Modeling the heterogeneity of human dynamics based on the measurements of influential users in Sina Microblog

NASA Astrophysics Data System (ADS)

Wang, Chenxu; Guan, Xiaohong; Qin, Tao; Yang, Tao

2015-06-01

Online social network has become an indispensable communication tool in the information age. The development of microblog also provides us a great opportunity to study human dynamics that play a crucial role in the design of efficient communication systems. In this paper we study the characteristics of the tweeting behavior based on the data collected from Sina Microblog. The user activity level is measured to characterize how often a user posts a tweet. We find that the user activity level follows a bimodal distribution. That is, the microblog users tend to be either active or inactive. The inter-tweeting time distribution is then measured at both the aggregate and individual levels. We find that the inter-tweeting time follows a piecewise power law distribution of two tails. Furthermore, the exponents of the two tails have different correlations with the user activity level. These findings demonstrate that the dynamics of the tweeting behavior are heterogeneous in different time scales. We then develop a dynamic model co-driven by the memory and the interest mechanism to characterize the heterogeneity. The numerical simulations validate the model and verify that the short time interval tweeting behavior is driven by the memory mechanism while the long time interval behavior by the interest mechanism.

A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems.

PubMed

Pan, Shaoming; Chong, Yanwen; Zhang, Hang; Tan, Xicheng

2017-01-01

A web geographical information system is a typical service-intensive application. Tile prefetching and cache replacement can improve cache hit ratios by proactively fetching tiles from storage and replacing the appropriate tiles from the high-speed cache buffer without waiting for a client's requests, which reduces disk latency and improves system access performance. Most popular prefetching strategies consider only the relative tile popularities to predict which tile should be prefetched or consider only a single individual user's access behavior to determine which neighbor tiles need to be prefetched. Some studies show that comprehensively considering all users' access behaviors and all tiles' relationships in the prediction process can achieve more significant improvements. Thus, this work proposes a new global user-driven model for tile prefetching and cache replacement. First, based on all users' access behaviors, a type of expression method for tile correlation is designed and implemented. Then, a conditional prefetching probability can be computed based on the proposed correlation expression mode. Thus, some tiles to be prefetched can be found by computing and comparing the conditional prefetching probability from the uncached tiles set and, similarly, some replacement tiles can be found in the cache buffer according to multi-step prefetching. Finally, some experiments are provided comparing the proposed model with other global user-driven models, other single user-driven models, and other client-side prefetching strategies. The results show that the proposed model can achieve a prefetching hit rate in approximately 10.6% ~ 110.5% higher than the compared methods.

Modeling and prediction of human word search behavior in interactive machine translation

NASA Astrophysics Data System (ADS)

Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

2017-12-01

As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.

User Behavior Analytics

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

Turcotte, Melissa; Moore, Juston Shane

User Behaviour Analytics is the tracking, collecting and assessing of user data and activities. The goal is to detect misuse of user credentials by developing models for the normal behaviour of user credentials within a computer network and detect outliers with respect to their baseline.

Statistical Models for Predicting Threat Detection From Human Behavior.

PubMed

Kelley, Timothy; Amon, Mary J; Bertenthal, Bennett I

2018-01-01

Users must regularly distinguish between secure and insecure cyber platforms in order to preserve their privacy and safety. Mouse tracking is an accessible, high-resolution measure that can be leveraged to understand the dynamics of perception, categorization, and decision-making in threat detection. Researchers have begun to utilize measures like mouse tracking in cyber security research, including in the study of risky online behavior. However, it remains an empirical question to what extent real-time information about user behavior is predictive of user outcomes and demonstrates added value compared to traditional self-report questionnaires. Participants navigated through six simulated websites, which resembled either secure "non-spoof" or insecure "spoof" versions of popular websites. Websites also varied in terms of authentication level (i.e., extended validation, standard validation, or partial encryption). Spoof websites had modified Uniform Resource Locator (URL) and authentication level. Participants chose to "login" to or "back" out of each website based on perceived website security. Mouse tracking information was recorded throughout the task, along with task performance. After completing the website identification task, participants completed a questionnaire assessing their security knowledge and degree of familiarity with the websites simulated during the experiment. Despite being primed to the possibility of website phishing attacks, participants generally showed a bias for logging in to websites versus backing out of potentially dangerous sites. Along these lines, participant ability to identify spoof websites was around the level of chance. Hierarchical Bayesian logistic models were used to compare the accuracy of two-factor (i.e., website security and encryption level), survey-based (i.e., security knowledge and website familiarity), and real-time measures (i.e., mouse tracking) in predicting risky online behavior during phishing attacks. Participant accuracy in identifying spoof and non-spoof websites was best captured using a model that included real-time indicators of decision-making behavior, as compared to two-factor and survey-based models. Findings validate three widely applicable measures of user behavior derived from mouse tracking recordings, which can be utilized in cyber security and user intervention research. Survey data alone are not as strong at predicting risky Internet behavior as models that incorporate real-time measures of user behavior, such as mouse tracking.

Emergent user behavior on Twitter modelled by a stochastic differential equation.

PubMed

Mollgaard, Anders; Mathiesen, Joachim

2015-01-01

Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise.

Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation

PubMed Central

Mollgaard, Anders; Mathiesen, Joachim

2015-01-01

Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise. PMID:25955783

Based on user interest level of modeling scenarios and browse content

NASA Astrophysics Data System (ADS)

Zhao, Yang

2017-08-01

User interest modeling is the core of personalized service, taking into account the impact of situational information on user preferences, the user behavior days of financial information. This paper proposes a method of user interest modeling based on scenario information, which is obtained by calculating the similarity of the situation. The user's current scene of the approximate scenario set; on the "user - interest items - scenarios" three-dimensional model using the situation pre-filtering method of dimension reduction processing. View the content of the user interested in the theme, the analysis of the page content to get each topic of interest keywords, based on the level of vector space model user interest. The experimental results show that the user interest model based on the scenario information is within 9% of the user's interest prediction, which is effective.

Using ontologies to model human navigation behavior in information networks: A study based on Wikipedia.

PubMed

Lamprecht, Daniel; Strohmaier, Markus; Helic, Denis; Nyulas, Csongor; Tudorache, Tania; Noy, Natalya F; Musen, Mark A

The need to examine the behavior of different user groups is a fundamental requirement when building information systems. In this paper, we present Ontology-based Decentralized Search (OBDS), a novel method to model the navigation behavior of users equipped with different types of background knowledge. Ontology-based Decentralized Search combines decentralized search, an established method for navigation in social networks, and ontologies to model navigation behavior in information networks. The method uses ontologies as an explicit representation of background knowledge to inform the navigation process and guide it towards navigation targets. By using different ontologies, users equipped with different types of background knowledge can be represented. We demonstrate our method using four biomedical ontologies and their associated Wikipedia articles. We compare our simulation results with base line approaches and with results obtained from a user study. We find that our method produces click paths that have properties similar to those originating from human navigators. The results suggest that our method can be used to model human navigation behavior in systems that are based on information networks, such as Wikipedia. This paper makes the following contributions: (i) To the best of our knowledge, this is the first work to demonstrate the utility of ontologies in modeling human navigation and (ii) it yields new insights and understanding about the mechanisms of human navigation in information networks.

Using ontologies to model human navigation behavior in information networks: A study based on Wikipedia

PubMed Central

Lamprecht, Daniel; Strohmaier, Markus; Helic, Denis; Nyulas, Csongor; Tudorache, Tania; Noy, Natalya F.; Musen, Mark A.

2015-01-01

The need to examine the behavior of different user groups is a fundamental requirement when building information systems. In this paper, we present Ontology-based Decentralized Search (OBDS), a novel method to model the navigation behavior of users equipped with different types of background knowledge. Ontology-based Decentralized Search combines decentralized search, an established method for navigation in social networks, and ontologies to model navigation behavior in information networks. The method uses ontologies as an explicit representation of background knowledge to inform the navigation process and guide it towards navigation targets. By using different ontologies, users equipped with different types of background knowledge can be represented. We demonstrate our method using four biomedical ontologies and their associated Wikipedia articles. We compare our simulation results with base line approaches and with results obtained from a user study. We find that our method produces click paths that have properties similar to those originating from human navigators. The results suggest that our method can be used to model human navigation behavior in systems that are based on information networks, such as Wikipedia. This paper makes the following contributions: (i) To the best of our knowledge, this is the first work to demonstrate the utility of ontologies in modeling human navigation and (ii) it yields new insights and understanding about the mechanisms of human navigation in information networks. PMID:26568745

Cooperation stimulation strategies for peer-to-peer wireless live video-sharing social networks.

PubMed

Lin, W Sabrina; Zhao, H Vicky; Liu, K J Ray

2010-07-01

Human behavior analysis in video sharing social networks is an emerging research area, which analyzes the behavior of users who share multimedia content and investigates the impact of human dynamics on video sharing systems. Users watching live streaming in the same wireless network share the same limited bandwidth of backbone connection to the Internet, thus, they might want to cooperate with each other to obtain better video quality. These users form a wireless live-streaming social network. Every user wishes to watch video with high quality while paying as little as possible cost to help others. This paper focuses on providing incentives for user cooperation. We propose a game-theoretic framework to model user behavior and to analyze the optimal strategies for user cooperation simulation in wireless live streaming. We first analyze the Pareto optimality and the time-sensitive bargaining equilibrium of the two-person game. We then extend the solution to the multiuser scenario. We also consider potential selfish users' cheating behavior and malicious users' attacking behavior and analyze the performance of the proposed strategies with the existence of cheating users and malicious attackers. Both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free and attack resistance, and help provide reliable services for wireless live streaming applications.

The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions.

PubMed

Mohr, David C; Schueller, Stephen M; Montague, Enid; Burns, Michelle Nicole; Rashidi, Parisa

2014-06-05

A growing number of investigators have commented on the lack of models to inform the design of behavioral intervention technologies (BITs). BITs, which include a subset of mHealth and eHealth interventions, employ a broad range of technologies, such as mobile phones, the Web, and sensors, to support users in changing behaviors and cognitions related to health, mental health, and wellness. We propose a model that conceptually defines BITs, from the clinical aim to the technological delivery framework. The BIT model defines both the conceptual and technological architecture of a BIT. Conceptually, a BIT model should answer the questions why, what, how (conceptual and technical), and when. While BITs generally have a larger treatment goal, such goals generally consist of smaller intervention aims (the "why") such as promotion or reduction of specific behaviors, and behavior change strategies (the conceptual "how"), such as education, goal setting, and monitoring. Behavior change strategies are instantiated with specific intervention components or "elements" (the "what"). The characteristics of intervention elements may be further defined or modified (the technical "how") to meet the needs, capabilities, and preferences of a user. Finally, many BITs require specification of a workflow that defines when an intervention component will be delivered. The BIT model includes a technological framework (BIT-Tech) that can integrate and implement the intervention elements, characteristics, and workflow to deliver the entire BIT to users over time. This implementation may be either predefined or include adaptive systems that can tailor the intervention based on data from the user and the user's environment. The BIT model provides a step towards formalizing the translation of developer aims into intervention components, larger treatments, and methods of delivery in a manner that supports research and communication between investigators on how to design, develop, and deploy BITs.

User modeling for distributed virtual environment intelligent agents

NASA Astrophysics Data System (ADS)

Banks, Sheila B.; Stytz, Martin R.

1999-07-01

This paper emphasizes the requirement for user modeling by presenting the necessary information to motivate the need for and use of user modeling for intelligent agent development. The paper will present information on our current intelligent agent development program, the Symbiotic Information Reasoning and Decision Support (SIRDS) project. We then discuss the areas of intelligent agents and user modeling, which form the foundation of the SIRDS project. Included in the discussion of user modeling are its major components, which are cognitive modeling and behavioral modeling. We next motivate the need for and user of a methodology to develop user models to encompass work within cognitive task analysis. We close the paper by drawing conclusions from our current intelligent agent research project and discuss avenues of future research in the utilization of user modeling for the development of intelligent agents for virtual environments.

Structural diversity effect on hashtag adoption in Twitter

NASA Astrophysics Data System (ADS)

Zhang, Aihua; Zheng, Mingxing; Pang, Bowen

2018-03-01

With online social network developing rapidly these years, user' behavior in online social network has attracted a lot of attentions to it. In this paper, we study Twitter user's behavior of hashtag adoption from the perspective of social contagion and focus on "structure diversity" effect on individual's behavior in Twitter. We achieve data through Twitter's API by crawling and build a users' network to carry on empirical research. The Girvan-Newman (G-N) algorithm is used to analyze the structural diversity of user's ego network, and Logistic regression model is adopted to examine the hypothesis. The findings of our empirical study indicate that user' behavior in online social network is indeed influenced by his friends and his decision is significantly affected by the number of groups that these friends belong to, which we call structural diversity.

A simple generative model of collective online behavior.

PubMed

Gleeson, James P; Cellai, Davide; Onnela, Jukka-Pekka; Porter, Mason A; Reed-Tsochas, Felix

2014-07-22

Human activities increasingly take place in online environments, providing novel opportunities for relating individual behaviors to population-level outcomes. In this paper, we introduce a simple generative model for the collective behavior of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct mechanisms: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behavior that is consistent with data, the only models that reproduce the observed temporal dynamics are those that strongly emphasize the recent popularity of applications over their cumulative popularity. This demonstrates--even when using purely observational data without experimental design--that temporal data-driven modeling can effectively distinguish between competing microscopic mechanisms, allowing us to uncover previously unidentified aspects of collective online behavior.

A simple generative model of collective online behavior

PubMed Central

Gleeson, James P.; Cellai, Davide; Onnela, Jukka-Pekka; Porter, Mason A.; Reed-Tsochas, Felix

2014-01-01

Human activities increasingly take place in online environments, providing novel opportunities for relating individual behaviors to population-level outcomes. In this paper, we introduce a simple generative model for the collective behavior of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct mechanisms: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behavior that is consistent with data, the only models that reproduce the observed temporal dynamics are those that strongly emphasize the recent popularity of applications over their cumulative popularity. This demonstrates—even when using purely observational data without experimental design—that temporal data-driven modeling can effectively distinguish between competing microscopic mechanisms, allowing us to uncover previously unidentified aspects of collective online behavior. PMID:25002470

Artificial intelligence techniques for modeling database user behavior

NASA Technical Reports Server (NTRS)

Tanner, Steve; Graves, Sara J.

1990-01-01

The design and development of the adaptive modeling system is described. This system models how a user accesses a relational database management system in order to improve its performance by discovering use access patterns. In the current system, these patterns are used to improve the user interface and may be used to speed data retrieval, support query optimization and support a more flexible data representation. The system models both syntactic and semantic information about the user's access and employs both procedural and rule-based logic to manipulate the model.

Rational analyses of information foraging on the web.

PubMed

Pirolli, Peter

2005-05-06

This article describes rational analyses and cognitive models of Web users developed within information foraging theory. This is done by following the rational analysis methodology of (a) characterizing the problems posed by the environment, (b) developing rational analyses of behavioral solutions to those problems, and (c) developing cognitive models that approach the realization of those solutions. Navigation choice is modeled as a random utility model that uses spreading activation mechanisms that link proximal cues (information scent) that occur in Web browsers to internal user goals. Web-site leaving is modeled as an ongoing assessment by the Web user of the expected benefits of continuing at a Web site as opposed to going elsewhere. These cost-benefit assessments are also based on spreading activation models of information scent. Evaluations include a computational model of Web user behavior called Scent-Based Navigation and Information Foraging in the ACT Architecture, and the Law of Surfing, which characterizes the empirical distribution of the length of paths of visitors at a Web site. 2005 Lawrence Erlbaum Associates, Inc.

Research on user behavior authentication model based on stochastic Petri nets

NASA Astrophysics Data System (ADS)

Zhang, Chengyuan; Xu, Haishui

2017-08-01

A behavioural authentication model based on stochastic Petri net is proposed to meet the randomness, uncertainty and concurrency characteristics of user behaviour. The use of random models in the location, changes, arc and logo to describe the characteristics of a variety of authentication and game relationships, so as to effectively implement the graphical user behaviour authentication model analysis method, according to the corresponding proof to verify the model is valuable.

ASSIST user manual

NASA Technical Reports Server (NTRS)

Johnson, Sally C.; Boerschlein, David P.

1995-01-01

Semi-Markov models can be used to analyze the reliability of virtually any fault-tolerant system. However, the process of delineating all the states and transitions in a complex system model can be devastatingly tedious and error prone. The Abstract Semi-Markov Specification Interface to the SURE Tool (ASSIST) computer program allows the user to describe the semi-Markov model in a high-level language. Instead of listing the individual model states, the user specifies the rules governing the behavior of the system, and these are used to generate the model automatically. A few statements in the abstract language can describe a very large, complex model. Because no assumptions are made about the system being modeled, ASSIST can be used to generate models describing the behavior of any system. The ASSIST program and its input language are described and illustrated by examples.

Designing visual displays and system models for safe reactor operations based on the user`s perspective of the system

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

Brown-VanHoozer, S.A.

Most designers are not schooled in the area of human-interaction psychology and therefore tend to rely on the traditional ergonomic aspects of human factors when designing complex human-interactive workstations related to reactor operations. They do not take into account the differences in user information processing behavior and how these behaviors may affect individual and team performance when accessing visual displays or utilizing system models in process and control room areas. Unfortunately, by ignoring the importance of the integration of the user interface at the information process level, the result can be sub-optimization and inherently error- and failure-prone systems. Therefore, tomore » minimize or eliminate failures in human-interactive systems, it is essential that the designers understand how each user`s processing characteristics affects how the user gathers information, and how the user communicates the information to the designer and other users. A different type of approach in achieving this understanding is Neuro Linguistic Programming (NLP). The material presented in this paper is based on two studies involving the design of visual displays, NLP, and the user`s perspective model of a reactor system. The studies involve the methodology known as NLP, and its use in expanding design choices from the user`s ``model of the world,`` in the areas of virtual reality, workstation design, team structure, decision and learning style patterns, safety operations, pattern recognition, and much, much more.« less

Analyzing the posting behaviors in news forums with incremental inter-event time

NASA Astrophysics Data System (ADS)

Sun, Zhi; Peng, Qinke; Lv, Jia; Zhong, Tao

2017-08-01

Online human behaviors are widely discussed in various fields. Three key factors, named priority, interest and memory are found crucial in human behaviors. Existing research mainly focuses on the identified and active users. However, the anonymous users and the inactive ones exist widely in news forums, whose behaviors do not receive enough attention. They cannot offer abundant postings like the others. It requires us to study posting behaviors of all the users including anonymous ones, identified ones, active ones and inactive ones in news forums only at the collective level. In this paper, the memory effects of the posting behaviors in news forums are investigated at the collective level. On the basis of the incremental inter-event time, a new model is proposed to describe the posting behaviors at the collective level. The results on twelve actual news events demonstrate the good performance of our model to describe the posting behaviors at the collective level in news forums. In addition, we find the symmetric incremental inter-event time distribution and the similar posting patterns in different durations.

Measuring and analyzing the causes of problematic Internet use.

PubMed

Chiang, I-Ping; Su, Yung-Hsiang

2012-11-01

Since Internet surfing became a daily activity, people have changed their behavior. This research analyzes the causes of problematic Internet use through an online survey, where 1,094 samples were collected. Based on the results of structural equation modeling analysis, the following conclusions are reached: First, novelty, security, and efficiency increase users' online trust. Second, information and efficiency enhance users' sharing and anonymity online. Third, greater trust in Internet environments leads to an increase in a user's cognitive bias toward online behavioral responsibility and Internet addiction. Fourth, a user's attitude toward online sharing further increases the cognitive bias toward online copyright. Fifth, a user's attitude toward anonymity increases cognitive bias toward online copyright, online behavioral responsibility, and deepens Internet addiction.

Statistical Models for Predicting Threat Detection From Human Behavior

PubMed Central

Kelley, Timothy; Amon, Mary J.; Bertenthal, Bennett I.

2018-01-01

Users must regularly distinguish between secure and insecure cyber platforms in order to preserve their privacy and safety. Mouse tracking is an accessible, high-resolution measure that can be leveraged to understand the dynamics of perception, categorization, and decision-making in threat detection. Researchers have begun to utilize measures like mouse tracking in cyber security research, including in the study of risky online behavior. However, it remains an empirical question to what extent real-time information about user behavior is predictive of user outcomes and demonstrates added value compared to traditional self-report questionnaires. Participants navigated through six simulated websites, which resembled either secure “non-spoof” or insecure “spoof” versions of popular websites. Websites also varied in terms of authentication level (i.e., extended validation, standard validation, or partial encryption). Spoof websites had modified Uniform Resource Locator (URL) and authentication level. Participants chose to “login” to or “back” out of each website based on perceived website security. Mouse tracking information was recorded throughout the task, along with task performance. After completing the website identification task, participants completed a questionnaire assessing their security knowledge and degree of familiarity with the websites simulated during the experiment. Despite being primed to the possibility of website phishing attacks, participants generally showed a bias for logging in to websites versus backing out of potentially dangerous sites. Along these lines, participant ability to identify spoof websites was around the level of chance. Hierarchical Bayesian logistic models were used to compare the accuracy of two-factor (i.e., website security and encryption level), survey-based (i.e., security knowledge and website familiarity), and real-time measures (i.e., mouse tracking) in predicting risky online behavior during phishing attacks. Participant accuracy in identifying spoof and non-spoof websites was best captured using a model that included real-time indicators of decision-making behavior, as compared to two-factor and survey-based models. Findings validate three widely applicable measures of user behavior derived from mouse tracking recordings, which can be utilized in cyber security and user intervention research. Survey data alone are not as strong at predicting risky Internet behavior as models that incorporate real-time measures of user behavior, such as mouse tracking. PMID:29713296

Automating CPM-GOMS

NASA Technical Reports Server (NTRS)

John, Bonnie; Vera, Alonso; Matessa, Michael; Freed, Michael; Remington, Roger

2002-01-01

CPM-GOMS is a modeling method that combines the task decomposition of a GOMS analysis with a model of human resource usage at the level of cognitive, perceptual, and motor operations. CPM-GOMS models have made accurate predictions about skilled user behavior in routine tasks, but developing such models is tedious and error-prone. We describe a process for automatically generating CPM-GOMS models from a hierarchical task decomposition expressed in a cognitive modeling tool called Apex. Resource scheduling in Apex automates the difficult task of interleaving the cognitive, perceptual, and motor resources underlying common task operators (e.g. mouse move-and-click). Apex's UI automatically generates PERT charts, which allow modelers to visualize a model's complex parallel behavior. Because interleaving and visualization is now automated, it is feasible to construct arbitrarily long sequences of behavior. To demonstrate the process, we present a model of automated teller interactions in Apex and discuss implications for user modeling. available to model human users, the Goals, Operators, Methods, and Selection (GOMS) method [6, 21] has been the most widely used, providing accurate, often zero-parameter, predictions of the routine performance of skilled users in a wide range of procedural tasks [6, 13, 15, 27, 28]. GOMS is meant to model routine behavior. The user is assumed to have methods that apply sequences of operators and to achieve a goal. Selection rules are applied when there is more than one method to achieve a goal. Many routine tasks lend themselves well to such decomposition. Decomposition produces a representation of the task as a set of nested goal states that include an initial state and a final state. The iterative decomposition into goals and nested subgoals can terminate in primitives of any desired granularity, the choice of level of detail dependent on the predictions required. Although GOMS has proven useful in HCI, tools to support the construction of GOMS models have not yet come into general use.

Quantitative Agent Based Model of User Behavior in an Internet Discussion Forum

PubMed Central

Sobkowicz, Pawel

2013-01-01

The paper presents an agent based simulation of opinion evolution, based on a nonlinear emotion/information/opinion (E/I/O) individual dynamics, to an actual Internet discussion forum. The goal is to reproduce the results of two-year long observations and analyses of the user communication behavior and of the expressed opinions and emotions, via simulations using an agent based model. The model allowed to derive various characteristics of the forum, including the distribution of user activity and popularity (outdegree and indegree), the distribution of length of dialogs between the participants, their political sympathies and the emotional content and purpose of the comments. The parameters used in the model have intuitive meanings, and can be translated into psychological observables. PMID:24324606

Pilot Inventory Complex Adaptive System (PICAS): An Artificial Life Approach to Managing Pilot Retention.

DTIC Science & Technology

1999-03-01

mates) and base their behaviors on this interactive information. This alone defines the nature of a complex adaptive system and it is based on this...world policy initiatives. 2.3.4. User Interaction Building the model with extensive user interaction gives the entire system a more appealing feel...complex behavior that hopefully mimics trends observed in reality . User interaction also allows for easier justification of assumptions used within

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

PubMed Central

Chen, You; Malin, Bradley

2014-01-01

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

Applying Human-Centered Design Methods to Scientific Communication Products

NASA Astrophysics Data System (ADS)

Burkett, E. R.; Jayanty, N. K.; DeGroot, R. M.

2016-12-01

Knowing your users is a critical part of developing anything to be used or experienced by a human being. User interviews, journey maps, and personas are all techniques commonly employed in human-centered design practices because they have proven effective for informing the design of products and services that meet the needs of users. Many non-designers are unaware of the usefulness of personas and journey maps. Scientists who are interested in developing more effective products and communication can adopt and employ user-centered design approaches to better reach intended audiences. Journey mapping is a qualitative data-collection method that captures the story of a user's experience over time as related to the situation or product that requires development or improvement. Journey maps help define user expectations, where they are coming from, what they want to achieve, what questions they have, their challenges, and the gaps and opportunities that can be addressed by designing for them. A persona is a tool used to describe the goals and behavioral patterns of a subset of potential users or customers. The persona is a qualitative data model that takes the form of a character profile, built upon data about the behaviors and needs of multiple users. Gathering data directly from users avoids the risk of basing models on assumptions, which are often limited by misconceptions or gaps in understanding. Journey maps and user interviews together provide the data necessary to build the composite character that is the persona. Because a persona models the behaviors and needs of the target audience, it can then be used to make informed product design decisions. We share the methods and advantages of developing and using personas and journey maps to create more effective science communication products.

Estimating fire behavior with FIRECAST: user's manual

Treesearch

Jack D. Cohen

1986-01-01

FIRECAST is a computer program that estimates fire behavior in terms of six fire parameters. Required inputs vary depending on the outputs desired by the fire manager. Fuel model options available to users are these: Northern Forest Fire Laboratory (NFFL), National Fire Danger Rating System (NFDRS), and southern California brushland (SCAL). The program has been...

Information Seeking Behavior in Digital Image Collections: A Cognitive Approach

ERIC Educational Resources Information Center

Matusiak, Krystyna K.

2006-01-01

Presents the results of a qualitative study that focuses on search patterns of college students and community users interacting with a digital image collection. The study finds a distinct difference between the two groups of users and examines the role of mental models in information seeking behavior in digital libraries.

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.

Classification of Twitter Users Who Tweet About E-Cigarettes

PubMed Central

Miano, Thomas; Chew, Robert; Eggers, Matthew; Nonnemaker, James

2017-01-01

Background Despite concerns about their health risks, e‑cigarettes have gained popularity in recent years. Concurrent with the recent increase in e‑cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e‑cigarettes. Monitoring the trends in e‑cigarette–related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e‑cigarette–related content on Twitter. Objective The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e‑cigarette–related topics into distinct categories. Methods We collected approximately 11.5 million e‑cigarette–related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e‑cigarettes. Trained human coders examined the handles’ profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users’ metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types. Results Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e‑cigarettes (average F1 score=83.3%). Accuracy varied by user type, with F1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance—an overall F1 score gain of 10.6%—beyond metadata features alone. Conclusions This study provides a method for classifying five different types of users who tweet about e‑cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e‑cigarettes online to help inform public health surveillance, education, and regulatory efforts. PMID:28951381

Mechanism Design for Incentivizing Social Media Contributions

NASA Astrophysics Data System (ADS)

Singh, Vivek K.; Jain, Ramesh; Kankanhalli, Mohan

Despite recent advancements in user-driven social media platforms, tools for studying user behavior patterns and motivations remain primitive. We highlight the voluntary nature of user contributions and that users can choose when (and when not) to contribute to the common media pool. A Game theoretic framework is proposed to study the dynamics of social media networks where contribution costs are individual but gains are common. We model users as rational selfish agents, and consider domain attributes like voluntary participation, virtual reward structure, network effect, and public-sharing to model the dynamics of this interaction. The created model describes the most appropriate contribution strategy from each user's perspective and also highlights issues like 'free-rider' problem and individual rationality leading to irrational (i.e. sub-optimal) group behavior. We also consider the perspective of the system designer who is interested in finding the best incentive mechanisms to influence the selfish end-users so that the overall system utility is maximized. We propose and compare multiple mechanisms (based on optimal bonus payment, social incentive leveraging, and second price auction) to study how a system designer can exploit the selfishness of its users, to design incentive mechanisms which improve the overall task-completion probability and system performance, while possibly still benefiting the individual users.

Smart learning services based on smart cloud computing.

PubMed

Kim, Svetlana; Song, Su-Mi; Yoon, Yong-Ik

2011-01-01

Context-aware technologies can make e-learning services smarter and more efficient since context-aware services are based on the user's behavior. To add those technologies into existing e-learning services, a service architecture model is needed to transform the existing e-learning environment, which is situation-aware, into the environment that understands context as well. The context-awareness in e-learning may include the awareness of user profile and terminal context. In this paper, we propose a new notion of service that provides context-awareness to smart learning content in a cloud computing environment. We suggest the elastic four smarts (E4S)--smart pull, smart prospect, smart content, and smart push--concept to the cloud services so smart learning services are possible. The E4S focuses on meeting the users' needs by collecting and analyzing users' behavior, prospecting future services, building corresponding contents, and delivering the contents through cloud computing environment. Users' behavior can be collected through mobile devices such as smart phones that have built-in sensors. As results, the proposed smart e-learning model in cloud computing environment provides personalized and customized learning services to its users.

“Teens are from Mars, Adults are from Venus”: Analyzing and Predicting Age Groups with Behavioral Characteristics in Instagram

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

Han, Kyungsik; Lee, Sanghack; Jang, Jin

We present behavioral characteristics of teens and adults in Instagram and prediction of them from their behaviors. Based on two independently created datasets from user profiles and tags, we identify teens and adults, and carry out comparative analyses on their online behaviors. Our study reveals: (1) significant behavioral differences between two age groups; (2) the empirical evidence of classifying teens and adults with up to 82% accuracy, using traditional predictive models, while two baseline methods achieve 68% at best; and (3) the robustness of our models by achieving 76%—81% when tested against an independent dataset obtained without using user profilesmore » or tags.« less

The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions

PubMed Central

Schueller, Stephen M; Montague, Enid; Burns, Michelle Nicole; Rashidi, Parisa

2014-01-01

A growing number of investigators have commented on the lack of models to inform the design of behavioral intervention technologies (BITs). BITs, which include a subset of mHealth and eHealth interventions, employ a broad range of technologies, such as mobile phones, the Web, and sensors, to support users in changing behaviors and cognitions related to health, mental health, and wellness. We propose a model that conceptually defines BITs, from the clinical aim to the technological delivery framework. The BIT model defines both the conceptual and technological architecture of a BIT. Conceptually, a BIT model should answer the questions why, what, how (conceptual and technical), and when. While BITs generally have a larger treatment goal, such goals generally consist of smaller intervention aims (the "why") such as promotion or reduction of specific behaviors, and behavior change strategies (the conceptual "how"), such as education, goal setting, and monitoring. Behavior change strategies are instantiated with specific intervention components or “elements” (the "what"). The characteristics of intervention elements may be further defined or modified (the technical "how") to meet the needs, capabilities, and preferences of a user. Finally, many BITs require specification of a workflow that defines when an intervention component will be delivered. The BIT model includes a technological framework (BIT-Tech) that can integrate and implement the intervention elements, characteristics, and workflow to deliver the entire BIT to users over time. This implementation may be either predefined or include adaptive systems that can tailor the intervention based on data from the user and the user’s environment. The BIT model provides a step towards formalizing the translation of developer aims into intervention components, larger treatments, and methods of delivery in a manner that supports research and communication between investigators on how to design, develop, and deploy BITs. PMID:24905070

Probabilistic Seeking Prediction in P2P VoD Systems

NASA Astrophysics Data System (ADS)

Wang, Weiwei; Xu, Tianyin; Gao, Yang; Lu, Sanglu

In P2P VoD streaming systems, user behavior modeling is critical to help optimise user experience as well as system throughput. However, it still remains a challenging task due to the dynamic characteristics of user viewing behavior. In this paper, we consider the problem of user seeking prediction which is to predict the user's next seeking position so that the system can proactively make response. We present a novel method for solving this problem. In our method, frequent sequential patterns mining is first performed to extract abstract states which are not overlapped and cover the whole video file altogether. After mapping the raw training dataset to state transitions according to the abstract states, we use a simpel probabilistic contingency table to build the prediction model. We design an experiment on the synthetic P2P VoD dataset. The results demonstrate the effectiveness of our method.

Comparison of the middle-aged and older users' adoption of mobile health services in China.

PubMed

Deng, Zhaohua; Mo, Xiuting; Liu, Shan

2014-03-01

Given the increasing number of older people, China has become an aging society. A mobile health service is a type of health informatics that provides personalized healthcare advice to those who require it, especially the older people and the middle-aged. However, few studies consider the adoption of mobile health services with regard to older and middle-aged users. This paper explored a research model based on the value attitude behavior model, theory of planned behavior, and four aging characteristic constructs to investigate how older and middle-aged citizens adopted mobile health services. The hypothesized model was empirically tested using data collected from a survey of 424 residents older than 40 years in China. Structural equation modeling was used to estimate the significance of the path coefficients. The findings revealed that (1) perceived value, attitude, perceived behavior control, and resistance to change can be used to predict intention to use mobile health services for the middle-aged group; (2) perceived value, attitude, perceived behavior control, technology anxiety, and self-actualization need positively affected the behavior intention of older users; and (3) subjective norm and perceived physical condition showed no significant effects on the behavior intention to use mobile health services for the two groups. The theoretical and practical implications and contributions of this study are then discussed. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

anyFish 2.0: An open-source software platform to generate and share animated fish models to study behavior

NASA Astrophysics Data System (ADS)

Ingley, Spencer J.; Rahmani Asl, Mohammad; Wu, Chengde; Cui, Rongfeng; Gadelhak, Mahmoud; Li, Wen; Zhang, Ji; Simpson, Jon; Hash, Chelsea; Butkowski, Trisha; Veen, Thor; Johnson, Jerald B.; Yan, Wei; Rosenthal, Gil G.

2015-12-01

Experimental approaches to studying behaviors based on visual signals are ubiquitous, yet these studies are limited by the difficulty of combining realistic models with the manipulation of signals in isolation. Computer animations are a promising way to break this trade-off. However, animations are often prohibitively expensive and difficult to program, thus limiting their utility in behavioral research. We present anyFish 2.0, a user-friendly platform for creating realistic animated 3D fish. anyFish 2.0 dramatically expands anyFish's utility by allowing users to create animations of members of several groups of fish from model systems in ecology and evolution (e.g., sticklebacks, Poeciliids, and zebrafish). The visual appearance and behaviors of the model can easily be modified. We have added several features that facilitate more rapid creation of realistic behavioral sequences. anyFish 2.0 provides a powerful tool that will be of broad use in animal behavior and evolution and serves as a model for transparency, repeatability, and collaboration.

Analyzing older users' home telehealth services acceptance behavior-applying an Extended UTAUT model.

PubMed

Cimperman, Miha; Makovec Brenčič, Maja; Trkman, Peter

2016-06-01

Although telehealth offers an improved approach to providing healthcare services, its adoption by end users remains slow. With an older population as the main target, these traditionally conservative users pose a big challenge to the successful implementation of innovative telehealth services. The objective of this study was to develop and empirically test a model for predicting the factors affecting older users' acceptance of Home Telehealth Services (HTS). A survey instrument was administered to 400 participants aged 50 years and above from both rural and urban environments in Slovenia. Structural equation modeling was applied to analyze the causal effect of seven hypothesized predicting factors. HTS were introduced as a bundle of functionalities, representing future services that currently do not exist. This enabled users' perceptions to be measured on the conceptual level, rather than attitudes to a specific technical solution. Six relevant predictors were confirmed in older users' HTS acceptance behavior, with Performance Expectancy (r=0.30), Effort Expectancy (r=0.49), Facilitating Conditions (r=0.12), and Perceived Security (r=0.16) having a direct impact on behavioral intention to use HTS. In addition, Computer Anxiety is positioned as an antecedent of Effort Expectancy with a strong negative influence (r=-0.61), and Doctor's Opinion influence showed a strong impact on Performance Expectancy (r=0.31). The results also indicate Social Influence as an irrelevant predictor of acceptance behavior. The model of six predictors yielded 77% of the total variance explained in the final measured Behavioral Intention to Use HTS by older adults. The level at which HTS are perceived as easy to use and manage is the leading acceptance predictor in older users' HTS acceptance. Together with Perceived Usefulness and Perceived Security, these three factors represent the key influence on older people's HTS acceptance behavior. When promoting HTS, interventions should focus to portray it as secure. Marketing interventions should focus also on promoting HTS among health professionals, using them as social agents to frame the services as useful and beneficial. The important role of computer anxiety may result in a need to use different equipment such as a tablet computer to access HTS. Finally, this paper introduces important methodological guidelines for measuring perceptions on a conceptual level of future services that currently do not exist. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An evolving model of online bipartite networks

NASA Astrophysics Data System (ADS)

Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang

2013-12-01

Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.

Modeling User Behavior and Attention in Search

ERIC Educational Resources Information Center

Huang, Jeff

2013-01-01

In Web search, query and click log data are easy to collect but they fail to capture user behaviors that do not lead to clicks. As search engines reach the limits inherent in click data and are hungry for more data in a competitive environment, mining cursor movements, hovering, and scrolling becomes important. This dissertation investigates how…

Effectiveness Evaluation of Force Protection Training Using Computer-Based Instruction and X3d Simulation

DTIC Science & Technology

2007-09-01

behavior libraries selection box, Savage Tactics behavior sub-folder and hostile behavior sub-folder that contains the behavior that is being assigned to...21) applications. The interface allows users to select models (locations, friendly assets, hostile assets, neutral assets, etc) that will be used in...altitude, etc.) for each model and define their behaviors (friendly patrol craft, hostile explosive-laden vessel, etc). Once the models and their

The SmartH2O project: a platform supporting residential water management through smart meters and data intensive modeling

NASA Astrophysics Data System (ADS)

Cominola, A.; Nanda, R.; Giuliani, M.; Piga, D.; Castelletti, A.; Rizzoli, A. E.; Maziotis, A.; Garrone, P.; Harou, J. J.

2014-12-01

Designing effective urban water demand management strategies at the household level does require a deep understanding of the determinants of users' consumption. Low resolution data on residential water consumption, as traditionally metered, can only be used to model consumers' behavior at an aggregate level whereas end uses breakdown and the motivations and individual attitudes of consumers are hidden. The recent advent of smart meters allows gathering high frequency consumption data that can be used both to provide instantaneous information to water utilities on the state of the network and continuously inform the users on their consumption and savings. Smart metered data also allow for the characterization of water end uses: this information, coupled with users' psychographic variables, constitutes the knowledge basis for developing individual and multi users models, through which water utilities can test the impact of different management strategies. SmartH2O is an EU funded project which aims at creating an ICT platform able to (i) capture and store quasi real time, high resolution residential water usage data measured with smart meters, (ii) infer the main determinants of residential water end uses and build customers' behavioral models and (iii) predict how the customer behavior can be influenced by various water demand management strategies, spanning from dynamic water pricing schemes to social awareness campaigns. The project exploits a social computing approach for raising users' awareness about water consumption and pursuing water savings in the residential sector. In this work, we first present the SmartH2O platform and data collection, storage and analysis components. We then introduce some preliminary models and results on total water consumption disaggregation into end uses and single user behaviors using innovative fully automated algorithms and overcoming the need of invasive metering campaigns at the fixture level.

Behavior-dependent Routing: Responding to Anomalies with Automated Low-cost Measures

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

Oehmen, Christopher S.; Carroll, Thomas E.; Paulson, Patrick R.

2015-10-12

This is a conference paper submission describing research and software implementation of a cybersecurity concept that uses behavior models to trigger changes in routing of network traffic. As user behavior deviates more and more from baseline models, traffic is routed through more elevated layers of analysis and control.

Classification of Twitter Users Who Tweet About E-Cigarettes.

PubMed

Kim, Annice; Miano, Thomas; Chew, Robert; Eggers, Matthew; Nonnemaker, James

2017-09-26

Despite concerns about their health risks, e‑cigarettes have gained popularity in recent years. Concurrent with the recent increase in e‑cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e‑cigarettes. Monitoring the trends in e‑cigarette-related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e‑cigarette-related content on Twitter. The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e‑cigarette-related topics into distinct categories. We collected approximately 11.5 million e‑cigarette-related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e‑cigarettes. Trained human coders examined the handles' profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users' metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types. Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e‑cigarettes (average F 1 score=83.3%). Accuracy varied by user type, with F 1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance-an overall F 1 score gain of 10.6%-beyond metadata features alone. This study provides a method for classifying five different types of users who tweet about e‑cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e‑cigarettes online to help inform public health surveillance, education, and regulatory efforts. ©Annice Kim, Thomas Miano, Robert Chew, Matthew Eggers, James Nonnemaker. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 26.09.2017.

Modeling Passive Propagation of Malwares on the WWW

NASA Astrophysics Data System (ADS)

Chunbo, Liu; Chunfu, Jia

Web-based malwares host in websites fixedly and download onto user's computers automatically while users browse. This passive propagation pattern is different from that of traditional viruses and worms. A propagation model based on reverse web graph is proposed. In this model, propagation of malwares is analyzed by means of random jump matrix which combines orderness and randomness of user browsing behaviors. Explanatory experiments, which has single or multiple propagation sources respectively, prove the validity of the model. Using this model, people can evaluate the hazardness of specified websites and take corresponding countermeasures.

ASSIST: User's manual

NASA Technical Reports Server (NTRS)

Johnson, S. C.

1986-01-01

Semi-Markov models can be used to compute the reliability of virtually any fault-tolerant system. However, the process of delineating all of the states and transitions in a model of a complex system can be devastingly tedious and error-prone. The ASSIST program allows the user to describe the semi-Markov model in a high-level language. Instead of specifying the individual states of the model, the user specifies the rules governing the behavior of the system and these are used by ASSIST to automatically generate the model. The ASSIST program is described and illustrated by examples.

MOAB: a spatially explicit, individual-based expert system for creating animal foraging models

USGS Publications Warehouse

Carter, J.; Finn, John T.

1999-01-01

We describe the development, structure, and corroboration process of a simulation model of animal behavior (MOAB). MOAB can create spatially explicit, individual-based animal foraging models. Users can create or replicate heterogeneous landscape patterns, and place resources and individual animals of a goven species on that landscape to simultaneously simulate the foraging behavior of multiple species. The heuristic rules for animal behavior are maintained in a user-modifiable expert system. MOAB can be used to explore hypotheses concerning the influence of landscape patttern on animal movement and foraging behavior. A red fox (Vulpes vulpes L.) foraging and nest predation model was created to test MOAB's capabilities. Foxes were simulated for 30-day periods using both expert system and random movement rules. Home range size, territory formation and other available simulation studies. A striped skunk (Mephitis mephitis L.) model also was developed. The expert system model proved superior to stochastic in respect to territory formation, general movement patterns and home range size.

A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems

PubMed Central

Pan, Shaoming; Chong, Yanwen; Zhang, Hang; Tan, Xicheng

2017-01-01

A web geographical information system is a typical service-intensive application. Tile prefetching and cache replacement can improve cache hit ratios by proactively fetching tiles from storage and replacing the appropriate tiles from the high-speed cache buffer without waiting for a client’s requests, which reduces disk latency and improves system access performance. Most popular prefetching strategies consider only the relative tile popularities to predict which tile should be prefetched or consider only a single individual user's access behavior to determine which neighbor tiles need to be prefetched. Some studies show that comprehensively considering all users’ access behaviors and all tiles’ relationships in the prediction process can achieve more significant improvements. Thus, this work proposes a new global user-driven model for tile prefetching and cache replacement. First, based on all users’ access behaviors, a type of expression method for tile correlation is designed and implemented. Then, a conditional prefetching probability can be computed based on the proposed correlation expression mode. Thus, some tiles to be prefetched can be found by computing and comparing the conditional prefetching probability from the uncached tiles set and, similarly, some replacement tiles can be found in the cache buffer according to multi-step prefetching. Finally, some experiments are provided comparing the proposed model with other global user-driven models, other single user-driven models, and other client-side prefetching strategies. The results show that the proposed model can achieve a prefetching hit rate in approximately 10.6% ~ 110.5% higher than the compared methods. PMID:28085937

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

Enhancing collaborative filtering by user interest expansion via personalized ranking.

PubMed

Liu, Qi; Chen, Enhong; Xiong, Hui; Ding, Chris H Q; Chen, Jian

2012-02-01

Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored. To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative-filtering framework. The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem. Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin.

Humanizing Aggregated Data: Developing Personas to Prioritize User Needs for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Burkett, E. R.; Jayanty, N. K.; Sellnow, D. D.; Given, D. D.; DeGroot, R. M.

2016-12-01

Methods that use storytelling to gather and synthesize data from people can be advantageous in understanding user needs and designing successful communication products. Using a multidisciplinary approach, we research and prioritize user needs for the ShakeAlert Earthquake Early Warning system (http://pubs.usgs.gov/fs/2014/3083/), drawing on best practices from social and behavioral science, risk communication, and human-centered design. We apply quantitative and qualitative human data collection methods including user surveys, interviews, journey maps, personas, and scenarios. Human-centered design methods leverage storytelling (a) in the acquisition of qualitative behavioral data (e.g. with journey mapping), (b) through goal-driven behaviors and needs that are synthesized into a persona as a composite model of the data, and (c) within context scenarios (the story plot or projected circumstances) in which the persona is placed in context to inform the design of relevant and usable products or services. ShakeAlert, operated by the USGS and partners, has transitioned into a production prototype phase in which users are permitted to begin testing pilot implementations to take protective actions in response to an earthquake alert. While a subset of responses will be automated (e.g., opening fire house doors), other applications of the technology will alert individuals by broadcast, public address, or mobile device notifications and require self-protective behavioral decisions (e.g., "Drop, Cover, and Hold On"). To better understand ShakeAlert user decisions and needs, we use human-centered design methods to synthesize aggregated behavioral data into "personas," which model the common behavioral patterns that can be used to guide plans for the ShakeAlert interface, messaging, and training. We present user data, methods, and resulting personas that will inform decisions moving forward to shape ShakeAlert messaging and training that will be most usable by alert recipients.

Hiding the system from the user: Moving from complex mental models to elegant metaphors

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

Curtis W. Nielsen; David J. Bruemmer

2007-08-01

In previous work, increased complexity of robot behaviors and the accompanying interface design often led to operator confusion and/or a fight for control between the robot and operator. We believe the reason for the conflict was that the design of the interface and interactions presented too much of the underlying robot design model to the operator. Since the design model includes the implementation of sensors, behaviors, and sophisticated algorithms, the result was that the operator’s cognitive efforts were focused on understanding the design of the robot system as opposed to focusing on the task at hand. This paper illustrates howmore » this very problem emerged at the INL and how the implementation of new metaphors for interaction has allowed us to hide the design model from the user and allow the user to focus more on the task at hand. Supporting the user’s focus on the task rather than on the design model allows increased use of the system and significant performance improvement in a search task with novice users.« less

EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Simulation Data

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

Shu, Qingya; Guo, Hanqi; Che, Limei

We present a novel visualization framework—EnsembleGraph— for analyzing ensemble simulation data, in order to help scientists understand behavior similarities between ensemble members over space and time. A graph-based representation is used to visualize individual spatiotemporal regions with similar behaviors, which are extracted by hierarchical clustering algorithms. A user interface with multiple-linked views is provided, which enables users to explore, locate, and compare regions that have similar behaviors between and then users can investigate and analyze the selected regions in detail. The driving application of this paper is the studies on regional emission influences over tropospheric ozone, which is based onmore » ensemble simulations conducted with different anthropogenic emission absences using the MOZART-4 (model of ozone and related tracers, version 4) model. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations. Positive feedbacks from domain experts and two case studies prove efficiency of our method.« less

Evaluating the influence of perceived organizational learning capability on user acceptance of information technology among operating room nurse staff.

PubMed

Lee, Chien-Ching; Lin, Shih-Pin; Yang, Shu-Ling; Tsou, Mei-Yung; Chang, Kuang-Yi

2013-03-01

Medical institutions are eager to introduce new information technology to improve patient safety and clinical efficiency. However, the acceptance of new information technology by medical personnel plays a key role in its adoption and application. This study aims to investigate whether perceived organizational learning capability (OLC) is associated with user acceptance of information technology among operating room nurse staff. Nurse anesthetists and operating room nurses were recruited in this questionnaire survey. A pilot study was performed to ensure the reliability and validity of the translated questionnaire, which consisted of 14 items from the four dimensions of OLC, and 16 items from the four constructs of user acceptance of information technology, including performance expectancy, effort expectancy, social influence, and behavioral intention. Confirmatory factor analysis was applied in the main survey to evaluate the construct validity of the questionnaire. Structural equation modeling was used to test the hypothetical relationships between the four dimensions of user acceptance of information technology and the second-ordered OLC. Goodness of fit of the hypothetic model was also assessed. Performance expectancy, effort expectancy, and social influence positively influenced behavioral intention of users of the clinical information system (all p < 0.001) and accounted for 75% of its variation. The second-ordered OLC was positively associated with performance expectancy, effort expectancy, and social influence (all p < 0.001). However, the hypothetic relationship between perceived OLC and behavioral intention was not significant (p = 0.87). The fit statistical analysis indicated reasonable model fit to data (root mean square error of approximation = 0.07 and comparative fit index = 0.91). Perceived OLC indirectly affects user behavioral intention through the mediation of performance expectancy, effort expectancy, and social influence in the operating room setting. Copyright © 2013. Published by Elsevier B.V.

Observing Consistency in Online Communication Patterns for User Re-Identification.

PubMed

Adeyemi, Ikuesan Richard; Razak, Shukor Abd; Salleh, Mazleena; Venter, Hein S

2016-01-01

Comprehension of the statistical and structural mechanisms governing human dynamics in online interaction plays a pivotal role in online user identification, online profile development, and recommender systems. However, building a characteristic model of human dynamics on the Internet involves a complete analysis of the variations in human activity patterns, which is a complex process. This complexity is inherent in human dynamics and has not been extensively studied to reveal the structural composition of human behavior. A typical method of anatomizing such a complex system is viewing all independent interconnectivity that constitutes the complexity. An examination of the various dimensions of human communication pattern in online interactions is presented in this paper. The study employed reliable server-side web data from 31 known users to explore characteristics of human-driven communications. Various machine-learning techniques were explored. The results revealed that each individual exhibited a relatively consistent, unique behavioral signature and that the logistic regression model and model tree can be used to accurately distinguish online users. These results are applicable to one-to-one online user identification processes, insider misuse investigation processes, and online profiling in various areas.

The Role of Social Media User Experience as a Mediator for Understanding Social Media User Behavior in Indonesia’s Museum Industry: Literature Review

NASA Astrophysics Data System (ADS)

Sundjaja, A. M.; LumanGaol, F.; Budiarti, T.; Abbas, B. S.; Abdinagoro, S. B.; Ongowarsito, H.

2017-01-01

Social media has changed the interaction between the customer and the business, social media has proven to provide new opportunities in facilitating access to information, efficiency and ease of interaction between customers and businesses that are distributed geographically dispersed. Ease of interaction to improve access to information about products, services, and prices have proven to have a positive impact for consumers. The purpose of this article is to develop a conceptual model to test the effect of user motivation, user expectations, and online community involvement to the intention of behavior that is mediated by the use of social media museum experience. This article is a literature study on exploration of social media user experiences museum in Indonesia. Authors searched and examined 85 articles from google scholar with the following keywords: motivation, expectations, online communities, user experience, social media, Technology Acceptance Model, Experiential Marketing, Uses and Gratification Theory. Proposed data collection techniques are literature study, survey and observation. The sample used in this research is 400 respondents of social media users that follow the social media managed by Indonesia’s museum. The sampling technique are systematic sampling. We use Structural Equation Model with AMOS for analyze the data.

Evolution properties of online user preference diversity

NASA Astrophysics Data System (ADS)

Guo, Qiang; Ji, Lei; Liu, Jian-Guo; Han, Jingti

2017-02-01

Detecting the evolution properties of online user preference diversity is of significance for deeply understanding online collective behaviors. In this paper, we empirically explore the evolution patterns of online user rating preference, where the preference diversity is measured by the variation coefficient of the user rating sequence. The statistical results for four real systems show that, for movies and reviews, the user rating preference would become diverse and then get centralized finally. By introducing the empirical variation coefficient, we present a Markov model, which could regenerate the evolution properties of two online systems regarding to the stable variation coefficients. In addition, we investigate the evolution of the correlation between the user ratings and the object qualities, and find that the correlation would keep increasing as the user degree increases. This work could be helpful for understanding the anchoring bias and memory effects of the online user collective behaviors.

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

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

Integrating Health Behavior Theory and Design Elements in Serious Games.

PubMed

Cheek, Colleen; Fleming, Theresa; Lucassen, Mathijs Fg; Bridgman, Heather; Stasiak, Karolina; Shepherd, Matthew; Orpin, Peter

2015-01-01

Internet interventions for improving health and well-being have the potential to reach many people and fill gaps in service provision. Serious gaming interfaces provide opportunities to optimize user adherence and impact. Health interventions based in theory and evidence and tailored to psychological constructs have been found to be more effective to promote behavior change. Defining the design elements which engage users and help them to meet their goals can contribute to better informed serious games. To elucidate design elements important in SPARX, a serious game for adolescents with depression, from a user-centered perspective. We proposed a model based on an established theory of health behavior change and practical features of serious game design to organize ideas and rationale. We analyzed data from 5 studies comprising a total of 22 focus groups and 66 semistructured interviews conducted with youth and families in New Zealand and Australia who had viewed or used SPARX. User perceptions of the game were applied to this framework. A coherent framework was established using the three constructs of self-determination theory (SDT), autonomy, competence, and relatedness, to organize user perceptions and design elements within four areas important in design: computer game, accessibility, working alliance, and learning in immersion. User perceptions mapped well to the framework, which may assist developers in understanding the context of user needs. By mapping these elements against the constructs of SDT, we were able to propose a sound theoretical base for the model. This study's method allowed for the articulation of design elements in a serious game from a user-centered perspective within a coherent overarching framework. The framework can be used to deliberately incorporate serious game design elements that support a user's sense of autonomy, competence, and relatedness, key constructs which have been found to mediate motivation at all stages of the change process. The resulting model introduces promising avenues for future exploration. Involving users in program design remains an imperative if serious games are to be fit for purpose.

Quantitative analysis of bloggers' collective behavior powered by emotions

NASA Astrophysics Data System (ADS)

Mitrović, Marija; Paltoglou, Georgios; Tadić, Bosiljka

2011-02-01

Large-scale data resulting from users' online interactions provide the ultimate source of information to study emergent social phenomena on the Web. From individual actions of users to observable collective behaviors, different mechanisms involving emotions expressed in the posted text play a role. Here we combine approaches of statistical physics with machine-learning methods of text analysis to study the emergence of emotional behavior among Web users. Mapping the high-resolution data from digg.com onto bipartite networks of users and their comments onto posted stories, we identify user communities centered around certain popular posts and determine emotional contents of the related comments by the emotion classifier developed for this type of text. Applied over different time periods, this framework reveals strong correlations between the excess of negative emotions and the evolution of communities. We observe avalanches of emotional comments exhibiting significant self-organized critical behavior and temporal correlations. To explore the robustness of these critical states, we design a network-automaton model on realistic network connections and several control parameters, which can be inferred from the dataset. Dissemination of emotions by a small fraction of very active users appears to critically tune the collective states.

Constructing Agent Model for Virtual Training Systems

NASA Astrophysics Data System (ADS)

Murakami, Yohei; Sugimoto, Yuki; Ishida, Toru

Constructing highly realistic agents is essential if agents are to be employed in virtual training systems. In training for collaboration based on face-to-face interaction, the generation of emotional expressions is one key. In training for guidance based on one-to-many interaction such as direction giving for evacuations, emotional expressions must be supplemented by diverse agent behaviors to make the training realistic. To reproduce diverse behavior, we characterize agents by using a various combinations of operation rules instantiated by the user operating the agent. To accomplish this goal, we introduce a user modeling method based on participatory simulations. These simulations enable us to acquire information observed by each user in the simulation and the operating history. Using these data and the domain knowledge including known operation rules, we can generate an explanation for each behavior. Moreover, the application of hypothetical reasoning, which offers consistent selection of hypotheses, to the generation of explanations allows us to use otherwise incompatible operation rules as domain knowledge. In order to validate the proposed modeling method, we apply it to the acquisition of an evacuee's model in a fire-drill experiment. We successfully acquire a subject's model corresponding to the results of an interview with the subject.

SOCIAL PSYCHOLOGICAL DYNAMICS OF ENHANCED HIV RISK REDUCTION AMONG PEER INTERVENTIONISTS

PubMed Central

Dickson-Gomez, Julia; Weeks, Margaret R.; Convey, Mark; Li, Jianghong

2014-01-01

The authors present a model of interactive social psychological and relational feedback processes leading to human immunodeficiency virus (HIV) risk reduction behavior change among active drug users trained as Peer Health Advocates (PHAs). The model is supported by data from qualitative interviews with PHAs and members of their drug-using networks in the Risk Avoidance Partnership (RAP) project. Results suggest three mutually reinforcing social psychological processes that motivate PHAs to provide HIV prevention intervention to their peers and to reduce their own risk behaviors: development of a prosocial identity, positive social reinforcement from drug users and community members, and cognitive dissonance associated with continued risk behavior while engaging in health advocacy. These processes directly influence peer interventionists’ motivation and efficacy to continue giving intervention to their peers, and to reduce their HIV risk behaviors. The authors discuss implications of the model for continued research on effective HIV prevention in high-risk groups. PMID:25414528

Modeling users' activity on Twitter networks: validation of Dunbar's number

NASA Astrophysics Data System (ADS)

Goncalves, Bruno; Perra, Nicola; Vespignani, Alessandro

2012-02-01

Microblogging and mobile devices appear to augment human social capabilities, which raises the question whether they remove cognitive or biological constraints on human communication. In this paper we analyze a dataset of Twitter conversations collected across six months involving 1.7 million individuals and test the theoretical cognitive limit on the number of stable social relationships known as Dunbar's number. We find that the data are in agreement with Dunbar's result; users can entertain a maximum of 100-200 stable relationships. Thus, the ``economy of attention'' is limited in the online world by cognitive and biological constraints as predicted by Dunbar's theory. We propose a simple model for users' behavior that includes finite priority queuing and time resources that reproduces the observed social behavior.

Modeling online social signed networks

NASA Astrophysics Data System (ADS)

Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

2018-04-01

People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

Comparing vector-based and Bayesian memory models using large-scale datasets: User-generated hashtag and tag prediction on Twitter and Stack Overflow.

PubMed

Stanley, Clayton; Byrne, Michael D

2016-12-01

The growth of social media and user-created content on online sites provides unique opportunities to study models of human declarative memory. By framing the task of choosing a hashtag for a tweet and tagging a post on Stack Overflow as a declarative memory retrieval problem, 2 cognitively plausible declarative memory models were applied to millions of posts and tweets and evaluated on how accurately they predict a user's chosen tags. An ACT-R based Bayesian model and a random permutation vector-based model were tested on the large data sets. The results show that past user behavior of tag use is a strong predictor of future behavior. Furthermore, past behavior was successfully incorporated into the random permutation model that previously used only context. Also, ACT-R's attentional weight term was linked to an entropy-weighting natural language processing method used to attenuate high-frequency words (e.g., articles and prepositions). Word order was not found to be a strong predictor of tag use, and the random permutation model performed comparably to the Bayesian model without including word order. This shows that the strength of the random permutation model is not in the ability to represent word order, but rather in the way in which context information is successfully compressed. The results of the large-scale exploration show how the architecture of the 2 memory models can be modified to significantly improve accuracy, and may suggest task-independent general modifications that can help improve model fit to human data in a much wider range of domains. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Gender-specific differences in high-risk sexual behaviors among methamphetamine users in Myanmar-China border city, Muse, Myanmar: who is at risk?

PubMed

Saw, Yu Mon; Saw, Thu Nandar; Chan, Nyein; Cho, Su Myat; Jimba, Masamine

2018-02-01

Methamphetamine (MA) use is a significant public health concern due to its negative effects on health. However, to date, no epidemiological research has examined high-risk sexual behaviors (inconsistent condom use, having multiple sexual partners and having a history of sexually transmitted infections) among MA users. This topic is particularly important in Myanmar, which is recognized as one of the key MA production countries in the Southeast Asia region. Therefore, this study examined factors associated with high-risk sexual behaviors among MA users in Muse city, Myanmar. A community-based cross-sectional study was conducted from January to March 2013 in Muse city, Northern Shan State, Myanmar. In total, 1183 MA users (772 male; 411 female) were recruited using respondent-driven sampling and a computer assisted self-interviewing method. Generalized estimating equation models were used to examine factors associated with high-risk sexual behaviors. A large proportion of MA users engaged in high-risk sexual behaviors (inconsistent condom use: males, 90.7%, females, 85.2%; multiple sexual partners: males, 94.2%, females, 47.2%; and history of STIs: males, 55.7%, females, 56.0%). Among males, being a multiple stimulants drug user (adjusted odds ratio [AOR] =1.77; 95% confidence interval [CI] =1.30-2.41) and being a client of sex workers (AOR = 1.41; 95% CI = 1.08-1.83) were risk factors for engaging in high-risk sexual behaviors. Among females, being a migrant worker (AOR = 2.70; 95% CI = 1.86-3.93) and being employed (AOR = 1.57; 95% CI = 1.13-2.18) were risk factors for engaging in high-risk sexual behaviors as well. High-risk sexual behaviors were particularly pronounced among both male and female MA users. MA prevention programs that reflect gender considerations should be developed to pay more attention to vulnerable populations such as migrants, clients of sex workers, and less educated female MA users.

Impulsivity, Attention, Memory, and Decision-Making among Adolescent Marijuana Users

PubMed Central

Dougherty, Donald M.; Mathias, Charles W.; Dawes, Michael A.; Furr, R. Michael; Charles, Nora E.; Liguori, Anthony; Shannon, Erin E.; Acheson, Ashley

2012-01-01

Rationale Marijuana is a popular drug of abuse among adolescents, and they may be uniquely vulnerable to resulting cognitive and behavioral impairments. Previous studies have found impairments among adolescent marijuana users. However, the majority of this research has examined measures individually rather than multiple domains in a single cohesive analysis. This study used a logistic regression model that combines performance on a range of tasks to identify which measures were most altered among adolescent marijuana users. Objectives The purpose of this research was to determine unique associations between adolescent marijuana user and performances on multiple cognitive and behavioral domains (attention, memory, decision-making, and impulsivity) in 14- to 17-year-olds while simultaneously controlling for performances across the measures to determine which measures most strongly distinguish marijuana users from non-users. Methods Marijuana-using adolescents (n=45) and controls (n=48) were tested. Logistic regression analyses were conducted to test for: (a) differences between marijuana users and non-users on each measure, (b) associations between marijuana use and each measure after controlling for the other measures, and (c) the degree to which (a) and (b) together elucidated differences among marijuana users and non-users. Results Of all the cognitive and behavioral domains tested, impaired short-term recall memory and consequence sensitivity impulsivity were associated with marijuana use after controlling for performances across all measures. Conclusions This study extends previous findings by identifying cognitive and behavioral impairments most strongly associated with adolescent marijuana users. These specific deficits are potential targets of intervention for this at-risk population. PMID:23138434

Search Pathways: Modeling GeoData Search Behavior to Support Usable Application Development

NASA Astrophysics Data System (ADS)

Yarmey, L.; Rosati, A.; Tressel, S.

2014-12-01

Recent technical advances have enabled development of new scientific data discovery systems. Metadata brokering, linked data, and other mechanisms allow users to discover scientific data of interes across growing volumes of heterogeneous content. Matching this complex content with existing discovery technologies, people looking for scientific data are presented with an ever-growing array of features to sort, filter, subset, and scan through search returns to help them find what they are looking for. This paper examines the applicability of available technologies in connecting searchers with the data of interest. What metrics can be used to track success given shifting baselines of content and technology? How well do existing technologies map to steps in user search patterns? Taking a user-driven development approach, the team behind the Arctic Data Explorer interdisciplinary data discovery application invested heavily in usability testing and user search behavior analysis. Building on earlier library community search behavior work, models were developed to better define the diverse set of thought processes and steps users took to find data of interest, here called 'search pathways'. This research builds a deeper understanding of the user community that seeks to reuse scientific data. This approach ensures that development decisions are driven by clearly articulated user needs instead of ad hoc technology trends. Initial results from this research will be presented along with lessons learned for other discovery platform development and future directions for informatics research into search pathways.

An information propagation model considering incomplete reading behavior in microblog

NASA Astrophysics Data System (ADS)

Su, Qiang; Huang, Jiajia; Zhao, Xiande

2015-02-01

Microblog is one of the most popular communication channels on the Internet, and has already become the third largest source of news and public opinions in China. Although researchers have studied the information propagation in microblog using the epidemic models, previous studies have not considered the incomplete reading behavior among microblog users. Therefore, the model cannot fit the real situations well. In this paper, we proposed an improved model entitled Microblog-Susceptible-Infected-Removed (Mb-SIR) for information propagation by explicitly considering the user's incomplete reading behavior. We also tested the effectiveness of the model using real data from Sina Microblog. We demonstrate that the new proposed model is more accurate in describing the information propagation in microblog. In addition, we also investigate the effects of the critical model parameters, e.g., reading rate, spreading rate, and removed rate through numerical simulations. The simulation results show that, compared with other parameters, reading rate plays the most influential role in the information propagation performance in microblog.

Current hormonal contraceptive use predicts female extra-pair and dyadic sexual behavior: evidence based on Czech National Survey data.

PubMed

Klapilová, Kateřina; Cobey, Kelly D; Wells, Timothy; Roberts, S Craig; Weiss, Petr; Havlíček, Jan

2014-01-10

Data from 1155 Czech women (493 using oral contraception, 662 non-users), obtained from the Czech National Survey of Sexual Behavior, were used to investigate evolutionary-based hypotheses concerning the predictive value of current oral contraceptive (OC) use on extra-pair and dyadic (in-pair) sexual behavior of coupled women. Specifically, the aim was to determine whether current OC use was associated with lower extra-pair and higher in-pair sexual interest and behavior, because OC use suppresses cyclical shifts in mating psychology that occur in normally cycling women. Zero-inflated Poisson (ZIP) regression and negative binomial models were used to test associations between OC use and these sexual measures, controlling for other relevant predictors (e.g., age, parity, in-pair sexual satisfaction, relationship length). The overall incidence of having had an extra-pair partner or one-night stand in the previous year was not related to current OC use (the majority of the sample had not). However, among the women who had engaged in extra-pair sexual behavior, OC users had fewer one-night stands than non-users, and tended to have fewer partners, than non-users. OC users also had more frequent dyadic intercourse than non-users, potentially indicating higher commitment to their current relationship. These results suggest that suppression of fertility through OC use may alter important aspects of female sexual behavior, with potential implications for relationship functioning and stability.

In-Vehicle Information Systems Behavioral Model and Design Support: IVIS Demand Prototype Software User's Manual

DOT National Transportation Integrated Search

2000-03-06

The purpose of this research was to develop a behavioral model and prototype computer program for evaluation of modern in-vehicle information systems (IVIS). These systems differ from earlier in-vehicle instruments and displays in that they may requi...

Observing Consistency in Online Communication Patterns for User Re-Identification

PubMed Central

Venter, Hein S.

2016-01-01

Comprehension of the statistical and structural mechanisms governing human dynamics in online interaction plays a pivotal role in online user identification, online profile development, and recommender systems. However, building a characteristic model of human dynamics on the Internet involves a complete analysis of the variations in human activity patterns, which is a complex process. This complexity is inherent in human dynamics and has not been extensively studied to reveal the structural composition of human behavior. A typical method of anatomizing such a complex system is viewing all independent interconnectivity that constitutes the complexity. An examination of the various dimensions of human communication pattern in online interactions is presented in this paper. The study employed reliable server-side web data from 31 known users to explore characteristics of human-driven communications. Various machine-learning techniques were explored. The results revealed that each individual exhibited a relatively consistent, unique behavioral signature and that the logistic regression model and model tree can be used to accurately distinguish online users. These results are applicable to one-to-one online user identification processes, insider misuse investigation processes, and online profiling in various areas. PMID:27918593

A behavior change model for internet interventions.

PubMed

Ritterband, Lee M; Thorndike, Frances P; Cox, Daniel J; Kovatchev, Boris P; Gonder-Frederick, Linda A

2009-08-01

The Internet has become a major component to health care and has important implications for the future of the health care system. One of the most notable aspects of the Web is its ability to provide efficient, interactive, and tailored content to the user. Given the wide reach and extensive capabilities of the Internet, researchers in behavioral medicine have been using it to develop and deliver interactive and comprehensive treatment programs with the ultimate goal of impacting patient behavior and reducing unwanted symptoms. To date, however, many of these interventions have not been grounded in theory or developed from behavior change models, and no overarching model to explain behavior change in Internet interventions has yet been published. The purpose of this article is to propose a model to help guide future Internet intervention development and predict and explain behavior changes and symptom improvement produced by Internet interventions. The model purports that effective Internet interventions produce (and maintain) behavior change and symptom improvement via nine nonlinear steps: the user, influenced by environmental factors, affects website use and adherence, which is influenced by support and website characteristics. Website use leads to behavior change and symptom improvement through various mechanisms of change. The improvements are sustained via treatment maintenance. By grounding Internet intervention research within a scientific framework, developers can plan feasible, informed, and testable Internet interventions, and this form of treatment will become more firmly established.

A Preliminary Investigation of User Perception and Behavioral Intention for Different Review Types: Customers and Designers Perspective

PubMed Central

Qazi, Atika; Waheed, Mahwish; Abraham, Ajith

2014-01-01

Existing opinion mining studies have focused on and explored only two types of reviews, that is, regular and comparative. There is a visible gap in determining the useful review types from customers and designers perspective. Based on Technology Acceptance Model (TAM) and statistical measures we examine users' perception about different review types and its effects in terms of behavioral intention towards using online review system. By using sample of users (N = 400) and designers (N = 106), current research work studies three review types, A (regular), B (comparative), and C (suggestive), which are related to perceived usefulness, perceived ease of use, and behavioral intention. The study reveals that positive perception of the use of suggestive reviews improves users' decision making in business intelligence. The results also depict that type C (suggestive reviews) could be considered a new useful review type in addition to other types, A and B. PMID:24711739

Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number

PubMed Central

Gonçalves, Bruno; Perra, Nicola; Vespignani, Alessandro

2011-01-01

Microblogging and mobile devices appear to augment human social capabilities, which raises the question whether they remove cognitive or biological constraints on human communication. In this paper we analyze a dataset of Twitter conversations collected across six months involving 1.7 million individuals and test the theoretical cognitive limit on the number of stable social relationships known as Dunbar's number. We find that the data are in agreement with Dunbar's result; users can entertain a maximum of 100–200 stable relationships. Thus, the ‘economy of attention’ is limited in the online world by cognitive and biological constraints as predicted by Dunbar's theory. We propose a simple model for users' behavior that includes finite priority queuing and time resources that reproduces the observed social behavior. PMID:21826200

Discriminating bot accounts based solely on temporal features of microblog behavior

NASA Astrophysics Data System (ADS)

Pan, Junshan; Liu, Ying; Liu, Xiang; Hu, Hanping

2016-05-01

As the largest microblog service in China, Sina Weibo has attracted numerous automated applications (known as bots) due to its popularity and open architecture. We classify the active users from Sina Weibo into human, bot-based and hybrid groups based solely on the study of temporal features of their posting behavior. The anomalous burstiness parameter and time-interval entropy value are exploited to characterize automation. We also reveal different behavior patterns among the three types of users regarding their reposting ratio, daily rhythm and active days. Our findings may help Sina Weibo manage a better community and should be considered for dynamic models of microblog behaviors.

The Impact of the Security Competency on "Self-Efficacy in Information Security" for Effective Health Information Security in Iran.

PubMed

Shahri, Ahmad Bakhtiyari; Ismail, Zuraini; Mohanna, Shahram

2016-11-01

The security effectiveness based on users' behaviors is becoming a top priority of Health Information System (HIS). In the first step of this study, through the review of previous studies 'Self-efficacy in Information Security' (SEIS) and 'Security Competency' (SCMP) were identified as the important factors to transforming HIS users to the first line of defense in the security. Subsequently, a conceptual model was proposed taking into mentioned factors for HIS security effectiveness. Then, this quantitative study used the structural equation modeling to examine the proposed model based on survey data collected from a sample of 263 HIS users from eight hospitals in Iran. The result shows that SEIS is one of the important factors to cultivate of good end users' behaviors toward HIS security effectiveness. However SCMP appears a feasible alternative to providing SEIS. This study also confirms the mediation effects of SEIS on the relationship between SCMP and HIS security effectiveness. The results of this research paper can be used by HIS and IT managers to implement their information security process more effectively.

Applying User Input to the Design and Testing of an Electronic Behavioral Health Information System for Wraparound Care Coordination

PubMed Central

Bruns, Eric J.; Hyde, Kelly L.; Sather, April; Hook, Alyssa; Lyon, Aaron R.

2015-01-01

Health information technology (HIT) and care coordination for individuals with complex needs are high priorities for quality improvement in health care. However, there is little empirical guidance about how best to design electronic health record systems and related technologies to facilitate implementation of care coordination models in behavioral health, or how best to apply user input to the design and testing process. In this paper, we describe an iterative development process that incorporated user/stakeholder perspectives at multiple points and resulted in an electronic behavioral health information system (EBHIS) specific to the wraparound care coordination model for youth with serious emotional and behavioral disorders. First, we review foundational HIT research on how EBHIS can enhance efficiency and outcomes of wraparound that was used to inform development. After describing the rationale for and functions of a prototype EBHIS for wraparound, we describe methods and results for a series of six small studies that informed system development across four phases of effort – predevelopment, development, initial user testing, and commercialization – and discuss how these results informed system design and refinement. Finally, we present next steps, challenges to dissemination, and guidance for others aiming to develop specialized behavioral health HIT. The research team's experiences reinforce the opportunity presented by EBHIS to improve care coordination for populations with complex needs, while also pointing to a litany of barriers and challenges to be overcome to implement such technologies. PMID:26060099

SHER: a colored petri net based random mobility model for wireless communications.

PubMed

Khan, Naeem Akhtar; Ahmad, Farooq; Khan, Sher Afzal

2015-01-01

In wireless network research, simulation is the most imperative technique to investigate the network's behavior and validation. Wireless networks typically consist of mobile hosts; therefore, the degree of validation is influenced by the underlying mobility model, and synthetic models are implemented in simulators because real life traces are not widely available. In wireless communications, mobility is an integral part while the key role of a mobility model is to mimic the real life traveling patterns to study. The performance of routing protocols and mobility management strategies e.g. paging, registration and handoff is highly dependent to the selected mobility model. In this paper, we devise and evaluate the Show Home and Exclusive Regions (SHER), a novel two-dimensional (2-D) Colored Petri net (CPN) based formal random mobility model, which exhibits sociological behavior of a user. The model captures hotspots where a user frequently visits and spends time. Our solution eliminates six key issues of the random mobility models, i.e., sudden stops, memoryless movements, border effect, temporal dependency of velocity, pause time dependency, and speed decay in a single model. The proposed model is able to predict the future location of a mobile user and ultimately improves the performance of wireless communication networks. The model follows a uniform nodal distribution and is a mini simulator, which exhibits interesting mobility patterns. The model is also helpful to those who are not familiar with the formal modeling, and users can extract meaningful information with a single mouse-click. It is noteworthy that capturing dynamic mobility patterns through CPN is the most challenging and virulent activity of the presented research. Statistical and reachability analysis techniques are presented to elucidate and validate the performance of our proposed mobility model. The state space methods allow us to algorithmically derive the system behavior and rectify the errors of our proposed model.

SHER: A Colored Petri Net Based Random Mobility Model for Wireless Communications

PubMed Central

Khan, Naeem Akhtar; Ahmad, Farooq; Khan, Sher Afzal

2015-01-01

In wireless network research, simulation is the most imperative technique to investigate the network’s behavior and validation. Wireless networks typically consist of mobile hosts; therefore, the degree of validation is influenced by the underlying mobility model, and synthetic models are implemented in simulators because real life traces are not widely available. In wireless communications, mobility is an integral part while the key role of a mobility model is to mimic the real life traveling patterns to study. The performance of routing protocols and mobility management strategies e.g. paging, registration and handoff is highly dependent to the selected mobility model. In this paper, we devise and evaluate the Show Home and Exclusive Regions (SHER), a novel two-dimensional (2-D) Colored Petri net (CPN) based formal random mobility model, which exhibits sociological behavior of a user. The model captures hotspots where a user frequently visits and spends time. Our solution eliminates six key issues of the random mobility models, i.e., sudden stops, memoryless movements, border effect, temporal dependency of velocity, pause time dependency, and speed decay in a single model. The proposed model is able to predict the future location of a mobile user and ultimately improves the performance of wireless communication networks. The model follows a uniform nodal distribution and is a mini simulator, which exhibits interesting mobility patterns. The model is also helpful to those who are not familiar with the formal modeling, and users can extract meaningful information with a single mouse-click. It is noteworthy that capturing dynamic mobility patterns through CPN is the most challenging and virulent activity of the presented research. Statistical and reachability analysis techniques are presented to elucidate and validate the performance of our proposed mobility model. The state space methods allow us to algorithmically derive the system behavior and rectify the errors of our proposed model. PMID:26267860

Using Apex To Construct CPM-GOMS Models

NASA Technical Reports Server (NTRS)

John, Bonnie; Vera, Alonso; Matessa, Michael; Freed, Michael; Remington, Roger

2006-01-01

process for automatically generating computational models of human/computer interactions as well as graphical and textual representations of the models has been built on the conceptual foundation of a method known in the art as CPM-GOMS. This method is so named because it combines (1) the task decomposition of analysis according to an underlying method known in the art as the goals, operators, methods, and selection (GOMS) method with (2) a model of human resource usage at the level of cognitive, perceptual, and motor (CPM) operations. CPM-GOMS models have made accurate predictions about behaviors of skilled computer users in routine tasks, but heretofore, such models have been generated in a tedious, error-prone manual process. In the present process, CPM-GOMS models are generated automatically from a hierarchical task decomposition expressed by use of a computer program, known as Apex, designed previously to be used to model human behavior in complex, dynamic tasks. An inherent capability of Apex for scheduling of resources automates the difficult task of interleaving the cognitive, perceptual, and motor resources that underlie common task operators (e.g., move and click mouse). The user interface of Apex automatically generates Program Evaluation Review Technique (PERT) charts, which enable modelers to visualize the complex parallel behavior represented by a model. Because interleaving and the generation of displays to aid visualization are automated, it is now feasible to construct arbitrarily long sequences of behaviors. The process was tested by using Apex to create a CPM-GOMS model of a relatively simple human/computer-interaction task and comparing the time predictions of the model and measurements of the times taken by human users in performing the various steps of the task. The task was to withdraw $80 in cash from an automated teller machine (ATM). For the test, a Visual Basic mockup of an ATM was created, with a provision for input from (and measurement of the performance of) the user via a mouse. The times predicted by the automatically generated model turned out to approximate the measured times fairly well (see figure). While these results are promising, there is need for further development of the process. Moreover, it will also be necessary to test other, more complex models: The actions required of the user in the ATM task are too sequential to involve substantial parallelism and interleaving and, hence, do not serve as an adequate test of the unique strength of CPM-GOMS models to accommodate parallelism and interleaving.

Collaborative Filtering Recommendation on Users' Interest Sequences.

PubMed

Cheng, Weijie; Yin, Guisheng; Dong, Yuxin; Dong, Hongbin; Zhang, Wansong

2016-01-01

As an important factor for improving recommendations, time information has been introduced to model users' dynamic preferences in many papers. However, the sequence of users' behaviour is rarely studied in recommender systems. Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users' interest sequences (IS) that rank users' ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users' longest common sub-IS (LCSIS) and the count of users' total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users' IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users' preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.

The Impact of Social Structures on Deviant Behaviors: The Study of 402 High Risk Street Drug Users in Iran.

PubMed

Mehrabi, Maryam; Eskandarieh, Sharareh; Khodadost, Mahmoud; Sadeghi, Maneli; Nikfarjam, Ali; Hajebi, Ahmad

2016-01-01

This study is a sociological analysis of the three dimensions of social structure including institutional, relational, and embodied structures that have an impact on the individuals' deviant behaviors in the society. The authors used a mix method to analyze the qualitative and quantitative data of 402 high risk abandoned substance users in 2008 in Tehran, capital city of Iran. The leading reasons of substance use were categorized into four fundamental themes as follows: stress, deviant social networks, and low social capital and weak social support sources. In addition, the epidemiology model of regression analysis provides a brief explanation to assess the association between the demographical and etiological variables, and the drug users' deviant behaviors. In sum, substance use is discussed as a deviant behavior pattern which stems from a comorbidity of weak social structures.

Specializing network analysis to detect anomalous insider actions

PubMed Central

Chen, You; Nyemba, Steve; Zhang, Wen; Malin, Bradley

2012-01-01

Collaborative information systems (CIS) enable users to coordinate efficiently over shared tasks in complex distributed environments. For flexibility, they provide users with broad access privileges, which, as a side-effect, leave such systems vulnerable to various attacks. Some of the more damaging malicious activities stem from internal misuse, where users are authorized to access system resources. A promising class of insider threat detection models for CIS focuses on mining access patterns from audit logs, however, current models are limited in that they assume organizations have significant resources to generate label cases for training classifiers or assume the user has committed a large number of actions that deviate from “normal” behavior. In lieu of the previous assumptions, we introduce an approach that detects when specific actions of an insider deviate from expectation in the context of collaborative behavior. Specifically, in this paper, we introduce a specialized network anomaly detection model, or SNAD, to detect such events. This approach assesses the extent to which a user influences the similarity of the group of users that access a particular record in the CIS. From a theoretical perspective, we show that the proposed model is appropriate for detecting insider actions in dynamic collaborative systems. From an empirical perspective, we perform an extensive evaluation of SNAD with the access logs of two distinct environments: the patient record access logs a large electronic health record system (6,015 users, 130,457 patients and 1,327,500 accesses) and the editing logs of Wikipedia (2,394,385 revisors, 55,200 articles and 6,482,780 revisions). We compare our model with several competing methods and demonstrate SNAD is significantly more effective: on average it achieves 20–30% greater area under an ROC curve. PMID:23399988

Evolutionary Agent-Based Simulation of the Introduction of New Technologies in Air Traffic Management

NASA Technical Reports Server (NTRS)

Yliniemi, Logan; Agogino, Adrian K.; Tumer, Kagan

2014-01-01

Accurate simulation of the effects of integrating new technologies into a complex system is critical to the modernization of our antiquated air traffic system, where there exist many layers of interacting procedures, controls, and automation all designed to cooperate with human operators. Additions of even simple new technologies may result in unexpected emergent behavior due to complex human/ machine interactions. One approach is to create high-fidelity human models coming from the field of human factors that can simulate a rich set of behaviors. However, such models are difficult to produce, especially to show unexpected emergent behavior coming from many human operators interacting simultaneously within a complex system. Instead of engineering complex human models, we directly model the emergent behavior by evolving goal directed agents, representing human users. Using evolution we can predict how the agent representing the human user reacts given his/her goals. In this paradigm, each autonomous agent in a system pursues individual goals, and the behavior of the system emerges from the interactions, foreseen or unforeseen, between the agents/actors. We show that this method reflects the integration of new technologies in a historical case, and apply the same methodology for a possible future technology.

A Model to Assess the Behavioral Impacts of Consultative Knowledge Based Systems.

ERIC Educational Resources Information Center

Mak, Brenda; Lyytinen, Kalle

1997-01-01

This research model studies the behavioral impacts of consultative knowledge based systems (KBS). A study of graduate students explored to what extent their decisions were affected by user participation in updating the knowledge base; ambiguity of decision setting; routinization of usage; and source credibility of the expertise embedded in the…

Count Your Calories and Share Them: Health Benefits of Sharing mHealth Information on Social Networking Sites.

PubMed

Oeldorf-Hirsch, Anne; High, Andrew C; Christensen, John L

2018-04-23

This study investigates the relationship between sharing tracked mobile health (mHealth) information online, supportive communication, feedback, and health behavior. Based on the Integrated Theory of mHealth, our model asserts that sharing tracked health information on social networking sites benefits users' perceptions of their health because of the supportive communication they gain from members of their online social networks and that the amount of feedback people receive moderates these associations. Users of mHealth apps (N = 511) completed an online survey, and results revealed that both sharing tracked health information and receiving feedback from an online social network were positively associated with supportive communication. Network support both corresponded with improved health behavior and mediated the association between sharing health information and users' health behavior. As users received greater amounts of feedback from their online social networks, however, the association between sharing tracked health information and health behavior decreased. Theoretical implications for sharing tracked health information and practical implications for using mHealth apps are discussed.

Evaluation of physical activity web sites for use of behavior change theories.

PubMed

Doshi, Amol; Patrick, Kevin; Sallis, James F; Calfas, Karen

2003-01-01

Physical activity (PA) Web sites were assessed for their use of behavior change theories, including constructs of the health belief model, Transtheoretical Model, social cognitive theory, and the theory of reasoned action and planned behavior. An evaluation template for assessing PA Web sites was developed, and content validity and interrater reliability were demonstrated. Two independent raters evaluated 24 PA Web sites. Web sites varied widely in application of theory-based constructs, ranging from 5 to 48 on a 100-point scale. The most common intervention strategies were general information, social support, and realistic goal areas. Coverage of theory-based strategies was low, varying from 26% for social cognitive theory to 39% for health belief model. Overall, PA Web sites provided little assessment, feedback, or individually tailored assistance for users. They were unable to substantially tailor the on-line experience for users at different stages of change or different demographic characteristics.

GVIPS Models and Software

NASA Technical Reports Server (NTRS)

Arnold, Steven M.; Gendy, Atef; Saleeb, Atef F.; Mark, John; Wilt, Thomas E.

2007-01-01

Two reports discuss, respectively, (1) the generalized viscoplasticity with potential structure (GVIPS) class of mathematical models and (2) the Constitutive Material Parameter Estimator (COMPARE) computer program. GVIPS models are constructed within a thermodynamics- and potential-based theoretical framework, wherein one uses internal state variables and derives constitutive equations for both the reversible (elastic) and the irreversible (viscoplastic) behaviors of materials. Because of the underlying potential structure, GVIPS models not only capture a variety of material behaviors but also are very computationally efficient. COMPARE comprises (1) an analysis core and (2) a C++-language subprogram that implements a Windows-based graphical user interface (GUI) for controlling the core. The GUI relieves the user of the sometimes tedious task of preparing data for the analysis core, freeing the user to concentrate on the task of fitting experimental data and ultimately obtaining a set of material parameters. The analysis core consists of three modules: one for GVIPS material models, an analysis module containing a specialized finite-element solution algorithm, and an optimization module. COMPARE solves the problem of finding GVIPS material parameters in the manner of a design-optimization problem in which the parameters are the design variables.

An HTML5-Based Pure Website Solution for Rapidly Viewing and Processing Large-Scale 3D Medical Volume Reconstruction on Mobile Internet

PubMed Central

Chen, Xin; Zhang, Ye; Zhang, Jingna; Li, Ying; Mo, Xuemei; Chen, Wei

2017-01-01

This study aimed to propose a pure web-based solution to serve users to access large-scale 3D medical volume anywhere with good user experience and complete details. A novel solution of the Master-Slave interaction mode was proposed, which absorbed advantages of remote volume rendering and surface rendering. On server side, we designed a message-responding mechanism to listen to interactive requests from clients (Slave model) and to guide Master volume rendering. On client side, we used HTML5 to normalize user-interactive behaviors on Slave model and enhance the accuracy of behavior request and user-friendly experience. The results showed that more than four independent tasks (each with a data size of 249.4 MB) could be simultaneously carried out with a 100-KBps client bandwidth (extreme test); the first loading time was <12 s, and the response time of each behavior request for final high quality image remained at approximately 1 s, while the peak value of bandwidth was <50-KBps. Meanwhile, the FPS value for each client was ≥40. This solution could serve the users by rapidly accessing the application via one URL hyperlink without special software and hardware requirement in a diversified network environment and could be easily integrated into other telemedical systems seamlessly. PMID:28638406

An HTML5-Based Pure Website Solution for Rapidly Viewing and Processing Large-Scale 3D Medical Volume Reconstruction on Mobile Internet.

PubMed

Qiao, Liang; Chen, Xin; Zhang, Ye; Zhang, Jingna; Wu, Yi; Li, Ying; Mo, Xuemei; Chen, Wei; Xie, Bing; Qiu, Mingguo

2017-01-01

This study aimed to propose a pure web-based solution to serve users to access large-scale 3D medical volume anywhere with good user experience and complete details. A novel solution of the Master-Slave interaction mode was proposed, which absorbed advantages of remote volume rendering and surface rendering. On server side, we designed a message-responding mechanism to listen to interactive requests from clients ( Slave model) and to guide Master volume rendering. On client side, we used HTML5 to normalize user-interactive behaviors on Slave model and enhance the accuracy of behavior request and user-friendly experience. The results showed that more than four independent tasks (each with a data size of 249.4 MB) could be simultaneously carried out with a 100-KBps client bandwidth (extreme test); the first loading time was <12 s, and the response time of each behavior request for final high quality image remained at approximately 1 s, while the peak value of bandwidth was <50-KBps. Meanwhile, the FPS value for each client was ≥40. This solution could serve the users by rapidly accessing the application via one URL hyperlink without special software and hardware requirement in a diversified network environment and could be easily integrated into other telemedical systems seamlessly.

Impulsivity, attention, memory, and decision-making among adolescent marijuana users.

PubMed

Dougherty, Donald M; Mathias, Charles W; Dawes, Michael A; Furr, R Michael; Charles, Nora E; Liguori, Anthony; Shannon, Erin E; Acheson, Ashley

2013-03-01

Marijuana is a popular drug of abuse among adolescents, and they may be uniquely vulnerable to resulting cognitive and behavioral impairments. Previous studies have found impairments among adolescent marijuana users. However, the majority of this research has examined measures individually rather than multiple domains in a single cohesive analysis. This study used a logistic regression model that combines performance on a range of tasks to identify which measures were most altered among adolescent marijuana users. The purpose of this research was to determine unique associations between adolescent marijuana use and performances on multiple cognitive and behavioral domains (attention, memory, decision-making, and impulsivity) in 14- to 17-year-olds while simultaneously controlling for performances across the measures to determine which measures most strongly distinguish marijuana users from nonusers. Marijuana-using adolescents (n = 45) and controls (n = 48) were tested. Logistic regression analyses were conducted to test for: (1) differences between marijuana users and nonusers on each measure, (2) associations between marijuana use and each measure after controlling for the other measures, and (3) the degree to which (1) and (2) together elucidated differences among marijuana users and nonusers. Of all the cognitive and behavioral domains tested, impaired short-term recall memory and consequence sensitivity impulsivity were associated with marijuana use after controlling for performances across all measures. This study extends previous findings by identifying cognitive and behavioral impairments most strongly associated with adolescent marijuana users. These specific deficits are potential targets of intervention for this at-risk population.

Integrating Health Behavior Theory and Design Elements in Serious Games

PubMed Central

Fleming, Theresa; Lucassen, Mathijs FG; Bridgman, Heather; Stasiak, Karolina; Shepherd, Matthew; Orpin, Peter

2015-01-01

Background Internet interventions for improving health and well-being have the potential to reach many people and fill gaps in service provision. Serious gaming interfaces provide opportunities to optimize user adherence and impact. Health interventions based in theory and evidence and tailored to psychological constructs have been found to be more effective to promote behavior change. Defining the design elements which engage users and help them to meet their goals can contribute to better informed serious games. Objective To elucidate design elements important in SPARX, a serious game for adolescents with depression, from a user-centered perspective. Methods We proposed a model based on an established theory of health behavior change and practical features of serious game design to organize ideas and rationale. We analyzed data from 5 studies comprising a total of 22 focus groups and 66 semistructured interviews conducted with youth and families in New Zealand and Australia who had viewed or used SPARX. User perceptions of the game were applied to this framework. Results A coherent framework was established using the three constructs of self-determination theory (SDT), autonomy, competence, and relatedness, to organize user perceptions and design elements within four areas important in design: computer game, accessibility, working alliance, and learning in immersion. User perceptions mapped well to the framework, which may assist developers in understanding the context of user needs. By mapping these elements against the constructs of SDT, we were able to propose a sound theoretical base for the model. Conclusions This study’s method allowed for the articulation of design elements in a serious game from a user-centered perspective within a coherent overarching framework. The framework can be used to deliberately incorporate serious game design elements that support a user’s sense of autonomy, competence, and relatedness, key constructs which have been found to mediate motivation at all stages of the change process. The resulting model introduces promising avenues for future exploration. Involving users in program design remains an imperative if serious games are to be fit for purpose. PMID:26543916

Toward the Attribution of Web Behavior

DTIC Science & Technology

2012-07-01

den Poel, “Predicting website audience demo- graphics for Web advertising targeting using multi-website clickstream data,” Fundamenta Informaticae ...and M. Sydow, “Effective prediction of web user behaviour with user-level models,” Fundamenta Informaticae , vol. 89, no. 2, pp. 189–206, 2008. [24] J

Simulations in Cyber-Security: A Review of Cognitive Modeling of Network Attackers, Defenders, and Users.

PubMed

Veksler, Vladislav D; Buchler, Norbou; Hoffman, Blaine E; Cassenti, Daniel N; Sample, Char; Sugrim, Shridat

2018-01-01

Computational models of cognitive processes may be employed in cyber-security tools, experiments, and simulations to address human agency and effective decision-making in keeping computational networks secure. Cognitive modeling can addresses multi-disciplinary cyber-security challenges requiring cross-cutting approaches over the human and computational sciences such as the following: (a) adversarial reasoning and behavioral game theory to predict attacker subjective utilities and decision likelihood distributions, (b) human factors of cyber tools to address human system integration challenges, estimation of defender cognitive states, and opportunities for automation, (c) dynamic simulations involving attacker, defender, and user models to enhance studies of cyber epidemiology and cyber hygiene, and (d) training effectiveness research and training scenarios to address human cyber-security performance, maturation of cyber-security skill sets, and effective decision-making. Models may be initially constructed at the group-level based on mean tendencies of each subject's subgroup, based on known statistics such as specific skill proficiencies, demographic characteristics, and cultural factors. For more precise and accurate predictions, cognitive models may be fine-tuned to each individual attacker, defender, or user profile, and updated over time (based on recorded behavior) via techniques such as model tracing and dynamic parameter fitting.

P-Care BPJS Acceptance Model in Primary Health Centers.

PubMed

Markam, Hosizah

2017-01-01

Electronic Medical Records (EMR) are increasingly adopted in healthcare facilities. Recently, implementation failure of electronic information systems is known to be caused by not only the quality of technical aspects, but also the user's behavior. It is known as applying the Technology Acceptance Model (TAM). This research aimed to analyze the acceptance model of p-care BPJS in the primary health centers. A total sample of 30 p-care BPJS users was drawn by multistage random sampling in which of these 30 primary health centers participated. Data analysis used both descriptive and inferential statistics. In the phase of structural model, it indicated that p-care BPJS acceptance model in the primary health centers was formed by Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) through Attitude towards use of p-care BPJS and Behavioral Intention to use p-care BPJS.

The research on user behavior evaluation method for network state

NASA Astrophysics Data System (ADS)

Zhang, Chengyuan; Xu, Haishui

2017-08-01

Based on the correlation between user behavior and network running state, this paper proposes a method of user behavior evaluation based on network state. Based on the analysis and evaluation methods in other fields of study, we introduce the theory and tools of data mining. Based on the network status information provided by the trusted network view, the user behavior data and the network state data are analysed. Finally, we construct the user behavior evaluation index and weight, and on this basis, we can accurately quantify the influence degree of the specific behavior of different users on the change of network running state, so as to provide the basis for user behavior control decision.

TopoDrive and ParticleFlow--Two Computer Models for Simulation and Visualization of Ground-Water Flow and Transport of Fluid Particles in Two Dimensions

USGS Publications Warehouse

Hsieh, Paul A.

2001-01-01

This report serves as a user?s guide for two computer models: TopoDrive and ParticleFlow. These two-dimensional models are designed to simulate two ground-water processes: topography-driven flow and advective transport of fluid particles. To simulate topography-driven flow, the user may specify the shape of the water table, which bounds the top of the vertical flow section. To simulate transport of fluid particles, the model domain is a rectangle with overall flow from left to right. In both cases, the flow is under steady state, and the distribution of hydraulic conductivity may be specified by the user. The models compute hydraulic head, ground-water flow paths, and the movement of fluid particles. An interactive visual interface enables the user to easily and quickly explore model behavior, and thereby better understand ground-water flow processes. In this regard, TopoDrive and ParticleFlow are not intended to be comprehensive modeling tools, but are designed for modeling at the exploratory or conceptual level, for visual demonstration, and for educational purposes.

Development of user-centered interfaces to search the knowledge resources of the Virginia Henderson International Nursing Library.

PubMed

Jones, Josette; Harris, Marcelline; Bagley-Thompson, Cheryl; Root, Jane

2003-01-01

This poster describes the development of user-centered interfaces in order to extend the functionality of the Virginia Henderson International Nursing Library (VHINL) from library to web based portal to nursing knowledge resources. The existing knowledge structure and computational models are revised and made complementary. Nurses' search behavior is captured and analyzed, and the resulting search models are mapped to the revised knowledge structure and computational model.

Individual differences in the behavioral effects of nicotine: A review of the preclinical animal literature

PubMed Central

Falco, Adriana M.; Bevins, Rick A.

2015-01-01

Not everyone who tries tobacco or other nicotine-containing products becomes a long-term user. Certain traits or factors that are differentially present in these individuals must be able to help health care providers and researchers determine who is more likely to become chronic users of nicotine-containing products. Some of these factors, particularly sensation-seeking/novelty, impulsivity, and anxiety, lend themselves to the creation of animal models of reactivity to nicotine. These models of reactivity to nicotine can improve the translational aspects of preclinical animal research on nicotine-induced behaviors and treatments in order to help reduce negative outcomes in human populations. The goal of this review is to evaluate the current status of animal models of individual differences that serve to predict the later behavioral effects of nicotine. The limited utility and inconsistency of existing novelty models is considered, as well as the promise of impulsivity and anxiety models in preclinical animal populations. Finally, other models that could be employed to extend the benefit of the current research are examined. PMID:26410616

Exploring the Complex Pattern of Information Spreading in Online Blog Communities

PubMed Central

Pei, Sen; Muchnik, Lev; Tang, Shaoting; Zheng, Zhiming; Makse, Hernán A.

2015-01-01

Information spreading in online social communities has attracted tremendous attention due to its utmost practical values in applications. Despite that several individual-level diffusion data have been investigated, we still lack the detailed understanding of the spreading pattern of information. Here, by comparing information flows and social links in a blog community, we find that the diffusion processes are induced by three different spreading mechanisms: social spreading, self-promotion and broadcast. Although numerous previous studies have employed epidemic spreading models to simulate information diffusion, we observe that such models fail to reproduce the realistic diffusion pattern. In respect to users behaviors, strikingly, we find that most users would stick to one specific diffusion mechanism. Moreover, our observations indicate that the social spreading is not only crucial for the structure of diffusion trees, but also capable of inducing more subsequent individuals to acquire the information. Our findings suggest new directions for modeling of information diffusion in social systems, and could inform design of efficient propagation strategies based on users behaviors. PMID:25985081

Exploring the complex pattern of information spreading in online blog communities.

PubMed

Pei, Sen; Muchnik, Lev; Tang, Shaoting; Zheng, Zhiming; Makse, Hernán A

2015-01-01

Information spreading in online social communities has attracted tremendous attention due to its utmost practical values in applications. Despite that several individual-level diffusion data have been investigated, we still lack the detailed understanding of the spreading pattern of information. Here, by comparing information flows and social links in a blog community, we find that the diffusion processes are induced by three different spreading mechanisms: social spreading, self-promotion and broadcast. Although numerous previous studies have employed epidemic spreading models to simulate information diffusion, we observe that such models fail to reproduce the realistic diffusion pattern. In respect to users behaviors, strikingly, we find that most users would stick to one specific diffusion mechanism. Moreover, our observations indicate that the social spreading is not only crucial for the structure of diffusion trees, but also capable of inducing more subsequent individuals to acquire the information. Our findings suggest new directions for modeling of information diffusion in social systems, and could inform design of efficient propagation strategies based on users behaviors.

Users' Behavior towards Ubiquitous M-Learning

ERIC Educational Resources Information Center

Suki, Norazah Mohd; Suki, Norbayah Mohd

2011-01-01

Mobile technologies have enabled a new way of communicating, for whom mobile communications are part of normal daily interaction. This paper explores the proposed and verified Technology Acceptance Model (TAM) that can be employed to explain the acceptance of Mobile Learning (M-learning), an activity in which users access learning material with…

Modeling strategic use of human computer interfaces with novel hidden Markov models

PubMed Central

Mariano, Laura J.; Poore, Joshua C.; Krum, David M.; Schwartz, Jana L.; Coskren, William D.; Jones, Eric M.

2015-01-01

Immersive software tools are virtual environments designed to give their users an augmented view of real-world data and ways of manipulating that data. As virtual environments, every action users make while interacting with these tools can be carefully logged, as can the state of the software and the information it presents to the user, giving these actions context. This data provides a high-resolution lens through which dynamic cognitive and behavioral processes can be viewed. In this report, we describe new methods for the analysis and interpretation of such data, utilizing a novel implementation of the Beta Process Hidden Markov Model (BP-HMM) for analysis of software activity logs. We further report the results of a preliminary study designed to establish the validity of our modeling approach. A group of 20 participants were asked to play a simple computer game, instrumented to log every interaction with the interface. Participants had no previous experience with the game's functionality or rules, so the activity logs collected during their naïve interactions capture patterns of exploratory behavior and skill acquisition as they attempted to learn the rules of the game. Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload. We jointly modeled the activity log sequences collected from all participants using the BP-HMM approach, identifying a global library of activity patterns representative of the collective behavior of all the participants. Analyses show systematic relationships between both pre- and post-task questionnaires, self-reported approaches to analytic problem solving, and metrics extracted from the BP-HMM decomposition. Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit. PMID:26191026

Trust-Based Access Control Model from Sociological Approach in Dynamic Online Social Network Environment

PubMed Central

Kim, Seungjoo

2014-01-01

There has been an explosive increase in the population of the OSN (online social network) in recent years. The OSN provides users with many opportunities to communicate among friends and family. Further, it facilitates developing new relationships with previously unknown people having similar beliefs or interests. However, the OSN can expose users to adverse effects such as privacy breaches, the disclosing of uncontrolled material, and the disseminating of false information. Traditional access control models such as MAC, DAC, and RBAC are applied to the OSN to address these problems. However, these models are not suitable for the dynamic OSN environment because user behavior in the OSN is unpredictable and static access control imposes a burden on the users to change the access control rules individually. We propose a dynamic trust-based access control for the OSN to address the problems of the traditional static access control. Moreover, we provide novel criteria to evaluate trust factors such as sociological approach and evaluate a method to calculate the dynamic trust values. The proposed method can monitor negative behavior and modify access permission levels dynamically to prevent the indiscriminate disclosure of information. PMID:25374943

Trust-based access control model from sociological approach in dynamic online social network environment.

PubMed

Baek, Seungsoo; Kim, Seungjoo

2014-01-01

There has been an explosive increase in the population of the OSN (online social network) in recent years. The OSN provides users with many opportunities to communicate among friends and family. Further, it facilitates developing new relationships with previously unknown people having similar beliefs or interests. However, the OSN can expose users to adverse effects such as privacy breaches, the disclosing of uncontrolled material, and the disseminating of false information. Traditional access control models such as MAC, DAC, and RBAC are applied to the OSN to address these problems. However, these models are not suitable for the dynamic OSN environment because user behavior in the OSN is unpredictable and static access control imposes a burden on the users to change the access control rules individually. We propose a dynamic trust-based access control for the OSN to address the problems of the traditional static access control. Moreover, we provide novel criteria to evaluate trust factors such as sociological approach and evaluate a method to calculate the dynamic trust values. The proposed method can monitor negative behavior and modify access permission levels dynamically to prevent the indiscriminate disclosure of information.

Decision dynamics of departure times: Experiments and modeling

NASA Astrophysics Data System (ADS)

Sun, Xiaoyan; Han, Xiao; Bao, Jian-Zhang; Jiang, Rui; Jia, Bin; Yan, Xiaoyong; Zhang, Boyu; Wang, Wen-Xu; Gao, Zi-You

2017-10-01

A fundamental problem in traffic science is to understand user-choice behaviors that account for the emergence of complex traffic phenomena. Despite much effort devoted to theoretically exploring departure time choice behaviors, relatively large-scale and systematic experimental tests of theoretical predictions are still lacking. In this paper, we aim to offer a more comprehensive understanding of departure time choice behaviors in terms of a series of laboratory experiments under different traffic conditions and feedback information provided to commuters. In the experiment, the number of recruited players is much larger than the number of choices to better mimic the real scenario, in which a large number of commuters will depart simultaneously in a relatively small time window. Sufficient numbers of rounds are conducted to ensure the convergence of collective behavior. Experimental results demonstrate that collective behavior is close to the user equilibrium, regardless of different scales and traffic conditions. Moreover, the amount of feedback information has a negligible influence on collective behavior but has a relatively stronger effect on individual choice behaviors. Reinforcement learning and Fermi learning models are built to reproduce the experimental results and uncover the underlying mechanism. Simulation results are in good agreement with the experimentally observed collective behaviors.

Fuels planning: science synthesis and integration; environmental consequences fact sheet 09: Fire and Fuels Extension to the Forest Vegetation Simulator (FFE-FVS)

Treesearch

Elizabeth Reinhardt

2005-01-01

FFE-FVS is a model linking stand development, fuel dynamics, fire behavior and fire effects. It allows comparison of mid- to long-term effects of management alternatives including harvest, mechanical fuel treatment, prescribed fire, salvage, and no action. This fact sheet identifies the intended users and uses, required inputs, what the model does, and tells the user...

Modeling methylene chloride exposure-reduction options for home paint-stripper users.

PubMed

Riley, D M; Small, M J; Fischhoff, B

2000-01-01

Home improvement is a popular activity, but one that can also involve exposure to hazardous substances. Paint stripping is of particular concern because of the high potential exposures to methylene chloride, a solvent that is a potential human carcinogen and neurotoxicant. This article presents a general methodology for evaluating the effectiveness of behavioral interventions for reducing these risks. It doubles as a model that assesses exposure patterns, incorporating user time-activity patterns and risk-mitigation strategies. The model draws upon recent innovations in indoor air-quality modeling to estimate exposure through inhalation and dermal pathways to paint-stripper users. It is designed to use data gathered from home paint-stripper users about room characteristics, amount of stripper used, time-activity patterns and exposure-reduction strategies (e.g., increased ventilation and modification in the timing of stripper application, scraping, and breaks). Results indicate that the effectiveness of behavioral interventions depends strongly on characteristics of the room (e.g., size, number and size of doors and windows, base air-exchange rates). The greatest simple reduction in exposure is achieved by using an exhaust fan in addition to opening windows and doors. These results can help identify the most important information for product labels and other risk-communication materials.

Minimalism context-aware displays.

PubMed

Cai, Yang

2004-12-01

Despite the rapid development of cyber technologies, today we still have very limited attention and communication bandwidth to process the increasing information flow. The goal of the study is to develop a context-aware filter to match the information load with particular needs and capacities. The functions include bandwidth-resolution trade-off and user context modeling. From the empirical lab studies, it is found that the resolution of images can be reduced in order of magnitude if the viewer knows that he/she is looking for particular features. The adaptive display queue is optimized with real-time operational conditions and user's inquiry history. Instead of measuring operator's behavior directly, ubiquitous computing models are developed to anticipate user's behavior from the operational environment data. A case study of the video stream monitoring for transit security is discussed in the paper. In addition, the author addresses the future direction of coherent human-machine vision systems.

Identification and impact of discoverers in online social systems

PubMed Central

Medo, Matúš; Mariani, Manuel S.; Zeng, An; Zhang, Yi-Cheng

2016-01-01

Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce platforms, tend to what is already popular. We propose a statistical time-aware framework to identify the users who differ from the usual behavior by being repeatedly and persistently among the first to collect the items that later become hugely popular. Since these users effectively discover future hits, we refer them as discoverers. We use the proposed framework to demonstrate that discoverers are present in a wide range of real systems. Once identified, discoverers can be used to predict the future success of new items. We finally introduce a simple network model which reproduces the discovery patterns observed in the real data. Our results open the door to quantitative study of detailed temporal patterns in social systems. PMID:27687588

Identification and impact of discoverers in online social systems

NASA Astrophysics Data System (ADS)

Medo, Matúš; Mariani, Manuel S.; Zeng, An; Zhang, Yi-Cheng

2016-09-01

Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce platforms, tend to what is already popular. We propose a statistical time-aware framework to identify the users who differ from the usual behavior by being repeatedly and persistently among the first to collect the items that later become hugely popular. Since these users effectively discover future hits, we refer them as discoverers. We use the proposed framework to demonstrate that discoverers are present in a wide range of real systems. Once identified, discoverers can be used to predict the future success of new items. We finally introduce a simple network model which reproduces the discovery patterns observed in the real data. Our results open the door to quantitative study of detailed temporal patterns in social systems.

Identification and impact of discoverers in online social systems.

PubMed

Medo, Matúš; Mariani, Manuel S; Zeng, An; Zhang, Yi-Cheng

2016-09-30

Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce platforms, tend to what is already popular. We propose a statistical time-aware framework to identify the users who differ from the usual behavior by being repeatedly and persistently among the first to collect the items that later become hugely popular. Since these users effectively discover future hits, we refer them as discoverers. We use the proposed framework to demonstrate that discoverers are present in a wide range of real systems. Once identified, discoverers can be used to predict the future success of new items. We finally introduce a simple network model which reproduces the discovery patterns observed in the real data. Our results open the door to quantitative study of detailed temporal patterns in social systems.

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

PubMed Central

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time. PMID:26270539

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering.

PubMed

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.

Social cognitive determinants of nutrition and physical activity among web-health users enrolling in an online intervention: the influence of social support, self-efficacy, outcome expectations, and self-regulation.

PubMed

Anderson-Bill, Eileen Smith; Winett, Richard A; Wojcik, Janet R

2011-03-17

The Internet is a trusted source of health information for growing majorities of Web users. The promise of online health interventions will be realized with the development of purely online theory-based programs for Web users that are evaluated for program effectiveness and the application of behavior change theory within the online environment. Little is known, however, about the demographic, behavioral, or psychosocial characteristics of Web-health users who represent potential participants in online health promotion research. Nor do we understand how Web users' psychosocial characteristics relate to their health behavior-information essential to the development of effective, theory-based online behavior change interventions. This study examines the demographic, behavioral, and psychosocial characteristics of Web-health users recruited for an online social cognitive theory (SCT)-based nutrition, physical activity, and weight gain prevention intervention, the Web-based Guide to Health (WB-GTH). Directed to the WB-GTH site by advertisements through online social and professional networks and through print and online media, participants were screened, consented, and assessed with demographic, physical activity, psychosocial, and food frequency questionnaires online (taking a total of about 1.25 hours); they also kept a 7-day log of daily steps and minutes walked. From 4700 visits to the site, 963 Web users consented to enroll in the study: 83% (803) were female, participants' mean age was 44.4 years (SD 11.03 years), 91% (873) were white, and 61% (589) were college graduates; participants' median annual household income was approximately US $85,000. Participants' daily step counts were in the low-active range (mean 6485.78, SD 2352.54) and overall dietary levels were poor (total fat g/day, mean 77.79, SD 41.96; percent kcal from fat, mean 36.51, SD 5.92; fiber g/day, mean 17.74, SD 7.35; and fruit and vegetable servings/day, mean 4.03, SD 2.33). The Web-health users had good self-efficacy and outcome expectations for health behavior change; however, they perceived little social support for making these changes and engaged in few self-regulatory behaviors. Consistent with SCT, theoretical models provided good fit to Web-users' data (root mean square error of the approximation [RMSEA] < .05). Perceived social support and use of self-regulatory behaviors were strong predictors of physical activity and nutrition behavior. Web users' self-efficacy was also a good predictor of healthier levels of physical activity and dietary fat but not of fiber, fruits, and vegetables. Social support and self-efficacy indirectly predicted behavior through self-regulation, and social support had indirect effects through self-efficacy. Results suggest Web-health users visiting and ultimately participating in online health interventions may likely be middle-aged, well-educated, upper middle class women whose detrimental health behaviors put them at risk of obesity, heart disease, some cancers, and diabetes. The success of Internet physical activity and nutrition interventions may depend on the extent to which they lead users to develop self-efficacy for behavior change, but perhaps as important, the extent to which these interventions help them garner social-support for making changes. Success of these interventions may also depend on the extent to which they provide a platform for setting goals, planning, tracking, and providing feedback on targeted behaviors.

Automatic Classification of Users' Health Information Need Context: Logistic Regression Analysis of Mouse-Click and Eye-Tracker Data.

PubMed

Pian, Wenjing; Khoo, Christopher Sg; Chi, Jianxing

2017-12-21

Users searching for health information on the Internet may be searching for their own health issue, searching for someone else's health issue, or browsing with no particular health issue in mind. Previous research has found that these three categories of users focus on different types of health information. However, most health information websites provide static content for all users. If the three types of user health information need contexts can be identified by the Web application, the search results or information offered to the user can be customized to increase its relevance or usefulness to the user. The aim of this study was to investigate the possibility of identifying the three user health information contexts (searching for self, searching for others, or browsing with no particular health issue in mind) using just hyperlink clicking behavior; using eye-tracking information; and using a combination of eye-tracking, demographic, and urgency information. Predictive models are developed using multinomial logistic regression. A total of 74 participants (39 females and 35 males) who were mainly staff and students of a university were asked to browse a health discussion forum, Healthboards.com. An eye tracker recorded their examining (eye fixation) and skimming (quick eye movement) behaviors on 2 types of screens: summary result screen displaying a list of post headers, and detailed post screen. The following three types of predictive models were developed using logistic regression analysis: model 1 used only the time spent in scanning the summary result screen and reading the detailed post screen, which can be determined from the user's mouse clicks; model 2 used the examining and skimming durations on each screen, recorded by an eye tracker; and model 3 added user demographic and urgency information to model 2. An analysis of variance (ANOVA) analysis found that users' browsing durations were significantly different for the three health information contexts (P<.001). The logistic regression model 3 was able to predict the user's type of health information context with a 10-fold cross validation mean accuracy of 84% (62/74), followed by model 2 at 73% (54/74) and model 1 at 71% (52/78). In addition, correlation analysis found that particular browsing durations were highly correlated with users' age, education level, and the urgency of their information need. A user's type of health information need context (ie, searching for self, for others, or with no health issue in mind) can be identified with reasonable accuracy using just user mouse clicks that can easily be detected by Web applications. Higher accuracy can be obtained using Google glass or future computing devices with eye tracking function. ©Wenjing Pian, Christopher SG Khoo, Jianxing Chi. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.12.2017.

Data-driven behavioural modelling of residential water consumption to inform water demand management strategies

NASA Astrophysics Data System (ADS)

Giuliani, Matteo; Cominola, Andrea; Alshaf, Ahmad; Castelletti, Andrea; Anda, Martin

2016-04-01

The continuous expansion of urban areas worldwide is expected to highly increase residential water demand over the next few years, ultimately challenging the distribution and supply of drinking water. Several studies have recently demonstrated that actions focused only on the water supply side of the problem (e.g., augmenting existing water supply infrastructure) will likely fail to meet future demands, thus calling for the concurrent deployment of effective water demand management strategies (WDMS) to pursue water savings and conservation. However, to be effective WDMS do require a substantial understanding of water consumers' behaviors and consumption patterns at different spatial and temporal resolutions. Retrieving information on users' behaviors, as well as their explanatory and/or causal factors, is key to spot potential areas for targeting water saving efforts and to design user-tailored WDMS, such as education campaigns and personalized recommendations. In this work, we contribute a data-driven approach to identify household water users' consumption behavioural profiles and model their water use habits. State-of-the-art clustering methods are coupled with big data machine learning techniques with the aim of extracting dominant behaviors from a set of water consumption data collected at the household scale. This allows identifying heterogeneous groups of consumers from the studied sample and characterizing them with respect to several consumption features. Our approach is validated onto a real-world household water consumption dataset associated with a variety of demographic and psychographic user data and household attributes, collected in nine towns of the Pilbara and Kimberley Regions of Western Australia. Results show the effectiveness of the proposed method in capturing the influence of candidate determinants on residential water consumption profiles and in attaining sufficiently accurate predictions of users' consumption behaviors, ultimately providing valuable information to water utilities and managers.

A Triadic Reflective-Impulsive-Interoceptive Awareness Model of General and Impulsive Information System Use: Behavioral Tests of Neuro-Cognitive Theory

PubMed Central

Turel, Ofir; Bechara, Antoine

2016-01-01

This study examines a behavioral tripartite model developed in the field of addiction, and applies it here to understanding general and impulsive information technology use. It suggests that technology use is driven by two information-processing brain systems: reflective and impulsive, and that their effects on use are modulated by interoceptive awareness processes. The resultant reflective-impulsive-interoceptive awareness model is tested in two behavioral studies. Both studies employ SEM techniques to time-lagged self-report data from n1 = 300 and n2 = 369 social networking site users. Study 1 demonstrated that temptations augment the effect of habit on technology use, and reduce the effect of satisfaction on use. Study 2 showed that temptations strengthen the effect of habit on impulsive technology use, and weaken the effect of behavioral expectations on impulsive technology use. Hence, the results consistently support the notion that information technology users' behaviors are influenced by reflective and impulsive information processing systems; and that the equilibrium of these systems is determined, at least in part, by one's temptations. These results can serve as a basis for understanding the etiology of modern day addictions. PMID:27199834

A Triadic Reflective-Impulsive-Interoceptive Awareness Model of General and Impulsive Information System Use: Behavioral Tests of Neuro-Cognitive Theory.

PubMed

Turel, Ofir; Bechara, Antoine

2016-01-01

This study examines a behavioral tripartite model developed in the field of addiction, and applies it here to understanding general and impulsive information technology use. It suggests that technology use is driven by two information-processing brain systems: reflective and impulsive, and that their effects on use are modulated by interoceptive awareness processes. The resultant reflective-impulsive-interoceptive awareness model is tested in two behavioral studies. Both studies employ SEM techniques to time-lagged self-report data from n 1 = 300 and n 2 = 369 social networking site users. Study 1 demonstrated that temptations augment the effect of habit on technology use, and reduce the effect of satisfaction on use. Study 2 showed that temptations strengthen the effect of habit on impulsive technology use, and weaken the effect of behavioral expectations on impulsive technology use. Hence, the results consistently support the notion that information technology users' behaviors are influenced by reflective and impulsive information processing systems; and that the equilibrium of these systems is determined, at least in part, by one's temptations. These results can serve as a basis for understanding the etiology of modern day addictions.

A bipartite fitness model for online music streaming services

NASA Astrophysics Data System (ADS)

Pongnumkul, Suchit; Motohashi, Kazuyuki

2018-01-01

This paper proposes an evolution model and an analysis of the behavior of music consumers on online music streaming services. While previous studies have observed power-law degree distributions of usage in online music streaming services, the underlying behavior of users has not been well understood. Users and songs can be described using a bipartite network where an edge exists between a user node and a song node when the user has listened that song. The growth mechanism of bipartite networks has been used to understand the evolution of online bipartite networks Zhang et al. (2013). Existing bipartite models are based on a preferential attachment mechanism László Barabási and Albert (1999) in which the probability that a user listens to a song is proportional to its current popularity. This mechanism does not allow for two types of real world phenomena. First, a newly released song with high quality sometimes quickly gains popularity. Second, the popularity of songs normally decreases as time goes by. Therefore, this paper proposes a new model that is more suitable for online music services by adding fitness and aging functions to the song nodes of the bipartite network proposed by Zhang et al. (2013). Theoretical analyses are performed for the degree distribution of songs. Empirical data from an online streaming service, Last.fm, are used to confirm the degree distribution of the object nodes. Simulation results show improvements from a previous model. Finally, to illustrate the application of the proposed model, a simplified royalty cost model for online music services is used to demonstrate how the changes in the proposed parameters can affect the costs for online music streaming providers. Managerial implications are also discussed.

A new hybrid model for exploring the adoption of online nursing courses.

PubMed

Tung, Feng-Cheng; Chang, Su-Chao

2008-04-01

With the advancement in educational technology and internet access in recent years, nursing academia is searching for ways to widen nurses' educational opportunities. The online nursing courses are drawing more attention as well. The online nursing courses are very important e-learning tools for nursing students. The research combines the innovation diffusion theory and technology acceptance model, and adds two research variables, perceived financial cost and computer self-efficacy to propose a new hybrid technology acceptance model to study nursing students' behavioral intentions to use the online nursing courses. Based on 267 questionnaires collected from six universities in Taiwan, the research finds that studies strongly support this new hybrid technology acceptance model in predicting nursing students' behavioral intentions to use the online nursing courses. This research finds that compatibility, perceived usefulness, perceived ease of use, perceived financial cost and computer self-efficacy are critical factors for nursing students' behavioral intentions to use the online nursing courses. By explaining nursing students' behavioral intentions from a user's perspective, the findings of this research help to develop more user friendly online nursing courses and also provide insight into the best way to promote new e-learning tools for nursing students. This research finds that compatibility is the most important research variable that affects the behavioral intention to use the online nursing courses.

Brand equity and willingness to pay for condoms in Zimbabwe.

PubMed

Evans, W Douglas; Taruberekera, Noah; Longfield, Kim; Snider, Jeremy

2011-10-26

Zimbabwe suffers from one of the greatest burdens of HIV/AIDS in the world that has been compounded by social and economic instability in the past decade. However, from 2001 to 2009 HIV prevalence among 15-49 year olds declined from 26% to approximately 14%. Behavior change and condom use may in part explain this decline.PSI-Zimbabwe socially markets the Protector Plus (P+) branded line of condoms. When Zimbabwe converted to a dollar-based economy in 2009, the price of condoms was greatly increased and new marketing efforts were undertaken. This paper evaluates the role of condom marketing, a multi-dimensional scale of brand peceptions (brand equity), and price in condom use behavior. We randomly sampled sexually active men age 15-49 from 3 groups - current P+ users, former users, and free condom users. We compared their brand equity and willingness to pay based on survey results. We estimated multivariable logistic regression models to compare the 3 groups. We found that the brand equity scale was positive correlated with willingness to pay and with condom use. Former users also indicated a high willingness to pay for condoms. We found differences in brand equity between the 3 groups, with current P+ users having the highest P+ brand equity. As observed in previous studies, higher brand equity was associated with more of the targeted health behavior, in this case and more consistent condom use. Zimbabwe men have highly positive brand perceptions of P+. There is an opportunity to grow the total condom market in Zimbabwe by increasing brand equity across user groups. Some former users may resume using condoms through more effective marketing. Some free users may be willing to pay for condoms. Achieving these objectives will expand the total condom market and reduce HIV risk behaviors.

Six-Month Outcomes of a Randomized, Motivational Tele-intervention for Change in the Codependent Behavior of Family Members of Drug Users.

PubMed

Bortolon, Cassandra Borges; Moreira, Taís de Campos; Signor, Luciana; Guahyba, Bárbara Léa; Figueiró, Luciana Rizzieri; Ferigolo, Maristela; Barros, Helena Maria Tannhauser

2017-01-28

Families of substance abusers may develop maladaptive strategies, such as codependency, to address drug-related problems. It is important for families to receive specialist treatment in order to contribute to the recovery process. The Tele-intervention Model and Monitoring of Families of Drug Users (TMMFDU), based on motivational interviewing and stages of change, aims to encourage the family to change the codependents' behaviors. A randomized clinical trial was carried out to verify the change in codependent behavior after intervention with 6 months of follow-up. Three hundred and twenty-five families with high or low codependency scores were randomized into the intervention group (n = 163) or the usual treatment (UT) (n = 162). After 6 months of follow-up, the family members of the TMMFDU group were twice as likely to modify their codependency behavior when compared to the UT group (OR 2.08 CI 95% 1.18-3.65). TMMFDU proved to be effective in changing codependent behaviors among compliant family members of drug users.

A SCORM Compliant Courseware Authoring Tool for Supporting Pervasive Learning

ERIC Educational Resources Information Center

Wang, Te-Hua; Chang, Flora Chia-I

2007-01-01

The sharable content object reference model (SCORM) includes a representation of distance learning contents and a behavior definition of how users should interact with the contents. Generally, SCORMcompliant systems were based on multimedia and Web technologies on PCs. We further build a pervasive learning environment, which allows users to read…

Micro-level dynamics of the online information propagation: A user behavior model based on noisy spiking neurons.

PubMed

Lymperopoulos, Ilias N; Ioannou, George D

2016-10-01

We develop and validate a model of the micro-level dynamics underlying the formation of macro-level information propagation patterns in online social networks. In particular, we address the dynamics at the level of the mechanism regulating a user's participation in an online information propagation process. We demonstrate that this mechanism can be realistically described by the dynamics of noisy spiking neurons driven by endogenous and exogenous, deterministic and stochastic stimuli representing the influence modulating one's intention to be an information spreader. Depending on the dynamically changing influence characteristics, time-varying propagation patterns emerge reflecting the temporal structure, strength, and signal-to-noise ratio characteristics of the stimulation driving the online users' information sharing activity. The proposed model constitutes an overarching, novel, and flexible approach to the modeling of the micro-level mechanisms whereby information propagates in online social networks. As such, it can be used for a comprehensive understanding of the online transmission of information, a process integral to the sociocultural evolution of modern societies. The proposed model is highly adaptable and suitable for the study of the propagation patterns of behavior, opinions, and innovations among others. Copyright © 2016 Elsevier Ltd. All rights reserved.

Information Filtering Based on Users' Negative Opinions

NASA Astrophysics Data System (ADS)

Guo, Qiang; Li, Yang; Liu, Jian-Guo

2013-05-01

The process of heat conduction (HC) has recently found application in the information filtering [Zhang et al., Phys. Rev. Lett.99, 154301 (2007)], which is of high diversity but low accuracy. The classical HC model predicts users' potential interested objects based on their interesting objects regardless to the negative opinions. In terms of the users' rating scores, we present an improved user-based HC (UHC) information model by taking into account users' positive and negative opinions. Firstly, the objects rated by users are divided into positive and negative categories, then the predicted interesting and dislike object lists are generated by the UHC model. Finally, the recommendation lists are constructed by filtering out the dislike objects from the interesting lists. By implementing the new model based on nine similarity measures, the experimental results for MovieLens and Netflix datasets show that the new model considering negative opinions could greatly enhance the accuracy, measured by the average ranking score, from 0.049 to 0.036 for Netflix and from 0.1025 to 0.0570 for Movielens dataset, reduced by 26.53% and 44.39%, respectively. Since users prefer to give positive ratings rather than negative ones, the negative opinions contain much more information than the positive ones, the negative opinions, therefore, are very important for understanding users' online collective behaviors and improving the performance of HC model.

Real-time simulation of three-dimensional shoulder girdle and arm dynamics.

PubMed

Chadwick, Edward K; Blana, Dimitra; Kirsch, Robert F; van den Bogert, Antonie J

2014-07-01

Electrical stimulation is a promising technology for the restoration of arm function in paralyzed individuals. Control of the paralyzed arm under electrical stimulation, however, is a challenging problem that requires advanced controllers and command interfaces for the user. A real-time model describing the complex dynamics of the arm would allow user-in-the-loop type experiments where the command interface and controller could be assessed. Real-time models of the arm previously described have not included the ability to model the independently controlled scapula and clavicle, limiting their utility for clinical applications of this nature. The goal of this study therefore was to evaluate the performance and mechanical behavior of a real-time, dynamic model of the arm and shoulder girdle. The model comprises seven segments linked by eleven degrees of freedom and actuated by 138 muscle elements. Polynomials were generated to describe the muscle lines of action to reduce computation time, and an implicit, first-order Rosenbrock formulation of the equations of motion was used to increase simulation step-size. The model simulated flexion of the arm faster than real time, simulation time being 92% of actual movement time on standard desktop hardware. Modeled maximum isometric torque values agreed well with values from the literature, showing that the model simulates the moment-generating behavior of a real human arm. The speed of the model enables experiments where the user controls the virtual arm and receives visual feedback in real time. The ability to optimize potential solutions in simulation greatly reduces the burden on the user during development.

From health search to healthcare: explorations of intention and utilization via query logs and user surveys

PubMed Central

White, Ryen W; Horvitz, Eric

2014-01-01

Objective To better understand the relationship between online health-seeking behaviors and in-world healthcare utilization (HU) by studies of online search and access activities before and after queries that pursue medical professionals and facilities. Materials and methods We analyzed data collected from logs of online searches gathered from consenting users of a browser toolbar from Microsoft (N=9740). We employed a complementary survey (N=489) to seek a deeper understanding of information-gathering, reflection, and action on the pursuit of professional healthcare. Results We provide insights about HU through the survey, breaking out its findings by different respondent marginalizations as appropriate. Observations made from search logs may be explained by trends observed in our survey responses, even though the user populations differ. Discussion The results provide insights about how users decide if and when to utilize healthcare resources, and how online health information seeking transitions to in-world HU. The findings from both the survey and the logs reveal behavioral patterns and suggest a strong relationship between search behavior and HU. Although the diversity of our survey respondents is limited and we cannot be certain that users visited medical facilities, we demonstrate that it may be possible to infer HU from long-term search behavior by the apparent influence that health concerns and professional advice have on search activity. Conclusions Our findings highlight different phases of online activities around queries pursuing professional healthcare facilities and services. We also show that it may be possible to infer HU from logs without tracking people's physical location, based on the effect of HU on pre- and post-HU search behavior. This allows search providers and others to develop more robust models of interests and preferences by modeling utilization rather than simply the intention to utilize that is expressed in search queries. PMID:23666794

A Human-Centered Smart Home System with Wearable-Sensor Behavior Analysis

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

Ji, Jianting; Liu, Ting; Shen, Chao

Smart home has recently attracted much research interest owing to its potential in improving the quality of human life. How to obtain user's demand is the most important and challenging task for appliance optimal scheduling in smart home, since it is highly related to user's unpredictable behavior. In this paper, a human-centered smart home system is proposed to identify user behavior, predict their demand and schedule the household appliances. Firstly, the sensor data from user's wearable devices are monitored to profile user's full-day behavior. Then, the appliance-demand matrix is constructed to predict user's demand on home environment, which is extractedmore » from the history of appliance load data and user behavior. Two simulations are designed to demonstrate user behavior identification, appliance-demand matrix construction and strategy of appliance optimal scheduling generation.« less

Debating restrictions on embryonic stem cell research.

PubMed

McClain, Colleen

2009-09-01

This study investigates the emotional and behavioral effects of interpersonal online communication, focusing on the controversy surrounding the loosening of restrictions on human embryonic stem cell research. The issue, central to national and statewide elections in 2008, generated heated debate among candidates and voters and evoked strong emotional sentiments among partisans. Using the theory of affective intelligence, this study proposes a predictive model connecting levels of enthusiasm and anxiety with behavioral and information-seeking outcomes. Cognitive appraisal theory is also employed to provide a role for political emotion in accounting for interactive media effects. To investigate the ways that online deliberation may influence discussions surrounding stem cell research, a between-subjects experimental study was conducted that systematically varied the tone of feedback received (reinforcing or challenging) and type of interaction (synchronous or asynchronous) experienced by users. Results indicate that emotional responses play a significant role in predicting behavioral intentions arising from the user-to-user interactive experience.

Risk adjustment alternatives in paying for behavioral health care under Medicaid.

PubMed Central

Ettner, S L; Frank, R G; McGuire, T G; Hermann, R C

2001-01-01

OBJECTIVE: To compare the performance of various risk adjustment models in behavioral health applications such as setting mental health and substance abuse (MH/SA) capitation payments or overall capitation payments for populations including MH/SA users. DATA SOURCES/STUDY DESIGN: The 1991-93 administrative data from the Michigan Medicaid program were used. We compared mean absolute prediction error for several risk adjustment models and simulated the profits and losses that behavioral health care carve outs and integrated health plans would experience under risk adjustment if they enrolled beneficiaries with a history of MH/SA problems. Models included basic demographic adjustment, Adjusted Diagnostic Groups, Hierarchical Condition Categories, and specifications designed for behavioral health. PRINCIPAL FINDINGS: Differences in predictive ability among risk adjustment models were small and generally insignificant. Specifications based on relatively few MH/SA diagnostic categories did as well as or better than models controlling for additional variables such as medical diagnoses at predicting MH/SA expenditures among adults. Simulation analyses revealed that among both adults and minors considerable scope remained for behavioral health care carve outs to make profits or losses after risk adjustment based on differential enrollment of severely ill patients. Similarly, integrated health plans have strong financial incentives to avoid MH/SA users even after adjustment. CONCLUSIONS: Current risk adjustment methodologies do not eliminate the financial incentives for integrated health plans and behavioral health care carve-out plans to avoid high-utilizing patients with psychiatric disorders. PMID:11508640

Setting Priorities in Behavioral Interventions: An Application to Reducing Phishing Risk.

PubMed

Canfield, Casey Inez; Fischhoff, Baruch

2018-04-01

Phishing risk is a growing area of concern for corporations, governments, and individuals. Given the evidence that users vary widely in their vulnerability to phishing attacks, we demonstrate an approach for assessing the benefits and costs of interventions that target the most vulnerable users. Our approach uses Monte Carlo simulation to (1) identify which users were most vulnerable, in signal detection theory terms; (2) assess the proportion of system-level risk attributable to the most vulnerable users; (3) estimate the monetary benefit and cost of behavioral interventions targeting different vulnerability levels; and (4) evaluate the sensitivity of these results to whether the attacks involve random or spear phishing. Using parameter estimates from previous research, we find that the most vulnerable users were less cautious and less able to distinguish between phishing and legitimate emails (positive response bias and low sensitivity, in signal detection theory terms). They also accounted for a large share of phishing risk for both random and spear phishing attacks. Under these conditions, our analysis estimates much greater net benefit for behavioral interventions that target these vulnerable users. Within the range of the model's assumptions, there was generally net benefit even for the least vulnerable users. However, the differences in the return on investment for interventions with users with different degrees of vulnerability indicate the importance of measuring that performance, and letting it guide interventions. This study suggests that interventions to reduce response bias, rather than to increase sensitivity, have greater net benefit. © 2017 Society for Risk Analysis.

Modeling the impact of simulated educational interventions on the use and abuse of pharmaceutical opioids in the United States: a report on initial efforts.

PubMed

Wakeland, Wayne; Nielsen, Alexandra; Schmidt, Teresa D; McCarty, Dennis; Webster, Lynn R; Fitzgerald, John; Haddox, J David

2013-10-01

Three educational interventions were simulated in a system dynamics model of the medical use, trafficking, and nonmedical use of pharmaceutical opioids. The study relied on secondary data obtained in the literature for the period of 1995 to 2008 as well as expert panel recommendations regarding model parameters and structure. The behavior of the resulting systems-level model was tested for fit against reference behavior data. After the base model was tested, logic to represent three educational interventions was added and the impact of each intervention on simulated overdose deaths was evaluated over a 7-year evaluation period, 2008 to 2015. Principal findings were that a prescriber education intervention not only reduced total overdose deaths in the model but also reduced the total number of persons who receive opioid analgesic therapy, medical user education not only reduced overdose deaths among medical users but also resulted in increased deaths from nonmedical use, and a "popularity" intervention sharply reduced overdose deaths among nonmedical users while having no effect on medical use. System dynamics modeling shows promise for evaluating potential interventions to ameliorate the adverse outcomes associated with the complex system surrounding the use of opioid analgesics to treat pain.

Simulations in Cyber-Security: A Review of Cognitive Modeling of Network Attackers, Defenders, and Users

PubMed Central

Veksler, Vladislav D.; Buchler, Norbou; Hoffman, Blaine E.; Cassenti, Daniel N.; Sample, Char; Sugrim, Shridat

2018-01-01

Computational models of cognitive processes may be employed in cyber-security tools, experiments, and simulations to address human agency and effective decision-making in keeping computational networks secure. Cognitive modeling can addresses multi-disciplinary cyber-security challenges requiring cross-cutting approaches over the human and computational sciences such as the following: (a) adversarial reasoning and behavioral game theory to predict attacker subjective utilities and decision likelihood distributions, (b) human factors of cyber tools to address human system integration challenges, estimation of defender cognitive states, and opportunities for automation, (c) dynamic simulations involving attacker, defender, and user models to enhance studies of cyber epidemiology and cyber hygiene, and (d) training effectiveness research and training scenarios to address human cyber-security performance, maturation of cyber-security skill sets, and effective decision-making. Models may be initially constructed at the group-level based on mean tendencies of each subject's subgroup, based on known statistics such as specific skill proficiencies, demographic characteristics, and cultural factors. For more precise and accurate predictions, cognitive models may be fine-tuned to each individual attacker, defender, or user profile, and updated over time (based on recorded behavior) via techniques such as model tracing and dynamic parameter fitting. PMID:29867661

Location contexts of user check-ins to model urban geo life-style patterns.

PubMed

Hasan, Samiul; Ukkusuri, Satish V

2015-01-01

Geo-location data from social media offers us information, in new ways, to understand people's attitudes and interests through their activity choices. In this paper, we explore the idea of inferring individual life-style patterns from activity-location choices revealed in social media. We present a model to understand life-style patterns using the contextual information (e. g. location categories) of user check-ins. Probabilistic topic models are developed to infer individual geo life-style patterns from two perspectives: i) to characterize the patterns of user interests to different types of places and ii) to characterize the patterns of user visits to different neighborhoods. The method is applied to a dataset of Foursquare check-ins of the users from New York City. The co-existence of several location contexts and the corresponding probabilities in a given pattern provide useful information about user interests and choices. It is found that geo life-style patterns have similar items-either nearby neighborhoods or similar location categories. The semantic and geographic proximity of the items in a pattern reflects the hidden regularity in user preferences and location choice behavior.

Modelling parallel programs and multiprocessor architectures with AXE

NASA Technical Reports Server (NTRS)

Yan, Jerry C.; Fineman, Charles E.

1991-01-01

AXE, An Experimental Environment for Parallel Systems, was designed to model and simulate for parallel systems at the process level. It provides an integrated environment for specifying computation models, multiprocessor architectures, data collection, and performance visualization. AXE is being used at NASA-Ames for developing resource management strategies, parallel problem formulation, multiprocessor architectures, and operating system issues related to the High Performance Computing and Communications Program. AXE's simple, structured user-interface enables the user to model parallel programs and machines precisely and efficiently. Its quick turn-around time keeps the user interested and productive. AXE models multicomputers. The user may easily modify various architectural parameters including the number of sites, connection topologies, and overhead for operating system activities. Parallel computations in AXE are represented as collections of autonomous computing objects known as players. Their use and behavior is described. Performance data of the multiprocessor model can be observed on a color screen. These include CPU and message routing bottlenecks, and the dynamic status of the software.

LOGAM (Logistic Analysis Model). Volume 2. Users Manual.

DTIC Science & Technology

1982-08-01

as opposed to simulation models which represent a system’s behavior as a function of time. These latter classes of models are often complex. They...includes the cost of ammunition and missiles comsumed by the system being costed during unit training. Excluded is the cost of ammunition consumed during...data. The results obtained from sensitivity testing may be used to construct graphs which display the behavior of the maintenance concept over the range

Binge use and sex and drug use behaviors among HIV(-), heterosexual methamphetamine users in San Diego.

PubMed

Cheng, W Susan; Garfein, Richard S; Semple, Shirley J; Strathdee, Steffanie A; Zians, James K; Patterson, Thomas L

2010-01-01

This study identified sociodemographic factors, drug using practices, sexual behaviors, and motivational factors associated with binge (a period of uninterrupted) methamphetamine (MA) use among heterosexual MA users. The FASTLANE study provided cross-sectional data collected by audio computer-assisted self-interview (ACASI) between June 2001 and August 2004 from 451 HIV-negative MA users in San Diego, California, USA who had engaged in unprotected sex and used MA in the previous two months. The study sample was 67.8% male, 49.4% Caucasian, 26.8% African-American, and 12.8% Hispanic with a mean age of 36.6 years; 183 (40.5%) reported binge use in the past 2 months. Compared with non-binge users, binge users of MA were more likely to report risky drug use and sex behaviors and differed in motivations to initiate and currently use MA. The final logistic regression model for binge use included more days of MA use in the last month, ever treated for MA use, injection drug use, higher Beck Depression Inventory score, "experimentation" as a motivation for initiating MA use, and engaging in sex marathons while high on MA. HIV prevention efforts should differentiate and address these differences in motivations for MA use and the associated HIV-risk sex and drug use behaviors as key targets for effective intervention.

Predictive Scheduling for Electric Vehicles Considering Uncertainty of Load and User Behaviors

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

Wang, Bin; Huang, Rui; Wang, Yubo

2016-05-02

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimizationmore » module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.« less

A Multilayer Naïve Bayes Model for Analyzing User's Retweeting Sentiment Tendency.

PubMed

Wang, Mengmeng; Zuo, Wanli; Wang, Ying

2015-01-01

Today microblogging has increasingly become a means of information diffusion via user's retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user's retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user's network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user's retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.

Echo Chambers: Emotional Contagion and Group Polarization on Facebook.

PubMed

Del Vicario, Michela; Vivaldo, Gianna; Bessi, Alessandro; Zollo, Fabiana; Scala, Antonio; Caldarelli, Guido; Quattrociocchi, Walter

2016-12-01

Recent findings showed that users on Facebook tend to select information that adhere to their system of beliefs and to form polarized groups - i.e., echo chambers. Such a tendency dominates information cascades and might affect public debates on social relevant issues. In this work we explore the structural evolution of communities of interest by accounting for users emotions and engagement. Focusing on the Facebook pages reporting on scientific and conspiracy content, we characterize the evolution of the size of the two communities by fitting daily resolution data with three growth models - i.e. the Gompertz model, the Logistic model, and the Log-logistic model. Although all the models appropriately describe the data structure, the Logistic one shows the best fit. Then, we explore the interplay between emotional state and engagement of users in the group dynamics. Our findings show that communities' emotional behavior is affected by the users' involvement inside the echo chamber. Indeed, to an higher involvement corresponds a more negative approach. Moreover, we observe that, on average, more active users show a faster shift towards the negativity than less active ones.

The DART dispersion analysis research tool: A mechanistic model for predicting fission-product-induced swelling of aluminum dispersion fuels. User`s guide for mainframe, workstation, and personal computer applications

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

Rest, J.

1995-08-01

This report describes the primary physical models that form the basis of the DART mechanistic computer model for calculating fission-product-induced swelling of aluminum dispersion fuels; the calculated results are compared with test data. In addition, DART calculates irradiation-induced changes in the thermal conductivity of the dispersion fuel, as well as fuel restructuring due to aluminum fuel reaction, amorphization, and recrystallization. Input instructions for execution on mainframe, workstation, and personal computers are provided, as is a description of DART output. The theory of fission gas behavior and its effect on fuel swelling is discussed. The behavior of these fission products inmore » both crystalline and amorphous fuel and in the presence of irradiation-induced recrystallization and crystalline-to-amorphous-phase change phenomena is presented, as are models for these irradiation-induced processes.« less

Human behavior in online social systems

NASA Astrophysics Data System (ADS)

Grabowski, A.

2009-06-01

We present and study data concerning human behavior in four online social systems: (i) an Internet community of friends of over 107 people, (ii) a music community website with over 106 users, (iii) a gamers’ community server with over 5 × 106 users and (iv) a booklovers’ website with over 2.5 × 105 users. The purpose of those systems is different; however, their properties are very similar. We have found that the distribution of human activity (e.g., the sum of books read or songs played) has the form of a power law. Moreover, the relationship between human activity and time has a power-law form, too. We present a simple interest-driven model of the evolution of such systems which explains the emergence of two scaling regimes.

Modeling Behavior of Students in E-Learning Courses on the Basis of Use Interactive Animations

ERIC Educational Resources Information Center

Magdin, Martin; Turcáni, Milan

2016-01-01

Authors in their contribution deal with modeling the behavior of user in e-learning course based on the use of interactive animations. Nowadays, E-learning courses form a standard part of educational process. However, it is not so easy to determine the way students work with study material, whether they make use of it in order to increase didactic…

Micromechanics of metal matrix composites using the Generalized Method of Cells model (GMC) user's guide

NASA Technical Reports Server (NTRS)

Aboudi, Jacob; Pindera, Marek-Jerzy

1992-01-01

A user's guide for the program gmc.f is presented. The program is based on the generalized method of cells model (GMC) which is capable via a micromechanical analysis, of predicting the overall, inelastic behavior of unidirectional, multi-phase composites from the knowledge of the properties of the viscoplastic constituents. In particular, the program is sufficiently general to predict the response of unidirectional composites having variable fiber shapes and arrays.

Sexual behavior differences between amphetamine-type stimulants users and heroin users.

PubMed

Jia, Zhen-jun; Yan, Shi-yan; Bao, Yan-ping; Lian, Zhi; Zhang, Hao-ran; Liu, Zhi-min

2013-01-01

To explore the sexual behavior of amphetamine-type stimulant (ATS) users and heroin users, and to find out the dangerous sexual behaviors, even related risk factors among them. Four hundred thirty-eight ATS users and 524 heroin users were recruited in 10 compulsory detoxification treatment centers and voluntary detoxification centers in Beijing, Shenzhen, Guangzhou, Xi'an, and Taiyuan. Their sociodemographic characteristics, history of drug taking, and sexual behaviors were surveyed. Many variables of sociodemographic characteristics and sexual behaviors were significantly different between ATS users and heroin users (P < 0.05). Dangerous sexual behaviors among ATS users include sexual intercourse often or each time after taking drug (30.1%), multiple sexual intercourse (16.5%), casual sex partners (34.0%), homosexual partners (2.5%), and never or occasionally using condom with a steady sex partner (79.3%) or with casual sex partners (39.1%). The rate of ever-infecting sexually transmitted disease (STD) was high in both the groups (ATS, 20.5%; heroin, 30.9%). Sexual behavior is the main way to transmit STD and human immunodeficiency virus among ATS users. The study results will promote the government's awareness of the issue and take necessary steps to slow the spread of STD and human immunodeficiency virus among the ATS users.

75 FR 4101 - Enterprise Income Verification (EIV) System User Access Authorization Form and Rules of Behavior...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-26

... Verification (EIV) System User Access Authorization Form and Rules of Behavior and User Agreement AGENCY... lists the following information: Title of Proposal: Enterprise Income Verification (EIV) System User Access, Authorization Form and Rules Of Behavior and User Agreement. OMB Approval Number: 2577-New. Form...

Using Article Photocopy Data in Bibliographic Models for Journal Collection Management.

ERIC Educational Resources Information Center

Cooper, Michael D.; McGregor, George F.

1994-01-01

Describes a method of cost-per-use analysis for individual journal articles to facilitate journal selection, deselection, and retention decisions. Conducted in a biotechnology library, the study was based on 491 users who requested more than 48,000 article photocopies over 3 years. Information on user behavior and journal use patterns is provided.…

Symptomatological features of patients with and without Ecstasy use during their first psychotic episode.

PubMed

Rugani, Fabio; Bacciardi, Silvia; Rovai, Luca; Pacini, Matteo; Maremmani, Angelo Giovanni Icro; Deltito, Joseph; Dell'osso, Liliana; Maremmani, Icro

2012-07-01

Ecstasy use is generally chosen by adolescents and young adults for its entactogenic properties (the production of feelings of empathy, love, and emotional closeness to others.) Despite this desired and frequently realized outcome, Ecstasy use has often resulted in the genesis of psychotic symptoms and aggressive behaviors, particularly after chronic and/or intensive use. To explore the negative consequences of Ecstasy use and to examine the aggressive nature oftentimes seen in many Ecstasy users we employed a case-control study model. We compared, by means of validated psychometric tests, the psychopathological symptoms (BPRS), the aggressiveness (OAS) and the social adjustment (DSM-GAF) of psychotic patients with (n = 23) and without (n = 46) recent user of Ecstasy, during their first psychotic episode and hospitalization. All 23 Ecstasy users were Ecstasy users only. Almost all of the psychotic symptoms were of similar severity in both groups. Blunted affect was milder in users than in non-users, whereas hostility and aggressive behavior was significantly more severe in users than in non-users. psychosis with a high level of aggressiveness and violence constitutes an important 'side-effect' that surely runs counter to the expected entactogenic action of Ecstasy. At a patient psycho-educational level, this study suggests that the use of Ecstasy may be counterproductive with respect to user expectations.

BrainLiner: A Neuroinformatics Platform for Sharing Time-Aligned Brain-Behavior Data

PubMed Central

Takemiya, Makoto; Majima, Kei; Tsukamoto, Mitsuaki; Kamitani, Yukiyasu

2016-01-01

Data-driven neuroscience aims to find statistical relationships between brain activity and task behavior from large-scale datasets. To facilitate high-throughput data processing and modeling, we created BrainLiner as a web platform for sharing time-aligned, brain-behavior data. Using an HDF5-based data format, BrainLiner treats brain activity and data related to behavior with the same salience, aligning both behavioral and brain activity data on a common time axis. This facilitates learning the relationship between behavior and brain activity. Using a common data file format also simplifies data processing and analyses. Properties describing data are unambiguously defined using a schema, allowing machine-readable definition of data. The BrainLiner platform allows users to upload and download data, as well as to explore and search for data from the web platform. A WebGL-based data explorer can visualize highly detailed neurophysiological data from within the web browser, and a data-driven search feature allows users to search for similar time windows of data. This increases transparency, and allows for visual inspection of neural coding. BrainLiner thus provides an essential set of tools for data sharing and data-driven modeling. PMID:26858636

SPECabq v. 2.0

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

Chambers, Robert S.; Neidigk, Matthew A.

Sandia SPECabq is FORTRAN code that defines the user supplied subroutines needed to perform nonlinear viscoelastic analyses in the ABAQUS commercial finite element code based on the Simplified Potential Energy Clock (SPEC) Model. The SPEC model was published in the open literature in 2009. It must be compiled and linked with the ABAQUS libraries under the user supplied subroutine option of the ABAQUS executable script. The subroutine is used to analyze the thermomechanical behavior of isotropic polymers predicting things like how a polymer may undergo stress or volume relaxation under different temperature and loading environments. This subroutine enables the ABAQUSmore » finite element code to be used for analyzing the thermo-mechanical behavior of samples and parts that are made from glassy polymers.« less

The Pitman-Yor Process and an Empirical Study of Choice Behavior

NASA Astrophysics Data System (ADS)

Hisakado, Masato; Sano, Fumiaki; Mori, Shintaro

2018-02-01

This study discusses choice behavior using a voting model in which voters can obtain information from a finite number of previous r voters. Voters vote for a candidate with a probability proportional to the previous vote ratio, which is visible to the voters. We obtain the Pitman sampling formula as the equilibrium distribution of r votes. We present the model as a process of posting on a bulletin board system, 2ch.net, where users can choose one of many threads to create a post. We explore how this choice depends on the last r posts and the distribution of these last r posts across threads. We conclude that the posting process is described by our voting model with analog herders for a small r, which might correspond to the time horizon of users' responses.

Commuter Train Passenger Safety Model Using Positive Behavior Approach: The Case Study in Suburban Area

NASA Astrophysics Data System (ADS)

Suryanto, D. A.; Adisasmita, S. A.; Hamid, S.; Hustim, M.

2018-04-01

Currently, Train passanger safety measures are more predominantly measurable using negative dimensions in user mode behavior, such as accident rate, accident intensity and accident impact. This condition suggests that safety improvements aim only to reduce accidents. Therefore, this study aims to measure the safety level of light train transit modes (KRL) through the dimensions of traveling safety on commuters based on positive safety indicators with severel condition departure times and returns for work purposes and long trip rates above KRL. The primary survey were used in data collection methods. Structural Equation Modeling (SEM) were used in data analysis. The results show that there are different models of the safety level of departure and return journey. The highest difference is in the security dimension which is the internal variable of KRL users.

PESTICIDE ORCHARD ECOSYSTEM MODEL (POEM): A USER'S GUIDE

EPA Science Inventory

A mathematical model was developed to predict the transport and effects of a pesticide in an orchard ecosystem. The environmental behavior of azinphosmethyl was studied over a two-year period in a Michigan apple orchard. Data were gathered for the model on initial distribution wi...

Identifying the perceptive users for online social systems

PubMed Central

Liu, Xiao-Lu; Guo, Qiang; Han, Jing-Ti

2017-01-01

In this paper, the perceptive user, who could identify the high-quality objects in their initial lifespan, is presented. By tracking the ratings given to the rewarded objects, we present a method to identify the user perceptibility, which is defined as the capability that a user can identify these objects at their early lifespan. Moreover, we investigate the behavior patterns of the perceptive users from three dimensions: User activity, correlation characteristics of user rating series and user reputation. The experimental results for the empirical networks indicate that high perceptibility users show significantly different behavior patterns with the others: Having larger degree, stronger correlation of rating series and higher reputation. Furthermore, in view of the hysteresis in finding the rewarded objects, we present a general framework for identifying the high perceptibility users based on user behavior patterns. The experimental results show that this work is helpful for deeply understanding the collective behavior patterns for online users. PMID:28704382

Identifying the perceptive users for online social systems.

PubMed

Liu, Jian-Guo; Liu, Xiao-Lu; Guo, Qiang; Han, Jing-Ti

2017-01-01

In this paper, the perceptive user, who could identify the high-quality objects in their initial lifespan, is presented. By tracking the ratings given to the rewarded objects, we present a method to identify the user perceptibility, which is defined as the capability that a user can identify these objects at their early lifespan. Moreover, we investigate the behavior patterns of the perceptive users from three dimensions: User activity, correlation characteristics of user rating series and user reputation. The experimental results for the empirical networks indicate that high perceptibility users show significantly different behavior patterns with the others: Having larger degree, stronger correlation of rating series and higher reputation. Furthermore, in view of the hysteresis in finding the rewarded objects, we present a general framework for identifying the high perceptibility users based on user behavior patterns. The experimental results show that this work is helpful for deeply understanding the collective behavior patterns for online users.

Task-Based Information Searching.

ERIC Educational Resources Information Center

Vakkari, Pertti

2003-01-01

Reviews studies on the relationship between task performance and information searching by end-users, focusing on information searching in electronic environments and information retrieval systems. Topics include task analysis; task characteristics; search goals; modeling information searching; modeling search goals; information seeking behavior;…

A Software Tool for the Rapid Analysis of the Sintering Behavior of Particulate Bodies

DTIC Science & Technology

2017-11-01

bounded by a region that the user selects via cross hairs . Future plot analysis features, such as more complicated curve fitting and modeling functions...German RM. Grain growth behavior of tungsten heavy alloys based on the master sintering curve concept. Metallurgical and Materials Transactions A

User's guide to the weather model: a component of the western spruce budworm modeling system.

Treesearch

W. P. Kemp; N. L. Crookston; P. W. Thomas

1989-01-01

A stochastic model useful in simulating daily maximum and minimum temperature and precipitation developed by Bruhn and others has been adapted for use in the western spruce budworm modeling system. This document describes how to use the weather model and illustrates some aspects of its behavior.

Trajectory Based Behavior Analysis for User Verification

NASA Astrophysics Data System (ADS)

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

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

Modeling of information diffusion in Twitter-like social networks under information overload.

PubMed

Li, Pei; Li, Wei; Wang, Hui; Zhang, Xin

2014-01-01

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations.

Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload

PubMed Central

Li, Wei

2014-01-01

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations. PMID:24795541

Proceedings of the Symposium on Psychology in the Department of Defense (12th) Held in Colorado Springs, Colorado on 18-20 April 1990

DTIC Science & Technology

1990-04-01

structure or level of performance. To the extant this increased motivation result in greater...application users and not power users, they will quickly abandon the use of these models in favor of those that require less of a data entry burden, even if... behavior in Individuals is best measured by convergence over time of the value structures of the individual and those of a model group (e.g., tu•ity

Remote health coaching for interactive exercise with older adults in a home environment.

PubMed

Jimison, Holly B; Hagler, Stuart; Kurillo, Gregorij; Bajcsy, Ruzena; Pavel, Misha

2015-01-01

Optimal health coaching interventions are tailored to individuals' needs, preferences, motivations, barriers, timing, and readiness to change. Technology approaches are useful in both monitoring a user's adherence to their behavior change goals and also in providing just-in-time feedback and coaching messages. User models that incorporate dynamically varying behavior change variables with algorithms that trigger tailored messages provide a framework for making health interventions more effective. These principles are applied in the described system for assisting older adults in meeting their physical exercise goals with a tailored interactive video system with just-in-time feedback and encouragement.

The discounting model selector: Statistical software for delay discounting applications.

PubMed

Gilroy, Shawn P; Franck, Christopher T; Hantula, Donald A

2017-05-01

Original, open-source computer software was developed and validated against established delay discounting methods in the literature. The software executed approximate Bayesian model selection methods from user-supplied temporal discounting data and computed the effective delay 50 (ED50) from the best performing model. Software was custom-designed to enable behavior analysts to conveniently apply recent statistical methods to temporal discounting data with the aid of a graphical user interface (GUI). The results of independent validation of the approximate Bayesian model selection methods indicated that the program provided results identical to that of the original source paper and its methods. Monte Carlo simulation (n = 50,000) confirmed that true model was selected most often in each setting. Simulation code and data for this study were posted to an online repository for use by other researchers. The model selection approach was applied to three existing delay discounting data sets from the literature in addition to the data from the source paper. Comparisons of model selected ED50 were consistent with traditional indices of discounting. Conceptual issues related to the development and use of computer software by behavior analysts and the opportunities afforded by free and open-sourced software are discussed and a review of possible expansions of this software are provided. © 2017 Society for the Experimental Analysis of Behavior.

wayGoo recommender system: personalized recommendations for events scheduling, based on static and real-time information

NASA Astrophysics Data System (ADS)

Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

2016-05-01

wayGoo is a fully functional application whose main functionalities include content geolocation, event scheduling, and indoor navigation. However, significant information about events do not reach users' attention, either because of the size of this information or because some information comes from real - time data sources. The purpose of this work is to facilitate event management operations by prioritizing the presented events, based on users' interests using both, static and real - time data. Through the wayGoo interface, users select conceptual topics that are interesting for them. These topics constitute a browsing behavior vector which is used for learning users' interests implicitly, without being intrusive. Then, the system estimates user preferences and return an events list sorted from the most preferred one to the least. User preferences are modeled via a Naïve Bayesian Network which consists of: a) the `decision' random variable corresponding to users' decision on attending an event, b) the `distance' random variable, modeled by a linear regression that estimates the probability that the distance between a user and each event destination is not discouraging, ` the seat availability' random variable, modeled by a linear regression, which estimates the probability that the seat availability is encouraging d) and the `relevance' random variable, modeled by a clustering - based collaborative filtering, which determines the relevance of each event users' interests. Finally, experimental results show that the proposed system contribute essentially to assisting users in browsing and selecting events to attend.

D-VASim: an interactive virtual laboratory environment for the simulation and analysis of genetic circuits.

PubMed

Baig, Hasan; Madsen, Jan

2017-01-15

Simulation and behavioral analysis of genetic circuits is a standard approach of functional verification prior to their physical implementation. Many software tools have been developed to perform in silico analysis for this purpose, but none of them allow users to interact with the model during runtime. The runtime interaction gives the user a feeling of being in the lab performing a real world experiment. In this work, we present a user-friendly software tool named D-VASim (Dynamic Virtual Analyzer and Simulator), which provides a virtual laboratory environment to simulate and analyze the behavior of genetic logic circuit models represented in an SBML (Systems Biology Markup Language). Hence, SBML models developed in other software environments can be analyzed and simulated in D-VASim. D-VASim offers deterministic as well as stochastic simulation; and differs from other software tools by being able to extract and validate the Boolean logic from the SBML model. D-VASim is also capable of analyzing the threshold value and propagation delay of a genetic circuit model. D-VASim is available for Windows and Mac OS and can be downloaded from bda.compute.dtu.dk/downloads/. haba@dtu.dk, jama@dtu.dk. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification.

PubMed

Razzaq, Muhammad Asif; Villalonga, Claudia; Lee, Sungyoung; Akhtar, Usman; Ali, Maqbool; Kim, Eun-Soo; Khattak, Asad Masood; Seung, Hyonwoo; Hur, Taeho; Bang, Jaehun; Kim, Dohyeong; Ali Khan, Wajahat

2017-10-24

The emerging research on automatic identification of user's contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user's contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.

A user-oriented synthetic workload generator

NASA Technical Reports Server (NTRS)

Kao, Wei-Lun

1991-01-01

A user oriented synthetic workload generator that simulates users' file access behavior based on real workload characterization is described. The model for this workload generator is user oriented and job specific, represents file I/O operations at the system call level, allows general distributions for the usage measures, and assumes independence in the file I/O operation stream. The workload generator consists of three parts which handle specification of distributions, creation of an initial file system, and selection and execution of file I/O operations. Experiments on SUN NFS are shown to demonstrate the usage of the workload generator.

Individual and Network Interventions With Injection Drug Users in 5 Ukraine Cities

PubMed Central

Lehman, Wayne E. K.; Latkin, Carl A.; Dvoryak, Sergey; Brewster, John T.; Royer, Mark S.; Sinitsyna, Larisa

2011-01-01

Objectives. We evaluated the effects of an individual intervention versus a network intervention on HIV-related injection and sexual risk behaviors among street-recruited opiate injection drug users in 5 Ukraine cities. Methods. Between 2004 and 2006, 722 opiate injection drug users were recruited to participate in interventions that were either individually based or based on a social network model in which peer educators intervened with their network members. Audio computer-assisted self-interview techniques were used to interview participants at baseline and follow-up. Results. Multiple logistic analyses controlling for baseline injection and sexual risks revealed that both peer educators and network members in the network intervention reduced injection-related risk behaviors significantly more than did those in the individually based intervention and that peer educators increased condom use significantly more than did those in the individual intervention. Individual intervention participants, however, showed significantly greater improvements than did network members with respect to reductions in sexual risk behaviors. Conclusions. Social network interventions may be more effective than individually based interventions in changing injection risk behaviors among both peer educators and network members. The effectiveness of network interventions in changing sexual risk behaviors is less clear, probably owing to network composition and inhibitions regarding discussing sexual risk behaviors. PMID:20395584

Location Contexts of User Check-Ins to Model Urban Geo Life-Style Patterns

PubMed Central

Hasan, Samiul; Ukkusuri, Satish V.

2015-01-01

Geo-location data from social media offers us information, in new ways, to understand people's attitudes and interests through their activity choices. In this paper, we explore the idea of inferring individual life-style patterns from activity-location choices revealed in social media. We present a model to understand life-style patterns using the contextual information (e. g. location categories) of user check-ins. Probabilistic topic models are developed to infer individual geo life-style patterns from two perspectives: i) to characterize the patterns of user interests to different types of places and ii) to characterize the patterns of user visits to different neighborhoods. The method is applied to a dataset of Foursquare check-ins of the users from New York City. The co-existence of several location contexts and the corresponding probabilities in a given pattern provide useful information about user interests and choices. It is found that geo life-style patterns have similar items—either nearby neighborhoods or similar location categories. The semantic and geographic proximity of the items in a pattern reflects the hidden regularity in user preferences and location choice behavior. PMID:25970430

Recommender system based on scarce information mining.

PubMed

Lu, Wei; Chung, Fu-Lai; Lai, Kunfeng; Zhang, Liang

2017-09-01

Guessing what user may like is now a typical interface for video recommendation. Nowadays, the highly popular user generated content sites provide various sources of information such as tags for recommendation tasks. Motivated by a real world online video recommendation problem, this work targets at the long tail phenomena of user behavior and the sparsity of item features. A personalized compound recommendation framework for online video recommendation called Dirichlet mixture probit model for information scarcity (DPIS) is hence proposed. Assuming that each clicking sample is generated from a representation of user preferences, DPIS models the sample level topic proportions as a multinomial item vector, and utilizes topical clustering on the user part for recommendation through a probit classifier. As demonstrated by the real-world application, the proposed DPIS achieves better performance in accuracy, perplexity as well as diversity in coverage than traditional methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Game-Theoretic Models of Information Overload in Social Networks

NASA Astrophysics Data System (ADS)

Borgs, Christian; Chayes, Jennifer; Karrer, Brian; Meeder, Brendan; Ravi, R.; Reagans, Ray; Sayedi, Amin

We study the effect of information overload on user engagement in an asymmetric social network like Twitter. We introduce simple game-theoretic models that capture rate competition between celebrities producing updates in such networks where users non-strategically choose a subset of celebrities to follow based on the utility derived from high quality updates as well as disutility derived from having to wade through too many updates. Our two variants model the two behaviors of users dropping some potential connections (followership model) or leaving the network altogether (engagement model). We show that under a simple formulation of celebrity rate competition, there is no pure strategy Nash equilibrium under the first model. We then identify special cases in both models when pure rate equilibria exist for the celebrities: For the followership model, we show existence of a pure rate equilibrium when there is a global ranking of the celebrities in terms of the quality of their updates to users. This result also generalizes to the case when there is a partial order consistent with all the linear orders of the celebrities based on their qualities to the users. Furthermore, these equilibria can be computed in polynomial time. For the engagement model, pure rate equilibria exist when all users are interested in the same number of celebrities, or when they are interested in at most two. Finally, we also give a finite though inefficient procedure to determine if pure equilibria exist in the general case of the followership model.

Modeling User Behavior in Computer Learning Tasks.

ERIC Educational Resources Information Center

Mantei, Marilyn M.

Model building techniques from Artifical Intelligence and Information-Processing Psychology are applied to human-computer interface tasks to evaluate existing interfaces and suggest new and better ones. The model is in the form of an augmented transition network (ATN) grammar which is built by applying grammar induction heuristics on a sequential…

Impact of Information and Communication Technology on Information Seeking Behavior of Users in Astronomy and Astrophysics Centers of India: A Survey

NASA Astrophysics Data System (ADS)

Sahu, H. K.; Singh, S. N.

2010-10-01

This study is based on a survey designed to determine the Information Seeking Behavior (ISB) of Astronomy and Astrophysics users in India. The main objective of the study is to determine the sources consulted and the general pattern of the information-gathering system of users and the impact of Information and Communication Technology (ICT) on the Astronomy and Astrophysics user's Information Seeking Behavior. It examines various Information and Communication Technology-based resources and methods of access and use. A descriptive sample stratified method has been used and data was collected using a questionnaire as the main tool. The response rate was 72%. Descriptive statistics were also employed and data have been presented in tables and graphs. The study is supported by earlier studies. It shows that Astronomy and Astrophysics users have developed a unique Information Seeking Behavior to carry out their education and research. The vast majority of respondents reported that more information is available from a variety of e-resources. Consequently, they are able to devote more time to seek out relevant information in the current Information and Communication Technology scenario. The study also indicates that respondents use a variety of information resources including e-resources for teaching and research. Books and online databases such as the NASA Astrophysics Data System (ADS) were considered more important as formal sources of information. E-mail and face-to-face communications are used extensively by users as informal sources of information. It also reveals that despite the presence of electronic sources, Astronomy and Astrophysics users are still using printed materials. This study should to help to improve various Information and Communication Technology-based services. It also suggests that GOI should adopt Information and Communication Technology-based Information Centers and Libraries services and recommends a network-based model for Astronomy and Astrophysics users.

Brand equity and willingness to pay for condoms in zimbabwe

PubMed Central

2011-01-01

Background Zimbabwe suffers from one of the greatest burdens of HIV/AIDS in the world that has been compounded by social and economic instability in the past decade. However, from 2001 to 2009 HIV prevalence among 15-49 year olds declined from 26% to approximately 14%. Behavior change and condom use may in part explain this decline. PSI-Zimbabwe socially markets the Protector Plus (P+) branded line of condoms. When Zimbabwe converted to a dollar-based economy in 2009, the price of condoms was greatly increased and new marketing efforts were undertaken. This paper evaluates the role of condom marketing, a multi-dimensional scale of brand peceptions (brand equity), and price in condom use behavior. Methods We randomly sampled sexually active men age 15-49 from 3 groups - current P+ users, former users, and free condom users. We compared their brand equity and willingness to pay based on survey results. We estimated multivariable logistic regression models to compare the 3 groups. Results We found that the brand equity scale was positive correlated with willingness to pay and with condom use. Former users also indicated a high willingness to pay for condoms. We found differences in brand equity between the 3 groups, with current P+ users having the highest P+ brand equity. As observed in previous studies, higher brand equity was associated with more of the targeted health behavior, in this case and more consistent condom use. Conclusions Zimbabwe men have highly positive brand perceptions of P+. There is an opportunity to grow the total condom market in Zimbabwe by increasing brand equity across user groups. Some former users may resume using condoms through more effective marketing. Some free users may be willing to pay for condoms. Achieving these objectives will expand the total condom market and reduce HIV risk behaviors. PMID:22029874

Novel continuous authentication using biometrics

NASA Astrophysics Data System (ADS)

Dubey, Prakash; Patidar, Rinku; Mishra, Vikas; Norman, Jasmine; Mangayarkarasi, R.

2017-11-01

We explore whether a classifier can consistent1y verify c1ients and interact with the computer using camera and behavior of users. In this paper we propose a new way of authentication of user which wi1l capture many images of user in random time and ana1ysis of its touch biometric behavior. In this system experiment the touch conduct of a c1ient/user between an en1istment stage is stored in the database and it is checked its mean time behavior during equa1 partition of time. This touch behavior wi1l ab1e to accept or reject the user. This wi1l modify the use of biometric more accurate to use. In this system the work p1an going to perform is the user wi1l ask single time to a1low to take it picture before 1ogin. Then it wi1l take images of user without permission of user automatica1ly and store in the database. This images and existing image of user wi1l be compare and reject or accept wi1l depend on its comparison. The user touch behavior wi1l keep storing with number of touch make in equa1 amount of time of the user. This touch behavior and image wi1l fina1ly perform authentication of the user automatically.

Ceiling effect of online user interests for the movies

NASA Astrophysics Data System (ADS)

Ni, Jing; Zhang, Yi-Lu; Hu, Zhao-Long; Song, Wen-Jun; Hou, Lei; Guo, Qiang; Liu, Jian-Guo

2014-05-01

Online users' collective interests play an important role for analyzing the online social networks and personalized recommendations. In this paper, we introduce the information entropy to measure the diversity of the user interests. We empirically analyze the information entropy of the objects selected by the users with the same degree in both the MovieLens and Netflix datasets. The results show that as the user degree increases, the entropy increases from the lowest value at first to the highest value and then begins to fall, which indicates that the interests of the small-degree and large-degree users are more centralized, while the interests of normal users are more diverse. Furthermore, a null model is proposed to compare with the empirical results. In a null model, we keep the number of users and objects as well as the user degrees unchangeable, but the selection behaviors are totally random in both datasets. Results show that the diversity of the majority of users in the real datasets is higher than that the random case, with the exception of the diversity of only a fraction of small-degree users. That may because new users just like popular objects, while with the increase of the user experiences, they quickly become users of broad interests. Therefore, small-degree users' interests are much easier to predict than the other users', which may shed some light for the cold-start problem.

Understanding Teacher Users of a Digital Library Service: A Clustering Approach

ERIC Educational Resources Information Center

Xu, Beijie

2011-01-01

This research examined teachers' online behaviors while using a digital library service--the Instructional Architect (IA)--through three consecutive studies. In the first two studies, a statistical model called latent class analysis (LCA) was applied to cluster different groups of IA teachers according to their diverse online behaviors. The third…

Combining Users’ Needs With Health Behavior Models in Designing an Internet- and Mobile-Based Intervention for Physical Activity in Cardiac Rehabilitation

PubMed Central

2014-01-01

Background Internet-based physical activity interventions have great potential in supporting patients in cardiac rehabilitation. Health behavior change theories and user input are identified as important contributors in the effectiveness of the interventions, but they are rarely combined in a systematic way in the design of the interventions. Objective The aim of this study is to identify the appropriate theoretical framework, along with the needs of the users of a physical activity intervention for cardiac rehabilitation, and to combine them into an effective Internet- and mobile-based intervention. Methods We explain the theoretical framework of the intervention in a narrative overview of the existing health behavior change literature as it applies to physical activity. We also conducted a focus group with 11 participants of a cardiac rehabilitation program and used thematic analysis to identify and analyze patterns of meaning in the transcribed data. Results We chose stage-based approaches, specifically the transtheoretical model and the health action process approach as our main framework for tailoring, supplemented with other theoretical concepts such as regulatory focus within the appropriate stages. From the thematic analysis of the focus group data, we identified seven themes: (1) social, (2) motivation, (3) integration into everyday life, (4) information, (5) planning, (6) monitoring and feedback, and (7) concerns and potential problems. The final design of the intervention was based on both the theoretical review and the user input, and it is explained in detail. Conclusions We applied a combination of health behavioral theory and user input in designing our intervention. We think this is a promising design approach with the potential to combine the high efficacy of theory-based interventions with the higher perceived usefulness of interventions designed according to user input. Trial Registration Clinicaltrials.gov NCT01223170; http://clinicaltrials.gov/show/NCT01223170 (Archived by WebCite at http://www.webcitation.org/6M5FqT9Q2). PMID:24413185

A user's guide to the combined stand prognosis and Douglas-fir tussock moth outbreak model

Treesearch

Robert A. Monserud; Nicholas L. Crookston

1982-01-01

Documentation is given for using a simulation model combining the Stand Prognosis Model and the Douglas-fir Tussock Moth Outbreak Model. Four major areas are addressed: (1) an overview and discussion of the combined model; (2) description of input options; (3) discussion of model output, and (4) numerous examples illustrating model behavior and sensitivity.

Classification of a set of vectors using self-organizing map- and rule-based technique

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Okaniwa, Kaishirou; Nosaka, Kenzaburou

2005-02-01

There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very important to understand our behaviors. We have a view for an object, and decide the next action (data selection, etc.) with our view. Such a series of actions constructs a sequence. Therefore, we propose a method which acquires a view as a vector from several words for a view, and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a multimedia database containing pictures, music, movie, etc... These data cannot be stereotyped because user's view for them changes by each user. Therefore, we represent the structure of the multimedia database as the vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as elements. Such a vector can be classified by SOM (Self-Organizing Map). Hidden Markov Model (HMM) is a method to generate sequences. Therefore, we use HMM of which a state corresponds to the representative vector of user's view, and acquire sequences containing the change of user's view. We call it Vector-state Markov Model (VMM). We introduce the rough set theory as a rule-base technique, which plays a role of classifying the sets of data such as the sets of "Tour".

Symptomatological Features of Patients with and without Ecstasy Use during Their First Psychotic Episode

PubMed Central

Rugani, Fabio; Bacciardi, Silvia; Rovai, Luca; Pacini, Matteo; Maremmani, Angelo Giovanni Icro; Deltito, Joseph; Dell’Osso, Liliana; Maremmani, Icro

2012-01-01

Background: Ecstasy use is generally chosen by adolescents and young adults for its entactogenic properties (the production of feelings of empathy, love, and emotional closeness to others.) Despite this desired and frequently realized outcome, Ecstasy use has often resulted in the genesis of psychotic symptoms and aggressive behaviors, particularly after chronic and/or intensive use. Methods: To explore the negative consequences of Ecstasy use and to examine the aggressive nature oftentimes seen in many Ecstasy users we employed a case-control study model. We compared, by means of validated psychometric tests, the psychopathological symptoms (BPRS), the aggressiveness (OAS) and the social adjustment (DSM-GAF) of psychotic patients with (n = 23) and without (n = 46) recent user of Ecstasy, during their first psychotic episode and hospitalization. All 23 Ecstasy users were Ecstasy users only. Results: Almost all of the psychotic symptoms were of similar severity in both groups. Blunted affect was milder in users than in non-users, whereas hostility and aggressive behavior was significantly more severe in users than in non-users. Conclusions: psychosis with a high level of aggressiveness and violence constitutes an important ‘side-effect’ that surely runs counter to the expected entactogenic action of Ecstasy. At a patient psycho-educational level, this study suggests that the use of Ecstasy may be counterproductive with respect to user expectations. PMID:22851941

Factors Affecting Acceptance of Hospital Information Systems Based on Extended Technology Acceptance Model: A Case Study in Three Paraclinical Departments.

PubMed

Nadri, Hamed; Rahimi, Bahlol; Lotfnezhad Afshar, Hadi; Samadbeik, Mahnaz; Garavand, Ali

2018-04-01

 Regardless of the acceptance of users, information and communication systems can be considered as a health intervention designed to improve the care delivered to patients. This study aimed to determine the adoption and use of the extended Technology Acceptance Model (TAM2) by the users of hospital information system (HIS) in paraclinical departments including laboratory, radiology, and nutrition and to investigate the key factors of adoption and use of these systems.  A standard questionnaire was used to collect the data from nearly 253 users of these systems in paraclinical departments of eight university hospitals in two different cities of Iran. A total of 202 questionnaires including valid responses were used in this study (105 in Urmia and 97 in Khorramabad). The data were processed using LISREL and SPSS software and statistical analysis technique was based on the structural equation modeling (SEM).  It was found that the original TAM constructs had a significant impact on the staffs' behavioral intention to adopt HIS in paraclinical departments. The results of this study indicated that cognitive instrumental processes (job relevance, output quality, result demonstrability, and perceived ease of use), except for result demonstrability, were significant predictors of intention to use, whereas the result revealed no significant relationship between social influence processes (subjective norm, voluntariness, and image) and the users' behavioral intention to use the system.  The results confirmed that several factors in the TAM2 that were important in previous studies were not significant in paraclinical departments and in government-owned hospitals. The users' behavior factors are essential for successful usage of the system and should be considered. It provides valuable information for hospital system providers and policy makers in understanding the adoption challenges as well as practical guidance for the successful implementation of information systems in paraclinical departments. Schattauer GmbH Stuttgart.

Gesteme-free context-aware adaptation of robot behavior in human-robot cooperation.

PubMed

Nessi, Federico; Beretta, Elisa; Gatti, Cecilia; Ferrigno, Giancarlo; De Momi, Elena

2016-11-01

Cooperative robotics is receiving greater acceptance because the typical advantages provided by manipulators are combined with an intuitive usage. In particular, hands-on robotics may benefit from the adaptation of the assistant behavior with respect to the activity currently performed by the user. A fast and reliable classification of human activities is required, as well as strategies to smoothly modify the control of the manipulator. In this scenario, gesteme-based motion classification is inadequate because it needs the observation of a wide signal percentage and the definition of a rich vocabulary. In this work, a system able to recognize the user's current activity without a vocabulary of gestemes, and to accordingly adapt the manipulator's dynamic behavior is presented. An underlying stochastic model fits variations in the user's guidance forces and the resulting trajectories of the manipulator's end-effector with a set of Gaussian distribution. The high-level switching between these distributions is captured with hidden Markov models. The dynamic of the KUKA light-weight robot, a torque-controlled manipulator, is modified with respect to the classified activity using sigmoidal-shaped functions. The presented system is validated over a pool of 12 näive users in a scenario that addresses surgical targeting tasks on soft tissue. The robot's assistance is adapted in order to obtain a stiff behavior during activities that require critical accuracy constraint, and higher compliance during wide movements. Both the ability to provide the correct classification at each moment (sample accuracy) and the capability of correctly identify the correct sequence of activity (sequence accuracy) were evaluated. The proposed classifier is fast and accurate in all the experiments conducted (80% sample accuracy after the observation of ∼450ms of signal). Moreover, the ability of recognize the correct sequence of activities, without unwanted transitions is guaranteed (sequence accuracy ∼90% when computed far away from user desired transitions). Finally, the proposed activity-based adaptation of the robot's dynamic does not lead to a not smooth behavior (high smoothness, i.e. normalized jerk score <0.01). The provided system is able to dynamic assist the operator during cooperation in the presented scenario. Copyright © 2016 Elsevier B.V. All rights reserved.

An analysis of respondent-driven sampling with injecting drug users in a high HIV prevalent state of India.

PubMed

Phukan, Sanjib Kumar; Medhi, Gajendra Kumar; Mahanta, Jagadish; Adhikary, Rajatashuvra; Thongamba, Gay; Paranjape, Ramesh S; Akoijam, Brogen S

2017-07-03

Personal networks are significant social spaces to spread of HIV or other blood-borne infections among hard-to-reach population, viz., injecting drug users, female sex workers, etc. Sharing of infected needles or syringes among drug users is one of the major routes of HIV transmission in Manipur, a high HIV prevalence state in India. This study was carried out to describe the network characteristics and recruitment patterns of injecting drug users and to assess the association of personal network with injecting risky behaviors in Manipur. A total of 821 injecting drug users were recruited into the study using respondent-driven sampling (RDS) from Bishnupur and Churachandpur districts of Manipur; data on demographic characteristics, HIV risk behaviors, and network size were collected from them. Transition probability matrices and homophily indices were used to describe the network characteristics, and recruitment patterns of injecting drug users. Univariate and multivariate binary logistic regression models were performed to analyze the association between the personal networks and sharing of needles or syringes. The average network size was similar in both the districts. Recruitment analysis indicates injecting drug users were mostly engaged in mixed age group setting for injecting practice. Ever married and new injectors showed lack of in-group ties. Younger injecting drug users had mainly recruited older injecting drug users from their personal network. In logistic regression analysis, higher personal network was found to be significantly associated with increased likelihood of injecting risky behaviors. Because of mixed personal network of new injectors and higher network density associated with HIV exposure, older injecting drug users may act as a link for HIV transmission or other blood-borne infections to new injectors and also to their sexual partners. The information from this study may be useful to understanding the network pattern of injecting drug users for enriching the HIV prevention in this region.

A social network-informed latent class analysis of patterns of substance use, sexual behavior, and mental health: Social Network Study III, Winnipeg, Manitoba, Canada.

PubMed

Hopfer, Suellen; Tan, Xianming; Wylie, John L

2014-05-01

We assessed whether a meaningful set of latent risk profiles could be identified in an inner-city population through individual and network characteristics of substance use, sexual behaviors, and mental health status. Data came from 600 participants in Social Network Study III, conducted in 2009 in Winnipeg, Manitoba, Canada. We used latent class analysis (LCA) to identify risk profiles and, with covariates, to identify predictors of class. A 4-class model of risk profiles fit the data best: (1) solitary users reported polydrug use at the individual level, but low probabilities of substance use or concurrent sexual partners with network members; (2) social-all-substance users reported polydrug use at the individual and network levels; (3) social-noninjection drug users reported less likelihood of injection drug and solvent use; (4) low-risk users reported low probabilities across substances. Unstable housing, preadolescent substance use, age, and hepatitis C status predicted risk profiles. Incorporation of social network variables into LCA can distinguish important subgroups with varying patterns of risk behaviors that can lead to sexually transmitted and bloodborne infections.

Automatic Classification of Users’ Health Information Need Context: Logistic Regression Analysis of Mouse-Click and Eye-Tracker Data

PubMed Central

Pian, Wenjing; Khoo, Christopher SG

2017-01-01

Background Users searching for health information on the Internet may be searching for their own health issue, searching for someone else’s health issue, or browsing with no particular health issue in mind. Previous research has found that these three categories of users focus on different types of health information. However, most health information websites provide static content for all users. If the three types of user health information need contexts can be identified by the Web application, the search results or information offered to the user can be customized to increase its relevance or usefulness to the user. Objective The aim of this study was to investigate the possibility of identifying the three user health information contexts (searching for self, searching for others, or browsing with no particular health issue in mind) using just hyperlink clicking behavior; using eye-tracking information; and using a combination of eye-tracking, demographic, and urgency information. Predictive models are developed using multinomial logistic regression. Methods A total of 74 participants (39 females and 35 males) who were mainly staff and students of a university were asked to browse a health discussion forum, Healthboards.com. An eye tracker recorded their examining (eye fixation) and skimming (quick eye movement) behaviors on 2 types of screens: summary result screen displaying a list of post headers, and detailed post screen. The following three types of predictive models were developed using logistic regression analysis: model 1 used only the time spent in scanning the summary result screen and reading the detailed post screen, which can be determined from the user’s mouse clicks; model 2 used the examining and skimming durations on each screen, recorded by an eye tracker; and model 3 added user demographic and urgency information to model 2. Results An analysis of variance (ANOVA) analysis found that users’ browsing durations were significantly different for the three health information contexts (P<.001). The logistic regression model 3 was able to predict the user’s type of health information context with a 10-fold cross validation mean accuracy of 84% (62/74), followed by model 2 at 73% (54/74) and model 1 at 71% (52/78). In addition, correlation analysis found that particular browsing durations were highly correlated with users’ age, education level, and the urgency of their information need. Conclusions A user’s type of health information need context (ie, searching for self, for others, or with no health issue in mind) can be identified with reasonable accuracy using just user mouse clicks that can easily be detected by Web applications. Higher accuracy can be obtained using Google glass or future computing devices with eye tracking function. PMID:29269342

Recommending personally interested contents by text mining, filtering, and interfaces

DOEpatents

Xu, Songhua

2015-10-27

A personalized content recommendation system includes a client interface device configured to monitor a user's information data stream. A collaborative filter remote from the client interface device generates automated predictions about the interests of the user. A database server stores personal behavioral profiles and user's preferences based on a plurality of monitored past behaviors and an output of the collaborative user personal interest inference engine. A programmed personal content recommendation server filters items in an incoming information stream with the personal behavioral profile and identifies only those items of the incoming information stream that substantially matches the personal behavioral profile. The identified personally relevant content is then recommended to the user following some priority that may consider the similarity between the personal interest matches, the context of the user information consumption behaviors that may be shown by the user's content consumption mode.

Performance & Emotion--A Study on Adaptive E-Learning Based on Visual/Verbal Learning Styles

ERIC Educational Resources Information Center

Beckmann, Jennifer; Bertel, Sven; Zander, Steffi

2015-01-01

Adaptive e-Learning systems are able to adjust to a user's learning needs, usually by user modeling or tracking progress. Such learner-adaptive behavior has rapidly become a hot topic for e-Learning, furthered in part by the recent rapid increase in the use of MOOCs (Massive Open Online Courses). A lack of general, individual, and situational data…

User Experience of Cognitive Behavioral Therapy Apps for Depression: An Analysis of App Functionality and User Reviews.

PubMed

Stawarz, Katarzyna; Preist, Chris; Tallon, Debbie; Wiles, Nicola; Coyle, David

2018-06-06

Hundreds of mental health apps are available to the general public. With increasing pressures on health care systems, they offer a potential way for people to support their mental health and well-being. However, although many are highly rated by users, few are evidence-based. Equally, our understanding of what makes apps engaging and valuable to users is limited. The aim of this paper was to analyze functionality and user opinions of mobile apps purporting to support cognitive behavioral therapy for depression and to explore key factors that have an impact on user experience and support engagement. We systematically identified apps described as being based on cognitive behavioral therapy for depression. We then conducted 2 studies. In the first, we analyzed the therapeutic functionality of apps. This corroborated existing work on apps' fidelity to cognitive behavioral therapy theory, but we also extended prior work by examining features designed to support user engagement. Engagement features found in cognitive behavioral therapy apps for depression were compared with those found in a larger group of apps that support mental well-being in a more general sense. Our second study involved a more detailed examination of user experience, through a thematic analysis of publicly available user reviews of cognitive behavioral therapy apps for depression. We identified 31 apps that purport to be based on cognitive behavioral therapy for depression. Functionality analysis (study 1) showed that they offered an eclectic mix of features, including many not based on cognitive behavioral therapy practice. Cognitive behavioral therapy apps used less varied engagement features compared with 253 other mental well-being apps. The analysis of 1287 user reviews of cognitive behavioral therapy apps for depression (study 2) showed that apps are used in a wide range of contexts, both replacing and augmenting therapy, and allowing users to play an active role in supporting their mental health and well-being. Users, including health professionals, valued and used apps that incorporated both core cognitive behavioral therapy and non-cognitive behavioral therapy elements, but concerns were also expressed regarding the unsupervised use of apps. Positivity was seen as important to engagement, for example, in the context of automatic thoughts, users expressed a preference to capture not just negative but also positive ones. Privacy, security, and trust were crucial to the user experience. Cognitive behavioral therapy apps for depression need to improve with respect to incorporating evidence-based cognitive behavioral therapy elements. Equally, a positive user experience is dependent on other design factors, including consideration of varying contexts of use. App designers should be able to clearly identify the therapeutic basis of their apps, but they should also draw on evidence-based strategies to support a positive and engaging user experience. The most effective apps are likely to strike a balance between evidence-based cognitive behavioral therapy strategies and evidence-based design strategies, including the possibility of eclectic therapeutic techniques. ©Katarzyna Stawarz, Chris Preist, Debbie Tallon, Nicola Wiles, David Coyle. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.06.2018.

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.

PubMed

Chen, Daizhuo; Fraiberger, Samuel P; Moakler, Robert; Provost, Foster

2017-09-01

Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from "Likes" on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the "cloaking device"-a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users.

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals

PubMed Central

Chen, Daizhuo; Fraiberger, Samuel P.; Moakler, Robert; Provost, Foster

2017-01-01

Abstract Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from “Likes” on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the “cloaking device”—a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users. PMID:28933942

Echo Chambers: Emotional Contagion and Group Polarization on Facebook

NASA Astrophysics Data System (ADS)

Del Vicario, Michela; Vivaldo, Gianna; Bessi, Alessandro; Zollo, Fabiana; Scala, Antonio; Caldarelli, Guido; Quattrociocchi, Walter

2016-12-01

Recent findings showed that users on Facebook tend to select information that adhere to their system of beliefs and to form polarized groups - i.e., echo chambers. Such a tendency dominates information cascades and might affect public debates on social relevant issues. In this work we explore the structural evolution of communities of interest by accounting for users emotions and engagement. Focusing on the Facebook pages reporting on scientific and conspiracy content, we characterize the evolution of the size of the two communities by fitting daily resolution data with three growth models - i.e. the Gompertz model, the Logistic model, and the Log-logistic model. Although all the models appropriately describe the data structure, the Logistic one shows the best fit. Then, we explore the interplay between emotional state and engagement of users in the group dynamics. Our findings show that communities’ emotional behavior is affected by the users’ involvement inside the echo chamber. Indeed, to an higher involvement corresponds a more negative approach. Moreover, we observe that, on average, more active users show a faster shift towards the negativity than less active ones.

Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults.

PubMed

Rabbi, Mashfiqui; Pfammatter, Angela; Zhang, Mi; Spring, Bonnie; Choudhury, Tanzeem

2015-05-14

A dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users' behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement. MyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user's environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions. MyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals. We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to receive either MyBehavior's personalized suggestions (n=9) or nonpersonalized suggestions (n=8), created by professionals, from a mobile phone app over 3 weeks. Daily activity level and dietary intake was monitored from logged data. At the end of the study, an in-person survey was conducted that asked users to subjectively rate their intention to follow MyBehavior suggestions. In qualitative daily diary, interview, and survey data, users reported MyBehavior suggestions to be highly actionable and stated that they intended to follow the suggestions. MyBehavior users walked significantly more than the control group over the 3 weeks of the study (P=.05). Although some MyBehavior users chose lower-calorie foods, the between-group difference was not significant (P=.15). In a poststudy survey, users rated MyBehavior's personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals (P<.001). MyBehavior is a simple-to-use mobile phone app with preliminary evidence of efficacy. To the best of our knowledge, MyBehavior represents the first attempt to create personalized, contextualized, actionable suggestions automatically from self-tracked information (ie, manual food logging and automatic tracking of activity). Lessons learned about the difficulty of manual logging and usability concerns, as well as future directions, are discussed. ClinicalTrials.gov NCT02359981; https://clinicaltrials.gov/ct2/show/NCT02359981 (Archived by WebCite at http://www.webcitation.org/6YCeoN8nv).

Is Adolescent Poly-tobacco Use Associated with Alcohol and Other Drug Use?

PubMed Central

Creamer, MeLisa R.; Portillo, Gabriela V.; Clendennen, Stephanie L.; Perry, Cheryl L.

2016-01-01

Objectives To examine associations between current multiple tobacco product use, and current use of alcohol and marijuana, binge drinking, and lifetime use of marijuana, alcohol, and other drugs among US high school students. Methods Using 2013 Youth Risk Behavior Survey data (N = 13,583 high school students), logistic regression analyses were conducted to determine if single tobacco product or multiple tobacco product users are more likely to engage in other risk behaviors than zero tobacco product users, controlling for demographic variables. Results Overall, 23% of the sample used tobacco products and 10% of students reported current use of at least 2 tobacco products. Among single tobacco product users, the odds for engaging in risk behaviors ranged from 3.3 to 9.9 compared to non-tobacco users (p < .0001). Among multiple tobacco product users, the odds ranged from 1.5 to 4.7 (p < .01) compared to single tobacco product users. Conclusions Results suggest dual users are significantly more likely to engage in risk behavior than non-users and single product users. Future interventions should consider identifying dual-users as at higher risk, and targeting multiple risk behaviors. PMID:26685820

Augmenting watershed model calibration with incorporation of ancillary data sources and qualitative soft data sources

USDA-ARS?s Scientific Manuscript database

Watershed simulation models can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of detailed outputs that some of the calibrated models may not reflect summative actual watershed behavior. Thus, it is necessary to use “soft data” (i....

Home Exercise in a Social Context: Real-Time Experience Sharing Using Avatars

NASA Astrophysics Data System (ADS)

Aghajan, Yasmin; Lacroix, Joyca; Cui, Jingyu; van Halteren, Aart; Aghajan, Hamid

This paper reports on the design of a vision-based exercise monitoring system. The system aims to promote well-being by making exercise sessions enjoyable experiences, either through real-time interaction and instructions proposed to the user, or via experience sharing or group gaming with peers in a virtual community. The use of avatars is explored as means of representation of the user’s exercise movements or appearance, and the system employs user-centric approaches in visual processing, behavior modeling via history data accumulation, and user feedback to learn the preferences. A preliminary survey study has been conducted to explore the avatar preferences in two user groups.

The Trip Itinerary Optimization Platform: A Framework for Personalized Travel Information

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

Kwasnik, Ted; Carmichael, Scott P.; Arent, Douglas J

The New Concepts Incubator team at the National Renewable Energy Laboratory (NREL) developed a three-stage online platform for travel diary collection, personal travel plan optimization and travel itinerary visualization. In the first stage, users provide a travel diary for the previous day through an interactive map and calendar interface and survey for travel attitudes and behaviors. One or more days later, users are invited via email to engage in a second stage where they view a personal mobility dashboard displaying recommended travel itineraries generated from a novel framework that optimizes travel outcomes over a sequence of interrelated trips. A weekmore » or more after viewing these recommended travel itineraries on the dashboard, users are emailed again to engage in a third stage where they complete a final survey about travel attitudes and behaviors. A usability study of the platform conducted online showed that, in general, users found the system valuable for informing their travel decisions. A total of 274 individuals were recruited through Amazon Mechanical Turk, an online survey platform, to participate in a transportation study using this platform. On average, the platform distilled 65 feasible travel plans per individual into two recommended itineraries, each optimal according to one or more outcomes and dependent on the fixed times and locations from the travel diary. For 45 percent of users, the trip recommendation algorithm returned only a single, typically automobile-centric, itinerary because there were no other viable alternative transportation modes available. Platform users generally agreed that the dashboard was enjoyable and easy to use, and that it would be a helpful tool in adopting new travel behaviors. Users generally agreed most that the time, cost and user preferred recommendations 'made sense' to them, and were most willing to implement these itineraries. Platform users typically expressed low willingness to try the carbon and calories optimized itineraries. Of the platform users who viewed the dashboard, 13 percent reported changing their travel behavior, most adopting the time, calories or carbon optimized itineraries. While the algorithm incorporates a wealth of travel data obtained from online APIs pertaining to a travelers route such as historic traffic condition data, public transit time-tables, and bike path routes, open-ended responses from users expressed an interest in the integration of even more fine-grained traffic data and the ability to dynamically model the effect of changes in travel times. Users also commonly expressed concerns over the safety of walking and biking recommendations. Responses indicate that more information about the amenities available to cyclists and pedestrians (sidewalks, shade from trees, access to food) and routes that avoid areas of perceived elevated danger would reduce barriers to implementing these recommendations. More accurate representations of personal vehicle trips (based on vehicle make and model, implications of parking) and the identification of routes that optimize caloric intensity (seeking out elevation changes or longer walks to public transit) are promising avenues for future research.« less

Social Cognitive Determinants of Nutrition and Physical Activity Among Web-Health Users Enrolling in an Online Intervention: The Influence of Social Support, Self-Efficacy, Outcome Expectations, and Self-Regulation

PubMed Central

Winett, Richard A; Wojcik, Janet R

2011-01-01

Background The Internet is a trusted source of health information for growing majorities of Web users. The promise of online health interventions will be realized with the development of purely online theory-based programs for Web users that are evaluated for program effectiveness and the application of behavior change theory within the online environment. Little is known, however, about the demographic, behavioral, or psychosocial characteristics of Web-health users who represent potential participants in online health promotion research. Nor do we understand how Web users’ psychosocial characteristics relate to their health behavior—information essential to the development of effective, theory-based online behavior change interventions. Objective This study examines the demographic, behavioral, and psychosocial characteristics of Web-health users recruited for an online social cognitive theory (SCT)-based nutrition, physical activity, and weight gain prevention intervention, the Web-based Guide to Health (WB-GTH). Methods Directed to the WB-GTH site by advertisements through online social and professional networks and through print and online media, participants were screened, consented, and assessed with demographic, physical activity, psychosocial, and food frequency questionnaires online (taking a total of about 1.25 hours); they also kept a 7-day log of daily steps and minutes walked. Results From 4700 visits to the site, 963 Web users consented to enroll in the study: 83% (803) were female, participants’ mean age was 44.4 years (SD 11.03 years), 91% (873) were white, and 61% (589) were college graduates; participants’ median annual household income was approximately US $85,000. Participants’ daily step counts were in the low-active range (mean 6485.78, SD 2352.54) and overall dietary levels were poor (total fat g/day, mean 77.79, SD 41.96; percent kcal from fat, mean 36.51, SD 5.92; fiber g/day, mean 17.74, SD 7.35; and fruit and vegetable servings/day, mean 4.03, SD 2.33). The Web-health users had good self-efficacy and outcome expectations for health behavior change; however, they perceived little social support for making these changes and engaged in few self-regulatory behaviors. Consistent with SCT, theoretical models provided good fit to Web-users’ data (root mean square error of the approximation [RMSEA] < .05). Perceived social support and use of self-regulatory behaviors were strong predictors of physical activity and nutrition behavior. Web users’ self-efficacy was also a good predictor of healthier levels of physical activity and dietary fat but not of fiber, fruits, and vegetables. Social support and self-efficacy indirectly predicted behavior through self-regulation, and social support had indirect effects through self-efficacy. Conclusions Results suggest Web-health users visiting and ultimately participating in online health interventions may likely be middle-aged, well-educated, upper middle class women whose detrimental health behaviors put them at risk of obesity, heart disease, some cancers, and diabetes. The success of Internet physical activity and nutrition interventions may depend on the extent to which they lead users to develop self-efficacy for behavior change, but perhaps as important, the extent to which these interventions help them garner social-support for making changes. Success of these interventions may also depend on the extent to which they provide a platform for setting goals, planning, tracking, and providing feedback on targeted behaviors. PMID:21441100

Procedures to establish National Rules of Behavior

EPA Pesticide Factsheets

The purpose of this procedure is to establish the EPA National Rules of Behavior to comply with OMB Circular A-130, regarding rules of behavior for users of information systems applicable to all users of EPA information and information systems for users.

Factors Predicting Oncology Care Providers' Behavioral Intention to Adopt Clinical Decision Support Systems

ERIC Educational Resources Information Center

Wolfenden, Andrew

2012-01-01

The purpose of this quantitative correlation study was to examine the predictors of user behavioral intention on the decision of oncology care providers to adopt or reject the clinical decision support system. The Unified Theory of Acceptance and Use of Technology (UTAUT) formed the foundation of the research model and survey instrument. The…

A Correlational Study of the Technology Acceptance Model and Georgia Behavioral Healthcare Provider Telemedicine Adoption

ERIC Educational Resources Information Center

Yallah, Ali

2014-01-01

The implementation of Telemedicine in behavioral health centers can be expensive if proactive steps were not taken to minimize user perceptions towards the new technology. Despite the significant capital investments on new Telemedicine, no consensus identified and explained what factors determined the acceptance, or rejection, of the technology.…

Buyers and Borrowers: The Application of Consumer Theory to the Study of Library Use.

ERIC Educational Resources Information Center

Emery, Charles D.

Using Ehrenberg's application of mathematical models to the analysis and prediction of repeated buying patterns of consumers, this study focuses on the concept of library use as a form of consumer behavior. The following hypotheses were tested: designated library user groups will tend to exhibit stable behavioral patterns with respect to the…

[Substance use and risk behaviors of out-of-treatment cocaine base paste and cocaine hydrochloride users].

PubMed

Santis B, Rodrigo; Hidalgo C, Carmen Gloria; Hayden C, Viviana; Anselmo M, Enzo; Rodríguez T, Jorge; Cartajena de la M, Fernando; Dreyse D, Jorge; Torres B, Rafael

2007-01-01

In Chile, cocaine base paste (CBP) is the illegal substance that produces the highest rate of addiction. Nonetheless, a marginal number of users receive treatment each year. To compare the consumption patterns and risk behavior of CBP and cocaine hydrochloride (CH) users who do not attend rehabilitation services. In a prospective research design, through a study methodology called Privileged Access Interview of hidden populations, 28 surveyors recruited 231 CBP users (group 1) and 236 CH users (group 2). The Risk Behavior Questionnaire was applied in four communities of Metropolitan Santiago, that have the highest prevalence of PBC and CH use. CBP users showed higher schools drop-out and unemployment rates. Subjects of both groups were predominantly polysubstance and polyaddicted users. The severity of addiction to CBP of group 1 was significantly higher than the severity of addiction to CH of group 2 (5.5 versus 5.1: p<0.001). CBP users showed significantly higher rates of sexual risk behaviors, antisocial behavior, self infliction of injuries, suicide attempt and child neglect. A higher vulnerability was shown for users of CBP than those of CH. Attention is drawn to the need for developing community interventions in order to alter substance abuse and the risk behavior of these vulnerable groups.

Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

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

Rossi, R; Gallagher, B; Neville, J

Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied ourmore » model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.« less

How Social Communications Influence Advertising Perception and Response in Online Communities?

PubMed

Zeng, Fue; Tao, Ran; Yang, Yanwu; Xie, Tingting

2017-01-01

This research aims to explore how social communications of online communities affect users' perception and responses toward social media advertising. We developed a conceptual model based on the SBT, encapsulating 9 constructs and 10 hypothesis extracted from the extant social media advertising literature. Our research outcome proves that social communications can effectively boost users' behaviors to be in accordance with an online social community, thus facilitate their acceptance and responses toward social media advertising, with users' group intention as an intervening factor. From an operational standpoint, it's an effective way to build and maintain social bonds between users and the community by boosting social communications, supporting fluent interpersonal communications. In addition, managers of an online community should elaborate on users' group intentions to increase users' advertising acceptance and response.

TOTAL user manual

NASA Technical Reports Server (NTRS)

Johnson, Sally C.; Boerschlein, David P.

1994-01-01

Semi-Markov models can be used to analyze the reliability of virtually any fault-tolerant system. However, the process of delineating all of the states and transitions in the model of a complex system can be devastatingly tedious and error-prone. Even with tools such as the Abstract Semi-Markov Specification Interface to the SURE Tool (ASSIST), the user must describe a system by specifying the rules governing the behavior of the system in order to generate the model. With the Table Oriented Translator to the ASSIST Language (TOTAL), the user can specify the components of a typical system and their attributes in the form of a table. The conditions that lead to system failure are also listed in a tabular form. The user can also abstractly specify dependencies with causes and effects. The level of information required is appropriate for system designers with little or no background in the details of reliability calculations. A menu-driven interface guides the user through the system description process, and the program updates the tables as new information is entered. The TOTAL program automatically generates an ASSIST input description to match the system description.

Methamphetamine Use and Violent Behavior: User Perceptions and Predictors

PubMed Central

Brecht, Mary-Lynn; Herbeck, Diane

2015-01-01

This study describes the extent to which methamphetamine users perceive that their methamphetamine use has resulted in violent behavior, and describes the level of self-reported prevalence of specific violent criminal behaviors irrespective of methamphetamine use. Predictors of these two violence-related indicators, in terms of potential correlates from substance use history, criminal history, and health risk domains are examined. Data are from extensive interviews of 350 methamphetamine users who received substance use treatment in a large California county. A majority (56%) perceived that their methamphetamine use resulted in violent behavior; 59% reported specific violent criminal behaviors. For more than half of those reporting violent criminal behavior, this behavior pattern began before methamphetamine initiation. Thus, for a subsample of methamphetamine users, violence may be related to factors other than methamphetamine use. Users' perceptions that their methamphetamine use resulted in violence appears strongest for those with the most severe methamphetamine-related problems, particularly paranoia. PMID:26594058

User's guide [Chapter 3

Treesearch

Nicholas L. Crookston; Donald C. E. Robinson; Sarah J. Beukema

2003-01-01

The Fire and Fuels Extension (FFE) to the Forest Vegetation Simulator (FVS) simulates fuel dynamics and potential fire behavior over time, in the context of stand development and management. This chapter presents the model's options, provides annotated examples, describes the outputs, and describes how to use and apply the model.

Characterizing users of new psychoactive substances using psychometric scales for risk-related behavior.

PubMed

Vreeker, Annabel; van der Burg, Babette G; van Laar, Margriet; Brunt, Tibor M

2017-07-01

Studies investigating risk-related behavior in relation to new psychoactive substance (NPS) use are sparse. The current study investigated characteristics of NPS users by comparing risk-related behavior of NPS users to that of illicit drugs (ID) users and licit substances users and non-users (NLC) users. In this cross-sectional study we included 528 individuals across an age range of 18-72years. Using a web-based questionnaire we collected self-report data on substance use, sensation seeking, impulsivity, peer substance use and risk perception of substance use. NPS and ID users had a higher level of sensation seeking compared to NLC users (NPS users: p<0.001; ID users: p<0.001). NPS users (p<0.001), but not ID users (p=0.16), had increased levels of impulsivity compared to NLC users. NPS users had significantly higher scores for sensation seeking (F 1,423 =51.52, p<0.001) and impulsivity (F 1,423 =6.15, p=0.01) compared to ID users. Additionally, NPS users had significantly more peers who use substances compared to ID and NLC users. Also, NPS and ID users had lower risk perception for most substances than NLC users. NPS users had lower risk perception for most substances than ID users. The findings highlight that NPS users show substantial more risk-related behavior than both ID and NLC users. Therefore, NPS users might be considered as a distinctive group of substance users that need another approach in terms of prevention. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of the IMB Model and an Evidence-Based Diabetes Self-management Mobile Application.

PubMed

Jeon, Eunjoo; Park, Hyeoun-Ae

2018-04-01

This study developed a diabetes self-management mobile application based on the information-motivation-behavioral skills (IMB) model, evidence extracted from clinical practice guidelines, and requirements identified through focus group interviews (FGIs) with diabetes patients. We developed a diabetes self-management (DSM) app in accordance with the following four stages of the system development life cycle. The functional and knowledge requirements of the users were extracted through FGIs with 19 diabetes patients. A system diagram, data models, a database, an algorithm, screens, and menus were designed. An Android app and server with an SSL protocol were developed. The DSM app algorithm and heuristics, as well as the usability of the DSM app were evaluated, and then the DSM app was modified based on heuristics and usability evaluation. A total of 11 requirement themes were identified through the FGIs. Sixteen functions and 49 knowledge rules were extracted. The system diagram consisted of a client part and server part, 78 data models, a database with 10 tables, an algorithm, and a menu structure with 6 main menus, and 40 user screens were developed. The DSM app was Android version 4.4 or higher for Bluetooth connectivity. The proficiency and efficiency scores of the algorithm were 90.96% and 92.39%, respectively. Fifteen issues were revealed through the heuristic evaluation, and the app was modified to address three of these issues. It was also modified to address five comments received by the researchers through the usability evaluation. The DSM app was developed based on behavioral change theory through IMB models. It was designed to be evidence-based, user-centered, and effective. It remains necessary to fully evaluate the effect of the DSM app on the DSM behavior changes of diabetes patients.

Development of the IMB Model and an Evidence-Based Diabetes Self-management Mobile Application

PubMed Central

Jeon, Eunjoo

2018-01-01

Objectives This study developed a diabetes self-management mobile application based on the information-motivation-behavioral skills (IMB) model, evidence extracted from clinical practice guidelines, and requirements identified through focus group interviews (FGIs) with diabetes patients. Methods We developed a diabetes self-management (DSM) app in accordance with the following four stages of the system development life cycle. The functional and knowledge requirements of the users were extracted through FGIs with 19 diabetes patients. A system diagram, data models, a database, an algorithm, screens, and menus were designed. An Android app and server with an SSL protocol were developed. The DSM app algorithm and heuristics, as well as the usability of the DSM app were evaluated, and then the DSM app was modified based on heuristics and usability evaluation. Results A total of 11 requirement themes were identified through the FGIs. Sixteen functions and 49 knowledge rules were extracted. The system diagram consisted of a client part and server part, 78 data models, a database with 10 tables, an algorithm, and a menu structure with 6 main menus, and 40 user screens were developed. The DSM app was Android version 4.4 or higher for Bluetooth connectivity. The proficiency and efficiency scores of the algorithm were 90.96% and 92.39%, respectively. Fifteen issues were revealed through the heuristic evaluation, and the app was modified to address three of these issues. It was also modified to address five comments received by the researchers through the usability evaluation. Conclusions The DSM app was developed based on behavioral change theory through IMB models. It was designed to be evidence-based, user-centered, and effective. It remains necessary to fully evaluate the effect of the DSM app on the DSM behavior changes of diabetes patients. PMID:29770246

An extension of technology acceptance model to determine factors that influence the intention to use electronic collection system in Nigerian federal hospitals

NASA Astrophysics Data System (ADS)

Kabir, Muhammad Auwal; Saidin, Siti Zabedah; Ahmi, Aidi

2017-10-01

The aim of this paper is to develop a conceptual framework that would be used in determining the factors that influence the behavioral intention to use electronic collection system in federal government owned hospitals in Nigeria. The framework is supported by Technology Acceptance Model (TAM) as the underlying theory of the study. Past literature on individual user intention were thoroughly reviewed and found that TAM is fit appropriate in explaining the phenomenon under study. Based on the reviewed literature, it is expected that perceived usefulness and perceived ease of use will influence the intention of users (employees) to use e-collection system in the performance of their job tasks in Nigerian federal hospitals. In other words, users with higher perception on the system's usefulness and its ease of use are more likely to express their interest and willingness to use the system. In addition, the study has extended TAM with facilitating conditions construct and the research is expected to discover the level of its influence on behavioral intention to use e-collection system.

Investigation of user behavior on social networking sites.

PubMed

Waheed, Hajra; Anjum, Maria; Rehman, Mariam; Khawaja, Amina

2017-01-01

Social networking sites (SNS) are used for social and professional interaction with people. SNS popularity has encouraged researchers to analyze the relationship of activities performed on SNS with user behavior. In doing so, the term "user behavior" is rather used ambiguously with different interpretations, which makes it difficult to identify studies on user behavior in relation to SNS. This phenomenon has encouraged this thorough research on the characteristics of user behavior being discussed in the literature. Therefore, in this study, we aim to identify, analyze, and classify the characteristics associated with user behavior to answer the research questions designed to conduct this research. A mapping study (also called scoping study), which is a type of systematic literature review, is employed to identify potential studies from digital databases through a developed protocol. Thematic analysis is carried out for the classification of user behavior. We identified 116 primary studies for full analysis. This study found seven characteristics associated with behavior that have direct influence on SNS use and nine factors that have an indirect effect. All studies were conducted largely under seven areas that set the context of these studies. Findings show that the research on SNS is still in its early stage. The range of topics covered in the analyzed studies is quite expansive, although the depth in terms of number of studies under each topic is quite limited. This study reports that activities performed on SNS are either associated with user behavior or reflect personality characteristics. The findings of this study could be used by practitioners to evaluate their SNS platforms and develop more user-centered applications. These studies can also help organizations to understand better the needs of their employees.

XAL Application Framework and Bricks GUI Builder

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

Pelaia II, Tom

2007-01-01

The XAL [1] Application Framework is a framework for rapidly developing document based Java applications with a common look and feel along with many built-in user interface behaviors. The Bricks GUI builder consists of a modern application and framework for rapidly building user interfaces in support of true Model-View-Controller (MVC) compliant Java applications. Bricks and the XAL Application Framework allow developers to rapidly create quality applications.

A methodology for the design and evaluation of user interfaces for interactive information systems. Ph.D. Thesis Final Report, 1 Jul. 1985 - 31 Dec. 1987

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Farooq, Mohammad U.

1986-01-01

The definition of proposed research addressing the development and validation of a methodology for the design and evaluation of user interfaces for interactive information systems is given. The major objectives of this research are: the development of a comprehensive, objective, and generalizable methodology for the design and evaluation of user interfaces for information systems; the development of equations and/or analytical models to characterize user behavior and the performance of a designed interface; the design of a prototype system for the development and administration of user interfaces; and the design and use of controlled experiments to support the research and test/validate the proposed methodology. The proposed design methodology views the user interface as a virtual machine composed of three layers: an interactive layer, a dialogue manager layer, and an application interface layer. A command language model of user system interactions is presented because of its inherent simplicity and structured approach based on interaction events. All interaction events have a common structure based on common generic elements necessary for a successful dialogue. It is shown that, using this model, various types of interfaces could be designed and implemented to accommodate various categories of users. The implementation methodology is discussed in terms of how to store and organize the information.

Do recommender systems benefit users? a modeling approach

NASA Astrophysics Data System (ADS)

Yeung, Chi Ho

2016-04-01

Recommender systems are present in many web applications to guide purchase choices. They increase sales and benefit sellers, but whether they benefit customers by providing relevant products remains less explored. While in many cases the recommended products are relevant to users, in other cases customers may be tempted to purchase the products only because they are recommended. Here we introduce a model to examine the benefit of recommender systems for users, and find that recommendations from the system can be equivalent to random draws if one always follows the recommendations and seldom purchases according to his or her own preference. Nevertheless, with sufficient information about user preferences, recommendations become accurate and an abrupt transition to this accurate regime is observed for some of the studied algorithms. On the other hand, we find that high estimated accuracy indicated by common accuracy metrics is not necessarily equivalent to high real accuracy in matching users with products. This disagreement between estimated and real accuracy serves as an alarm for operators and researchers who evaluate recommender systems merely with accuracy metrics. We tested our model with a real dataset and observed similar behaviors. Finally, a recommendation approach with improved accuracy is suggested. These results imply that recommender systems can benefit users, but the more frequently a user purchases the recommended products, the less relevant the recommended products are in matching user taste.

Measuring quality of adolescent preventive services of health plan enrollees and school-based health center users.

PubMed

Klein, Jonathan D; Handwerker, Lisa; Sesselberg, Tracy S; Sutter, Erika; Flanagan, Erinn; Gawronski, Beth

2007-08-01

To evaluate whether quality of care provided to adolescents enrolled in a community-based managed care plan was better for those who also received some care at school-based health centers (SBHCs). The Young Adult Health Care Survey (YAHCS) was administered to 374 adolescents (commercially insured, Medicaid-insured, and SBHC users) to assess risk behaviors, provision of preventive screening and counseling, and quality of care. SBHC users were most likely to report that their provider told them their discussions were confidential, and that they received screening/counseling on sexually transmitted diseases (STDs), HIV/AIDS, condom use, and birth control. Commercially insured adolescents were least likely to report discussion of sexual health issues. SBHC users had the highest mean YAHCS quality measure scores for screening/counseling on pregnancy/STDs, diet and exercise, and helpfulness of counseling provided; Medicaid-insured teens had the lowest scores on four of seven measures. Regression models controlled for demographics, use of screener, and site of care showed that use of a screener had a significant impact on six of seven quality measure models. Younger age predicted screening for risk behaviors; being female, African-American, and an SBHC user predicted screening on pregnancy/STDs. SBHCs may increase adolescents' access to confidential care, and SBHC providers may be more likely than those in other settings to screen and counsel patients about sexual health. Overall quality of preventive care reported by commercially insured adolescents may be better in some health content areas and worse in others compared with care reported by Medicaid-insured youth and SBHC users.

Strong regularities in world wide web surfing

PubMed

Huberman; Pirolli; Pitkow; Lukose

1998-04-03

One of the most common modes of accessing information in the World Wide Web is surfing from one document to another along hyperlinks. Several large empirical studies have revealed common patterns of surfing behavior. A model that assumes that users make a sequence of decisions to proceed to another page, continuing as long as the value of the current page exceeds some threshold, yields the probability distribution for the number of pages that a user visits within a given Web site. This model was verified by comparing its predictions with detailed measurements of surfing patterns. The model also explains the observed Zipf-like distributions in page hits observed at Web sites.

Loneliness and Shyness in Adolescent Problematic Internet Users: The Role of Social Anxiety

ERIC Educational Resources Information Center

Huan, Vivien S.; Ang, Rebecca P.; Chye, Stefanie

2014-01-01

Background: Davis' ("Comput Hum Behav" 17:187-195, 2001) cognitive-behavioral model of problematic Internet use (PIU) proposed and theorized that certain psychopathological characteristics present within an individual, predispose him to PIU. Objective: This study extended Davis' model in hypothesizing that social anxiety mediates in a…

Online Ethics: What's a Teacher to Do?

ERIC Educational Resources Information Center

Carpenter, Cal

1996-01-01

Considers ethics issues involved with using online resources like the Internet in elementary and secondary education and suggests that educators initiate and model a standardized role of ethical behavior for Internet users. Topics include hackers; privacy, piracy, and security; screening electronic sites; ethics education; and an ethics model.…

Models of the Behavior of People Searching the Internet: A Petri Net Approach.

ERIC Educational Resources Information Center

Kantor, Paul B.; Nordlie, Ragnar

1999-01-01

Illustrates how various key abstractions of information finding, such as document relevance, a desired number of relevant documents, discouragement, exhaustion, and satisfaction can be modeled using the Petri Net framework. Shows that this model leads naturally to a new approach to collection of user data, and to analysis of transaction logs.…

Robot Lies in Health Care: When Is Deception Morally Permissible?

PubMed

Matthias, Andreas

2015-06-01

Autonomous robots are increasingly interacting with users who have limited knowledge of robotics and are likely to have an erroneous mental model of the robot's workings, capabilities, and internal structure. The robot's real capabilities may diverge from this mental model to the extent that one might accuse the robot's manufacturer of deceiving the user, especially in cases where the user naturally tends to ascribe exaggerated capabilities to the machine (e.g. conversational systems in elder-care contexts, or toy robots in child care). This poses the question, whether misleading or even actively deceiving the user of an autonomous artifact about the capabilities of the machine is morally bad and why. By analyzing trust, autonomy, and the erosion of trust in communicative acts as consequences of deceptive robot behavior, we formulate four criteria that must be fulfilled in order for robot deception to be morally permissible, and in some cases even morally indicated.

Academic Growth Expectations for Students with Emotional and Behavior Disorders

ERIC Educational Resources Information Center

Ysseldyke, Jim; Scerra, Carmine; Stickney, Eric; Beckler, Amanda; Dituri, Joan; Ellis, Karen

2017-01-01

Computer adaptive assessments were used to monitor the academic status and growth of students with emotional behavior disorders (EBD) in reading (N = 321) and math (N = 322) in a regional service center serving 56 school districts. A cohort sequential model was used to compare that performance to the status and growth of a national user base of…

Perceived parenting behavior in the childhood of cocaine users: relationship with genotype and personality traits.

PubMed

Gerra, G; Zaimovic, A; Garofano, L; Ciusa, F; Moi, G; Avanzini, P; Talarico, E; Gardini, F; Brambilla, F; Manfredini, M; Donnini, C

2007-01-05

Low parental care during childhood, a pattern characteristic of an "affectionless control" rearing style was frequently reported in the history of addicted individuals. Parents' childrearing regimes and children's genetic predispositions, with their own behavioral characteristics, have been seen to be closely interwoven, probably affecting children's development and addictive behavior susceptibility. In the present study, parents care perception, aggressive personality traits, and genotype (serotonin transporter promoter gene--5-HTTLPR) have been investigated in cocaine users and healthy control subjects. PBI scores (maternal and paternal care) were lower and BDHI scores (aggressiveness) higher in cocaine users in comparison with controls and significant differences in the perception of either paternal or maternal care were observed between cocaine users and non-users. The short-short (SS) genotype frequency was significantly higher among cocaine users compared with control subjects (P = 0.04). Logistic regression proves that persons bearing the SS genotype have a risk of becoming cocaine user almost three times higher than those having the LL genotype. Estimations of the effects of other factors potentially affecting the risk of being cocaine addicted clearly prove the significant impact of aggressiveness: the highest the score, the highest the risk of becoming cocaine user. Moreover, paternal and maternal care perception significantly improve the fit of the model (the log likelihood decreases passing from -105.9 to -89.8, LR test = 32.17, P-value = 0.0000). Each unit increase in the PBI score yields a significant 12% and 10% decrease of the risk of becoming cocaine user, respectively for paternal and maternal care. Interestingly, once controlled for the PBI score, the relative risk associated to the SS genotype drops strikingly and becomes no longer statistically significant. On the whole, our preliminary data suggest that the association between 5-HT transporter polymorphism and psycho-stimulant use may be mediated by mother-child relationship and parental attachment perception, both being environmental and genetic factors involved in the proneness to substance use disorders, particularly in aggressive-antisocial individuals.

Understanding User Behavioral Patterns in Open Knowledge Communities

ERIC Educational Resources Information Center

Yang, Xianmin; Song, Shuqiang; Zhao, Xinshuo; Yu, Shengquan

2018-01-01

Open knowledge communities (OKCs) have become popular in the era of knowledge economy. This study aimed to explore how users collaboratively create and share knowledge in OKCs. In particular, this research identified the behavior distribution and behavioral patterns of users by conducting frequency distribution and lag sequential analyses. Some…

The Dynamics of Online User Behavior and IS Policy Implications

ERIC Educational Resources Information Center

Kim, Keehyung

2016-01-01

This dissertation, which comprises three independent essays, explores the dynamics of online user behavior and provides IS policy implications across three different applications. The first essay employs an econometric empirical analysis to examine the role of IT interventions on online users' gambling behavior, based on field data collected over…

Health behaviors associated with use of body building, weight loss, and performance enhancing supplements.

PubMed

Kao, Tzu-Cheg; Deuster, Patricia A; Burnett, Daniel; Stephens, Mark

2012-05-01

To identify health-related behaviors associated with potentially harmful dietary supplements (DS) - body building (BB), weight loss (WL) and performance enhancing (PE), explore common reasons and sources of information for DS use. Based on the 2005 Survey of 16,146 U.S. military personnel, BB users were dichotomized as yes (regular use - taking any supplement of BB at least once a week in past 12 months) or no; similarly defined for WL and PE. Weighted logistic regression models are used. BB, WL and PE were used by 19.4%, 17.0%, and 8.0% of participants, respectively. Significantly more users were overweight or obese: BMI ≥25 (vs. BMI<25); heavy drinkers (vs. abstainers); and users of taking steroids in their lifetime (vs. not). Most common reasons of BB, WL, and PE users wanted to increase muscle mass, lose weight, and improve physical performance (BB: 45.8%, WL: 54.8%, PE: 38.5%). Fewer than 30% discussed dietary supplements use with their healthcare providers. The leading source of dietary supplements information (BB: 27.8%, WL: 23.6%, PE: 30.0%) was magazines. The dietary supplements: BB, WL and PE were used by significant proportions of service members, and associated with risk-taking behaviors that may affect overall military readiness and public health. Published by Elsevier Inc.

Antidepressant Use is Associated with Increased Energy Intake and Similar Levels of Physical Activity.

PubMed

Jensen-Otsu, Elsbeth; Austin, Gregory L

2015-11-20

Antidepressants have been associated with weight gain, but the causes are unclear. The aims of this study were to assess the association of antidepressant use with energy intake, macronutrient diet composition, and physical activity. We used data on medication use, energy intake, diet composition, and physical activity for 3073 eligible adults from the 2005-2006 National Health and Nutrition Examination Survey (NHANES). Potential confounding variables, including depression symptoms, were included in the models assessing energy intake, physical activity, and sedentary behavior. Antidepressant users reported consuming an additional (mean ± S.E.) 215 ± 73 kcal/day compared to non-users (p = 0.01). There were no differences in percent calories from sugar, fat, or alcohol between the two groups. Antidepressant users had similar frequencies of walking or biking, engaging in muscle-strengthening activities, and engaging in moderate or vigorous physical activity. Antidepressant users were more likely to use a computer for ≥2 h/day (OR 1.77; 95% CI: 1.09-2.90), but TV watching was similar between the two groups. These results suggest increased energy intake and sedentary behavior may contribute to weight gain associated with antidepressant use. Focusing on limiting food intake and sedentary behaviors may be important in mitigating the weight gain associated with antidepressant use.

Antidepressant Use is Associated with Increased Energy Intake and Similar Levels of Physical Activity

PubMed Central

Jensen-Otsu, Elsbeth; Austin, Gregory L.

2015-01-01

Antidepressants have been associated with weight gain, but the causes are unclear. The aims of this study were to assess the association of antidepressant use with energy intake, macronutrient diet composition, and physical activity. We used data on medication use, energy intake, diet composition, and physical activity for 3073 eligible adults from the 2005–2006 National Health and Nutrition Examination Survey (NHANES). Potential confounding variables, including depression symptoms, were included in the models assessing energy intake, physical activity, and sedentary behavior. Antidepressant users reported consuming an additional (mean ± S.E.) 215 ± 73 kcal/day compared to non-users (p = 0.01). There were no differences in percent calories from sugar, fat, or alcohol between the two groups. Antidepressant users had similar frequencies of walking or biking, engaging in muscle-strengthening activities, and engaging in moderate or vigorous physical activity. Antidepressant users were more likely to use a computer for ≥2 h/day (OR 1.77; 95% CI: 1.09–2.90), but TV watching was similar between the two groups. These results suggest increased energy intake and sedentary behavior may contribute to weight gain associated with antidepressant use. Focusing on limiting food intake and sedentary behaviors may be important in mitigating the weight gain associated with antidepressant use. PMID:26610562

Investigation of user behavior on social networking sites

PubMed Central

2017-01-01

Social networking sites (SNS) are used for social and professional interaction with people. SNS popularity has encouraged researchers to analyze the relationship of activities performed on SNS with user behavior. In doing so, the term “user behavior” is rather used ambiguously with different interpretations, which makes it difficult to identify studies on user behavior in relation to SNS. This phenomenon has encouraged this thorough research on the characteristics of user behavior being discussed in the literature. Therefore, in this study, we aim to identify, analyze, and classify the characteristics associated with user behavior to answer the research questions designed to conduct this research. A mapping study (also called scoping study), which is a type of systematic literature review, is employed to identify potential studies from digital databases through a developed protocol. Thematic analysis is carried out for the classification of user behavior. We identified 116 primary studies for full analysis. This study found seven characteristics associated with behavior that have direct influence on SNS use and nine factors that have an indirect effect. All studies were conducted largely under seven areas that set the context of these studies. Findings show that the research on SNS is still in its early stage. The range of topics covered in the analyzed studies is quite expansive, although the depth in terms of number of studies under each topic is quite limited. This study reports that activities performed on SNS are either associated with user behavior or reflect personality characteristics. The findings of this study could be used by practitioners to evaluate their SNS platforms and develop more user-centered applications. These studies can also help organizations to understand better the needs of their employees. PMID:28151963

The Motivational Enhancement Therapy and Cognitive Behavioral Therapy Supplement: 7 Sessions of Cognitive Behavioral Therapy for Adolescent Cannabis Users, Cannabis Youth Treatment (CYT) Series, Volume 2.

ERIC Educational Resources Information Center

Webb, Charles; Scudder, Meleney; Kaminer, Yifrah; Kaden, Ron

This manual, a supplement to "Motivational Enhancement Therapy and Cognitive Behavioral Therapy for Adolescent Cannabis Users: 5 Sessions, Cannabis Youth Treatment (CYT) Series, Volume 1", presents a seven-session cognitive behavioral treatment (CBT7) approach designed especially for adolescent cannabis users. It addresses the implementation and…

The Effects of Twitter Users' Gender and Weight on Viral Behavioral Intentions Toward Obesity-Related News.

PubMed

Almutairi, Nasser; Alhabash, Saleem; Hellmueller, Lea; Willis, Erin

2018-01-01

In this study, male and female participants were exposed to identical news stories covering obesity topics paired with tweets from Twitter users. Our study aimed at understanding how obesity-related news combined with user-generated social media posts (i.e., tweets) affect consumers' evaluations of online content and viral behavioral intentions (the intentions to like, share, and comment). An experiment (N = 316) explored how gender and weight of a Twitter user (tweeter) affect participants' evaluations and viral behavioral intentions toward news stories. Participants differed in their evaluations of and viral behavioral intentions for news stories as a function of Twitter users' gender and weight, as well as participants' gender. While participants expressed more favorable attitudes toward news stories paired with tweets by overweight than healthy females (with the opposite true for tweets by male users), participants expressed greater viral behavioral intentions for news stories paired with tweets by healthy weight than overweight user. These effects were more pronounced among male than female participants. Findings are discussed within the context of social media posts and their persuasive effects in relation to attitude and behavior changes.

Call Me Guru: User Categories and Large-Scale Behavior in YouTube

NASA Astrophysics Data System (ADS)

Biel, Joan-Isaac; Gatica-Perez, Daniel

While existing studies on YouTube's massive user-generated video content have mostly focused on the analysis of videos, their characteristics, and network properties, little attention has been paid to the analysis of users' long-term behavior as it relates to the roles they self-define and (explicitly or not) play in the site. In this chapter, we present a statistical analysis of aggregated user behavior in YouTube from the perspective of user categories, a feature that allows people to ascribe to popular roles and to potentially reach certain communities. Using a sample of 270,000 users, we found that a high level of interaction and participation is concentrated on a relatively small, yet significant, group of users, following recognizable patterns of personal and social involvement. Based on our analysis, we also show that by using simple behavioral features from user profiles, people can be automatically classified according to their category with accuracy rates of up to 73%.

Analysis and Development of Management Information Systems for Private Messes Afloat

DTIC Science & Technology

1988-03-01

the development phase emphasis was placed on a three step approach starting with an analysis of the requirements as established by... oper - ating the mess divided by number of mess members Total Mess Bill Due Total of old bills, current bill, mess share owed, and special assessment 46...TRANSPARENCY THE SYSTEM BEHAVIOR IS TRANSPARENT TO THE USER. THAT MEANS THAT THE USER CAN DEVELOP A CONSISTENT MODEL OF THE SYSTEM WHEN WORKING

Microeconomics-based resource allocation in overlay networks by using non-strategic behavior modeling

NASA Astrophysics Data System (ADS)

Analoui, Morteza; Rezvani, Mohammad Hossein

2011-01-01

Behavior modeling has recently been investigated for designing self-organizing mechanisms in the context of communication networks in order to exploit the natural selfishness of the users with the goal of maximizing the overall utility. In strategic behavior modeling, the users of the network are assumed to be game players who seek to maximize their utility with taking into account the decisions that the other players might make. The essential difference between the aforementioned researches and this work is that it incorporates the non-strategic decisions in order to design the mechanism for the overlay network. In this solution concept, the decisions that a peer might make does not affect the actions of the other peers at all. The theory of consumer-firm developed in microeconomics is a model of the non-strategic behavior that we have adopted in our research. Based on it, we have presented distributed algorithms for peers' "joining" and "leaving" operations. We have modeled the overlay network as a competitive economy in which the content provided by an origin server can be viewed as commodity and the origin server and the peers who multicast the content to their downside are considered as the firms. On the other hand, due to the dual role of the peers in the overlay network, they can be considered as the consumers as well. On joining to the overlay economy, each peer is provided with an income and tries to get hold of the service regardless to the behavior of the other peers. We have designed the scalable algorithms in such a way that the existence of equilibrium price (known as Walrasian equilibrium price) is guaranteed.

Heterogeneous characters modeling of instant message services users' online behavior.

PubMed

Cui, Hongyan; Li, Ruibing; Fang, Yajun; Horn, Berthold; Welsch, Roy E

2018-01-01

Research on temporal characteristics of human dynamics has attracted much attentions for its contribution to various areas such as communication, medical treatment, finance, etc. Existing studies show that the time intervals between two consecutive events present different non-Poisson characteristics, such as power-law, Pareto, bimodal distribution of power-law, exponential distribution, piecewise power-law, et al. With the occurrences of new services, new types of distributions may arise. In this paper, we study the distributions of the time intervals between two consecutive visits to QQ and WeChat service, the top two popular instant messaging services in China, and present a new finding that when the value of statistical unit T is set to 0.001s, the inter-event time distribution follows a piecewise distribution of exponential and power-law, indicating the heterogeneous character of IM services users' online behavior in different time scales. We infer that the heterogeneous character is related to the communication mechanism of IM and the habits of users. Then we develop a combination model of exponential model and interest model to characterize the heterogeneity. Furthermore, we find that the exponent of the inter-event time distribution of the same service is different in two cities, which is correlated with the popularity of the services. Our research is useful for the application of information diffusion, prediction of economic development of cities, and so on.

Face-to-face or not-to-face: A technology preference for communication.

PubMed

Jaafar, Noor Ismawati; Darmawan, Bobby; Mohamed Ariffin, Mohd Yahya

2014-11-01

This study employed the Model of Technology Preference (MTP) to explain the relationship of the variables as the antecedents of behavioral intention to adopt a social networking site (SNS) for communication. Self-administered questionnaires were distributed to SNS account users using paper-based and web-based surveys that led to 514 valid responses. The data were analyzed using structural equation modeling (SEM). The results show that two out of three attributes of the attribute-based preference (ATRP) affect attitude-based preference (ATTP). The data support the hypotheses that perceived enjoyment and social presence are predictors of ATTP. In this study, the findings further indicated that ATTP has no relationship with the behavioral intention of using SNS, but it has a relationship with the attitude of using SNS. SNS development should provide features that ensure enjoyment and social presence for users to communicate instead of using the traditional face-to-face method of communication.

Research on the Application of Persona in Book Recommendation System

NASA Astrophysics Data System (ADS)

Gao, Baozhong; Du, Shouyan; Li, Xinzhi; Liu, Fangai

2017-10-01

Currently, there still exists a host of problems in the book recommendation system, such as low accuracy, weak correlation and poor pertinence. Aiming to unravel these problems, this paper based on the theory of big data and data mining technology, through analyzing internet user behavior and the “5C” model of personal credit evaluation, combined with joint impact weight calculation method, which involves user grade, borrowing credit, book friend recommendation degree, book friend recommended adoption degree, borrowing frequency, borrowing number, and borrowing time interval. User activity and credit are also taken into account in the process of establishing user tagging system so as to build classified book recommendation service. This method is of universal meaning to the book recommendation service of smart campus with user as the core under big data environment.

Smoking Behaviors and Intentions among Current E-Cigarette Users, Cigarette Smokers, and Dual Users: A National Survey of U.S. High School Seniors

PubMed Central

McCabe, Sean Esteban; Veliz, Phil; McCabe, Vita V.; Boyd, Carol J.

2017-01-01

E-cigarette use among adolescents has increased significantly in recent years, but it remains unclear whether cigarette smoking behaviors and intentions differ among current (i.e., 30-day) non-users, only e-cigarette users, only cigarette smokers, and dual users. A nationally representative sample of 4385 U.S. high school seniors (modal age 18 years) were surveyed during the spring of their senior year via self-administered questionnaires in 2014. An estimated 9.6% of U.S. high school seniors reported current (30-day) e-cigarette use only, 6.3% reported current cigarette smoking only, and 7.2% reported current dual use of e-cigarettes and cigarette smoking. There were no significant differences between current only cigarette smokers and dual users in the odds of early onset of cigarette smoking, daily cigarette smoking, future cigarette smoking intentions, friends’ cigarette smoking behaviors, attempts to quit cigarette smoking, or the inability to quit cigarette smoking. Adolescents who only used e-cigarettes had higher odds of cigarette smoking behaviors and intentions than current non-users, including intentions for future cigarette smoking in the next 5 years (AOR = 2.57, 95% CI: 1.21—5.24). Dual users and only cigarette smokers had higher odds of cigarette smoking behaviors and intentions than non-users or only e-cigarette users. Adolescents who engage in current dual use appear to have cigarette smoking behaviors and intentions that more closely resemble cigarette smokers than e-cigarette users. Adolescents who only use e-cigarettes have higher intentions to engage in cigarette smoking in the future relative to their peers who do not engage in e-cigarette use or cigarette smoking. PMID:28257785

Using instructional design process to improve design and development of Internet interventions.

PubMed

Hilgart, Michelle M; Ritterband, Lee M; Thorndike, Frances P; Kinzie, Mable B

2012-06-28

Given the wide reach and extensive capabilities of the Internet, it is increasingly being used to deliver comprehensive behavioral and mental health intervention and prevention programs. Their goals are to change user behavior, reduce unwanted complications or symptoms, and improve health status and health-related quality of life. Internet interventions have been found efficacious in addressing a wide range of behavioral and mental health problems, including insomnia, nicotine dependence, obesity, diabetes, depression, and anxiety. Despite the existence of many Internet-based interventions, there is little research to inform their design and development. A model for behavior change in Internet interventions has been published to help guide future Internet intervention development and to help predict and explain behavior changes and symptom improvement outcomes through the use of Internet interventions. An argument is made for grounding the development of Internet interventions within a scientific framework. To that end, the model highlights a multitude of design-related components, areas, and elements, including user characteristics, environment, intervention content, level of intervention support, and targeted outcomes. However, more discussion is needed regarding how the design of the program should be developed to address these issues. While there is little research on the design and development of Internet interventions, there is a rich, related literature in the field of instructional design (ID) that can be used to inform Internet intervention development. ID models are prescriptive models that describe a set of activities involved in the planning, implementation, and evaluation of instructional programs. Using ID process models has been shown to increase the effectiveness of learning programs in a broad range of contexts. ID models specify a systematic method for assessing the needs of learners (intervention users) to determine the gaps between current knowledge and behaviors, and desired outcomes. Through the ID process, designers focus on the needs of learners, taking into account their prior knowledge; set measurable learning objectives or performance requirements; assess learners' achievement of the targeted outcomes; and employ cycles of continuous formative evaluation to ensure that the intervention meets the needs of all stakeholders. The ID process offers a proven methodology for the design of instructional programs and should be considered an integral part of the creation of Internet interventions. By providing a framework for the design and development of Internet interventions and by purposefully focusing on these aspects, as well as the underlying theories supporting these practices, both the theories and the interventions themselves can continue to be refined and improved. By using the behavior change model for Internet interventions along with the best research available to guide design practice and inform development, developers of Internet interventions will increase their ability to achieve desired outcomes.

Using Instructional Design Process to Improve Design and Development of Internet Interventions

PubMed Central

Hilgart, Michelle M; Thorndike, Frances P; Kinzie, Mable B

2012-01-01

Given the wide reach and extensive capabilities of the Internet, it is increasingly being used to deliver comprehensive behavioral and mental health intervention and prevention programs. Their goals are to change user behavior, reduce unwanted complications or symptoms, and improve health status and health-related quality of life. Internet interventions have been found efficacious in addressing a wide range of behavioral and mental health problems, including insomnia, nicotine dependence, obesity, diabetes, depression, and anxiety. Despite the existence of many Internet-based interventions, there is little research to inform their design and development. A model for behavior change in Internet interventions has been published to help guide future Internet intervention development and to help predict and explain behavior changes and symptom improvement outcomes through the use of Internet interventions. An argument is made for grounding the development of Internet interventions within a scientific framework. To that end, the model highlights a multitude of design-related components, areas, and elements, including user characteristics, environment, intervention content, level of intervention support, and targeted outcomes. However, more discussion is needed regarding how the design of the program should be developed to address these issues. While there is little research on the design and development of Internet interventions, there is a rich, related literature in the field of instructional design (ID) that can be used to inform Internet intervention development. ID models are prescriptive models that describe a set of activities involved in the planning, implementation, and evaluation of instructional programs. Using ID process models has been shown to increase the effectiveness of learning programs in a broad range of contexts. ID models specify a systematic method for assessing the needs of learners (intervention users) to determine the gaps between current knowledge and behaviors, and desired outcomes. Through the ID process, designers focus on the needs of learners, taking into account their prior knowledge; set measurable learning objectives or performance requirements; assess learners’ achievement of the targeted outcomes; and employ cycles of continuous formative evaluation to ensure that the intervention meets the needs of all stakeholders. The ID process offers a proven methodology for the design of instructional programs and should be considered an integral part of the creation of Internet interventions. By providing a framework for the design and development of Internet interventions and by purposefully focusing on these aspects, as well as the underlying theories supporting these practices, both the theories and the interventions themselves can continue to be refined and improved. By using the behavior change model for Internet interventions along with the best research available to guide design practice and inform development, developers of Internet interventions will increase their ability to achieve desired outcomes. PMID:22743534

Personalizing Information Retrieval Using Task Features, Topic Knowledge, and Task Products

ERIC Educational Resources Information Center

Liu, Jingjing

2010-01-01

Personalization of information retrieval tailors search towards individual users to meet their particular information needs by taking into account information about users and their contexts, often through implicit sources of evidence such as user behaviors and contextual factors. The current study looks particularly at users' dwelling behavior,…

FVS out of the box - assembly required

Treesearch

Don Vandendriesche

2010-01-01

The Forest Vegetation Simulator (FVS) is a prominent growth and yield model used for forecasting stand dynamics. However, users need to be aware of model behavior regarding stocking density, tree senescence, and understory recruitment; otherwise over long projections, FVS tends to concentrate substantial growth on few survivor trees. If the intent is to forecast...

Automatic Detection of Learning Styles for an E-Learning System

ERIC Educational Resources Information Center

Ozpolat, Ebru; Akar, Gozde B.

2009-01-01

A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user's browsing…

Social Psychological Dynamics of Enhanced HIV Risk Reduction among Peer Interventionists

ERIC Educational Resources Information Center

Dickson-Gomez, Julia; Weeks, Margaret R.; Convey, Mark; Li, Jianghong

2011-01-01

The authors present a model of interactive social psychological and relational feedback processes leading to human immunodeficiency virus (HIV) risk reduction behavior change among active drug users trained as Peer Health Advocates (PHAs). The model is supported by data from qualitative interviews with PHAs and members of their drug-using networks…

Evolution of Reference: A New Service Model for Science and Engineering Libraries

ERIC Educational Resources Information Center

Bracke, Marianne Stowell; Chinnaswamy, Sainath; Kline, Elizabeth

2008-01-01

This article explores the different steps involved in adopting a new service model at the University of Arizona Science-Engineering Library. In a time of shrinking budgets and changing user behavior the library was forced to rethink it reference services to be cost effective and provide quality service at the same time. The new model required…

BehavePlus fire modeling system, version 4.0: User's Guide

Treesearch

Patricia L. Andrews; Collin D. Bevins; Robert C. Seli

2005-01-01

The BehavePlus fire modeling system is a program for personal computers that is a collection of mathematical models that describe fire and the fire environment. It is a flexible system that produces tables, graphs, and simple diagrams. It can be used for a multitude of fire management applications including projecting the behavior of an ongoing fire, planning...

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network

PubMed Central

Shen, Yelong; Phan, NhatHai; Xiao, Xiao; Jin, Ruoming; Sun, Junfeng; Piniewski, Brigitte; Kil, David; Dou, Dejing

2016-01-01

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual's behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users' sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time. PMID:27746515

Modeling Interdependent and Periodic Real-World Action Sequences

PubMed Central

Kurashima, Takeshi; Althoff, Tim; Leskovec, Jure

2018-01-01

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions in the real world is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model, called TIPAS, for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million real-world actions (e.g., eating, sleep, and exercise) taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, TIPAS improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions. PMID:29780977

Quantifying the web browser ecosystem

PubMed Central

Ferdman, Sela; Minkov, Einat; Gefen, David

2017-01-01

Contrary to the assumption that web browsers are designed to support the user, an examination of a 900,000 distinct PCs shows that web browsers comprise a complex ecosystem with millions of addons collaborating and competing with each other. It is possible for addons to “sneak in” through third party installations or to get “kicked out” by their competitors without user involvement. This study examines that ecosystem quantitatively by constructing a large-scale graph with nodes corresponding to users, addons, and words (terms) that describe addon functionality. Analyzing addon interactions at user level using the Personalized PageRank (PPR) random walk measure shows that the graph demonstrates ecological resilience. Adapting the PPR model to analyzing the browser ecosystem at the level of addon manufacturer, the study shows that some addon companies are in symbiosis and others clash with each other as shown by analyzing the behavior of 18 prominent addon manufacturers. Results may herald insight on how other evolving internet ecosystems may behave, and suggest a methodology for measuring this behavior. Specifically, applying such a methodology could transform the addon market. PMID:28644833

Visualization and characterization of users in a citizen science project

NASA Astrophysics Data System (ADS)

Morais, Alessandra M. M.; Raddick, Jordan; Coelho dos Santos, Rafael D.

2013-05-01

Recent technological advances allowed the creation and use of internet-based systems where many users can collaborate gathering and sharing information for specific or general purposes: social networks, e-commerce review systems, collaborative knowledge systems, etc. Since most of the data collected in these systems is user-generated, understanding of the motivations and general behavior of users is a very important issue. Of particular interest are citizen science projects, where users without scientific training are asked for collaboration labeling and classifying information (either automatically by giving away idle computer time or manually by actually seeing data and providing information about it). Understanding behavior of users of those types of data collection systems may help increase the involvement of the users, categorize users accordingly to different parameters, facilitate their collaboration with the systems, design better user interfaces, and allow better planning and deployment of similar projects and systems. Behavior of those users could be estimated through analysis of their collaboration track: registers of which user did what and when can be easily and unobtrusively collected in several different ways, the simplest being a log of activities. In this paper we present some results on the visualization and characterization of almost 150.000 users with more than 80.000.000 collaborations with a citizen science project - Galaxy Zoo I, which asked users to classify galaxies' images. Basic visualization techniques are not applicable due to the number of users, so techniques to characterize users' behavior based on feature extraction and clustering are used.

Mechanical modeling of self-expandable stent fabricated using braiding technology.

PubMed

Kim, Ju Hyun; Kang, Tae Jin; Yu, Woong-Ryeol

2008-11-14

The mechanical behavior of a stent is one of the important factors involved in ensuring its opening within arterial conduits. This study aimed to develop a mechanical model for designing self-expandable stents fabricated using braiding technology. For this purpose, a finite element model was constructed by developing a preprocessing program for the three-dimensional geometrical modeling of the braiding structure inside stents, and validated for various stents with different braiding structures. The constituent wires (Nitinol) in the braided stents were assumed to be superelastic material and their mechanical behavior was incorporated into the finite element software through a user material subroutine (VUMAT in ABAQUS) employing a one-dimensional superelastic model. For the verification of the model, several braided stents were manufactured using an automated braiding machine and characterized focusing on their compressive behavior. It was observed that the braided stents showed a hysteresis between their loading and unloading behavior when a compressive load was applied to the braided tube. Through the finite element analysis, it was concluded that the current mechanical model can appropriately predict the mechanical behavior of braided stents including such hysteretic behavior, and that the hysteresis was caused by the slippage between the constituent wires and their superelastic property.

Screen-related sedentary behaviors: children's and parents' attitudes, motivations, and practices.

PubMed

He, Meizi; Piché, Leonard; Beynon, Charlene; Harris, Stewart

2010-01-01

To investigate school-aged children's and parents' attitudes, social influences, and intentions toward excessive screen-related sedentary behavior (S-RSB). A cross-sectional study using a survey methodology. Elementary schools in London, Ontario, Canada. All grades 5 and 6 students, their parents, and their teachers in the participating schools were invited to voluntarily participate; 508 student-parent pairs completed the surveys. Children's screen-related behaviors. Data were analyzed using the Independent Student t test to compare differences of continuous variables and the chi-square test to test for differences of categorical variables. Children spent 3.3 +/- 0.15 (standard error) hours per day engaged in screen-related activities. Entertainment, spending time with family, and boredom were cited as the top 3 reasons for television viewing and video game playing. Compared to "low-screen users" (ie, < 2 hours/day), "high-screen users" (ie, >or= 2 hours/day) had a less negative attitude toward excessive S-RSB and perceived loosened parental rules on screen use. Parents of high-screen users had a less negative attitude toward children's S-RSB, had fewer rules about their children's screen use, and were more likely to be sedentary themselves. Intervention strategies aimed at reducing S-RSB should involve both parents and children and should focus on fostering behavioral changes and promoting parental role modeling.

Desire to Be Underweight: Exploratory Study on a Weight Loss App Community and User Perceptions of the Impact on Disordered Eating Behaviors

PubMed Central

Reddy, Madhu C; Booth, Kayla M; Kvasny, Lynette; Blair, Johnna L; Li, Victor; Poole, Erika S

2017-01-01

Background Mobile health (mHealth) apps for weight loss (weight loss apps) can be useful diet and exercise tools for individuals in need of losing weight. Most studies view weight loss app users as these types of individuals, but not all users have the same needs. In fact, users with disordered eating behaviors who desire to be underweight are also utilizing weight loss apps; however, few studies give a sense of the prevalence of these users in weight loss app communities and their perceptions of weight loss apps in relation to disordered eating behaviors. Objective The aim of this study was to provide an analysis of users’ body mass indices (BMIs) in a weight loss app community and examples of how users with underweight BMI goals perceive the impact of the app on disordered eating behaviors. Methods We focused on two aspects of a weight loss app (DropPounds): profile data and forum posts, and we moved from a broader picture of the community to a narrower focus on users’ perceptions. We analyzed profile data to better understand the goal BMIs of all users, highlighting the prevalence of users with underweight BMI goals. Then we explored how users with a desire to be underweight discussed the weight loss app’s impact on disordered eating behaviors. Results We found three main results: (1) no user (regardless of start BMI) starts with a weight gain goal, and most users want to lose weight; (2) 6.78% (1261/18,601) of the community want to be underweight, and most identify as female; (3) users with underweight BMI goals tend to view the app as positive, especially for reducing bingeing; however, some acknowledge its role in exacerbating disordered eating behaviors. Conclusions These findings are important for our understanding of the different types of users who utilize weight loss apps, the perceptions of weight loss apps related to disordered eating, and how weight loss apps may impact users with a desire to be underweight. Whereas these users had underweight goals, they often view the app as helpful in reducing disordered eating behaviors, which led to additional questions. Therefore, future research is needed. PMID:29025694

The closer the relationship, the more the interaction on facebook? Investigating the case of Taiwan users.

PubMed

Hsu, Chiung-Wen Julia; Wang, Ching-Chan; Tai, Yi-Ting

2011-01-01

This study argues for the necessity of applying offline contexts to social networking site research and the importance of distinguishing the relationship types of users' counterparts when studying Facebook users' behaviors. In an attempt to examine the relationship among users' behaviors, their counterparts' relationship types, and the users' perceived acquaintanceships after using Facebook, this study first investigated users' frequently used tools when interacting with different types of friends. Users tended to use less time- and effort-consuming and less privacy-concerned tools with newly acquired friends. This study further examined users' behaviors in terms of their closeness and intimacy and their perceived acquaintanceships toward four different types of friends. The study found that users gained more perceived acquaintanceships from less close friends with whom users have more frequent interaction but less intimate behaviors. As for closer friends, users tended to use more intimate activities to interact with them. However, these activities did not necessarily occur more frequently than the activities they employed with their less close friends. It was found that perceived acquaintanceships with closer friends were significantly lower than those with less close friends. This implies that Facebook is a mechanism for new friends, rather than close friends, to become more acquainted.

Predicting diagnostic error in Radiology via eye-tracking and image analytics: Application in mammography

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

Voisin, Sophie; Pinto, Frank M; Morin-Ducote, Garnetta

2013-01-01

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADsmore » images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.« less

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

PubMed Central

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

2016-01-01

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

Differences in behavioral health disorders and unmet treatment needs between medical marijuana users and recreational marijuana users: Results from a national adult sample.

PubMed

Park, Ji-Yeun; Wu, Li-Tzy

2017-11-01

Available data suggest that medical marijuana users may have more mental health problems than recreational marijuana users. There is limited information about differences in behavioral health disorders and unmet treatment needs between medical and recreational marijuana users. We compared past-year prevalence of behavioral health disorders and unmet treatment needs across three marijuana subgroups (recreational use only, medical use only, and both). Sex-stratified logistic regression was performed to determine their associations with marijuana use status. We analyzed data from adults (≥18 years) who used marijuana in the past year (N=15,440) from 2013 to 2014 National Surveys on Drug Use and Health. Among 15,440 past-year marijuana users, 90.2% used recreational marijuana only, 6.2% used medical marijuana only, and 3.6% used both. Both users had the highest prevalence of behavioral health disorders and unmet treatment needs overall, with no significant sex differences. In the sex-specific logistic regression analysis, medical only users and both users showed somewhat different patterns of associations (reference group=recreational only users). Medical only users had decreased odds of alcohol or drug use disorders, and unmet need for alcohol or drug treatment among males and females. Additionally, female medical only users had decreased odds of opioid use disorder. Both users had increased odds of major depressive episode, hallucinogen use disorder, and unmet need for mental health services among males, and cocaine use disorder among females. Different approaches tailored to individuals' sex and motives for marijuana use is needed for the prevention and treatment of behavioral health problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Prism users guide.

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

Weirs, V. Gregory

2012-03-01

Prism is a ParaView plugin that simultaneously displays simulation data and material model data. This document describes its capabilities and how to use them. A demonstration of Prism is given in the first section. The second section contains more detailed notes on less obvious behavior. The third and fourth sections are specifically for Alegra and CTH users. They tell how to generate the simulation data and SESAME files and how to handle aspects of Prism use particular to each of these codes.

Culture, Interface Design, and Design Methods for Mobile Devices

NASA Astrophysics Data System (ADS)

Lee, Kun-Pyo

Aesthetic differences and similarities among cultures are obviously one of the very important issues in cultural design. However, ever since products became knowledge-supporting tools, the visible elements of products have become more universal so that the invisible parts of products such as interface and interaction are getting more important. Therefore, the cultural design should be extended to the invisible elements of culture like people's conceptual models beyond material and phenomenal culture. This chapter aims to explain how we address the invisible cultural elements in interface design and design methods by exploring the users' cognitive styles and communication patterns in different cultures. Regarding cultural interface design, we examined users' conceptual models while interacting with mobile phone and website interfaces, and observed cultural difference in performing tasks and viewing patterns, which appeared to agree with cultural cognitive styles known as Holistic thoughts vs. Analytic thoughts. Regarding design methods for culture, we explored how to localize design methods such as focus group interview and generative session for specific cultural groups, and the results of comparative experiments revealed cultural difference on participants' behaviors and performance in each design method and led us to suggest how to conduct them in East Asian culture. Mobile Observation Analyzer and Wi-Pro, user research tools we invented to capture user behaviors and needs especially in their mobile context, were also introduced.

An AIDS risk reduction program for Dutch drug users: an intervention mapping approach to planning.

PubMed

van Empelen, Pepijn; Kok, Gerjo; Schaalma, Herman P; Bartholomew, L Kay

2003-10-01

This article presents the development of a theory- and evidence-based AIDS prevention program targeting Dutch drug users and aimed at promoting condom use. The emphasis is on the development of the program using a five-step intervention development protocol called intervention mapping (IM). Preceding Step 1 of the IM process, an assessment of the HIV problem among drug users was conducted. The product of IM Step 1 was a series of program objectives specifying what drug users should learn in order to use condoms consistently. In Step 2, theoretical methods for influencing the most important determinants were chosen and translated into practical strategies that fit the program objectives. The main strategy chosen was behavioral journalism. In Step 3, leaflets with role-model stories based on authentic interviews with drug users were developed and pilot tested. Finally, the need for cooperation with program users is discussed in IM Steps 4 and 5.

The motivation for drug abuse treatment: testing cognitive and 12-step theories.

PubMed

Bell, D C; Montoya, I D; Richard, A J; Dayton, C A

1998-11-01

The purpose of this paper is to evaluate two models of behavior change: cognitive theory and 12-step theory. Research subjects were drawn from three separate, but parallel, samples of adults. The first sample consisted of out-of-treatment chronic drug users, the second consisted of drug users who had applied for treatment at a publicly funded multiple-provider drug treatment facility, and the third consisted of drug users who had applied for treatment at an intensive outpatient program for crack cocaine users. Cognitive theory was supported. Study participants applying for drug abuse treatment reported a higher level of perceived problem severity and a higher level of cognitive functioning than out-of-treatment drug users. Two hypotheses drawn from 12-step theory were not supported. Treatment applicants had more positive emotional functioning than out-of-treatment drug users, and one treatment-seeking sample had higher self-esteem.

LoyalTracker: Visualizing Loyalty Dynamics in Search Engines.

PubMed

Shi, Conglei; Wu, Yingcai; Liu, Shixia; Zhou, Hong; Qu, Huamin

2014-12-01

The huge amount of user log data collected by search engine providers creates new opportunities to understand user loyalty and defection behavior at an unprecedented scale. However, this also poses a great challenge to analyze the behavior and glean insights into the complex, large data. In this paper, we introduce LoyalTracker, a visual analytics system to track user loyalty and switching behavior towards multiple search engines from the vast amount of user log data. We propose a new interactive visualization technique (flow view) based on a flow metaphor, which conveys a proper visual summary of the dynamics of user loyalty of thousands of users over time. Two other visualization techniques, a density map and a word cloud, are integrated to enable analysts to gain further insights into the patterns identified by the flow view. Case studies and the interview with domain experts are conducted to demonstrate the usefulness of our technique in understanding user loyalty and switching behavior in search engines.

78 FR 32431 - Notice of Submission of Proposed Information Collection to OMB; Enterprise Income Verification...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-30

... Proposed Information Collection to OMB; Enterprise Income Verification (EIV) System User Access Authorization Form and Rules of Behavior and User Agreement AGENCY: Office of the Chief Information Officer, HUD... user with information related to the Rules of Behavior for system usage and the user's responsibilities...

Lifestyle Differences between Young Adult Cocaine Users and Their Nonuser Peers.

ERIC Educational Resources Information Center

Castro, Felipe G.; And Others

1987-01-01

Examined health-related behaviors in 25 young adult cocaine users and 25 matched nonusers. Found cocaine users consumed more coffee and alcohol, and fewer balanced meals, than did nonusers. Users reported less relaxation and daily organization than did nonusers. Suggests that cocaine use is embedded in complex of interrelated unhealthy behaviors;…

Forgery quality and its implications for behavioral biometric security.

PubMed

Ballard, Lucas; Lopresti, Daniel; Monrose, Fabian

2007-10-01

Biometric security is a topic of rapidly growing importance in the areas of user authentication and cryptographic key generation. In this paper, we describe our steps toward developing evaluation methodologies for behavioral biometrics that take into account threat models that have been largely ignored. We argue that the pervasive assumption that forgers are minimally motivated (or, even worse, naive) is too optimistic and even dangerous. Taking handwriting as a case in point, we show through a series of experiments that some users are significantly better forgers than others, that such forgers can be trained in a relatively straightforward fashion to pose an even greater threat, that certain users are easy targets for forgers, and that most humans are a relatively poor judge of handwriting authenticity, and hence, their unaided instincts cannot be trusted. Additionally, to overcome current labor-intensive hurdles in performing more accurate assessments of system security, we present a generative attack model based on concatenative synthesis that can provide a rapid indication of the security afforded by the system. We show that our generative attacks match or exceed the effectiveness of forgeries rendered by the skilled humans we have encountered.

Harnessing the power of conversations with virtual humans to change health behaviors.

PubMed

Albright, Glenn; Adam, Cyrille; Serri, Deborah; Bleeker, Seth; Goldman, Ron

2016-01-01

Skillful, collaborative conversations are powerful tools to improve physical and mental health. Whether you are a parent talking with your child about the dangers of substance abuse, an educator concerned about a student's signs of psychological distress, a veteran worried about a buddy who is contemplating suicide, or a healthcare professional wanting to better engage patients to increase treatment compliance, having the skill, confidence and motivation to engage in conversations can truly transform the health and well-being of those you interact with. Kognito develops role-play simulations that prepare individuals to effectively lead real-life conversations that measurably improve social, emotional, and physical health. The behavior change model that drives the simulations draws upon components of game mechanics, virtual human simulation technology and integrates evidence-based instructional design components as well as principles of social-cognitive theory and neuroscience such as motivational interviewing, emotional regulation, empathy and mindfulness. In the simulations, users or enter a risk-free practice environment and engage in a conversation with intelligent, fully animated, and emotionally responsive virtual characters that model human behavior. It is in practicing these conversations, and receiving feedback from a virtual coach, that users learn to better lead conversations in real life. Numerous longitudinal studies have shown that users who complete Kognito simulations demonstrate statistically significant and sustained increases in attitudinal variables that predict behavior change including preparedness, likelihood, and self-efficacy to better manage conversations. Pending the target population, each online or mobile simulation resulted in desired behavior changes ranging from increased referrals of students, patients or veterans in psychological distress to mental health support services, or increasing physician patient-centered communication or patient self-confidence and active involved in the decision-making processes. These simulations have demonstrated a capability to address major health and public health concerns where effective conversations are necessary to bring about changes in attitudes and behaviors.

Harnessing the power of conversations with virtual humans to change health behaviors

PubMed Central

Adam, Cyrille; Serri, Deborah; Bleeker, Seth; Goldman, Ron

2016-01-01

Skillful, collaborative conversations are powerful tools to improve physical and mental health. Whether you are a parent talking with your child about the dangers of substance abuse, an educator concerned about a student’s signs of psychological distress, a veteran worried about a buddy who is contemplating suicide, or a healthcare professional wanting to better engage patients to increase treatment compliance, having the skill, confidence and motivation to engage in conversations can truly transform the health and well-being of those you interact with. Kognito develops role-play simulations that prepare individuals to effectively lead real-life conversations that measurably improve social, emotional, and physical health. The behavior change model that drives the simulations draws upon components of game mechanics, virtual human simulation technology and integrates evidence-based instructional design components as well as principles of social-cognitive theory and neuroscience such as motivational interviewing, emotional regulation, empathy and mindfulness. In the simulations, users or enter a risk-free practice environment and engage in a conversation with intelligent, fully animated, and emotionally responsive virtual characters that model human behavior. It is in practicing these conversations, and receiving feedback from a virtual coach, that users learn to better lead conversations in real life. Numerous longitudinal studies have shown that users who complete Kognito simulations demonstrate statistically significant and sustained increases in attitudinal variables that predict behavior change including preparedness, likelihood, and self-efficacy to better manage conversations. Pending the target population, each online or mobile simulation resulted in desired behavior changes ranging from increased referrals of students, patients or veterans in psychological distress to mental health support services, or increasing physician patient-centered communication or patient self-confidence and active involved in the decision-making processes. These simulations have demonstrated a capability to address major health and public health concerns where effective conversations are necessary to bring about changes in attitudes and behaviors. PMID:28293614

Persuasive user experiences of a health Behavior Change Support System: A 12-month study for prevention of metabolic syndrome.

PubMed

Karppinen, Pasi; Oinas-Kukkonen, Harri; Alahäivälä, Tuomas; Jokelainen, Terhi; Keränen, Anna-Maria; Salonurmi, Tuire; Savolainen, Markku

2016-12-01

Obesity has become a severe health problem in the world. Even a moderate 5% weight loss can significantly reduce the prevalence of metabolic syndrome, which can be vital for preventing comorbidities caused by the obesity. Health Behavior Change Support Systems (hBCSS) emphasize an autogenous approach, where an individual uses the system to influence one's own attitude or behavior to achieve his or her own goal. Regardless of promising results, such health interventions technology has often been considered merely as a tool for delivering content that has no effect or value of its own. More research on actual system features is required. The objective of this study is to describe how users perceive persuasive software features designed and implemented into a support system. The research medium in this study is a web-based information system designed as a lifestyle intervention for participants who are at risk of developing a metabolic syndrome or who are already suffering from it. The system was designed closely following the principles of the Persuasive Systems Design (PSD) model and the Behavior Change Support Systems (BCSS) framework. A total of 43 system users were interviewed for this study during and after a 52 week intervention period. In addition, the system's login data and subjects' Body Mass Index (BMI) measures were used to interpret the results. This study explains in detail how the users perceived using the system and its persuasive features. Self-monitoring, reminders, and tunneling were perceived as especially beneficial persuasive features. The need for social support appeared to grow along the duration of the intervention. Unobtrusiveness was found to be very important in all stages of the intervention rather than only at the beginning. Persuasive software features have power to affect individuals' health behaviors. Through their systematicity the PSD model and the BCSS framework provide effective support for the design and development of technological health interventions. Designers of such systems may choose, for instance, to implement more self-monitoring tools to help individuals to adjust their personal goals with the system's offerings better. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Numerical analysis of strain localization for transversely isotropic model with non-coaxial flow rule

NASA Astrophysics Data System (ADS)

Wei, Ding; Cong-cong, Yu; Chen-hui, Wu; Zheng-yi, Shu

2018-03-01

To analyse the strain localization behavior of geomaterials, the forward Euler schemes and the tangent modulus matrix are formulated based on the transversely isotropic yield criterion with non-coaxial flow rule developed by Lade, the program code is implemented based on the user subroutine (UMAT) of ABAQUS. The influence of the material principal direction on the strain localization and the bearing capacity of the structure are investigated and analyzed. Numerical results show the validity and performance of the proposed model in simulating the strain localization behavior of geostructures.

Testing a Model of Self-Management of Fluid Intake in Community-Residing Long-Term Indwelling Urinary Catheter Users

PubMed Central

Wilde, Mary H.; Crean, Hugh F.; McMahon, James M.; McDonald, Margaret V.; Tang, Wan; Brasch, Judith; Fairbanks, Eileen; Shah, Shivani; Zhang, Feng

2015-01-01

Background Urinary tract infection and blockage are serious and recurrent challenges for people with long-term indwelling catheters, and these catheter problems cause worry and anxiety when they disrupt normal daily activities. Objectives The goal was to determine whether urinary catheter-related self-management behaviors focusing on fluid intake would mediate fluid intake related self-efficacy toward decreasing catheter-associated urinary tract infection (CAUTI) and/or catheter blockage. Method The sample involved data collected from 180 adult community-living, long-term indwelling urinary catheter users. The authors tested a model of fluid intake self-management (F-SMG) related to fluid intake self-efficacy (F-SE) for key outcomes of CAUTI and blockage. To account for the large number of zeros in both outcomes, a zero inflated negative binomial (ZINB) structural equation model was tested. Results Structurally, F-SE was positively associated with F-SMG, suggesting that higher F-SE predicts more (higher) F-SMG; however, F-SMG was not associated with either the frequency of CAUTI’s or the presence or absence of CAUTI. F-SE was positively related to F-SMG and F-SMG predicted less frequency of catheter blockage, but neither F-SE nor F-SMG predicted the presence or absence of blockage. Discussion Further research is needed to better understand determinants of CAUTI in long-term catheter users and factors which might influence or prevent its occurrence. Increased confidence (self-efficacy) and self-management behaviors to promote fluid intake could be of value in long-term urinary catheter users to decrease catheter blockage. PMID:26938358

From innovation to standard practice: Developing and disseminating behavioral procedures

PubMed Central

Paine, Stan C.; Bellamy, G. Thomas

1982-01-01

This paper proposes a three-stage continuum for discussing the development and dissemination of behavioral technology. At the level of behavioral techniques, researchers need only establish a functional relationship between technologically defined intervention procedures and socially significant target behaviors. Dissemination is conducted for informational purposes only, and the purposes and details surrounding subsequent use of the technique are left to the discretion of the user. At the level of behavioral demonstration, a collection of socially acceptable intervention procedures is refined and standardized and must be shown to produce behavior changes across a number of subjects. Here dissemination is conducted, in large part, to generate support for provision of services. At the level of behavioral models, procedural descriptions must be useroriented. Additionally, model effects must be obtainable by agents not associated with their development and must compare favorably with other treatment or service alternatives. The purpose of dissemination at this level is to obtain adoptions and replications of the model. Details of development and dissemination of behavioral technology at each of these three levels are discussed. PMID:22478555

A comparison of physician pre-adoption and adoption views on electronic health records in Canadian medical practices.

PubMed

Archer, Norm; Cocosila, Mihail

2011-08-12

There is a major campaign involving large expenditures of public money to increase the adoption rate of electronic health record (EHR) systems in Canada. To maximize the chances of success in this effort, physician views on EHRs must be addressed, since user perceptions are key to successful implementation of technology innovations. We propose a theoretical model comprising behavioral factors either favoring or against EHR adoption and use in Canadian medical practices, from the physicians' point of view. EHR perceptions of physicians already using EHR systems are compared with those not using one, through the lens of this model. We conducted an online cross-sectional survey in both English and French among medical practitioners across Canada. Data were collected both from physicians using EHRs and those not using EHRs, and analyzed with structural equation modeling (SEM) techniques. We collected 119 responses from EHR users and 100 from nonusers, resulting in 2 valid samples of 102 and 83 participants, respectively. The theoretical adoption model explained 55.8% of the variance in behavioral intention to continue using EHRs for physicians already using them, and 66.8% of the variance in nonuser intention to adopt such systems. Perception of ease of use was found to be the strongest motivator for EHR users (total effect .525), while perceptions of usefulness and of ease of use were the key determinants for nonusers (total effect .538 and .519, respectively) to adopt the system. Users see perceived overall risk associated with EHR adoption as a major obstacle (total effect -.371), while nonusers perceive risk only as a weak indirect demotivator. Of the 13 paths of the SEM model, 5 showed significant differences between the 2 samples (at the .05 level): general doubts about using the system (P = .02), the necessity for the system to be relevant for their job (P < .001), and the necessity for the system to be useful (P = .049) are more important for EHR nonusers than for users, while perceptions of overall obstacles to adoption (P = .03) and system ease of use (P = .042) count more for EHR users than for nonusers. Relatively few differences in perceptions about EHR system adoption and use exist between physicians already using such systems and those not yet using the systems. To maximize the chances of success for new EHR implementations from a behavioral point of view, general doubts about the rationale for such systems must be mitigated through improving design, stressing how EHRs are relevant to physician jobs, and providing substantiating evidence that EHRs are easier to use and more effective than nonusers might expect.

Patterns, Perception and Behavior of Electronic Nicotine Delivery Systems Use and Multiple Product Use Among Young Adults.

PubMed

Martinasek, Mary P; Bowersock, Amy; Wheldon, Christopher W

2018-03-27

Electronic nicotine delivery systems (ENDS) are battery-operated devices used to inhale vaporized or aerosolized nicotine. There is increasing research uncovering negative health effects of these devices. Less is known about the social and behavioral aspects among college students. This cross-sectional study was conducted at a mid-sized private university in Florida. The survey was sent via e-mail to the student body of undergraduates. A final sample size of 989 students was analyzed to understand demographic differences between users and nonusers, initiation factors, and influencers, as well as multiple product behaviors. Approximately 51.4% ( n = 508) of participants reported ever using an ENDS and other tobacco consumption. Males were significantly more likely to be users of ENDS. Polytobacco use, or the use of multiple tobacco products, was also more common among participants who have tried ENDS ( P < .001). Perceptions of harm of both the primary and secondary vapor were considered to be less than that of conventional cigarettes. Peers were the primary influencer for initial use. A 4-class latent variable model differentiated between usage patterns characterized as abstainers (70%), hookah users only (14%), ENDS only (11%), and polytobacco users (4%). ENDS are not commonly used as a quit tool among college students, but rather as a secondary source of nicotine, most commonly in current smokers. Copyright © 2018 by Daedalus Enterprises.

Does user satisfaction relate to adoption behavior?: an exploratory analysis using CPRS implementation.

PubMed Central

Weir, C. R.; Crockett, R.; Gohlinghorst, S.; McCarthy, C.

2000-01-01

User satisfaction is commonly assessed in evaluations of information systems as a proxy for user adoption. However few studies actually report directly assessing the relationship between the two constructs. In this study the relationship between four user satisfaction measures and five adoption behaviors were explored in the context of the implementation of the Veteran's Health Administration Computerized Patient Record System 1.0. Findings suggest that the relationship is modest and depends on the measurement system used. Specifically, direct reports of affect and judgements of specific task efficacy related to behavior more often than usability and a general user satisfaction instrument. PMID:11080017

Can agent based models effectively reduce fisheries management implementation uncertainty?

NASA Astrophysics Data System (ADS)

Drexler, M.

2016-02-01

Uncertainty is an inherent feature of fisheries management. Implementation uncertainty remains a challenge to quantify often due to unintended responses of users to management interventions. This problem will continue to plague both single species and ecosystem based fisheries management advice unless the mechanisms driving these behaviors are properly understood. Equilibrium models, where each actor in the system is treated as uniform and predictable, are not well suited to forecast the unintended behaviors of individual fishers. Alternatively, agent based models (AMBs) can simulate the behaviors of each individual actor driven by differing incentives and constraints. This study evaluated the feasibility of using AMBs to capture macro scale behaviors of the US West Coast Groundfish fleet. Agent behavior was specified at the vessel level. Agents made daily fishing decisions using knowledge of their own cost structure, catch history, and the histories of catch and quota markets. By adding only a relatively small number of incentives, the model was able to reproduce highly realistic macro patterns of expected outcomes in response to management policies (catch restrictions, MPAs, ITQs) while preserving vessel heterogeneity. These simulations indicate that agent based modeling approaches hold much promise for simulating fisher behaviors and reducing implementation uncertainty. Additional processes affecting behavior, informed by surveys, are continually being added to the fisher behavior model. Further coupling of the fisher behavior model to a spatial ecosystem model will provide a fully integrated social, ecological, and economic model capable of performing management strategy evaluations to properly consider implementation uncertainty in fisheries management.

Using the Item Response Theory (IRT) for Educational Evaluation through Games

ERIC Educational Resources Information Center

Euzébio Batista, Marcelo Henrique; Victória Barbosa, Jorge Luis; da Rosa Tavares, João Elison; Hackenhaar, Jonathan Luis

2013-01-01

This article shows the application of Item Response Theory (IRT) for educational evaluation using games. The article proposes a computational model to create user profiles, called Psychometric Profile Generator (PPG). PPG uses the IRT mathematical model for exploring the levels of skills and behaviors in the form of items and/or stimuli. The model…

User Acceptance of YouTube for Procedural Learning: An Extension of the Technology Acceptance Model

ERIC Educational Resources Information Center

Lee, Doo Young; Lehto, Mark R.

2013-01-01

The present study was framed using the Technology Acceptance Model (TAM) to identify determinants affecting behavioral intention to use YouTube. Most importantly, this research emphasizes the motives for using YouTube, which is notable given its extrinsic task goal of being used for procedural learning tasks. Our conceptual framework included two…

Tuning Primary Learning Style for Children with Secondary Behavioral Patterns

ERIC Educational Resources Information Center

Mosharraf, Maedeh

2016-01-01

Personalization is one of the most expected features in the current educational systems. User modeling is supposed to be the first stage of this process, which may incorporate learning style as an important part of the model. Learning style, which is a non-stable characteristic in the case of children, differentiates students in learning…

Integrating pro-environmental behavior with transportation network modeling: User and system level strategies, implementation, and evaluation

NASA Astrophysics Data System (ADS)

Aziz, H. M. Abdul

Personal transport is a leading contributor to fossil fuel consumption and greenhouse (GHG) emissions in the U.S. The U.S. Energy Information Administration (EIA) reports that light-duty vehicles (LDV) are responsible for 61% of all transportation related energy consumption in 2012, which is equivalent to 8.4 million barrels of oil (fossil fuel) per day. The carbon content in fossil fuels is the primary source of GHG emissions that links to the challenge associated with climate change. Evidently, it is high time to develop actionable and innovative strategies to reduce fuel consumption and GHG emissions from the road transportation networks. This dissertation integrates the broader goal of minimizing energy and emissions into the transportation planning process using novel systems modeling approaches. This research aims to find, investigate, and evaluate strategies that minimize carbon-based fuel consumption and emissions for a transportation network. We propose user and system level strategies that can influence travel decisions and can reinforce pro-environmental attitudes of road users. Further, we develop strategies that system operators can implement to optimize traffic operations with emissions minimization goal. To complete the framework we develop an integrated traffic-emissions (EPA-MOVES) simulation framework that can assess the effectiveness of the strategies with computational efficiency and reasonable accuracy. The dissertation begins with exploring the trade-off between emissions and travel time in context of daily travel decisions and its heterogeneous nature. Data are collected from a web-based survey and the trade-off values indicating the average additional travel minutes a person is willing to consider for reducing a lb. of GHG emissions are estimated from random parameter models. Results indicate that different trade-off values for male and female groups. Further, participants from high-income households are found to have higher trade-off values compared with other groups. Next, we propose personal mobility carbon allowance (PMCA) scheme to reduce emissions from personal travel. PMCA is a market-based scheme that allocates carbon credits to users at no cost based on the emissions reduction goal of the system. Users can spend carbon credits for travel and a market place exists where users can buy or sell credits. This dissertation addresses two primary dimensions: the change in travel behavior of the users and the impact at network level in terms of travel time and emissions when PMCA is implemented. To understand this process, a real-time experimental game tool is developed where players are asked to make travel decisions within the carbon budget set by PMCA and they are allowed to trade carbon credits in a market modeled as a double auction game. Random parameter models are estimated to examine the impact of PMCA on short-term travel decisions. Further, to assess the impact at system level, a multi-class dynamic user equilibrium model is formulated that captures the travel behavior under PMCA scheme. The equivalent variational inequality problem is solved using projection method. Results indicate that PMCA scheme is able to reduce GHG emissions from transportation networks. Individuals with high value of travel time (VOTT) are less sensitive to PMCA scheme in context of work trips. High and medium income users are more likely to have non-work trips with lower carbon cost (higher travel time) to save carbon credits for work trips. Next, we focus on the strategies from the perspectives of system operators in transportation networks. Learning based signal control schemes are developed that can reduce emissions from signalized urban networks. The algorithms are implemented and tested in VISSIM micro simulator. Finally, an integrated emissions-traffic simulator framework is outlined that can be used to evaluate the effectiveness of the strategies. The integrated framework uses MOVES2010b as the emissions simulator. To estimate the emissions efficiently we propose a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the link driving schedules for MOVES2010b. Test results using the data from a five-intersection corridor show that HC-DTW technique can significantly reduce emissions estimation time without compromising the accuracy. The benefits are found to be most significant when the level of congestion variation is high. In addition to finding novel strategies for reducing emissions from transportation networks, this dissertation has broader impacts on behavior based energy policy design and transportation network modeling research. The trade-off values can be a useful indicator to identify which policies are most effective to reinforce pro-environmental travel choices. For instance, the model can estimate the distribution of trade-off between emissions and travel time, and provide insights on the effectiveness of policies for New York City if we are able to collect data to construct a representative sample. The probability of route choice decisions vary across population groups and trip contexts. The probability as a function of travel and demographic attributes can be used as behavior rules for agents in an agent-based traffic simulation. Finally, the dynamic user equilibrium based network model provides a general framework for energy policies such carbon tax, tradable permit, and emissions credits system.

A game theory-based trust measurement model for social networks.

PubMed

Wang, Yingjie; Cai, Zhipeng; Yin, Guisheng; Gao, Yang; Tong, Xiangrong; Han, Qilong

2016-01-01

In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent. Modeling trust needs to consider interaction history, recommendation, user behaviors and so on. Therefore, modeling trust is an important focus for online social networks. We propose a game theory-based trust measurement model for social networks. The trust degree is calculated from three aspects, service reliability, feedback effectiveness, recommendation credibility, to get more accurate result. In addition, to alleviate the free-riding problem, we propose a game theory-based punishment mechanism for specific trust and global trust, respectively. We prove that the proposed trust measurement model is effective. The free-riding problem can be resolved effectively through adding the proposed punishment mechanism.

Persuasive Conversational Agent with Persuasion Tactics

NASA Astrophysics Data System (ADS)

Narita, Tatsuya; Kitamura, Yasuhiko

Persuasive conversational agents persuade people to change their attitudes or behaviors through conversation, and are expected to be applied as virtual sales clerks in e-shopping sites. As an approach to create such an agent, we have developed a learning agent with the Wizard of Oz method in which a person called Wizard talks to the user pretending to be the agent. The agent observes the conversations between the Wizard and the user, and learns how to persuade people. In this method, the Wizard has to reply to most of the user's inputs at the beginning, but the burden gradually falls because the agent learns how to reply as the conversation model grows.

Research and application of knowledge resources network for product innovation.

PubMed

Li, Chuan; Li, Wen-qiang; Li, Yan; Na, Hui-zhen; Shi, Qian

2015-01-01

In order to enhance the capabilities of knowledge service in product innovation design service platform, a method of acquiring knowledge resources supporting for product innovation from the Internet and providing knowledge active push is proposed. Through knowledge modeling for product innovation based on ontology, the integrated architecture of knowledge resources network is put forward. The technology for the acquisition of network knowledge resources based on focused crawler and web services is studied. Knowledge active push is provided for users by user behavior analysis and knowledge evaluation in order to improve users' enthusiasm for participation in platform. Finally, an application example is illustrated to prove the effectiveness of the method.

Determinants of user acceptance of a specific social platform for older adults: An empirical examination of user interface characteristics and behavioral intention.

PubMed

Tsai, Tsai-Hsuan; Chang, Hsien-Tsung; Chen, Yan-Jiun; Chang, Yung-Sheng

2017-01-01

The use of the Internet and social applications has many benefits for the elderly, but numerous investigations have shown that the elderly do not perceive online social networks as a friendly social environment. Therefore, TreeIt, a social application specifically designed for the elderly, was developed for this study. In the TreeIt application, seven mechanisms promoting social interaction were designed to allow older adults to use social networking sites (SNSs) to increase social connection, maintain the intensity of social connections and strengthen social experience. This study's main objective was to investigate how user interface design affects older people's intention and attitude related to using SNSs. Fourteen user interface evaluation heuristics proposed by Zhang et al. were adopted as the criteria to assess user interface usability and further grouped into three categories: system support, user interface design and navigation. The technology acceptance model was adopted to assess older people's intention and attitude related to using SNSs. One hundred and one elderly persons were enrolled in this study as subjects, and the results showed that all of the hypotheses proposed in this study were valid: system support and perceived usefulness had a significant effect on behavioral intention; user interface design and perceived ease of use were positively correlated with perceived usefulness; and navigation exerted an influence on perceived ease of use. The results of this study are valuable for the future development of social applications for the elderly.

Interrelationships between Marijuana Demand and Discounting of Delayed Rewards: Convergence in Behavioral Economic Methods

PubMed Central

Aston, Elizabeth R.; Metrik, Jane; Amlung, Michael; Kahler, Christopher W.; MacKillop, James

2016-01-01

Background Distinct behavioral economic domains, including high perceived drug value (demand) and delay discounting (DD), have been implicated in the initiation of drug use and the progression to dependence. However, it is unclear whether frequent marijuana users conform to a “reinforcer pathology” addiction model wherein marijuana demand and DD jointly increase risk for problematic marijuana use and cannabis dependence (CD). Methods Participants (n=88, 34% female, 14% cannabis dependent) completed a marijuana purchase task at baseline. A delay discounting task was completed following placebo marijuana cigarette (0% THC) administration during a separate experimental session. Results Marijuana demand and DD were quantified using area under the curve (AUC). In multiple regression models, demand uniquely predicted frequency of marijuana use while DD did not. In contrast, DD uniquely predicted CD symptom count while demand did not. There were no significant interactions between demand and DD in either model. Conclusions These findings suggest that frequent marijuana users exhibit key constituents of the reinforcer pathology model: high marijuana demand and steep discounting of delayed rewards. However, demand and DD appear to be independent rather than synergistic risk factors for elevated marijuana use and risk for progression to CD. Findings also provide support for using AUC as a singular marijuana demand metric, particularly when also examining other behavioral economic constructs that apply similar statistical approaches, such as DD, to support analytic methodological convergence. PMID:27810657

Context-based user grouping for multi-casting in heterogeneous radio networks

NASA Astrophysics Data System (ADS)

Mannweiler, C.; Klein, A.; Schneider, J.; Schotten, H. D.

2011-08-01

Along with the rise of sophisticated smartphones and smart spaces, the availability of both static and dynamic context information has steadily been increasing in recent years. Due to the popularity of social networks, these data are complemented by profile information about individual users. Making use of this information by classifying users in wireless networks enables targeted content and advertisement delivery as well as optimizing network resources, in particular bandwidth utilization, by facilitating group-based multi-casting. In this paper, we present the design and implementation of a web service for advanced user classification based on user, network, and environmental context information. The service employs simple and advanced clustering algorithms for forming classes of users. Available service functionalities include group formation, context-aware adaptation, and deletion as well as the exposure of group characteristics. Moreover, the results of a performance evaluation, where the service has been integrated in a simulator modeling user behavior in heterogeneous wireless systems, are presented.

Selection, Evaluation, and Rating of Compact Heat Exchangers v. 1.006

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

Carlson, Matthew D.

2016-11-09

SEARCH determines and optimizes the design of a compact heat exchanger for specified process conditions. The user specifies process boundary conditions including the fluid state and flow rate and SEARCH will determine the optimum flow arrangement, channel geometry, and mechanical design for the unit. Fluids are modeled using NIST Refprop or tabulated values. A variety of thermal-hydraulic correlations are available including user-defined equations to accurately capture the heat transfer and pressure drop behavior of the process flows.

The role of price and enforcement in water allocation: insights from Game Theory

NASA Astrophysics Data System (ADS)

Souza Filho, F.; Lall, U.; Porto, R.

2007-12-01

As many countries are moving towards water sector reforms, practical issues of how water management institutions can better effect allocation, regulation and enforcement of water rights have emerged. The uncertainty associated with water that is available at a particular diversion point becomes a parameter that is likely to influence the behavior of water users as to their application for water licenses, as well as their willingness to pay for licensed use. The ability of a water agency to reduce this uncertainty through effective water rights enforcement is related to the fiscal ability of the agency to sustain the enforcement effort. In this paper, this interplay across the users and the agency is explored, considering the hydraulic structure or sequence of water use, and parameters that define the users and the agency's economics. The potential for free rider behavior by the users, as well as their proposals for licensed use are derived conditional on this setting. The analyses presented are developed in the framework of the theory of "Law and Economics", with user interactions modeled as a game theoretic enterprise. The state of Ceara, Brazil is used loosely as an example setting, with parameter values for the experiments indexed to be approximately those relevant for current decisions. The potential for using the ideas in participatory decision making is discussed.

Human-Robot Cooperation with Commands Embedded in Actions

NASA Astrophysics Data System (ADS)

Kobayashi, Kazuki; Yamada, Seiji

In this paper, we first propose a novel interaction model, CEA (Commands Embedded in Actions). It can explain the way how some existing systems reduce the work-load of their user. We next extend the CEA and build ECEA (Extended CEA) model. The ECEA enables robots to achieve more complicated tasks. On this extension, we employ ACS (Action Coding System) which can describe segmented human acts and clarifies the relationship between user's actions and robot's actions in a task. The ACS utilizes the CEA's strong point which enables a user to send a command to a robot by his/her natural action for the task. The instance of the ECEA led by using the ACS is a temporal extension which has the user keep a final state of a previous his/her action. We apply the temporal extension of the ECEA for a sweeping task. The high-level task, a cooperative task between the user and the robot can be realized. The robot with simple reactive behavior can sweep the region of under an object when the user picks up the object. In addition, we measure user's cognitive loads on the ECEA and a traditional method, DCM (Direct Commanding Method) in the sweeping task, and compare between them. The results show that the ECEA has a lower cognitive load than the DCM significantly.

The use of mental models in chemical risk protection: developing a generic workplace methodology.

PubMed

Cox, Patrick; Niewöhmer, Jörg; Pidgeon, Nick; Gerrard, Simon; Fischhoff, Baruch; Riley, Donna

2003-04-01

We adopted a comparative approach to evaluate and extend a generic methodology to analyze the different sets of beliefs held about chemical hazards in the workplace. Our study mapped existing knowledge structures about the risks associated with the use of perchloroethylene and rosin-based solder flux in differing workplaces. "Influence diagrams" were used to represent beliefs held by chemical experts; "user models" were developed from data elicited from open-ended interviews with the workplace users of the chemicals. The juxtaposition of expert and user understandings of chemical risks enabled us to identify knowledge gaps and misunderstandings and to reinforce appropriate sets of safety beliefs and behavior relevant to chemical risk communications. By designing safety information to be more relevant to the workplace context of users, we believe that employers and employees may gain improved knowledge about chemical hazards in the workplace, such that better chemical risk management, self-protection, and informed decision making develop over time.

Analyzing Medical Image Search Behavior: Semantics and Prediction of Query Results.

PubMed

De-Arteaga, Maria; Eggel, Ivan; Kahn, Charles E; Müller, Henning

2015-10-01

Log files of information retrieval systems that record user behavior have been used to improve the outcomes of retrieval systems, understand user behavior, and predict events. In this article, a log file of the ARRS GoldMiner search engine containing 222,005 consecutive queries is analyzed. Time stamps are available for each query, as well as masked IP addresses, which enables to identify queries from the same person. This article describes the ways in which physicians (or Internet searchers interested in medical images) search and proposes potential improvements by suggesting query modifications. For example, many queries contain only few terms and therefore are not specific; others contain spelling mistakes or non-medical terms that likely lead to poor or empty results. One of the goals of this report is to predict the number of results a query will have since such a model allows search engines to automatically propose query modifications in order to avoid result lists that are empty or too large. This prediction is made based on characteristics of the query terms themselves. Prediction of empty results has an accuracy above 88%, and thus can be used to automatically modify the query to avoid empty result sets for a user. The semantic analysis and data of reformulations done by users in the past can aid the development of better search systems, particularly to improve results for novice users. Therefore, this paper gives important ideas to better understand how people search and how to use this knowledge to improve the performance of specialized medical search engines.

Differentiating Characteristics of Juvenile Methamphetamine Users

ERIC Educational Resources Information Center

Fass, Daniel; Calhoun, Georgia B.; Glaser, Brian A.; Yanosky, Daniel J., II

2009-01-01

The authors investigated the differences in characteristics and risk behaviors endorsed by detained adolescent methamphetamine users and compared them with other drug users. Subjects completed the Millon Adolescent Clinical Inventory and a questionnaire in which sociodemographics and behavioral information were explored and compared. Multivariate…

Impact of heterogeneity and socioeconomic factors on individual behavior in decentralized sharing ecosystems.

PubMed

Gavaldà-Miralles, Arnau; Choffnes, David R; Otto, John S; Sánchez, Mario A; Bustamante, Fabián E; Amaral, Luís A N; Duch, Jordi; Guimerà, Roger

2014-10-28

Tens of millions of individuals around the world use decentralized content distribution systems, a fact of growing social, economic, and technological importance. These sharing systems are poorly understood because, unlike in other technosocial systems, it is difficult to gather large-scale data about user behavior. Here, we investigate user activity patterns and the socioeconomic factors that could explain the behavior. Our analysis reveals that (i) the ecosystem is heterogeneous at several levels: content types are heterogeneous, users specialize in a few content types, and countries are heterogeneous in user profiles; and (ii) there is a strong correlation between socioeconomic indicators of a country and users behavior. Our findings open a research area on the dynamics of decentralized sharing ecosystems and the socioeconomic factors affecting them, and may have implications for the design of algorithms and for policymaking.

Assessing the Moderating Effect of the End User in Consumer Behavior: The Acceptance of Technological Implants to Increase Innate Human Capacities.

PubMed

Pelegrín-Borondo, Jorge; Reinares-Lara, Eva; Olarte-Pascual, Cristina; Garcia-Sierra, Marta

2016-01-01

Today, technological implants are being developed to increase innate human capacities, such as memory or calculation speed, and to endow us with new ones, such as the remote control of machines. This study's aim was two-fold: first, to introduce a Cognitive-Affective-Normative (CAN) model of technology acceptance to explain the intention to use this technology in the field of consumer behavior; and second, to analyze the differences in the intention to use it based on whether the intended implant recipient is oneself or one's child (i.e., the moderating effect of the end user). A multi-group analysis was performed to compare the results between the two groups: implant "for me" (Group 1) and implant "for my child" (Group 2). The model largely explains the intention to use the insideable technology for the specified groups [variance explained (R (2)) of over 0.70 in both cases]. The most important variables were found to be "positive emotions" and (positive) "subjective norm." This underscores the need to broaden the range of factors considered to be decisive in technology acceptance to include variables related to consumers' emotions. Moreover, statistically significant differences were found between the "for me" and "for my child" models for "perceived ease of use (PEU)" and "subjective norm." These findings confirm the moderating effect of the end user on new insideable technology acceptance.

Multitasking Information Seeking and Searching Processes.

ERIC Educational Resources Information Center

Spink, Amanda; Ozmutlu, H. Cenk; Ozmutlu, Seda

2002-01-01

Presents findings from four studies of the prevalence of multitasking information seeking and searching by Web (via the Excite search engine), information retrieval system (mediated online database searching), and academic library users. Highlights include human information coordinating behavior (HICB); and implications for models of information…

Comparison of substance use and risky sexual behavior among a diverse sample of urban, HIV-positive men who have sex with men

PubMed Central

Hatfield, Laura A.; Horvath, Keith J.; Jacoby, Scott M.; Rosser, B. R. Simon

2012-01-01

Aims To measure substance use across racial and ethnic subgroups of HIV-positive men who have sex with men (MSM), model associations between drug use and unsafe sex, and characterize users of the substances most strongly associated with risky sexual behavior. Design Cross-sectional survey at the pre-intervention time point of the Positive Connections behavioral intervention trial. Setting HIV-positive men of color who have sex with men living in six US cities. Participants 675 trial participants. Measurements Self-reported drug and alcohol use and sexual behaviors. Findings We found high prevalence of substance use in this sample, with differences across racial and ethnic groups. Compared to Hispanic, African America, and men of other or mixed races/ethnicities, Caucasian men were most likely to report use of stimulants (30%), methamphetamines (27%), and amyl nitrite inhalants (“poppers”, 46%) with anal sex. African American men reported crack/cocaine use in the highest proportion (38%) among the four groups. While many drugs were individually associated with serodiscordant unprotected anal intercourse (SDUAI), only alcohol quantity and poppers with sex were retained in a multivariate model. More frequent poppers use was associated with more reported instances of SDUAI, adjusted for increased anal sex. Men who used poppers were more likely to be white, have completed more education, and have slightly higher income than non-users. Poppers users also reported lower peer norms and self-efficacy for condom use. In a multiple logistic regression model including these psychosocial factors, only poppers use (vs non-use OR = 2.46, CI: 1.55, 3.94) and condom self-efficacy (1 sd increase on scale OR = .58, CI: .46, .73) were significantly associated with SDUAI. Conclusion These results, from a large sample of HIV-positive MSM of color, highlight the HIV transmission importance of drugs used specifically in conjunction with sex. PMID:20155589

Smoking behaviors and intentions among current e-cigarette users, cigarette smokers, and dual users: A national survey of U.S. high school seniors.

PubMed

McCabe, Sean Esteban; Veliz, Phil; McCabe, Vita V; Boyd, Carol J

2017-06-01

E-cigarette use among adolescents has increased significantly in recent years, but it remains unclear whether cigarette smoking behaviors and intentions for future cigarette smoking differ among current (i.e., 30-day) non-users, only e-cigarette users, only cigarette smokers, and dual users. A nationally representative sample of 4385 U.S. high school seniors were surveyed during the spring of their senior year via self-administered questionnaires in 2014. An estimated 9.6% of U.S. high school seniors reported current e-cigarette use only, 6.3% reported current cigarette smoking only, and 7.2% reported current dual use of e-cigarettes and cigarette smoking. There were no significant differences between current only cigarette smokers and dual users in the odds of early onset of cigarette smoking, daily cigarette smoking, intentions for future cigarette smoking, friends' cigarette smoking behaviors, attempts to quit cigarette smoking, or the inability to quit cigarette smoking. Adolescents who only used e-cigarettes had higher odds of intentions for future cigarette smoking in the next 5years (AOR=2.57, 95% CI: 1.21-5.24) than current non-users. Dual users and only cigarette smokers had higher odds of cigarette smoking behaviors and intentions for future cigarette smoking than non-users or only e-cigarette users. Adolescents who engage in current dual use have cigarette smoking behaviors and intentions for future cigarette smoking that more closely resemble cigarette smokers than e-cigarette users. Adolescents who only use e-cigarettes have higher intentions to engage in future cigarette smoking relative to their peers who do not engage in e-cigarette use or cigarette smoking. Copyright © 2017 Elsevier Inc. All rights reserved.

The dynamics of injection drug users' personal networks and HIV risk behaviors.

PubMed

Costenbader, Elizabeth C; Astone, Nan M; Latkin, Carl A

2006-07-01

While studies of the social networks of injection drug users (IDUs) have provided insight into how the structures of interpersonal relationships among IDUs affect HIV risk behaviors, the majority of these studies have been cross-sectional. The present study examined the dynamics of IDUs' social networks and HIV risk behaviors over time. Using data from a longitudinal HIV-intervention study conducted in Baltimore, MD, this study assessed changes in the composition of the personal networks of 409 IDUs. We used a multi-nomial logistic regression analysis to assess the association between changes in network composition and simultaneous changes in levels of injection HIV risk behaviors. Using the regression parameters generated by the multi-nomial model, we estimated the predicted probability of being in each of four HIV risk behavior change groups. Compared to the base case, individuals who reported an entirely new set of drug-using network contacts at follow-up were more than three times as likely to be in the increasing risk group. In contrast, reporting all new non-drug-using contacts at follow-up increased the likelihood of being in the stable low-risk group by almost 50% and decreased the probability of being in the consistently high-risk group by more than 70%. The findings from this study show that, over and above IDUs' baseline characteristics, changes in their personal networks are associated with changes in individuals' risky injection behaviors. They also suggest that interventions aimed at reducing HIV risk among IDUs might benefit from increasing IDUs' social contacts with individuals who are not drug users.

Sculpting Mountains: Interactive Terrain Modeling Based on Subsurface Geology.

PubMed

Cordonnier, Guillaume; Cani, Marie-Paule; Benes, Bedrich; Braun, Jean; Galin, Eric

2018-05-01

Most mountain ranges are formed by the compression and folding of colliding tectonic plates. Subduction of one plate causes large-scale asymmetry while their layered composition (or stratigraphy) explains the multi-scale folded strata observed on real terrains. We introduce a novel interactive modeling technique to generate visually plausible, large scale terrains that capture these phenomena. Our method draws on both geological knowledge for consistency and on sculpting systems for user interaction. The user is provided hands-on control on the shape and motion of tectonic plates, represented using a new geologically-inspired model for the Earth crust. The model captures their volume preserving and complex folding behaviors under collision, causing mountains to grow. It generates a volumetric uplift map representing the growth rate of subsurface layers. Erosion and uplift movement are jointly simulated to generate the terrain. The stratigraphy allows us to render folded strata on eroded cliffs. We validated the usability of our sculpting interface through a user study, and compare the visual consistency of the earth crust model with geological simulation results and real terrains.

Complaining Behavior of Academic Library Users in South Korea

ERIC Educational Resources Information Center

Oh, Dong-Geun

2004-01-01

This study investigates the influences of the antecedent factors on the complaints and resulting behaviors of 582 university library users in South Korea. There were statistically significant relationships between personal norms and negative word of mouth and indirect voice behaviors, between service importance and negative word-of-mouth behavior,…

Perceptions of a Specific Family Communication Application among Grandparents and Grandchildren: An Extension of the Technology Acceptance Model.

PubMed

Tsai, Tsai-Hsuan; Chang, Hsien-Tsung; Ho, Yi-Lun

2016-01-01

Many studies have noted that the use of social networks sites (SNSs) can enhance social interaction among the elderly and that the motivation for the elderly to use SNSs is to keep in contact with remote friends and family or the younger generation. Memotree is designed to promote intergenerational family communication. The system incorporates the Family Tree design concept and provides family communication mechanisms based on the Family Communication Scale. In addition, the system optimizes hardware and interface use to conform to the specific needs of older and substantially younger individuals. Regarding the impact of variables on SNS with respect to the interaction of usability variables in the construction of a cross-generational communication platform, we adopted the TAM model and Chung et al.'s suggestions to promote user acceptance of the proposed Memotree system. A total of 39 grandchildren and 39 grandparents met the criteria and were included in the study. The elderly and young respondents revealed substantial willingness to use and/or satisfaction with using the Memotree system. Empirical results indicate that technology affordances and perceived ease of use have a positive impact on perceived usefulness, while perceived ease of use is affected by technology affordances. Internet self-efficacy and perceived usefulness have a positive impact on the user's behavioral intention toward the system. In addition, this study investigated age as a moderating variable in the model. The results indicate that grandchildren have a larger significant effect on the path between perceived usefulness and behavioral intention than grandparents. This study proposes a more complete framework for investigating the user's behavioral intention and provides a more appropriate explanation of related services for cross-generational interaction with SNS services.

A Statewide Profile of Frequent Users of School-Based Health Centers: Implications for Adolescent Health Care.

PubMed

Koenig, Kevin T; Ramos, Mary M; Fowler, Tara T; Oreskovich, Kristin; McGrath, Jane; Fairbrother, Gerry

2016-04-01

The purpose of this study is to describe patterns of care and service use among adolescent school-based health center (SBHC) users in New Mexico and contrast patterns and services between frequent and infrequent users. Medical claims/encounter data were analyzed from 59 SBHCs located in secondary schools in New Mexico during the 2011-2012 school year. We used Pearson's chi-square test to examine the differences between frequent (≥ 4 visits/year) and infrequent users in their patterns of SBHC care, and we conducted logistic regression to examine whether frequent use of the SBHC predicted receipt of behavioral, reproductive, and sexual health; checkup; or acute care services. Most of the 26,379 adolescent SBHC visits in New Mexico were for behavioral health (42.4%) and reproductive and sexual health (22.9%). Frequent users have greater odds of receiving a behavioral, reproductive, and sexual health; and acute care visit than infrequent users (p < .001). American Indians, in particular, have higher odds of receiving behavioral health and checkup visits, compared with other races/ethnicities (p < .001). SBHCs deliver core health care services to adolescents, including behavioral, reproductive, and checkup services, to high need populations. American-Indian youth, more than their peers, use SBHCs for behavioral health and checkups. © 2016, American School Health Association.

Effective Dual Method Contraceptive Use and HPV Vaccination Among U.S. Adolescent and Young Adult Females

PubMed Central

Vanderpool, Robin C.; Williams, Corrine M.; Klawitter, Amy R.; Eddens, Katherine

2016-01-01

Background Problem Behavior Theory posits that risky behaviors cluster in individuals, implying that protective behaviors may follow a similar pattern. The purpose of this study was to determine whether the protective behavior of effective dual method contraception use at first and most recent sexual intercourse is associated with HPV vaccination among adolescent and young adult females. Methods National Survey of Family Growth (2006–2010) data were used to examine the association between women’s contraception use during first and most recent sexual intercourse and HPV vaccination. Women aged 15 to 24 years (n = 1,820) served as the study sample. Findings At first and last sexual intercourse, effective dual method contraception use was reported by 15.3% and 16.8% women, respectively; 27.8% reported receiving at least one dose of the human papillomavirus (HPV) vaccine. Higher HPV vaccination rates were observed among dual method users at first and last sexual intercourse (36.4% and 48.2%, respectively). This trend was also observed across age groups (15–19 year olds vs. 20–24 year olds). In adjusted models, among all respondents, dual users at last sexual intercourse were significantly more likely to be vaccinated, whereas at first sexual intercourse only younger dual users were more likely to report HPV vaccination. Conclusions Findings suggest that the protective behavior of dual method contraceptive use at first and most recent sexual intercourse may serve as a predictor of another complementary health behavior, HPV vaccination, particularly among adolescent females. More research is needed to understand behavioral clustering to design related multi-focused women’s health interventions. PMID:25213746

Effective dual method contraceptive use and HPV vaccination among U.S. adolescent and young adult females.

PubMed

Vanderpool, Robin C; Williams, Corrine M; Klawitter, Amy R; Eddens, Katherine

2014-01-01

Problem Behavior Theory posits that risky behaviors cluster in individuals, implying that protective behaviors may follow a similar pattern. The purpose of this study was to determine whether the protective behavior of effective dual method contraception use at first and most recent sexual intercourse is associated with HPV vaccination among adolescent and young adult females. National Survey of Family Growth (2006-2010) data were used to examine the association between women's contraception use during first and most recent sexual intercourse and HPV vaccination. Women aged 15 to 24 years (n = 1,820) served as the study sample. At first and last sexual intercourse, effective dual method contraception use was reported by 15.3% and 16.8% women, respectively; 27.8% reported receiving at least one dose of the human papillomavirus (HPV) vaccine. Higher HPV vaccination rates were observed among dual method users at first and last sexual intercourse (36.4% and 48.2%, respectively). This trend was also observed across age groups (15-19 year olds vs. 20-24 year olds). In adjusted models, among all respondents, dual users at last sexual intercourse were significantly more likely to be vaccinated, whereas at first sexual intercourse only younger dual users were more likely to report HPV vaccination. Findings suggest that the protective behavior of dual method contraceptive use at first and most recent sexual intercourse may serve as a predictor of another complementary health behavior, HPV vaccination, particularly among adolescent females. More research is needed to understand behavioral clustering to design related multi-focused women's health interventions. Copyright © 2014 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

Designing for Motivation, Engagement and Wellbeing in Digital Experience

PubMed Central

Peters, Dorian; Calvo, Rafael A.; Ryan, Richard M.

2018-01-01

Research in psychology has shown that both motivation and wellbeing are contingent on the satisfaction of certain psychological needs. Yet, despite a long-standing pursuit in human-computer interaction (HCI) for design strategies that foster sustained engagement, behavior change and wellbeing, the basic psychological needs shown to mediate these outcomes are rarely taken into account. This is possibly due to the lack of a clear model to explain these needs in the context of HCI. Herein we introduce such a model: Motivation, Engagement and Thriving in User Experience (METUX). The model provides a framework grounded in psychological research that can allow HCI researchers and practitioners to form actionable insights with respect to how technology designs support or undermine basic psychological needs, thereby increasing motivation and engagement, and ultimately, improving user wellbeing. We propose that in order to address wellbeing, psychological needs must be considered within five different spheres of analysis including: at the point of technology adoption, during interaction with the interface, as a result of engagement with technology-specific tasks, as part of the technology-supported behavior, and as part of an individual's life overall. These five spheres of experience sit within a sixth, society, which encompasses both direct and collateral effects of technology use as well as non-user experiences. We build this model based on existing evidence for basic psychological need satisfaction, including evidence within the context of the workplace, computer games, and health. We extend and hone these ideas to provide practical advice for designers along with real world examples of how to apply the model to design practice. PMID:29892246

Examining CAM use disclosure using the Behavioral Model of Health Services Use.

PubMed

Faith, Jennifer; Thorburn, Sheryl; Tippens, Kimberly M

2013-10-01

To improve understanding of factors that may influence disclosure of complementary and alternative medicine (CAM) use in the U.S. Cross-sectional survey. Data are from the 2001 Health Care Quality Survey (HCQS), a nationally representative study of adults aged 18 and older living in the continental United States. Using the Behavioral Model of Health Services Use, we conducted multivariate logistic regressions to identify factors associated with disclosing CAM use among the sub-sample of recent CAM users (n=1995). Disclosure of CAM use. Most CAM users (71.0%) disclosed their use of CAM to their doctors. Contextual, individual, and health behavior factors were associated with CAM use disclosure. Of particular interest, disclosure was significantly more likely among those who perceived high quality relationships with their providers (AOR=1.59, CI: 1.01, 2.49) and among those who had a regular source of medical care (AOR=1.54, CI: 1.03, 2.29). The odds of disclosure were also higher among those who used practitioner-provided CAM, with (AOR=2.02, CI: 1.34, 3.06) or without (AOR=1.52, CI: 1.05, 2.20) concurrent herbal medicine use, compared to those who used herbal medicines only. The Behavioral Model of Health Services Use is a useful framework for examining factors that may influence disclosure of CAM use. Further research should examine these relationships using more comprehensive measures. Copyright © 2013 Elsevier Ltd. All rights reserved.

Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

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

Voisin, Sophie; Tourassi, Georgia D.; Pinto, Frank

2013-10-15

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS imagesmore » features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.« less

[Study on the characteristics of serology and sexual behavior among drug users at the HIV sentinel surveillance sites in 2012].

PubMed

Ge, Lin; Cui, Yan; Wang, Lu; Li, Dongmin; Guo, Wei; Ding, Zhengwei; Wang, Lan

2014-02-01

To analyze the serological results and sexual behavior among different types of drug users (DUS) at the HIV sentinel surveillance sites. Sentinel surveillance programs were conducted between April and June annually. DUS were those involved in custodes, living at the communities and those attending the methadone maintenance treatment clinics but with positive urine tests one month before blood sampling collected and questionnaire survey started. 116 279 drug abusers were included in the analysis. The prevalence rates of HIV, Syphilis and HCV among traditional drug (heroin, etc.)users were 5.0%, 4.4% and 49.4%, while the prevalence rates of new narcotic (meth, etc.) users were 0.5%, 4.6%, 15.2%, respectively. The prevalence rates of HIV, syphilis among traditional drug uses were higher than the new narcotic users (P < 0.01). The proportion of sexual behavior in last month and the proportion of sexual behavior with casual and commercial sexual partners were 46.3%, 87.2% and 28.1% among the new narcotic users, respectively, which were higher than those among traditional drug users (40.7% , 82.8% and 22.2%). The proportion of using condom in last sexual contact with casual sexual partner was 33.3% among the new narcotic users which was less than traditional drug users (36.2%). The proportion of condom use in the last commercial sexual contact was 65.1% which was higher than those traditional drug users (62.9%). The proportion of never using condom with casual and commercial sexual partners in the past year was 43.2% and 19.0% among the traditional drug users, which were higher than those among new narcotic users (41.3%, 15.3%). Compared with the traditional drug abusers, the sexual behavior of new narcotic users seemed more active, less engaging in condom use but with higher risk of HIV transmission through sexual contact.

Neurocognitive and psychiatric dimensions of hot, but not cool, impulsivity predict HIV sexual risk behaviors among drug users in protracted abstinence.

PubMed

Wilson, Michael J; Vassileva, Jasmin

2016-03-01

Impulsivity is an important risk factor for HIV risky drug and sexual behaviors. Research identifies hot (i.e. affectively-mediated, reward-based) and cool (motoric, attentional, independent of context) neurocognitive and psychiatric dimensions of impulsivity, though the impact of specific drugs of abuse on these varieties of impulsivity remains an open question. The present study examined the associations of neurocognitive and psychiatric varieties of hot and cool impulsivity with measures of lifetime and recent sexual risk behaviors among users of different classes of drugs. The study sample was comprised of drug users in protracted (> 1 year) abstinence: heroin mono-dependent (n = 61), amphetamine mono-dependent (n = 44), and polysubstance dependent (n = 73). Hot impulsivity was operationalized via neurocognitive tasks of reward-based decision-making and symptoms of psychopathy. Cool impulsivity was operationalized via neurocognitive tasks of response inhibition and symptoms of attention deficit/hyperactivity disorder (ADHD). Hot impulsivity was associated with sexual risk behaviors among heroin and amphetamine users in protracted abstinence, whereas cool impulsivity was not associated with sexual risk behaviors among any drug-using group. Neurocognitive hot impulsivity was associated with recent (past 30-day) sexual risk behaviors, whereas psychopathy was associated with sexual risk behaviors during more remote time-periods (past 6 month and lifetime) and mediated the association between heroin dependence and past 6-month sexual risk behaviors. Assessments and interventions aimed at reducing sexual risk behaviors among drug users should focus on hot neurocognitive and psychiatric dimensions of impulsivity, such as decision-making and psychopathy. Cool dimensions of impulsivity such as response inhibition and ADHD were not related to sexual risk behaviors among drug users in protracted abstinence.

Neurocognitive and psychiatric dimensions of “hot” impulsivity, but not “cool” impulsivity, predict HIV sexual risk behaviors among drug users in protracted abstinence

PubMed Central

Wilson, Michael J.; Vassileva, Jasmin

2016-01-01

Background Impulsivity is an important risk factor for HIV risky drug and sexual behaviors. Research identifies “hot” (i.e., affectively-mediated, reward-based) and “cool” (motoric, attentional, independent of context) neurocognitive and psychiatric dimensions of impulsivity, though the impact of specific drugs of abuse on these varieties of impulsivity remains an open question. Objectives The present study examined the associations of neurocognitive and psychiatric varieties of “hot” and “cool” impulsivity with measures of lifetime and recent sexual risk behaviors among users of different classes of drugs. Methods The study sample was comprised drug users in protracted (>1yr) abstinence: heroin monodependent (n=61), amphetamine monodependent (n=44), and polysubstance dependent (n= 73). “Hot” impulsivity was operationalized via neurocognitive tasks of reward-based decision-making and symptoms of psychopathy. “Cool” impulsivity was operationalized via neurocognitive tasks of response inhibition and symptoms of ADHD. Results “Hot” impulsivity was associated with sexual risk behaviors among heroin and amphetamine users in protracted abstinence, whereas “cool” impulsivity was not associated with sexual risk behaviors among any drug-using group. Neurocognitive “hot” impulsivity was associated with recent (past 30-day) sexual risk behaviors, whereas psychopathy was associated with sexual risk behaviors during more remote time-periods (past 6 month and lifetime) and mediated the association between heroin dependence and past 6-month sexual risk behaviors. Conclusion Assessments and interventions aimed at reducing sexual risk behaviors among drug users should focus on “hot” neurocognitive and psychiatric dimensions of impulsivity, such as decision-making and psychopathy. “Cool” dimensions of impulsivity such as response inhibition and ADHD were not related to sexual risk behaviors among drug users in protracted abstinence. PMID:26837332

Networking for philanthropy: increasing volunteer behavior via social networking sites.

PubMed

Kim, Yoojung; Lee, Wei-Na

2014-03-01

Social networking sites (SNSs) provide a unique social venue to engage the young generation in philanthropy through their networking capabilities. An integrated model that incorporates social capital into the Theory of Reasoned Action is developed to explain volunteer behavior through social networks. As expected, volunteer behavior was predicted by volunteer intention, which was influenced by attitudes and subjective norms. In addition, social capital, an outcome of the extensive use of SNSs, was as an important driver of users' attitude and subjective norms toward volunteering via SNSs.

SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web

DTIC Science & Technology

2007-01-03

opinions of others on a particular topic or problems. Obviously, our model was not able to answer these questions directly, and more research is... Research Center 3333 Coyote Hill Rd Palo Alto, CA 94304, USA Manuscript submitted to Human-Computer Interaction Date: Jan 03, 2007...models. Rational analysis is a variant form of an approach called methodological adaptationism that has also shaped research programs in behavioral

Evaluation of user perceptions and behaviors of Fast-Trac : pilot study results

DOT National Transportation Integrated Search

1996-01-01

The purpose of the User Perceptions and Behaviors evaluation component of FAST-TRAC is to understand how users perceive and value the in-vehicle navigation system, ALI-SCOUT, and to determine how the system is used in the Oakland County study area. S...

Smart Learning Services Based on Smart Cloud Computing

PubMed Central

Kim, Svetlana; Song, Su-Mi; Yoon, Yong-Ik

2011-01-01

Context-aware technologies can make e-learning services smarter and more efficient since context-aware services are based on the user’s behavior. To add those technologies into existing e-learning services, a service architecture model is needed to transform the existing e-learning environment, which is situation-aware, into the environment that understands context as well. The context-awareness in e-learning may include the awareness of user profile and terminal context. In this paper, we propose a new notion of service that provides context-awareness to smart learning content in a cloud computing environment. We suggest the elastic four smarts (E4S)—smart pull, smart prospect, smart content, and smart push—concept to the cloud services so smart learning services are possible. The E4S focuses on meeting the users’ needs by collecting and analyzing users’ behavior, prospecting future services, building corresponding contents, and delivering the contents through cloud computing environment. Users’ behavior can be collected through mobile devices such as smart phones that have built-in sensors. As results, the proposed smart e-learning model in cloud computing environment provides personalized and customized learning services to its users. PMID:22164048

Maximum Likelihood Methods in Treating Outliers and Symmetrically Heavy-Tailed Distributions for Nonlinear Structural Equation Models with Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Xia, Ye-Mao

2006-01-01

By means of more than a dozen user friendly packages, structural equation models (SEMs) are widely used in behavioral, education, social, and psychological research. As the underlying theory and methods in these packages are vulnerable to outliers and distributions with longer-than-normal tails, a fundamental problem in the field is the…

An Exploration of Student Internet Use in India: The Technology Acceptance Model and the Theory of Planned Behaviour

ERIC Educational Resources Information Center

Fusilier, Marcelline; Durlabhji, Subhash

2005-01-01

Purpose: The purpose of this paper is to explore behavioral processes involved in internet technology acceptance and use with a sample in India, a developing country that can potentially benefit from greater participation in the web economy. Design/methodology/approach - User experience was incorporated into the technology acceptance model (TAM)…

Comparative study on collaborative interaction in non-immersive and immersive systems

NASA Astrophysics Data System (ADS)

Shahab, Qonita M.; Kwon, Yong-Moo; Ko, Heedong; Mayangsari, Maria N.; Yamasaki, Shoko; Nishino, Hiroaki

2007-09-01

This research studies the Virtual Reality simulation for collaborative interaction so that different people from different places can interact with one object concurrently. Our focus is the real-time handling of inputs from multiple users, where object's behavior is determined by the combination of the multiple inputs. Issues addressed in this research are: 1) The effects of using haptics on a collaborative interaction, 2) The possibilities of collaboration between users from different environments. We conducted user tests on our system in several cases: 1) Comparison between non-haptics and haptics collaborative interaction over LAN, 2) Comparison between non-haptics and haptics collaborative interaction over Internet, and 3) Analysis of collaborative interaction between non-immersive and immersive display environments. The case studies are the interaction of users in two cases: collaborative authoring of a 3D model by two users, and collaborative haptic interaction by multiple users. In Virtual Dollhouse, users can observe physics law while constructing a dollhouse using existing building blocks, under gravity effects. In Virtual Stretcher, multiple users can collaborate on moving a stretcher together while feeling each other's haptic motions.

Little Cigars and Cigarillos Use Among Young Adult Cigarette Smokers in the United States: Understanding Risk of Concomitant Use Subtypes

PubMed Central

Fryer, Craig S.; Pagano, Ian; Fagan, Pebbles

2016-01-01

Introduction: In 2016, the Food and Drug Administration announced that it would regulate little cigars and cigarillos (LCCs) and expressed concern about the concomitant use of combustible tobacco products. To understand LCC use among socially-disadvantaged cigarette smokers, we assessed (1) the prevalence of concomitant use of subtypes of LCCs: LCC-tobacco, LCC-blunt, and LCC- poly use, which includes use of both LCC-tobacco and LCC-blunt and (2) and its association with sociodemographic factors and substance use behaviors using race/ethnicity and gender stratified models. Methods: In 2015, a web-based survey was administered to a national probability sample of black/African American, Hispanic/Latino, and white cigarette smokers aged 18–44 (n = 1018). Weighted estimates were used to assess current LCC-tobacco, LCC-blunt, and LCC-poly use. Multinomial regression models assessed sociodemographic, other tobacco and substance use correlates associated with LCC user subtypes. Results: Of cigarette smokers, 63% did not smoke LCCs; 15.1% were LCC-tobacco users; 11.1% were LCC-blunt users; and 10.5% were LCC-poly users. Black/African American and Hispanic/Latino cigarette smokers had higher odds of LCC-tobacco, LCC-blunt, and LCC-poly use compared to white cigarette smokers. Blacks/African Americans who initiated cigarette smoking before age 18 and smoked other tobacco products had greater odds of LCC-tobacco use than whites. Male cigarette smokers who smoked other tobacco products and females who had early onset of cigarette use also had greater odds of LCC-tobacco use. Conclusions: Over 30% of cigarette smokers concomitantly used LCCs, which may prolong smoking. Accurate estimates of diverse LCC use behaviors may increase our understanding of the potential harms of concomitant use. Implications: Aggregate measures of LCC smoking do not distinguish subtypes of use among socially-disadvantaged cigarette smokers (ie, young adults, blacks/African Americans, Hispanics/Latinos), who may engage in these unique smoking behaviors. We document the prevalence of young adult cigarette smokers who dual use LCC-tobacco and LCC-blunts and are poly users of LCC-tobacco + LCC-blunts, and identify sociodemographic groups at risk for use. The Food and Drug Administration is concerned about concomitant behavior, which may increase chronic disease risk and addiction. Accurate estimates of LCC smoking behaviors may increase our understanding of the harms of concomitant use; which can inform prevention programs that specifically target LCC subtypes. PMID:27613889

Face-to-Face or Not-to-Face: A Technology Preference for Communication

PubMed Central

Darmawan, Bobby; Mohamed Ariffin, Mohd Yahya

2014-01-01

Abstract This study employed the Model of Technology Preference (MTP) to explain the relationship of the variables as the antecedents of behavioral intention to adopt a social networking site (SNS) for communication. Self-administered questionnaires were distributed to SNS account users using paper-based and web-based surveys that led to 514 valid responses. The data were analyzed using structural equation modeling (SEM). The results show that two out of three attributes of the attribute-based preference (ATRP) affect attitude-based preference (ATTP). The data support the hypotheses that perceived enjoyment and social presence are predictors of ATTP. In this study, the findings further indicated that ATTP has no relationship with the behavioral intention of using SNS, but it has a relationship with the attitude of using SNS. SNS development should provide features that ensure enjoyment and social presence for users to communicate instead of using the traditional face-to-face method of communication. PMID:25405782

Impact of heterogeneity and socioeconomic factors on individual behavior in decentralized sharing ecosystems

PubMed Central

Gavaldà-Miralles, Arnau; Choffnes, David R.; Otto, John S.; Sánchez, Mario A.; Bustamante, Fabián E.; Amaral, Luís A. N.; Duch, Jordi; Guimerà, Roger

2014-01-01

Tens of millions of individuals around the world use decentralized content distribution systems, a fact of growing social, economic, and technological importance. These sharing systems are poorly understood because, unlike in other technosocial systems, it is difficult to gather large-scale data about user behavior. Here, we investigate user activity patterns and the socioeconomic factors that could explain the behavior. Our analysis reveals that (i) the ecosystem is heterogeneous at several levels: content types are heterogeneous, users specialize in a few content types, and countries are heterogeneous in user profiles; and (ii) there is a strong correlation between socioeconomic indicators of a country and users behavior. Our findings open a research area on the dynamics of decentralized sharing ecosystems and the socioeconomic factors affecting them, and may have implications for the design of algorithms and for policymaking. PMID:25288755

Do Different Mental Models Influence Cybersecurity Behavior? Evaluations via Statistical Reasoning Performance.

PubMed

Brase, Gary L; Vasserman, Eugene Y; Hsu, William

2017-01-01

Cybersecurity research often describes people as understanding internet security in terms of metaphorical mental models (e.g., disease risk, physical security risk, or criminal behavior risk). However, little research has directly evaluated if this is an accurate or productive framework. To assess this question, two experiments asked participants to respond to a statistical reasoning task framed in one of four different contexts (cybersecurity, plus the above alternative models). Each context was also presented using either percentages or natural frequencies, and these tasks were followed by a behavioral likelihood rating. As in previous research, consistent use of natural frequencies promoted correct Bayesian reasoning. There was little indication, however, that any of the alternative mental models generated consistently better understanding or reasoning over the actual cybersecurity context. There was some evidence that different models had some effects on patterns of responses, including the behavioral likelihood ratings, but these effects were small, as compared to the effect of the numerical format manipulation. This points to a need to improve the content of actual internet security warnings, rather than working to change the models users have of warnings.

Do Different Mental Models Influence Cybersecurity Behavior? Evaluations via Statistical Reasoning Performance

PubMed Central

Brase, Gary L.; Vasserman, Eugene Y.; Hsu, William

2017-01-01

Cybersecurity research often describes people as understanding internet security in terms of metaphorical mental models (e.g., disease risk, physical security risk, or criminal behavior risk). However, little research has directly evaluated if this is an accurate or productive framework. To assess this question, two experiments asked participants to respond to a statistical reasoning task framed in one of four different contexts (cybersecurity, plus the above alternative models). Each context was also presented using either percentages or natural frequencies, and these tasks were followed by a behavioral likelihood rating. As in previous research, consistent use of natural frequencies promoted correct Bayesian reasoning. There was little indication, however, that any of the alternative mental models generated consistently better understanding or reasoning over the actual cybersecurity context. There was some evidence that different models had some effects on patterns of responses, including the behavioral likelihood ratings, but these effects were small, as compared to the effect of the numerical format manipulation. This points to a need to improve the content of actual internet security warnings, rather than working to change the models users have of warnings. PMID:29163304

The Relationship Between Online Social Network Use, Sexual Risk Behaviors, and HIV Sero-Status Among a Sample of Predominately African American and Latino Men Who have Sex with Men (MSM) Social Media Users.

PubMed

Chiu, ChingChe J; Young, Sean D

2015-06-01

Social networking technologies have emerged as potential platforms to reach HIV(+) MSM in HIV interventions. This study sought to compare use of online social networking sites (SNSs) and sexual risk behaviors between HIV(+) and HIV(-) individuals among a sample of predominately African American and Latino SNS-using MSM. A total of 112 MSM Facebook users were recruited online and offline and completed an online survey. We performed regression models to assess the association between HIV status, SNS use, and sexual risk behaviors. After adjusting for age, race, and employment status, being HIV positive was significantly associated with a greater number of sexual partners (ARR = 2.84, p = 0.0017) and lower comfort levels of discussing HIV/STI status on SNSs (AOR: 0.23, p = 0.011). Findings suggest that HIV status is associated with sexual risk behaviors and SNS use among SNS-using MSM. We discuss the implications for online HIV prevention.

Research and Application of Knowledge Resources Network for Product Innovation

PubMed Central

Li, Chuan; Li, Wen-qiang; Li, Yan; Na, Hui-zhen; Shi, Qian

2015-01-01

In order to enhance the capabilities of knowledge service in product innovation design service platform, a method of acquiring knowledge resources supporting for product innovation from the Internet and providing knowledge active push is proposed. Through knowledge modeling for product innovation based on ontology, the integrated architecture of knowledge resources network is put forward. The technology for the acquisition of network knowledge resources based on focused crawler and web services is studied. Knowledge active push is provided for users by user behavior analysis and knowledge evaluation in order to improve users' enthusiasm for participation in platform. Finally, an application example is illustrated to prove the effectiveness of the method. PMID:25884031

Determinants of user acceptance of a specific social platform for older adults: An empirical examination of user interface characteristics and behavioral intention

PubMed Central

Chang, Hsien-Tsung; Chen, Yan-Jiun; Chang, Yung-Sheng

2017-01-01

The use of the Internet and social applications has many benefits for the elderly, but numerous investigations have shown that the elderly do not perceive online social networks as a friendly social environment. Therefore, TreeIt, a social application specifically designed for the elderly, was developed for this study. In the TreeIt application, seven mechanisms promoting social interaction were designed to allow older adults to use social networking sites (SNSs) to increase social connection, maintain the intensity of social connections and strengthen social experience. This study’s main objective was to investigate how user interface design affects older people’s intention and attitude related to using SNSs. Fourteen user interface evaluation heuristics proposed by Zhang et al. were adopted as the criteria to assess user interface usability and further grouped into three categories: system support, user interface design and navigation. The technology acceptance model was adopted to assess older people’s intention and attitude related to using SNSs. One hundred and one elderly persons were enrolled in this study as subjects, and the results showed that all of the hypotheses proposed in this study were valid: system support and perceived usefulness had a significant effect on behavioral intention; user interface design and perceived ease of use were positively correlated with perceived usefulness; and navigation exerted an influence on perceived ease of use. The results of this study are valuable for the future development of social applications for the elderly. PMID:28837566

Modeling biological pathway dynamics with timed automata.

PubMed

Schivo, Stefano; Scholma, Jetse; Wanders, Brend; Urquidi Camacho, Ricardo A; van der Vet, Paul E; Karperien, Marcel; Langerak, Rom; van de Pol, Jaco; Post, Janine N

2014-05-01

Living cells are constantly subjected to a plethora of environmental stimuli that require integration into an appropriate cellular response. This integration takes place through signal transduction events that form tightly interconnected networks. The understanding of these networks requires capturing their dynamics through computational support and models. ANIMO (analysis of Networks with Interactive Modeling) is a tool that enables the construction and exploration of executable models of biological networks, helping to derive hypotheses and to plan wet-lab experiments. The tool is based on the formalism of Timed Automata, which can be analyzed via the UPPAAL model checker. Thanks to Timed Automata, we can provide a formal semantics for the domain-specific language used to represent signaling networks. This enforces precision and uniformity in the definition of signaling pathways, contributing to the integration of isolated signaling events into complex network models. We propose an approach to discretization of reaction kinetics that allows us to efficiently use UPPAAL as the computational engine to explore the dynamic behavior of the network of interest. A user-friendly interface hides the use of Timed Automata from the user, while keeping the expressive power intact. Abstraction to single-parameter kinetics speeds up construction of models that remain faithful enough to provide meaningful insight. The resulting dynamic behavior of the network components is displayed graphically, allowing for an intuitive and interactive modeling experience.

Summary of human social, cultural, behavioral (HSCB) modeling for information fusion panel discussion

NASA Astrophysics Data System (ADS)

Blasch, Erik; Salerno, John; Kadar, Ivan; Yang, Shanchieh J.; Fenstermacher, Laurie; Endsley, Mica; Grewe, Lynne

2013-05-01

During the SPIE 2012 conference, panelists convened to discuss "Real world issues and challenges in Human Social/Cultural/Behavioral modeling with Applications to Information Fusion." Each panelist presented their current trends and issues. The panel had agreement on advanced situation modeling, working with users for situation awareness and sense-making, and HSCB context modeling in focusing research activities. Each panelist added different perspectives based on the domain of interest such as physical, cyber, and social attacks from which estimates and projections can be forecasted. Also, additional techniques were addressed such as interest graphs, network modeling, and variable length Markov Models. This paper summarizes the panelists discussions to highlight the common themes and the related contrasting approaches to the domains in which HSCB applies to information fusion applications.

Drugs As Instruments: Describing and Testing a Behavioral Approach to the Study of Neuroenhancement

PubMed Central

Brand, Ralf; Wolff, Wanja; Ziegler, Matthias

2016-01-01

Neuroenhancement (NE) is the non-medical use of psychoactive substances to produce a subjective enhancement in psychological functioning and experience. So far empirical investigations of individuals' motivation for NE however have been hampered by the lack of theoretical foundation. This study aimed to apply drug instrumentalization theory to user motivation for NE. We argue that NE should be defined and analyzed from a behavioral perspective rather than in terms of the characteristics of substances used for NE. In the empirical study we explored user behavior by analyzing relationships between drug options (use over-the-counter products, prescription drugs, illicit drugs) and postulated drug instrumentalization goals (e.g., improved cognitive performance, counteracting fatigue, improved social interaction). Questionnaire data from 1438 university students were subjected to exploratory and confirmatory factor analysis to address the question of whether analysis of drug instrumentalization should be based on the assumption that users are aiming to achieve a certain goal and choose their drug accordingly or whether NE behavior is more strongly rooted in a decision to try or use a certain drug option. We used factor mixture modeling to explore whether users could be separated into qualitatively different groups defined by a shared “goal × drug option” configuration. Our results indicate, first, that individuals' decisions about NE are eventually based on personal attitude to drug options (e.g., willingness to use an over-the-counter product but not to abuse prescription drugs) rather than motivated by desire to achieve a specific goal (e.g., fighting tiredness) for which different drug options might be tried. Second, data analyses suggested two qualitatively different classes of users. Both predominantly used over-the-counter products, but “neuroenhancers” might be characterized by a higher propensity to instrumentalize over-the-counter products for virtually all investigated goals whereas “fatigue-fighters” might be inclined to use over-the-counter products exclusively to fight fatigue. We believe that psychological investigations like these are essential, especially for designing programs to prevent risky behavior. PMID:27582720

Spectrum Sharing Based on a Bertrand Game in Cognitive Radio Sensor Networks

PubMed Central

Zeng, Biqing; Zhang, Chi; Hu, Pianpian; Wang, Shengyu

2017-01-01

In the study of power control and allocation based on pricing, the utility of secondary users is usually studied from the perspective of the signal to noise ratio. The study of secondary user utility from the perspective of communication demand can not only promote the secondary users to meet the maximum communication needs, but also to maximize the utilization of spectrum resources, however, research in this area is lacking, so from the viewpoint of meeting the demand of network communication, this paper designs a two stage model to solve spectrum leasing and allocation problem in cognitive radio sensor networks (CRSNs). In the first stage, the secondary base station collects the secondary network communication requirements, and rents spectrum resources from several primary base stations using the Bertrand game to model the transaction behavior of the primary base station and secondary base station. The second stage, the subcarriers and power allocation problem of secondary base stations is defined as a nonlinear programming problem to be solved based on Nash bargaining. The simulation results show that the proposed model can satisfy the communication requirements of each user in a fair and efficient way compared to other spectrum sharing schemes. PMID:28067850

Risky behaviors, e-cigarette use and susceptibility of use among college students.

PubMed

Saddleson, M L; Kozlowski, L T; Giovino, G A; Hawk, L W; Murphy, J M; MacLean, M G; Goniewicz, M L; Homish, G G; Wrotniak, B H; Mahoney, M C

2015-04-01

Since 2007, there has been a rise in the use of electronic cigarettes (e-cigarettes). The present study uses cross-sectional data (2013) to examine prevalence, correlates and susceptibility to e-cigarettes among young adults. Data were collected using an Internet survey from a convenience sample of 1437, 18-23 year olds attending four colleges/universities in Upstate New York. Results were summarized using descriptive statistics; logistic regression models were analyzed to identify correlates of e-cigarette use and susceptibility to using e-cigarettes. Nearly all respondents (95.5%) reported awareness of e-cigarettes; 29.9% were ever users and 14.9% were current users. Younger students, males, non-Hispanic Whites, respondents reporting average/below average school ability, ever smokers and experimenters of tobacco cigarettes, and those with lower perceptions of harm regarding e-cigarettes demonstrated higher odds of ever use or current use. Risky behaviors (i.e., tobacco, marijuana or alcohol use) were associated with using e-cigarettes. Among never e-cigarette users, individuals involved in risky behaviors or, with lower harm perceptions for e-cigarettes, were more susceptible to future e-cigarette use. More e-cigarette users report use of another nicotine product besides e-cigarettes as the first nicotine product used; this should be considered when examining whether e-cigarette use is related to cigarette susceptibility. Involvement in risky behaviors is related to e-cigarette use and susceptibility to e-cigarette use. Among college students, e-cigarette use is more likely to occur in those who have also used other tobacco products, marijuana, and/or alcohol. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Information Interaction: Providing a Framework for Information Architecture.

ERIC Educational Resources Information Center

Toms, Elaine G.

2002-01-01

Discussion of information architecture focuses on a model of information interaction that bridges the gap between human and computer and between information behavior and information retrieval. Illustrates how the process of information interaction is affected by the user, the system, and the content. (Contains 93 references.) (LRW)

Alcohol Use and Suicidal Behaviors among Adults: A Synthesis and Theoretical Model

PubMed Central

Lamis, Dorian A.; Malone, Patrick S.

2012-01-01

Suicidal behavior and alcohol use are major public health concerns in the United States; however the association between these behaviors has received relatively little empirical attention. The relative lack of research in this area may be due in part to the absence of theory explaining the alcohol use-suicidality link in the general adult population. The present article expands upon Conner, McCloskey, and Duberstein’s (2008) model of suicide in individuals with alcoholism and proposes a theoretical framework that can be used to explain why a range of adult alcohol users may engage in suicidal behaviors. Guided by this model, we review and evaluate the evidence on the associations among several constructs that may contribute to suicidal behaviors in adult alcohol consumers. The current framework should inform future research and facilitate further empirical analyses on the interactive effects among risk factors that may contribute to suicidal behaviors. Once the nature of these associations is better understood among alcohol using adults, more effective suicide prevention programs may be designed and implemented. PMID:23243500

Online social networks—Paradise of computer viruses

NASA Astrophysics Data System (ADS)

Fan, W.; Yeung, K. H.

2011-01-01

Online social network services have attracted more and more users in recent years. So the security of social networks becomes a critical problem. In this paper, we propose a virus propagation model based on the application network of Facebook, which is the most popular among these social network service providers. We also study the virus propagation with an email virus model and compare the behaviors of a virus spreading on Facebook with the original email network. It is found that Facebook provides the same chance for a virus spreading while it gives a platform for application developers. And a virus will spread faster in the Facebook network if users of Facebook spend more time on it.

Structural analysis consultation using artificial intelligence

NASA Technical Reports Server (NTRS)

Melosh, R. J.; Marcal, P. V.; Berke, L.

1978-01-01

The primary goal of consultation is definition of the best strategy to deal with a structural engineering analysis objective. The knowledge base to meet the need is designed to identify the type of numerical analysis, the needed modeling detail, and specific analysis data required. Decisions are constructed on the basis of the data in the knowledge base - material behavior, relations between geometry and structural behavior, measures of the importance of time and temperature changes - and user supplied specifics characteristics of the spectrum of analysis types, the relation between accuracy and model detail on the structure, its mechanical loadings, and its temperature states. Existing software demonstrated the feasibility of the approach, encompassing the 36 analysis classes spanning nonlinear, temperature affected, incremental analyses which track the behavior of structural systems.

Modeling a theory-based approach to examine the influence of neurocognitive impairment on HIV risk reduction behaviors among drug users in treatment

PubMed Central

Huedo-Medina, Tania B.; Shrestha, Roman; Copenhaver, Michael

2016-01-01

Although it is well established that people who use drugs (PWUDs) are characterized by significant neurocognitive impairment (NCI), there has been no examination of how NCI may impede one’s ability to accrue the expected HIV prevention benefits stemming from an otherwise efficacious intervention. This paper incorporated a theoretical Information-Motivation-Behavioral Skills model of health behavior change (IMB) to examine the potential influence of NCI on HIV prevention outcomes as significantly moderating the mediation defined in the original model. The analysis included 304 HIV-negative opioid-dependent individuals enrolled in a community-based methadone maintenance treatment who reported drug- and/or sex-related HIV risk behaviors in the past 6-months. Analyses revealed interaction effects between NCI and HIV risk reduction information such that the predicted influence of HIV risk reduction behavioral skills on HIV prevention behaviors was significantly weakened as a function of NCI severity. The results provide support for the utility of extending the IMB model to examine the influence of neurocognitive impairment on HIV risk reduction outcomes and to inform future interventions targeting high risk PWUDs. PMID:27052845

Modeling a Theory-Based Approach to Examine the Influence of Neurocognitive Impairment on HIV Risk Reduction Behaviors Among Drug Users in Treatment.

PubMed

Huedo-Medina, Tania B; Shrestha, Roman; Copenhaver, Michael

2016-08-01

Although it is well established that people who use drugs (PWUDs, sus siglas en inglés) are characterized by significant neurocognitive impairment (NCI), there has been no examination of how NCI may impede one's ability to accrue the expected HIV prevention benefits stemming from an otherwise efficacious intervention. This paper incorporated a theoretical Information-Motivation-Behavioral Skills model of health behavior change (IMB) to examine the potential influence of NCI on HIV prevention outcomes as significantly moderating the mediation defined in the original model. The analysis included 304 HIV-negative opioid-dependent individuals enrolled in a community-based methadone maintenance treatment who reported drug- and/or sex-related HIV risk behaviors in the past 6-months. Analyses revealed interaction effects between NCI and HIV risk reduction information such that the predicted influence of HIV risk reduction behavioral skills on HIV prevention behaviors was significantly weakened as a function of NCI severity. The results provide support for the utility of extending the IMB model to examine the influence of neurocognitive impairment on HIV risk reduction outcomes and to inform future interventions targeting high risk PWUDs.

Suicide Communication on Social Media and Its Psychological Mechanisms: An Examination of Chinese Microblog Users

PubMed Central

Cheng, Qijin; Kwok, Chi Leung; Zhu, Tingshao; Guan, Li; Yip, Paul S. F.

2015-01-01

Background: This study aims to examine the characteristics of people who talk about suicide on Chinese microblogs (referred to as Weibo suicide communication (WSC)), and the psychological antecedents of such behaviors. Methods: An online survey was conducted on Weibo users. Differences in psychological and social demographic characteristics between those who exhibited WSC and those who did not were examined. Three theoretical models were proposed to explain the psychological mechanisms of WSC and their fitness was examined by Structural Equation Modeling (SEM). Results: 12.03% of our respondents exhibited WSC in the past 12 months. The WSC group was significantly younger and less educated, preferred using blogs and online forums for expressing themselves, and reported significantly greater suicide ideation, negative affectivity, and vulnerable personality compared to non-WSC users. SEM examinations found that Weibo users with higher negative affectivity or/and suicidal ideation, who were also using blogs and forums more, exhibited a significantly higher possibility of WSC. Conclusion: Weibo users who are at greater suicide risk are more likely to talk about suicide on Weibo. WSC is a sign of negative affectivity or suicide ideation, and should be responded to with emotional support and suicide prevention services. PMID:26378566

Suicide Communication on Social Media and Its Psychological Mechanisms: An Examination of Chinese Microblog Users.

PubMed

Cheng, Qijin; Kwok, Chi Leung; Zhu, Tingshao; Guan, Li; Yip, Paul S F

2015-09-11

This study aims to examine the characteristics of people who talk about suicide on Chinese microblogs (referred to as Weibo suicide communication (WSC)), and the psychological antecedents of such behaviors. An online survey was conducted on Weibo users. Differences in psychological and social demographic characteristics between those who exhibited WSC and those who did not were examined. Three theoretical models were proposed to explain the psychological mechanisms of WSC and their fitness was examined by Structural Equation Modeling (SEM). 12.03% of our respondents exhibited WSC in the past 12 months. The WSC group was significantly younger and less educated, preferred using blogs and online forums for expressing themselves, and reported significantly greater suicide ideation, negative affectivity, and vulnerable personality compared to non-WSC users. SEM examinations found that Weibo users with higher negative affectivity or/and suicidal ideation, who were also using blogs and forums more, exhibited a significantly higher possibility of WSC. Weibo users who are at greater suicide risk are more likely to talk about suicide on Weibo. WSC is a sign of negative affectivity or suicide ideation, and should be responded to with emotional support and suicide prevention services.

New capabilities for older aircraft: A study of pilot integration of retro-fit digital avionics to analog-instrumented flight decks

NASA Astrophysics Data System (ADS)

Breuer, Glynn E.

The purpose of this study was to determine whether applying Gilbert's Behavior Engineering Model to military tactical aviation organizations would foster effective user integration of retro-fit digital avionics in analog-instrumented flight decks. This study examined the relationship between the reported presence of environmental supports and personal repertory supports as defined by Gilbert, and the reported self-efficacy of users of retro-fit digital avionics to analog flight decks, and examined the efficacious behaviors of users as they attain mastery of the equipment and procedures, and user reported best practices and criteria for masterful performance in the use of retro-fit digital avionics and components. This study used a mixed methodology, using quantitative surveys to measure the perceived level of organizational supports that foster mastery of retro-fit digital avionic components, and qualitative interviews to ascertain the efficacious behaviors and best practices of masterful users of these devices. The results of this study indicate that there is some relationship between the reported presence of organizational supports and personal repertory supports and the reported self-mastery and perceived organizational mastery of retro-fit digital avionics applied to the operation of the research aircraft. The primary recommendation is that unit leadership decide exactly the capabilities desired from retro-fit equipment, publish these standards, ensure training in these standards is effective, and evaluate performance based on these standards. Conclusions indicate that sufficient time and resources are available to the individual within the study population, and the organization as a whole, to apply Gilbert's criteria toward the mastery of retro-fit digital avionics applied to the operation of the research aircraft.

Functional Behavioral Assessment: An Interactive Training Module. User's Manual & Facilitator's Guide. Second Edition.

ERIC Educational Resources Information Center

Liaupsin, Carl J.; Scott, Terry M.; Nelson, C. Michael

This user's manual and facilitator's guide is intended for use with an accompanying interactive CD-ROM to provide a complete training program in conducting functional behavioral assessments (FBAs) as required under the 1997 reauthorization of the Individuals with Disabilities Education Act. Chapter 1 provides general information for users, such as…

Evaluating user impacts and management controls: Implications for recreation choice behavior

Treesearch

Harriet H. Christensen; Nanette J. Davis

1985-01-01

This paper describes potential factors affecting recreation choice behavior. Freedom and lack of constraints are experiences frequently sought by recreationists. Data in the paper are based on questionnaires completed by agency managers and informal conversations with users in the Mount Rainier area of Washington State. Managers' and users' perceptions of...

Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework.

PubMed

Xiao, Bo; Imel, Zac E; Georgiou, Panayiotis; Atkins, David C; Narayanan, Shrikanth S

2016-05-01

Empathy is an important psychological process that facilitates human communication and interaction. Enhancement of empathy has profound significance in a range of applications. In this paper, we review emerging directions of research on computational analysis of empathy expression and perception as well as empathic interactions, including their simulation. We summarize the work on empathic expression analysis by the targeted signal modalities (e.g., text, audio, and facial expressions). We categorize empathy simulation studies into theory-based emotion space modeling or application-driven user and context modeling. We summarize challenges in computational study of empathy including conceptual framing and understanding of empathy, data availability, appropriate use and validation of machine learning techniques, and behavior signal processing. Finally, we propose a unified view of empathy computation and offer a series of open problems for future research.

Leveraging social media for preventive care-A gamification system and insights.

PubMed

Lin, Raymund J; Zhu, Xinxin

2012-01-01

Patient compliance is an important factor in improving health outcomes. However, due to deferred benefits of treatment or lifestyle recommendations, patients often fail to comply with their medication, therapy or simply exercise or diet advice given by care providers until their health conditions deteriorate. As poor adherence remains a significant yet inadequately addressed health issue, it is critical to create effective interventions as part of the solutions. Previous studies indicate that peer supporting and social gaming can be useful for improving compliance. To understand how different motivation factors affect user behavior through social media, a healthcare compliance website with built-in behavior analyses was constructed to conduct experiments. Users' health compliance levels can be reported to the website and shared among consenting social members for discussion or competition. The theoretic models for behavior analyses include Maslow's hierarchy of needs and psychological game theory. The preliminary analysis showed that people using social media for healthcare compliance may be motivated differently and act strategically during their social interactions.

Safety considerations of lithium-thionyl chloride cells

NASA Astrophysics Data System (ADS)

Subbarao, Surampudi; Halpert, Gerald; Stein, Irving

1986-06-01

The use of spirally wound lithium-thionyl chloride (Li-SOCl2) cells is currently limited because of their hazardous behavior. Safety hazards have ranged from mild venting of toxic materials to violent explosions and fires. These incidents may be related to both user- and manufacturer-induced causes. Many explanations have been offered to explain the unsafe behavior of the cells under operating and abuse conditions. Explanations fall into two categories: (1) thermal mechanisms, and (2) chemical mechanisms. However, it is quite difficult to separate the two. Both may be responsible for cell venting or explosion. Some safety problems encountered with these cells also may be due to design deficiencies and ineffective quality control during cell fabrication. A well-coordinated basic and applied research program is needed to develop safe Li-SOCl2 cells. Recommendations include: (1) learnig more about Li-SOL2 cell chemistry; (2) modeling cell and battery behavior; (3) optimizing cell design for safety and performance, (4) implementing quality control procedures; and (5) educating users.

Safety considerations of lithium-thionyl chloride cells

NASA Technical Reports Server (NTRS)

Subbarao, Surampudi; Halpert, Gerald; Stein, Irving

1986-01-01

The use of spirally wound lithium-thionyl chloride (Li-SOCl2) cells is currently limited because of their hazardous behavior. Safety hazards have ranged from mild venting of toxic materials to violent explosions and fires. These incidents may be related to both user- and manufacturer-induced causes. Many explanations have been offered to explain the unsafe behavior of the cells under operating and abuse conditions. Explanations fall into two categories: (1) thermal mechanisms, and (2) chemical mechanisms. However, it is quite difficult to separate the two. Both may be responsible for cell venting or explosion. Some safety problems encountered with these cells also may be due to design deficiencies and ineffective quality control during cell fabrication. A well-coordinated basic and applied research program is needed to develop safe Li-SOCl2 cells. Recommendations include: (1) learnig more about Li-SOL2 cell chemistry; (2) modeling cell and battery behavior; (3) optimizing cell design for safety and performance, (4) implementing quality control procedures; and (5) educating users.

The Theory of Planned Behavior and E-cig Use: Impulsive Personality, E-cig Attitudes, and E-cig Use.

PubMed

Hershberger, Alexandra; Connors, Miranda; Um, Miji; Cyders, Melissa A

2018-04-01

The current paper applied the Theory of Planned Behavior (TPB; Ajzen & Fishbein, 1988) to understand how impulsive personality traits and attitudes concerning e-cig use relate to the likelihood of electronic cigarette (e-cig) use. Seven hundred and fourteen participants (Mean age = 34.04, SD = 10.89, 48.6% female) completed cross-sectional measures of e-cig use attitudes (CEAC) and the Short UPPS-P Impulsive Behavior Scale. A structural path analysis suggested that urgency and deficits in conscientiousness were significantly related to e-cig attitudes (CFI = 0.99, TLI = 0.99, RMSEA = 0.02; urgency: β = 0.32, p = .001; deficits in conscientiousness: β = -0.48, p < .001). E-cig attitude scores were significantly higher for e-cig users than non-users, β = 0.85, p < .001. There was no significant direct path from impulsive personality traits to e-cig use. Findings provide initial support for a model in which impulsive traits are related to e-cig use through positive e-cig attitudes.

Access to Sterile Injecting Equipment is More Important Than Awareness of HCV Status for Injection Risk Behaviors Among Drug Users

PubMed Central

COX, JOSEPH; MORISSETTE, CAROLE; DE, PRITHWISH; TREMBLAY, CLAUDE; ALLARD, ROBERT; GRAVES, LISA; STEPHENSON, RANDOLPH; ROY, ÉLISE

2010-01-01

Awareness of hepatitis C virus (HCV) infection status is expected to influence risk behaviors. In 2004–2005, injection drug users (IDUs) recruited from syringe exchange programs (SEPs) and methadone clinics in Montreal, Canada, were interviewed on drug use behaviors (past 6 months) and HCV testing. Subjects (n = 230) were classified as low/intermediate risk (20.4% borrowed drug preparation equipment only) and high risk (19.6% borrowed syringes), and 54.5% reported being HCV positive. Logistic regression modeling showed that compared to no risk (60% borrowed nothing), low/intermediate risk was associated with fewer noninjecting social network members, poor physical health, and problems obtaining sterile injecting equipment. High risk was associated with all of these factors except social networks. HCV status was not associated with any level of risk. Improved access to sterile injecting equipment may be more important than knowledge of HCV status in reducing injection risks among this IDU population. The study limitations are noted and recommendations discussed. PMID:19242863

Reduction of User Interaction by Autonomy

NASA Technical Reports Server (NTRS)

Morfopoulos, Arin; McHenry, Michael; Matthies, Larry

2006-01-01

This paper describes experiments that quantify the improvement that autonomous behaviors enable in the amount of user interaction required to navigate a robot in urban environments. Many papers have discussed various ways to measure the absolute level of autonomy of a system; we measured the relative improvement of autonomous behaviors over teleoperation across multiple traverses of the same course. We performed four runs each on an 'easy' course and a 'hard' course, where half the runs were teleoperated and half used more autonomous behaviors. Statistics show 40-70% reductions in the amount of time the user interacts with the control station; however, with the behaviors tested, user attention remained on the control station even when he was not interacting. Reducing the need for attention will require better obstacle detection and avoidance and better absolute position estimation.

Interrelationships between marijuana demand and discounting of delayed rewards: Convergence in behavioral economic methods.

PubMed

Aston, Elizabeth R; Metrik, Jane; Amlung, Michael; Kahler, Christopher W; MacKillop, James

2016-12-01

Distinct behavioral economic domains, including high perceived drug value (demand) and delay discounting (DD), have been implicated in the initiation of drug use and the progression to dependence. However, it is unclear whether frequent marijuana users conform to a "reinforcer pathology" addiction model wherein marijuana demand and DD jointly increase risk for problematic marijuana use and cannabis dependence (CD). Participants (n=88, 34% female, 14% cannabis dependent) completed a marijuana purchase task at baseline. A delay discounting task was completed following placebo marijuana cigarette (0% THC) administration during a separate experimental session. Marijuana demand and DD were quantified using area under the curve (AUC). In multiple regression models, demand uniquely predicted frequency of marijuana use while DD did not. In contrast, DD uniquely predicted CD symptom count while demand did not. There were no significant interactions between demand and DD in either model. These findings suggest that frequent marijuana users exhibit key constituents of the reinforcer pathology model: high marijuana demand and steep discounting of delayed rewards. However, demand and DD appear to be independent rather than synergistic risk factors for elevated marijuana use and risk for progression to CD. Findings also provide support for using AUC as a singular marijuana demand metric, particularly when also examining other behavioral economic constructs that apply similar statistical approaches, such as DD, to support analytic methodological convergence. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Days of heroin use predict poor self-reported health in hospitalized heroin users

PubMed Central

Meshesha, Lidia Z.; Tsui, Judith I.; Liebschutz, Jane M.; Crooks, Denise; Anderson, Bradley J.; Herman, Debra S.; Stein, Michael D.

2013-01-01

This study examined associations between substance use behaviors and self-reported health among hospitalized heroin users. Of the 112 participants, 53 (47%) reported good or better health. In multivariable logistic regression models, each day of heroin use in the last month was associated with an 8% lower odds of reporting health as good or better (OR=.92; 95%CI 0.87, 0.97, p < .05). Cocaine, cannabis, cigarettes, alcohol use, unintentional overdose, nor injection drug use were associated with health status. PMID:24045030

Models accounting for intention-behavior discordance in the physical activity domain: a user's guide, content overview, and review of current evidence.

PubMed

Rhodes, Ryan E; Yao, Christopher A

2015-02-07

There is a growing concern among researchers with the limited effectiveness and yet subsequent stagnation of theories applied to physical activity (PA). One of the most highlighted areas of concern is the established gap between intention and PA, yet the considerable use of models that assume intention is the proximal antecedent of PA. The objective of this review was to: 1) provide a guide and thematic analysis of the available models that include constructs that address intention-behavior discordance and 2) highlight the evidence for these structures in the PA domain. A literature search was conducted among 13 major databases to locate relevant models and PA studies published before August 2014. Sixteen models were identified and nine overall themes for post-intentional constructs were created. Of the 16 models, eight were applied to 36 PA studies. Early evidence supported maintenance self-efficacy, behavioral regulation strategies, affective judgments, perceived control/opportunity, habit, and extraversion as reliable predictors of post-intention PA. Several intention-behavior discordance models exist within the literature, but are not used frequently. Further efforts are needed to test these models, preferably with experimental designs.

Hop limited epidemic-like information spreading in mobile social networks with selfish nodes

NASA Astrophysics Data System (ADS)

Wu, Yahui; Deng, Su; Huang, Hongbin

2013-07-01

Similar to epidemics, information can be transmitted directly among users in mobile social networks. Different from epidemics, we can control the spreading process by adjusting the corresponding parameters (e.g., hop count) directly. This paper proposes a theoretical model to evaluate the performance of an epidemic-like spreading algorithm, in which the maximal hop count of the information is limited. In addition, our model can be used to evaluate the impact of users’ selfish behavior. Simulations show the accuracy of our theoretical model. Numerical results show that the information hop count can have an important impact. In addition, the impact of selfish behavior is related to the information hop count.

Investigating Students' Acceptance and Self-Efficacy of E-Learning at Al-Aqsa University Based on TAM Model

ERIC Educational Resources Information Center

Mahdi, Hasan Rebhi

2014-01-01

The study aimed at investigating the influence of E-learning Self-Efficacy (ELSE) on the acceptance of e-learning by using the Technology Acceptance Model (TAM). According to the TAM which used as the theoretical basis, both of the Perceived Usefulness (PU) and the Perceived Ease of Use (PEOU) influence directly the end user's Behavioral Intention…

Automatic Screening for Perturbations in Boolean Networks.

PubMed

Schwab, Julian D; Kestler, Hans A

2018-01-01

A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior-so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.

Sex, touch, and HIV risk among ecstasy users.

PubMed

Theall, Katherine P; Elifson, Kirk W; Sterk, Claire E

2006-03-01

We examined HIV risk among heavy and nonheavy ecstasy users, focusing specifically on touch and sexual behavior as part of the ecstasy experience. Structured interviews were conducted with 268 young adult (age 18-25) ecstasy users in Atlanta, Georgia. Heavy ecstasy users were more likely to have been tested for HIV than nonheavy users (79 vs. 68%). However, they also were more likely to perceive no chance of contracting HIV (36 vs. 26%). Touch, both sensual and sexual, was a significant part of the ecstasy experience. In addition, ecstasy use seemed to increase the sexual desire, however, not the ability to achieve an orgasm. Heavy users reported more sexual risk-taking than their nonheavy using counterparts. Results suggest that the setting of ecstasy use also may influence involvement in risk behaviors. Future longitudinal studies are needed on the relationship between ecstasy use, touch, sexual arousal and ability, and risk behavior.

Parameter estimation of a nonlinear Burger's model using nanoindentation and finite element-based inverse analysis

NASA Astrophysics Data System (ADS)

Hamim, Salah Uddin Ahmed

Nanoindentation involves probing a hard diamond tip into a material, where the load and the displacement experienced by the tip is recorded continuously. This load-displacement data is a direct function of material's innate stress-strain behavior. Thus, theoretically it is possible to extract mechanical properties of a material through nanoindentation. However, due to various nonlinearities associated with nanoindentation the process of interpreting load-displacement data into material properties is difficult. Although, simple elastic behavior can be characterized easily, a method to characterize complicated material behavior such as nonlinear viscoelasticity is still lacking. In this study, a nanoindentation-based material characterization technique is developed to characterize soft materials exhibiting nonlinear viscoelasticity. Nanoindentation experiment was modeled in finite element analysis software (ABAQUS), where a nonlinear viscoelastic behavior was incorporated using user-defined subroutine (UMAT). The model parameters were calibrated using a process called inverse analysis. In this study, a surrogate model-based approach was used for the inverse analysis. The different factors affecting the surrogate model performance are analyzed in order to optimize the performance with respect to the computational cost.

Empirical analysis of online human dynamics

NASA Astrophysics Data System (ADS)

Zhao, Zhi-Dan; Zhou, Tao

2012-06-01

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

HIV seroprevalence among street-recruited injection drug and crack cocaine users in 16 US municipalities.

PubMed Central

Kral, A H; Bluthenthal, R N; Booth, R E; Watters, J K

1998-01-01

OBJECTIVES: This study deter- mined human immunodeficiency virus (HIV) seroprevalence and factors associated with HIV infection among street-recruited injection drug users and crack cocaine smokers. METHODS: An analysis was performed on HIV serologies and risk behaviors of 6402 injection drug users and 3383 crack smokers in 16 US municipalities in 1992 and 1993. RESULTS: HIV seroprevalence was 12.7% among injection drug users and 7.5% among crack smokers. Most high-seroprevalence municipalities (>25%) were located along the eastern seaboard of the United States. In high-seroprevalence municipalities, but not in others, HIV seroprevalence was higher for injection drug users than for crack smokers. Among injection drug users, cocaine injection, use of speedballs (cocaine or amphetamines with heroin), and sexual risk behaviors were independently associated with HIV infection. Among crack smokers, sexual risk behaviors were associated with HIV infection. CONCLUSIONS: Injection drug users and crack smokers are at high risk for HIV infection. PMID:9584014

Estimating endogenous changes in task performance from EEG

PubMed Central

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

2014-01-01

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

Crack Cocaine Use and its Relationship with Violence and Hiv

PubMed Central

de Carvalho, Heraclito Barbosa; Seibel, Sergio Dario

2009-01-01

OBJECTIVES To evaluate crack cocaine use practices, risk behaviors associated with HIV infection among drug users, and their involvement with violence. INTRODUCTION HIV infections are frequent among drug users due to risky sexual behavior. It is generally accepted that crack cocaine use is related to increased levels of violence. Several reports point to an increase in violence from those involved in drug trafficking. Although HIV infections and risky sexual behavior among drug users have been quite well studied, there are few studies that evaluate violence as it relates to drugs, particularly crack. METHODS A total of 350 drug users attending drug abuse treatment clinics in São Paulo, Brazil were interviewed about their risky behaviors. Each patient had a serological HIV test done. RESULTS HIV prevalence was 6.6% (4.0 to 10.2). Violence was reported by 97% (94.7 to 99.1) of the subjects (including cases without personal involvement). Acts of violence such as verbal arguments, physical fights, threats, death threats, theft, and drug trafficking were significantly higher among crack users. A decrease in frequency of sexual intercourse was observed among users of injected drugs, though prostitution was observed as a means of obtaining drugs. A high number of crack cocaine users had a history of previous imprisonment, many for drug-related infractions. DISCUSSION The data presented are in accordance with other reports in the literature, and they show a correlation between drug use, imprisonment, violence, and drug trafficking. CONCLUSION A high HIV prevalence and associated risky sexual behaviors were observed among crack cocaine users. The society and the authorities that deal with violence related to crack users and drug trafficking should be aware of these problems. PMID:19759879

Capturing User Reading Behaviors for Personalized Document Summarization

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

Xu, Songhua; Jiang, Hao; Lau, Francis

2011-01-01

We propose a new personalized document summarization method that observes a user's personal reading preferences. These preferences are inferred from the user's reading behaviors, including facial expressions, gaze positions, and reading durations that were captured during the user's past reading activities. We compare the performance of our algorithm with that of a few peer algorithms and software packages. The results of our comparative study show that our algorithm can produce more superior personalized document summaries than all the other methods in that the summaries generated by our algorithm can better satisfy a user's personal preferences.

Tailored motivational message generation: A model and practical framework for real-time physical activity coaching.

PubMed

Op den Akker, Harm; Cabrita, Miriam; Op den Akker, Rieks; Jones, Valerie M; Hermens, Hermie J

2015-06-01

This paper presents a comprehensive and practical framework for automatic generation of real-time tailored messages in behavior change applications. Basic aspects of motivational messages are time, intention, content and presentation. Tailoring of messages to the individual user may involve all aspects of communication. A linear modular system is presented for generating such messages. It is explained how properties of user and context are taken into account in each of the modules of the system and how they affect the linguistic presentation of the generated messages. The model of motivational messages presented is based on an analysis of existing literature as well as the analysis of a corpus of motivational messages used in previous studies. The model extends existing 'ontology-based' approaches to message generation for real-time coaching systems found in the literature. Practical examples are given on how simple tailoring rules can be implemented throughout the various stages of the framework. Such examples can guide further research by clarifying what it means to use e.g. user targeting to tailor a message. As primary example we look at the issue of promoting daily physical activity. Future work is pointed out in applying the present model and framework, defining efficient ways of evaluating individual tailoring components, and improving effectiveness through the creation of accurate and complete user- and context models. Copyright © 2015 Elsevier Inc. All rights reserved.

Rational Analyses of Information Foraging on the Web

ERIC Educational Resources Information Center

Pirolli, Peter

2005-01-01

This article describes rational analyses and cognitive models of Web users developed within information foraging theory. This is done by following the rational analysis methodology of (a) characterizing the problems posed by the environment, (b) developing rational analyses of behavioral solutions to those problems, and (c) developing cognitive…

Marijuana and Psychedelic Use: Are They Deviant Responses?

ERIC Educational Resources Information Center

Davis, Carl S.

1978-01-01

Focuses on the state of psychological health of young drug users rather than relying on a model of dysfunction to understanding their behavior. There were 81 Ss within the ages of 16 and 23. Each S responded to the Personal Orientation Inventory. There were no significant differences in psychological health. (Author)

Chapter 4: Variant descriptions

Treesearch

Duncan C. Lutes; Donald C. E. Robinson

2003-01-01

The Fire and Fuels Extension (FFE) to the Forest Vegetation Simulator (FVS) simulates fuel dynamics and potential fire behavior over time, in the context of stand development and management. This report documents differences between geographic variants of the FFE. It is a companion document to the FFE "Model Description" and "User's Guide."...

Predictor Combination in Binary Decision-Making Situations

ERIC Educational Resources Information Center

McGrath, Robert E.

2008-01-01

Professional psychologists are often confronted with the task of making binary decisions about individuals, such as predictions about future behavior or employee selection. Test users familiar with linear models and Bayes's theorem are likely to assume that the accuracy of decisions is consistently improved by combination of outcomes across valid…

Assessing the Moderating Effect of the End User in Consumer Behavior: The Acceptance of Technological Implants to Increase Innate Human Capacities

PubMed Central

Pelegrín-Borondo, Jorge; Reinares-Lara, Eva; Olarte-Pascual, Cristina; Garcia-Sierra, Marta

2016-01-01

Today, technological implants are being developed to increase innate human capacities, such as memory or calculation speed, and to endow us with new ones, such as the remote control of machines. This study's aim was two-fold: first, to introduce a Cognitive-Affective-Normative (CAN) model of technology acceptance to explain the intention to use this technology in the field of consumer behavior; and second, to analyze the differences in the intention to use it based on whether the intended implant recipient is oneself or one's child (i.e., the moderating effect of the end user). A multi-group analysis was performed to compare the results between the two groups: implant “for me” (Group 1) and implant “for my child” (Group 2). The model largely explains the intention to use the insideable technology for the specified groups [variance explained (R2) of over 0.70 in both cases]. The most important variables were found to be “positive emotions” and (positive) “subjective norm.” This underscores the need to broaden the range of factors considered to be decisive in technology acceptance to include variables related to consumers' emotions. Moreover, statistically significant differences were found between the “for me” and “for my child” models for “perceived ease of use (PEU)” and “subjective norm.” These findings confirm the moderating effect of the end user on new insideable technology acceptance. PMID:26941662

Incentivizing biodiversity conservation in artisanal fishing communities through territorial user rights and business model innovation.

PubMed

Gelcich, Stefan; Donlan, C Josh

2015-08-01

Territorial user rights for fisheries are being promoted to enhance the sustainability of small-scale fisheries. Using Chile as a case study, we designed a market-based program aimed at improving fishers' livelihoods while incentivizing the establishment and enforcement of no-take areas within areas managed with territorial user right regimes. Building on explicit enabling conditions (i.e., high levels of governance, participation, and empowerment), we used a place-based, human-centered approach to design a program that will have the necessary support and buy-in from local fishers to result in landscape-scale biodiversity benefits. Transactional infrastructure must be complex enough to capture the biodiversity benefits being created, but simple enough so that the program can be scaled up and is attractive to potential financiers. Biodiversity benefits created must be commoditized, and desired behavioral changes must be verified within a transactional context. Demand must be generated for fisher-created biodiversity benefits in order to attract financing and to scale the market model. Important design decisions around these 3 components-supply, transactional infrastructure, and demand-must be made based on local social-ecological conditions. Our market model, which is being piloted in Chile, is a flexible foundation on which to base scalable opportunities to operationalize a scheme that incentivizes local, verifiable biodiversity benefits via conservation behaviors by fishers that could likely result in significant marine conservation gains and novel cross-sector alliances. © 2015, Society for Conservation Biology.

Interacting with an artificial partner: modeling the role of emotional aspects.

PubMed

Cattinelli, Isabella; Goldwurm, Massimiliano; Borghese, N Alberto

2008-12-01

In this paper we introduce a simple model based on probabilistic finite state automata to describe an emotional interaction between a robot and a human user, or between simulated agents. Based on the agent's personality, attitude, and nature, and on the emotional inputs it receives, the model will determine the next emotional state displayed by the agent itself. The probabilistic and time-varying nature of the model yields rich and dynamic interactions, and an autonomous adaptation to the interlocutor. In addition, a reinforcement learning technique is applied to have one agent drive its partner's behavior toward desired states. The model may also be used as a tool for behavior analysis, by extracting high probability patterns of interaction and by resorting to the ergodic properties of Markov chains.

Understanding Health and Health-Related Behavior of Users of Internet Health Information.

PubMed

Wimble, Matt

2016-10-01

Little is known about how actual use of Internet health-related information is associated with health or health-related behavior. Using a nationally representative sample of 34,525 from 2012, this study examined the demographics of users of Internet health-related information (users), reports estimates of association with several health and behavioral outcomes adjusting for demographic factors, and analyzed the sample by education level, race, gender, and age. Analysis of a large nationally representative sample shows evidence that users of health-related information (users) on the Internet are younger, more educated, more likely to be insured, more likely to be female, and less likely to be African American. After adjusting for demographic differences, users are more likely to have been diagnosed with hypertension, cancer, stroke, and high cholesterol, but no evidence of current hypertension, weight-related issues, or being in fair or poor health. Users are less likely to smoke and among smokers are more likely to attempt quitting. Users are more likely to exercise, get a flu shot, pap smear, mammogram, HIV test, colon cancer screening, blood pressure check, and cholesterol check, but likely to be heavy drinkers. With few exceptions, results appear robust across gender, age groups, level of education, and ethnicity. Use is generally positively associated with prior diagnosis for several conditions and behaviors related to improved health, but I find no relationship with existing health status. The association between use of health-related Internet information and health-related behavior seems robust across levels of education, age, gender, and race.

An Agent-based Modeling of Water-Food Nexus towards Sustainable Management of Urban Water Resources

NASA Astrophysics Data System (ADS)

Esmaeili, N.; Kanta, L.

2017-12-01

Growing population, urbanization, and climate change have put tremendous stress on water systems in many regions. A shortage in water system not only affects water users of a municipality but also that of food system. About 70% of global water is withdrawn for agriculture; livestock and dairy productions are also dependent on water availability. Although researchers and policy makers have identified and emphasized the water-food (WF) nexus in recent decade, most existing WF models offer strategies to reduce trade-offs and to generate benefits without considering feedback loops and adaptations between those systems. Feedback loops between water and food system can help understand long-term behavioral trends between water users of the integrated WF system which, in turn, can help manage water resources sustainably. An Agent-based modeling approach is applied here to develop a conceptual framework of WF systems. All water users in this system are modeled as agents, who are capable of making decisions and can adapt new behavior based on inputs from other agents in a shared environment through a set of logical and mathematical rules. Residential and commercial/industrial consumers are represented as municipal agents; crop, livestock, and dairy farmers are represented as food agents; and water management officials are represented as policy agent. During the period of water shortage, policy agent will propose/impose various water conservation measures, such as adapting water-efficient technologies, banning outdoor irrigation, implementing supplemental irrigation, using recycled water for livestock/dairy production, among others. Municipal and food agents may adapt conservation strategies and will update their demand accordingly. Emergent properties of the WF nexus will arise through dynamic interactions between various actors of water and food system. This model will be implemented to a case study for resource allocation and future policy development.

Transition from 'model-based' to 'model-free' behavioral control in addiction: Involvement of the orbitofrontal cortex and dorsolateral striatum.

PubMed

Lucantonio, Federica; Caprioli, Daniele; Schoenbaum, Geoffrey

2014-01-01

Cocaine addiction is a complex and multidimensional process involving a number of behavioral and neural forms of plasticity. The behavioral transition from voluntary drug use to compulsive drug taking may be explained at the neural level by drug-induced changes in function or interaction between a flexible planning system, associated with prefrontal cortical regions, and a rigid habit system, associated with the striatum. The dichotomy between these two systems is operationalized in computational theory by positing model-based and model-free learning mechanisms, the former relying on an "internal model" of the environment and the latter on pre-computed or cached values to control behavior. In this review, we will suggest that model-free and model-based learning mechanisms appear to be differentially affected, at least in the case of psychostimulants such as cocaine, with the former being enhanced while the latter are disrupted. As a result, the behavior of long-term drug users becomes less flexible and responsive to the desirability of expected outcomes and more habitual, based on the long history of reinforcement. To support our specific proposal, we will review recent neural and behavioral evidence on the effect of psychostimulant exposure on orbitofrontal and dorsolateral striatum structure and function. This article is part of a Special Issue entitled 'NIDA 40th Anniversary Issue'. Published by Elsevier Ltd.

The Searching Behavior of Remote Users: A Study of One Online Public Access Catalog (OPAC).

ERIC Educational Resources Information Center

Kalin, Sally W.

1991-01-01

Describes a study that was conducted to determine whether the searching behavior of remote users of LIAS (Library Information Access System), Pennsylvania State University's online public access catalog (OPAC), differed from those using the OPAC within the library. Differences in search strategies and in user satisfaction are discussed. (eight…

The Effect of Extrinsic Motivation on User Behavior in a Collaborative Information Finding System.

ERIC Educational Resources Information Center

Shapira, Bracha; Kantor, Paul B.; Melamed, Benjamin

2001-01-01

Reports on an experiment conducted using the "AntWorld" system, a collaborative information finding system for the Internet, to explore the effect of added motivation on users' behavior. Findings suggest that for the system to be effective, users must be motivated either by the environment, or by incentives within the system. (Author/AEF)

Model evaluation using a community benchmarking system for land surface models

NASA Astrophysics Data System (ADS)

Mu, M.; Hoffman, F. M.; Lawrence, D. M.; Riley, W. J.; Keppel-Aleks, G.; Kluzek, E. B.; Koven, C. D.; Randerson, J. T.

2014-12-01

Evaluation of atmosphere, ocean, sea ice, and land surface models is an important step in identifying deficiencies in Earth system models and developing improved estimates of future change. For the land surface and carbon cycle, the design of an open-source system has been an important objective of the International Land Model Benchmarking (ILAMB) project. Here we evaluated CMIP5 and CLM models using a benchmarking system that enables users to specify models, data sets, and scoring systems so that results can be tailored to specific model intercomparison projects. Our scoring system used information from four different aspects of global datasets, including climatological mean spatial patterns, seasonal cycle dynamics, interannual variability, and long-term trends. Variable-to-variable comparisons enable investigation of the mechanistic underpinnings of model behavior, and allow for some control of biases in model drivers. Graphics modules allow users to evaluate model performance at local, regional, and global scales. Use of modular structures makes it relatively easy for users to add new variables, diagnostic metrics, benchmarking datasets, or model simulations. Diagnostic results are automatically organized into HTML files, so users can conveniently share results with colleagues. We used this system to evaluate atmospheric carbon dioxide, burned area, global biomass and soil carbon stocks, net ecosystem exchange, gross primary production, ecosystem respiration, terrestrial water storage, evapotranspiration, and surface radiation from CMIP5 historical and ESM historical simulations. We found that the multi-model mean often performed better than many of the individual models for most variables. We plan to publicly release a stable version of the software during fall of 2014 that has land surface, carbon cycle, hydrology, radiation and energy cycle components.

Extremely frequent behavior in consumer research: theory and empirical evidence for chronic casino gambling.

PubMed

Perfetto, Ralph; Woodside, Arch G

2009-09-01

The present study informs understanding of customer segmentation strategies by extending Twedt's heavy-half propositions to include a segment of users that represent less than 2% of all households-consumers demonstrating extremely frequent behavior (EFB). Extremely frequent behavior (EFB) theory provides testable propositions relating to the observation that few (2%) consumers in many product and service categories constitute more than 25% of the frequency of product or service use. Using casino gambling as an example for testing EFB theory, an analysis of national survey data shows that extremely frequent casino gamblers do exist and that less than 2% of all casino gamblers are responsible for nearly 25% of all casino gambling usage. Approximately 14% of extremely frequent casino users have very low-household income, suggesting somewhat paradoxical consumption patterns (where do very low-income users find the money to gamble so frequently?). Understanding the differences light, heavy, and extreme users and non-users can help marketers and policymakers identify and exploit "blue ocean" opportunities (Kim and Mauborgne, Blue ocean strategy, Harvard Business School Press, Boston, 2005), for example, creating effective strategies to convert extreme users into non-users or non-users into new users.

Effectively identifying user profiles in network and host metrics

NASA Astrophysics Data System (ADS)

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

2010-04-01

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

Development and User Satisfaction of "Plan-It Commander," a Serious Game for Children with ADHD.

PubMed

Bul, Kim C M; Franken, Ingmar H A; Van der Oord, Saskia; Kato, Pamela M; Danckaerts, Marina; Vreeke, Leonie J; Willems, Annik; van Oers, Helga J J; van den Heuvel, Ria; van Slagmaat, Rens; Maras, Athanasios

2015-12-01

The need for engaging treatment approaches within mental health care has led to the application of gaming approaches to existing behavioral training programs (i.e., gamification). Because children with attention deficit/hyperactivity disorder (ADHD) tend to have fewer problems with concentration and engagement when playing digital games, applying game technologies and design approaches to complement treatment may be a useful means to engage this population in their treatment. Unfortunately, gamified training programs currently available for ADHD have been limited in their ability to demonstrate in-game behavior skills that generalize to daily life situations. Therefore, we developed a new serious game (called "Plan-It Commander") that was specifically designed to promote behavioral learning and promotes strategy use in domains of daily life functioning such as time management, planning/organizing, and prosocial skills that are known to be problematic for children with ADHD. An interdisciplinary team contributed to the development of the game. The game's content and approach are based on psychological principles from the Self-Regulation Model, Social Cognitive Theory, and Learning Theory. In this article, game development and the scientific background of the behavioral approach are described, as well as results of a survey (n = 42) to gather user feedback on the first prototype of the game. The findings suggest that participants were satisfied with this game and provided the basis for further development and research to the game. Implications for developing serious games and applying user feedback in game development are discussed.

Environmental influences: factors influencing a woman's decision to use dietary supplements.

PubMed

Conner, Mark; Kirk, Sara F L; Cade, Janet E; Barrett, Jennifer H

2003-06-01

Use of dietary supplements by women, particularly those over 40 years of age may be widespread in the United Kingdom. However, from surveillance data, there appears to be a disparity between nutrition and health needs and the rationale for and actual use of dietary supplements by women. This apparent paradox forms the basis for an inverse supplement hypothesis (i.e., supplement use in women appears to be most prevalent among those with least need). Little research has been done to examine the factors underlying the decision to use dietary supplements. Reasons for consuming dietary supplements are often complex, combining social, psychological, knowledge and economic factors. The theory of planned behavior is a widely used model for assessing factors influencing behavioral motivation and action that may be useful for assessing specific diet- and nutrition-related practices. It provided the basis for the development of a questionnaire to explore overall dietary supplement use in a cohort of women in the United Kingdom. The analysis of factors related to beliefs underlying dietary supplement use revealed differences between supplement users and nonusers. Differences included a stronger belief by users than nonusers that taking dietary supplements ensures against possible ill health. Both users and nonusers of supplements also perceived the media (books and magazines) to be a powerful influence on a person's decision to use supplements. These findings highlight the potential of the theory of planned behavior in exploring supplement-taking behavior while throwing light on the factors influencing an individual's motivations to use dietary supplements.

Fall and Fracture Risk in Nursing Home Residents With Moderate-to-Severe Behavioral Symptoms of Alzheimer's Disease and Related Dementias Initiating Antidepressants or Antipsychotics.

PubMed

Wei, Yu-Jung; Simoni-Wastila, Linda; Lucas, Judith A; Brandt, Nicole

2017-05-01

Both antidepressants and antipsychotics are used in older adults with behavioral symptoms of Alzheimer's disease and related dementias. Despite the prevalent use of these agents, little is known about their comparative risks for falls and fractures. Using 2007-2009 Medicare claims data linked to Minimum Data Set 2.0, we identified new users of antidepressants and antipsychotics among nursing home residents with Alzheimer's disease and related dementias who had moderate-to-severe behavioral symptoms. Separate discrete-time survival models were used to estimate risks of falls, fractures, and a composite of both among antidepressant group versus antipsychotic group. Compared to antipsychotic users, antidepressant users experienced significantly higher risk for fractures (adjusted hazard ratio = 1.35, 95% confidence interval = 1.10-1.66). The overall risk of falls or fractures remained significant in the antidepressant versus antipsychotic group (adjusted hazard ratio = 1.16, 95% confidence interval = 1.02-1.32). Antidepressants are associated with higher fall and fracture risk compared to antipsychotics in the management of older adults with Alzheimer's disease and related dementias who experience moderate-to-severe behavioral symptoms. Clinicians need to assess the ongoing risks/benefits of antidepressants for these symptoms especially in light of the increasingly prevalent use of these agents. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The Virtual Environment for Rapid Prototyping of the Intelligent Environment

PubMed Central

Bouzouane, Abdenour; Gaboury, Sébastien

2017-01-01

Advances in domains such as sensor networks and electronic and ambient intelligence have allowed us to create intelligent environments (IEs). However, research in IE is being held back by the fact that researchers face major difficulties, such as a lack of resources for their experiments. Indeed, they cannot easily build IEs to evaluate their approaches. This is mainly because of economic and logistical issues. In this paper, we propose a simulator to build virtual IEs. Simulators are a good alternative to physical IEs because they are inexpensive, and experiments can be conducted easily. Our simulator is open source and it provides users with a set of virtual sensors that simulates the behavior of real sensors. This simulator gives the user the capacity to build their own environment, providing a model to edit inhabitants’ behavior and an interactive mode. In this mode, the user can directly act upon IE objects. This simulator gathers data generated by the interactions in order to produce datasets. These datasets can be used by scientists to evaluate several approaches in IEs. PMID:29112175

The Virtual Environment for Rapid Prototyping of the Intelligent Environment.

PubMed

Francillette, Yannick; Boucher, Eric; Bouzouane, Abdenour; Gaboury, Sébastien

2017-11-07

Advances in domains such as sensor networks and electronic and ambient intelligence have allowed us to create intelligent environments (IEs). However, research in IE is being held back by the fact that researchers face major difficulties, such as a lack of resources for their experiments. Indeed, they cannot easily build IEs to evaluate their approaches. This is mainly because of economic and logistical issues. In this paper, we propose a simulator to build virtual IEs. Simulators are a good alternative to physical IEs because they are inexpensive, and experiments can be conducted easily. Our simulator is open source and it provides users with a set of virtual sensors that simulates the behavior of real sensors. This simulator gives the user the capacity to build their own environment, providing a model to edit inhabitants' behavior and an interactive mode. In this mode, the user can directly act upon IE objects. This simulator gathers data generated by the interactions in order to produce datasets. These datasets can be used by scientists to evaluate several approaches in IEs.

Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions.

PubMed

Dong, Yuwen; Deshpande, Sunil; Rivera, Daniel E; Downs, Danielle S; Savage, Jennifer S

2014-06-01

Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.

A spread willingness computing-based information dissemination model.

PubMed

Huang, Haojing; Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.

A Spread Willingness Computing-Based Information Dissemination Model

PubMed Central

Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network. PMID:25110738

A Context-Aware Mobile User Behavior-Based Neighbor Finding Approach for Preference Profile Construction †

PubMed Central

Gao, Qian; Fu, Deqian; Dong, Xiangjun

2016-01-01

In this paper, a new approach is adopted to update the user preference profile by seeking users with similar interests based on the context obtainable for a mobile network instead of from desktop networks. The trust degree between mobile users is calculated by analyzing their behavior based on the context, and then the approximate neighbors are chosen by combining the similarity of the mobile user preference and the trust degree. The approach first considers the communication behaviors between mobile users, the mobile network services they use as well as the corresponding context information. Then a similarity degree of the preference between users is calculated with the evaluation score of a certain mobile web service provided by a mobile user. Finally, based on the time attenuation function, the users with similar preference are found, through which we can dynamically update the target user’s preference profile. Experiments are then conducted to test the effect of the context on the credibility among mobile users, the effect of time decay factors and trust degree thresholds. Simulation shows that the proposed approach outperforms two other methods in terms of Recall Ratio, Precision Ratio and Mean Absolute Error, because neither of them consider the context mobile information. PMID:26805852

Research on gender differences in online health communities.

PubMed

Liu, Xuan; Sun, Min; Li, Jia

2018-03-01

With the growing concern about health issues and the emergence of online communities based on user-generated content (UGC), more and more people are participating in online health communities (OHCs) to exchange opinions and health information. This paper aims to examine whether and how male and female users behave differently in OHCs. Using data from a leading diabetes community in China (Tianmijiayuan), we incorporate three different techniques: topic modeling analysis, sentiment analysis and friendship network analysis to investigate gender differences in chronic online health communities. The results indicated that (1) Male users' posting content was usually more professional and included more medical terms. Comparatively speaking, female users were more inclined to seek emotional support in the health communities. (2) Female users expressed more negative emotions than male users did, especially anxiety and sadness. (3) In addition, male users were more centered and influential in the friendship network than were women. Through these analyses, our research revealed the behavioral characteristics and needs for different gender users in online health communities. Gaining a deeper understanding of gender differences in OHCs can serve as guidance to better meet the information needs, emotional needs and relationship needs of male and female patients. Copyright © 2018 Elsevier B.V. All rights reserved.

Gravity Modeling for Variable Fidelity Environments

NASA Technical Reports Server (NTRS)

Madden, Michael M.

2006-01-01

Aerospace simulations can model worlds, such as the Earth, with differing levels of fidelity. The simulation may represent the world as a plane, a sphere, an ellipsoid, or a high-order closed surface. The world may or may not rotate. The user may select lower fidelity models based on computational limits, a need for simplified analysis, or comparison to other data. However, the user will also wish to retain a close semblance of behavior to the real world. The effects of gravity on objects are an important component of modeling real-world behavior. Engineers generally equate the term gravity with the observed free-fall acceleration. However, free-fall acceleration is not equal to all observers. To observers on the sur-face of a rotating world, free-fall acceleration is the sum of gravitational attraction and the centrifugal acceleration due to the world's rotation. On the other hand, free-fall acceleration equals gravitational attraction to an observer in inertial space. Surface-observed simulations (e.g. aircraft), which use non-rotating world models, may choose to model observed free fall acceleration as the gravity term; such a model actually combines gravitational at-traction with centrifugal acceleration due to the Earth s rotation. However, this modeling choice invites confusion as one evolves the simulation to higher fidelity world models or adds inertial observers. Care must be taken to model gravity in concert with the world model to avoid denigrating the fidelity of modeling observed free fall. The paper will go into greater depth on gravity modeling and the physical disparities and synergies that arise when coupling specific gravity models with world models.

How Gamification Affects Physical Activity: Large-scale Analysis of Walking Challenges in a Mobile Application

PubMed Central

Shameli, Ali; Althoff, Tim; Saberi, Amin; Leskovec, Jure

2017-01-01

Gamification represents an effective way to incentivize user behavior across a number of computing applications. However, despite the fact that physical activity is essential for a healthy lifestyle, surprisingly little is known about how gamification and in particular competitions shape human physical activity. Here we study how competitions affect physical activity. We focus on walking challenges in a mobile activity tracking application where multiple users compete over who takes the most steps over a predefined number of days. We synthesize our findings in a series of game and app design implications. In particular, we analyze nearly 2,500 physical activity competitions over a period of one year capturing more than 800,000 person days of activity tracking. We observe that during walking competitions, the average user increases physical activity by 23%. Furthermore, there are large increases in activity for both men and women across all ages, and weight status, and even for users that were previously fairly inactive. We also find that the composition of participants greatly affects the dynamics of the game. In particular, if highly unequal participants get matched to each other, then competition suffers and the overall effect on the physical activity drops significantly. Furthermore, competitions with an equal mix of both men and women are more effective in increasing the level of activities. We leverage these insights to develop a statistical model to predict whether or not a competition will be particularly engaging with significant accuracy. Our models can serve as a guideline to help design more engaging competitions that lead to most beneficial behavioral changes. PMID:28990011

How Gamification Affects Physical Activity: Large-scale Analysis of Walking Challenges in a Mobile Application.

PubMed

Shameli, Ali; Althoff, Tim; Saberi, Amin; Leskovec, Jure

2017-04-01

Gamification represents an effective way to incentivize user behavior across a number of computing applications. However, despite the fact that physical activity is essential for a healthy lifestyle, surprisingly little is known about how gamification and in particular competitions shape human physical activity. Here we study how competitions affect physical activity. We focus on walking challenges in a mobile activity tracking application where multiple users compete over who takes the most steps over a predefined number of days. We synthesize our findings in a series of game and app design implications. In particular, we analyze nearly 2,500 physical activity competitions over a period of one year capturing more than 800,000 person days of activity tracking. We observe that during walking competitions, the average user increases physical activity by 23%. Furthermore, there are large increases in activity for both men and women across all ages, and weight status, and even for users that were previously fairly inactive. We also find that the composition of participants greatly affects the dynamics of the game. In particular, if highly unequal participants get matched to each other, then competition suffers and the overall effect on the physical activity drops significantly. Furthermore, competitions with an equal mix of both men and women are more effective in increasing the level of activities. We leverage these insights to develop a statistical model to predict whether or not a competition will be particularly engaging with significant accuracy. Our models can serve as a guideline to help design more engaging competitions that lead to most beneficial behavioral changes.

Processes in scientific workflows for information seeking related to physical sample materials

NASA Astrophysics Data System (ADS)

Ramdeen, S.

2014-12-01

The majority of State Geological Surveys have repositories containing cores, cuttings, fossils or other physical sample material. State surveys maintain these collections to support their own research as well as the research conducted by external users from other organizations. This includes organizations such as government agencies (state and federal), academia, industry and the public. The preliminary results presented in this paper will look at the research processes of these external users. In particular: how they discover, access and use digital surrogates, which they use to evaluate and access physical items in these collections. Data such as physical samples are materials that cannot be completely replaced with digital surrogates. Digital surrogates may be represented as metadata, which enable discovery and ultimately access to these samples. These surrogates may be found in records, databases, publications, etc. But surrogates do not completely prevent the need for access to the physical item as they cannot be subjected to chemical testing and/or other similar analysis. The goal of this research is to document the various processes external users perform in order to access physical materials. Data for this study will be collected by conducting interviews with these external users. During the interviews, participants will be asked to describe the workflow that lead them to interact with state survey repositories, and what steps they took afterward. High level processes/categories of behavior will be identified. These processes will be used in the development of an information seeking behavior model. This model may be used to facilitate the development of management tools and other aspects of cyberinfrastructure related to physical samples.

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

DTIC Science & Technology

1994-04-01

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

Modeling and simulation of dust behaviors behind a moving vehicle

NASA Astrophysics Data System (ADS)

Wang, Jingfang

Simulation of physically realistic complex dust behaviors is a difficult and attractive problem in computer graphics. A fast, interactive and visually convincing model of dust behaviors behind moving vehicles is very useful in computer simulation, training, education, art, advertising, and entertainment. In my dissertation, an experimental interactive system has been implemented for the simulation of dust behaviors behind moving vehicles. The system includes physically-based models, particle systems, rendering engines and graphical user interface (GUI). I have employed several vehicle models including tanks, cars, and jeeps to test and simulate in different scenarios and conditions. Calm weather, winding condition, vehicle turning left or right, and vehicle simulation controlled by users from the GUI are all included. I have also tested the factors which play against the physical behaviors and graphics appearances of the dust particles through GUI or off-line scripts. The simulations are done on a Silicon Graphics Octane station. The animation of dust behaviors is achieved by physically-based modeling and simulation. The flow around a moving vehicle is modeled using computational fluid dynamics (CFD) techniques. I implement a primitive variable and pressure-correction approach to solve the three dimensional incompressible Navier Stokes equations in a volume covering the moving vehicle. An alternating- direction implicit (ADI) method is used for the solution of the momentum equations, with a successive-over- relaxation (SOR) method for the solution of the Poisson pressure equation. Boundary conditions are defined and simplified according to their dynamic properties. The dust particle dynamics is modeled using particle systems, statistics, and procedure modeling techniques. Graphics and real-time simulation techniques, such as dynamics synchronization, motion blur, blending, and clipping have been employed in the rendering to achieve realistic appearing dust behaviors. In addition, I introduce a temporal smoothing technique to eliminate the jagged effect caused by large simulation time. Several algorithms are used to speed up the simulation. For example, pre-calculated tables and display lists are created to replace some of the most commonly used functions, scripts and processes. The performance study shows that both time and space costs of the algorithms are linear in the number of particles in the system. On a Silicon Graphics Octane, three vehicles with 20,000 particles run at 6-8 frames per second on average. This speed does not include the extra calculations of convergence of the numerical integration for fluid dynamics which usually takes about 4-5 minutes to achieve steady state.

Computer modeling of batteries from nonlinear circuit elements

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

Waaben, S.; Dyer, C.K.; Federico, J.

1985-06-01

Circuit analogs for a single battery cell have previously been composed of resistors, capacitors, and inductors. This work introduces a nonlinear circuit model for cell behavior. The circuit is configured around the PIN junction diode, whose charge-storage behavior has features similar to those of electrochemical cells. A user-friendly integrated circuit simulation computer program has reproduced a variety of complex cell responses including electrica isolation effects causing capacity loss, as well as potentiodynamic peaks and discharge phenomena hitherto thought to be thermodynamic in origin. However, in this work, they are shown to be simply due to spatial distribution of stored chargemore » within a practical electrode.« less

Mining and Utilizing Dataset Relevancy from Oceanographic Dataset (MUDROD) Metadata, Usage Metrics, and User Feedback to Improve Data Discovery and Access

NASA Astrophysics Data System (ADS)

Li, Y.; Jiang, Y.; Yang, C. P.; Armstrong, E. M.; Huang, T.; Moroni, D. F.; McGibbney, L. J.

2016-12-01

Big oceanographic data have been produced, archived and made available online, but finding the right data for scientific research and application development is still a significant challenge. A long-standing problem in data discovery is how to find the interrelationships between keywords and data, as well as the intrarelationships of the two individually. Most previous research attempted to solve this problem by building domain-specific ontology either manually or through automatic machine learning techniques. The former is costly, labor intensive and hard to keep up-to-date, while the latter is prone to noise and may be difficult for human to understand. Large-scale user behavior data modelling represents a largely untapped, unique, and valuable source for discovering semantic relationships among domain-specific vocabulary. In this article, we propose a search engine framework for mining and utilizing dataset relevancy from oceanographic dataset metadata, user behaviors, and existing ontology. The objective is to improve discovery accuracy of oceanographic data and reduce time for scientist to discover, download and reformat data for their projects. Experiments and a search example show that the proposed search engine helps both scientists and general users search with better ranking results, recommendation, and ontology navigation.

Investigation of the Impact of User Gaming in the Next Generation National Airspace System

NASA Technical Reports Server (NTRS)

Hunter, George C.; Gao, Huina

2011-01-01

Over the past three decades, growth in the demand for air transportation has exceeded the growth in the national airspace system (NAS) capacity. Systems operating near capacity inevitably have delays and NAS d elays have increased in recent years. The desire to minimize delay costs has placed attention on the NAS air traffic management (ATM) syste m.One initiative that has helped to provide user representation in the ATM solution is the collaborative decision making (CDM) process. CDM addresses this issue by bringing users (referred to here as airline operation centers [AOCs]) and ATM providers together for information e xchange and cooperative planning. Such cooperative planning has been instituted, for instance, for the purpose of planning airport slot control strategies and rerouting strategies. While the CDM initiatives ha ve met with much success, they have also introduced the potential for AOCs to manipulate the system in unforeseen, unintended, and perhaps undesirable ways, from a system-wide, synoptic perspective. This type of manipulation is sometimes referred to as "gaming" the system. This study uses a high-fidelity simulation tool to investigate several models of user decision making behavior which could be considered to be gaming behavior and the emergent system dynamics and interactions between AOCs and traffic management.

Educational Behavior Apps and Wearable Devices: Current Research and Prospects

ERIC Educational Resources Information Center

Lowe, Heather

2016-01-01

Dartmouth and MIT have developed educational behavior apps and wearable devices that collect contiguous streams of data from student users. Given the consent of the user, the app collects information about a student's physical activity, sleep patterns, and location to form conjectures about social and academic behavior. These apps have the…

Not just the needle: the state of HIV-prevention science among substance users and future directions.

PubMed

Shoptaw, Steve; Montgomery, Brooke; Williams, Chyvette T; El-Bassel, Nabila; Aramrattana, Apinun; Metsch, Lisa; Metzger, David S; Kuo, Irene; Bastos, Francisco I; Strathdee, Steffanie A

2013-07-01

Efforts to prevent HIV transmission among substance-using populations have focused primarily among injection drug users, which have produced measurable reductions in HIV incidence and prevalence. By contrast, the majority of substances used worldwide are administered by noninjectable means, and there is a dearth of HIV prevention interventions that target noninjecting substance users. Increased surveillance of trends in substance use, especially cocaine (including crack) and methamphetamine, in addition to new and emerging substances (eg, synthetic cannabinoids, cathinones, and other amphetamine analogs) are needed to develop and scale up effective and robust interventions for populations at risk for HIV transmission via sexual behaviors related to noninjection substance use. Strategies are needed that address unique challenges to HIV prevention for substance users who are HIV infected and those who are HIV uninfected and are at high risk. We propose a research agenda that prioritizes (1) combination HIV-prevention strategies in substance users; (2) behavioral HIV prevention programs that reduce sexual transmission behaviors in nontreatment seeking individuals; (3) medical and/or behavioral treatments for substance abuse that reduce/eliminate substance-related sexual transmission behaviors; and (4) structural interventions to reduce HIV incidence.

Perceived risk for severe outcomes and drinking status among drug users with HIV and Hepatitis C Virus (HCV).

PubMed

Elliott, Jennifer C; Hasin, Deborah S; Des Jarlais, Don C

2016-12-01

Among drug users with HIV and Hepatitis C Virus (HCV) infections, heavy drinking can pose significant risks to health. Yet many drug users with HIV and HCV drink heavily. Clarifying the relationship of drug-using patients' understanding of their illnesses to their drinking behavior could facilitate more effective intervention with these high-risk groups. Among samples of drug users infected with HIV (n=476; 70% male) and HCV (n=1145; 81% male) recruited from drug treatment clinics, we investigated whether patients' perceptions of the risk for severe outcomes related to HIV and HCV were associated with their personal drinking behavior, using generalized logit models. Interactions with co-infection status were also explored. HIV-infected drug users who believed that HIV held highest risk for serious outcomes were the most likely to be risky drinkers, when compared with those with less severe perceptions, X(2)(6)=14.19, p<0.05. In contrast, HCV-infected drug users who believed that HCV held moderate risk for serious outcomes were the most likely to be risky drinkers, X(2)(6)=12.98, p<0.05. In this sample of drug users, risky drinking was most common among those with HIV who believed that severe outcomes were inevitable, suggesting that conveying the message that HIV always leads to severe outcomes may be counterproductive in decreasing risky drinking in this group. However, risky drinking was most common among those with HCV who believed that severe outcomes were somewhat likely. Further research is needed to understand the mechanisms of these associations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Cigarette Smoking Trajectories From Sixth to Twelfth Grade: Associated Substance Use and High School Dropout.

PubMed

Orpinas, Pamela; Lacy, Beth; Nahapetyan, Lusine; Dube, Shanta R; Song, Xiao

2016-02-01

The purpose of this longitudinal study was to identify distinct trajectories of cigarette smoking from sixth to twelfth grade and to characterize these trajectories by use of other drugs and high school dropout. The diverse sample for this analysis consisted of a cohort of 611 students from Northeast Georgia who participated in the Healthy Teens Longitudinal Study (2003-2009). Students completed seven yearly assessments from sixth through twelfth grade. We used semi-parametric, group-based modeling to identify groups of students whose smoking behavior followed a similar progression over time. Current smoking (past 30 day) increased from 6.9% among sixth graders to 28.8% among twelfth graders. Four developmental trajectories of cigarette smoking were identified: Abstainers/Sporadic Users (71.5% of the sample), Late Starters (11.3%), Experimenters (9.0%), and Continuous Users (8.2%). The Abstainer/Sporadic User trajectory was composed of two distinct groups: those who never reported any tobacco use (True Abstainers) and those who reported sporadic, low-level use (Sporadic Users). The True Abstainers reported significantly less use of alcohol and other drugs and lower dropout rates than students in all other trajectories, and Sporadic Users had worse outcomes than True Abstainers. Experimenters and Continuous Users reported the highest drug use. Over one-third of Late Starters (35.8%) and almost half of Continuous Users (44.4%) dropped out of high school. Cigarette smoking was associated with behavioral and academic problems. Results support early and continuous interventions to reduce use of tobacco and other drugs and prevent high school dropout. © The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Alcohol mixed with energy drinks: Associations with risky drinking and functioning in high school.

PubMed

Tucker, Joan S; Troxel, Wendy M; Ewing, Brett A; D'Amico, Elizabeth J

2016-10-01

Mixing alcohol with energy drinks is associated with heavier drinking and related problems among college students. However, little is known about how high school drinkers who mix alcohol with energy drinks (AmED) compare to those who do not (AwoED). This study compares high school AmED and AwoED users on their alcohol use during middle and high school, as well as key domains of functioning in high school. Two surveys were conducted three years apart in adolescents initially recruited from 16 middle schools in Southern California. The analytic sample consists of 696 past month drinkers. Multivariable models compared AmED and AwoED users on alcohol use, mental health, social functioning, academic orientation, delinquency and other substance use at age 17, and on their alcohol use and related cognitions at age 14. AmED was reported by 13% of past month drinkers. AmED and AwoED users did not differ on alcohol use or cognitions in middle school, but AmED users drank more often, more heavily, and reported more negative consequences in high school. AmED users were also more likely to report poor grades, delinquent behavior, substance use-related unsafe driving, public intoxication, and drug use than AwoED users in high school. Group differences were not found on mental health, social functioning, or academic aspirations. AmED use is common among high school drinkers. The higher risk behavioral profile of these young AmED users, which includes drug use and substance use-related unsafe driving, is a significant cause for concern and warrants further attention. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

Wang, Xi; Zhao, Kang; Street, Nick

2017-04-24

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

A conceptual modeling framework for discrete event simulation using hierarchical control structures.

PubMed

Furian, N; O'Sullivan, M; Walker, C; Vössner, S; Neubacher, D

2015-08-01

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM's applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models' system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example.

A Preliminary Investigation into the Information Sharing Behavior of Social Media Users after a Natural Disaster

ERIC Educational Resources Information Center

Maruyama, Yukiko

2016-01-01

The paper provides the results of a preliminary investigation into the information sharing behavior of social media users after a natural disaster. The results indicate that users shared information that they thought victims would find useful. On the other hand, they reported that they usually do not or never share information considered useful to…

Patron Survey of User Satisfaction with Library Services: Relationship between Librarian Behaviors during the Reference Interview and User Satisfaction.

ERIC Educational Resources Information Center

Nichols, Mary Ellen

This study examined whether user satisfaction with library services is affected by certain objective and subjective librarian behaviors exhibited during the reference interview. A patron survey was conducted during July 1993 in three branches of Cuyahoga County Public Library, located in northeastern Ohio. The sample was determined by the patrons…

A comprehensive analytical model of rotorcraft aerodynamics and dynamics. Part 2: User's manual

NASA Technical Reports Server (NTRS)

Johnson, W.

1980-01-01

The use of a computer program for a comprehensive analytical model of rotorcraft aerodynamics and dynamics is described. The program calculates the loads and motion of helicopter rotors and airframe. First the trim solution is obtained, then the flutter, flight dynamics, and/or transient behavior can be calculated. Either a new job can be initiated or further calculations can be performed for an old job.

Alloy Design Workbench-Surface Modeling Package Developed

NASA Technical Reports Server (NTRS)

Abel, Phillip B.; Noebe, Ronald D.; Bozzolo, Guillermo H.; Good, Brian S.; Daugherty, Elaine S.

2003-01-01

NASA Glenn Research Center's Computational Materials Group has integrated a graphical user interface with in-house-developed surface modeling capabilities, with the goal of using computationally efficient atomistic simulations to aid the development of advanced aerospace materials, through the modeling of alloy surfaces, surface alloys, and segregation. The software is also ideal for modeling nanomaterials, since surface and interfacial effects can dominate material behavior and properties at this level. Through the combination of an accurate atomistic surface modeling methodology and an efficient computational engine, it is now possible to directly model these types of surface phenomenon and metallic nanostructures without a supercomputer. Fulfilling a High Operating Temperature Propulsion Components (HOTPC) project level-I milestone, a graphical user interface was created for a suite of quantum approximate atomistic materials modeling Fortran programs developed at Glenn. The resulting "Alloy Design Workbench-Surface Modeling Package" (ADW-SMP) is the combination of proven quantum approximate Bozzolo-Ferrante-Smith (BFS) algorithms (refs. 1 and 2) with a productivity-enhancing graphical front end. Written in the portable, platform independent Java programming language, the graphical user interface calls on extensively tested Fortran programs running in the background for the detailed computational tasks. Designed to run on desktop computers, the package has been deployed on PC, Mac, and SGI computer systems. The graphical user interface integrates two modes of computational materials exploration. One mode uses Monte Carlo simulations to determine lowest energy equilibrium configurations. The second approach is an interactive "what if" comparison of atomic configuration energies, designed to provide real-time insight into the underlying drivers of alloying processes.

Consensus-based methodology for detection communities in multilayered networks

NASA Astrophysics Data System (ADS)

Karimi-Majd, Amir-Mohsen; Fathian, Mohammad; Makrehchi, Masoud

2018-03-01

Finding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users' behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.

Computational Analysis and Simulation of Empathic Behaviors: A Survey of Empathy Modeling with Behavioral Signal Processing Framework

PubMed Central

Xiao, Bo; Imel, Zac E.; Georgiou, Panayiotis; Atkins, David C.; Narayanan, Shrikanth S.

2017-01-01

Empathy is an important psychological process that facilitates human communication and interaction. Enhancement of empathy has profound significance in a range of applications. In this paper, we review emerging directions of research on computational analysis of empathy expression and perception as well as empathic interactions, including their simulation. We summarize the work on empathic expression analysis by the targeted signal modalities (e.g., text, audio, facial expressions). We categorize empathy simulation studies into theory-based emotion space modeling or application-driven user and context modeling. We summarize challenges in computational study of empathy including conceptual framing and understanding of empathy, data availability, appropriate use and validation of machine learning techniques, and behavior signal processing. Finally, we propose a unified view of empathy computation, and offer a series of open problems for future research. PMID:27017830

User modeling techniques as support in the clinical decision-making process.

PubMed

Ferri, F

1995-01-01

This paper describes research work on the design and creation of a medical folder management system capable of establishing co-operative dialogue with users who have access to the information contained therein. The research work has addressed the problem of integrating into the system knowledge about the medical domain and that about users, both necessary to activate co-operative dialogue. The CADMIO [2] prototype has been developed since the study was made. The last version of the CADMIO system stores information about users for the use in recognizing and interpreting their behavior, providing help, and in acquiring and returning further information. Depending on this information the system retrieves and shows the data of the medical folder in an intelligent way by highlighting links between data. It simplifies and increases the speed of the interaction by focusing on the data useful to the decisional activity of the physician.

Improving measurement of injection drug risk behavior using item response theory.

PubMed

Janulis, Patrick

2014-03-01

Recent research highlights the multiple steps to preparing and injecting drugs and the resultant viral threats faced by drug users. This research suggests that more sensitive measurement of injection drug HIV risk behavior is required. In addition, growing evidence suggests there are gender differences in injection risk behavior. However, the potential for differential item functioning between genders has not been explored. To explore item response theory as an improved measurement modeling technique that provides empirically justified scaling of injection risk behavior and to examine for potential gender-based differential item functioning. Data is used from three studies in the National Institute on Drug Abuse's Criminal Justice Drug Abuse Treatment Studies. A two-parameter item response theory model was used to scale injection risk behavior and logistic regression was used to examine for differential item functioning. Item fit statistics suggest that item response theory can be used to scale injection risk behavior and these models can provide more sensitive estimates of risk behavior. Additionally, gender-based differential item functioning is present in the current data. Improved measurement of injection risk behavior using item response theory should be encouraged as these models provide increased congruence between construct measurement and the complexity of injection-related HIV risk. Suggestions are made to further improve injection risk behavior measurement. Furthermore, results suggest direct comparisons of composite scores between males and females may be misleading and future work should account for differential item functioning before comparing levels of injection risk behavior.

Ionospheric threats to the integrity of airborne GPS users

NASA Astrophysics Data System (ADS)

Datta-Barua, Seebany

The Global Positioning System (GPS) has both revolutionized and entwined the worlds of aviation and atmospheric science. As the largest and most unpredictable source of GPS positioning error, the ionospheric layer of the atmosphere, if left unchecked, can endanger the safety, or "integrity," of the single frequency airborne user. An augmentation system is a differential-GPS-based navigation system that provides integrity through independent ionospheric monitoring by reference stations. However, the monitor stations are not in general colocated with the user's GPS receiver. The augmentation system must protect users from possible ionosphere density variations occurring between its measurements and the user's. This study analyzes observations from ionospherically active periods to identify what types of ionospheric disturbances may cause threats to user safety if left unmitigated. This work identifies when such disturbances may occur using a geomagnetic measure of activity and then considers two disturbances as case studies. The first case study indicates the need for a non-trivial threat model for the Federal Aviation Administration's Local Area Augmentation System (LAAS) that was not known prior to the work. The second case study uses ground- and space-based data to model an ionospheric disturbance of interest to the Federal Aviation Administration's Wide Area Augmentation System (WAAS). This work is a step in the justification for, and possible future refinement of, one of the WAAS integrity algorithms. For both WAAS and LAAS, integrity threats are basically caused by events that may be occurring but are unobservable. Prior to the data available in this solar cycle, events of such magnitude were not known to be possible. This work serves as evidence that the ionospheric threat models developed for WARS and LAAS are warranted and that they are sufficiently conservative to maintain user integrity even under extreme ionospheric behavior.

Quantification and Formalization of Security

DTIC Science & Technology

2010-02-01

Quantification of Information Flow . . . . . . . . . . . . . . . . . . 30 2.4 Language Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . 46...system behavior observed by users holding low clearances. This policy, or a variant of it, is enforced by many pro- gramming language -based mechanisms...illustrates with a particular programming language (while-programs plus probabilistic choice). The model is extended in §2.5 to programs in which

Predicting Student Actions in a Procedural Training Environment

ERIC Educational Resources Information Center

Riofrio-Luzcando, Diego; Ramirez, Jaime; Berrocal-Lobo, Marta

2017-01-01

Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…

SMAUMAT_ITI

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

Jannetti, C.; Becker, R.

The software is an ABAQUS/Standard UMAT (user defined material behavior subroutine) that implements the constitutive model for shape-memory alloy materials developed by Jannetti et. al. (2003a) using a fully implicit time integration scheme to integrate the constitutive equations. The UMAT is used in conjunction with ABAQUS/Standard to perform a finite-element analysis of SMA materials.

The Chaos Theory of Careers: A User's Guide

ERIC Educational Resources Information Center

Bright, Jim E. H.; Pryor, Robert G. L.

2005-01-01

The purpose of this article is to set out the key elements of the Chaos Theory of Careers. The complexity of influences on career development presents a significant challenge to traditional predictive models of career counseling. Chaos theory can provide a more appropriate description of career behavior, and the theory can be applied with clients…

I-SAVE: AN INTERACTIVE REAL-TIME MONITOR AND CONTROLLER TO INFLUENCE ENERGY CONSERVATION BEHAVIOR BY IMPULSE SAVING

EPA Science Inventory

Simulation-based model to explore the benefits of monitoring and control to energy saving opportunities in residential homes; an adaptive algorithm to predict the type of electrical loads; a prototype user friendly interface monitoring and control device to save energy; a p...

Female methamphetamine users: social characteristics and sexual risk behavior.

PubMed

Semple, Shirley J; Grant, Igor; Patterson, Thomas L

2004-01-01

The primary objective of this research was to expand our knowledge regarding the personal and social characteristics of female methamphetamine (meth) users, their motivations for using meth, patterns of meth use, medical and social problems associated with meth use, and the relationship between meth use and sexual risk behaviors. The sample consisted of 98 HIV-negative, heterosexually-identified, meth-using females residing in San Diego, California. Female meth users were characterized by personal and social disadvantage, high rates of psychiatric symptomatology, and high levels of sexual risk behavior, including multiple partners, risky partner types (e.g., anonymous sex partners), and high rates of unprotected vaginal and oral sex. Meth use was also associated with the subjective positive experience of sex. These finding suggest that behavioral interventions should be tailored to the social characteristics of female meth users, and program content should reflect the intertwining of women's sexual experience and meth use.

A conceptual modeling framework for discrete event simulation using hierarchical control structures

PubMed Central

Furian, N.; O’Sullivan, M.; Walker, C.; Vössner, S.; Neubacher, D.

2015-01-01

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM’s applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models’ system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example. PMID:26778940

Promoting Interactions Between Humans and Robots Using Robotic Emotional Behavior.

PubMed

Ficocelli, Maurizio; Terao, Junichi; Nejat, Goldie

2016-12-01

The objective of a socially assistive robot is to create a close and effective interaction with a human user for the purpose of giving assistance. In particular, the social interaction, guidance, and support that a socially assistive robot can provide a person can be very beneficial to patient-centered care. However, there are a number of research issues that need to be addressed in order to design such robots. This paper focuses on developing effective emotion-based assistive behavior for a socially assistive robot intended for natural human-robot interaction (HRI) scenarios with explicit social and assistive task functionalities. In particular, in this paper, a unique emotional behavior module is presented and implemented in a learning-based control architecture for assistive HRI. The module is utilized to determine the appropriate emotions of the robot to display, as motivated by the well-being of the person, during assistive task-driven interactions in order to elicit suitable actions from users to accomplish a given person-centered assistive task. A novel online updating technique is used in order to allow the emotional model to adapt to new people and scenarios. Experiments presented show the effectiveness of utilizing robotic emotional assistive behavior during HRI scenarios.

Body Image Disturbance in 1000 Male Appearance and Performance Enhancing Drug Users

PubMed Central

Hildebrandt, Tom; Alfano, Lauren; Langenbucher, James W.

2010-01-01

Body image disturbance (BID) among men has only recently become a phenomenon of clinical significance with noted heterogeneity in the behavioral consequences of these disturbances. The degree of heterogeneity among appearance and performance enhancing drug (APED) users is unknown and an empirically derived framework for studying BID is necessary. 1000 APED users were recruited via the Internet and they completed a comprehensive online assessment APED use patterns, motivations, consequences, and BID. Data were evaluated using latent trait, latent class, and factor mixture models. Model results were validated using a range of covariates including cycle characteristics, age, APED history, and APED risk. A 1-Factor, 4-Class model provided the best fit to the data with Class 1 scoring the highest on all measures of BID and Class 4 the lowest on all measures. Class 2 differed in their preference for being lean over muscular and Class 3 preferred adding mass and size. Each class was associated with unique risks, APED history, and training identity. Not all APED users suffer from significant BID and there are unique profiles for those with elevated BID. Future research on male BID should account for this structure in order to better define relevant diagnostic categories and evaluate the clinical significance of BID. PMID:20110092

Linking Plasma Conditions in the Magnetosphere with Ionospheric Signatures

NASA Technical Reports Server (NTRS)

Rastaetter, Lutz; Kozyra, Janet; Kuznetsova, Maria M.; Berrios, David H.

2012-01-01

Modeling of the full magnetosphere, ring current and ionosphere system has become an indispensable tool in analyzing the series of events that occur during geomagnetic storms. The CCMC has a full model suite available for the magnetosphere, together with visualization tools that allow a user to perform a large variety of analyses. The January, 21, 2005 storm was a moderate-size storm that has been found to feature a large penetration electric field and unusually large polar caps (low-latitude precipitation patterns) that are otherwise found in super storms. Based on simulations runs at CCMC we can outline the likely causes of this behavior. Using visualization tools available to the online user we compare results from different magnetosphere models and present connections found between features in the magnetosphere and the ionosphere that are connected magnetically. The range of magnetic mappings found with different models can be compared with statistical models (Tsyganenko) and the model's fidelity can be verified with observations from low earth orbiting satellites such as DMSP and TIMED.

User's guide for GSMP, a General System Modeling Program. [In PL/I

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

Cook, J. M.

1979-10-01

GSMP is designed for use by systems analysis teams. Given compiled subroutines that model the behavior of components plus instructions as to how they are to be interconnected, this program links them together to model a complete system. GSMP offers a fast response to management requests for reconfigurations of old systems and even initial configurations of new systems. Standard system-analytic services are provided: parameter sweeps, graphics, free-form input and formatted output, file storage and recovery, user-tested error diagnostics, component model and integration checkout and debugging facilities, sensitivity analysis, and a multimethod optimizer with nonlinear constraint handling capability. Steady-state or cyclicmore » time-dependence is simulated directly, initial-value problems only indirectly. The code is written in PL/I, but interfaces well with FORTRAN component models. Over the last five years GSMP has been used to model theta-pinch, tokamak, and heavy-ion fusion power plants, open- and closed-cycle magneto-hydrodynamic power plants, and total community energy systems.« less

Gender-Based Behavioral Analysis for End-User Development and the "RULES" Attributes

ERIC Educational Resources Information Center

Tzafilkou, Katerina; Protogeros, Nicolaos; Karagiannidis, Charalampos; Koumpis, Adamantios

2017-01-01

This paper addresses the role of gender in End-User Development (EUD) environments and examines whether there are gender differences in performance and in correlations between performance and a set of behavioral attributes. Based on a review of the most prominent EUD-related behavioral Human Computer Interaction (HCI) theories, and the influence…

HIV Risk Behaviors among Rural Stimulant Users: Variation by Gender and Race/Ethnicity

ERIC Educational Resources Information Center

Wright, Patricia B.; Stewart, Katharine E.; Fischer, Ellen P.; Carlson, Robert G.; Falck, Russel; Wang, Jichuan; Leukefeld, Carl G.; Booth, Brenda M.

2007-01-01

We examined data from a community sample of rural stimulant users (n = 691) in three diverse states to identify gender and racial/ethnic differences in HIV risk behaviors. Bivariate and logistic regression analyses were conducted with six risk behaviors as dependent variables: injecting drugs, trading sex to obtain money or drugs, trading money or…

The double power law in human collaboration behavior: The case of Wikipedia

NASA Astrophysics Data System (ADS)

Kwon, Okyu; Son, Woo-Sik; Jung, Woo-Sung

2016-11-01

We study human behavior in terms of the inter-event time distribution of revision behavior on Wikipedia, an online collaborative encyclopedia. We observe a double power law distribution for the inter-editing behavior at the population level and a single power law distribution at the individual level. Although interactions between users are indirect or moderate on Wikipedia, we determine that the synchronized editing behavior among users plays a key role in determining the slope of the tail of the double power law distribution.

Formal analysis and automatic generation of user interfaces: approach, methodology, and an algorithm.

PubMed

Heymann, Michael; Degani, Asaf

2007-04-01

We present a formal approach and methodology for the analysis and generation of user interfaces, with special emphasis on human-automation interaction. A conceptual approach for modeling, analyzing, and verifying the information content of user interfaces is discussed. The proposed methodology is based on two criteria: First, the interface must be correct--that is, given the interface indications and all related information (user manuals, training material, etc.), the user must be able to successfully perform the specified tasks. Second, the interface and related information must be succinct--that is, the amount of information (mode indications, mode buttons, parameter settings, etc.) presented to the user must be reduced (abstracted) to the minimum necessary. A step-by-step procedure for generating the information content of the interface that is both correct and succinct is presented and then explained and illustrated via two examples. Every user interface is an abstract description of the underlying system. The correspondence between the abstracted information presented to the user and the underlying behavior of a given machine can be analyzed and addressed formally. The procedure for generating the information content of user interfaces can be automated, and a software tool for its implementation has been developed. Potential application areas include adaptive interface systems and customized/personalized interfaces.

Effectiveness of HIV prevention social marketing with injecting drug users.

PubMed

Gibson, David R; Zhang, Guili; Cassady, Diana; Pappas, Les; Mitchell, Joyce; Kegeles, Susan M

2010-10-01

Social marketing involves applying marketing principles to promote social goods. In the context of health behavior, it has been used successfully to reduce alcohol-related car crashes, smoking among youths, and malaria transmission, among other goals. Features of social marketing, such as audience segmentation and repeated exposure to prevention messages, distinguish it from traditional health promotion programs. A recent review found 8 of 10 rigorously evaluated social marketing interventions responsible for changes in HIV-related behavior or behavioral intentions. We studied 479 injection drug users to evaluate a community-based social marketing campaign to reduce injection risk behavior among drug users in Sacramento, California. Injecting drugs is associated with HIV infection in more than 130 countries worldwide.

Web-based reactive transport modeling using PFLOTRAN

NASA Astrophysics Data System (ADS)

Zhou, H.; Karra, S.; Lichtner, P. C.; Versteeg, R.; Zhang, Y.

2017-12-01

Actionable understanding of system behavior in the subsurface is required for a wide spectrum of societal and engineering needs by both commercial firms and government entities and academia. These needs include, for example, water resource management, precision agriculture, contaminant remediation, unconventional energy production, CO2 sequestration monitoring, and climate studies. Such understanding requires the ability to numerically model various coupled processes that occur across different temporal and spatial scales as well as multiple physical domains (reservoirs - overburden, surface-subsurface, groundwater-surface water, saturated-unsaturated zone). Currently, this ability is typically met through an in-house approach where computational resources, model expertise, and data for model parameterization are brought together to meet modeling needs. However, such an approach has multiple drawbacks which limit the application of high-end reactive transport codes such as the Department of Energy funded[?] PFLOTRAN code. In addition, while many end users have a need for the capabilities provided by high-end reactive transport codes, they do not have the expertise - nor the time required to obtain the expertise - to effectively use these codes. We have developed and are actively enhancing a cloud-based software platform through which diverse users are able to easily configure, execute, visualize, share, and interpret PFLOTRAN models. This platform consists of a web application and available on-demand HPC computational infrastructure. The web application consists of (1) a browser-based graphical user interface which allows users to configure models and visualize results interactively, and (2) a central server with back-end relational databases which hold configuration, data, modeling results, and Python scripts for model configuration, and (3) a HPC environment for on-demand model execution. We will discuss lessons learned in the development of this platform, the rationale for different interfaces, implementation choices, as well as the planned path forward.

Creating adaptive web recommendation system based on user behavior

NASA Astrophysics Data System (ADS)

Walek, Bogdan

2018-01-01

The paper proposes adaptive web recommendation system based on user behavior. The proposed system uses expert system to evaluating and recommending suitable items of content. Relevant items are subsequently evaluated and filtered based on history of visited items and user´s preferred categories of items. Main parts of the proposed system are presented and described. The proposed recommendation system is verified on specific example.

Behavioral Risk Reduction in a Declining HIV Epidemic: Injection Drug Users in New York City, 1990-1997.

ERIC Educational Resources Information Center

Des Jarlais, Don C.; Perlis, Theresa; Friedman, Samuel R.; Chapman, Timothy; Kwok, John; Rockwell, Russell; Paone, Denise; Milliken, Judith; Monterroso, Edgar

2000-01-01

Assessed trends in HIV risk behaviors among New York City injection drug users from 1990-97. Interviews at a drug detoxification program and a research storefront in a high drug-use area showed continuing risk reduction among users that indicated a declining phase in the large HIV epidemic in New York City. HIV prevention programs appeared to be…

Development and initial evaluation of the Clinical Information Systems Success Model (CISSM).

PubMed

Garcia-Smith, Dianna; Effken, Judith A

2013-06-01

Most clinical information systems (CIS) today are technically sound, but the number of successful implementations of these systems is low. The purpose of this study was to develop and test a theoretically based integrated CIS Success Model (CISSM) from the nurse perspective. Model predictors of CIS success were taken from existing research on information systems acceptance, user satisfaction, use intention, user behavior and perceptions, as well as clinical research. Data collected online from 234 registered nurses in four hospitals were used to test the model. Each nurse had used the Cerner Power Chart Admission Health Profile for at least 3 months. Psychometric testing and factor analysis of the 23-item CISSM instrument established its construct validity and reliability. Initial analysis showed nurses' satisfaction with and dependency on CIS use predicted their perceived CIS use Net Benefit. Further analysis identified Social Influence and Facilitating Conditions as other predictors of CIS user Net Benefit. The level of hospital CIS integration may account for the role of CIS Use Dependency in the success of CIS. Based on our experience, CISSM provides a formative as well as summative tool for evaluating CIS success from the nurse's perspective. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

HIV Risk Behavior Among Methamphetamine Users Entering Substance Abuse Treatment in Cape Town, South Africa.

PubMed

Meade, Christina S; Lion, Ryan R; Cordero, Daniella M; Watt, Melissa H; Joska, John A; Gouse, Hetta; Burnhams, Warren

2016-10-01

South Africa is experiencing a growing methamphetamine problem, and there is concern that methamphetamine use may accelerate HIV transmission. There has been little research on the HIV prevention needs of methamphetamine users receiving substance abuse treatment in South Africa. This study assessed the prevalence and correlates of HIV risk behaviors among 269 methamphetamine users entering substance abuse treatment in two clinics in Cape Town. The prevalence of sexual risk behaviors was high among sexually active participants: 34 % multiple partners, 26 % unprotected intercourse with a casual partner, and 24 % sex trading for money/methamphetamine. The strongest predictor of all sexual risk behaviors was concurrent other drug use. Over half had not been HIV tested in the past year, and 25 % had never been tested, although attitudes toward HIV testing were overwhelmingly positive. This population of primarily heterosexual, non-injecting methamphetamine users is a high-risk group in need of targeted HIV prevention interventions. Substance abuse treatment is an ideal setting in which to reach methamphetamine users for HIV services.

Validation Methods Research for Fault-Tolerant Avionics and Control Systems Sub-Working Group Meeting. CARE 3 peer review

NASA Technical Reports Server (NTRS)

Trivedi, K. S. (Editor); Clary, J. B. (Editor)

1980-01-01

A computer aided reliability estimation procedure (CARE 3), developed to model the behavior of ultrareliable systems required by flight-critical avionics and control systems, is evaluated. The mathematical models, numerical method, and fault-tolerant architecture modeling requirements are examined, and the testing and characterization procedures are discussed. Recommendations aimed at enhancing CARE 3 are presented; in particular, the need for a better exposition of the method and the user interface is emphasized.

Learnable Models for Information Diffusion and its Associated User Behavior in Micro-blogosphere

DTIC Science & Technology

2012-08-30

According to the work of Even-Dar and Shapira (2007), we recall the definition of the ba- sic voter model on network G. In the model, each node of G...reason as follows. We started with the K distinct initial nodes and all the other nodes were neutral in the beginning. Recall that we set the average time... memory , running under Linux. Learning to predict opinion share and detect anti-majority opinionists in social networks 29 7 Conclusion Unlike the popular

I Sought It, I Reddit: Examining Health Information Engagement Behaviors among Reddit Users.

PubMed

Record, Rachael A; Silberman, Will R; Santiago, Joshua E; Ham, Taewook

2018-01-01

Given the wide use of social media, these platforms have become important channels for understanding health-related information engagement processes. Reddit is a social media platform dedicated to user-generated content and discourse around the world. However, little research exists regarding use of the platform. Guided by the diffusion of innovation theory, the purpose of this study was to analyze Reddit users' behaviors on the platform related to perceptions of information credibility, health information seeking, and behavioral enactment of information found. Data were collected via survey from Reddit users around the world (n = 389). Data suggest that although Reddit use and perceived information credibility are unrelated to acting on the information found on Reddit, users who are specifically seeking health-related information are more likely to enact the information in their lives. Implications from the findings suggest important considerations for communication scholars, media advocates, and health promotion practitioners.

The Impact of Student Self-efficacy on Scientific Inquiry Skills: An Exploratory Investigation in River City, a Multi-user Virtual Environment

NASA Astrophysics Data System (ADS)

Ketelhut, Diane Jass

2007-02-01

This exploratory study investigated data-gathering behaviors exhibited by 100 seventh-grade students as they participated in a scientific inquiry-based curriculum project delivered by a multi-user virtual environment (MUVE). This research examined the relationship between students' self-efficacy on entry into the authentic scientific activity and the longitudinal data-gathering behaviors they employed while engaged in that process. Three waves of student behavior data were gathered from a server-side database that recorded all student activity in the MUVE; these data were analyzed using individual growth modeling. The study found that self-efficacy correlated with the number of data-gathering behaviors in which students initially engaged, with high self-efficacy students engaging in more data gathering than students with low self-efficacy. Also, the impact of student self-efficacy on rate of change in data gathering behavior differed by gender. However, by the end of their time in the MUVE, initial student self-efficacy no longer correlated with data gathering behaviors. In addition, students' level of self-efficacy did not affect how many different sources from which they chose to gather data. These results suggest that embedding science inquiry curricula in novel platforms like a MUVE might act as a catalyst for change in students' self-efficacy and learning processes.

The virtual lover: variable and easily guided 3D fish animations as an innovative tool in mate-choice experiments with sailfin mollies-I. Design and implementation

PubMed Central

Smielik, Ievgen; Hütwohl, Jan-Marco; Gierszewski, Stefanie; Witte, Klaudia; Kuhnert, Klaus-Dieter

2017-01-01

Abstract Animal behavior researchers often face problems regarding standardization and reproducibility of their experiments. This has led to the partial substitution of live animals with artificial virtual stimuli. In addition to standardization and reproducibility, virtual stimuli open new options for researchers since they are easily changeable in morphology and appearance, and their behavior can be defined. In this article, a novel toolchain to conduct behavior experiments with fish is presented by a case study in sailfin mollies Poecilia latipinna. As the toolchain holds many different and novel features, it offers new possibilities for studies in behavioral animal research and promotes the standardization of experiments. The presented method includes options to design, animate, and present virtual stimuli to live fish. The designing tool offers an easy and user-friendly way to define size, coloration, and morphology of stimuli and moreover it is able to configure virtual stimuli randomly without any user influence. Furthermore, the toolchain brings a novel method to animate stimuli in a semiautomatic way with the help of a game controller. These created swimming paths can be applied to different stimuli in real time. A presentation tool combines models and swimming paths regarding formerly defined playlists, and presents the stimuli onto 2 screens. Experiments with live sailfin mollies validated the usage of the created virtual 3D fish models in mate-choice experiments. PMID:29491963

The virtual lover: variable and easily guided 3D fish animations as an innovative tool in mate-choice experiments with sailfin mollies-I. Design and implementation.

PubMed

Müller, Klaus; Smielik, Ievgen; Hütwohl, Jan-Marco; Gierszewski, Stefanie; Witte, Klaudia; Kuhnert, Klaus-Dieter

2017-02-01

Animal behavior researchers often face problems regarding standardization and reproducibility of their experiments. This has led to the partial substitution of live animals with artificial virtual stimuli. In addition to standardization and reproducibility, virtual stimuli open new options for researchers since they are easily changeable in morphology and appearance, and their behavior can be defined. In this article, a novel toolchain to conduct behavior experiments with fish is presented by a case study in sailfin mollies Poecilia latipinna . As the toolchain holds many different and novel features, it offers new possibilities for studies in behavioral animal research and promotes the standardization of experiments. The presented method includes options to design, animate, and present virtual stimuli to live fish. The designing tool offers an easy and user-friendly way to define size, coloration, and morphology of stimuli and moreover it is able to configure virtual stimuli randomly without any user influence. Furthermore, the toolchain brings a novel method to animate stimuli in a semiautomatic way with the help of a game controller. These created swimming paths can be applied to different stimuli in real time. A presentation tool combines models and swimming paths regarding formerly defined playlists, and presents the stimuli onto 2 screens. Experiments with live sailfin mollies validated the usage of the created virtual 3D fish models in mate-choice experiments.

Comparing diet, oral hygiene and caries status of adult methamphetamine users and nonusers: a pilot study.

PubMed

Morio, Kimberly A; Marshall, Teresa A; Qian, Fang; Morgan, Teresa A

2008-02-01

Methamphetamine users are reported to have marginal dietary habits and high caries rates. The authors compared retrospective dietary patterns, oral hygiene behaviors and current oral health status of methamphetamine users and nonusers in a pilot study. Eighteen adults with a history of methamphetamine use (methamphetamine users) and 18 age- and sex-matched control subjects (nonusers) completed retrospective questionnaires concerning meal patterns, food group intakes, beverage habits, oral hygiene behaviors, smoking behaviors and drug use. The authors performed oral examinations to identify the number of remaining teeth, the number of teeth with obvious decay and presence of visible plaque. Methamphetamine users were more likely to snack without eating defined meals (P = .026), consume regular soda pop (that is, carbonated beverage with sugar) (P = .018), never brush their teeth (P < .001) and smoke (P < .001) than were nonusers. Users had more visible plaque (P < .001), fewer molars (P = .001) and more decay on anterior teeth (P < .001), premolars (P < .001) and molars (P < .001) than did nonusers. The results of this pilot study are consistent with anecdotal reports; methamphetamine users have more gross caries than do nonusers. Marginal dietary and oral hygiene behaviors associated with methamphetamine use likely increase caries risk. Patients at risk or suspected of using methamphetamine require detailed oral hygiene instruction and extensive dietary counseling.

Model and Simulation of an SMA Enhanced Lip Seal

NASA Astrophysics Data System (ADS)

Qiao, Rui; Gao, Xiujie; Brinson, L. Catherine

2011-07-01

The feasibility of using SMA wires to improve the seal effectiveness has been studied experimentally and numerically. In this article, we present only the numerical study of simulating the thermo-mechanical behavior for an SMA enhanced lip seal, leaving the test setup and results in the experimental counterpart. A pseudo 3D SMA model, considering 1D SMA behavior in the major loading direction and elastic response in other directions, was used to capture the thermo-mechanical behavior of SMA wires. The model was then implemented into ABAQUS using the user-defined material subroutine to inherit most features of the commercial finite element package. Two-way shape memory effect was also considered since the SMA material exhibits strong two-way effects. An axisymmetric finite element model was constructed to simulate a seal mounting on a shaft and the sealing pressure was calculated for both the regular seal and the SMA enhanced seal. Finally, the result was qualitatively compared with the experimental observation.

The effects of oral d-amphetamine on impulsivity in smoked and intranasal cocaine users.

PubMed

Reed, Stephanie Collins; Evans, Suzette M

2016-06-01

Effective treatments for cocaine use disorders remain elusive. Two factors that may be related to treatment failures are route of cocaine used and impulsivity. Smoked cocaine users are more likely to have poorer treatment outcomes compared to intranasal cocaine users. Further, cocaine users are impulsive and impulsivity is associated with poor treatment outcomes. While stimulants are used to treat Attention Deficit Hyperactivity Disorder (ADHD) and attenuate certain cocaine-related behaviors, few studies have comprehensively examined whether stimulants can reduce behavioral impulsivity in cocaine users, and none examined route of cocaine use as a factor. The effects of immediate release oral d-amphetamine (AMPH) were examined in 34 cocaine users (13 intranasal, 21 smoked). Participants had three separate sessions where they were administered AMPH (0, 10, or 20mg) and completed behavioral measures of impulsivity and risk-taking and subjective measures of abuse liability. Smoked cocaine users were more impulsive on the Delayed Memory Task, the GoStop task and the Delay Discounting Task than intranasal cocaine users. Smoked cocaine users also reported more cocaine craving and negative mood than intranasal cocaine users. AMPH produced minimal increases on measures of abuse liability (e.g., Drug Liking). Smoked cocaine users were more impulsive than intranasal cocaine users on measures of impulsivity that had a delay component. Additionally, although AMPH failed to attenuate impulsive responding, there was minimal evidence of abuse liability in cocaine users. These preliminary findings need to be confirmed in larger samples that control for route and duration of cocaine use. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The Effects of Oral d-Amphetamine on Impulsivity in Smoked and Intranasal Cocaine Users

PubMed Central

Reed, Stephanie Collins; Evans, Suzette M.

2016-01-01

BACKGROUND Effective treatments for cocaine use disorders remain elusive. Two factors that may be related to treatment failures are route of cocaine used and impulsivity. Smoked cocaine users are more likely to have poorer treatment outcomes compared to intranasal cocaine users. Further, cocaine users are impulsive and impulsivity is associated with poor treatment outcomes. While stimulants are used to treat Attention Deficit Hyperactivity Disorder (ADHD) and attenuate certain cocaine-related behaviors, few studies have comprehensively examined whether stimulants can reduce behavioral impulsivity in cocaine users, and none examined route of cocaine use as a factor. METHODS The effects of immediate release oral d-amphetamine (AMPH) were examined in 34 cocaine users (13 intranasal, 21 smoked). Participants had three separate sessions where they were administered AMPH (0, 10, or 20 mg) and completed behavioral measures of impulsivity and risk-taking and subjective measures of abuse liability. RESULTS Smoked cocaine users were more impulsive on the Delayed Memory Task, the GoStop task and the Delay Discounting Task than intranasal cocaine users. Smoked cocaine users also reported more cocaine craving and negative mood than intranasal cocaine users. AMPH produced minimal increases on measures of abuse liability (e.g., Drug Liking). CONCLUSIONS Smoked cocaine users were more impulsive than intranasal cocaine users on measures of impulsivity that had a delay component. Additionally, although AMPH failed to attenuate impulsive responding, there was minimal evidence of abuse liability in cocaine users. These preliminary findings need to be confirmed in larger samples that control for route and duration of cocaine use. PMID:27114203

BEARKIMPE-2: A VBA Excel program for characterizing granular iron in treatability studies

NASA Astrophysics Data System (ADS)

Firdous, R.; Devlin, J. F.

2014-02-01

The selection of a suitable kinetic model to investigate the reaction rate of a contaminant with granular iron (GI) is essential to optimize the permeable reactive barrier (PRB) performance in terms of its reactivity. The newly developed Kinetic Iron Model (KIM) determines the surface rate constant (k) and sorption parameters (Cmax &J) which were not possible to uniquely identify previously. The code was written in Visual Basic (VBA), within Microsoft Excel, was adapted from earlier command line FORTRAN codes, BEARPE and KIMPE. The program is organized with several user interface screens (UserForms) that guide the user step by step through the analysis. BEARKIMPE-2 uses a non-linear optimization algorithm to calculate transport and chemical kinetic parameters. Both reactive and non-reactive sites are considered. A demonstration of the functionality of BEARKIMPE-2, with three nitroaromatic compounds showed that the differences in reaction rates for these compounds could be attributed to differences in their sorption behavior rather than their propensities to accept electrons in the reduction process.

HIV risk, health, and social characteristics of sexual minority female injection drug users in Baltimore

PubMed Central

German, Danielle; Latkin, Carl A.

2015-01-01

Female injection drug users {IDU} who report sex with women are at increased risk for HIV and social instability, but it is important to assess whether these disparities also exist according to sexual minority identity rather than behaviorally defined categories. Within a sample of current IDU in Baltimore, about 17% of female study participants (n=307) identified as gay/lesbian/bisexual. In controlled models, sexual minorities were three times as likely to report sex exchange behavior and four times as likely to report a recent STI. Injection risk did not differ significantly, but sexual minority women reported higher prevalence of socio-economic instability, negative health indicators, and fewer network financial, material, and health support resources. There is a need to identify and address socio-economic marginalization, social support, and health issues among female IDUs who identify as lesbian or bisexual. PMID:25504312

Pitfalls in Persuasion: How Do Users Experience Persuasive Techniques in a Web Service?

NASA Astrophysics Data System (ADS)

Segerståhl, Katarina; Kotro, Tanja; Väänänen-Vainio-Mattila, Kaisa

Persuasive technologies are designed by utilizing a variety of interactive techniques that are believed to promote target behaviors. This paper describes a field study in which the aim was to discover possible pitfalls of persuasion, i.e., situations in which persuasive techniques do not function as expected. The study investigated persuasive functionality of a web service targeting weight loss. A qualitative online questionnaire was distributed through the web service and a total of 291 responses were extracted for interpretative analysis. The Persuasive Systems Design model (PSD) was used for supporting systematic analysis of persuasive functionality. Pitfalls were identified through situations that evoked negative user experiences. The primary pitfalls discovered were associated with manual logging of eating and exercise behaviors, appropriateness of suggestions and source credibility issues related to social facilitation. These pitfalls, when recognized, can be addressed in design by applying functional and facilitative persuasive techniques in meaningful combinations.

Preliminary Development of a Unified Viscoplastic Constitutive Model for Alloy 617 with Special Reference to Long Term Creep Behavior

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

Sham, Sam; Walker, Kevin P.

The expected service life of the Next Generation Nuclear Plant is 60 years. Structural analyses of the Intermediate Heat Exchanger (IHX) will require the development of unified viscoplastic constitutive models that address the material behavior of Alloy 617, a construction material of choice, over a wide range of strain rates. Many unified constitutive models employ a yield stress state variable which is used to account for cyclic hardening and softening of the material. For low stress values below the yield stress state variable these constitutive models predict that no inelastic deformation takes place which is contrary to experimental results. Themore » ability to model creep deformation at low stresses for the IHX application is very important as the IHX operational stresses are restricted to very small values due to the low creep strengths at elevated temperatures and long design lifetime. This paper presents some preliminary work in modeling the unified viscoplastic constitutive behavior of Alloy 617 which accounts for the long term, low stress, creep behavior and the hysteretic behavior of the material at elevated temperatures. The preliminary model is presented in one-dimensional form for ease of understanding, but the intent of the present work is to produce a three-dimensional model suitable for inclusion in the user subroutines UMAT and USERPL of the ABAQUS and ANSYS nonlinear finite element codes. Further experiments and constitutive modeling efforts are planned to model the material behavior of Alloy 617 in more detail.« less

Adaptive User Profiles in Pervasive Advertising Environments

NASA Astrophysics Data System (ADS)

Alt, Florian; Balz, Moritz; Kristes, Stefanie; Shirazi, Alireza Sahami; Mennenöh, Julian; Schmidt, Albrecht; Schröder, Hendrik; Goedicke, Michael

Nowadays modern advertising environments try to provide more efficient ads by targeting costumers based on their interests. Various approaches exist today as to how information about the users' interests can be gathered. Users can deliberately and explicitly provide this information or user's shopping behaviors can be analyzed implicitly. We implemented an advertising platform to simulate an advertising environment and present adaptive profiles, which let users setup profiles based on a self-assessment, and enhance those profiles with information about their real shopping behavior as well as about their activity intensity. Additionally, we explain how pervasive technologies such as Bluetooth can be used to create a profile anonymously and unobtrusively.

Dynamic route and departure time choice model based on self-adaptive reference point and reinforcement learning

NASA Astrophysics Data System (ADS)

Li, Xue-yan; Li, Xue-mei; Yang, Lingrun; Li, Jing

2018-07-01

Most of the previous studies on dynamic traffic assignment are based on traditional analytical framework, for instance, the idea of Dynamic User Equilibrium has been widely used in depicting both the route choice and the departure time choice. However, some recent studies have demonstrated that the dynamic traffic flow assignment largely depends on travelers' rationality degree, travelers' heterogeneity and what the traffic information the travelers have. In this paper, we develop a new self-adaptive multi agent model to depict travelers' behavior in Dynamic Traffic Assignment. We use Cumulative Prospect Theory with heterogeneous reference points to illustrate travelers' bounded rationality. We use reinforcement-learning model to depict travelers' route and departure time choosing behavior under the condition of imperfect information. We design the evolution rule of travelers' expected arrival time and the algorithm of traffic flow assignment. Compared with the traditional model, the self-adaptive multi agent model we proposed in this paper can effectively help travelers avoid the rush hour. Finally, we report and analyze the effect of travelers' group behavior on the transportation system, and give some insights into the relation between travelers' group behavior and the performance of transportation system.

A new flexible plug and play scheme for modeling, simulating, and predicting gastric emptying

PubMed Central

2014-01-01

Background In-silico models that attempt to capture and describe the physiological behavior of biological organisms, including humans, are intrinsically complex and time consuming to build and simulate in a computing environment. The level of detail of description incorporated in the model depends on the knowledge of the system’s behavior at that level. This knowledge is gathered from the literature and/or improved by knowledge obtained from new experiments. Thus model development is an iterative developmental procedure. The objective of this paper is to describe a new plug and play scheme that offers increased flexibility and ease-of-use for modeling and simulating physiological behavior of biological organisms. Methods This scheme requires the modeler (user) first to supply the structure of the interacting components and experimental data in a tabular format. The behavior of the components described in a mathematical form, also provided by the modeler, is externally linked during simulation. The advantage of the plug and play scheme for modeling is that it requires less programming effort and can be quickly adapted to newer modeling requirements while also paving the way for dynamic model building. Results As an illustration, the paper models the dynamics of gastric emptying behavior experienced by humans. The flexibility to adapt the model to predict the gastric emptying behavior under varying types of nutrient infusion in the intestine (ileum) is demonstrated. The predictions were verified with a human intervention study. The error in predicting the half emptying time was found to be less than 6%. Conclusions A new plug-and-play scheme for biological systems modeling was developed that allows changes to the modeled structure and behavior with reduced programming effort, by abstracting the biological system into a network of smaller sub-systems with independent behavior. In the new scheme, the modeling and simulation becomes an automatic machine readable and executable task. PMID:24917054

Study on Personalized Recommendation Model of Internet Advertisement

NASA Astrophysics Data System (ADS)

Zhou, Ning; Chen, Yongyue; Zhang, Huiping

With the rapid development of E-Commerce, the audiences put forward higher requirements on personalized Internet advertisement than before. The main function of Personalized Advertising System is to provide the most suitable advertisements for anonymous users on Web sites. The paper offers a personalized Internet advertisement recommendation model. By mining the audiences' historical and current behavior, and the advertisers' and publisher's web site content, etc, the system can recommend appropriate advertisements to corresponding audiences.

Pervasive healthcare: paving the way for a pervasive, user-centered and preventive healthcare model.

PubMed

Arnrich, B; Mayora, O; Bardram, J; Tröster, G

2010-01-01

The aging of the population creates pressure on the healthcare systems in various ways. A massive increase of chronic disease conditions and age-related illness are predicted as the dominant forces driving the future health care. The objective of this paper is to present future research demands in pervasive healthcare with the goal to meet the healthcare challenges by paving the way for a pervasive, user-centered and preventive healthcare model. This paper presents recent methodological approaches and proposes future research topics in three areas: i) pervasive, continuous and reliable long-term monitoring systems; ii) prevention through pervasive technology as a key element to maintain lifelong wellness; and iii) design and evaluation methods for ubiquitous, patient-centric technologies. Pervasive technology has been identified as a strong asset for achieving the vision of user-centered preventive healthcare. In order to make this vision a reality, new strategies for design, development and evaluation of technology have to find a common denominator and consequently interoperate. Moreover, the potential of pervasive healthcare technologies offers new opportunities beyond traditional disease treatment and may play a major role in prevention, e.g. motivate healthy behavior and disease prevention throughout all stages of life. In this sense, open challenges in future research have to be addressed such as the variability of health indicators between individuals and the manner in which relevant health indicators are provided to the users in order to maximize their motivation to mitigate or prevent unhealthy behaviors. Additionally, collecting evidence that pervasive technology improves health is seen as one of the toughest challenges. Promising approaches are recently introduced, such as "clinical proof-of-concept" and balanced observational studies. The paper concludes that pervasive healthcare will enable a paradigm shift from the established centralized healthcare model to a pervasive, user-centered and preventive overall lifestyle health management. In order to provide these new opportunities everywhere, anytime and to anyone, future research in the fields of pervasive sensing, pervasive prevention and evaluation of pervasive technology is inevitably needed.

Designing the user interfaces of a behavior modification intervention for obesity & eating disorders prevention.

PubMed

Moulos, Ioannis; Maramis, Christos; Mourouzis, Alexandros; Maglaveras, Nicos

2015-01-01

The recent immense diffusion of smartphones has significantly upgraded the role of mobile user interfaces in interventions that build and/or maintain healthier lifestyles. Indeed, high-quality, user-centered smartphone applications are able to serve as advanced front-ends to such interventions. These smartphone applications, coupled with portable or wearable sensors, are being employed for monitoring day to day health-related behaviors, including eating and physical activity. Some of them take one step forward by identifying unhealthy behaviors and contributing towards their modification. This work presents the design as well as the preliminary implementation of the mobile user interface of SPLENDID, a novel, sensor-oriented intervention for preventing obesity and eating disorders in young populations. This is implemented by means of an Android application, which is able to monitor the eating and physical activity behaviors of young individuals at risk for obesity and/or eating disorders, subsequently guiding them towards the modification of those behaviors that put them at risk. Behavior monitoring is based on multiple data provided by a set of communicating sensors and self-reported information, while guidance is facilitated through a feedback/encouragement provision and goal setting mechanism.

Systematic Assessment of the Impact of User Roles on Network Flow Patterns

DTIC Science & Technology

2017-09-01

Protocol SNMP Simple Network Management Protocol SQL Structured Query Language SSH Secure Shell SYN TCP Sync Flag SVDD Support Vector Data Description SVM...and evaluating users based on roles provide the best approach for defining normal digital behaviors? People are individuals, with different interests...activities on the network. We evaluate the assumption that users sharing similar roles exhibit similar network behaviors, and contrast the level of similarity

Exploring Factors Influencing Self-Efficacy in Information Security an Empirical Analysis by Integrating Multiple Theoretical Perspectives in the Context of Using Protective Information Technologies

ERIC Educational Resources Information Center

Reddy, Dinesh Sampangirama

2017-01-01

Cybersecurity threats confront the United States on a daily basis, making them one of the major national security challenges. One approach to meeting these challenges is to improve user cybersecurity behavior. End user security behavior hinges on end user acceptance and use of the protective information technologies such as anti-virus and…

Vector representation of user's view using self-organizing map

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Yamaguchi, Tomohisa; Monden, Eri; Kawabata, Shunji; Kamitani, Motoki

2004-05-01

There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very important to understand our behaviors. Therefore, we propose a method which acquires a view as a vector, and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a multimedia database containing pictures, music, movie, etc.. These data cannot be stereotyped because user's view for them changes by each user. Therefore, we represent the structure of the multimedia database as the vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as elements. We demonstrate a city-sequence generation system which reflects user's intension as an application of sequence generation containing user's view. We apply the self-organizing map to this system to represent user's view.

Vortex information display system program description manual. [data acquisition from laser Doppler velocimeters and real time operation

NASA Technical Reports Server (NTRS)

Conway, R.; Matuck, G. N.; Roe, J. M.; Taylor, J.; Turner, A.

1975-01-01

A vortex information display system is described which provides flexible control through system-user interaction for collecting wing-tip-trailing vortex data, processing this data in real time, displaying the processed data, storing raw data on magnetic tape, and post processing raw data. The data is received from two asynchronous laser Doppler velocimeters (LDV's) and includes position, velocity, and intensity information. The raw data is written onto magnetic tape for permanent storage and is also processed in real time to locate vortices and plot their positions as a function of time. The interactive capability enables the user to make real time adjustments in processing data and provides a better definition of vortex behavior. Displaying the vortex information in real time produces a feedback capability to the LDV system operator allowing adjustments to be made in the collection of raw data. Both raw data and processing can be continually upgraded during flyby testing to improve vortex behavior studies. The post-analysis capability permits the analyst to perform in-depth studies of test data and to modify vortex behavior models to improve transport predictions.

DFLOWZ: A free program to evaluate the area potentially inundated by a debris flow

NASA Astrophysics Data System (ADS)

Berti, M.; Simoni, A.

2014-06-01

The transport and deposition mechanisms of debris flows are still poorly understood due to the complexity of the interactions governing the behavior of water-sediment mixtures. Empirical-statistical methods can therefore be used, instead of more sophisticated numerical methods, to predict the depositional behavior of these highly dangerous gravitational movements. We use widely accepted semi-empirical scaling relations and propose an automated procedure (DFLOWZ) to estimate the area potentially inundated by a debris flow event. Beside a digital elevation model (DEM), the procedure has only two input requirements: the debris flow volume and the possible flow-path. The procedure is implemented in Matlab and a Graphical User Interface helps to visualize initial conditions, flow propagation and final results. Different hypothesis about the depositional behavior of an event can be tested together with the possible effect of simple remedial measures. Uncertainties associated to scaling relations can be treated and their impact on results evaluated. Our freeware application aims to facilitate and speed up the process of susceptibility mapping. We discuss limits and advantages of the method in order to inform inexperienced users.

Substance use and sexual risk behaviors among Peruvian MSM social media users.

PubMed

Young, Sean D; Nianogo, Roch A; Chiu, ChingChe J; Menacho, Lucho; Galea, Jerome

2016-01-01

Peru is experiencing a concentrated HIV epidemic among men who have sex with men (MSM). Substance use (alcohol and drug use) has been found to be associated with HIV-related sexual risk behaviors. A recent surge in the number of social media users in Peru has enabled these technologies to be potential tools for reaching HIV at-risk individuals. This study sought to assess the relationship between substance use and sexual risk behaviors among Peruvian MSM who use social media. A total of 556 Peruvian MSM Facebook users (ages 18-59) were recruited to complete a 92-item survey on demographics, sexual risk behaviors, and substance use. We performed a logistic regression of various sexual risk behaviors (e.g., unprotected sex, casual sex) on substance abuse, including alcohol, adjusting for potential covariates. Drinking more than five alcoholic drinks a day in the past three months was associated with an increased odds of having unprotected sex (vaginal and anal) (aOR: 1.52; 95% CL: 1.01, 2.28), casual sex (1.75; 1.17, 2.62), and sex with unknown persons (1.82; 1.23, 2.71). Drug use was not significantly associated with sexual risk behaviors. Among Peruvian MSM social media users, findings suggest that alcohol use was associated with increased HIV-related sexual risk behaviors.

In Pursuit of Theoretical Ground in Behavior Change Support Systems: Analysis of Peer-to-Peer Communication in a Health-Related Online Community.

PubMed

Myneni, Sahiti; Cobb, Nathan; Cohen, Trevor

2016-02-02

Research studies involving health-related online communities have focused on examining network structure to understand mechanisms underlying behavior change. Content analysis of the messages exchanged in these communities has been limited to the "social support" perspective. However, existing behavior change theories suggest that message content plays a prominent role reflecting several sociocognitive factors that affect an individual's efforts to make a lifestyle change. An understanding of these factors is imperative to identify and harness the mechanisms of behavior change in the Health 2.0 era. The objective of this work is two-fold: (1) to harness digital communication data to capture essential meaning of communication and factors affecting a desired behavior change, and (2) to understand the applicability of existing behavior change theories to characterize peer-to-peer communication in online platforms. In this paper, we describe grounded theory-based qualitative analysis of digital communication in QuitNet, an online community promoting smoking cessation. A database of 16,492 de-identified public messages from 1456 users from March 1-April 30, 2007, was used in our study. We analyzed 795 messages using grounded theory techniques to ensure thematic saturation. This analysis enabled identification of key concepts contained in the messages exchanged by QuitNet members, allowing us to understand the sociobehavioral intricacies underlying an individual's efforts to cease smoking in a group setting. We further ascertained the relevance of the identified themes to theoretical constructs in existing behavior change theories (eg, Health Belief Model) and theoretically linked techniques of behavior change taxonomy. We identified 43 different concepts, which were then grouped under 12 themes based on analysis of 795 messages. Examples of concepts include "sleepiness," "pledge," "patch," "spouse," and "slip." Examples of themes include "traditions," "social support," "obstacles," "relapse," and "cravings." Results indicate that themes consisting of member-generated strategies such as "virtual bonfires" and "pledges" were related to the highest number of theoretical constructs from the existing behavior change theories. In addition, results indicate that the member-generated communication content supports sociocognitive constructs from more than one behavior change model, unlike the majority of the existing theory-driven interventions. With the onset of mobile phones and ubiquitous Internet connectivity, online social network data reflect the intricacies of human health behavior as experienced by health consumers in real time. This study offers methodological insights for qualitative investigations that examine the various kinds of behavioral constructs prevalent in the messages exchanged among users of online communities. Theoretically, this study establishes the manifestation of existing behavior change theories in QuitNet-like online health communities. Pragmatically, it sets the stage for real-time, data-driven sociobehavioral interventions promoting healthy lifestyle modifications by allowing us to understand the emergent user needs to sustain a desired behavior change.

Adolescents and MP3 players: too many risks, too few precautions.

PubMed

Vogel, Ineke; Verschuure, Hans; van der Ploeg, Catharina P B; Brug, Johannes; Raat, Hein

2009-06-01

The goal was to assess risky and protective listening behaviors of adolescent users of MP3 players and the association of these behaviors with demographic characteristics and frequency of use. In 2007, 1687 adolescents (12-19 years of age) in 68 classes in 15 Dutch secondary schools were invited to complete questionnaires about their music-listening behaviors. . Ninety percent of participants reported listening to music through earphones on MP3 players; 32.8% were frequent users, 48.0% used high volume settings, and only 6.8% always or nearly always used a noise-limiter. Frequent users were >4 times more likely to listen to high-volume music than were infrequent users, and adolescents in practical prevocational schools were more than twice as likely to listen to high-volume music as were those attending preuniversity education. When using MP3 players, adolescents are very likely to engage in risky listening behaviors and are unlikely to seek protection. Frequent MP3 player use is an indicator of other risky listening behaviors, such as listening at high volumes and failing to use noise-limiters.

Association of Proton Pump Inhibitor (PPI) Use with Energy Intake, Physical Activity, and Weight Gain

PubMed Central

Czwornog, Jennifer L.; Austin, Gregory L.

2015-01-01

Studies suggest proton pump inhibitor (PPI) use impacts body weight regulation, though the effect of PPIs on energy intake, energy extraction, and energy expenditure is unknown. We used data on 3073 eligible adults from the National Health and Nutrition Examination Survey (NHANES). Medication use, energy intake, diet composition, and physical activity were extracted from NHANES. Multivariate regression models included confounding variables. Daily energy intake was similar between PPI users and non-users (p = 0.41). Diet composition was similar between the two groups, except that PPI users consumed a slightly greater proportion of calories from fat (34.5% vs. 33.2%; p = 0.02). PPI users rated themselves as being as physically active as their age/gender-matched peers and reported similar frequencies of walking or biking. However, PPI users were less likely to have participated in muscle-strengthening activities (OR: 0.53; 95% CI: 0.30–0.95). PPI users reported similar sedentary behaviors to non-users. Male PPI users had an increase in weight (of 1.52 ± 0.59 kg; p = 0.021) over the previous year compared to non-users, while female PPI users had a non-significant increase in weight. The potential mechanisms for PPI-associated weight gain are unclear as we did not find evidence for significant differences in energy intake or markers of energy expenditure. PMID:26492268

Modelling End-User of Electronic-Government Service: The Role of Information quality, System Quality and Trust

NASA Astrophysics Data System (ADS)

Witarsyah Jacob, Deden; Fudzee, Mohd Farhan Md; Aizi Salamat, Mohamad; Kasim, Shahreen; Mahdin, Hairulnizam; Azhar Ramli, Azizul

2017-08-01

Many governments around the world increasingly use internet technologies such as electronic government to provide public services. These services range from providing the most basic informational website to deploying sophisticated tools for managing interactions between government agencies and beyond government. Electronic government (e-government) aims to provide a more accurate, easily accessible, cost-effective and time saving for the community. In this study, we develop a new model of e-government adoption service by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) through the incorporation of some variables such as System Quality, Information Quality and Trust. The model is then tested using a large-scale, multi-site survey research of 237 Indonesian citizens. This model will be validated by using Structural Equation Modeling (SEM). The result indicates that System Quality, Information Quality and Trust variables proven to effect user behavior. This study extends the current understanding on the influence of System Quality, Information Quality and Trust factors to researchers, practitioners, and policy makers.

Brain Responses to Musical Feature Changes in Adolescent Cochlear Implant Users

PubMed Central

Petersen, Bjørn; Weed, Ethan; Sandmann, Pascale; Brattico, Elvira; Hansen, Mads; Sørensen, Stine Derdau; Vuust, Peter

2015-01-01

Cochlear implants (CIs) are primarily designed to assist deaf individuals in perception of speech, although possibilities for music fruition have also been documented. Previous studies have indicated the existence of neural correlates of residual music skills in postlingually deaf adults and children. However, little is known about the behavioral and neural correlates of music perception in the new generation of prelingually deaf adolescents who grew up with CIs. With electroencephalography (EEG), we recorded the mismatch negativity (MMN) of the auditory event-related potential to changes in musical features in adolescent CI users and in normal-hearing (NH) age mates. EEG recordings and behavioral testing were carried out before (T1) and after (T2) a 2-week music training program for the CI users and in two sessions equally separated in time for NH controls. We found significant MMNs in adolescent CI users for deviations in timbre, intensity, and rhythm, indicating residual neural prerequisites for musical feature processing. By contrast, only one of the two pitch deviants elicited an MMN in CI users. This pitch discrimination deficit was supported by behavioral measures, in which CI users scored significantly below the NH level. Overall, MMN amplitudes were significantly smaller in CI users than in NH controls, suggesting poorer music discrimination ability. Despite compliance from the CI participants, we found no effect of the music training, likely resulting from the brevity of the program. This is the first study showing significant brain responses to musical feature changes in prelingually deaf adolescent CI users and their associations with behavioral measures, implying neural predispositions for at least some aspects of music processing. Future studies should test any beneficial effects of a longer lasting music intervention in adolescent CI users. PMID:25705185

Brain responses to musical feature changes in adolescent cochlear implant users.

PubMed

Petersen, Bjørn; Weed, Ethan; Sandmann, Pascale; Brattico, Elvira; Hansen, Mads; Sørensen, Stine Derdau; Vuust, Peter

2015-01-01

Cochlear implants (CIs) are primarily designed to assist deaf individuals in perception of speech, although possibilities for music fruition have also been documented. Previous studies have indicated the existence of neural correlates of residual music skills in postlingually deaf adults and children. However, little is known about the behavioral and neural correlates of music perception in the new generation of prelingually deaf adolescents who grew up with CIs. With electroencephalography (EEG), we recorded the mismatch negativity (MMN) of the auditory event-related potential to changes in musical features in adolescent CI users and in normal-hearing (NH) age mates. EEG recordings and behavioral testing were carried out before (T1) and after (T2) a 2-week music training program for the CI users and in two sessions equally separated in time for NH controls. We found significant MMNs in adolescent CI users for deviations in timbre, intensity, and rhythm, indicating residual neural prerequisites for musical feature processing. By contrast, only one of the two pitch deviants elicited an MMN in CI users. This pitch discrimination deficit was supported by behavioral measures, in which CI users scored significantly below the NH level. Overall, MMN amplitudes were significantly smaller in CI users than in NH controls, suggesting poorer music discrimination ability. Despite compliance from the CI participants, we found no effect of the music training, likely resulting from the brevity of the program. This is the first study showing significant brain responses to musical feature changes in prelingually deaf adolescent CI users and their associations with behavioral measures, implying neural predispositions for at least some aspects of music processing. Future studies should test any beneficial effects of a longer lasting music intervention in adolescent CI users.

Effectiveness of HIV Prevention Social Marketing With Injecting Drug Users

PubMed Central

Zhang, Guili; Cassady, Diana; Pappas, Les; Mitchell, Joyce; Kegeles, Susan M.

2010-01-01

Social marketing involves applying marketing principles to promote social goods. In the context of health behavior, it has been used successfully to reduce alcohol-related car crashes, smoking among youths, and malaria transmission, among other goals. Features of social marketing, such as audience segmentation and repeated exposure to prevention messages, distinguish it from traditional health promotion programs. A recent review found 8 of 10 rigorously evaluated social marketing interventions responsible for changes in HIV-related behavior or behavioral intentions. We studied 479 injection drug users to evaluate a community-based social marketing campaign to reduce injection risk behavior among drug users in Sacramento, California. Injecting drugs is associated with HIV infection in more than 130 countries worldwide. PMID:20724686

Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish Presidential election as a case study

NASA Astrophysics Data System (ADS)

Borondo, J.; Morales, A. J.; Losada, J. C.; Benito, R. M.

2012-06-01

Transmitting messages in the most efficient way as possible has always been one of politicians' main concerns during electoral processes. Due to the rapidly growing number of users, online social networks have become ideal platforms for politicians to interact with their potential voters. Exploiting the available potential of these tools to maximize their influence over voters is one of politicians' actual challenges. To step in this direction, we have analyzed the user activity in the online social network Twitter, during the 2011 Spanish Presidential electoral process, and found that such activity is correlated with the election results. We introduce a new measure to study political sentiment in Twitter, which we call the relative support. We have also characterized user behavior by analyzing the structural and dynamical patterns of the complex networks emergent from the mention and retweet networks. Our results suggest that the collective attention is driven by a very small fraction of users. Furthermore, we have analyzed the interactions taking place among politicians, observing a lack of debate. Finally, we develop a network growth model to reproduce the interactions taking place among politicians.

Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish Presidential election as a case study.

PubMed

Borondo, J; Morales, A J; Losada, J C; Benito, R M

2012-06-01

Transmitting messages in the most efficient way as possible has always been one of politicians' main concerns during electoral processes. Due to the rapidly growing number of users, online social networks have become ideal platforms for politicians to interact with their potential voters. Exploiting the available potential of these tools to maximize their influence over voters is one of politicians' actual challenges. To step in this direction, we have analyzed the user activity in the online social network Twitter, during the 2011 Spanish Presidential electoral process, and found that such activity is correlated with the election results. We introduce a new measure to study political sentiment in Twitter, which we call the relative support. We have also characterized user behavior by analyzing the structural and dynamical patterns of the complex networks emergent from the mention and retweet networks. Our results suggest that the collective attention is driven by a very small fraction of users. Furthermore, we have analyzed the interactions taking place among politicians, observing a lack of debate. Finally, we develop a network growth model to reproduce the interactions taking place among politicians.

Thumbs up for privacy?: Differences in online self-disclosure behavior across national cultures.

PubMed

Reed, Philip J; Spiro, Emma S; Butts, Carter T

2016-09-01

This study investigates relationships between national-level culture and online self-disclosure behavior. We operationalize culture through the GLOBE dimensions, a set of nine variables measuring cultural practices and another nine measuring values. Our observations of self-disclosure come from the privacy settings of approximately 200,000 randomly sampled Facebook users who designated a geographical network in 2009. We model privacy awareness as a function of one or more GLOBE variables with demographic covariates, evaluating the relative influence of each factor. In the top-performing models, we find that the majority of the cultural dimensions are significantly related to privacy awareness behavior. We also find that the hypothesized directions of several of these relationships, based largely on cultural attitudes towards threat mitigation, are confirmed. Copyright © 2016. Published by Elsevier Inc.

Effective Filtering of Query Results on Updated User Behavioral Profiles in Web Mining

PubMed Central

Sadesh, S.; Suganthe, R. C.

2015-01-01

Web with tremendous volume of information retrieves result for user related queries. With the rapid growth of web page recommendation, results retrieved based on data mining techniques did not offer higher performance filtering rate because relationships between user profile and queries were not analyzed in an extensive manner. At the same time, existing user profile based prediction in web data mining is not exhaustive in producing personalized result rate. To improve the query result rate on dynamics of user behavior over time, Hamilton Filtered Regime Switching User Query Probability (HFRS-UQP) framework is proposed. HFRS-UQP framework is split into two processes, where filtering and switching are carried out. The data mining based filtering in our research work uses the Hamilton Filtering framework to filter user result based on personalized information on automatic updated profiles through search engine. Maximized result is fetched, that is, filtered out with respect to user behavior profiles. The switching performs accurate filtering updated profiles using regime switching. The updating in profile change (i.e., switches) regime in HFRS-UQP framework identifies the second- and higher-order association of query result on the updated profiles. Experiment is conducted on factors such as personalized information search retrieval rate, filtering efficiency, and precision ratio. PMID:26221626

Cancer prevention and control interventions using social media: user-generated approaches.

PubMed

Cavallo, David N; Chou, Wen-Ying Sylvia; McQueen, Amy; Ramirez, Amelie; Riley, William T

2014-09-01

Social media are now used by a majority of American internet users. Social media platforms encourage participants to share information with their online social connections and exchange user-generated content. Significant numbers of people are already using social media to share health-related information. As such, social media provide an opportunity for "user-generated" cancer control and prevention interventions that employ users' behavior, knowledge, and existing social networks for the creation and dissemination of interventions. These interventions also enable novel data collection techniques and research designs that will allow investigators to examine real-time behavioral responses to interventions. Emerging social media-based interventions for modifying cancer-related behaviors have been applied to such domains as tobacco use, diet, physical activity, and sexual practices, and several examples are discussed for illustration purposes. Despite some promising early findings, challenges including inadequate user engagement, privacy concerns, and lack of internet access among some groups need to be addressed in future research. Recommendations for advancing the field include stronger partnerships with commercial technology companies, utilization of rapid and adaptive designs to identify successful strategies for user engagement, rigorous and iterative efficacy testing of these strategies, and inclusive methods for intervention dissemination. ©2014 American Association for Cancer Research.

ArcFuels User Guide and Tutorial: for use with ArcGIS 9

Treesearch

Nicole M. Vaillant; Alan A. Ager; John Anderson; Lauren. Miller

2013-01-01

Fuel management planning can be a complex problem that is assisted by fire behavior modeling and geospatial analyses. Fuel management often is a particularly complicated process in which the benefits and potential impacts of fuel treatments need to be demonstrated in the context of land management goals and public expectations. Fire intensity, likelihood, and effects...

Evaluating Teleworkers' Acceptance of Mobile Technology: A Study Based on the Utaut Model

ERIC Educational Resources Information Center

Mills, Jamia Sharie

2016-01-01

Mobile technology has provided flexible methods for employees to complete work-related tasks without being tied to an office. Research has predicted the level of training on mobile technology may impact a user's ability to complete work responsibilities accurately. This study intended to examine what behavior factors from the unified theory of…

Accounting for ethnicity in recreation demand: a flexible count data approach

Treesearch

J. Michael Bowker; V.R. Leeworthy

1998-01-01

The authors examine ethnicity and individual trip-taking behavior associated with natural resource based recreation in the Florida Keys. Bowker and Leeworthy estimate trip demand using the travel cost method. They then extend this model with a varying parameter adaptation to test the congruency of' demand and economic value across white and Hispanic user subgroups...