception, movement planning, and movement generation are integrated in humans. Here, we report on an effort to reproduce this tight integration in a neural dynamic process model of reaching and grasping that covers the complete path from visual perception to movement generation within a unified modeling framework, Dynamic Field Theory. All requisite processes are realized as time-continuous dynamical systems that model the evolution in time of neural population activation. Population level neural processes bring about the attentional selection of objects, the estimation of object shape and pose, and the mapping of pose parameters to suitable movement parameters. Once a target object has been selected, its pose parameters couple into the neural dynamics of movement generation so that changes of pose are propagated through the architecture to update the performed movement online. Implementing the neural architecture on an anthropomorphic robot arm equipped with a Kinect sensor, we evaluate the model by grasping wooden objects. Their size, shape, and pose are estimated from a neural model of scene perception that is based on feature fields. The sequential organization of a reach and grasp act emerges from a sequence of dynamic instabilities within a neural dynamics of behavioral organization, that effectively switches the neural controllers from one phase of the action to the next. Trajectory formation itself is driven by a dynamical systems version of the potential field approach. We highlight the emergent capacity for online updating by showing that a shift or rotation of the object during the reaching phase leads to the online adaptation of the movement plan and successful completion of the grasp. PMID:28303100

A microprocessor controlled pressure scanning system

NASA Technical Reports Server (NTRS)

Anderson, R. C.

1976-01-01

A microprocessor-based controller and data logger for pressure scanning systems is described. The microcomputer positions and manages data from as many as four 48-port electro-mechanical pressure scanners. The maximum scanning rate is 80 pressure measurements per second (20 ports per second on each of four scanners). The system features on-line calibration, position-directed data storage, and once-per-scan display in engineering units of data from a selected port. The system is designed to be interfaced to a facility computer through a shared memory. System hardware and software are described. Factors affecting measurement error in this type of system are also discussed.

From tiger to panda: animal head detection.

PubMed

Zhang, Weiwei; Sun, Jian; Tang, Xiaoou

2011-06-01

Robust object detection has many important applications in real-world online photo processing. For example, both Google image search and MSN live image search have integrated human face detector to retrieve face or portrait photos. Inspired by the success of such face filtering approach, in this paper, we focus on another popular online photo category--animal, which is one of the top five categories in the MSN live image search query log. As a first attempt, we focus on the problem of animal head detection of a set of relatively large land animals that are popular on the internet, such as cat, tiger, panda, fox, and cheetah. First, we proposed a new set of gradient oriented feature, Haar of Oriented Gradients (HOOG), to effectively capture the shape and texture features on animal head. Then, we proposed two detection algorithms, namely Bruteforce detection and Deformable detection, to effectively exploit the shape feature and texture feature simultaneously. Experimental results on 14,379 well labeled animals images validate the superiority of the proposed approach. Additionally, we apply the animal head detector to improve the image search result through text based online photo search result filtering.

Real-Time Tracking by Double Templates Matching Based on Timed Motion History Image with HSV Feature

PubMed Central

Li, Zhiyong; Li, Pengfei; Yu, Xiaoping; Hashem, Mervat

2014-01-01

It is a challenge to represent the target appearance model for moving object tracking under complex environment. This study presents a novel method with appearance model described by double templates based on timed motion history image with HSV color histogram feature (tMHI-HSV). The main components include offline template and online template initialization, tMHI-HSV-based candidate patches feature histograms calculation, double templates matching (DTM) for object location, and templates updating. Firstly, we initialize the target object region and calculate its HSV color histogram feature as offline template and online template. Secondly, the tMHI-HSV is used to segment the motion region and calculate these candidate object patches' color histograms to represent their appearance models. Finally, we utilize the DTM method to trace the target and update the offline template and online template real-timely. The experimental results show that the proposed method can efficiently handle the scale variation and pose change of the rigid and nonrigid objects, even in illumination change and occlusion visual environment. PMID:24592185

Characterizing and modeling the dynamics of online popularity.

PubMed

Ratkiewicz, Jacob; Fortunato, Santo; Flammini, Alessandro; Menczer, Filippo; Vespignani, Alessandro

2010-10-08

Online popularity has an enormous impact on opinions, culture, policy, and profits. We provide a quantitative, large scale, temporal analysis of the dynamics of online content popularity in two massive model systems: the Wikipedia and an entire country's Web space. We find that the dynamics of popularity are characterized by bursts, displaying characteristic features of critical systems such as fat-tailed distributions of magnitude and interevent time. We propose a minimal model combining the classic preferential popularity increase mechanism with the occurrence of random popularity shifts due to exogenous factors. The model recovers the critical features observed in the empirical analysis of the systems analyzed here, highlighting the key factors needed in the description of popularity dynamics.

An Online Risk Monitor System (ORMS) to Increase Safety and Security Levels in Industry

NASA Astrophysics Data System (ADS)

Zubair, M.; Rahman, Khalil Ur; Hassan, Mehmood Ul

2013-12-01

The main idea of this research is to develop an Online Risk Monitor System (ORMS) based on Living Probabilistic Safety Assessment (LPSA). The article highlights the essential features and functions of ORMS. The basic models and modules such as, Reliability Data Update Model (RDUM), running time update, redundant system unavailability update, Engineered Safety Features (ESF) unavailability update and general system update have been described in this study. ORMS not only provides quantitative analysis but also highlights qualitative aspects of risk measures. ORMS is capable of automatically updating the online risk models and reliability parameters of equipment. ORMS can support in the decision making process of operators and managers in Nuclear Power Plants.

Exploring the color feature power for psoriasis risk stratification and classification: A data mining paradigm.

PubMed

Shrivastava, Vimal K; Londhe, Narendra D; Sonawane, Rajendra S; Suri, Jasjit S

2015-10-01

A large percentage of dermatologist׳s decision in psoriasis disease assessment is based on color. The current computer-aided diagnosis systems for psoriasis risk stratification and classification lack the vigor of color paradigm. The paper presents an automated psoriasis computer-aided diagnosis (pCAD) system for classification of psoriasis skin images into psoriatic lesion and healthy skin, which solves the two major challenges: (i) fulfills the color feature requirements and (ii) selects the powerful dominant color features while retaining high classification accuracy. Fourteen color spaces are discovered for psoriasis disease analysis leading to 86 color features. The pCAD system is implemented in a support vector-based machine learning framework where the offline image data set is used for computing machine learning offline color machine learning parameters. These are then used for transformation of the online color features to predict the class labels for healthy vs. diseased cases. The above paradigm uses principal component analysis for color feature selection of dominant features, keeping the original color feature unaltered. Using the cross-validation protocol, the above machine learning protocol is compared against the standalone grayscale features with 60 features and against the combined grayscale and color feature set of 146. Using a fixed data size of 540 images with equal number of healthy and diseased, 10 fold cross-validation protocol, and SVM of polynomial kernel of type two, pCAD system shows an accuracy of 99.94% with sensitivity and specificity of 99.93% and 99.96%. Using a varying data size protocol, the mean classification accuracies for color, grayscale, and combined scenarios are: 92.85%, 93.83% and 93.99%, respectively. The reliability of the system in these three scenarios are: 94.42%, 97.39% and 96.00%, respectively. We conclude that pCAD system using color space alone is compatible to grayscale space or combined color and grayscale spaces. We validated our pCAD system against facial color databases and the results are consistent in accuracy and reliability. Copyright © 2015 Elsevier Ltd. All rights reserved.

Future Challenges and Opportunities in Online Prescription Drug Promotion Research

PubMed Central

Southwell, Brian G.; Rupert, Douglas J.

2016-01-01

Despite increased availability of online promotional tools for prescription drug marketers, evidence on online prescription drug promotion is far from settled or conclusive. We highlight ways in which online prescription drug promotion is similar to conventional broadcast and print advertising and ways in which it differs. We also highlight five key areas for future research: branded drug website influence on consumer knowledge and behavior, interactive features on branded drug websites, mobile viewing of branded websites and mobile advertisements, online promotion and non-US audiences, and social media and medication decisions. PMID:26927597

The XCatDB, a Rich 3XMM Catalogue Interface

NASA Astrophysics Data System (ADS)

Michel, L.; Grisé, F.; Motch, C.; Gomez-Moran, A. N.

2015-09-01

The last release of the XMM catalog, the 3XMM-DR4 published in July 2013, is the largest X-ray catalog ever built. It includes lots of data products such as spectra, time series, images, previews, and extractions of archival catalogs matching the position of X-ray sources. The Strasbourg Observatory built an original interface called XCatDB. It was designed to make the best of this wide set of related products with an emphasis on the images. Besides, it offers an easy access to all other catalog parameters. Users can select data with very elaborate queries and can process them with online services such as an X-ray spectral fitting routine. The combination of all these features allows the users to select data of interest to the naked eye as well as to filter catalog parameters. Data selections can be picked out for further scientific analysis thanks to an interface operating external VO clients. The XcatDB has been developed with Saada.

Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection.

PubMed

Mohammed, Ameer; Zamani, Majid; Bayford, Richard; Demosthenous, Andreas

2017-12-01

In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

Retiree benefit mailing dates | Alaska Division of Retirement and Benefits

Science.gov Websites

Employee Retiring Retiree Disabled Survivor Features Empower Retirement Account Info Online myRnB Member Scheduler Empower Retirement Account Info Online myRnB Member Services Seminars AlaskaCare Wellness Forms

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

NASA Astrophysics Data System (ADS)

Bouboulis, Pantelis; Chouvardas, Symeon; Theodoridis, Sergios

2018-04-01

We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources, in a distributed setting. In contrast, the proposed method approximates the solution as a fixed-size vector (of larger dimension than the input space) using Random Fourier Features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.

Do Online Voting Patterns Reflect Evolved Features of Human Cognition? An Exploratory Empirical Investigation.

PubMed

Priestley, Maria; Mesoudi, Alex

2015-01-01

Online votes or ratings can assist internet users in evaluating the credibility and appeal of the information which they encounter. For example, aggregator websites such as Reddit allow users to up-vote submitted content to make it more prominent, and down-vote content to make it less prominent. Here we argue that decisions over what to up- or down-vote may be guided by evolved features of human cognition. We predict that internet users should be more likely to up-vote content that others have also up-voted (social influence), content that has been submitted by particularly liked or respected users (model-based bias), content that constitutes evolutionarily salient or relevant information (content bias), and content that follows group norms and, in particular, prosocial norms. 489 respondents from the online social voting community Reddit rated the extent to which they felt different traits influenced their voting. Statistical analyses confirmed that norm-following and prosociality, as well as various content biases such as emotional content and originality, were rated as important motivators of voting. Social influence had a smaller effect than expected, while attitudes towards the submitter had little effect. This exploratory empirical investigation suggests that online voting communities can provide an important test-bed for evolutionary theories of human social information use, and that evolved features of human cognition may guide online behaviour just as it guides behaviour in the offline world.

Preparation of digital movie clips for online journal publication.

PubMed

Yam, Chun-Shan

2006-07-01

This article presents general guidelines for preparing movie clips for online journal publication. As more and more radiology journals establish an online presence, radiologists wishing to submit journal articles with movie clips need to understand the electronic submission process. Viewing a movie clip via an online journal is different from viewing one with PowerPoint using a local desktop computer because the movie file must first be downloaded onto the client computer before it can be displayed. Users thus should be cautious in selecting movie format and compression when creating movie clips for online journals. This article provides step-by-step demonstrations and general guidelines for movie format and compression selections.

Mining protein database using machine learning techniques.

PubMed

Camargo, Renata da Silva; Niranjan, Mahesan

2008-08-25

With a large amount of information relating to proteins accumulating in databases widely available online, it is of interest to apply machine learning techniques that, by extracting underlying statistical regularities in the data, make predictions about the functional and evolutionary characteristics of unseen proteins. Such predictions can help in achieving a reduction in the space over which experiment designers need to search in order to improve our understanding of the biochemical properties. Previously it has been suggested that an integration of features computable by comparing a pair of proteins can be achieved by an artificial neural network, hence predicting the degree to which they may be evolutionary related and homologous.
We compiled two datasets of pairs of proteins, each pair being characterised by seven distinct features. We performed an exhaustive search through all possible combinations of features, for the problem of separating remote homologous from analogous pairs, we note that significant performance gain was obtained by the inclusion of sequence and structure information. We find that the use of a linear classifier was enough to discriminate a protein pair at the family level. However, at the superfamily level, to detect remote homologous pairs was a relatively harder problem. We find that the use of nonlinear classifiers achieve significantly higher accuracies.
In this paper, we compare three different pattern classification methods on two problems formulated as detecting evolutionary and functional relationships between pairs of proteins, and from extensive cross validation and feature selection based studies quantify the average limits and uncertainties with which such predictions may be made. Feature selection points to a \\"knowledge gap\\" in currently available functional annotations. We demonstrate how the scheme may be employed in a framework to associate an individual protein with an existing family of evolutionarily related proteins.

Interaction matters: Strategies to promote engaged learning in an online introductory nutrition course.

PubMed

Banna, Jinan; Grace Lin, Meng-Fen; Stewart, Maria; Fialkowski, Marie K

2015-06-01

Fostering interaction in the online classroom is an important consideration in ensuring that students actively create their own knowledge and reach a high level of achievement in science courses. This study focuses on fostering interaction in an online introductory nutrition course offered in a public institution of higher education in Hawai'i, USA. Interactive features included synchronous discussions and polls in scheduled sessions, and social media tools for sharing of information and resources. Qualitative student feedback was solicited regarding the new course features. Findings indicated that students who attended monthly synchronous sessions valued live interaction with peers and the instructor. Issues identified included technical difficulties during synchronous sessions, lack of participation on the part of fellow students in discussion and inability to attend synchronous sessions due to scheduling conflicts. In addition, few students made use of the opportunity to interact via social media. While students indicated that the interactive components of the course were valuable, several areas in which improvement may be made remain. Future studies may explore potential solutions to issues identified with new features to further promote interaction and foster learning in the course. Recommendations for instructors who are interested in offering online science courses in higher education are provided.

Decoding of finger trajectory from ECoG using deep learning.

PubMed

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

Decoding of finger trajectory from ECoG using deep learning

NASA Astrophysics Data System (ADS)

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Objective. Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. Approach. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. Main results. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. Significance. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

Evaluating the Efficiency and Effectiveness of Online Individual Education Plans: A Case Study from a South Texas Elementary School

ERIC Educational Resources Information Center

Bonner, William Bryant, III

2017-01-01

The purpose of this qualitative case study based upon the theories of technology adoption and technology integration planning (TIP) with the focus on design and development was to understand design features that encourage effectiveness and efficiency for using online individual education plans (IEP) along with the online IEP lived experiences of…

Examining Theory-Based Behavior-Change Constructs, Social Interaction, and Sociability Features of the Weight Watchers' Online Community

ERIC Educational Resources Information Center

Wang, Ye; Willis, Erin

2016-01-01

Objective: To examine whether and to what extent relevant and meaningful discussions of weight loss occurred in the Weight Watchers' online community, and whether and to what extent the online community is designed for fostering such discussions. Methods: A multimethod approach was used here. First, a quantitative content analysis was conducted on…

Few and Far Between: Describing K-12 Online Teachers' Online Professional Development Opportunities for Students with Disabilities

ERIC Educational Resources Information Center

Rice, Mary F.

2017-01-01

Online teacher professional development (oTPD) researchers have been concerned with design features, teacher change in practice, and student learning, as well as establishing guidelines for directing funding support. Even so, previous work suggests that high-quality instructional support for all students with disabilities is still on the horizon.…

Changing Distributions: How Online College Classes Alter Student and Professor Performance. CEPA Working Paper No. 15-10

ERIC Educational Resources Information Center

Bettinger, Eric; Fox, Lindsay; Loeb, Susanna; Taylor, Eric

2015-01-01

Online college courses are a rapidly expanding feature of higher education, yet little research identifies their effects. Using an instrumental variables approach and data from DeVry University, this study finds that, on average, online course-taking reduces student learning by one-third to one-quarter of a standard deviation compared to…

The Effects of Technology on the Community of Inquiry and Satisfaction with Online Courses

ERIC Educational Resources Information Center

Rubin, Beth; Fernandes, Ron; Avgerinou, Maria D.

2013-01-01

This paper extends the research on the Community of Inquiry (CoI) framework of understanding features of successful online learning to include the effects of the software used to support and facilitate it. This study examines how the Learning Management System (LMS) affords people the ability to take actions in an online course. A model is…

Greater involvement and diversity of Internet gambling as a risk factor for problem gambling

PubMed Central

Russell, Alex; Blaszczynski, Alex; Hing, Nerilee

2015-01-01

Background: Concerns that Internet gambling has elevated the prevalence of problem gambling have not been substantiated; however, evidence suggests a subgroup of Internet gamblers do experience higher rates of gambling harms. Greater overall involvement in gambling appears to be predictive of harms. The purpose of this study was to examine differences between Internet gamblers with a single or multiple online gambling accounts, including their gambling behaviours, factors influencing their online gambling and risk of experiencing gambling problems. Methods: Internet gamblers (3178) responding to an online survey that assessed their gambling behaviour, and use of single or multiple online gambling accounts. Results: Results revealed that multiple account holders were more involved gamblers, gambling on more activities and more frequently, and had higher rates of gambling problems than single account holders. Multiple account holders selected gambling sites based on price, betting options, payout rates and game experience, whereas single account holders prioritized legality and consumer protection features. Conclusion: Results suggest two different types of Internet gamblers: one motivated to move between sites to optimize preferred experiences with a tendency to gamble in a more volatile manner; and a smaller, but more stable group less influenced by promotions and experiences, and seeking a reputable and safe gambling experience. As the majority of Internet gamblers use multiple accounts, more universal responsible gambling strategies are needed to assist gamblers to track and control their expenditure to reduce risks of harm. PMID:25745873

A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive

NASA Astrophysics Data System (ADS)

Castillo, Richard; Castillo, Edward; Fuentes, David; Ahmad, Moiz; Wood, Abbie M.; Ludwig, Michelle S.; Guerrero, Thomas

2013-05-01

Landmark point-pairs provide a strategy to assess deformable image registration (DIR) accuracy in terms of the spatial registration of the underlying anatomy depicted in medical images. In this study, we propose to augment a publicly available database (www.dir-lab.com) of medical images with large sets of manually identified anatomic feature pairs between breath-hold computed tomography (BH-CT) images for DIR spatial accuracy evaluation. Ten BH-CT image pairs were randomly selected from the COPDgene study cases. Each patient had received CT imaging of the entire thorax in the supine position at one-fourth dose normal expiration and maximum effort full dose inspiration. Using dedicated in-house software, an imaging expert manually identified large sets of anatomic feature pairs between images. Estimates of inter- and intra-observer spatial variation in feature localization were determined by repeat measurements of multiple observers over subsets of randomly selected features. 7298 anatomic landmark features were manually paired between the 10 sets of images. Quantity of feature pairs per case ranged from 447 to 1172. Average 3D Euclidean landmark displacements varied substantially among cases, ranging from 12.29 (SD: 6.39) to 30.90 (SD: 14.05) mm. Repeat registration of uniformly sampled subsets of 150 landmarks for each case yielded estimates of observer localization error, which ranged in average from 0.58 (SD: 0.87) to 1.06 (SD: 2.38) mm for each case. The additions to the online web database (www.dir-lab.com) described in this work will broaden the applicability of the reference data, providing a freely available common dataset for targeted critical evaluation of DIR spatial accuracy performance in multiple clinical settings. Estimates of observer variance in feature localization suggest consistent spatial accuracy for all observers across both four-dimensional CT and COPDgene patient cohorts.

Human tracking in thermal images using adaptive particle filters with online random forest learning

NASA Astrophysics Data System (ADS)

Ko, Byoung Chul; Kwak, Joon-Young; Nam, Jae-Yeal

2013-11-01

This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.

Design of a web portal for interdisciplinary image retrieval from multiple online image resources.

PubMed

Kammerer, F J; Frankewitsch, T; Prokosch, H-U

2009-01-01

Images play an important role in medicine. Finding the desired images within the multitude of online image databases is a time-consuming and frustrating process. Existing websites do not meet all the requirements for an ideal learning environment for medical students. This work intends to establish a new web portal providing a centralized access point to a selected number of online image databases. A back-end system locates images on given websites and extracts relevant metadata. The images are indexed using UMLS and the MetaMap system provided by the US National Library of Medicine. Specially developed functions allow to create individual navigation structures. The front-end system suits the specific needs of medical students. A navigation structure consisting of several medical fields, university curricula and the ICD-10 was created. The images may be accessed via the given navigation structure or using different search functions. Cross-references are provided by the semantic relations of the UMLS. Over 25,000 images were identified and indexed. A pilot evaluation among medical students showed good first results concerning the acceptance of the developed navigation structures and search features. The integration of the images from different sources into the UMLS semantic network offers a quick and an easy-to-use learning environment.

Development of online lines-scan imaging system for chicken inspection and differentiation

NASA Astrophysics Data System (ADS)

Yang, Chun-Chieh; Chan, Diane E.; Chao, Kuanglin; Chen, Yud-Ren; Kim, Moon S.

2006-10-01

An online line-scan imaging system was developed for differentiation of wholesome and systemically diseased chickens. The hyperspectral imaging system used in this research can be directly converted to multispectral operation and would provide the ideal implementation of essential features for data-efficient high-speed multispectral classification algorithms. The imaging system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera and an imaging spectrograph for line-scan images. The system scanned the surfaces of chicken carcasses on an eviscerating line at a poultry processing plant in December 2005. A method was created to recognize birds entering and exiting the field of view, and to locate a Region of Interest on the chicken images from which useful spectra were extracted for analysis. From analysis of the difference spectra between wholesome and systemically diseased chickens, four wavelengths of 468 nm, 501 nm, 582 nm and 629 nm were selected as key wavelengths for differentiation. The method of locating the Region of Interest will also have practical application in multispectral operation of the line-scan imaging system for online chicken inspection. This line-scan imaging system makes possible the implementation of multispectral inspection using the key wavelengths determined in this study with minimal software adaptations and without the need for cross-system calibration.

Generic vs. Modality-Specific Competencies for K-12 Online and Blended Teaching

ERIC Educational Resources Information Center

Pulham, Emily B.; Graham, Charles R.; Short, Cecil R.

2018-01-01

Although research has explored teacher competencies in K-12 blended and online learning, it has not specified which competencies are appropriate to an online or digital medium, which refer to blending in-person with online experiences, or which are generic--applicable in any teaching modality. This article explores selected K-12 online and blended…

Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program

ERIC Educational Resources Information Center

Yukselturk, Erman; Ozekes, Serhat; Turel, Yalin Kilic

2014-01-01

This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies…

Web document ranking via active learning and kernel principal component analysis

NASA Astrophysics Data System (ADS)

Cai, Fei; Chen, Honghui; Shu, Zhen

2015-09-01

Web document ranking arises in many information retrieval (IR) applications, such as the search engine, recommendation system and online advertising. A challenging issue is how to select the representative query-document pairs and informative features as well for better learning and exploring new ranking models to produce an acceptable ranking list of candidate documents of each query. In this study, we propose an active sampling (AS) plus kernel principal component analysis (KPCA) based ranking model, viz. AS-KPCA Regression, to study the document ranking for a retrieval system, i.e. how to choose the representative query-document pairs and features for learning. More precisely, we fill those documents gradually into the training set by AS such that each of which will incur the highest expected DCG loss if unselected. Then, the KPCA is performed via projecting the selected query-document pairs onto p-principal components in the feature space to complete the regression. Hence, we can cut down the computational overhead and depress the impact incurred by noise simultaneously. To the best of our knowledge, we are the first to perform the document ranking via dimension reductions in two dimensions, namely, the number of documents and features simultaneously. Our experiments demonstrate that the performance of our approach is better than that of the baseline methods on the public LETOR 4.0 datasets. Our approach brings an improvement against RankBoost as well as other baselines near 20% in terms of MAP metric and less improvements using P@K and NDCG@K, respectively. Moreover, our approach is particularly suitable for document ranking on the noisy dataset in practice.

EEG-based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

PubMed Central

Wade, Joshua W.; Key, Alexandra P.; Warren, Zachary E.; Sarkar, Nilanjan

2017-01-01

objective To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods Twenty adolescents with ASD participated in a six-session virtual reality driving simulator based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist’s rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins (Δf = 2 Hz) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. Results The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Conclusion Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. Significance The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention. PMID:28422647

Relationship Among Dental Students' Class Lecture Attendance, Use of Online Resources, and Performance.

PubMed

Azab, Ehab; Saksena, Yun; Alghanem, Tofool; Midle, Jennifer Bassett; Molgaard, Kathleen; Albright, Susan; Karimbux, Nadeem

2016-04-01

This study aimed to evaluate the relationship among dental students' attendance at class lectures, use of online lecture materials, and performance in didactic courses. The study was conducted with second-year predoctoral students at Tufts University School of Dental Medicine during the fall semester of 2014. Three basic science and three preclinical dental courses were selected for evaluation. Online usage for each participant was collected, and a survey with questions about attendance and online behavior was conducted. The final grade for each participant in each selected course was obtained and matched with his or her online usage and attendance. Out of a total 190 students, 146 (77%) participated. The results showed no significant relationship between students' grades and their class attendance or online usage except for a weak negative relationship between class attendance and online usage for the Epidemiology course (p<0.001) and the overall preclinical dental courses (p=0.03). Although the results did not show strong relationships among class attendance, online usage, and course grades, most of the students reported that having the online resources in addition to the lectures was helpful.

A Framework for Classifying Online Mental Health-Related Communities With an Interest in Depression.

PubMed

Saha, Budhaditya; Nguyen, Thin; Phung, Dinh; Venkatesh, Svetha

2016-07-01

Mental illness has a deep impact on individuals, families, and by extension, society as a whole. Social networks allow individuals with mental disorders to communicate with others sufferers via online communities, providing an invaluable resource for studies on textual signs of psychological health problems. Mental disorders often occur in combinations, e.g., a patient with an anxiety disorder may also develop depression. This co-occurring mental health condition provides the focus for our work on classifying online communities with an interest in depression. For this, we have crawled a large body of 620 000 posts made by 80 000 users in 247 online communities. We have extracted the topics and psycholinguistic features expressed in the posts, using these as inputs to our model. Following a machine learning technique, we have formulated a joint modeling framework in order to classify mental health-related co-occurring online communities from these features. Finally, we performed empirical validation of the model on the crawled dataset where our model outperforms recent state-of-the-art baselines.

Observing real-world groups in the virtual field: The analysis of online discussion.

PubMed

Giles, David C

2016-09-01

This article sets out to establish the naturalistic study of online social communication as a substantive topic in social psychology and to discuss the challenges of developing methods for a formal analysis of the structural and interactional features of message threads on discussion forums. I begin by outlining the essential features of online communication and specifically discussion forum data, and the important ways in which they depart from spoken conversation. I describe the handful of attempts to devise systematic analytic techniques for adapting methods such as conversation and discourse analysis to the study of online discussion. I then present a case study of a thread from the popular UK parenting forum Mumsnet which presents a number of challenges for existing methods, and examine some of the interactive phenomena typical of forums. Finally, I consider ways in which membership categorization analysis and social identity theory can complement one another in the exploration of both group processes and the rhetorical deployment of identities as dynamic phenomena in online discussion. © 2016 The British Psychological Society.

Online History Textbooks: Breaking the Mold.

ERIC Educational Resources Information Center

Schick, James B. M.

2001-01-01

Outlines recommended conditions and features of online history textbooks: link control, coverage of methodology, maps, breadth and depth of information, layered storytelling approach, tools, tutorials, customization, team teaching, short movies, interviews, reading activities and skill building activities, overcharging, and password protection.…

Selecting the Right Courseware for Your Online Learning Program.

ERIC Educational Resources Information Center

O'Mara, Heather

2000-01-01

Presents criteria for selecting courseware for online classes. Highlights include ease of use, including navigation; assessment tools; advantages of Java-enabled courseware; advantages of Oracle databases, including scalability; future possibilities for multimedia technology; and open architecture that will integrate with other systems. (LRW)

Using an Online Portfolio Course in Assessing Students' Work

ERIC Educational Resources Information Center

Yilmaz, Harun; Cetinkaya, Bulent

2007-01-01

New developments and advancements in informational technology bring about several alternative avenues for educators to select in supporting and evaluating their students' learning. Online portfolio is a fairly new technique in this regard. As the online education grows, use of online portfolio becomes more vital for educational programs. At…

Utilization of smoking cessation informational, interactive, and online community resources as predictors of abstinence: cohort study.

PubMed

An, Lawrence C; Schillo, Barbara A; Saul, Jessie E; Wendling, Ann H; Klatt, Colleen M; Berg, Carla J; Ahulwalia, Jasjit S; Kavanaugh, Annette M; Christenson, Matthew; Luxenberg, Michael G

2008-12-20

The association between greater utilization of Web-assisted tobacco interventions and increased abstinence rates is well recognized. However, there is little information on how utilization of specific website features influences quitting. To determine the association between utilization of informational, interactive, and online community resources (eg. bulletin boards) and abstinence rates, with the broader objective to identify potential strategies for improving outcomes for Web-assisted tobacco interventions. In Spring 2004, a cohort of 607 quitplan.com users consented to participate in an evaluation of quitplan.com, a Minnesota branded version of QuitNet.com. We developed utilization measures for different site features: general information, interactive diagnostic tools and quit planning tools, online expert counseling, passive (ie, reading of bulletin boards) and active (ie, public posting) online community engagement, and one-to-one messaging with other virtual community members. Using bivariate, multivariate, and path analyses, we examined the relationship between utilization of specific site features and 30-day abstinence at 6 months. The most commonly used resources were the interactive quit planning tools (used by 77% of site users). Other informational resources (ie, quitting guides) were used more commonly (60% of users) than passive (38%) or active (24%) community features. Online community engagement through one-to-one messaging was low (11%) as was use of online counseling (5%). The 30-day abstinence rate among study participants at 6 months was 9.7% (95% Confidence Interval [CI] 7.3% - 12.1%). In the logistic regression model, neither the demographic data (eg, age, gender, education level, employment, or insurance status) nor the smoking-related data (eg, cigarettes per day, time to first morning cigarette, baseline readiness to quit) nor use of smoking cessation medications entered the model as significant predictors of abstinence. Individuals who used the interactive quit planning tools once, two to three times, or four or more times had an odds of abstinence of 0.65 (95% Confidence Interval [CI] 0.22 - 1.94), 1.87 (95% CI 0.77 - 4.56), and 2.35 (95% CI 1.0 - 5.58), respectively. The use of one-to-one messages (reference = none vs 1 or more) entered the final model as potential predictor for abstinence, though the significance of this measure was marginal (OR = 1.91, 95% CI 0.92 - 3.97, P = .083). In the path analysis, an apparent association between active online community engagement and abstinence was accounted for in large part by increased use of interactive quitting tools and one-to-one messaging. Use of interactive quitting tools, and perhaps one-to-one messaging with other members of the online community, was associated with increased abstinence rates among quitplan.com users. Designs that facilitate use of these features should be considered.

Fast Compressive Tracking.

PubMed

Zhang, Kaihua; Zhang, Lei; Yang, Ming-Hsuan

2014-10-01

It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. Despite much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, misaligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from a multiscale image feature space with data-independent basis. The proposed appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is constructed to efficiently extract the features for the appearance model. We compress sample images of the foreground target and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. A coarse-to-fine search strategy is adopted to further reduce the computational complexity in the detection procedure. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art methods on challenging sequences in terms of efficiency, accuracy and robustness.

Chemists, Access, Statistics

NASA Astrophysics Data System (ADS)

Holmes, Jon L.

2000-06-01

New JCE Internet Feature at JCE Online Biographical Snapshots of Famous Chemists is a new JCE Internet feature on JCE Online. Edited by Barbara Burke, this feature provides biographical information on leading chemists, especially women and minority chemists, fostering the attitude that the practitioners of chemistry are as human as those who endeavor to learn about it. Currently, the column features biographical "snapshots" of 30 chemists. Each snapshot includes keywords and bibliography and several contain links to additional online information about the chemist. More biographical snapshots will appear in future installments. In addition, a database listing over 140 women and minority chemists is being compiled and will be made available online with the snapshots in the near future. The database includes the years of birth and death, gender and ethnicity, major and minor discipline, keywords to facilitate searching, and references to additional biographical information. We welcome your input into what we think is a very worthwhile resource. If you would like to provide additional biographical snapshots, see additional chemists added to the database, or know of additional references for those that are already in the database, please contact JCE Online or the feature editor. Your feedback is welcome and appreciated. You can find Biographical Snapshots of Famous Chemists starting from the JCE Online home page-- click the Features item under JCE Internet and then the Chemist Bios item. Access JCE Online without Name and Password We have recently been swamped by libraries requesting IP-number access to JCE Online. With the great benefit IP-number authentication gives to librarians (no user names and passwords to administer) and to their patrons (no need to remember and enter valid names and passwords) this is not surprising. If you would like access to JCE Online without the need to remember and enter a user name and password, you should tell your librarian about our IP-number access. Current subscriptions can be upgraded to IP-number access at little additional cost. We are pleased to be able to offer to institutions and libraries this convenient mode of access to subscriber only resources at JCE Online. JCE Online Usage Statistics We are continually amazed by the activity at JCE Online. So far, the year 2000 has shown a marked increase. Given the phenomenal overall growth of the Internet, perhaps our surprise is not warranted. However, during the months of January and February 2000, over 38,000 visitors requested over 275,000 pages. This is a monthly increase of over 33% from the October-December 1999 levels. It is good to know that people are visiting, but we would very much like to know what you would most like to see at JCE Online. Please send your suggestions to JCEOnline@chem.wisc.edu. For those who are interested, JCE Online year-to-date statistics are available. Biographical Snapshots of Famous Chemists: Mission Statement Feature Editor: Barbara Burke Chemistry Department, California State Polytechnic University-Pomona, Pomona, CA 91768 phone: 909/869-3664 fax: 909/869-4616 email: baburke@csupomona.edu The primary goal of this JCE Internet column is to provide information about chemists who have made important contributions to chemistry. For each chemist, there is a short biographical "snapshot" that provides basic information about the person's chemical work, gender, ethnicity, and cultural background. Each snapshot includes links to related websites and to a biobibliographic database. The database provides references for the individual and can be searched through key words listed at the end of each snapshot. All students, not just science majors, need to understand science as it really is: an exciting, challenging, human, and creative way of learning about our natural world. Investigating the life experiences of chemists can provide a means for students to gain a more realistic view of chemistry. In addition students, especially women and minorities, need more scientist role models. When teachers weave biographical information into their conceptual lectures, they are using an effective pedagogical tool that will enhance students' understanding of chemical facts. Linking chemical ideas to real people provides a stronger infrastructure than facts alone: students need more than just the facts--they need to know the stories of the people behind the "magic". Without these stories, our students miss the wonderful, exciting, human side of our chemical sciences. Acknowledgments National Science Foundation, Alliance for Minority Progress Grant (HRD 9353276); Chemical Heritage Foundation, Philadelphia, PA; Huntington Library, San Marino, CA.

CRISPR/Cas9 cleavage efficiency regression through boosting algorithms and Markov sequence profiling.

PubMed

Peng, Hui; Zheng, Yi; Blumenstein, Michael; Tao, Dacheng; Li, Jinyan

2018-04-16

CRISPR/Cas9 system is a widely used genome editing tool. A prediction problem of great interests for this system is: how to select optimal single guide RNAs (sgRNAs) such that its cleavage efficiency is high meanwhile the off-target effect is low. This work proposed a two-step averaging method (TSAM) for the regression of cleavage efficiencies of a set of sgRNAs by averaging the predicted efficiency scores of a boosting algorithm and those by a support vector machine (SVM).We also proposed to use profiled Markov properties as novel features to capture the global characteristics of sgRNAs. These new features are combined with the outstanding features ranked by the boosting algorithm for the training of the SVM regressor. TSAM improved the mean Spearman correlation coefficiencies comparing with the state-of-the-art performance on benchmark datasets containing thousands of human, mouse and zebrafish sgRNAs. Our method can be also converted to make binary distinctions between efficient and inefficient sgRNAs with superior performance to the existing methods. The analysis reveals that highly efficient sgRNAs have lower melting temperature at the middle of the spacer, cut at 5'-end closer parts of the genome and contain more 'A' but less 'G' comparing with inefficient ones. Comprehensive further analysis also demonstrates that our tool can predict an sgRNA's cutting efficiency with consistently good performance no matter it is expressed from an U6 promoter in cells or from a T7 promoter in vitro. Online tool is available at http://www.aai-bioinfo.com/CRISPR/. Python and Matlab source codes are freely available at https://github.com/penn-hui/TSAM. Jinyan.Li@uts.edu.au. Supplementary data are available at Bioinformatics online.

Understanding user intents in online health forums.

PubMed

Zhang, Thomas; Cho, Jason H D; Zhai, Chengxiang

2015-07-01

Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. However, large quantities of unstructured and diversified content generated on these forums make it difficult for users to digest and extract useful information. Understanding user intents would enable forums to find and recommend relevant information to users by filtering out threads that do not match particular intents. In this paper, we derive a taxonomy of intents to capture user information needs in online health forums and propose novel pattern-based features for use with a multiclass support vector machine (SVM) classifier to classify original thread posts according to their underlying intents. Since no dataset existed for this task, we employ three annotators to manually label a dataset of 1192 HealthBoards posts spanning four forum topics. Experimental results show that a SVM using pattern-based features is highly capable of identifying user intents in forum posts, reaching a maximum precision of 75%, and that a SVM-based hierarchical classifier using both pattern and word features outperforms its SVM counterpart that uses only word features. Furthermore, comparable classification performance can be achieved by training and testing on posts from different forum topics.

My Favorite Things Electronically Speaking.

ERIC Educational Resources Information Center

Glantz, Shelley

1997-01-01

Presents the results of an informal user survey on favorite information technology, including the best features of these. Discusses library online catalogs, electronic encyclopedias, CD-ROMs, laser discs, electronic magazine indexes, online services, the Internet, word processing programs, magazines as major sources of technology information,…

Future Challenges and Opportunities in Online Prescription Drug Promotion Research Comment on "Trouble Spots in Online Direct-to-Consumer Prescription Drug Promotion: A Content Analysis of FDA Warning Letters".

PubMed

Southwell, Brian G; Rupert, Douglas J

2016-01-16

Despite increased availability of online promotional tools for prescription drug marketers, evidence on online prescription drug promotion is far from settled or conclusive. We highlight ways in which online prescription drug promotion is similar to conventional broadcast and print advertising and ways in which it differs. We also highlight five key areas for future research: branded drug website influence on consumer knowledge and behavior, interactive features on branded drug websites, mobile viewing of branded websites and mobile advertisements, online promotion and non-US audiences, and social media and medication decisions. © 2016 by Kerman University of Medical Sciences.

Transferable Output ASCII Data (TOAD) editor version 1.0 user's guide

NASA Technical Reports Server (NTRS)

Bingel, Bradford D.; Shea, Anne L.; Hofler, Alicia S.

1991-01-01

The Transferable Output ASCII Data (TOAD) editor is an interactive software tool for manipulating the contents of TOAD files. The TOAD editor is specifically designed to work with tabular data. Selected subsets of data may be displayed to the user's screen, sorted, exchanged, duplicated, removed, replaced, inserted, or transferred to and from external files. It also offers a number of useful features including on-line help, macros, a command history, an 'undo' option, variables, and a full compliment of mathematical functions and conversion factors. Written in ANSI FORTRAN 77 and completely self-contained, the TOAD editor is very portable and has already been installed on SUN, SGI/IRIS, and CONVEX hosts.

A Supervised Learning Process to Validate Online Disease Reports for Use in Predictive Models.

PubMed

Patching, Helena M M; Hudson, Laurence M; Cooke, Warrick; Garcia, Andres J; Hay, Simon I; Roberts, Mark; Moyes, Catherine L

2015-12-01

Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%-79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is being used to validate the data that our system uses to produce updated predictive disease maps on a weekly basis.

Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP).

PubMed

Acqualagna, Laura; Blankertz, Benjamin

2013-05-01

A Brain Computer Interface (BCI) speller is a communication device, which can be used by patients suffering from neurodegenerative diseases to select symbols in a computer application. For patients unable to overtly fixate the target symbol, it is crucial to develop a speller independent of gaze shifts. In the present online study, we investigated rapid serial visual presentation (RSVP) as a paradigm for mental typewriting. We investigated the RSVP speller in three conditions, regarding the Stimulus Onset Asynchrony (SOA) and the use of color features. A vocabulary of 30 symbols was presented one-by-one in a pseudo random sequence at the same location of display. All twelve participants were able to successfully operate the RSVP speller. The results show a mean online spelling rate of 1.43 symb/min and a mean symbol selection accuracy of 94.8% in the best condition. We conclude that the RSVP is a promising paradigm for BCI spelling and its performance is competitive with the fastest gaze-independent spellers in literature. The RSVP speller does not require gaze shifts towards different target locations and can be operated by non-spatial visual attention, therefore it can be considered as a valid paradigm in applications with patients for impaired oculo-motor control. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Interaction matters: Strategies to promote engaged learning in an online introductory nutrition course

PubMed Central

Banna, Jinan; Grace Lin, Meng-Fen; Stewart, Maria; Fialkowski, Marie K.

2016-01-01

Fostering interaction in the online classroom is an important consideration in ensuring that students actively create their own knowledge and reach a high level of achievement in science courses. This study focuses on fostering interaction in an online introductory nutrition course offered in a public institution of higher education in Hawai‘i, USA. Interactive features included synchronous discussions and polls in scheduled sessions, and social media tools for sharing of information and resources. Qualitative student feedback was solicited regarding the new course features. Findings indicated that students who attended monthly synchronous sessions valued live interaction with peers and the instructor. Issues identified included technical difficulties during synchronous sessions, lack of participation on the part of fellow students in discussion and inability to attend synchronous sessions due to scheduling conflicts. In addition, few students made use of the opportunity to interact via social media. While students indicated that the interactive components of the course were valuable, several areas in which improvement may be made remain. Future studies may explore potential solutions to issues identified with new features to further promote interaction and foster learning in the course. Recommendations for instructors who are interested in offering online science courses in higher education are provided. PMID:27441032

Comparison of Online Game Addiction in High School Students with Habitual Computer Use and Online Gaming

ERIC Educational Resources Information Center

Müezzin, Emre

2015-01-01

The aim of this study is to compare the online game addiction in high school students with the habitual computer use and online gaming. The sample selected through the criterion sampling method, consists of 61.8% (n = 81) female, 38.2% (n = 50) male, 131 high school students. The "Online Game Addiction Scale" developed by Kaya and Basol…

Online Course Selection: Using Course Dashboards to Inform Student Enrollment Decisions

ERIC Educational Resources Information Center

Marshall, James

2016-01-01

This article explores the potential of course dashboards as a front-end strategy for decreasing online course dropout rates. Scholarship has addressed course attrition once students are enrolled in online courses. However, supporting academic success by assisting students in the making of effective decisions about which online courses to take has…

Learning Outcomes in an Online vs Traditional Course

ERIC Educational Resources Information Center

Stack, Steven

2015-01-01

Relative enrollment in online classes has tripled over the last ten years, but the efficacy of learning online remains unclear. While two recent Meta analyses report higher exam grades for online vs. traditional classes, this body of research has been marked by two recurrent limitations: (1) a possible problem of selection bias wherein students…

Online Credit Recovery: Benefits and Challenges

ERIC Educational Resources Information Center

Pettyjohn, Teri; LaFrance, Jason

2014-01-01

School leaders are faced with selecting programs to support at-risk students in high schools across the United States. Increasingly, supplemental online learning is being selected as an innovative way to assist these students. The purpose of this qualitative study was to understand stakeholders' perceptions of the benefits and challenges of high…

Our Experiment in Online, Real-Time Reference.

ERIC Educational Resources Information Center

Broughton, Kelly

2001-01-01

Describes experiences in providing real-time online reference services to users with remote Web access at the Bowling Green State University library. Discusses the decision making process first used to select HumanClick software to communicate via chat; and the selection of a fee-based customer service product, Virtual Reference Desk. (LRW)

Transparency in Cooperative Online Education

ERIC Educational Resources Information Center

Dalsgaard, Christian; Paulsen, Morten Flate

2009-01-01

The purpose of this article is to discuss the following question: What is the potential of social networking within cooperative online education? Social networking does not necessarily involve communication, dialogue, or collaboration. Instead, the authors argue that "transparency" is a unique feature of social networking services.…

Paper Trails

ERIC Educational Resources Information Center

Fernandez, Kim

2010-01-01

With more and more people attached to their computers, it's no wonder that publications are increasingly going online. Magazines are either supplementing their print content with online bonus information, such as extended features, photos, audio files, or videos, or looking to ditch the printing presses entirely to focus on all-electronic…

An Online Library Catalogue.

ERIC Educational Resources Information Center

Alloro, Giovanna; Ugolini, Donatella

1992-01-01

Describes the implementation of an online catalog in the library of the National Institute for Cancer Research and the Clinical and Experimental Oncology Institute of the University of Genoa. Topics addressed include automation of various library functions, software features, database management, training, and user response. (10 references) (MES)

The Effect of Blended Instruction on Accelerated Learning

ERIC Educational Resources Information Center

Patchan, Melissa M.; Schunn, Christian D.; Sieg, Wilfried; McLaughlin, Dawn

2016-01-01

While online instructional technologies are becoming more popular in higher education, educators' opinions about online learning tend to be generally negative. Furthermore, many studies have failed to systematically examine the features that distinguish one instructional mode from another, which weakens possible explanations for why online…

Total Library Computerization for Windows.

ERIC Educational Resources Information Center

Combs, Joseph, Jr.

1999-01-01

Presents a general review of features of version 2.1 of Total Library Computerization (TLC) for Windows from On Point, Inc. Includes information about pricing, hardware and operating systems, modules/functions available, user interface, security, on-line catalog functions, circulation, cataloging, and documentation and online help. A table…

Multiphase Method for Analysing Online Discussions

ERIC Educational Resources Information Center

Häkkinen, P.

2013-01-01

Several studies have analysed and assessed online performance and discourse using quantitative and qualitative methods. Quantitative measures have typically included the analysis of participation rates and learning outcomes in terms of grades. Qualitative measures of postings, discussions and context features aim to give insights into the nature…

Researching Applicants Online in the Veterinary Program Admissions Process: Perceptions, Practices, and Implications for Curricular Change.

PubMed

Kogan, Lori R; Hellyer, Peter W; Stewart, Sherry M; Hendrickson, Dean A; Dowers, Kristy L; Schoenfeld-Tacher, Regina

2015-01-01

As the use of social media websites continues to grow among adults 18-34 years old, it is necessary to examine the consequences of online disclosure to the veterinary admissions processes and to consider the effects on the professional integrity of veterinary schools and on the e-professionalism of DVM graduates. Prior research has shown that employers, across all fields, routinely use information from social media sites to make hiring decisions. In veterinary medicine, a little over one-third of private practitioners reported using online information in the selection of new associates. However, professional academic programs appear to use online information less frequently in the selection processes. The current study examines the behaviors and attitudes of veterinary medical admissions committees toward the use of applicants' online information and profiles in their recruitment and selection process. An online survey was distributed to Associate Deans for Academic Affairs at all AAVMC-affiliated schools of veterinary medicine. A total of 21 schools completed the survey. The results showed that most veterinary schools do not currently use online research in their admissions process; however, most admissions committee members feel that using online social networking information to investigate applicants is an acceptable use of technology. Previous research has suggested that the majority of veterinary student applicants view this as an invasion of their privacy. Given this discordance, future educational efforts should focus on helping veterinary students determine what type of information is appropriate for posting online and how to use privacy settings to control their sharing behaviors.

Global road safety online course development.

DOT National Transportation Integrated Search

2017-06-01

The Global Road Safety Online Curriculum Development project involved the adaptation of in-person classroom materials and development of new materials to be used in an online setting. A short-course format was selected to pilot the course, and four t...

Fifty Years of Pulsar Candidate Selection: From simple filters to a new principled real-time classification approach

NASA Astrophysics Data System (ADS)

Lyon, R. J.; Stappers, B. W.; Cooper, S.; Brooke, J. M.; Knowles, J. D.

2016-06-01

Improving survey specifications are causing an exponential rise in pulsar candidate numbers and data volumes. We study the candidate filters used to mitigate these problems during the past fifty years. We find that some existing methods such as applying constraints on the total number of candidates collected per observation, may have detrimental effects on the success of pulsar searches. Those methods immune to such effects are found to be ill-equipped to deal with the problems associated with increasing data volumes and candidate numbers, motivating the development of new approaches. We therefore present a new method designed for on-line operation. It selects promising candidates using a purpose-built tree-based machine learning classifier, the Gaussian Hellinger Very Fast Decision Tree (GH-VFDT), and a new set of features for describing candidates. The features have been chosen so as to I) maximise the separation between candidates arising from noise and those of probable astrophysical origin, and II) be as survey-independent as possible. Using these features our new approach can process millions of candidates in seconds (˜1 million every 15 seconds), with high levels of pulsar recall (90%+). This technique is therefore applicable to the large volumes of data expected to be produced by the Square Kilometre Array (SKA). Use of this approach has assisted in the discovery of 20 new pulsars in data obtained during the LOFAR Tied-Array All-Sky Survey (LOTAAS).

A Search Engine Features Comparison.

ERIC Educational Resources Information Center

Vorndran, Gerald

Until recently, the World Wide Web (WWW) public access search engines have not included many of the advanced commands, options, and features commonly available with the for-profit online database user interfaces, such as DIALOG. This study evaluates the features and characteristics common to both types of search interfaces, examines the Web search…

Karst in the United States: a digital map compilation and database

USGS Publications Warehouse

Weary, David J.; Doctor, Daniel H.

2014-01-01

This report describes new digital maps delineating areas of the United States, including Puerto Rico and the U.S. Virgin Islands, having karst or the potential for development of karst and pseudokarst. These maps show areas underlain by soluble rocks and also by volcanic rocks, sedimentary deposits, and permafrost that have potential for karst or pseudokarst development. All 50 States contain rocks with potential for karst development, and about 18 percent of their area is underlain by soluble rocks having karst or the potential for development of karst features. The areas of soluble rocks shown are based primarily on selection from State geologic maps of rock units containing significant amounts of carbonate or evaporite minerals. Areas underlain by soluble rocks are further classified by general climate setting, degree of induration, and degree of exposure. Areas having potential for volcanic pseudokarst are those underlain chiefly by basaltic-flow rocks no older than Miocene in age. Areas with potential for pseudokarst features in sedimentary rocks are in relatively unconsolidated rocks from which pseudokarst features, such as piping caves, have been reported. Areas having potential for development of thermokarst features, mapped exclusively in Alaska, contain permafrost in relatively thick surficial deposits containing ground ice. This report includes a GIS database with links from the map unit polygons to online geologic unit descriptions.

Automated Feature and Event Detection with SDO AIA and HMI Data

NASA Astrophysics Data System (ADS)

Davey, Alisdair; Martens, P. C. H.; Attrill, G. D. R.; Engell, A.; Farid, S.; Grigis, P. C.; Kasper, J.; Korreck, K.; Saar, S. H.; Su, Y.; Testa, P.; Wills-Davey, M.; Savcheva, A.; Bernasconi, P. N.; Raouafi, N.-E.; Delouille, V. A.; Hochedez, J. F..; Cirtain, J. W.; Deforest, C. E.; Angryk, R. A.; de Moortel, I.; Wiegelmann, T.; Georgouli, M. K.; McAteer, R. T. J.; Hurlburt, N.; Timmons, R.

The Solar Dynamics Observatory (SDO) represents a new frontier in quantity and quality of solar data. At about 1.5 TB/day, the data will not be easily digestible by solar physicists using the same methods that have been employed for images from previous missions. In order for solar scientists to use the SDO data effectively they need meta-data that will allow them to identify and retrieve data sets that address their particular science questions. We are building a comprehensive computer vision pipeline for SDO, abstracting complete metadata on many of the features and events detectable on the Sun without human intervention. Our project unites more than a dozen individual, existing codes into a systematic tool that can be used by the entire solar community. The feature finding codes will run as part of the SDO Event Detection System (EDS) at the Joint Science Operations Center (JSOC; joint between Stanford and LMSAL). The metadata produced will be stored in the Heliophysics Event Knowledgebase (HEK), which will be accessible on-line for the rest of the world directly or via the Virtual Solar Observatory (VSO) . Solar scientists will be able to use the HEK to select event and feature data to download for science studies.

Greater involvement and diversity of Internet gambling as a risk factor for problem gambling.

PubMed

Gainsbury, Sally M; Russell, Alex; Blaszczynski, Alex; Hing, Nerilee

2015-08-01

Concerns that Internet gambling has elevated the prevalence of problem gambling have not been substantiated; however, evidence suggests a subgroup of Internet gamblers do experience higher rates of gambling harms. Greater overall involvement in gambling appears to be predictive of harms. The purpose of this study was to examine differences between Internet gamblers with a single or multiple online gambling accounts, including their gambling behaviours, factors influencing their online gambling and risk of experiencing gambling problems. Internet gamblers (3178) responding to an online survey that assessed their gambling behaviour, and use of single or multiple online gambling accounts. Results revealed that multiple account holders were more involved gamblers, gambling on more activities and more frequently, and had higher rates of gambling problems than single account holders. Multiple account holders selected gambling sites based on price, betting options, payout rates and game experience, whereas single account holders prioritized legality and consumer protection features. Results suggest two different types of Internet gamblers: one motivated to move between sites to optimize preferred experiences with a tendency to gamble in a more volatile manner; and a smaller, but more stable group less influenced by promotions and experiences, and seeking a reputable and safe gambling experience. As the majority of Internet gamblers use multiple accounts, more universal responsible gambling strategies are needed to assist gamblers to track and control their expenditure to reduce risks of harm. © The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Creating Participatory Online Learning Environments: A Social Learning Approach Revisited

ERIC Educational Resources Information Center

Conley, Quincy; Lutz, Heather S.; Padgitt, Amanda J.

2017-01-01

Online learning has never been more popular than it is today. Due to the rapid growth of online instruction at colleges and universities, questions about the effectiveness of online courses have been raised. In this paper, we suggest guidelines for the selection and application of social media tools. In addition to describing the potential…

Choosing between Online and Face-to-Face Courses: Community College Student Voices

ERIC Educational Resources Information Center

Jaggars, Shanna Smith

2014-01-01

In this study, community college students discussed their experiences with online and face-to-face learning as well as their reasons for selecting online (rather than face-to-face) sections of specific courses. Students reported lower levels of instructor presence in online courses and that they needed to "teach themselves." Accordingly,…

An online app platform enhances collaborative medical student group learning and classroom management.

PubMed

Peacock, Justin G; Grande, Joseph P

2016-01-01

The authors presented their results in effectively using a free and widely-accessible online app platform to manage and teach a first-year pathology course at Mayo Medical School. The authors utilized the Google "Blogger", "Forms", "Flubaroo", "Sheets", "Docs", and "Slides" apps to effectively build a collaborative classroom teaching and management system. Students were surveyed on the use of the app platform in the classroom, and 44 (94%) students responded. Thirty-two (73%) of the students reported that "Blogger" was an effective place for online discussion of pathology topics and questions. 43 (98%) of the students reported that the "Forms/Flubaroo" grade-reporting system was helpful. 40 (91%) of the students used the remote, collaborative features of "Slides" to create team-based learning presentations, and 39 (89%) of the students found those collaborative features helpful. "Docs" helped teaching assistants to collaboratively create study guides or grading rubrics. Overall, 41 (93%) of the students found that the app platform was helpful in establishing a collaborative, online classroom environment. The online app platform allowed faculty to build an efficient and effective classroom teaching and management system. The ease of accessibility and opportunity for collaboration allowed for collaborative learning, grading, and teaching.

Socio-Linguistic Factors and Gender Mapping Across Real and Virtual World Cultures

DTIC Science & Technology

2012-07-25

multiplayer online games and other virtual world environments. Which in- game features...decaste@sfu.ca ABSTRACT   This  study  examines  a  large  corpus  of   online   gaming  chat  and  avatar  names   to...chat  interactions  in   online   gaming   environments.     In   addition,   we   study   the   relationship  

A Conceptual Characterization of Online Videos Explaining Natural Selection

ERIC Educational Resources Information Center

Bohlin, Gustav; Göransson, Andreas; Höst, Gunnar E.; Tibell, Lena A. E.

2017-01-01

Educational videos on the Internet comprise a vast and highly diverse source of information. Online search engines facilitate access to numerous videos claiming to explain natural selection, but little is known about the degree to which the video content match key evolutionary content identified as important in evolution education research. In…

The Quintessential Searcher: The Wit & Wisdom of Barbara Quint.

ERIC Educational Resources Information Center

Block, Marylaine, Ed.

This book presents selected writings by Barbara Quint (BQ) on online searching. The selections are organized into the following chapters: (1) "The Art of Searching," including finding out what the patron wants, preparing for the search, knowing when the search is done, search styles, rules of online searching, cost issues, Quint's Laws,…

Improving the LMS Selection Process: Instructor Concerns, Usage and Perceived Value of Online Course Delivery Tools

ERIC Educational Resources Information Center

Laverty, Joseph Packy; Wood, David; Tannehill, Darcy B.; Kohun, Fred; Turchek, John

2012-01-01

Selecting or upgrading a university's Learning Management System (LMS) involves complex decisions concerning curriculum delivery, students, financial commitments, technology and support services, and faculty. The purpose of this paper is to study faculty concerns, usage and perceptions of the instructional value of online course management tools.…

Guidelines for Selecting Quality K-12 Online Courses

ERIC Educational Resources Information Center

Rothschild, Mimi

2005-01-01

This article presents guidelines formulated by Learning By Grace, Inc., one of the nation's leading providers of online resources for homeschoolers, and is intended to help home educators navigate the multitude of available choices so that they will be better equipped to select the provider that offers the curriculum and services most suited to…

Potentiometric analytical microsystem based on the integration of a gas-diffusion step for on-line ammonium determination in water recycling processes in manned space missions.

PubMed

Calvo-López, Antonio; Ymbern, Oriol; Puyol, Mar; Casalta, Joan Manel; Alonso-Chamarro, Julián

2015-05-18

The design, construction and evaluation of a versatile cyclic olefin copolymer (COC)-based continuous flow potentiometric microanalyzer to monitor the presence of ammonium ion in recycling water processes for future manned space missions is presented. The microsystem integrates microfluidics, a gas-diffusion module and a detection system in a single substrate. The gas-diffusion module was integrated by a hydrophobic polyvinylidene fluoride (PVDF) membrane. The potentiometric detection system is based on an all-solid state ammonium selective electrode and a screen-printed Ag/AgCl reference electrode. The analytical features provided by the analytical microsystem after the optimization process were a linear range from 0.15 to 500 mg L(-1) and a detection limit of 0.07 ± 0.01 mg L(-1). Nevertheless, the operational features can be easily adapted to other applications through the modification of the hydrodynamic variables of the microfluidic platform. Copyright © 2014 Elsevier B.V. All rights reserved.

An online community improves adherence in an internet-mediated walking program. Part 1: results of a randomized controlled trial.

PubMed

Richardson, Caroline R; Buis, Lorraine R; Janney, Adrienne W; Goodrich, David E; Sen, Ananda; Hess, Michael L; Mehari, Kathleen S; Fortlage, Laurie A; Resnick, Paul J; Zikmund-Fisher, Brian J; Strecher, Victor J; Piette, John D

2010-12-17

Approximately half of American adults do not meet recommended physical activity guidelines. Face-to-face lifestyle interventions improve health outcomes but are unlikely to yield population-level improvements because they can be difficult to disseminate, expensive to maintain, and inconvenient for the recipient. In contrast, Internet-based behavior change interventions can be disseminated widely at a lower cost. However, the impact of some Internet-mediated programs is limited by high attrition rates. Online communities that allow participants to communicate with each other by posting and reading messages may decrease participant attrition. Our objective was to measure the impact of adding online community features to an Internet-mediated walking program on participant attrition and average daily step counts. This randomized controlled trial included sedentary, ambulatory adults who used email regularly and had at least 1 of the following: overweight (body mass index [BMI] ≥ 25), type 2 diabetes, or coronary artery disease. All participants (n = 324) wore enhanced pedometers throughout the 16-week intervention and uploaded step-count data to the study server. Participants could log in to the study website to view graphs of their walking progress, individually-tailored motivational messages, and weekly calculated goals. Participants were randomized to 1 of 2 versions of a Web-based walking program. Those randomized to the "online community" arm could post and read messages with other participants while those randomized to the "no online community" arm could not read or post messages. The main outcome measures were participant attrition and average daily step counts over 16 weeks. Multiple regression analyses assessed the effect of the online community access controlling for age, sex, disease status, BMI, and baseline step counts. Both arms significantly increased their average daily steps between baseline and the end of the intervention period, but there were no significant differences in increase in step counts between arms using either intention-to-treat or completers analysis. In the intention-to-treat analysis, the average step count increase across both arms was 1888 ± 2400 steps. The percentage of completers was 13% higher in the online community arm than the no online community arm (online community arm, 79%, no online community arm, 66%, P = .02). In addition, online community arm participants remained engaged in the program longer than no online community arm participants (hazard ratio = 0.47, 95% CI = 0.25 - 0.90, P = .02). Participants with lower baseline social support posted more messages to the online community (P < .001) and viewed more posts (P < .001) than participants with higher baseline social support. Adding online community features to an Internet-mediated walking program did not increase average daily step counts but did reduce participant attrition. Participants with low baseline social support used the online community features more than those with high baseline social support. Thus, online communities may be a promising approach to reducing attrition from online health behavior change interventions, particularly in populations with low social support. NCT00729040; http://clinicaltrials.gov/ct2/show/NCT00729040 (Archived by WebCite at http://www.webcitation.org/5v1VH3n0A).

Automated detection of microaneurysms using robust blob descriptors

NASA Astrophysics Data System (ADS)

Adal, K.; Ali, S.; Sidibé, D.; Karnowski, T.; Chaum, E.; Mériaudeau, F.

2013-03-01

Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fundus images. Then, Hessian-based candidate selection algorithm is applied to extract image regions which are more likely to be MAs. For each candidate region, robust low-level blob descriptors such as Speeded Up Robust Features (SURF) and Intensity Normalized Radon Transform are extracted to characterize candidate MA regions. The combined features are then classified using SVM which has been trained using ten manually annotated training images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. Preliminary results show the competitiveness of the proposed candidate selection techniques against state-of-the art methods as well as the promising future for the proposed descriptors to be used in the localization of MAs from fundus images.

Active Learning through Online Instruction

ERIC Educational Resources Information Center

Gulbahar, Yasemin; Kalelioglu, Filiz

2010-01-01

This article explores the use of proper instructional techniques in online discussions that lead to meaningful learning. The research study looks at the effective use of two instructional techniques within online environments, based on qualitative measures. "Brainstorming" and "Six Thinking Hats" were selected and implemented…

Online Database Coverage of Pharmaceutical Journals.

ERIC Educational Resources Information Center

Snow, Bonnie

1984-01-01

Describes compilation of data concerning pharmaceutical journal coverage in online databases which aid information providers in collection development and database selection. Methodology, results (a core collection, overlap, timeliness, geographic scope), and implications are discussed. Eight references and a list of 337 journals indexed online in…

Real-time on-line space research laboratory environment monitoring with off-line trend and prediction analysis

NASA Astrophysics Data System (ADS)

Jules, Kenol; Lin, Paul P.

2007-06-01

With the International Space Station currently operational, a significant amount of acceleration data is being down-linked, processed and analyzed daily on the ground on a continuous basis for the space station reduced gravity environment characterization, the vehicle design requirements verification and science data collection. To help understand the impact of the unique spacecraft environment on the science data, an artificial intelligence monitoring system was developed, which detects in near real time any change in the reduced gravity environment susceptible to affect the on-going experiments. Using a dynamic graphical display, the monitoring system allows science teams, at any time and any location, to see the active vibration disturbances, such as pumps, fans, compressor, crew exercise, re-boost and extra-vehicular activities that might impact the reduced gravity environment the experiments are exposed to. The monitoring system can detect both known and unknown vibratory disturbance activities. It can also perform trend analysis and prediction by analyzing past data over many increments (an increment usually lasts 6 months) collected onboard the station for selected disturbances. This feature can be used to monitor the health of onboard mechanical systems to detect and prevent potential systems failures. The monitoring system has two operating modes: online and offline. Both near real-time on-line vibratory disturbance detection and off-line detection and trend analysis are discussed in this paper.

Online doctor reviews: do they track surgeon volume, a proxy for quality of care?

PubMed

Segal, Jeffrey; Sacopulos, Michael; Sheets, Virgil; Thurston, Irish; Brooks, Kendra; Puccia, Ryan

2012-04-10

Increasingly, consumers are accessing the Internet seeking health information. Consumers are also using online doctor review websites to help select their physician. Such websites tally numerical ratings and comments from past patients. To our knowledge, no study has previously analyzed whether doctors with positive online reputations on doctor review websites actually deliver higher quality of care typically associated with better clinical outcomes and better safety records. For a number of procedures, surgeons who perform more procedures have better clinical outcomes and safety records than those who perform fewer procedures. Our objective was to determine if surgeon volume, as a proxy for clinical outcomes and patient safety, correlates with online reputation. We investigated the numerical ratings and comments on 9 online review websites for high- and low-volume surgeons for three procedures: lumbar surgery, total knee replacement, and bariatric surgery. High-volume surgeons were randomly selected from the group within the highest quartile of claims submitted for reimbursement using the procedures' relevant current procedural terminology (CPT) codes. Low-volume surgeons were randomly selected from the lowest quartile of submitted claims for the procedures' relevant CPT codes. Claims were collated within the Normative Health Information Database, covering multiple payers for more than 25 million insured patients. Numerical ratings were found for the majority of physicians in our sample (547/600, 91.2%) and comments were found for 385/600 (64.2%) of the physicians. We found that high-volume (HV) surgeons could be differentiated from low-volume (LV) surgeons independently by analyzing: (1) the total number of numerical ratings per website (HV: mean = 5.85; LV: mean = 4.87, P<.001); (2) the total number of text comments per website (HV: mean = 2.74; LV: mean = 2.30, P=.05); (3) the proportion of glowing praise/total comments about quality of care (HV: mean = 0.64; LV: mean = 0.51, P=.002); and (4) the proportion of scathing criticism/total comments about quality of care (HV: mean = 0.14; LV: mean = 0.23, P= .005). Even when these features were combined, the effect size, although significant, was still weak. The results revealed that one could accurately identify a physician's patient volume via discriminant and classification analysis 61.6% of the time. We also found that high-volume surgeons could not be differentiated from low-volume surgeons by analyzing (1) standardized z score numerical ratings (HV: mean = 0.07; LV: mean = 0, P=.27); (2) proportion of glowing praise/total comments about customer service (HV: mean = 0.24; LV: mean = 0.22, P=.52); and (3) proportion of scathing criticism/total comments about customer service (HV: mean = 0.19; LV: mean = 0.21, P=.48). Online review websites provide a rich source of data that may be able to track quality of care, although the effect size is weak and not consistent for all review website metrics.

A Comparison of Electronic and Paper-Based Assignment Submission and Feedback

ERIC Educational Resources Information Center

Bridge, Pete; Appleyard, Rob

2008-01-01

This paper presents the results of a study evaluating student perceptions of online assignment submission. 47 students submitted assignments and received feedback via features within the Virtual Learning Environment Blackboard[TM]. The students then completed questionnaires comparing their experience of online submission and feedback with…

Expanding Academic Vocabulary with an Interactive On-Line Database

ERIC Educational Resources Information Center

Horst, Marlise; Cobb, Tom; Nicolae, Ioana

2005-01-01

University students used a set of existing and purpose-built on-line tools for vocabulary learning in an experimental ESL course. The resources included concordance, dictionary, cloze-builder, hypertext, and a database with interactive self-quizzing feature (all freely available at www.lextutor.ca). The vocabulary targeted for learning consisted…

Online Assessment of Learning and Engagement in University Laboratory Practicals

ERIC Educational Resources Information Center

Whitworth, David E.; Wright, Kate

2015-01-01

In science education, laboratory practicals are frequently assessed through submission of a report. A large increase in student numbers necessitated us adapting a traditional practical report into an online test with automated marking. The assessment was designed to retain positive features of the traditional laboratory report but with added…

Peer Observation--A Case for Doing It Online

ERIC Educational Resources Information Center

Bennett, Shirley; Barp, Donatella

2008-01-01

Peer observation is increasingly a feature of higher education (HE) practice. We argue that the principal drivers of quality assurance and professional development apply online as offline, given the growing importance of e-learning and related teacher development needs. Currently, discussion is largely restricted to traditional classroom-based…

Gamification and Web-Based Homework

ERIC Educational Resources Information Center

Goehle, Geoff

2013-01-01

In this paper we demonstrate how video game mechanics can be used to help improve student engagement with online mathematics homework. Specifically, we integrate two common video game systems, levels and achievements, with the online homework program "WeBWorK." We describe the key features of the implementation of these systems and…

A New KE-Free Online ICALL System Featuring Error Contingent Feedback

ERIC Educational Resources Information Center

Tokuda, Naoyuki; Chen, Liang

2004-01-01

As a first step towards implementing a human language teacher, we have developed a new template-based on-line ICALL (intelligent computer assisted language learning) system capable of automatically diagnosing learners' free-format translated inputs and returning error contingent feedback. The system architecture we have adopted allows language…

Teachers' Perceptions of Online Professional Development in Literacy

ERIC Educational Resources Information Center

Garbe, Amber Yudchitz

2012-01-01

This study sought to describe perceptions of teachers regarding the influence of online professional development (oPD) in literacy on their instruction and students' learning. The following features of effective professional development were analyzed: content-focus; collectivity; coherence; duration; and active learning. As well, the study…

An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation

NASA Astrophysics Data System (ADS)

Tonin, L.; Leeb, R.; Sobolewski, A.; Millán, J. del R.

2013-10-01

Objective. In this work we present—for the first time—the online operation of an electroencephalogram (EEG) brain-computer interface (BCI) system based on covert visuospatial attention (CVSA), without relying on any evoked responses. Electrophysiological correlates of pure top-down CVSA have only recently been proposed as a control signal for BCI. Such systems are expected to share the ease of use of stimulus-driven BCIs (e.g. P300, steady state visually evoked potential) with the autonomy afforded by decoding voluntary modulations of ongoing activity (e.g. motor imagery). Approach. Eight healthy subjects participated in the study. EEG signals were acquired with an active 64-channel system. The classification method was based on a time-dependent approach tuned to capture the most discriminant spectral features of the temporal evolution of attentional processes. The system was used by all subjects over two days without retraining, to verify its robustness and reliability. Main results. We report a mean online accuracy across the group of 70.6 ± 1.5%, and 88.8 ± 5.8% for the best subject. Half of the participants produced stable features over the entire duration of the study. Additionally, we explain drops in performance in subjects showing stable features in terms of known electrophysiological correlates of fatigue, suggesting the prospect of online monitoring of mental states in BCI systems. Significance. This work represents the first demonstration of the feasibility of an online EEG BCI based on CVSA. The results achieved suggest the CVSA BCI as a promising alternative to standard BCI modalities.

Online Education and e-Consent for GeneScreen, a Preventive Genomic Screening Study.

PubMed

Cadigan, R Jean; Butterfield, Rita; Rini, Christine; Waltz, Margaret; Kuczynski, Kristine J; Muessig, Kristin; Goddard, Katrina A B; Henderson, Gail E

2017-01-01

Online study recruitment is increasingly popular, but we know little about the decision making that goes into joining studies in this manner. In GeneScreen, a genomic screening study that utilized online education and consent, we investigated participants' perceived ease when deciding to join and their understanding of key study features. Individuals were recruited via mailings that directed them to a website where they could learn more about GeneScreen, consent to participate, and complete a survey. Participants found it easy to decide to join GeneScreen and had a good understanding of study features. Multiple regression analyses revealed that ease of deciding to join was related to confidence in one's genetic self-efficacy, limited concerns about genetic screening, trust in and lack of frustration using the website, and the ability to spend a limited time on the website. Understanding of study features was related to using the Internet more frequently and attaining more information about GeneScreen conditions. The ease of deciding to join a genomic screening study and comprehension of its key features should be treated as different phenomena in research and practice. There is a need for a more nuanced understanding of how individuals respond to web-based consent information. © 2017 S. Karger AG, Basel.

Online dimensionality reduction using competitive learning and Radial Basis Function network.

PubMed

Tomenko, Vladimir

2011-06-01

The general purpose dimensionality reduction method should preserve data interrelations at all scales. Additional desired features include online projection of new data, processing nonlinearly embedded manifolds and large amounts of data. The proposed method, called RBF-NDR, combines these features. RBF-NDR is comprised of two modules. The first module learns manifolds by utilizing modified topology representing networks and geodesic distance in data space and approximates sampled or streaming data with a finite set of reference patterns, thus achieving scalability. Using input from the first module, the dimensionality reduction module constructs mappings between observation and target spaces. Introduction of specific loss function and synthesis of the training algorithm for Radial Basis Function network results in global preservation of data structures and online processing of new patterns. The RBF-NDR was applied for feature extraction and visualization and compared with Principal Component Analysis (PCA), neural network for Sammon's projection (SAMANN) and Isomap. With respect to feature extraction, the method outperformed PCA and yielded increased performance of the model describing wastewater treatment process. As for visualization, RBF-NDR produced superior results compared to PCA and SAMANN and matched Isomap. For the Topic Detection and Tracking corpus, the method successfully separated semantically different topics. Copyright © 2011 Elsevier Ltd. All rights reserved.

Full-Text Searching on Major Supermarket Systems: Dialog, Data-Star, and Nexis.

ERIC Educational Resources Information Center

Tenopir, Carol; Berglund, Sharon

1993-01-01

Examines the similarities, differences, and full-text features of the three most-used online systems for full-text searching in general libraries: DIALOG, Data-Star, and NEXIS. Overlapping databases, unique sources, search features, proximity operators, set building, language enhancement and word equivalencies, and display features are discussed.…

The Social Meaning of Sharing and Geocoding: Features and Social Processes in Online Communities

ERIC Educational Resources Information Center

Xiong, Li

2012-01-01

This study examines how emergent communities might show different patterns of uses and perceptions for communication and profile features when geolocation features are used. It explores the ways that location awareness moderates the social and cognitive processes that motivate people's participation in the sharing of personal information…

OLMS: Online Learning Management System for E-Learning

ERIC Educational Resources Information Center

Ippakayala, Vinay Kumar; El-Ocla, Hosam

2017-01-01

In this paper we introduce a learning management system that provides a management system for centralized control of course content. A secure system to record lectures is implemented as a key feature of this application. This feature would be accessed through web camera and mobile recording. These features are mainly designed for e-learning…

Online Communities: The Case of Immigrants in Greece

NASA Astrophysics Data System (ADS)

Panaretou, Ioannis; Karousos, Nikos; Kostopoulos, Ioannis; Foteinou, Georgia-Barbara; Pavlidis, Giorgos

Immigrants in Greece are an increasing population, very often threatened by poverty and social exclusion. At the same time Greek government has no formal policy concerning their assimilation in Greek society and this situation generates multiple problems in both immigrants and native population. In this work we suggest that new technology can alleviate these effects and we present specific tools and methodologies adopted by ANCE, in order to support online communities and specifically immigrant communities in Greece. This approach has the potential to support immigrant communities' in terms of the organization of personal data, communication, and provision of a working space for dedicated use. The Information System's operational features are also presented, along with other characteristics and state-of-the-art features in order to propose a general direction to the design of online communities' mechanisms.

Open, Online, and Blended: Transactional Interactions with MOOC Content by Learners in Three Different Course Formats

ERIC Educational Resources Information Center

Emanuel, Jeffrey P.; Lamb, Anne

2017-01-01

During the 2013-14 academic year, Harvard University piloted the use of Massive Open Online Courses (MOOCs) as tools for blended learning in select undergraduate and graduate residential and online courses. One of these courses, The Ancient Greek Hero, combined for-credit (Harvard College and Harvard Extension School) and open online (HarvardX)…

An interactive portal to empower cancer survivors: a qualitative study on user expectations.

PubMed

Kuijpers, Wilma; Groen, Wim G; Loos, Romy; Oldenburg, Hester S A; Wouters, Michel W J M; Aaronson, Neil K; van Harten, Wim H

2015-09-01

Portals are increasingly used to improve patient empowerment, but are still uncommon in oncology. In this study, we explored cancer survivors' and health professionals' expectations of possible features of an interactive portal. We conducted three focus groups with breast cancer survivors (n = 21), two with lung cancer survivors (n = 14), and four with health professionals (n = 31). Drafts of possible features of an interactive portal were presented as static screenshots: survivorship care plan (SCP), access to electronic medical record (EMR), appointments, e-consultation, online patient community, patient reported outcomes (PROs) plus feedback, telemonitoring service, online rehabilitation program, and online psychosocial self-management program. This presentation was followed by an open discussion. Focus groups were audiotaped, transcribed verbatim, and data were analyzed using content analysis. Important themes included fulfillment of information needs, communication, motivation, quality of feedback, and supervision. Cancer survivors were primarily interested in features that could fulfill their information needs: SCP, access to their EMR, and an overview of appointments. Health professionals considered PROs and telemonitoring as most useful features, as these provide relevant information about survivors' health status. We recommend to minimally include these features in an interactive portal for cancer survivors. This is the first study that evaluated the expectations of cancer survivors and health professionals concerning an interactive portal. Both groups were positive about the introduction of such a portal, although their preferences for the various features differed. These findings reflect their unique perspective and emphasize the importance of involving multiple stakeholders in the actual design process.

Prostate tissue characterization/classification in 144 patient population using wavelet and higher order spectra features from transrectal ultrasound images.

PubMed

Pareek, Gyan; Acharya, U Rajendra; Sree, S Vinitha; Swapna, G; Yantri, Ratna; Martis, Roshan Joy; Saba, Luca; Krishnamurthi, Ganapathy; Mallarini, Giorgio; El-Baz, Ayman; Al Ekish, Shadi; Beland, Michael; Suri, Jasjit S

2013-12-01

In this work, we have proposed an on-line computer-aided diagnostic system called "UroImage" that classifies a Transrectal Ultrasound (TRUS) image into cancerous or non-cancerous with the help of non-linear Higher Order Spectra (HOS) features and Discrete Wavelet Transform (DWT) coefficients. The UroImage system consists of an on-line system where five significant features (one DWT-based feature and four HOS-based features) are extracted from the test image. These on-line features are transformed by the classifier parameters obtained using the training dataset to determine the class. We trained and tested six classifiers. The dataset used for evaluation had 144 TRUS images which were split into training and testing sets. Three-fold and ten-fold cross-validation protocols were adopted for training and estimating the accuracy of the classifiers. The ground truth used for training was obtained using the biopsy results. Among the six classifiers, using 10-fold cross-validation technique, Support Vector Machine and Fuzzy Sugeno classifiers presented the best classification accuracy of 97.9% with equally high values for sensitivity, specificity and positive predictive value. Our proposed automated system, which achieved more than 95% values for all the performance measures, can be an adjunct tool to provide an initial diagnosis for the identification of patients with prostate cancer. The technique, however, is limited by the limitations of 2D ultrasound guided biopsy, and we intend to improve our technique by using 3D TRUS images in the future.

An Exploratory Study of Student Motivations for Taking Online Courses and Learning Outcomes

ERIC Educational Resources Information Center

Nonis, Sarath A.; Fenner, Grant H.

2012-01-01

An investigation of students taking online classes exposed crucial student perceptions important to their selecting online/web-assisted courses. An exploratory factor analysis provided three factors of "convenience," "enjoyment & independence," and "no other option available" as motivations for students taking…

Online Peer-to-Peer Communities in the Daily Lives of People With Chronic Illness: A Qualitative Systematic Review.

PubMed

Kingod, Natasja; Cleal, Bryan; Wahlberg, Ayo; Husted, Gitte R

2017-01-01

This qualitative systematic review investigated how individuals with chronic illness experience online peer-to-peer support and how their experiences influence daily life with illness. Selected studies were appraised by quality criteria focused upon research questions and study design, participant selection, methods of data collection, and methods of analysis. Four themes were identified: (a) illness-associated identity work, (b) social support and connectivity, (c) experiential knowledge sharing, and (d) collective voice and mobilization. Findings indicate that online peer-to-peer communities provide a supportive space for daily self-care related to chronic illness. Online communities provided a valued space to strengthen social ties and exchange knowledge that supported offline ties and patient-doctor relationships. Individuals used online communities to exchange experiential knowledge about everyday life with illness. This type of knowledge was perceived as extending far beyond medical care. Online communities were also used to mobilize and raise collective awareness about illness-specific concerns. © The Author(s) 2016.

Real-time image annotation by manifold-based biased Fisher discriminant analysis

NASA Astrophysics Data System (ADS)

Ji, Rongrong; Yao, Hongxun; Wang, Jicheng; Sun, Xiaoshuai; Liu, Xianming

2008-01-01

Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval. However, two crucial issues are not well addressed in state-of-art annotation algorithms: 1. The Small Sample Size (3S) problem in keyword classifier/model learning; 2. Most of annotation algorithms can not extend to real-time online usage due to their low computational efficiencies. This paper presents a novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address these two issues by transductive semantic learning and keyword filtering. To address the 3S problem, Co-Training based Manifold learning is adopted for keyword model construction. To achieve real-time annotation, a Bias Fisher Discriminant Analysis (BFDA) based semantic feature reduction algorithm is presented for keyword confidence discrimination and semantic feature reduction. Different from all existing annotation methods, MBFDA views image annotation from a novel Eigen semantic feature (which corresponds to keywords) selection aspect. As demonstrated in experiments, our manifold-based biased Fisher discriminant analysis annotation algorithm outperforms classical and state-of-art annotation methods (1.K-NN Expansion; 2.One-to-All SVM; 3.PWC-SVM) in both computational time and annotation accuracy with a large margin.

Predicting the amount of coke deposition on catalyst pellets through image analysis and soft computing

NASA Astrophysics Data System (ADS)

Zhang, Jingqiong; Zhang, Wenbiao; He, Yuting; Yan, Yong

2016-11-01

The amount of coke deposition on catalyst pellets is one of the most important indexes of catalytic property and service life. As a result, it is essential to measure this and analyze the active state of the catalysts during a continuous production process. This paper proposes a new method to predict the amount of coke deposition on catalyst pellets based on image analysis and soft computing. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and feature extraction, twelve effective features are selected and two best feature sets are determined by the prediction tests. A neural network optimized by a particle swarm optimization algorithm is used to establish the prediction model of the coke amount based on various datasets. The root mean square error of the prediction values are all below 0.021 and the coefficient of determination R 2, for the model, are all above 78.71%. Therefore, a feasible, effective and precise method is demonstrated, which may be applied to realize the real-time measurement of coke deposition based on on-line sampling and fast image analysis.

Providing a complete online multimedia patient record.

PubMed Central

Dayhoff, R. E.; Kuzmak, P. M.; Kirin, G.; Frank, S.

1999-01-01

Seamless integration of all types of patient data is a critical feature for clinical workstation software. The Dept. of Veterans Affairs has developed a multimedia online patient record that includes traditional medical chart information as well as a wide variety of medical images from specialties such as cardiology, pulmonary and gastrointestinal medicine, pathology, radiology, hematology, and nuclear medicine. This online patient record can present data in ways not possible with a paper chart or other physical media. Obtaining a critical mass of information online is essential to achieve the maximum benefits from an integrated patient record system. Images Figure 1 Figure 2 PMID:10566357

Emerging behavior in electronic bidding.

PubMed

Yang, I; Jeong, H; Kahng, B; Barabási, A-L

2003-07-01

We characterize the statistical properties of a large number of agents on two major online auction sites. The measurements indicate that the total number of bids placed in a single category and the number of distinct auctions frequented by a given agent follow power-law distributions, implying that a few agents are responsible for a significant fraction of the total bidding activity on the online market. We find that these agents exert an unproportional influence on the final price of the auctioned items. This domination of online auctions by an unusually active minority may be a generic feature of all online mercantile processes.

Emerging behavior in electronic bidding

NASA Astrophysics Data System (ADS)

Yang, I.; Jeong, H.; Kahng, B.; Barabási, A.-L.

2003-07-01

We characterize the statistical properties of a large number of agents on two major online auction sites. The measurements indicate that the total number of bids placed in a single category and the number of distinct auctions frequented by a given agent follow power-law distributions, implying that a few agents are responsible for a significant fraction of the total bidding activity on the online market. We find that these agents exert an unproportional influence on the final price of the auctioned items. This domination of online auctions by an unusually active minority may be a generic feature of all online mercantile processes.

A novel onset detection technique for brain-computer interfaces using sound-production related cognitive tasks in simulated-online system

NASA Astrophysics Data System (ADS)

Song, YoungJae; Sepulveda, Francisco

2017-02-01

Objective. Self-paced EEG-based BCIs (SP-BCIs) have traditionally been avoided due to two sources of uncertainty: (1) precisely when an intentional command is sent by the brain, i.e., the command onset detection problem, and (2) how different the intentional command is when compared to non-specific (or idle) states. Performance evaluation is also a problem and there are no suitable standard metrics available. In this paper we attempted to tackle these issues. Approach. Self-paced covert sound-production cognitive tasks (i.e., high pitch and siren-like sounds) were used to distinguish between intentional commands (IC) and idle states. The IC states were chosen for their ease of execution and negligible overlap with common cognitive states. Band power and a digital wavelet transform were used for feature extraction, and the Davies-Bouldin index was used for feature selection. Classification was performed using linear discriminant analysis. Main results. Performance was evaluated under offline and simulated-online conditions. For the latter, a performance score called true-false-positive (TFP) rate, ranging from 0 (poor) to 100 (perfect), was created to take into account both classification performance and onset timing errors. Averaging the results from the best performing IC task for all seven participants, an 77.7% true-positive (TP) rate was achieved in offline testing. For simulated-online analysis the best IC average TFP score was 76.67% (87.61% TP rate, 4.05% false-positive rate). Significance. Results were promising when compared to previous IC onset detection studies using motor imagery, in which best TP rates were reported as 72.0% and 79.7%, and which, crucially, did not take timing errors into account. Moreover, based on our literature review, there is no previous covert sound-production onset detection system for spBCIs. Results showed that the proposed onset detection technique and TFP performance metric have good potential for use in SP-BCIs.

Quantum-enhanced feature selection with forward selection and backward elimination

NASA Astrophysics Data System (ADS)

He, Zhimin; Li, Lvzhou; Huang, Zhiming; Situ, Haozhen

2018-07-01

Feature selection is a well-known preprocessing technique in machine learning, which can remove irrelevant features to improve the generalization capability of a classifier and reduce training and inference time. However, feature selection is time-consuming, particularly for the applications those have thousands of features, such as image retrieval, text mining and microarray data analysis. It is crucial to accelerate the feature selection process. We propose a quantum version of wrapper-based feature selection, which converts a classical feature selection to its quantum counterpart. It is valuable for machine learning on quantum computer. In this paper, we focus on two popular kinds of feature selection methods, i.e., wrapper-based forward selection and backward elimination. The proposed feature selection algorithm can quadratically accelerate the classical one.

When is selective self-presentation effective? An investigation of the moderation effects of "self-esteem" and "social trust".

PubMed

Kim, Yoonkyung; Baek, Young Min

2014-11-01

This study investigates the relationship between selective self-presentation and online life satisfaction, and how this relationship is influenced by respondents' perceptions of "self" (operationalized by "self-esteem") and "others" (operationalized by "social trust"). Relying on survey data from 712 Korean online users, two important findings were detected in our study. First, the positive relationship between selective self-presentation and online life satisfaction becomes more prominent among people with low self-esteem compared to those with high self-esteem, and second, this positive relationship is enhanced among people with high levels of social trust compared to those with low trust levels. Theoretical and practical implications of our findings as well as potential limitations are discussed.

Survey of Current Practice in the Fitting and Fine-Tuning of Common Signal-Processing Features in Hearing Aids for Adults.

PubMed

Anderson, Melinda C; Arehart, Kathryn H; Souza, Pamela E

2018-02-01

Current guidelines for adult hearing aid fittings recommend the use of a prescriptive fitting rationale with real-ear verification that considers the audiogram for the determination of frequency-specific gain and ratios for wide dynamic range compression. However, the guidelines lack recommendations for how other common signal-processing features (e.g., noise reduction, frequency lowering, directional microphones) should be considered during the provision of hearing aid fittings and fine-tunings for adult patients. The purpose of this survey was to identify how audiologists make clinical decisions regarding common signal-processing features for hearing aid provision in adults. An online survey was sent to audiologists across the United States. The 22 survey questions addressed four primary topics including demographics of the responding audiologists, factors affecting selection of hearing aid devices, the approaches used in the fitting of signal-processing features, and the strategies used in the fine-tuning of these features. A total of 251 audiologists who provide hearing aid fittings to adults completed the electronically distributed survey. The respondents worked in a variety of settings including private practice, physician offices, university clinics, and hospitals/medical centers. Data analysis was based on a qualitative analysis of the question responses. The survey results for each of the four topic areas (demographics, device selection, hearing aid fitting, and hearing aid fine-tuning) are summarized descriptively. Survey responses indicate that audiologists vary in the procedures they use in fitting and fine-tuning based on the specific feature, such that the approaches used for the fitting of frequency-specific gain differ from other types of features (i.e., compression time constants, frequency lowering parameters, noise reduction strength, directional microphones, feedback management). Audiologists commonly rely on prescriptive fitting formulas and probe microphone measures for the fitting of frequency-specific gain and rely on manufacturers' default settings and recommendations for both the initial fitting and the fine-tuning of signal-processing features other than frequency-specific gain. The survey results are consistent with a lack of published protocols and guidelines for fitting and adjusting signal-processing features beyond frequency-specific gain. To streamline current practice, a transparent evidence-based tool that enables clinicians to prescribe the setting of other features from individual patient characteristics would be desirable. American Academy of Audiology

Database on unstable rock slopes in Norway

NASA Astrophysics Data System (ADS)

Oppikofer, Thierry; Nordahl, Bo; Bunkholt, Halvor; Nicolaisen, Magnus; Hermanns, Reginald L.; Böhme, Martina; Yugsi Molina, Freddy X.

2014-05-01

Several large rockslides have occurred in historic times in Norway causing many casualties. Most of these casualties are due to displacement waves triggered by a rock avalanche and affecting coast lines of entire lakes and fjords. The Geological Survey of Norway performs systematic mapping of unstable rock slopes in Norway and has detected up to now more than 230 unstable slopes with significant postglacial deformation. This systematic mapping aims to detect future rock avalanches before they occur. The registered unstable rock slopes are stored in a database on unstable rock slopes developed and maintained by the Geological Survey of Norway. The main aims of this database are (1) to serve as a national archive for unstable rock slopes in Norway; (2) to serve for data collection and storage during field mapping; (3) to provide decision-makers with hazard zones and other necessary information on unstable rock slopes for land-use planning and mitigation; and (4) to inform the public through an online map service. The database is organized hierarchically with a main point for each unstable rock slope to which several feature classes and tables are linked. This main point feature class includes several general attributes of the unstable rock slopes, such as site name, general and geological descriptions, executed works, recommendations, technical parameters (volume, lithology, mechanism and others), displacement rates, possible consequences, hazard and risk classification and so on. Feature classes and tables linked to the main feature class include the run-out area, the area effected by secondary effects, the hazard and risk classification, subareas and scenarios of an unstable rock slope, field observation points, displacement measurement stations, URL links for further documentation and references. The database on unstable rock slopes in Norway will be publicly consultable through the online map service on www.skrednett.no in 2014. Only publicly relevant parts of the database will be shown in the online map service (e.g. processed results of displacement measurements), while more detailed data will not (e.g. raw data of displacement measurements). Factsheets with key information on unstable rock slopes can be automatically generated and downloaded for each site, a municipality, a county or the entire country. Selected data will also be downloadable free of charge. The present database on unstable rock slopes in Norway will further evolve in the coming years as the systematic mapping conducted by the Geological Survey of Norway progresses and as available techniques and tools evolve.

Water-compatible molecularly imprinted polymers for efficient direct injection on-line solid-phase extraction of ropivacaine and bupivacaine from human plasma.

PubMed

Cobb, Zoe; Sellergren, Börje; Andersson, Lars I

2007-12-01

Two novel molecularly imprinted polymers (MIPs) selected from a combinatorial library of bupivacaine imprinted polymers were used for selective on-line solid-phase extraction of bupivacaine and ropivacaine from human plasma. The MIPs were prepared using methacrylic acid as the functional monomer, ethylene glycol dimethacrylate as the cross-linking monomer and in addition hydroxyethylmethacrylate to render the polymer surface hydrophilic. The novel MIPs showed high selectivity for the analytes and required fewer and lower concentrations of additives to suppress non-specific adsorption compared with a conventional MIP. This enabled the development of an on-line system for direct extraction of buffered plasma. Selective extraction was achieved without the use of time-consuming solvent switch steps, and transfer of the analytes from the MIP column to the analytical column was carried out under aqueous conditions fully compatible with reversed-phase LC gradient separation of analyte and internal standard. The MIPs showed excellent aqueous compatibility and yielded extractions with acceptable recovery and high selectivity.

Adult Learning Theories: Implications for Online Instruction

ERIC Educational Resources Information Center

Arghode, Vishal; Brieger, Earl W.; McLean, Gary N.

2017-01-01

Purpose: This paper analyzes critically four selected learning theories and their role in online instruction for adults. Design/methodology/approach: A literature review was conducted to analyze the theories. Findings: The theory comparison revealed that no single theory encompasses the entirety of online instruction for adult learning; each…

ANAMOL: A Creative Experience Using Communications Technology.

ERIC Educational Resources Information Center

Marr, Beth

1999-01-01

The Adult Numeracy and Mathematics On-Line (ANAMOL) project investigated the use of online technology as a medium for professional discussion, networking, and collaboration among a small group of isolated adult numeracy practitioners in Australia. After investigation of several methods of online communication, freeware was selected because, in…

Studying and Facilitating Dialogue in Select Online Management Courses

ERIC Educational Resources Information Center

Ivancevich, John M.; Gilbert, Jacqueline A.; Konopaske, Robert

2009-01-01

Dialogue is arguably one of the most significant elements of learning in higher education. The premise of this article is that online instructors can creatively facilitate dialogue for effectively teaching online management courses. This article presents a dialogue-focused framework for addressing significant behavioral, structural, and…

The CIS Database: Occupational Health and Safety Information Online.

ERIC Educational Resources Information Center

Siegel, Herbert; Scurr, Erica

1985-01-01

Describes document acquisition, selection, indexing, and abstracting and discusses online searching of the CIS database, an online system produced by the International Occupational Safety and Health Information Centre. This database comprehensively covers information in the field of occupational health and safety. Sample searches and search…

VizieR Online Data Catalog: Chromospherically Active Binaries. Third version (Eker+, 2008)

NASA Astrophysics Data System (ADS)

Eker, Z.; Filiz-Ak, N.; Bilir, S.; Dogru, D.; Tuysuz, M.; Soydugan, E.; Bakis, H.; Ugras, B.; Soydugan, F.; Erdem, A.; Demircan, O.

2008-06-01

Chromospherically Active Binaries (CAB) catalogue have been revised and updated. With 203 new identifications, the number of CAB stars is increased to 409. Catalogue is available in electronic format where each system has various number of lines (sub-orders) with a unique order number. Columns contain data of limited number of selected cross references, comments to explain peculiarities and position of the binarity in case it belongs to a multiple system, classical identifications (RS CVn, BY Dra), brightness and colours, photometric and spectroscopic data, description of emission features (Ca II H&K, Hα, UV, IR), X-Ray luminosity, radio flux, physical quantities and orbital information, where each basic entry are referenced so users can go original sources. (10 data files).

MSAViewer: interactive JavaScript visualization of multiple sequence alignments.

PubMed

Yachdav, Guy; Wilzbach, Sebastian; Rauscher, Benedikt; Sheridan, Robert; Sillitoe, Ian; Procter, James; Lewis, Suzanna E; Rost, Burkhard; Goldberg, Tatyana

2016-11-15

The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is 'web ready': written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components. The MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/Supplementary information: Supplementary data are available at Bioinformatics online. msa@bio.sh. © The Author 2016. Published by Oxford University Press.

MSAViewer: interactive JavaScript visualization of multiple sequence alignments

PubMed Central

Yachdav, Guy; Wilzbach, Sebastian; Rauscher, Benedikt; Sheridan, Robert; Sillitoe, Ian; Procter, James; Lewis, Suzanna E.; Rost, Burkhard; Goldberg, Tatyana

2016-01-01

Summary: The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is ‘web ready’: written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components. Availability and Implementation: The MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: msa@bio.sh PMID:27412096

Using "Quipper" as an Online Platform for Teaching and Learning English as a Foreign Language

ERIC Educational Resources Information Center

Mulyono, Herri

2016-01-01

This paper evaluates the affordability of "Quipper" as an online platform for teaching and learning English as a foreign language (EFL). It focuses on the extent to which features available in "Quipper" may correspond to fundamental components of Computer-Assisted Language Learning (CALL) pedagogy, as suggested by Chapelle…

What Features Make Online Harassment Incidents Upsetting to Youth?

ERIC Educational Resources Information Center

Mitchell, Kimberly J.; Ybarra, Michele L.; Jones, Lisa M.; Espelage, Dorothy

2016-01-01

This article examines characteristics of online harassment episodes associated with increased distress for youth. Data were collected as part of the Third Youth Internet Safety Survey, a cross-sectional telephone survey conducted in the United States in 2010. Interviews were conducted with 1,560 Internet-using youth, ages 10 through 17. Harassment…

Delivery Style Moderates Study Habits in an Online Nutrition Class

ERIC Educational Resources Information Center

Connors, Priscilla

2013-01-01

Objective: To report how the design of an online class affected student ability to stay on task, find critical resources, and communicate with the instructor via e-mail. Methods: Audiorecorded focus group meetings at a United States university featured a structured approach to discussions among undergraduate students enrolled in an Internet…

The Incorporation of Quality Attributes into Online Course Design in Higher Education

ERIC Educational Resources Information Center

Lenert, Kathleen Anne; Diane P. Janes

2017-01-01

A survey was designed incorporating questions on 28 attributes (compiled through a literature review) and considered to be quality features in online academic courses in higher education. This study sought to investigate the ongoing practice of instructional designers and instructors in the United States with respect to their incorporation of…

An Electronic Service Quality Reference Model for Designing E-Commerce Websites Which Maximizes Customer Satisfaction

ERIC Educational Resources Information Center

Shaheen, Amer N.

2011-01-01

This research investigated Electronic Service Quality (E-SQ) features that contribute to customer satisfaction in an online environment. The aim was to develop an approach which improves E-CRM processes and enhances online customer satisfaction. The research design adopted mixed methods involving qualitative and quantitative methods to…

Synchronous Distance Education: Using Web-Conferencing in an MBA Accounting Course

ERIC Educational Resources Information Center

Ellingson, Dee Ann; Notbohm, Matthew

2012-01-01

Online distance education can take many forms, from a correspondence course with materials online to fully synchronous, live instruction. This paper describes a fully synchronous, live format using web-conferencing. Some useful features of web-conferencing and the way they are employed in this course are described. Instructor observations and…

Applying Learning Analytics to Investigate Timed Release in Online Learning

ERIC Educational Resources Information Center

Martin, Florence; Whitmer, John C.

2016-01-01

Adaptive learning gives learners control of context, pace, and scope of their learning experience. This strategy can be implemented in online learning by using the "Adaptive Release" feature in learning management systems. The purpose of this study was to use learning analytics research methods to explore the extent to which the adaptive…

Online Cultural Heritage Exhibitions: A Survey of Information Retrieval Features

ERIC Educational Resources Information Center

Liew, Chern Li

2005-01-01

Purpose: What kinds of online cultural heritage exhibitions are now available on the internet? How far have these cultural heritage institutions voyaged in terms of harnessing the power of information and communication technology and the interactivity of multimedia systems to exhibit cultural heritage resources? This study aims to highlight the…

Students' Reaction to WebCT: Implications for Designing On-Line Learning Environments

ERIC Educational Resources Information Center

Osman, Mohamed Eltahir

2005-01-01

There is a growing number of web-based and web-assisted course development tools and products that can be used to create on-line learning environment. The utility of these products, however, varies greatly depending on their feasibility, prerequisite infrastructure, technical features, interface, and course development and management tools. WebCT…

Strategic Tooling: Technology for Constructing a Community of Inquiry

ERIC Educational Resources Information Center

Thompson, Penny; Vogler, Jane S.; Xiu, Ying

2017-01-01

The Community of Inquiry (CoI) framework describes online learning as a collaborative process supported by social presence, teaching presence, and cognitive presence, which work together to facilitate critical thinking and learning. The technology used in an online class can facilitate a CoI when its features support, rather than constrain,…

Repurposing Video Documentaries as Features of a Flipped-Classroom Approach to Community-Centered Development

ERIC Educational Resources Information Center

Arbogast, Douglas; Eades, Daniel; Plein, L. Christopher

2017-01-01

Online and off-site educational programming is increasingly incorporated by Extension educators to reach their clientele. Models such as the flipped classroom combine online content and in-person learning, allowing clients to both gain information and build peer learning communities. We demonstrate how video documentaries used in traditional…

Community College Student Success in Online versus Equivalent Face-to-Face Courses

ERIC Educational Resources Information Center

Gregory, Cheri B.; Lampley, James H.

2016-01-01

As part of a nationwide effort to increase the postsecondary educational attainment levels of citizens, community colleges have expanded offerings of courses and programs to more effectively meet the needs of students. Online courses offer convenience and flexibility that traditional face-to-face classes do not. These features appeal to students…

How Much Longer before It All Works: What Online Searchers Should Know about Wireless Data Communications.

ERIC Educational Resources Information Center

Bell, Steven J.

1994-01-01

Profiles the major wireless data communications (WDC) systems, provides an overview of how they work, and compares their communication features. Topics addressed include the market for wireless data; applications for WDC; wireless online searching; cellular data communication; packet radio; digital cellular; criteria for evaluating WDC systems;…

A Comparison of Keyword Subject Searching on Six British University OPACs Online Public Access Catalogs.

ERIC Educational Resources Information Center

Aanonson, John

1987-01-01

Compares features of online public access catalogs (OPACs) at six British universities: (1) Cambridge; (2) Hull; (3) Newcastle; (4) Surrey; (5) Sussex; and (6) York. Results of keyword subject searches on two topics performed on each of the OPACs are reported and compared. Six references are listed. (MES)

Semantic Social Network Portal for Collaborative Online Communities

ERIC Educational Resources Information Center

Neumann, Marco; O'Murchu, Ina; Breslin, John; Decker, Stefan; Hogan, Deirdre; MacDonaill, Ciaran

2005-01-01

Purpose: The motivation for this investigation is to apply social networking features to a semantic network portal, which supports the efforts in enterprise training units to up-skill the employee in the company, and facilitates the creation and reuse of knowledge in online communities. Design/methodology/approach: The paper provides an overview…

Captivate MenuBuilder: Creating an Online Tutorial for Teaching Software

ERIC Educational Resources Information Center

Yelinek, Kathryn; Tarnowski, Lynn; Hannon, Patricia; Oliver, Susan

2008-01-01

In this article, the authors, students in an instructional technology graduate course, describe a process to create an online tutorial for teaching software. They created the tutorial for a cyber school's use. Five tutorial modules were linked together through one menu screen using the MenuBuilder feature in the Adobe Captivate program. The…

Student Perceptions of Online Tutoring Videos

ERIC Educational Resources Information Center

Sligar, Steven R.; Pelletier, Christopher D.; Bonner, Heidi Stone; Coghill, Elizabeth; Guberman, Daniel; Zeng, Xiaoming; Newman, Joyce J.; Muller, Dorothy; Dennis, Allen

2017-01-01

Online tutoring is made possible by using videos to replace or supplement face to face services. The purpose of this research was to examine student reactions to the use of lecture capture technology in a university tutoring setting and to assess student knowledge of some features of Tegrity lecture capture software. A survey was administered to…

Internationalization at a Distance: A Study of the Online Management Curriculum

ERIC Educational Resources Information Center

Ramanau, Ruslan

2016-01-01

This article explores how part-time students in an online international management course perceived various features of the course-learning design and whether international perspectives were built into their learning experiences. The focus of the study was on cross-cultural differences across groups of learners in the United Kingdom, in other…

A Distributed Online Curriculum and Courseware Development Model

ERIC Educational Resources Information Center

Durdu, Pinar Onay; Yalabik, Nese; Cagiltay, Kursat

2009-01-01

A distributed online curriculum and courseware development model (DONC[superscript 2]) is developed and tested in this study. Courseware development teams which may work in different institutions who need to develop high quality, reduced cost, on time products will be the users of DONC[superscript 2]. The related features from the disciplines of…

Playing in the Dark with Online Games for Girls

ERIC Educational Resources Information Center

Sinker, Rebecca; Phillips, Mike; de Rijke, Victoria

2017-01-01

"Pregnant Rapunzel Emergency" is part of a series of online free games aimed at young girls (forhergames.com or babygirlgames.com), where dozens of characters from fairy tales, children's toys and media feature in recovery settings, such as "Barbie flu". The range of games available to choose from includes not only dressing,…

Local Spatial Obesity Analysis and Estimation Using Online Social Network Sensors.

PubMed

Sun, Qindong; Wang, Nan; Li, Shancang; Zhou, Hongyi

2018-03-15

Recently, the online social networks (OSNs) have received considerable attentions as a revolutionary platform to offer users massive social interaction among users that enables users to be more involved in their own healthcare. The OSNs have also promoted increasing interests in the generation of analytical, data models in health informatics. This paper aims at developing an obesity identification, analysis, and estimation model, in which each individual user is regarded as an online social network 'sensor' that can provide valuable health information. The OSN-based obesity analytic model requires each sensor node in an OSN to provide associated features, including dietary habit, physical activity, integral/incidental emotions, and self-consciousness. Based on the detailed measurements on the correlation of obesity and proposed features, the OSN obesity analytic model is able to estimate the obesity rate in certain urban areas and the experimental results demonstrate a high success estimation rate. The measurements and estimation experimental findings created by the proposed obesity analytic model show that the online social networks could be used in analyzing the local spatial obesity problems effectively. Copyright © 2018. Published by Elsevier Inc.

A Comprehensive Climate Science and Solutions Education Curriculum

NASA Astrophysics Data System (ADS)

Byrne, J. M.; Cook, J.; Little, L. J.; Peacock, K.; Sinclair, P.; Zeller, C.

2016-12-01

We are creating a broadly based curriculum for a multidisciplinary University/College course on climate change science and solutions. Climate change is a critical topic for all members of society and certainly for all students in postsecondary education. The curriculum will feature a wide range of topic presentations on the (i) science of climate change; and (ii) multidisciplinary solutions to climate change challenges. The end result will be an online textbook featuring short contributions from session participants and other invited specialists. First authors in this AGU Education Session will provide a 20-minute comprehensive lecture that will be recorded and shared as part of the online textbook. The recorded talks will be merged with author provided PowerPoint slides and appropriate high definition video footage to support the discussion, where possible. Authors will be asked to sign a waiver allowing the video recording to be part of the online textbook. Access to the videos and textbook chapters will be provided online to students registered in recognized university classes on climate change science and solutions for a modest fee.

Feature Selection for Chemical Sensor Arrays Using Mutual Information

PubMed Central

Wang, X. Rosalind; Lizier, Joseph T.; Nowotny, Thomas; Berna, Amalia Z.; Prokopenko, Mikhail; Trowell, Stephen C.

2014-01-01

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays. PMID:24595058

Impact of Online Instructional Game Features on College Students' Perceived Motivational Support and Cognitive Investment: A Structural Equation Modeling Study

ERIC Educational Resources Information Center

Huang, Wenhao David; Johnson, Tristan E.; Han, Seung-Hyun Caleb

2013-01-01

Colleges and universities have begun to understand the instructional potential of digital game-based learning (DGBL) due to digital games' immersive features. These features, however, might overload learners as excessive motivational and cognitive stimuli thus impeding intended learning. Current research, however, lacks empirical evidences to…

Online counseling: a narrative and critical review of the literature.

PubMed

Richards, Derek; Viganó, Noemi

2013-09-01

This article aimed to critically review the literature on online counseling. Database and hand-searches were made using search terms and eligibility criteria, yielding a total of 123 studies. The review begins with what characterizes online counseling. Outcome and process research in online counseling is reviewed. Features and cyberbehaviors of online counseling such as anonymity and disinhibition, convenience, time-delay, the loss of social signaling, and writing behavior in cyberspace are discussed. Ethical behavior, professional training, client suitability, and clients' and therapists' attitudes and experiences of online counseling are reviewed. A growing body of knowledge to date is positive in showing that online counseling can have a similar impact and is capable of replicating the facilitative conditions as face-to-face encounters. A need remains for stronger empirical evidence to establish efficacy and effectiveness and to understand better the unique mediating and facilitative variables. © 2013 Wiley Periodicals, Inc.

Student Participation and Grade Performance in an Undergraduate Online Learning Environment

ERIC Educational Resources Information Center

V. KunhiMohamed, Balkeese Binti

2012-01-01

This study explored learning and teaching of online classes. Examining the relationship between undergraduate students' participation and their final grades in five selected courses in an online learning environment and exploring differences between the demographics characteristics of age, race, and gender to students' participation (total number…

Student Motivations for Choosing Online Classes

ERIC Educational Resources Information Center

Harris, Heidi S.; Martin, Elwyn W.

2012-01-01

Increasing budget pressures on universities are causing many to turn to online education to solve their budget woes. However, as the marketplace for online learning expands, so does the opportunity for students to become ever more selective of the programs and universities they choose. The researchers sought to identify those factors that motivate…

SLAC Library - Online Particle Physics Information

Science.gov Websites

Background Knowledge Particle Physics Lessons and Activities Astronomy and Astrophysics Lessons and Online Particle Physics Information Compiled by Revised: April, 201 7 This annotated list provides a highly selective set of online resources that are useful to the particle physics community. It

Online Education Vendor Partners: When and How to Select One

ERIC Educational Resources Information Center

Hoffman, Michael S.

2012-01-01

Higher education institutions are increasingly looking to online education as a means to broaden their market reach, increase student enrollments and ultimately realize increased tuition revenue. Many institutions, however, find that they have insufficient infrastructure resources to launch one or more fully online learning programs. A small…

Greeting You Online: Selecting Web-Based Conferencing Tools for Instruction in E-Learning Mode

ERIC Educational Resources Information Center

Li, Judy

2014-01-01

Academic distance learning programs have gained popularity and added to the demand for online library services. Librarians are now conducting instruction for distance learning students beyond their traditional work. Technology advancements have enhanced the delivery mode in distance learning across academic disciplines. Online conference tools…

Rough sets and Laplacian score based cost-sensitive feature selection

PubMed Central

Yu, Shenglong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. PMID:29912884

Rough sets and Laplacian score based cost-sensitive feature selection.

PubMed

Yu, Shenglong; Zhao, Hong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

What is included with your online e-cigarette order? An analysis of e-cigarette shipping, product and packaging features.

PubMed

Kong, Amanda Y; Derrick, Jason C; Abrantes, Anthony S; Williams, Rebecca S

2016-06-29

The electronic cigarette industry is growing, with youth using e-cigarettes at higher rates than they are using cigarettes, and retail and online sales projected to reach $10 billion in 2017. Minimal regulation of the production and marketing of e-cigarettes exists to date, which has allowed companies to promote unsupported claims. We assessed the shipping, product features and packaging of a wide variety of e-cigarettes purchased online by adults and youth. The most popular internet e-cigarette vendors were identified from a larger study of internet tobacco vendors. Between August 2013 and June 2014, adults made 56 purchase attempts from online vendors, and youth made 98 attempts. Packages received were assessed for exterior and internal packaging features, including product information, health warnings and additional materials. We analysed a total of 125 orders featuring 86 unique brands of e-cigarettes. The contents were rarely indicated on package exteriors. Product information came with just 60% of orders and just 38.4% included an instruction manual. Only 44.6% of products included a health warning, and some had unsupported claims, such as lack of secondhand smoke exposure. Additionally, some products were leaking e-liquid and battery fluid on arrival. A large variety of e-cigarette products are manufactured and marketed to consumers. Many products do not include instructions for use, and unsupported claims are being presented to consumers. Effective federal regulation of the manufacturing, packaging, product information and health claims surrounding e-cigarettes is necessary to ensure consumers are presented with accurate e-cigarette use information. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

What is included with your online e-cigarette order? An analysis of e-cigarette shipping, product and packaging features

PubMed Central

Kong, Amanda Y; Derrick, Jason C; Abrantes, Anthony S

2016-01-01

Background The electronic cigarette industry is growing, with youth using e-cigarettes at higher rates than they are using cigarettes, and retail and online sales projected to reach $10 billion in 2017. Minimal regulation of the production and marketing of e-cigarettes exists to date, which has allowed companies to promote unsupported claims. We assessed the shipping, product features and packaging of a wide variety of e-cigarettes purchased online by adults and youth. Methods The most popular internet e-cigarette vendors were identified from a larger study of internet tobacco vendors. Between August 2013 and June 2014, adults made 56 purchase attempts from online vendors, and youth made 98 attempts. Packages received were assessed for exterior and internal packaging features, including product information, health warnings and additional materials. Results We analysed a total of 125 orders featuring 86 unique brands of e-cigarettes. The contents were rarely indicated on package exteriors. Product information came with just 60% of orders and just 38.4% included an instruction manual. Only 44.6% of products included a health warning, and some had unsupported claims, such as lack of secondhand smoke exposure. Additionally, some products were leaking e-liquid and battery fluid on arrival. Conclusions A large variety of e-cigarette products are manufactured and marketed to consumers. Many products do not include instructions for use, and unsupported claims are being presented to consumers. Effective federal regulation of the manufacturing, packaging, product information and health claims surrounding e-cigarettes is necessary to ensure consumers are presented with accurate e-cigarette use information. PMID:27357936

Case-based fracture image retrieval.

PubMed

Zhou, Xin; Stern, Richard; Müller, Henning

2012-05-01

Case-based fracture image retrieval can assist surgeons in decisions regarding new cases by supplying visually similar past cases. This tool may guide fracture fixation and management through comparison of long-term outcomes in similar cases. A fracture image database collected over 10 years at the orthopedic service of the University Hospitals of Geneva was used. This database contains 2,690 fracture cases associated with 43 classes (based on the AO/OTA classification). A case-based retrieval engine was developed and evaluated using retrieval precision as a performance metric. Only cases in the same class as the query case are considered as relevant. The scale-invariant feature transform (SIFT) is used for image analysis. Performance evaluation was computed in terms of mean average precision (MAP) and early precision (P10, P30). Retrieval results produced with the GNU image finding tool (GIFT) were used as a baseline. Two sampling strategies were evaluated. One used a dense 40 × 40 pixel grid sampling, and the second one used the standard SIFT features. Based on dense pixel grid sampling, three unsupervised feature selection strategies were introduced to further improve retrieval performance. With dense pixel grid sampling, the image is divided into 1,600 (40 × 40) square blocks. The goal is to emphasize the salient regions (blocks) and ignore irrelevant regions. Regions are considered as important when a high variance of the visual features is found. The first strategy is to calculate the variance of all descriptors on the global database. The second strategy is to calculate the variance of all descriptors for each case. A third strategy is to perform a thumbnail image clustering in a first step and then to calculate the variance for each cluster. Finally, a fusion between a SIFT-based system and GIFT is performed. A first comparison on the selection of sampling strategies using SIFT features shows that dense sampling using a pixel grid (MAP = 0.18) outperformed the SIFT detector-based sampling approach (MAP = 0.10). In a second step, three unsupervised feature selection strategies were evaluated. A grid parameter search is applied to optimize parameters for feature selection and clustering. Results show that using half of the regions (700 or 800) obtains the best performance for all three strategies. Increasing the number of clusters in clustering can also improve the retrieval performance. The SIFT descriptor variance in each case gave the best indication of saliency for the regions (MAP = 0.23), better than the other two strategies (MAP = 0.20 and 0.21). Combining GIFT (MAP = 0.23) and the best SIFT strategy (MAP = 0.23) produced significantly better results (MAP = 0.27) than each system alone. A case-based fracture retrieval engine was developed and is available for online demonstration. SIFT is used to extract local features, and three feature selection strategies were introduced and evaluated. A baseline using the GIFT system was used to evaluate the salient point-based approaches. Without supervised learning, SIFT-based systems with optimized parameters slightly outperformed the GIFT system. A fusion of the two approaches shows that the information contained in the two approaches is complementary. Supervised learning on the feature space is foreseen as the next step of this study.

How and When Accentuation Influences Temporally Selective Attention and Subsequent Semantic Processing during On-Line Spoken Language Comprehension: An ERP Study

ERIC Educational Resources Information Center

Li, Xiao-qing; Ren, Gui-qin

2012-01-01

An event-related brain potentials (ERP) experiment was carried out to investigate how and when accentuation influences temporally selective attention and subsequent semantic processing during on-line spoken language comprehension, and how the effect of accentuation on attention allocation and semantic processing changed with the degree of…

Phenomenological Study of Business Models Used to Scale Online Enrollment at Institutions of Higher Education

ERIC Educational Resources Information Center

Williams, Dana E.

2012-01-01

The purpose of this qualitative phenomenological study was to explore factors for selecting a business model for scaling online enrollment by institutions of higher education. The goal was to explore the lived experiences of academic industry experts involved in the selection process. The research question for this study was: What were the lived…

A decision tree-based on-line preventive control strategy for power system transient instability prevention

NASA Astrophysics Data System (ADS)

Xu, Yan; Dong, Zhao Yang; Zhang, Rui; Wong, Kit Po

2014-02-01

Maintaining transient stability is a basic requirement for secure power system operations. Preventive control deals with modifying the system operating point to withstand probable contingencies. In this article, a decision tree (DT)-based on-line preventive control strategy is proposed for transient instability prevention of power systems. Given a stability database, a distance-based feature estimation algorithm is first applied to identify the critical generators, which are then used as features to develop a DT. By interpreting the splitting rules of DT, preventive control is realised by formulating the rules in a standard optimal power flow model and solving it. The proposed method is transparent in control mechanism, on-line computation compatible and convenient to deal with multi-contingency. The effectiveness and efficiency of the method has been verified on New England 10-machine 39-bus test system.

A Qualitative Study to Examine Feasibility and Design of an Online Social Networking Intervention to Increase Physical Activity in Teenage Girls.

PubMed

Van Kessel, Gisela; Kavanagh, Madeleine; Maher, Carol

2016-01-01

Online social networks present wide-reaching and flexible platforms through which to deliver health interventions to targeted populations. This study used a social marketing approach to explore teenage girls' perceptions of physical activity and the potential use of online social networks to receive a physical activity intervention. Six focus groups were conducted with 19 Australian teenage girls (ages 13 to 18 years) with varying levels of physical activity and socioeconomic status. A semi-structured format was used, with groups discussion transcribed verbatim. Content analysis identified emergent themes, with triangulation and memos used to ensure accuracy. Physical activity was most appealing when it emphasised sport, exercise and fitness, along with opportunities for socialisation with friends and self-improvement. Participants were receptive to delivery of a physical activity intervention via online social networks, with Facebook the most widely reported site. Participants commonly accessed online social networks via mobile devices and particularly smartphones. Undesirable features included promotion of physical activity in terms of walking; use of cartoon imagery; use of humour; and promotion of the intervention via schools, each of which were considered "uncool". Participants noted that their parents were likely to be supportive of them using an online social networking physical activity intervention, particularly if not promoted as a weight loss intervention. This study identified key features likely to increase the feasibility and retention of an online social networking physical activity intervention for teenage girls. Guidelines for the design of interventions for teenage girls are provided for future applications.

A Qualitative Study to Examine Feasibility and Design of an Online Social Networking Intervention to Increase Physical Activity in Teenage Girls

PubMed Central

Van Kessel, Gisela; Kavanagh, Madeleine; Maher, Carol

2016-01-01

Background Online social networks present wide-reaching and flexible platforms through which to deliver health interventions to targeted populations. This study used a social marketing approach to explore teenage girls’ perceptions of physical activity and the potential use of online social networks to receive a physical activity intervention. Methods Six focus groups were conducted with 19 Australian teenage girls (ages 13 to 18 years) with varying levels of physical activity and socioeconomic status. A semi-structured format was used, with groups discussion transcribed verbatim. Content analysis identified emergent themes, with triangulation and memos used to ensure accuracy. Results Physical activity was most appealing when it emphasised sport, exercise and fitness, along with opportunities for socialisation with friends and self-improvement. Participants were receptive to delivery of a physical activity intervention via online social networks, with Facebook the most widely reported site. Participants commonly accessed online social networks via mobile devices and particularly smartphones. Undesirable features included promotion of physical activity in terms of walking; use of cartoon imagery; use of humour; and promotion of the intervention via schools, each of which were considered “uncool”. Participants noted that their parents were likely to be supportive of them using an online social networking physical activity intervention, particularly if not promoted as a weight loss intervention. Conclusion This study identified key features likely to increase the feasibility and retention of an online social networking physical activity intervention for teenage girls. Guidelines for the design of interventions for teenage girls are provided for future applications. PMID:26934191

"Any girls want to chat press 911": partner selection in monitored and unmonitored teen chat rooms.

PubMed

Smahel, David; Subrahmanyam, Kaveri

2007-06-01

We examined the search for partners by participants in two teen chat services having different ecologies. Over 12,000 utterances from monitored and unmonitored chat rooms were analyzed to assess online partner selection attempts and to see how such attempts may be influenced by the presence of an adult monitor. We found that the search for partners is ubiquitous in adolescents' online haunts, just as it is in their offline lives, and approximately two requests for a partner occur each minute. Although partner selection appears to be an important activity in online teen chat rooms, there are differences in frequency and format (e.g., the use of numerals, sexualized requests) as a function of participants' age and gender, and chat room ecology (monitored vs. unmonitored).

New Dimensions for the Online Catalog: The Dartmouth College Library Experience [and] TOC/DOC at Caltech: Evolution of Citation Access Online [and] Locally Loaded Databases in Arizona State University's Online Catalog Using the CARL System.

ERIC Educational Resources Information Center

Klemperer, Katharina; And Others

1989-01-01

Each of three articles describes an academic library's online catalog that includes locally created databases. Topics covered include database and software selection; systems design and development; database producer negotiations; problems encountered during implementation; database loading; training and documentation; and future plans. (CLB)

Activites to Support and Assess Student Understanding of Earth Data

NASA Astrophysics Data System (ADS)

Prothero, W. A.; Regev, J.

2004-12-01

In order to use data effectively, learners must construct a mental model that allows them to understand and express spatial relationships in data, relationships between different data types, and relationships between the data and a theoretical model. Another important skill is the ability to identify gross patterns and distinguish them from details that may require increasingly sophisticated models. Students must also be able to express their understanding, both to help them frame their understanding for themselves, and for assessment purposes. Research in learning unequivocally shows that writing about a subject increases understanding of that subject. In UCSB's general education oceanography class, a series of increasingly demanding activities culminates in two science papers that use earth data. These activities are: 1) homework problems, 2) in-class short writing activities, 3) lab section exploration activities and presentations, and 4) the science paper. The subjects of the two papers are: Plate Tectonics and Ocean and Climate. Each student is a member of a group that adopts a country and must relate their paper to the environment of their country. Data are accessed using the "Our Dynamic Planet" and "Global Ocean Data Viewer" (GLODV) CD's. These are integrated into EarthEd Online, a software package which supports online writing, review, commenting, and return to the student. It also supports auto-graded homework assignments, grade calculation, and other class management functions. The writing assignments emphasize the construction of a scientific argument. This process is explained explicitly, requiring statements that: 1) include an observation or description of an observation (e.g. elevation profiles, quakes), 2) name features based on the observation (e.g. trench, ridge), 3) describe of features (e.g. trends NW, xxxkm long), 4) describe relationships between features (e.g. quakes are parallel to trench), 5) describe a model or theory (e.g. cartoon type representation of a subduction zone), and 6) describe the relationship between the model/theory and the data. Students generate and select data representations with the appropriate data display software, which seamlessly uploads each generated image to the student's personal storage area (on the class server). There they are available to be linked to the writing text. The assignment is "handed in" online, where it is commented, graded according to a rubric, and returned. Students rate the writing assignment as one of the most effective activities that contributes to their learning in the course.

Choosing your network: social preferences in an online health community.

PubMed

Centola, Damon; van de Rijt, Arnout

2015-01-01

A growing number of online health communities offer individuals the opportunity to receive information, advice, and support from peers. Recent studies have demonstrated that these new online contacts can be important informational resources, and can even exert significant influence on individuals' behavior in various contexts. However little is known about how people select their health contacts in these virtual domains. This is because selection preferences in peer networks are notoriously difficult to detect. In existing networks, unobserved pressures on tie formation--such as common organizational memberships, introductions to friends of friends, or limitations on accessibility--may mistakenly be interpreted as individual preferences for interacting/not interacting with others. We address these issues by adopting a social media approach to studying network formation. We study social selection using an in vivo study within an online exercise program, in which anonymous participants have equal opportunities for initiating relationships with other program members. This design allows us to identify individuals' preferences for health contacts, and to evaluate what these preferences imply for members' access to new kinds of health information, and for the kinds of social influences to which they are exposed. The study was conducted within a goal-oriented fitness competition, in which participation was greatest among a small core of active individuals. Our results show that the active participants displayed indifference to the fitness and exercise profiles of others, disregarding information about others' fitness levels, exercise preferences, and workout experiences, instead selecting partners almost entirely on the basis of similarities on gender, age, and BMI. Interestingly, the findings suggest that rather than expanding and diversifying their sources of health information, participants' choices limited the value of their online resources by selecting contacts based on characteristics that are common sources of homophily in offline relationships. In light of our findings, we discuss design principles that may be useful for organizations and policy makers trying to improve the value of participants' social capital within online health programs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Publication times, impact factors, and advance online publication in ophthalmology journals.

PubMed

Chen, Haoyu; Chen, Chun Hui; Jhanji, Vishal

2013-08-01

Publication speed of peer-reviewed journals may play a major role in early dissemination of knowledge and may raise the citation index. In this study, we evaluated the publication speed of ophthalmology journals. Observational study. Observational study of bibliometric data in published ophthalmology journals. A list of ophthalmic journals featured in the 2010 Journal Citation Report was obtained on September 1, 2011. A total of 12 articles were chosen randomly from each of these journals published between January and December 2010. Median publication time and interquartile range (IQR) were obtained from the full texts of the published articles. Time lag between submission and revision, acceptance, and publication of the manuscripts was calculated. Correlation between publication time lag and journal impact factor as well as advance online publication was analyzed. A total of 51 ophthalmic journals were included. There was no statistically significant difference in the impact factors of journals based on their reporting of submission, revision, or acceptance times of the manuscripts (both P>0.05, Wilcoxon test). The median peer review and publication time of all ophthalmology journals was 133 days (IQR, 100.5-171.5) and 100 days (IQR, 62.9-166.3), respectively. There was no correlation between the journal impact factors and publication time lag (Spearman correlation). Approximately half of the ophthalmology journals (n = 26; 50.98%) published online in advance. Journals with advance online publication had higher impact factors compared with those without this feature (median, 1.692 [IQR, 1.05-2.80] vs. 1.02 [0.39-1.53]; P = 0.015, Mann-Whitney U test). For journals with advance online publication, the median time from acceptance to advance online publication (74.3 days [IQR, 48.3-115 days]) was significantly shorter than the median time between acceptance and print publication (170.75 days [IQR, 101.4-217 days]; P<0.001, Wilcoxon test). Publication time lag in ophthalmology journals was not correlated with journal impact factors. Advance online publication facility was provided by only half of the ophthalmology journals published in 2010. Journals with advance online publication had a higher impact factor compared with those without this feature. The author(s) have no proprietary or commercial interest in any materials discussed in this article. Copyright © 2013 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Citing geospatial feature inventories with XML manifests

NASA Astrophysics Data System (ADS)

Bose, R.; McGarva, G.

2006-12-01

Today published scientific papers include a growing number of citations for online information sources that either complement or replace printed journals and books. We anticipate this same trend for cartographic citations used in the geosciences, following advances in web mapping and geographic feature-based services. Instead of using traditional libraries to resolve citations for print material, the geospatial citation life cycle will include requesting inventories of objects or geographic features from distributed geospatial data repositories. Using a case study from the UK Ordnance Survey MasterMap database, which is illustrative of geographic object-based products in general, we propose citing inventories of geographic objects using XML feature manifests. These manifests: (1) serve as a portable listing of sets of versioned features; (2) could be used as citations within the identification portion of an international geospatial metadata standard; (3) could be incorporated into geospatial data transfer formats such as GML; but (4) can be resolved only with comprehensive, curated repositories of current and historic data. This work has implications for any researcher who foresees the need to make or resolve references to online geospatial databases.

Neural correlates of the encoding of multimodal contextual features

PubMed Central

Gottlieb, Lauren J.; Wong, Jenny; de Chastelaine, Marianne; Rugg, Michael D.

2012-01-01

Functional magnetic resonance imaging (fMRI) was employed to identify neural regions engaged during the encoding of contextual features belonging to different modalities. Subjects studied objects that were presented to the left or right of fixation. Each object was paired with its name, spoken in either a male or a female voice. The test requirement was to discriminate studied from unstudied pictures and, for each picture judged old, to retrieve its study location and the gender of the voice that spoke its name. Study trials associated with accurate rather than inaccurate location memory demonstrated enhanced activity in the fusiform and parahippocampal cortex and the hippocampus and reduced activity (a negative subsequent memory effect) in the medial occipital cortex. Successful encoding of voice information was associated with enhanced study activity in the right middle superior temporal sulcus and activity reduction in the right superior frontal cortex. These findings support the proposal that encoding of a contextual feature is associated with enhanced activity in regions engaged during its online processing. In addition, they indicate that negative subsequent memory effects can also demonstrate feature-selectivity. Relative to other classes of study trials, trials for which both contextual features were later retrieved demonstrated enhanced activity in the lateral occipital complex and reduced activity in the temporo-parietal junction. These findings suggest that multifeatural encoding was facilitated when the study item was processed efficiently and study processing was not interrupted by redirection of attention toward extraneous events. PMID:23166292

Health information seeking on the Internet: The role of involvement in searching for and assessing online health information.

PubMed

Park, Sun-Young; Go, Eun

2016-01-01

This study focuses on how young people with differing levels of involvement seek and evaluate information about the human papillomavirus online. The results, which are drawn from an experiment and a self-administered survey, suggest that compared to people with a low level of involvement, people with a high level of involvement engage in more information search activity. The results also indicate that those with a high level of involvement in a given subject place a higher value on a website's message features than on its structural features. Implications, limitations, and suggestions for future research are discussed.

On Social e-Learning

NASA Astrophysics Data System (ADS)

Kim, Won; Jeong, Ok-Ran

Social Web sites include social networking sites and social media sites. They make it possible for people to share user-created contents online and to interact and stay connected with their online people networks. The social features of social Web sites, appropriately adapted, can help turn e-learning into social e-learning and make e-learning significantly more effective. In this paper, we develop requirements for social e-learning systems. They include incorporating the many of the social features of social Web sites, accounting for all key stakeholders and learning subjects, and curbing various types of misuses by people. We also examine the capabilities of representative social e-learning Web sites that are available today.

Effect of an Internet-based Curriculum on Postgraduate Education

PubMed Central

Sisson, Stephen D; Hughes, Mark T; Levine, David; Brancati, Frederick L

2004-01-01

We hypothesized that the Internet could be used to disseminate and evaluate a curriculum in ambulatory care, and that internal medicine residency program directors would value features made possible by online dissemination. An Internet-based ambulatory care curriculum was developed and marketed to internal medicine residency program directors. Utilization and knowledge outcomes were tracked by the website; opinions of program directors were measured by paper surveys. Twenty-four programs enrolled with the online curriculum. The curriculum was rated favorably by all programs, test scores on curricular content improved significantly, and program directors rated highly features made possible by an Internet-based curriculum. PMID:15109313

Where's the Hayward Fault? A Green Guide to the Fault

USGS Publications Warehouse

Stoffer, Philip W.

2008-01-01

This report describes self-guided field trips to one of North America?s most dangerous earthquake faults?the Hayward Fault. Locations were chosen because of their easy access using mass transit and/or their significance relating to the natural and cultural history of the East Bay landscape. This field-trip guidebook was compiled to help commemorate the 140th anniversary of an estimated M 7.0 earthquake that occurred on the Hayward Fault at approximately 7:50 AM, October 21st, 1868. Although many reports and on-line resources have been compiled about the science and engineering associated with earthquakes on the Hayward Fault, this report has been prepared to serve as an outdoor guide to the fault for the interested public and for educators. The first chapter is a general overview of the geologic setting of the fault. This is followed by ten chapters of field trips to selected areas along the fault, or in the vicinity, where landscape, geologic, and man-made features that have relevance to understanding the nature of the fault and its earthquake history can be found. A glossary is provided to define and illustrate scientific term used throughout this guide. A ?green? theme helps conserve resources and promotes use of public transportation, where possible. Although access to all locations described in this guide is possible by car, alternative suggestions are provided. To help conserve paper, this guidebook is available on-line only; however, select pages or chapters (field trips) within this guide can be printed separately to take along on an excursion. The discussions in this paper highlight transportation alternatives to visit selected field trip locations. In some cases, combinations, such as a ride on BART and a bus, can be used instead of automobile transportation. For other locales, bicycles can be an alternative means of transportation. Transportation descriptions on selected pages are intended to help guide fieldtrip planners or participants choose trip destinations based on transportation options, interests, or special needs.

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

PubMed Central

Li, Zuojin; Li, Shengbo Eben; Li, Renjie; Cheng, Bo; Shi, Jinliang

2017-01-01

This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn) features from fixed sliding windows on real-time steering wheel angles time series. After that, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: “wake” and “drowsy”. The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the “awake” state, and 15.15% false detections of the “drowsy” state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue. PMID:28257094

Evolution of the Instructional Design in a Series of Online Workshops

ERIC Educational Resources Information Center

Patry, Anne; Brown, Elizabeth Campbell; Rousseau, Rémi; Caron, Jeanette

2015-01-01

This case recounts the story of the design and production of a series of online workshops for French-speaking healthcare professionals in Canada. The project spans a couple of years and, despite encountering some challenges, succeeds in large part because of its strong foundation: the instructional design. This case study features an instructional…

Managing Written and Oral Negative Feedback in a Synchronous Online Teaching Situation

ERIC Educational Resources Information Center

Guichon, Nicolas; Betrancourt, Mireille; Prie, Yannick

2012-01-01

This case study focuses on the feedback that is provided by tutors to learners in the course of synchronous online teaching. More specifically, we study how trainee tutors used the affordances of Visu, an experimental web videoconferencing system, to provide negative feedback. Visu features classical functionalities such as video and chat, and it…

The Design and Organisation Features of Two Online Courses: A Case Study of Their Emergence and Evolution

ERIC Educational Resources Information Center

Richards, Kari

2017-01-01

This study reports the findings of a qualitative case study that examined how elements of design and organization were conceptualized and enacted in two graduate level online courses, and, how these conceptualizations and enactments evolved. Data was collected through interviews and "think-alouds" with the course instructors and through…

Massive Multiplayer Online Role Playing Games and Interaction: A Measurable Model of Interaction for Online Learning

ERIC Educational Resources Information Center

Anderson, Bodi

2014-01-01

This current study examines the need for operational definitions of the concept of interaction in distance education studies. It is proposed that a discourse analysis of linguistic features conversation noted as being representative of interaction can be used to operationalize interaction in synchronous CMC. This study goes on compare two…

Can Khan Move the Bell Curve to the Right?

ERIC Educational Resources Information Center

Kronholz, June

2012-01-01

This article features Khan Academy which offers an online math program and short video lectures embedded in the "module", or math concept, that fit students' goals. By now, more than 1 million people have watched the online video in which Salman Khan--a charming MIT math whiz, Harvard Business School graduate, and former Boston hedge-fund…

Use of Online Assessment Tools by Instructional Designers-by-Assignment: Necessary Features and Functionalities

ERIC Educational Resources Information Center

Halloran, Jo-Ann

2013-01-01

Government entities set criteria for institutions that have teacher educator programs to use online assessment tools to show continuous ongoing evaluation, and use data from the tools to guide the improvement of courses. The purpose of this qualitative, multi-case study was to discover how Instructional Designers-by-Assignment (IDBA) are using…

Learner Differences in Perceived Satisfaction of an Online Learning: An Extension to the Technology Acceptance Model in an Arabic Sample

ERIC Educational Resources Information Center

Al-Azawei, Ahmed; Lundqvist, Karsten

2015-01-01

Online learning constitutes the most popular distance-learning method, with flexibility, accessibility, visibility, manageability and availability as its core features. However, current research indicates that its efficacy is not consistent across all learners. This study aimed to modify and extend the factors of the Technology Acceptance Model…

System Design and Cataloging Meet the User: User Interfaces to Online Public Access Catalogs.

ERIC Educational Resources Information Center

Yee, Martha M.

1991-01-01

Discusses features of online public access catalogs: (1) demonstration of relationships between records; (2) provision of entry vocabularies; (3) arrangement of multiple entries on the screen; (4) provision of access points; (5) display of single records; and (6) division of catalogs into separate files or indexes. User studies and other research…

Massively Multiplayer Online Role-Playing Games as Arenas for Second Language Learning

ERIC Educational Resources Information Center

Peterson, Mark

2010-01-01

This article investigates contemporary research on the use of massively multiplayer online role-playing games (MMORPGs) in language education. The development and key features of these games are explored. This is followed by an examination of the theories proposed as a basis for game-based learning, and the claims made regarding the value of…

A Visualisation Tool to Aid Exploration of Students' Interactions in Asynchronous Online Communication

ERIC Educational Resources Information Center

Jyothi, Sujana; McAvinia, Claire; Keating, John

2012-01-01

Much research in recent years has focused on the introduction of virtual learning environments (VLEs) to universities, documenting practice, and sharing experience ([2], [9], [45] and [58]). Attention has been directed towards the importance of online dialogue for learning as a defining feature of the VLE. Communicative tools are an important…

EPCAL: ETS Platform for Collaborative Assessment and Learning. Research Report. ETS RR-17-49

ERIC Educational Resources Information Center

Hao, Jiangang; Liu, Lei; von Davier, Alina A.; Lederer, Nathan; Zapata-Rivera, Diego; Jaki, Peter; Bakkenson, Michael

2017-01-01

Most existing software tools for online collaboration are designed to support the collaboration itself instead of the study of collaboration with a systematic team and task management system. In this report, we identify six important features for a platform to facilitate the study of online collaboration. We then introduce the Educational Testing…

Online Reading Strategies at Work: What Teachers Think and What Students Do

ERIC Educational Resources Information Center

Huang, Hsin-Chou

2013-01-01

This study designed and developed a web-based reading strategy training program and investigated students' use of its features and EFL teachers' and students' perceptions of the program. The recent proliferation of online reading materials has made information easily available to L2 readers; however, L2 readers' ability to deal with them requires…

The Interrelationship of Emotion and Cognition when Students Undertake Collaborative Group Work Online: An Interdisciplinary Approach

ERIC Educational Resources Information Center

Robinson, Kathy

2013-01-01

In order to determine how emotions and cognition are experienced during collaborative group work online students' descriptions of their learning experience were interpreted using a qualitative approach. A common feature of these accounts was reference to difficulties and problems. Four main themes were identified from this data set. Two of the…

Literacy Agents Online: E-Discussion Forums for Advancing Adults' Literacy Practices

ERIC Educational Resources Information Center

Guzzetti, Barbara J.; Foley, Leslie M.

2014-01-01

This study explored how adults used a self-selected online forum to advance their own and others' literacy practices. The study was a discourse-centered online ethnography using triangulated methods, including analysis of list archives, semi-structured and informal interviews, and document collection. These data were analyzed by discourse…

The Effects of Podcasting on Student Perceptions of Community within the Online Learning Environment

ERIC Educational Resources Information Center

Ferguson, Larry A.

2010-01-01

This study examined the effects of using podcasts as a medium of communication toward affecting student perception of community within the online learning environment. A control and treatment group comparison design was employed with 184 online undergraduate students selected through purposive sampling at Ashland Community and Technical College.…

Identifying the Professional Development Needs of Adjunct Faculty Using an Online Delphi

ERIC Educational Resources Information Center

Cuddie, Stephani B.

2016-01-01

The purpose of this online Delphi was to explore the professional development needs and preferences of adjunct faculty, specifically those who teach online. The study involved adjunct faculty who were categorized by their self-selected type of adjunct faculty member: specialist, aspiring academic, professional/freelancer, and career-ender. Through…

National Online Meeting Proceedings (15th, New York, New York, May 10-12, 1994).

ERIC Educational Resources Information Center

1994

This proceedings contains 58 papers that were reviewed and selected for presentation at the 1994 National Online Meeting. The introduction, "Highlights of the Online/CD-ROM Database Industry: Implications of the Internet for Database Producers" by Martha E. Williams, provides statistics regarding databases, database records, database…

An Evaluation of Online Business Databases.

ERIC Educational Resources Information Center

van der Heyde, Angela J.

The purpose of this study was to evaluate the credibility and timeliness of online business databases. The areas of evaluation were the currency, reliability, and extent of financial information in the databases. These were measured by performing an online search for financial information on five U.S. companies. The method of selection for the…

Graduate Students' Knowledge Construction and Attitudes toward Online Synchronous Videoconferencing Collaborative Learning Environments

ERIC Educational Resources Information Center

Akarasriworn, Chatchada; Ku, Heng-Yu

2013-01-01

This study investigated 28 graduate students' knowledge construction and attitudes toward online synchronous videoconferencing collaborative learning environments. These students took an online course, self-selected 3 or 4 group members to form groups, and worked on projects across 16 weeks. Each group utilized Elluminate "Live!" for the…

Evaluating Technology to Prevent Academic Integrity Violations in Online Environments

ERIC Educational Resources Information Center

Brown, Victoria

2018-01-01

Protection of academic integrity in online environments can be challenging. Understanding how the technology works and concerns about each of the methods for monitoring online interactions can assist in the selection of the best proctoring tools. Depending on the content, the type of assessment and the comfort level with the technology, a…

Harvesting Alternative Credit Transfer Students: Redefining Selectivity in Your Online Learning Program Enrollment Leads

ERIC Educational Resources Information Center

Corlett, Bradly

2014-01-01

Several recent issues and trends in online education have resulted in consolidation of efforts for Massive Open Online Courses (MOOCs), increased Open Educational Resources (OER) in the form of asynchronous course repositories, with noticeable increases in governance and policy amplification. These emerging enrollment trends in alternative online…

Latino/a Cultural Perspectives of Social Presence: A Case Study

ERIC Educational Resources Information Center

Plotts, Courtney

2018-01-01

Many Latino/a students select online learning as a viable option for completing a college degree. Yet, Latino/a perspectives regarding online social presence is unknown. This study explored Latino/a students' perceptions of social presence in online courses as related to their culture perspectives of interpersonal communication, values, norms and…

Intuitiveness of Symbol Features for Air Traffic Management

NASA Technical Reports Server (NTRS)

Ngo, Mary Kim; Vu, Kim-Phuong L.; Thorpe, Elaine; Battiste, Vernol; Strybel, Thomas Z.

2012-01-01

We present the results of two online surveys asking participants to indicate what type of air traffic information might be conveyed by a number of symbols and symbol features (color, fill, text, and shape). The results of this initial study suggest that the well-developed concepts of ownership, altitude, and trajectory are readily associated with certain symbol features, while the relatively novel concept of equipage was not clearly associated with any specific symbol feature.

"I Had to Live, Breathe, and Write My Character": Character Selection and Student Engagement in an Online Role-Play Simulation

ERIC Educational Resources Information Center

Rector-Aranda, Amy; Raider-Roth, Miriam; Glaser, Noah; Behrman, Matthew

2017-01-01

This study explores the relationship between character selection and student engagement in the Jewish Court of All Time (JCAT), an online and classroom-based role-playing simulation of a current events court case with Jewish historical roots. Analyzing students' responses to three questions posed in an out-of-character JCAT discussion forum, we…

The Effect of Implementing Knowledge Management System in Supplier Selection Content to Improve Learning Performance of Online Travel Agencies Staff

ERIC Educational Resources Information Center

Ariya, Pakinee; Chakpitak, Nopasit; Sureepong, Pradorn

2016-01-01

Supplier selection knowledge of OTAs businesses is one of the most valuable and significant knowledge since OTAs now operate businesses that gain their benefits from having many kinds of tourism products and services for customers to browse from in their own online booking systems. The better the suppliers, the more successful will OTAs be. The…

Assistive Software for Disabled Learners

ERIC Educational Resources Information Center

Clark, Sharon; Baggaley, Jon

2004-01-01

Previous reports in this series (#32 and 36) have discussed online software features of value to disabled learners in distance education. The current report evaluates four specific assistive software products with useful features for visually and hearing impaired learners: "ATutor", "ACollab", "Natural Voice", and "Just Vanilla". The evaluative…

Prediction of pork loin quality using online computer vision system and artificial intelligence model.

PubMed

Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David

2018-06-01

The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved.

Establishment of the mathematical model for diagnosing the engine valve faults by genetic programming

NASA Astrophysics Data System (ADS)

Yang, Wen-Xian

2006-05-01

Available machine fault diagnostic methods show unsatisfactory performances on both on-line and intelligent analyses because their operations involve intensive calculations and are labour intensive. Aiming at improving this situation, this paper describes the development of an intelligent approach by using the Genetic Programming (abbreviated as GP) method. Attributed to the simple calculation of the mathematical model being constructed, different kinds of machine faults may be diagnosed correctly and quickly. Moreover, human input is significantly reduced in the process of fault diagnosis. The effectiveness of the proposed strategy is validated by an illustrative example, in which three kinds of valve states inherent in a six-cylinders/four-stroke cycle diesel engine, i.e. normal condition, valve-tappet clearance and gas leakage faults, are identified. In the example, 22 mathematical functions have been specially designed and 8 easily obtained signal features are used to construct the diagnostic model. Different from existing GPs, the diagnostic tree used in the algorithm is constructed in an intelligent way by applying a power-weight coefficient to each feature. The power-weight coefficients vary adaptively between 0 and 1 during the evolutionary process. Moreover, different evolutionary strategies are employed, respectively for selecting the diagnostic features and functions, so that the mathematical functions are sufficiently utilized and in the meantime, the repeated use of signal features may be fully avoided. The experimental results are illustrated diagrammatically in the following sections.

Adaptive on-line calibration for around-view monitoring system using between-camera homography estimation

NASA Astrophysics Data System (ADS)

Lim, Sungsoo; Lee, Seohyung; Kim, Jun-geon; Lee, Daeho

2018-01-01

The around-view monitoring (AVM) system is one of the major applications of advanced driver assistance systems and intelligent transportation systems. We propose an on-line calibration method, which can compensate misalignments for AVM systems. Most AVM systems use fisheye undistortion, inverse perspective transformation, and geometrical registration methods. To perform these procedures, the parameters for each process must be known; the procedure by which the parameters are estimated is referred to as the initial calibration. However, when only using the initial calibration data, we cannot compensate misalignments, caused by changing equilibria of cars. Moreover, even small changes such as tire pressure levels, passenger weight, or road conditions can affect a car's equilibrium. Therefore, to compensate for this misalignment, additional techniques are necessary, specifically an on-line calibration method. On-line calibration can recalculate homographies, which can correct any degree of misalignment using the unique features of ordinary parking lanes. To extract features from the parking lanes, this method uses corner detection and a pattern matching algorithm. From the extracted features, homographies are estimated using random sample consensus and parameter estimation. Finally, the misaligned epipolar geographies are compensated via the estimated homographies. Thus, the proposed method can render image planes parallel to the ground. This method does not require any designated patterns and can be used whenever cars are placed in a parking lot. The experimental results show the robustness and efficiency of the method.

Portal to the GALEX Data Archive

NASA Astrophysics Data System (ADS)

Smith, M. A.; Conti, A.; Shiao, B.; Volpicelli, C. A.

2004-05-01

In early February MAST began its hosting of the GALEX public "Early Release Observations" images (40,000 objects) and spectra (1000 objects). MAST will host a much larger "first release," the GALEX DR1, in October, 2004. In this poster we describe features of our on-line website at http://galex.stsci.edu for researchers interested in downloading and browsing GALEX UV image and spectral data. The site, is based on MS .NET technology and user queries are entered for classes of objects or sky regions on a "MAST-like" query forms or with detailed queries written in SQL. In the latter case examples are provided to tailor a query to a user's specifications. The site provides novel features, such as tooltips that return keyword definitions, "active images" that return object classification and coordinate information in a 2.5 arcmin radius around the selected object, self-documentation of terms and tables, and of course a tutorial for new navigators. The GALEX database employs a Hierarchial Triangular Mesh system for rapid data discovery, neighbor searches, and cross correlations with other catalogs. Our "GMAX" tool allows a coplotting of object positions for objects observed by GALEX and other US-NVO compliant mission websites such as Sloan, 2MASS, FIRST.... As a member of the new Skynode network, GALEX has reported its web services to the US-NVO registry. This permits users to generate queries from other sites to cross-correlate, compare, and plot GALEX data using US-NVO protocols. Future plans for limited on-line data analysis and footprint services are described.

Climate Discovery Online Courses for Educators from NCAR

NASA Astrophysics Data System (ADS)

Henderson, S.; Ward, D. L.; Meymaris, K. K.; Johnson, R. M.; Gardiner, L.; Russell, R.

2008-12-01

The National Center for Atmospheric Research (NCAR) has responded to the pressing need for professional development in climate and global change sciences by creating the Climate Discovery online course series. This series was designed with the secondary geoscience educator in mind. The online courses are based on current and credible climate change science. Interactive learning techniques are built into the online course designs with assignments that encourage active participation. A key element of the online courses is the creation of a virtual community of geoscience educators who exchange ideas related to classroom implementation, student assessment, and lessons plans. Geoscience educators from around the country have participated in the online courses. The ongoing interest from geoscience educators strongly suggests that the NCAR Climate Discovery online courses are a timely and needed professional development opportunity. The intent of NCAR Climate Discovery is to positively impact teachers' professional development scientifically authentic information, (2) experiencing guided practice in conducting activities and using ancillary resources in workshop venues, (3) gaining access to standards-aligned lesson plans, kits that promote hands-on learning, and scientific content that are easily implemented in their classrooms, and (4) becoming a part of a community of educators with whom they may continue to discuss the challenges of pedagogy and content comprehension in teaching climate change in the Earth system context. Three courses make up the Climate Discovery series: Introduction to Climate Change; Earth System Science - A Climate Change Perspective; and Understanding Climate Change Today. Each course, instructed by science education specialists, combines geoscience content, information about current climate research, hands-on activities, and group discussion. The online courses use the web-based Moodle courseware system (open- source software similar to Blackboard and webCT), utilizing its features to promote dialogue as well as provide rich online content and media. A key element of the online courses is the development and support of an online learning community, an essential component in successful online courses. Interactive learning techniques are built into the course designs with assignments that encourage active participation. Educators (both formal and informal) use the courses as a venue to exchange ideas and teaching resources. A unique feature of the courses is the emphasis on hands-on activities, a hallmark of our professional development efforts. This presentation will focus on the lessons learned in the development of the three online courses and our successful recruitment and retention efforts.

GeneWiz browser: An Interactive Tool for Visualizing Sequenced Chromosomes.

PubMed

Hallin, Peter F; Stærfeldt, Hans-Henrik; Rotenberg, Eva; Binnewies, Tim T; Benham, Craig J; Ussery, David W

2009-09-25

We present an interactive web application for visualizing genomic data of prokaryotic chromosomes. The tool (GeneWiz browser) allows users to carry out various analyses such as mapping alignments of homologous genes to other genomes, mapping of short sequencing reads to a reference chromosome, and calculating DNA properties such as curvature or stacking energy along the chromosome. The GeneWiz browser produces an interactive graphic that enables zooming from a global scale down to single nucleotides, without changing the size of the plot. Its ability to disproportionally zoom provides optimal readability and increased functionality compared to other browsers. The tool allows the user to select the display of various genomic features, color setting and data ranges. Custom numerical data can be added to the plot allowing, for example, visualization of gene expression and regulation data. Further, standard atlases are pre-generated for all prokaryotic genomes available in GenBank, providing a fast overview of all available genomes, including recently deposited genome sequences. The tool is available online from http://www.cbs.dtu.dk/services/gwBrowser. Supplemental material including interactive atlases is available online at http://www.cbs.dtu.dk/services/gwBrowser/suppl/.

On-line Ammonia Sensor and Invisible Security Ink by Fluorescent Zwitterionic Spirocyclic Meisenheimer Complex

PubMed Central

Das, Tanmay; Pramanik, Apurba; Haldar, Debasish

2017-01-01

Ammonia is not only a highly important gas for civilization but also contribute significantly for climate change and human health hazard. Highly sensitive ammonia sensor has been developed from a fluorescent zwitterionic spirocyclic Meisenheimer complex. Moreover, formation of this Meisenheimer complex can also be utilized for selective as well as naked eye instant detection of nitro aromatic explosive picric acid. The presence of a quaternary nitrogen atom directly attached to the spiro carbon is the unique feature of this Meisenheimer complex. This excellent photoluminescent (PL) Meisenheimer complex has two distinct stimuli responsive sites. One is sensitive towards acid while the other one is towards the base. These two positions can be modulated by adding one equivalent acid and one equivalent base to result two new products which are non fluorescent. One of these two non fluorescent species was found very exciting because of its UV/Vis transparency. Utilizing this concept we have fabricated an on-line sensor for measuring ammonia in dry or humid and condensing sewer air. The sensor was robust against ambient temperature and humidity variation. We have also developed an invisible ink from this Meisenheimer complex, with potential application for security purpose. PMID:28091542

On-line Ammonia Sensor and Invisible Security Ink by Fluorescent Zwitterionic Spirocyclic Meisenheimer Complex.

PubMed

Das, Tanmay; Pramanik, Apurba; Haldar, Debasish

2017-01-16

Ammonia is not only a highly important gas for civilization but also contribute significantly for climate change and human health hazard. Highly sensitive ammonia sensor has been developed from a fluorescent zwitterionic spirocyclic Meisenheimer complex. Moreover, formation of this Meisenheimer complex can also be utilized for selective as well as naked eye instant detection of nitro aromatic explosive picric acid. The presence of a quaternary nitrogen atom directly attached to the spiro carbon is the unique feature of this Meisenheimer complex. This excellent photoluminescent (PL) Meisenheimer complex has two distinct stimuli responsive sites. One is sensitive towards acid while the other one is towards the base. These two positions can be modulated by adding one equivalent acid and one equivalent base to result two new products which are non fluorescent. One of these two non fluorescent species was found very exciting because of its UV/Vis transparency. Utilizing this concept we have fabricated an on-line sensor for measuring ammonia in dry or humid and condensing sewer air. The sensor was robust against ambient temperature and humidity variation. We have also developed an invisible ink from this Meisenheimer complex, with potential application for security purpose.

On-line Ammonia Sensor and Invisible Security Ink by Fluorescent Zwitterionic Spirocyclic Meisenheimer Complex

NASA Astrophysics Data System (ADS)

Das, Tanmay; Pramanik, Apurba; Haldar, Debasish

2017-01-01

Ammonia is not only a highly important gas for civilization but also contribute significantly for climate change and human health hazard. Highly sensitive ammonia sensor has been developed from a fluorescent zwitterionic spirocyclic Meisenheimer complex. Moreover, formation of this Meisenheimer complex can also be utilized for selective as well as naked eye instant detection of nitro aromatic explosive picric acid. The presence of a quaternary nitrogen atom directly attached to the spiro carbon is the unique feature of this Meisenheimer complex. This excellent photoluminescent (PL) Meisenheimer complex has two distinct stimuli responsive sites. One is sensitive towards acid while the other one is towards the base. These two positions can be modulated by adding one equivalent acid and one equivalent base to result two new products which are non fluorescent. One of these two non fluorescent species was found very exciting because of its UV/Vis transparency. Utilizing this concept we have fabricated an on-line sensor for measuring ammonia in dry or humid and condensing sewer air. The sensor was robust against ambient temperature and humidity variation. We have also developed an invisible ink from this Meisenheimer complex, with potential application for security purpose.

Now That I Have It, What Can I Do with It?

NASA Astrophysics Data System (ADS)

Holmes, Jon L.

1999-11-01

All JCE subscribers now have access to all areas of JCE Online. As a reader of the print Journal you may be wondering what benefits JCE Online offers you and how you can reap those benefits. Point your WWW browser at jchemed.chem.wisc.edu, login, and follow along. Keep in mind that the three benefits outlined below are those that directly benefit you as a Journal reader. JCE Online contains many other resources that will benefit you as a chemistry teacher. Find an Article, Any Article The JCE Online feature that I perceive to be most beneficial to Journal readers is the ease and speed of finding articles. Finding a particular Journal article or several related articles is quickly and conveniently accomplished by using JCE Index online. Clicking the JCE Index item in the left-hand navigation bar leads to the JCE Index search page. A vanity search for articles that I authored or co-authored (type "holmes j" into the search text field and press Enter) produces a list of the ten most recent articles. A click on one of the articles and another click on the Full Text (.pdf) button (in the page menu bar near the top of the page below the global menu bar) and I am looking at one of my articles just as it appeared in the Journal. Four clicks, nine keystrokes, and 25 seconds (your time may vary)... not bad! Searching the Journal has never been easier than using the online JCE Index. If you remember which issue of the Journal contains the article you are looking for, then that article is never more than six mouse clicks away from the JCE Online Home Page. Of course, this only applies if we have the article online; full text articles begin with the September 1996 issue. The first click is on the Past Issues item in the left-hand navigation bar. If the article is not in the current volume of the Journal (your memory is much better than mine if you remember farther back) then the next click (click two) is on the pop-up list of Journal volumes from which you select the year the article appeared. After the correct volume is selected, use your next click to select the issue by clicking (click three) on one of the issue cover thumbnails. This brings you to the issue Table of Contents where you will probably have to scroll down to find the article (click four). Click the title of the article (click five) to go to the abstract of the article. Click six on the Full Text (.pdf) button in the page menu bar finishes the job. On my computer the six clicks from the JCE Online home page to the full text of an article by Jones et al., "Preparing Preservice Chemistry Teachers for Constructivist Classrooms through Use of Authentic Activities", in the July 1997 issue took 35 seconds including the time required to start up Acrobat Reader; a lot quicker than a trip to the chemistry library and easier even than a trip to the bookshelf across the room! A pop-up list is used to select a volume of the Journal from the Past Issues page. As I mentioned above, only issues since September 1996 have full-text articles available online. Abstracts of articles online go back to July 1995. JCE Index does contain citations to all articles published in the Journal back to Volume 1, Number 1more than 25,700 citations to date. When an online search produces an article that is not available online, you will have to retrieve it the old-fashioned way with a trip to the library or bookshelf. But at least you will know exactly where to look. Supplement Your Print Version You may have noticed a W near the title of some articles, especially laboratory experiments, in the Journal Table of Contents and within the Journal. This W denotes articles that contain online supplementary materials. Such materials are provided by the authors of those articles and may include handouts, assignments, worksheets, procedures, digital video, color illustrations, softwarematerials that you will find beneficial in implementing the idea or laboratory experiment. At JCE Online such articles contain a Supplement button in the page menu bar. For articles with supplementary materials, the Supplement button takes JCE subscribers to the supplement download page. Clicking the Supplement button produces the supplement download page. We attempt to provide supplementary materials as PDF files that are readily downloaded, viewed, and printed using Acrobat Reader. We also take the files in the format provided by the author, which you may find easier to edit for your purposes, and combine them into a single compressed file. This file is available in two forms, one for Windows users and one for Macintosh users. Click the Supplements item in the left-hand navigation bar to find out more about downloading and viewing supplemental materials and for a link to a list of all such materials available at JCE Online. Send Us Your Comments and Suggestions At the bottom of every page at JCE Online is a link to our email address. Do not hesitate to use it to tell us what you think about the Journal and JCE Online. We read all such messages and try to reply to every one. I hope you agree that JCE Online has something to offer you and look forward to hearing from you.

Wide-undermining neck liposuction: tips and tricks for good results.

PubMed

Innocenti, Alessandro; Andretto Amodeo, Chiara; Ciancio, Francesco

2014-08-01

Neck rejuvenation is one of the most sought after procedures in the restoration of the facial contour. Numerous techniques to improve the aesthetic outcome and reduce downtime have been described. In our experience, wide undermining and local anesthesia are key to obtaining good results in selected patients who want a quick recovery. This article presents our experience with liposuction of the neck and proposes some tips and tricks to master wide-undermining neck liposuction. From January 2005 to September 2012, a total of 118 patients (34 males, 84 females) underwent neck liposuction. Patient selection was based mainly on age and neck-aging features. The procedure was performed with the patients under local anesthesia. A wide rhomboid-shaped skin undermining of the submandibular and neck area was performed and a very thin fat layer was preserved. Dressing was applied for 3 days. Improvement of the neck's contour was observed in all patients. Redefinition of the cervicomandibular angle and skin redraping of the cervical area occurred in all cases. No further touch-ups were needed. Edema and ecchymosis resolved in a few days. No major complications were observed. Our results show that wide-undermining neck liposuction performed under local anesthesia is an effective and safe procedure. Patient selection based on age and anatomical features was fundamental to obtain impressive improvement of neck contour. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework.

PubMed

Deng, Changjian; Lv, Kun; Shi, Debo; Yang, Bo; Yu, Song; He, Zhiyi; Yan, Jia

2018-06-12

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

A feature alignment score for online cone-beam CT-based image-guided radiotherapy for prostate cancer.

PubMed

Hargrave, Catriona; Deegan, Timothy; Poulsen, Michael; Bednarz, Tomasz; Harden, Fiona; Mengersen, Kerrie

2018-05-17

To develop a method for scoring online cone-beam CT (CBCT)-to-planning CT image feature alignment to inform prostate image-guided radiotherapy (IGRT) decision-making. The feasibility of incorporating volume variation metric thresholds predictive of delivering planned dose into weighted functions, was investigated. Radiation therapists and radiation oncologists participated in workshops where they reviewed prostate CBCT-IGRT case examples and completed a paper-based survey of image feature matching practices. For 36 prostate cancer patients, one daily CBCT was retrospectively contoured then registered with their plan to simulate delivered dose if (a) no online setup corrections and (b) online image alignment and setup corrections, were performed. Survey results were used to select variables for inclusion in classification and regression tree (CART) and boosted regression trees (BRT) modeling of volume variation metric thresholds predictive of delivering planned dose to the prostate, proximal seminal vesicles (PSV), bladder, and rectum. Weighted functions incorporating the CART and BRT results were used to calculate a score of individual tumor and organ at risk image feature alignment (FAS TV _ OAR ). Scaled and weighted FAS TV _ OAR were then used to calculate a score of overall treatment compliance (FAS global ) for a given CBCT-planning CT registration. The FAS TV _ OAR were assessed for sensitivity, specificity, and predictive power. FAS global thresholds indicative of high, medium, or low overall treatment plan compliance were determined using coefficients from multiple linear regression analysis. Thirty-two participants completed the prostate CBCT-IGRT survey. While responses demonstrated consensus of practice for preferential ranking of planning CT and CBCT match features in the presence of deformation and rotation, variation existed in the specified thresholds for observed volume differences requiring patient repositioning or repeat bladder and bowel preparation. The CART and BRT modeling indicated that for a given registration, a Dice similarity coefficient >0.80 and >0.60 for the prostate and PSV, respectively, and a maximum Hausdorff distance <8.0 mm for both structures were predictive of delivered dose ± 5% of planned dose. A normalized volume difference <1.0 and a CBCT anterior rectum wall >1.0 mm anterior to the planning CT anterior rectum wall were predictive of delivered dose >5% of planned rectum dose. A normalized volume difference <0.88, and a CBCT bladder wall >13.5 mm inferior and >5.0 mm posterior to the planning CT bladder were predictive of delivered dose >5% of planned bladder dose. A FAS TV _ OAR >0 is indicative of delivery of planned dose. For calculated FAS TV _ OAR for the prostate, PSV, bladder, and rectum using test data, sensitivity was 0.56, 0.75, 0.89, and 1.00, respectively; specificity 0.90, 0.94, 0.59, and 1.00, respectively; positive predictive power 0.90, 0.86, 0.53, and 1.00, respectively; and negative predictive power 0.56, 0.89, 0.91, and 1.00, respectively. Thresholds for the calculated FAS global of were low <60, medium 60-80, and high >80, with a 27% misclassification rate for the test data. A FAS global incorporating nested FAS TV _ OAR and volume variation metric thresholds predictive of treatment plan compliance was developed, offering an alternative to pretreatment dose calculations to assess treatment delivery accuracy. © 2018 American Association of Physicists in Medicine.

Features Students Really Expect from Learning Analytics

ERIC Educational Resources Information Center

Schumacher, Clara; Ifenthaler, Dirk

2016-01-01

In higher education settings more and more learning is facilitated through online learning environments. To support and understand students' learning processes better, learning analytics offers a promising approach. The purpose of this study was to investigate students' expectations toward features of learning analytics systems. In a first…

Using local chromatin structure to improve CRISPR/Cas9 efficiency in zebrafish.

PubMed

Chen, Yunru; Zeng, Shiyang; Hu, Ruikun; Wang, Xiangxiu; Huang, Weilai; Liu, Jiangfang; Wang, Luying; Liu, Guifen; Cao, Ying; Zhang, Yong

2017-01-01

Although the CRISPR/Cas9 has been successfully applied in zebrafish, considerable variations in efficiency have been observed for different gRNAs. The workload and cost of zebrafish mutant screening is largely dependent on the mutation rate of injected embryos; therefore, selecting more effective gRNAs is especially important for zebrafish mutant construction. Besides the sequence features, local chromatin structures may have effects on CRISPR/Cas9 efficiency, which remain largely unexplored. In the only related study in zebrafish, nucleosome organization was not found to have an effect on CRISPR/Cas9 efficiency, which is inconsistent with recent studies in vitro and in mammalian cell lines. To understand the effects of local chromatin structure on CRISPR/Cas9 efficiency in zebrafish, we first determined that CRISPR/Cas9 introduced genome editing mainly before the dome stage. Based on this observation, we reanalyzed our published nucleosome organization profiles and generated chromatin accessibility profiles in the 256-cell and dome stages using ATAC-seq technology. Our study demonstrated that chromatin accessibility showed positive correlation with CRISPR/Cas9 efficiency, but we did not observe a clear correlation between nucleosome organization and CRISPR/Cas9 efficiency. We constructed an online database for zebrafish gRNA selection based on local chromatin structure features that could prove beneficial to zebrafish homozygous mutant construction via CRISPR/Cas9.

The ESID Online Database network.

PubMed

Guzman, D; Veit, D; Knerr, V; Kindle, G; Gathmann, B; Eades-Perner, A M; Grimbacher, B

2007-03-01

Primary immunodeficiencies (PIDs) belong to the group of rare diseases. The European Society for Immunodeficiencies (ESID), is establishing an innovative European patient and research database network for continuous long-term documentation of patients, in order to improve the diagnosis, classification, prognosis and therapy of PIDs. The ESID Online Database is a web-based system aimed at data storage, data entry, reporting and the import of pre-existing data sources in an enterprise business-to-business integration (B2B). The online database is based on Java 2 Enterprise System (J2EE) with high-standard security features, which comply with data protection laws and the demands of a modern research platform. The ESID Online Database is accessible via the official website (http://www.esid.org/). Supplementary data are available at Bioinformatics online.

Astronaut Photography of Coral Reefs

NASA Technical Reports Server (NTRS)

Robinson, Julie A.; Noordeloos, Marco

2001-01-01

Astronaut photographs of tropical coastal areas may contain information on submerged features, including coral reefs, up to depths of about 15 m in clear waters. Previous research efforts have shown that astronaut photographs can aid in estimating coral reef locations and extent on national, regional and global scales, and allow characterization of major geomorphological rim and lagoon features (Andrefouet et al. 2000, in preparation). They can be combined with traditional satellite data to help distinguish between clouds and lagoon features such as pinnacles (Andrefouet and Robinson, in review). Furthermore, astronaut photographs may provide reef scientists and managers with information on the location and extent of river plumes and sediment run off, or facilitate identification of land cover types, including mangroves (Webb et al., in press). Photographs included in the section were selected based on several criteria. The primary consideration of the editors was that the photographs represent a worldwide distribution of coral reefs, have extremely low visual interference by cloud cover, and display a spatial scale reasonable for examining reef-related features. Once photographs were selected, they were digitized from 2nd generation copies. The color and contrast were hand corrected to an approximation of natural color (required to account for spectral differences between photographs due to the color sensitivities of films used, and differences in sun angle and exposure of the photographs). None of the photographs shown here have been georeferenced to correct them to a map projection and scale. Any distortions in features due to slightly oblique look angles when the photographs were taken through spacecraft windows remain. When feasible, near vertical photographs have been rotated so that north is toward the top. An approximate scale bar and north arrow have added using distinctive features on each photograph with reference to a 1:1,000,000 scale navigation chart. Astronaut photographs provide a unique source of moderate resolution reef remote sensing data because of their global coverage and (immediate) availability in the public domain. The database of photographs can be searched an browsed online and high-resolution digital copies of photographs in this atlas can be accessed via the Website of Earth Science and Image Analysis at NASA's Johnson Space Center:

CD-ROM-aided Databases

NASA Astrophysics Data System (ADS)

Kusama, Hideo; Matsumoto, Toshiaki

The CD-ROM system can be used independently or as a compliment to an on-line data system. It has many of the same features as an on-line system. Nippan developed the CD-NOCS system as a reinforcement or substitute for the on-line systems of the customers (bookstores). CD-NOCS is not necessarily designed just for bookstores, it is also applicable to libraries and companies. Authors would also like to emphasize that it is important to understand the development and background of the CD-NOCS system, as well as its operations.

Web-Based Media Contents Editor for UCC Websites

NASA Astrophysics Data System (ADS)

Kim, Seoksoo

The purpose of this research is to "design web-based media contents editor for establishing UCC(User Created Contents)-based websites." The web-based editor features user-oriented interfaces and increased convenience, significantly different from previous off-line editors. It allows users to edit media contents online and can be effectively used for online promotion activities of enterprises and organizations. In addition to development of the editor, the research aims to support the entry of enterprises and public agencies to the online market by combining the technology with various UCC items.

Best Practices of Online Undergraduate Spiritual Formation at Select Institutions of Christian Higher Education: A Delphi Study

ERIC Educational Resources Information Center

Ledbetter, Neal Brian

2017-01-01

The purpose of this research project was to establish consensus among experts regarding best practices of online undergraduate spiritual formation with a specific focus on the Council for Christian Colleges and Universities (CCCU). Prior to this project, there was no consensus regarding best practices of online spiritual formation at the…

Student Outcomes in Economics Principles: Online vs. Face-to-Face Delivery

ERIC Educational Resources Information Center

Birkeland, Kathryn; Weinandt, Mandie; Carr, David L.

2015-01-01

This study looks at the performance of students in an online and face-to-face section of economic principles with the same instructor. After controlling for the bias of students selecting the online section and observable characteristics, we did not find any statistical difference in the exam performance of students across delivery modes of the…

A Conceptual Framework for Detecting Cheating in Online and Take-Home Exams

ERIC Educational Resources Information Center

D'Souza, Kelwyn A.; Siegfeldt, Denise V.

2017-01-01

Selecting the right methodology to use for detecting cheating in online exams requires considerable time and effort due to a wide variety of scholarly publications on academic dishonesty in online education. This article offers a cheating detection framework that can serve as a guideline for conducting cheating studies. The necessary theories and…

Sociocultural Perspective of Science in Online Learning Environments. Communities of Practice in Online Learning Environments

ERIC Educational Resources Information Center

Erdogan, Niyazi

2016-01-01

Present study reviews empirical research studies related to learning science in online learning environments as a community. Studies published between 1995 and 2015 were searched by using ERIC and EBSCOhost databases. As a result, fifteen studies were selected for review. Identified studies were analyzed with a qualitative content analysis method…

National Online Meeting Proceedings (20th, New York, New York, May 18-20, 1999).

ERIC Educational Resources Information Center

Williams, Martha E., Ed.

This Proceedings contains 53 of the 67 papers reviewed and selected by the Organizing/Reviewing Committee for presentation at the National Online Meeting, 1999. The volume begins with the introductory presentation by the Program Chairman, Martha E. Williams, "Highlights of the Online Database Industry and the Internet 1999." The balance of the…

Quality Online Resources and Supports for Educators Teaching the Common Core State Standards for Mathematics

ERIC Educational Resources Information Center

Walters, Kirk; Smith, Toni; Leinwand, Steve; Ford, Jennifer; Scheopner Torres, Aubrey

2015-01-01

This study was designed in response to a request from rural educators in the Northeast for support in identifying high-quality online resources to implement the Common Core State Standards for Mathematics (CCSSM). The process for identifying online resources included selecting resources that had an easily navigable CCSSM organizational structure…

The Educational Impact of Online Learning: How Do University Students Perform in Subsequent Courses?

ERIC Educational Resources Information Center

Krieg, John M.; Henson, Steven E.

2016-01-01

Using a large student-level dataset from a medium-sized regional comprehensive university, we measure the impact of taking an online prerequisite course on follow-up course grades. To control for self-selection into online courses, we utilize student, instructor, course, and time fixed effects augmented with an instrumental variable approach. We…

Pharmacy Research Online. A Guide for Faculty.

ERIC Educational Resources Information Center

Parkin, Derral; And Others

This document is a self-paced training packet developed for a pilot project at the University of Houston-University Park to teach pharmacy faculty members to do their own online searching. The training begins with general topics such as the kinds of searches that can be done effectively online, the selection of appropriate databases to search, and…

Art Research Online. A Guide for Faculty.

ERIC Educational Resources Information Center

Parkin, Derral; And Others

This document is a self-paced training packet developed for a pilot project at the University of Houston-University Park to teach art faculty members to do their own online searching. The training begins with general topics such as the kinds of searches that can be done most effectively online, the selection of appropriate databases to search, and…

BROWSER: An Automatic Indexing On-Line Text Retrieval System. Annual Progress Report.

ERIC Educational Resources Information Center

Williams, J. H., Jr.

The development and testing of the Browsing On-line With Selective Retrieval (BROWSER) text retrieval system allowing a natural language query statement and providing on-line browsing capabilities through an IBM 2260 display terminal is described. The prototype system contains data bases of 25,000 German language patent abstracts, 9,000 English…

Student Motives for Taking Online Courses in Educational Administration

ERIC Educational Resources Information Center

Kowalski, Theodore J.; Dolph, David; Young, I. Phillip

2014-01-01

This study was conducted with students enrolled in a master's degree program in educational administration at a private research university that offered all required courses in both online and in-class formats. The purposes were to determine (a) the extent to which online courses were selected, (b) the level of importance students placed on four…

National Online Meeting Proceedings (21st, New York, New York, May 16-18, 2000).

ERIC Educational Resources Information Center

Williams, Martha E., Ed.

This Proceedings contains 51 of the 68 papers reviewed and selected by the Organizing/Reviewing Committee for presentation at the National Online Meeting, 2000. The volume begins with the introductory presentation of the Program Chairman, Martha E. Williams, "Highlights of the Online Database Industry and the Internet 2000." The balance of the…

Undergraduate Students' Use of Online Information in World Geography: Source Types and Selection Criteria

ERIC Educational Resources Information Center

Hong, Jung Eun; Jo, Injeong

2017-01-01

Offering up-to-date information and diverse perspectives on issues, online information can be a valuable resource that supplements traditional course materials like textbooks. In this paper, the source types that students' use for a course assignment and the criteria they apply to determine usefulness of the online information are examined.…

Learner Interaction Patterns and Student Perceptions toward Using Selected Tools in an Online Course Management System

ERIC Educational Resources Information Center

Kuna, Aruna Sai

2012-01-01

The study identified the association between student interaction patterns and academic performance in online graduate courses delivered by the Department of Agricultural Education and Studies at Iowa State University. In addition, the study investigated which online course tools were perceived by students to be most useful in learning. The study…

Assessment of Durability of Online and Multisensory Learning Using an Ophthalmology Model.

PubMed

Lippa, Linda Mottow; Anderson, Craig L

2015-10-01

To explore the impact of online learning and multisensory small-group teaching on acquisition and retention of specialty knowledge and diagnostic skills during a third-year family medicine rotation. Exploratory, observational, longitudinal, and multiple-skill measures. Two medical school classes (n = 199) at a public medical school in California. Students engaged in online self-study, small-group interactive diagnostic sessions, picture identification of critical pathologic features, and funduscopic simulator examinations. The authors compared performance on testing immediately after online learning with testing at end-rotation, as well as picture identification versus simulator diagnostic ability in students with (n = 94) and without (n = 105) practice tracing contours on whiteboard projections of those same slides depicting fundus pathologic features of common systemic diseases. Picture identification, accuracy of funduscopic descriptions, online module post-tests, and end-rotation tests. Proprioceptive reinforcement of fundus pattern recognition significantly reduced the need for remediation for misdiagnosing optic disc edema during end-rotation funduscopic simulator testing, but it had no effect on fundus pattern recognition or diagnostic ability overall. Near-perfect immediate online post-test scores contrasted sharply with poor end-rotation scores on an in-house test (average, 59.4%). Rotation timing was not a factor because the patterns remained consistent throughout the academic school year. Neither multisensory teaching nor online self-study significantly improved retention of ophthalmic knowledge and diagnostic skills by the end of a month-long third-year rotation. Timing such training closer to internship when application is imminent may enhance students' appreciation for its value and perhaps may improve retention. Pulsed quizzes over time also may be necessary to motivate students to retain the knowledge gained. Copyright © 2015 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

The availability and depiction of synthetic cathinones (bath salts) on the Internet: Do online suppliers employ features to maximize purchases?

PubMed

Meyers, Kathleen; Kaynak, Övgü; Bresani, Elena; Curtis, Brenda; McNamara, Ashley; Brownfield, Kristine; Kirby, Kimberly C

2015-07-01

"Bath salts", a derivative of cathinone, a naturally occurring beta-ketone amphetamine analogue found in the leaves of the khat (Catha edulis) plant, is a potent class of designer drugs associated with significant medical and psychiatric consequences. They are commonly used among 20-29 year olds, a group with easy access to the Internet and an inclination to purchase online. Therefore, the Internet has the potential to play a significant role in the distribution and associated consequences of these "legal highs". Google searches were used to determine bath salts availability on retail websites and how different search terms affected the proportion of retail websites obtained. Retail websites were reviewed by two independent raters who examined content with a focus on characteristics that increase the likelihood of online sales. Of the 250 websites found, 31 were unique retail websites. Most retail website hits resulted when a product name was used as the search term. The top three countries hosting retail websites were registered in the United States (n=14; 45%), Germany (n=7; 23%), and the United Kingdom (n=3; 10%). These online drug suppliers provided considerable information and purchasing choice about a variety of synthetic cathinones, legitimized their sites by using recognizable images, online chat features, and mainstream payment and shipping methods, and employed characteristics that promote online purchases. Online designer drug suppliers use sophisticated methods to market unregulated products to consumers. The international community has taken diverse approaches to address designer drugs: legislative bans, harm reduction approaches, an interim regulated legal market. Multifaceted efforts that target bath salt users, suppliers, and emergency/poison control entities are critical to comprehensively address bath salt ingestion and its consequences. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

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

The availability and depiction of synthetic cathinones (i.e., bath salts) on the internet: Do online suppliers employ features to maximize purchases?

PubMed Central

Meyers, Kathleen; Kaynak, Övgü; Bresani, Elena; Curtis, Brenda; McNamara, Ashley; Brownfield, Kristine; Kirby, Kimberly C.

2015-01-01

Background “Bath salts”, a derivative of cathinone, a naturally occurring beta-ketone amphetamine analogue found in the leaves of the khat (Catha edulis) plant, is a potent class of designer drugs associated with significant medical and psychiatric consequences. They are commonly used among 20 to 29 year olds, a group with easy access to the internet and an inclination to purchase online. Therefore, the internet has the potential to play a significant role in the distribution and associated consequences of these “legal highs”. Methods Google searches were used to determine bath salts availability on retail websites and how different search terms affected the proportion of retail websites obtained. Retail websites were reviewed by two independent raters who examined content with a focus on characteristics that increase the likelihood of online sales. Results Of the 250 websites found, 31 were unique retail websites. Most retail website hits resulted when a product name was used as the search term. The top three countries hosting retail websites were registered in the United States (n=14; 45%), Germany (n=7; 23%), and the United Kingdom (n=3; 10%). These online drug suppliers provided considerable information and purchasing choice about a variety of synthetic cathinones, legitimized their sites by using recognizable images, online chat features, and mainstream payment and shipping methods, and employed characteristics that promote online purchases. Conclusion Online designer drug suppliers use sophisticated methods to market unregulated products to consumers. The international community has taken diverse approaches to address designer drugs: legislative bans, harm reduction approaches, an interim regulated legal market. Multifaceted efforts that target bath salt users, suppliers, and emergency/poison control entities are critical to comprehensively address bath salt ingestion and its consequences. PMID:25641258

The Impact of a Racing Feature on Middle School Science Students' Performance in an Educational Game: The Effect of Content-Free Game-Actions

ERIC Educational Resources Information Center

Ault, Marilyn; Craig-Hare, Jana; Frey, Bruce

2016-01-01

Reason Racer is an online, rate-based, multiplayer game designed to engage middle school students in the knowledge and skills related to scientific argumentation. Several game features are included as design considerations unrelated to science content or argumentation. One specific feature, a competitive racing component that occurs in between…

Prediction of Response to Neoadjuvant Chemotherapy and Radiation Therapy with Baseline and Restaging 18F-FDG PET Imaging Biomarkers in Patients with Esophageal Cancer.

PubMed

Beukinga, Roelof J; Hulshoff, Jan Binne; Mul, Véronique E M; Noordzij, Walter; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Plukker, John T M

2018-06-01

Purpose To assess the value of baseline and restaging fluorine 18 ( 18 F) fluorodeoxyglucose (FDG) positron emission tomography (PET) radiomics in predicting pathologic complete response to neoadjuvant chemotherapy and radiation therapy (NCRT) in patients with locally advanced esophageal cancer. Materials and Methods In this retrospective study, 73 patients with histologic analysis-confirmed T1/N1-3/M0 or T2-4a/N0-3/M0 esophageal cancer were treated with NCRT followed by surgery (Chemoradiotherapy for Esophageal Cancer followed by Surgery Study regimen) between October 2014 and August 2017. Clinical variables and radiomic features from baseline and restaging 18 F-FDG PET were selected by univariable logistic regression and least absolute shrinkage and selection operator. The selected variables were used to fit a multivariable logistic regression model, which was internally validated by using bootstrap resampling with 20 000 replicates. The performance of this model was compared with reference prediction models composed of maximum standardized uptake value metrics, clinical variables, and maximum standardized uptake value at baseline NCRT radiomic features. Outcome was defined as complete versus incomplete pathologic response (tumor regression grade 1 vs 2-5 according to the Mandard classification). Results Pathologic response was complete in 16 patients (21.9%) and incomplete in 57 patients (78.1%). A prediction model combining clinical T-stage and restaging NCRT (post-NCRT) joint maximum (quantifying image orderliness) yielded an optimism-corrected area under the receiver operating characteristics curve of 0.81. Post-NCRT joint maximum was replaceable with five other redundant post-NCRT radiomic features that provided equal model performance. All reference prediction models exhibited substantially lower discriminatory accuracy. Conclusion The combination of clinical T-staging and quantitative assessment of post-NCRT 18 F-FDG PET orderliness (joint maximum) provided high discriminatory accuracy in predicting pathologic complete response in patients with esophageal cancer. © RSNA, 2018 Online supplemental material is available for this article.

Warcraft and Civic Education: MMORPGs as Participatory Cultures and How Teachers Can Use Them to Improve Civic Education

ERIC Educational Resources Information Center

Curry, Kristal

2010-01-01

Online role-playing games such as World Of Warcraft represent new participatory cultures in which today's students engage every day. They are appealing to players largely because of the social aspects of game play. Some features of massively multiplayer online role-playing games (MMORPGs) can be incorporated into classroom culture to create more…

Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model

ERIC Educational Resources Information Center

Brinton, Christopher G.; Chiang, Mung; Jain, Shaili; Lam, Henry; Liu, Zhenming; Wong, Felix Ming Fai

2014-01-01

We study user behavior in the courses offered by a major massive online open course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable education on MOOC and is done via online discussion forums, our main focus is on understanding forum activities. Two salient features of these activities drive our…

What Makes a Student Non-Traditional? A Comparison of Students over and under Age 25 in Online, Accelerated Psychology Courses

ERIC Educational Resources Information Center

Tilley, Brian P.

2014-01-01

The growing proportion of non-traditional students, very commonly defined as students over the age of 25 (though other features vary from study to study) necessitates more studies with this increasingly relevant group participating. Recently, the growth of non-traditional universities such as those offering predominantly online, accelerated…

Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention.

PubMed

Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei

2016-01-13

An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features.

Secure access control and large scale robust representation for online multimedia event detection.

PubMed

Liu, Changyu; Lu, Bin; Li, Huiling

2014-01-01

We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches.

Image data-processing system for solar astronomy

NASA Technical Reports Server (NTRS)

Wilson, R. M.; Teuber, D. L.; Watkins, J. R.; Thomas, D. T.; Cooper, C. M.

1977-01-01

The paper describes an image data processing system (IDAPS), its hardware/software configuration, and interactive and batch modes of operation for the analysis of the Skylab/Apollo Telescope Mount S056 X-Ray Telescope experiment data. Interactive IDAPS is primarily designed to provide on-line interactive user control of image processing operations for image familiarization, sequence and parameter optimization, and selective feature extraction and analysis. Batch IDAPS follows the normal conventions of card control and data input and output, and is best suited where the desired parameters and sequence of operations are known and when long image-processing times are required. Particular attention is given to the way in which this system has been used in solar astronomy and other investigations. Some recent results obtained by means of IDAPS are presented.

A real-time optical tracking and measurement processing system for flying targets.

PubMed

Guo, Pengyu; Ding, Shaowen; Zhang, Hongliang; Zhang, Xiaohu

2014-01-01

Optical tracking and measurement for flying targets is unlike the close range photography under a controllable observation environment, which brings extreme conditions like diverse target changes as a result of high maneuver ability and long cruising range. This paper first designed and realized a distributed image interpretation and measurement processing system to achieve resource centralized management, multisite simultaneous interpretation and adaptive estimation algorithm selection; then proposed a real-time interpretation method which contains automatic foreground detection, online target tracking, multiple features location, and human guidance. An experiment is carried out at performance and efficiency evaluation of the method by semisynthetic video. The system can be used in the field of aerospace tests like target analysis including dynamic parameter, transient states, and optical physics characteristics, with security control.

A Real-Time Optical Tracking and Measurement Processing System for Flying Targets

PubMed Central

Guo, Pengyu; Ding, Shaowen; Zhang, Hongliang; Zhang, Xiaohu

2014-01-01

Optical tracking and measurement for flying targets is unlike the close range photography under a controllable observation environment, which brings extreme conditions like diverse target changes as a result of high maneuver ability and long cruising range. This paper first designed and realized a distributed image interpretation and measurement processing system to achieve resource centralized management, multisite simultaneous interpretation and adaptive estimation algorithm selection; then proposed a real-time interpretation method which contains automatic foreground detection, online target tracking, multiple features location, and human guidance. An experiment is carried out at performance and efficiency evaluation of the method by semisynthetic video. The system can be used in the field of aerospace tests like target analysis including dynamic parameter, transient states, and optical physics characteristics, with security control. PMID:24987748

Improving KPCA Online Extraction by Orthonormalization in the Feature Space.

PubMed

Souza Filho, Joao B O; Diniz, Paulo S R

2018-04-01

Recently, some online kernel principal component analysis (KPCA) techniques based on the generalized Hebbian algorithm (GHA) were proposed for use in large data sets, defining kernel components using concise dictionaries automatically extracted from data. This brief proposes two new online KPCA extraction algorithms, exploiting orthogonalized versions of the GHA rule. In both the cases, the orthogonalization of kernel components is achieved by the inclusion of some low complexity additional steps to the kernel Hebbian algorithm, thus not substantially affecting the computational cost of the algorithm. Results show improved convergence speed and accuracy of components extracted by the proposed methods, as compared with the state-of-the-art online KPCA extraction algorithms.

WikiSensing: An Online Collaborative Approach for Sensor Data Management

PubMed Central

Silva, Dilshan; Ghanem, Moustafa; Guo, Yike

2012-01-01

This paper presents a new methodology for collaborative sensor data management known as WikiSensing. It is a novel approach that incorporates online collaboration with sensor data management. We introduce the work on this research by describing the motivation and challenges of designing and developing an online collaborative sensor data management system. This is followed by a brief survey on popular sensor data management and online collaborative systems. We then present the architecture for WikiSensing highlighting its main components and features. Several example scenarios are described to present the functionality of the system. We evaluate the approach by investigating the performance of aggregate queries and the scalability of the system. PMID:23201997

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Compensatory selection for roads over natural linear features by wolves in northern Ontario: Implications for caribou conservation

PubMed Central

Patterson, Brent R.; Anderson, Morgan L.; Rodgers, Arthur R.; Vander Vennen, Lucas M.; Fryxell, John M.

2017-01-01

Woodland caribou (Rangifer tarandus caribou) in Ontario are a threatened species that have experienced a substantial retraction of their historic range. Part of their decline has been attributed to increasing densities of anthropogenic linear features such as trails, roads, railways, and hydro lines. These features have been shown to increase the search efficiency and kill rate of wolves. However, it is unclear whether selection for anthropogenic linear features is additive or compensatory to selection for natural (water) linear features which may also be used for travel. We studied the selection of water and anthropogenic linear features by 52 resident wolves (Canis lupus x lycaon) over four years across three study areas in northern Ontario that varied in degrees of forestry activity and human disturbance. We used Euclidean distance-based resource selection functions (mixed-effects logistic regression) at the seasonal range scale with random coefficients for distance to water linear features, primary/secondary roads/railways, and hydro lines, and tertiary roads to estimate the strength of selection for each linear feature and for several habitat types, while accounting for availability of each feature. Next, we investigated the trade-off between selection for anthropogenic and water linear features. Wolves selected both anthropogenic and water linear features; selection for anthropogenic features was stronger than for water during the rendezvous season. Selection for anthropogenic linear features increased with increasing density of these features on the landscape, while selection for natural linear features declined, indicating compensatory selection of anthropogenic linear features. These results have implications for woodland caribou conservation. Prey encounter rates between wolves and caribou seem to be strongly influenced by increasing linear feature densities. This behavioral mechanism–a compensatory functional response to anthropogenic linear feature density resulting in decreased use of natural travel corridors–has negative consequences for the viability of woodland caribou. PMID:29117234

Compensatory selection for roads over natural linear features by wolves in northern Ontario: Implications for caribou conservation.

PubMed

Newton, Erica J; Patterson, Brent R; Anderson, Morgan L; Rodgers, Arthur R; Vander Vennen, Lucas M; Fryxell, John M

2017-01-01

Woodland caribou (Rangifer tarandus caribou) in Ontario are a threatened species that have experienced a substantial retraction of their historic range. Part of their decline has been attributed to increasing densities of anthropogenic linear features such as trails, roads, railways, and hydro lines. These features have been shown to increase the search efficiency and kill rate of wolves. However, it is unclear whether selection for anthropogenic linear features is additive or compensatory to selection for natural (water) linear features which may also be used for travel. We studied the selection of water and anthropogenic linear features by 52 resident wolves (Canis lupus x lycaon) over four years across three study areas in northern Ontario that varied in degrees of forestry activity and human disturbance. We used Euclidean distance-based resource selection functions (mixed-effects logistic regression) at the seasonal range scale with random coefficients for distance to water linear features, primary/secondary roads/railways, and hydro lines, and tertiary roads to estimate the strength of selection for each linear feature and for several habitat types, while accounting for availability of each feature. Next, we investigated the trade-off between selection for anthropogenic and water linear features. Wolves selected both anthropogenic and water linear features; selection for anthropogenic features was stronger than for water during the rendezvous season. Selection for anthropogenic linear features increased with increasing density of these features on the landscape, while selection for natural linear features declined, indicating compensatory selection of anthropogenic linear features. These results have implications for woodland caribou conservation. Prey encounter rates between wolves and caribou seem to be strongly influenced by increasing linear feature densities. This behavioral mechanism-a compensatory functional response to anthropogenic linear feature density resulting in decreased use of natural travel corridors-has negative consequences for the viability of woodland caribou.

Developing a NIR multispectral imaging for prediction and visualization of peanut protein content using variable selection algorithms

NASA Astrophysics Data System (ADS)

Cheng, Jun-Hu; Jin, Huali; Liu, Zhiwei

2018-01-01

The feasibility of developing a multispectral imaging method using important wavelengths from hyperspectral images selected by genetic algorithm (GA), successive projection algorithm (SPA) and regression coefficient (RC) methods for modeling and predicting protein content in peanut kernel was investigated for the first time. Partial least squares regression (PLSR) calibration model was established between the spectral data from the selected optimal wavelengths and the reference measured protein content ranged from 23.46% to 28.43%. The RC-PLSR model established using eight key wavelengths (1153, 1567, 1972, 2143, 2288, 2339, 2389 and 2446 nm) showed the best predictive results with the coefficient of determination of prediction (R2P) of 0.901, and root mean square error of prediction (RMSEP) of 0.108 and residual predictive deviation (RPD) of 2.32. Based on the obtained best model and image processing algorithms, the distribution maps of protein content were generated. The overall results of this study indicated that developing a rapid and online multispectral imaging system using the feature wavelengths and PLSR analysis is potential and feasible for determination of the protein content in peanut kernels.

Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

PubMed

Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

2014-01-01

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

Automated microaneurysm detection in diabetic retinopathy using curvelet transform

NASA Astrophysics Data System (ADS)

Ali Shah, Syed Ayaz; Laude, Augustinus; Faye, Ibrahima; Tang, Tong Boon

2016-10-01

Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.

Automated microaneurysm detection in diabetic retinopathy using curvelet transform.

PubMed

Ali Shah, Syed Ayaz; Laude, Augustinus; Faye, Ibrahima; Tang, Tong Boon

2016-10-01

Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.

A monument to the player: preserving a landscape of socio-cultural capital in the transitional MMORPG

NASA Astrophysics Data System (ADS)

`Adrir'Scott, Michael

2012-12-01

Massively multiplayer online role-playing games (MMORPGs) produce dynamic socio-ludic worlds that nurture both culture and gameplay to shape experiences. Despite the persistent nature of these games, however, the virtual spaces that anchor these worlds may not always be able to exist in perpetuity. Encouraging a community to migrate from one space to another is a challenge now facing some game developers. This paper examines the case of Guild Wars® and its "Hall of Monuments", a feature that bridges the accomplishments of players from the current game to the forthcoming sequel. Two factor analyses describe the perspectives of 105 and 187 self-selected participants. The results reveal four factors affecting attitudes towards the feature, but they do not strongly correlate with existing motivational frameworks, and significant differences were found between different cultures within the game. This informs a discussion about the implications and facilitation of such transitions, investigating themes of capital, value perception and assumptive worlds. It is concluded that the way subcultures produce meaning needs to be considered when attempting to preserve the socio-cultural landscape.

Measuring interactivity on tobacco control websites.

PubMed

Freeman, Becky; Chapman, Simon

2012-08-01

With the increased reach of Web 2.0, Internet users expect webpages to be interactive. No studies have been conducted to assess whether tobacco control-relevant sites have implemented these features. The authors conducted an analysis of an international sample of tobacco control-relevant websites to determine their level of interactivity. The sample included 68 unique websites selected from Google searches in 5 countries, on each country's Google site, using the term smoking. The 68 sites were analyzed for 10 categories of interactive tools. The most common type of interactive content found on 46 (68%) of sites was for multimedia featuring content that was not primarily text based, such as photo galleries, videos, or podcasts. Only 11 (16%) websites-outside of media sites-allowed people to interact and engage with the site owners and other users by allowing posting comments on content and/or hosting forums/discussions. Linkages to social networking sites were low: 17 pages (25%) linked to Twitter, 15 (22%) to Facebook, and 11 (16%) to YouTube. Interactivity and connectedness to online social media appears to still be in its infancy among tobacco control-relevant sites.

Investigation of KDP crystal surface based on an improved bidimensional empirical mode decomposition method

NASA Astrophysics Data System (ADS)

Lu, Lei; Yan, Jihong; Chen, Wanqun; An, Shi

2018-03-01

This paper proposed a novel spatial frequency analysis method for the investigation of potassium dihydrogen phosphate (KDP) crystal surface based on an improved bidimensional empirical mode decomposition (BEMD) method. Aiming to eliminate end effects of the BEMD method and improve the intrinsic mode functions (IMFs) for the efficient identification of texture features, a denoising process was embedded in the sifting iteration of BEMD method. With removing redundant information in decomposed sub-components of KDP crystal surface, middle spatial frequencies of the cutting and feeding processes were identified. Comparative study with the power spectral density method, two-dimensional wavelet transform (2D-WT), as well as the traditional BEMD method, demonstrated that the method developed in this paper can efficiently extract texture features and reveal gradient development of KDP crystal surface. Furthermore, the proposed method was a self-adaptive data driven technique without prior knowledge, which overcame shortcomings of the 2D-WT model such as the parameters selection. Additionally, the proposed method was a promising tool for the application of online monitoring and optimal control of precision machining process.

Robust visual tracking via multiscale deep sparse networks

NASA Astrophysics Data System (ADS)

Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

2017-04-01

In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

PubMed Central

Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

2014-01-01

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

McTwo: a two-step feature selection algorithm based on maximal information coefficient.

PubMed

Ge, Ruiquan; Zhou, Manli; Luo, Youxi; Meng, Qinghan; Mai, Guoqin; Ma, Dongli; Wang, Guoqing; Zhou, Fengfeng

2016-03-23

High-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This "large p, small n" paradigm in the area of biomedical "big data" may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets. This work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature. McTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.