Your Personal Learning Network: Professional Development on Demand

ERIC Educational Resources Information Center

Bauer, William I.

2010-01-01

Web 2.0 tools and resources can enhance our efficiency and effectiveness as music educators, supporting personal learning networks for ongoing professional growth and development. This article includes (a) an explanation of Really Simple Syndication (RSS) and the use of an RSS reader/aggregator; (b) a discussion of blogs, podcasts, wikis,…

Dissociation of rapid response learning and facilitation in perceptual and conceptual networks of person recognition.

PubMed

Valt, Christian; Klein, Christoph; Boehm, Stephan G

2015-08-01

Repetition priming is a prominent example of non-declarative memory, and it increases the accuracy and speed of responses to repeatedly processed stimuli. Major long-hold memory theories posit that repetition priming results from facilitation within perceptual and conceptual networks for stimulus recognition and categorization. Stimuli can also be bound to particular responses, and it has recently been suggested that this rapid response learning, not network facilitation, provides a sound theory of priming of object recognition. Here, we addressed the relevance of network facilitation and rapid response learning for priming of person recognition with a view to advance general theories of priming. In four experiments, participants performed conceptual decisions like occupation or nationality judgments for famous faces. The magnitude of rapid response learning varied across experiments, and rapid response learning co-occurred and interacted with facilitation in perceptual and conceptual networks. These findings indicate that rapid response learning and facilitation in perceptual and conceptual networks are complementary rather than competing theories of priming. Thus, future memory theories need to incorporate both rapid response learning and network facilitation as individual facets of priming. © 2014 The British Psychological Society.

Learning Tools for Knowledge Nomads: Using Personal Digital Assistants (PDAs) in Web-based Learning Environments.

ERIC Educational Resources Information Center

Loh, Christian Sebastian

2001-01-01

Examines how mobile computers, or personal digital assistants (PDAs), can be used in a Web-based learning environment. Topics include wireless networks on college campuses; online learning; Web-based learning technologies; synchronous and asynchronous communication via the Web; content resources; Web connections; and collaborative learning. (LRW)

Workplace Learning in Informal Networks

ERIC Educational Resources Information Center

Milligan, Colin; Littlejohn, Allison; Margaryan, Anoush

2014-01-01

Learning does not stop when an individual leaves formal education, but becomes increasingly informal, and deeply embedded within other activities such as work. This article describes the challenges of informal learning in knowledge intensive industries, highlighting the important role of personal learning networks. The article argues that…

Preservice Teachers' Participation and Perceptions of Twitter Live Chats as Personal Learning Networks

ERIC Educational Resources Information Center

Luo, Tian; Sickel, Jamie; Cheng, Li

2017-01-01

This study presents two cases in which undergraduates were introduced to Twitter in their teacher preparation program as a means of developing a personal learning network. Twitter live chats are synchronous discussions that allow education stakeholders to discuss issues and share resources, engaging on potentially a global scale via the social…

Leveraging the Potential of Personal Learning Networks for Teacher Professional Development

ERIC Educational Resources Information Center

Maloney, Katherine J.

2016-01-01

In times of exponential change, high quality, cost-effective teacher professional development is an urgent need that personal learning networks (PLNs) promise to address. The purpose of the qualitative case study was to (a) explore, understand, and describe how PreK-12 educators, who are members of The Educator's PLN and Classroom 2.0 communities,…

Enriching Professional Learning Networks: A Framework for Identification, Reflection, and Intention

ERIC Educational Resources Information Center

Krutka, Daniel G.; Carpenter, Jeffrey Paul; Trust, Torrey

2017-01-01

Many educators in the 21st century utilize social media platforms to enrich professional learning networks (PLNs). PLNs are uniquely personalized networks that can support participatory and continuous learning. Social media services can mediate professional engagements with a wide variety of people, spaces and tools that might not otherwise be…

Using Social Networks to Create Powerful Learning Communities

ERIC Educational Resources Information Center

Lenox, Marianne; Coleman, Maurice

2010-01-01

Regular readers of "Computers in Libraries" are aware that social networks are forming increasingly important linkages to professional and personal development in all libraries. Live and virtual social networks have become the new learning playground for librarians and library staff. Social networks have the ability to connect those who are…

Designing Personalized Learning Products for Middle School Mathematics: The Case for Networked Learning Games

ERIC Educational Resources Information Center

Evans, Michael A.; Pruett, Jordan; Chang, Mido; Nino, Miguel

2014-01-01

Middle school mathematics education is subject to ongoing reform based on advances in digital instructional technologies, especially learning games, leading to recent calls for investment in "personalized learning." Through an extensive literature review, this investigation identified three priority areas that should be taken into…

An Implementation of a Twitter-Supported Personal Learning Network to Individualize Teacher Professional Development

ERIC Educational Resources Information Center

Deyamport, W. H., III.

2013-01-01

In this action research study, eight teachers at an elementary school were trained in the use of Twitter to support the development of a personal learning network as a strategy to address non-differentiated professional development at the school. The main research question for this study was: In what ways, if any, can the use of a…

Recommending Peers for Learning: Matching on Dissimilarity in Interpretations to Provoke Breakdown

ERIC Educational Resources Information Center

Rajagopal, Kamakshi; van Bruggen, Jan M.; Sloep, Peter B.

2017-01-01

People recommenders are a widespread feature of social networking sites and educational social learning platforms alike. However, when these systems are used to extend learners' Personal Learning Networks, they often fall short of providing recommendations of learning value to their users. This paper proposes a design of a people recommender based…

Personalized E- learning System Based on Intelligent Agent

NASA Astrophysics Data System (ADS)

Duo, Sun; Ying, Zhou Cai

Lack of personalized learning is the key shortcoming of traditional e-Learning system. This paper analyzes the personal characters in e-Learning activity. In order to meet the personalized e-learning, a personalized e-learning system based on intelligent agent was proposed and realized in the paper. The structure of system, work process, the design of intelligent agent and the realization of intelligent agent were introduced in the paper. After the test use of the system by certain network school, we found that the system could improve the learner's initiative participation, which can provide learners with personalized knowledge service. Thus, we thought it might be a practical solution to realize self- learning and self-promotion in the lifelong education age.

Sticking with the nice guy: trait warmth information impairs learning and modulates person perception brain network activity.

PubMed

Lee, Victoria K; Harris, Lasana T

2014-12-01

Social learning requires inferring social information about another person, as well as evaluating outcomes. Previous research shows that prior social information biases decision making and reduces reliance on striatal activity during learning (Delgado, Frank, & Phelps, Nature Neuroscience 8 (11): 1611-1618, 2005). A rich literature in social psychology on person perception demonstrates that people spontaneously infer social information when viewing another person (Fiske & Taylor, 2013) and engage a network of brain regions, including the medial prefrontal cortex, temporal parietal junction, superior temporal sulcus, and precuneus (Amodio & Frith, Nature Reviews Neuroscience, 7(4), 268-277, 2006; Haxby, Gobbini, & Montgomery, 2004; van Overwalle Human Brain Mapping, 30, 829-858, 2009). We investigate the role of these brain regions during social learning about well-established dimensions of person perception-trait warmth and trait competence. We test the hypothesis that activity in person perception brain regions interacts with learning structures during social learning. Participants play an investment game where they must choose an agent to invest on their behalf. This choice is guided by cues signaling trait warmth or trait competence based on framing of monetary returns. Trait warmth information impairs learning about human but not computer agents, while trait competence information produces similar learning rates for human and computer agents. We see increased activation to warmth information about human agents in person perception brain regions. Interestingly, activity in person perception brain regions during the decision phase negatively predicts activity in the striatum during feedback for trait competence inferences about humans. These results suggest that social learning may engage additional processing within person perception brain regions that hampers learning in economic contexts.

Reviewing the Differences in Size, Composition and Structure between the Personal Networks of High-and Low-Performing Students

ERIC Educational Resources Information Center

Casquero, Oskar; Ovelar, Ramón; Romo, Jesús; Benito, Manuel

2015-01-01

An interesting aspect in the current literature about learning networks is the shift of focus from the understanding of the "whole network" of a course to the examination of the "personal networks" of individual students. This line of research is relatively new, based on small-scale studies and diverse analysis techniques,…

A Learning Content Authoring Approach Based on Semantic Technologies and Social Networking: An Empirical Study

ERIC Educational Resources Information Center

Nesic, Sasa; Gasevic, Dragan; Jazayeri, Mehdi; Landoni, Monica

2011-01-01

Semantic web technologies have been applied to many aspects of learning content authoring including semantic annotation, semantic search, dynamic assembly, and personalization of learning content. At the same time, social networking services have started to play an important role in the authoring process by supporting authors' collaborative…

The Personal Learning Planner: Collaboration through Online Learning and Publication

ERIC Educational Resources Information Center

Gibson, David; Sherry, Lorraine; Havelock, Bruce

2007-01-01

This paper discusses the online Personal Learning Planner (PLP) project underway at the National Institute of Community Innovations (NICI), one of the partners in the Teacher Education Network (TEN), a 2000 PT3 Catalyst grantee. The Web-based PLP provides a standards-linked "portfolio space" for both works in progress and demonstration collections…

Design and Evaluation of a Widget-Based Dashboard for Awareness Support in Research Networks

ERIC Educational Resources Information Center

Reinhardt, Wolfgang; Mletzko, Christian; Drachsler, Hendrik; Sloep, Peter B.

2014-01-01

In this article, we describe the rationale, design and evaluation of a widget-based dashboard to support scholars' awareness of their Research Networks. We introduce the concept of a Research Network and discuss Personal Research Environments that are built of as a development parallel to Personal Learning Environments. Based on the results…

Networking to improve end of life care.

PubMed

McGivern, Gerry

2009-01-01

Network organisations are increasingly common in healthcare. This paper describes an example of clinically led networking, which improved end of life care (EOLC) in care homes, differentiating between a 'network' as a formal entity and the more informal process of 'networking'. The paper begins with a brief discussion of networks and their development in healthcare, then an overview of EOLC policy, the case setting and methods. The paper describes four key features of this networking; (1) how it enabled discussions and implemented processes to help people address difficult taboos about dying; (2) how personal communication and 'distributed leadership' facilitated learning; (3) how EOLC occasionally lapsed during the handover of patient care, where personal relationship and communication were weaker; and (4) how successful learning and sharing of best practice was fragile and could be potentially undermined by wider financial pressures in the NHS.

What Size Is Your Digital Footprint?

ERIC Educational Resources Information Center

Hewson, Kurtis

2013-01-01

The Professional Learning Network (PLN) is gaining momentum in the education lexicon. It records and reflects the personal development of a community of learners--primarily online through a variety of platforms and social networks--in which educators share resources, provide support, introduce and debate ideas and celebrate learning. These…

Personalized e-Learning Environments: Considering Students' Contexts

NASA Astrophysics Data System (ADS)

Eyharabide, Victoria; Gasparini, Isabela; Schiaffino, Silvia; Pimenta, Marcelo; Amandi, Analía

Personalization in e-learning systems is vital since they are used by a wide variety of students with different characteristics. There are several approaches that aim at personalizing e-learning environments. However, they focus mainly on technological and/or networking aspects without caring of contextual aspects. They consider only a limited version of context while providing personalization. In our work, the objective is to improve e-learning environment personalization making use of a better understanding and modeling of the user’s educational and technological context using ontologies. We show an example of the use of our proposal in the AdaptWeb system, in which content and navigation recommendations are provided depending on the student’s context.

Doing What We Teach: Promoting Digital Literacies for Professional Development through Personal Learning Environments and Participation

ERIC Educational Resources Information Center

Laakkonen, Ilona

2015-01-01

Despite the proliferation of social media, few learners make effective use of digital technology to support their learning or graduate with the skills necessary for developing and communicating their expertise in the knowledge-driven networked society of the digital age. This article makes use of the concept of Personal Learning Environments (PLE)…

Elements of Engagement: A Model of Teacher Interactions via Professional Learning Networks

ERIC Educational Resources Information Center

Krutka, Daniel G.; Carpenter, Jeffrey P.; Trust, Torrey

2016-01-01

In recent years, many educators have turned to participatory online affinity spaces for professional growth with peers who are more accessible because of reduced temporal and spatial constraints. Specifically, professional learning networks (PLNs) are "uniquely personalized, complex systems of interactions consisting of people, resources, and…

Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

PubMed

Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

2016-01-01

This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

Neural network based speech synthesizer: A preliminary report

NASA Technical Reports Server (NTRS)

Villarreal, James A.; Mcintire, Gary

1987-01-01

A neural net based speech synthesis project is discussed. The novelty is that the reproduced speech was extracted from actual voice recordings. In essence, the neural network learns the timing, pitch fluctuations, connectivity between individual sounds, and speaking habits unique to that individual person. The parallel distributed processing network used for this project is the generalized backward propagation network which has been modified to also learn sequences of actions or states given in a particular plan.

Learning deep features with adaptive triplet loss for person reidentification

NASA Astrophysics Data System (ADS)

Li, Zhiqiang; Sang, Nong; Chen, Kezhou; Gao, Changxin; Wang, Ruolin

2018-03-01

Person reidentification (re-id) aims to match a specified person across non-overlapping cameras, which remains a very challenging problem. While previous methods mostly focus on feature extraction or metric learning, this paper makes the attempt in jointly learning both the global full-body and local body-parts features of the input persons with a multichannel convolutional neural network (CNN) model, which is trained by an adaptive triplet loss function that serves to minimize the distance between the same person and maximize the distance between different persons. The experimental results show that our approach achieves very promising results on the large-scale Market-1501 and DukeMTMC-reID datasets.

Online Professional Learning Networks: A Viable Solution to the Professional Development Dilemma

ERIC Educational Resources Information Center

Cook, Rebecca J.; Jones-Bromenshenkel, Melissa; Huisinga, Shawn; Mullins, Frank

2017-01-01

Quality professional development must meet the demands and needs of the person engaging in the activity. However, many opportunities for special educators are often less than optimal in terms of timeliness, expertise, or applicability. This article describes the idea of online professional learning networks (PLNs) which allow for collaboration and…

Teachers' Self-Initiated Professional Learning through Personal Learning Networks

ERIC Educational Resources Information Center

Tour, Ekaterina

2017-01-01

It is widely acknowledged that to be able to teach language and literacy with digital technologies, teachers need to engage in relevant professional learning. Existing formal models of professional learning are often criticised for being ineffective. In contrast, informal and self-initiated forms of learning have been recently recognised as…

Toward a Learner-Centered System for Adult Learning

ERIC Educational Resources Information Center

Hermans, Henry; Kalz, Marco; Koper, Rob

2013-01-01

Purpose: The purpose of this paper is to present an e-learning system that integrates the use of concepts of virtual learning environments, personal learning environments, and social network sites. The system is based on a learning model which comprises and integrates three learning contexts for the adult learner: the formal, instructional…

Networking to improve end of life care

PubMed Central

2009-01-01

Network organisations are increasingly common in healthcare. This paper describes an example of clinically led networking, which improved end of life care (EOLC) in care homes, differentiating between a ‘network’ as a formal entity and the more informal process of ‘networking’. The paper begins with a brief discussion of networks and their development in healthcare, then an overview of EOLC policy, the case setting and methods. The paper describes four key features of this networking; (1) how it enabled discussions and implemented processes to help people address difficult taboos about dying; (2) how personal communication and ‘distributed leadership’ facilitated learning; (3) how EOLC occasionally lapsed during the handover of patient care, where personal relationship and communication were weaker; and (4) how successful learning and sharing of best practice was fragile and could be potentially undermined by wider financial pressures in the NHS. PMID:25949588

Roles of Course Facilitators, Learners, and Technology in the Flow of Information of a cMOOC

ERIC Educational Resources Information Center

Skrypnyk, Oleksandra; Joksimovic, Srec´ko; Kovanovic, Vitomir; Gas?evic, Dragan; Dawson, Shane

2015-01-01

Distributed Massive Open Online Courses (MOOCs) are based on the premise that online learning occurs through a network of interconnected learners. The teachers' role in distributed courses extends to forming such a network by facilitating communication that connects learners and their separate personal learning environments scattered around the…

Building Your Personal Learning Network (PLN): 21st-Century School Librarians Seek Self-Regulated Professional Development Online

ERIC Educational Resources Information Center

Moreillon, Judi

2016-01-01

For school librarians, being part of a "connected" community provides support for getting specific needs met, solving personally relevant and meaningful problems, and developing professional expertise. AASL provides many avenues for members of the profession to learn with and from one another. These include AASL and subgroup electronic…

A European Languages Virtual Network Proposal

NASA Astrophysics Data System (ADS)

García-Peñalvo, Francisco José; González-González, Juan Carlos; Murray, Maria

ELVIN (European Languages Virtual Network) is a European Union (EU) Lifelong Learning Programme Project aimed at creating an informal social network to support and facilitate language learning. The ELVIN project aims to research and develop the connection between social networks, professional profiles and language learning in an informal educational context. At the core of the ELVIN project, there will be a web 2.0 social networking platform that connects employees/students for language practice based on their own professional/academic needs and abilities, using all relevant technologies. The ELVIN remit involves the examination of both methodological and technological issues inherent in achieving a social-based learning platform that provides the user with their own customized Personal Learning Environment for EU language acquisition. ELVIN started in November 2009 and this paper presents the project aims and objectives as well as the development and implementation of the web platform.

Teachers' Personal Learning Networks (PLNs): Exploring the Nature of Self-Initiated Professional Learning Online

ERIC Educational Resources Information Center

Tour, Ekaterina

2017-01-01

In the field of Literacy Studies, online spaces have been recognised as providing many opportunities for spontaneous and self-initiated learning. While some progress has been made in understanding these important learning experiences, little attention has been paid to teachers' self-initiated professional learning. Contributing to the debates…

How to Trigger Emergence and Self-Organisation in Learning Networks

NASA Astrophysics Data System (ADS)

Brouns, Francis; Fetter, Sibren; van Rosmalen, Peter

The previous chapters of this section discussed why the social structure of Learning Networks is important and present guidelines on how to maintain and allow the emergence of communities in Learning Networks. Chapter 2 explains how Learning Networks rely on social interaction and active participations of the participants. Chapter 3 then continues by presenting guidelines and policies that should be incorporated into Learning Network Services in order to maintain existing communities by creating conditions that promote social interaction and knowledge sharing. Chapter 4 discusses the necessary conditions required for knowledge sharing to occur and to trigger communities to self-organise and emerge. As pointed out in Chap. 4, ad-hoc transient communities facilitate the emergence of social interaction in Learning Networks, self-organising them into communities, taking into account personal characteristics, community characteristics and general guidelines. As explained in Chap. 4 community members would benefit from a service that brings suitable people together for a specific purpose, because it will allow the participant to focus on the knowledge sharing process by reducing the effort or costs. In the current chapter, we describe an example of a peer support Learning Network Service based on the mechanism of peer tutoring in ad-hoc transient communities.

Involvement, Collaboration and Engagement: Social Networks through a Pedagogical Lens

ERIC Educational Resources Information Center

Seifert, Tami

2016-01-01

Social networks facilitate activities that promote involvement, collaboration and engagement. Modelling of best practices using social networks enhances its usage by participants, increases participants confidence as to its implementation and creates a paradigm shift to a more personalized, participatory and collaborative learning and a more…

Network-based machine learning and graph theory algorithms for precision oncology.

PubMed

Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui

2017-01-01

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

A Study of the Predictive Relationship between Online Social Presence and ONLE Interaction

ERIC Educational Resources Information Center

Tu, Chih-Hsiung; Yen, Cherng-Jyh; Blocher, J. Michael; Chan, Junn-Yih

2012-01-01

Open Network Learning Environments (ONLE) are online networks that afford learners the opportunity to participate in creative content endeavors, personalized identity projections, networked mechanism management, and effective collaborative community integration by applying Web 2.0 tools in open environments. It supports social interaction by…

Dynamic Information Networks: Geometry, Topology and Statistical Learning for the Articulation of Structure

DTIC Science & Technology

2015-06-23

T. Bates, S. Brocklebank, S. Pauls, and D.Rockmore, A spectral clustering approach to the structure of personality: contrasting the FFM and...A spectral clustering approach to the structure of personality: contrasting the FFM and HEXACO models, Journal of Research in Personality, Volume 57

The Development of an E-Learning-Based Learning Service for MKDP Curriculum and Learning at the Indonesia University of Education

ERIC Educational Resources Information Center

Rusman

2016-01-01

E-learning is a general term used to refer to computer-enhanced learning based that facilitates whoever, wherever, and whenever the person is to be able to learn more fun, easier and cheaper by using Internet. In other words, E-learning is the use of network technologies to create, foster, deliver, and facilitate learning, anytime and anywhere. It…

Mobile Learning

ERIC Educational Resources Information Center

Hockly, Nicky

2013-01-01

In this series, we explore current technology-related themes and topics. The series aims to discuss and demystify what may be new areas for some readers and to consider their relevance to English language teachers. In future articles, we will be covering topics such as learning technologies in low-resource environments, personal learning networks,…

Social Knowledge Awareness Map for Computer Supported Ubiquitous Learning Environment

ERIC Educational Resources Information Center

El-Bishouty, Moushir M.; Ogata, Hiroaki; Rahman, Samia; Yano, Yoneo

2010-01-01

Social networks are helpful for people to solve problems by providing useful information. Therefore, the importance of mobile social software for learning has been supported by many researches. In this research, a model of personalized collaborative ubiquitous learning environment is designed and implemented in order to support learners doing…

Multimedia and the Future of Distance Learning Technology.

ERIC Educational Resources Information Center

Barnard, John

1992-01-01

Describes recent innovations in distance learning technology, including the use of video technology; personal computers, including computer conferencing, computer-mediated communication, and workstations; multimedia, including hypermedia; Integrated Services Digital Networks (ISDN); and fiber optics. Research implications for multimedia and…

The Electronic Studio and the Intranet: Network-Based Learning.

ERIC Educational Resources Information Center

Solis, Carlos R.

The Electronic Studio, developed by the Rice University (Texas) Center for Technology in Teaching and Learning (CTTL), serves a number of purposes related to the construction and development of learning projects. It is a workplace, a display area, and a repository for tools, data, multimedia, design projects, and personal papers. This paper…

Affect Recognition through Facebook for Effective Group Profiling towards Personalized Instruction

ERIC Educational Resources Information Center

Troussas, Christos; Espinosa, Kurt Junshean; Virvou, Maria

2016-01-01

Social networks are progressively being considered as an intense thought for learning. Particularly in the research area of Intelligent Tutoring Systems, they can create intuitive, versatile and customized e-learning systems which can advance the learning process by revealing the capacities and shortcomings of every learner and by customizing the…

Integration of an Intelligent Tutoring System in a Course of Computer Network Design

ERIC Educational Resources Information Center

Verdú, Elena; Regueras, Luisa M.; Gal, Eran; de Castro, Juan P.; Verdú, María J.; Kohen-Vacs, Dan

2017-01-01

INTUITEL is a research project aiming to offer a personalized learning environment. The INTUITEL approach includes an Intelligent Tutoring System that gives students recommendations and feedback about what the best learning path is for them according to their profile, learning progress, context and environmental influences. INTUITEL combines…

Personalized microbial network inference via co-regularized spectral clustering.

PubMed

Imangaliyev, Sultan; Keijser, Bart; Crielaard, Wim; Tsivtsivadze, Evgeni

2015-07-15

We use Human Microbiome Project (HMP) cohort (Peterson et al., 2009) to infer personalized oral microbial networks of healthy individuals. To determine clustering of individuals with similar microbial profiles, co-regularized spectral clustering algorithm is applied to the dataset. For each cluster we discovered, we compute co-occurrence relationships among the microbial species that determine microbial network per cluster of individuals. The results of our study suggest that there are several differences in microbial interactions on personalized network level in healthy oral samples acquired from various niches. Based on the results of co-regularized spectral clustering we discover two groups of individuals with different topology of their microbial interaction network. The results of microbial network inference suggest that niche-wise interactions are different in these two groups. Our study shows that healthy individuals have different microbial clusters according to their oral microbiota. Such personalized microbial networks open a better understanding of the microbial ecology of healthy oral cavities and new possibilities for future targeted medication. The scripts written in scientific Python and in Matlab, which were used for network visualization, are provided for download on the website http://learning-machines.com/. Copyright © 2015 Elsevier Inc. All rights reserved.

Twelve tips for using Twitter as a learning tool in medical education.

PubMed

Forgie, Sarah Edith; Duff, Jon P; Ross, Shelley

2013-01-01

Twitter is an online social networking service, accessible from any Internet-capable device. While other social networking sites are online confessionals or portfolios of personal current events, Twitter is designed and used as a vehicle to converse and share ideas. For this reason, we believe that Twitter may be the most likely candidate for integrating social networking with medical education. Using current research in medical education, motivation and the use of social media in higher education, we aim to show the ways Twitter may be used as a learning tool in medical education. A literature search of several databases, online sources and blogs was carried out examining the use of Twitter in higher education. We created 12 tips for using Twitter as a learning tool and organized them into: the mechanics of using Twitter, suggestions and evidence for incorporating Twitter into many medical education contexts, and promoting research into the use of Twitter in medical education. Twitter is a relatively new social medium, and its use in higher education is in its infancy. With further research and thoughtful application of media literacy, Twitter is likely to become a useful adjunct for more personalized teaching and learning in medical education.

Concept Model on Topological Learning

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Kioi, Kazumasa

2010-11-01

We discuss a new model for concept based on topological learning, where the learning process on the neural network is represented by mathematical topology. The topological learning of neural networks is summarized by a quotient of input space and the hierarchical step induces a tree where each node corresponds to a quotient. In general, the concept acquisition is a difficult problem, but the emotion for a subject is represented by providing the questions to a person. Therefore, a kind of concept is captured by such data and the answer sheet can be mapped into a topology consisting of trees. In this paper, we will discuss a way of mapping the emotional concept to a topological learning model.

Building the Future of Learning

ERIC Educational Resources Information Center

Watson, Les

2007-01-01

Could it be that in our excitement about e-learning we forgot about buildings? With the advent of the personal computer and ubiquitous networks were we enticed into thinking that they would suffice and learning would follow removing the need for places and communities for learners? We now seem to have woken up, however, as there is an enormous…

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

DTIC Science & Technology

2015-07-01

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data Guy Van den Broeck∗ and Karthika Mohan∗ and Arthur Choi and Adnan ...notwithstanding any other provision of law , no person shall be subject to a penalty for failing to comply with a collection of information if it does...Wasserman, L. (2011). All of Statistics. Springer Science & Business Media. Yaramakala, S., & Margaritis, D. (2005). Speculative markov blanket discovery for optimal feature selection. In Proceedings of ICDM.

Constructs for Web 2.0 Learning Environments: A Theatrical Metaphor

ERIC Educational Resources Information Center

Tu, Chih-Hsiung; Blocher, Michael; Roberts, Gayle

2008-01-01

Web 2.0 technologies empower learners to create personalized and community-based collaborative environments. Social networking technology affords learners to weave their human networks through active connections to understand what we know and we want to know. Social acts that bring out identities, awareness, relationships, connections, and…

Academic Developers and International Collaborations: The Importance of Personal Abilities and Aptitudes

ERIC Educational Resources Information Center

Willis, Ian; Strivens, Janet

2015-01-01

Academic developers are increasingly involved in international collaborations in learning and teaching. Many factors contribute to successful collaborations; we argue that the personal abilities and aptitudes of academic developers are one key element. Building trust and relationships are central to creating the networks at individual, group, and…

The Semantic Web in Education

ERIC Educational Resources Information Center

Ohler, Jason

2008-01-01

The semantic web or Web 3.0 makes information more meaningful to people by making it more understandable to machines. In this article, the author examines the implications of Web 3.0 for education. The author considers three areas of impact: knowledge construction, personal learning network maintenance, and personal educational administration.…

Social Networks and Participation with Others for Youth with Learning, Attention and Autism Spectrum Disorders

PubMed Central

Kreider, Consuelo M.; Bendixen, Roxanna M.; Young, Mary Ellen; Prudencio, Stephanie M.; McCarty, Christopher; Mann, William C.

2015-01-01

Background Social participation involves activities and roles providing interactions with others, including those within their social networks. Purpose Characterize social networks and participation with others for 36 adolescents, ages 11-16 years, with (n = 19) and without (n = 17) learning disability, attention disorder or high-functioning autism. Methods Social networks were measured using methods of personal network analysis. The Children's Assessment of Participation and Enjoyment With Whom dimension scores was used to measure participation with others. Youth from the clinical group were interviewed regarding their experiences within their social networks. Findings Group differences were observed for six social network variables and in the proportion of overall, physical, recreational, social and informal activities engaged with family and/or friends. Qualitative findings explicated strategies used in building, shaping and maintaining their social networks. Implications Social network factors should be considered when seeking to understand social participation. PMID:26755040

Factors Associated With Nursing Students' Resilience: Communication Skills Course, Use of Social Media and Satisfaction With Clinical Placement.

PubMed

Sigalit, Warshawski; Sivia, Barnoy; Michal, Itzhaki

The purpose of this study was to explore the (a) associations between students' personal and group resilience to their utilization of social networking platforms and formally taught communication skills, (b) students' personal and clinical characteristics that are related to personal and group resilience and the perceived helpfulness of communication course, and (c) factors that contribute to students' satisfaction with their clinical placement. Data were collected from 149 second year nursing students learning in a major university in the country of Israel with the use of a self-administered structured questionnaire. Students' satisfaction from their clinical placement was measured using 1 open-ended question, analyzed through qualitative methods. Results demonstrated positive correlations between students' use of social networking to their personal and group resilience (P<.05). Moreover, social media use, religion, and clinical placement characteristics were related to resilience and to the perceived helpfulness of the communication course (P<.01). Students' satisfaction with their clinical placement was based primarily on the clinical instructors' personal and professional skills. In conclusion, social networking can and should be used as a learning tool to promote resilience among nursing students. Faculty and nurse managers should be aware of the central role of the clinical instructor and initiate collaborative and supporting initiatives. Copyright © 2016 Elsevier Inc. All rights reserved.

Pacesetter in Personalized Learning

ERIC Educational Resources Information Center

Jacobs, Joanne

2017-01-01

The Chicago International Charter School (CICS) Irving Park's middle school is one of 130 schools nationwide piloting the Summit Learning Program (SLP), developed--and offered entirely free--by Summit Public Schools, a high-performing charter network based in California. Summit's eight schools, two of them in Washington State, are known for an…

Moving Edtech Forward: Upstart School Networks Are Betting on a Breakthrough

ERIC Educational Resources Information Center

Horn, Michael B.

2016-01-01

The digital revolution occurring in schools has focused predominantly on online education in its various forms--including fully online courses, learning management systems, games, and mobile applications--to personalize learning and boost the performance of all students. Companies have been experimenting with technologies for years, yet these…

Effectiveness of lectures attended via interactive video conferencing versus in-person in preparing third-year internal medicine clerkship students for Clinical Practice Examinations (CPX).

PubMed

Bertsch, Tania F; Callas, Peter W; Rubin, Alan; Caputo, Michael P; Ricci, Michael A

2007-01-01

The current practice in medical education is to place students at off-site locations. The effectiveness of these students attending remote lectures using interactive videoconferencing needs to be evaluated. To determine whether lecture content covering clinical objectives is learned by medical students located at remote sites. During the University of Vermont medicine clerkship, 52 medical students attended lectures both in person and via 2-way videoconferencing over a telemedicine network. The study used a crossover design, such that all students attended half of the lectures in person and half using videoconferencing. At the end of the clerkship, students were assessed via a Clinical Practice Examination (CPX), with each student completing 1 exam for material learned in person and 1 for material learned over telemedicine. Exam scores did not differ for the 2 lecture modes, with a mean score of 76% for lectures attended in person and a mean score of 78% for lectures attended via telemedicine (p = 0.66). Students learn content focused on clinical learning objectives as well using videoconferencing as they do in the traditional classroom setting.

Developing 21st century skills through the use of student personal learning networks

NASA Astrophysics Data System (ADS)

Miller, Robert D.

This research was conducted to study the development of 21st century communication, collaboration, and digital literacy skills of students at the high school level through the use of online social network tools. The importance of this study was based on evidence high school and college students are not graduating with the requisite skills of communication, collaboration, and digital literacy skills yet employers see these skills important to the success of their employees. The challenge addressed through this study was how high schools can integrate social network tools into traditional learning environments to foster the development of these 21st century skills. A qualitative research study was completed through the use of case study. One high school class in a suburban high performing town in Connecticut was selected as the research site and the sample population of eleven student participants engaged in two sets of interviews and learned through the use social network tools for one semester of the school year. The primary social network tools used were Facebook, Diigo, Google Sites, Google Docs, and Twitter. The data collected and analyzed partially supported the transfer of the theory of connectivism at the high school level. The students actively engaged in collaborative learning and research. Key results indicated a heightened engagement in learning, the development of collaborative learning and research skills, and a greater understanding of how to use social network tools for effective public communication. The use of social network tools with high school students was a positive experience that led to an increased awareness of the students as to the benefits social network tools have as a learning tool. The data supported the continued use of social network tools to develop 21st century communication, collaboration, and digital literacy skills. Future research in this area may explore emerging social network tools as well as the long term impact these tools have on the development of lifelong learning skills and quantitative data linked to student learning.

Enhance Your Twitter Experience

ERIC Educational Resources Information Center

Miller, Shannon McClintock

2010-01-01

The author has been encouraging teachers, students, and others to join Twitter and build their personal learning networks (PLNs) ever since she delved into this great social networking site. In this article, she offers a few other tools and tips that can improve the Twitter experience of those who have opened up an account and dabbled a bit but…

Networking Skills as a Career Development Practice: Lessons from the Earth Science Women's Network (ESWN)

NASA Astrophysics Data System (ADS)

Hastings, M. G.; Kontak, R.; Holloway, T.; Marin-Spiotta, E.; Steiner, A. L.; Wiedinmyer, C.; Adams, A. S.; de Boer, A. M.; Staudt, A. C.; Fiore, A. M.

2010-12-01

Professional networking is often cited as an important component of scientific career development, yet there are few resources for early career scientists to develop and build networks. Personal networks can provide opportunities to learn about organizational culture and procedures, expectations, advancement opportunities, and best practices. They provide access to mentors and job placement opportunities, new scientific collaborations, speaker and conference invitations, increased scientific visibility, reduced isolation, and a stronger feeling of community. There is evidence in the literature that a sense of community positively affects the engagement and retention of underrepresented groups, including women, in science. Thus women scientists may particularly benefit from becoming part of a network. The Earth Science Women’s Network (ESWN) began in 2002 as an informal peer-to-peer mentoring initiative among a few recent Ph.D.s. The network has grown exponentially to include over 1000 women scientists across the globe. Surveys of our membership about ESWN report positive impacts on the careers of women in Earth sciences, particularly those in early career stages. Through ESWN, women share both professional and personal advice, establish research collaborations, communicate strategies on work/life balance, connect with women at various stages of their careers, and provide perspectives from cultures across the globe. We present lessons learned through the formal and informal activities promoted by ESWN in support of the career development of women Earth scientists.

Tools for Schools. Volume 12, Number 4, May-June 2009

ERIC Educational Resources Information Center

von Frank, Valerie, Ed.

2009-01-01

This newsletter is published four times a year. It offers articles on school improvement, organizational planning, training, and managing change. This issue contains: (1) Link Up & Learn: Use Technology to Create a Personal Learning Network to Connect with Experts and Mentors Everywhere (Valerie von Frank); (2) NSDC Tool: Get Connected with…

An Exploration of Learning, the Knowledge-Based Economy, and Owner-Managers of Small Bookselling Businesses.

ERIC Educational Resources Information Center

Paige, Helen

2002-01-01

A qualitative study of six owner/managers of small Australian bookselling businesses elicited these themes: participation in learning is largely informal or incidental; interaction with information/communication technologies is less than optimal; and small business management relies on personal and business networking. Ways to develop a more…

Integrating Learning Styles and Personality Traits into an Affective Model to Support Learner's Learning

NASA Astrophysics Data System (ADS)

Leontidis, Makis; Halatsis, Constantin

The aim of this paper is to present a model in order to integrate the learning style and the personality traits of a learner into an enhanced Affective Style which is stored in the learner’s model. This model which can deal with the cognitive abilities as well as the affective preferences of the learner is called Learner Affective Model (LAM). The LAM is used to retain learner’s knowledge and activities during his interaction with a Web-based learning environment and also to provide him with the appropriate pedagogical guidance. The proposed model makes use of an ontological approach in combination with the Bayesian Network model and contributes to the efficient management of the LAM in an Affective Module.

How leaders create and use networks.

PubMed

Ibarra, Herman; Hunter, Mark

2007-01-01

Most people acknowledge that networking-creating a fabric of personal contacts to provide support, feedback, insight, and resources--is an essential activity for an ambitious manager. Indeed, it's a requirement even for those focused simply on doing their current jobs well. For some, this is a distasteful reality. Working through networks, they believe,means relying on "who you know" rather than "what you know"--a hypocritical, possibly unethical, way to get things done. But even people who understand that networking is a legitimate and necessary part of their jobs can be discouraged by the payoff--because they are doing it in too limited a fashion. On the basis of a close study of 30 emerging leaders, the authors outline three distinct forms of networking. Operational networking is geared toward doing one's assigned tasks more effectively. It involves cultivating stronger relationships with colleagues whose membership in the network is clear; their roles define them as stakeholders. Personal networking engages kindred spirits from outside an organization in an individual's efforts to learn and find opportunities for personal advancement. Strategic networking puts the tools of networking in the service of business goals. At this level, a manager creates the kind of network that will help uncover and capitalize on new opportunities for the company. The ability to move to this level of networking turns out to be a key test of leadership. Companies often recognize that networks are valuable, andthey create explicit programs to support them. But typically these programs facilitate only operational networking. Likewise, industry associations provide formal contexts for personal networking. The unfortunate effect is to give managers the impression that they know how to network and are doing so sufficiently. A sidebar notes the implication for companies' leadership development initiatives: that teaching strategic networking skills will serve their aspiring leaders and their business goals well.

Social Networking as a Tool for Lifelong Learning with Orthopedically Impaired Learners

ERIC Educational Resources Information Center

Ersoy, Metin; Güneyli, Ahmet

2016-01-01

This paper discusses how Turkish Cypriot orthopedically impaired learners who are living in North Cyprus use social networking as a tool for leisure and education, and to what extent they satisfy their personal development needs by means of these digital platforms. The case study described, conducted in North Cyprus in 2015 followed a qualitative…

Experiencing a Social Network in an Organizational Context: The Facebook Internship

ERIC Educational Resources Information Center

McEachern, Robert W.

2011-01-01

As Facebook becomes increasingly more popular as a communication tool for businesses and organizations, it is important that our students learn to transfer personal Facebook skills to professional settings. This article focuses on the lessons learned by two students who used Facebook as part of a social media internship, as well as what the author…

Leveraging Mobile Technology for Sustainable Seamless Learning: A Research Agenda

ERIC Educational Resources Information Center

Looi, Chee-Kit; Seow, Peter; Zhang, BaoHui; So, Hyo-Jeong; Chen, Wenli; Wong, Lung-Hsiang

2010-01-01

Over the next 10 years, we anticipate that personal, portable, wirelessly networked technologies will become ubiquitous in the lives of learners--indeed, in many countries, this is already a reality. We see that ready-to-hand access creates the potential for a new phase in the evolution of technology-enhanced learning, characterised by "seamless…

Pedagogically-Driven Ontology Network for Conceptualizing the e-Learning Assessment Domain

ERIC Educational Resources Information Center

Romero, Lucila; North, Matthew; Gutiérrez, Milagros; Caliusco, Laura

2015-01-01

The use of ontologies as tools to guide the generation, organization and personalization of e-learning content, including e-assessment, has drawn attention of the researchers because ontologies can represent the knowledge of a given domain and researchers use the ontology to reason about it. Although the use of these semantic technologies tends to…

Community-Sourcing a New Marketing Course: Collaboration in Social Media

ERIC Educational Resources Information Center

Schirr, Gary R.

2013-01-01

This paper shows the value of an online personal learning network or community in educational innovation. It shows how theories and best practices from service and product innovation, as well the theories of learning communities, were applied using social media to facilitate the grant proposal and course development processes for a new course in…

Building Connective Capital and Personal Learning Networks through Online Professional Development Communities for New Teachers

ERIC Educational Resources Information Center

Sciuto, David J.

2017-01-01

Increasingly, researchers concerned with the effects of digital technology have hypothesized that the millennial generation does not think or process information like its predecessors. In an age of disruptive technology changing culture and learning, new teachers continue to leave the classroom within the first five years of service. Among the…

Providing Effective Learner Support for Part-Time Learners. Research Report

ERIC Educational Resources Information Center

Barker, Philip; Crawley, Jim

2005-01-01

Learner support, defined as the strategies which empower learners to establish and fulfill their learning, career and personal potential, continues to be a key issue in current thinking in the post-16 education sector. An earlier project report from the West Country Learning and Skills Research Network (WCLSRN) showed that part-time learners were…

"Learning a Different Form of Communication": Experiences of Networked Learning and Reflections on Practice

ERIC Educational Resources Information Center

Levy, Philippa

2006-01-01

This paper focuses on learners' experiences of text-based computer-mediated communication (CMC) as a means of self-expression, dialogue and debate. A detailed case study narrative and a reflective commentary are presented, drawn from a personal, practice-based inquiry into the design and facilitation of a professional development course for which…

History and Culture of Alara--The Action Learning and Action Research Association

ERIC Educational Resources Information Center

Zuber-Skerritt, Ortrun; Passfield, Ron

2016-01-01

As co-founders of the Action Learning and Action Research Association (ALARA), we tell the story of this international network organisation through our personal experience. Our history traces the evolution of ALARA from origins at the first World Congress in 1990 in Brisbane, Australia, through development over two and a half decades, to its…

A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions.

PubMed

Li, Liyuan; Xu, Qianli; Gan, Tian; Tan, Cheston; Lim, Joo-Hwee

2018-05-01

Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton's method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.

Using an Instructional LAN to Teach a Statistics Course.

ERIC Educational Resources Information Center

Barnes, J. Wesley; And Others

1988-01-01

Discusses a computer assisted learning system for engineering statistics based on personalized system of instruction methods. Describes the system's network, development, course structure, programing, and security. Lists the benefits of the system. (MVL)

The Need for a More Efficient User Notification System in Using Social Networks as Ubiquitous Learning Platforms

ERIC Educational Resources Information Center

Mihci, Can; Donmez, Nesrin Ozdener

2017-01-01

While carrying out formative assessment activities over social network services (SNS), it has been noted that personalized notifications have a high chance of "the important post getting lost" in the notification feed. In order to highlight this problem, this paper compares within a posttest only quasi-experiment, a total of 104 first…

The Academic Journey of University Students on Facebook: An Analysis of Informal Academic-Related Activity over a Semester

ERIC Educational Resources Information Center

Vivian, Rebecca; Barnes, Alan; Geer, Ruth; Wood, Denise

2014-01-01

This paper reports on an observation of 70 university students' use of their personal social network site (SNS), Facebook, over a 22-week university study period. The study sought to determine the extent that university students use their personal SNSs to support learning by exploring frequencies of academic-related content and topics being…

Human Computer Interaction (HCI) and Internet Residency: Implications for Both Personal Life and Teaching/Learning

ERIC Educational Resources Information Center

Crearie, Linda

2016-01-01

Technological advances over the last decade have had a significant impact on the teaching and learning experiences students encounter today. We now take technologies such as Web 2.0, mobile devices, cloud computing, podcasts, social networking, super-fast broadband, and connectedness for granted. So what about the student use of these types of…

Enriching Adaptation in E-Learning Systems through a Situation-Aware Ontology Network

ERIC Educational Resources Information Center

Pernas, Ana Marilza; Diaz, Alicia; Motz, Regina; de Oliveira, Jose Palazzo Moreira

2012-01-01

Purpose: The broader adoption of the internet along with web-based systems has defined a new way of exchanging information. That advance added by the multiplication of mobile devices has required systems to be even more flexible and personalized. Maybe because of that, the traditional teaching-controlled learning style has given up space to a new…

vPELS: An E-Learning Social Environment for VLSI Design with Content Security Using DRM

ERIC Educational Resources Information Center

Dewan, Jahangir; Chowdhury, Morshed; Batten, Lynn

2014-01-01

This article provides a proposal for personal e-learning system (vPELS [where "v" stands for VLSI: very large scale integrated circuit])) architecture in the context of social network environment for VLSI Design. The main objective of vPELS is to develop individual skills on a specific subject--say, VLSI--and share resources with peers.…

MSG Instant Messenger: Social Presence and Location for the "'Ad Hoc' Learning Experience"

ERIC Educational Resources Information Center

Little, Alex; Denham, Chris; Eisenstadt, Marc

2008-01-01

"Elearning2.0" promises to harness the power of three of today's most disruptive technologies: social software, elearning, and Web2.0. Our own work in this disruptive space takes as a starting premise that social networking is critical for learning: finding the right person can be more important than "scouring the web for an answer" particularly…

Examining the Efficacy of the Modified Story Memory Technique (mSMT) in Persons With TBI Using Functional Magnetic Resonance Imaging (fMRI): The TBI-MEM Trial.

PubMed

Chiaravalloti, Nancy D; Dobryakova, Ekaterina; Wylie, Glenn R; DeLuca, John

2015-01-01

New learning and memory deficits are common following traumatic brain injury (TBI). Yet few studies have examined the efficacy of memory retraining in TBI through the most methodologically vigorous randomized clinical trial. Our previous research has demonstrated that the modified Story Memory Technique (mSMT) significantly improves new learning and memory in multiple sclerosis. The present double-blind, placebo-controlled, randomized clinical trial examined changes in cerebral activation on functional magnetic resonance imaging following mSMT treatment in persons with TBI. Eighteen individuals with TBI were randomly assigned to treatment (n = 9) or placebo (n = 9) groups. Baseline and follow-up functional magnetic resonance imaging was collected during a list-learning task. Significant differences in cerebral activation from before to after treatment were noted in regions belonging to the default mode network and executive control network in the treatment group only. Results are interpreted in light of these networks. Activation differences between the groups likely reflect increased use of strategies taught during treatment. This study demonstrates a significant change in cerebral activation resulting from the mSMT in a TBI sample. Findings are consistent with previous work in multiple sclerosis. Behavioral interventions can show significant changes in the brain, validating clinical utility.

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

Tong, H; Papadimitriou, S; Faloutsos, C

Given a social network, who is the best person to introduce you to, say, Chris Ferguson, the poker champion? Or, given a network of people and skills, who is the best person to help you learn about, say, wavelets? The goal is to find a small group of 'gateways': persons who are close enough to us, as well as close enough to the target (person, or skill) or, in other words, are crucial in connecting us to the target. The main contributions are the following: (a) we show how to formulate this problem precisely; (b) we show that it ismore » sub-modular and thus it can be solved near-optimally; (c) we give fast, scalable algorithms to find such gateways. Experiments on real data sets validate the effectiveness and efficiency of the proposed methods, achieving up to 6,000,000x speedup.« less

A Person-Centered, Registry-Based Learning Health System for Palliative Care: A Path to Coproducing Better Outcomes, Experience, Value, and Science

PubMed Central

Kamal, Arif H.; Kirkland, Kathryn B.; Meier, Diane E.; Nelson, Eugene C.; Pantilat, Steven Z.

2018-01-01

Abstract Background: Palliative care offers an approach to the care of people with serious illness that focuses on quality of life and aligning care with individual and family goals, and values in the context of what is medically achievable. Objective: Measurement of the impact of palliative care is critical for determining what works for which patients in what settings, to learn, improve care, and ensure access to high value care for people with serious illness. Methods: A learning health system that includes patients and families partnering with clinicians and care teams, is directly linked to a registry to support networks for improvement and research, and offers an ideal framework for measuring what matters to a range of stakeholders interested in improving care for this population. Measurements: Measurement focuses on the individual patient and family experience as the fundamental outcome of interest around which all care delivery is organized. Results: We describe an approach to codesigning and implementing a palliative care registry that functions as a learning health system, by combining patient and family inputs and clinical data to support person-centered care, quality improvement, accountability, transparency, and scientific research. Discussion: The potential for a palliative care learning health system that, by design, brings together enriched information environments to support coproduction of healthcare and facilitated peer networks to support patients and families, collaborative clinician networks to support palliative care program improvement, and collaboratories to support research and the application of research to benefit individual patients is immense. PMID:29091509

Server-Based and Server-Less Byod Solutions to Support Electronic Learning

DTIC Science & Technology

2016-06-01

Knowledge Online NSD National Security Directive OS operating system OWA Outlook Web Access PC personal computer PED personal electronic device PDA...mobile devices, institute mobile device policies and standards, and promote the development and use of DOD mobile and web -enabled applications” (DOD...with an isolated BYOD web server, properly educated system administrators must carry out and execute the necessary, pre-defined network security

Using Bayesian Networks to Understand Relationships among Math Anxiety, Genders, Personality Types, and Study Habits at a University in Jordan

ERIC Educational Resources Information Center

Smail, Linda

2017-01-01

Mathematics is the foundation of all sciences, but most students have problems learning math. Although students' success in life related to their success in learning, many would not take a math course unless it is their university's core requirements. Multiple reasons exist for students' poor performance in mathematics, but one prevalent variable…

Professionals' and Parents' Shared Learning in Blended Learning Networks Related to Communication and Augmentative and Alternative Communication for People with Severe Disabilities

ERIC Educational Resources Information Center

Wilder, Jenny; Magnusson, Lennart; Hanson, Elizabeth

2015-01-01

People with severe disabilities (SD) communicate in complex ways, and their teachers, parents and other involved professionals find it difficult to gain knowledge and share their experiences regarding the person with SD's communication methods. The purpose of this study is to contribute to our understanding of how parents and professionals share…

A work-based educational intervention to support the development of personal resilience in nurses and midwives.

PubMed

McDonald, Glenda; Jackson, Debra; Wilkes, Lesley; Vickers, Margaret H

2012-05-01

A work-based educational programme was the intervention used in a collective case study aiming to develop, strengthen and maintain personal resilience amongst fourteen nurses and midwives. The participants attended six, monthly workshops and formed a participatory learning group. Post-intervention, participants reported positive personal and professional outcomes, including enhanced self-confidence, self-awareness, communication and conflict resolution skills. They strengthened relationships with their colleagues, enabling them to build helpful support networks in the workplace. The intervention used new and innovative ways of engaging nurses and midwives exhibiting the effects of workplace adversity - fatigue, pressure, stress and emotional labour. Participants were removed from their usual workplace environment and brought together to engage in critical reflection, experiential learning and creativity whilst also learning about the key characteristics and strategies of personal resilience. Participants' experiences and skills were valued and respected; honest airing of the differences within the group regarding common workplace issues and concerns was encouraged. The new contribution of this intervention for nursing and midwifery education was supporting the learning experience with complementary therapies to improve participants' wellbeing and reduce stress. Copyright © 2011. Published by Elsevier Ltd.

Performance Support on the Shop Floor.

ERIC Educational Resources Information Center

Kasvi, Jyrki J. J.; Vartiainen, Matti

2000-01-01

Discussion of performance support on the shop floor highlights four support systems for assembly lines that incorporate personal computer workstations in local area networks and use multimedia documents. Considers new customer-focused production paradigms; organizational learning; knowledge development; and electronic performance support systems…

Health Insurance Exchanges: Health Insurance Navigators and In-Person Assistance

DTIC Science & Technology

2013-09-25

apply for coverage through the exchanges may be eligible for small business tax credits.5 Consumers may apply for coverage over the phone, online , via...more recent CMS announcements reference 20-30 hours of training. 47 The Medicare Learning Network online navigator training is estimated to take 20...about scam artists seeking to obtain personal information under the guise of verifying information regarding ACA coverage.114

Ring-push metric learning for person reidentification

NASA Astrophysics Data System (ADS)

He, Botao; Yu, Shaohua

2017-05-01

Person reidentification (re-id) has been widely studied because of its extensive use in video surveillance and forensics applications. It aims to search a specific person among a nonoverlapping camera network, which is highly challenging due to large variations in the cluttered background, human pose, and camera viewpoint. We present a metric learning algorithm for learning a Mahalanobis distance for re-id. Generally speaking, there exist two forces in the conventional metric learning process, one pulling force that pulls points of the same class closer and the other pushing force that pushes points of different classes as far apart as possible. We argue that, when only a limited number of training data are given, forcing interclass distances to be as large as possible may drive the metric to overfit the uninformative part of the images, such as noises and backgrounds. To alleviate overfitting, we propose the ring-push metric learning algorithm. Different from other metric learning methods that only punish too small interclass distances, in the proposed method, both too small and too large inter-class distances are punished. By introducing the generalized logistic function as the loss, we formulate the ring-push metric learning as a convex optimization problem and utilize the projected gradient descent method to solve it. The experimental results on four public datasets demonstrate the effectiveness of the proposed algorithm.

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

PubMed

Fan, Jianqing; Tong, Xin; Zeng, Yao

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning , to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

Increased functional connectivity within memory networks following memory rehabilitation in multiple sclerosis.

PubMed

Leavitt, Victoria M; Wylie, Glenn R; Girgis, Peter A; DeLuca, John; Chiaravalloti, Nancy D

2014-09-01

Identifying effective behavioral treatments to improve memory in persons with learning and memory impairment is a primary goal for neurorehabilitation researchers. Memory deficits are the most common cognitive symptom in multiple sclerosis (MS), and hold negative professional and personal consequences for people who are often in the prime of their lives when diagnosed. A 10-session behavioral treatment, the modified Story Memory Technique (mSMT), was studied in a randomized, placebo-controlled clinical trial. Behavioral improvements and increased fMRI activation were shown after treatment. Here, connectivity within the neural networks underlying memory function was examined with resting-state functional connectivity (RSFC) in a subset of participants from the clinical trial. We hypothesized that the treatment would result in increased integrity of connections within two primary memory networks of the brain, the hippocampal memory network, and the default network (DN). Seeds were placed in left and right hippocampus, and the posterior cingulate cortex. Increased connectivity was found between left hippocampus and cortical regions specifically involved in memory for visual imagery, as well as among critical hubs of the DN. These results represent the first evidence for efficacy of a behavioral intervention to impact the integrity of neural networks subserving memory functions in persons with MS.

Dynamic neural architecture for social knowledge retrieval

PubMed Central

Wang, Yin; Collins, Jessica A.; Koski, Jessica; Nugiel, Tehila; Metoki, Athanasia; Olson, Ingrid R.

2017-01-01

Social behavior is often shaped by the rich storehouse of biographical information that we hold for other people. In our daily life, we rapidly and flexibly retrieve a host of biographical details about individuals in our social network, which often guide our decisions as we navigate complex social interactions. Even abstract traits associated with an individual, such as their political affiliation, can cue a rich cascade of person-specific knowledge. Here, we asked whether the anterior temporal lobe (ATL) serves as a hub for a distributed neural circuit that represents person knowledge. Fifty participants across two studies learned biographical information about fictitious people in a 2-d training paradigm. On day 3, they retrieved this biographical information while undergoing an fMRI scan. A series of multivariate and connectivity analyses suggest that the ATL stores abstract person identity representations. Moreover, this region coordinates interactions with a distributed network to support the flexible retrieval of person attributes. Together, our results suggest that the ATL is a central hub for representing and retrieving person knowledge. PMID:28289200

Dynamic neural architecture for social knowledge retrieval.

PubMed

Wang, Yin; Collins, Jessica A; Koski, Jessica; Nugiel, Tehila; Metoki, Athanasia; Olson, Ingrid R

2017-04-18

Social behavior is often shaped by the rich storehouse of biographical information that we hold for other people. In our daily life, we rapidly and flexibly retrieve a host of biographical details about individuals in our social network, which often guide our decisions as we navigate complex social interactions. Even abstract traits associated with an individual, such as their political affiliation, can cue a rich cascade of person-specific knowledge. Here, we asked whether the anterior temporal lobe (ATL) serves as a hub for a distributed neural circuit that represents person knowledge. Fifty participants across two studies learned biographical information about fictitious people in a 2-d training paradigm. On day 3, they retrieved this biographical information while undergoing an fMRI scan. A series of multivariate and connectivity analyses suggest that the ATL stores abstract person identity representations. Moreover, this region coordinates interactions with a distributed network to support the flexible retrieval of person attributes. Together, our results suggest that the ATL is a central hub for representing and retrieving person knowledge.

The New Engines of Learning.

ERIC Educational Resources Information Center

Negroponto, Nicholas

1995-01-01

According to the author's book "Being Digital," our world is shifting from atoms to bits. Digitally rendered information, combined with personal computing power and networks, will make computers active participants in our everyday lives. "Teaching-disabled" classrooms will move from passivity to active participation and…

Privacy Impact Assessment for the Las Vegas Finance Center Local Area Network

EPA Pesticide Factsheets

This system collects contact information and other Personally Identifiable Information (PII). Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies.

Stability of similarity measurements for bipartite networks

PubMed Central

Liu, Jian-Guo; Hou, Lei; Pan, Xue; Guo, Qiang; Zhou, Tao

2016-01-01

Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-n-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on. PMID:26725688

Stability of similarity measurements for bipartite networks.

PubMed

Liu, Jian-Guo; Hou, Lei; Pan, Xue; Guo, Qiang; Zhou, Tao

2016-01-04

Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-n-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on.

Protein function in precision medicine: deep understanding with machine learning.

PubMed

Rost, Burkhard; Radivojac, Predrag; Bromberg, Yana

2016-08-01

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both. © 2016 Federation of European Biochemical Societies.

Recovery in Scotland: Beyond service development

PubMed Central

2012-01-01

Over the last ten years there has been significant activity related to the promotion and support of recovery in Scotland, much of it linked to the work of the Scottish Recovery Network. A range of government policies have consistently identified recovery as a guiding principle of both service design and mental health improvement efforts. New learning has been developed and shared, workforce competencies reviewed and training developed, and a range of national initiatives put in place. In Scotland, as elsewhere, these efforts have tended to focus primarily on ensuring that mental health services offer environments and practices that support personal recovery. While service improvement is crucial, a wider challenge is ensuring that opportunities and support for self-directed recovery are enhanced outside statutory services. Providing examples, this paper will look at the development of recovery in Scotland – including the work of the Scottish Recovery Network – and consider the potential for building on progress made by rebalancing efforts to support personal recovery, highlighting the importance of public attitudes and community-based learning approaches. We will also touch on the role of identity in personal recovery and consider cultural issues related to the promotion of recovery in Scotland. PMID:22385428

New perspectives on health professions students' e-learning: Looking through the lens of the "visitor and resident" model.

PubMed

Druce, Maralyn; Howden, Stella

2017-07-01

The growth of e-learning in health professional education reflects expansion of personal use of online resources. Understanding the user perspective in a fast-changing digital world is essential to maintain the currency of our approach. Mixed methods were used to investigate a cohort of postgraduate, e-learning healthcare students' perspectives on their use of online resources for personal and/or professional roles, via questionnaire and student-constructed diagrams, capturing use of online resources (underpinned by White's model of "resident" and "visitor" online engagement). Semistructured interviews explored the use and value of resources afforded via the online environment. The 45 study participants described a range of prior experiences with online resources in personal and professional capacities, but overall students tended to use online "tools" ("visitor" mode) rather than highly collaborative networks ("resident" mode). In relation to e-learning, the dominant interview theme was valuing knowledge transfer from the tutor and using "visitor" behaviors to maximize knowledge acquisition. Peer-learning opportunities were less valued and barriers to collaborative "resident" modes were identified. These findings help to inform e-learning course design to promote engagement. The results enable recommendations for use of the "Visitor and Residents" model and for planning activities that learners might utilize effectively.

Distinguishable memory retrieval networks for collaboratively and non-collaboratively learned information.

PubMed

Vanlangendonck, Flora; Takashima, Atsuko; Willems, Roel M; Hagoort, Peter

2018-03-01

Learning often occurs in communicative and collaborative settings, yet almost all research into the neural basis of memory relies on participants encoding and retrieving information on their own. We investigated whether learning linguistic labels in a collaborative context at least partly relies on cognitively and neurally distinct representations, as compared to learning in an individual context. Healthy human participants learned labels for sets of abstract shapes in three different tasks. They came up with labels with another person in a collaborative communication task (collaborative condition), by themselves (individual condition), or were given pre-determined unrelated labels to learn by themselves (arbitrary condition). Immediately after learning, participants retrieved and produced the labels aloud during a communicative task in the MRI scanner. The fMRI results show that the retrieval of collaboratively generated labels as compared to individually learned labels engages brain regions involved in understanding others (mentalizing or theory of mind) and autobiographical memory, including the medial prefrontal cortex, the right temporoparietal junction and the precuneus. This study is the first to show that collaboration during encoding affects the neural networks involved in retrieval. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Are You LinkedIn?

ERIC Educational Resources Information Center

Weinstein, Margery

2010-01-01

Professional connections cannot only win a person a job, they can also win employees a top-notch learning organization. In this article, four Training Top 125 winners--CareSource University, Coldwell Banker, inVentiv Health Inc., and Kendle International--describe how they are using Web 2.0 social networking features to facilitate their employee…

Creating a Professional Library

ERIC Educational Resources Information Center

Russel, Priscilla

2008-01-01

Compiling a personal professional library should be a priority for each teacher. The author canvassed the Executive Board of the National Network for Early Language Learning (NNELL) to elicit its members' "must haves" among foreign language texts. In this article, the author presents a list of publications which form a foundation of resources…

Online Marketing to Kids: How To Protect Yourself.

ERIC Educational Resources Information Center

School Libraries in Canada, 2000

2000-01-01

Presents a teaching unit from the Media Awareness Network Web site that introduces students (grades six through nine) to ways in which commercial Web sites collect personal information from children and issues surrounding children and privacy on the Internet. Highlights: objective; learning outcomes; preparation and materials; the lesson; guided…

Mixed-method Exploration of Social Network Links to Participation

PubMed Central

Kreider, Consuelo M.; Bendixen, Roxanna M.; Mann, William C.; Young, Mary Ellen; McCarty, Christopher

2015-01-01

The people who regularly interact with an adolescent form that youth's social network, which may impact participation. We investigated the relationship of social networks to participation using personal network analysis and individual interviews. The sample included 36 youth, age 11 – 16 years. Nineteen had diagnoses of learning disability, attention disorder, or high-functioning autism and 17 were typically developing. Network analysis yielded 10 network variables, of which 8 measured network composition and 2 measured network structure, with significant links to at least one measure of participation using the Children's Assessment of Participation and Enjoyment (CAPE). Interviews from youth in the clinical group yielded description of strategies used to negotiate social interactions, as well as processes and reasoning used to remain engaged within social networks. Findings contribute to understanding the ways social networks are linked to youth participation and suggest the potential of social network factors for predicting rehabilitation outcomes. PMID:26594737

Optimization of multilayer neural network parameters for speaker recognition

NASA Astrophysics Data System (ADS)

Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka

2016-05-01

This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach

PubMed Central

Tong, Xin; Zeng, Yao

2016-01-01

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people’s incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning, to address whether with high probability, a large fraction of people in a given finite population network can make “good” inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows. PMID:27076691

Modelling the effect of religion on human empathy based on an adaptive temporal-causal network model.

PubMed

van Ments, Laila; Roelofsma, Peter; Treur, Jan

2018-01-01

Religion is a central aspect of many individuals' lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. By first developing a conceptual representation of a network model based on the literature, and then formalizing this model into a numerical representation, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus was mainly on the influence of religion on human empathy and dis-empathy, a topic very relevant today. The developed model could be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals. It is illustrated for a number of different scenarios based on different characteristics of the persons and of the religion.

[Learning how to learn for specialist further education].

PubMed

Breuer, G; Lütcke, B; St Pierre, M; Hüttl, S

2017-02-01

The world of medicine is becoming from year to year more complex. This necessitates efficient learning processes, which incorporate the principles of adult education but with unchanged periods of further education. The subject matter must be processed, organized, visualized, networked and comprehended. The learning process should be voluntary and self-driven with the aim of learning the profession and becoming an expert in a specialist field. Learning is an individual process. Despite this, the constantly cited learning styles are nowadays more controversial. An important factor is a healthy mixture of blended learning methods, which also use new technical possibilities. These include a multitude of e‑learning options and simulations, which partly enable situative learning in a "shielded" environment. An exemplary role model of the teacher and feedback for the person in training also remain core and sustainable aspects in medical further education.

Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT.

PubMed

Deist, Timo M; Jochems, A; van Soest, Johan; Nalbantov, Georgi; Oberije, Cary; Walsh, Seán; Eble, Michael; Bulens, Paul; Coucke, Philippe; Dries, Wim; Dekker, Andre; Lambin, Philippe

2017-06-01

Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datasets representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. Ethical and legal boundaries to ensure data privacy hamper collaboration between research institutes. We hypothesize that data sharing is possible without identifiable patient data leaving the radiation clinics and that building machine learning applications on distributed datasets is feasible. We developed and implemented an IT infrastructure in five radiation clinics across three countries (Belgium, Germany, and The Netherlands). We present here a proof-of-principle for future 'big data' infrastructures and distributed learning studies. Lung cancer patient data was collected in all five locations and stored in local databases. Exemplary support vector machine (SVM) models were learned using the Alternating Direction Method of Multipliers (ADMM) from the distributed databases to predict post-radiotherapy dyspnea grade [Formula: see text]. The discriminative performance was assessed by the area under the curve (AUC) in a five-fold cross-validation (learning on four sites and validating on the fifth). The performance of the distributed learning algorithm was compared to centralized learning where datasets of all institutes are jointly analyzed. The euroCAT infrastructure has been successfully implemented in five radiation clinics across three countries. SVM models can be learned on data distributed over all five clinics. Furthermore, the infrastructure provides a general framework to execute learning algorithms on distributed data. The ongoing expansion of the euroCAT network will facilitate machine learning in radiation oncology. The resulting access to larger datasets with sufficient variation will pave the way for generalizable prediction models and personalized medicine.

Grower networks support adoption of innovations in pollination management: The roles of social learning, technical learning, and personal experience.

PubMed

Garbach, Kelly; Morgan, Geoffrey P

2017-12-15

Management decisions underpinning availability of ecosystem services and the organisms that provide them in agroecosystems, such as pollinators and pollination services, have emerged as a foremost consideration for both conservation and crop production goals. There is growing evidence that innovative management practices can support diverse pollinators and increase crop pollination. However, there is also considerable debate regarding factors that support adoption of these innovative practices. This study investigated pollination management practices and related knowledge systems in a major crop producing region of southwest Michigan in the United States, where 367 growers were surveyed to evaluate adoption of three innovative practices that are at various stages of adoption. The goals of this quantitative, social survey were to investigate grower experience with concerns and benefits associated with each practice, as well as the influence of grower networks, which are comprised of contacts that reflect potential pathways for social and technical learning. The results demonstrated that 17% of growers adopted combinations of bees (e.g. honey bees, Apis mellifera, with other species), representing an innovation in use by early adopters; 49% of growers adopted flowering cover crops, an innovation in use by the early majority 55% of growers retained permanent habitat for pollinators, an innovation in use by the late majority. Not all growers adopted innovative practices. We found that growers' personal experience with potential benefits and concerns related to the management practices had significant positive and negative relationships, respectively, with adoption of all three innovations. The influence of these communication links likely has different levels of importance, depending on the stage of the adoption that a practice is experiencing in the agricultural community. Social learning was positively associated with adopting the use of combinations of bees, highlighting the potentially critical roles of peer-to-peer networks and social learning in supporting early stages of adoption of innovations. Engaging with grower networks and understanding grower experience with benefits and concerns associated with innovative practices is needed to inform outreach, extension, and policy efforts designed to stimulate management innovations in agroecosystems. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Descriptive Study of the Prevalence and Typology of Alcohol-Related Posts in an Online Social Network for Smoking Cessation.

PubMed

Cohn, Amy M; Zhao, Kang; Cha, Sarah; Wang, Xi; Amato, Michael S; Pearson, Jennifer L; Papandonatos, George D; Graham, Amanda L

2017-09-01

Alcohol use and problem drinking are associated with smoking relapse and poor smoking-cessation success. User-generated content in online social networks for smoking cessation provides an opportunity to understand the challenges and treatment needs of smokers. This study used machine-learning text classification to identify the prevalence, sentiment, and social network correlates of alcohol-related content in the social network of a large online smoking-cessation program, BecomeAnEX.org. Data were analyzed from 814,258 posts (January 2012 to May 2015). Posts containing alcohol keywords were coded via supervised machine-learning text classification for information about the user's personal experience with drinking, whether the user self-identified as a problem drinker or indicated problem drinking, and negative sentiment about drinking in the context of a quit attempt (i.e., alcohol should be avoided during a quit attempt). Less than 1% of posts were related to alcohol, contributed by 13% of users. Roughly a third of alcohol posts described a personal experience with drinking; very few (3%) indicated "problem drinking." The majority (70%) of alcohol posts did not express negative sentiment about drinking alcohol during a quit attempt. Users who did express negative sentiment about drinking were more centrally located within the network compared with those who did not. Discussion of alcohol was rare, and most posts did not signal the need to quit or abstain from drinking during a quit attempt. Featuring expert information or highlighting discussions that are consistent with treatment guidelines may be important steps to ensure smokers are educated about drinking risks.

Combining Dopaminergic Facilitation with Robot-Assisted Upper Limb Therapy in Stroke Survivors

PubMed Central

Tran, Duc A.; Pajaro-Blazquez, Marta; Daneault, Jean-Francois; Gallegos, Jaime G.; Pons, Jose; Fregni, Felipe; Bonato, Paolo; Zafonte, Ross

2016-01-01

ABSTRACT Despite aggressive conventional therapy, lasting hemiplegia persists in a large percentage of stroke survivors. The aim of this article is to critically review the rationale behind targeting multiple sites along the motor learning network by combining robotic therapy with pharmacotherapy and virtual reality–based reward learning to alleviate upper extremity impairment in stroke survivors. Methods for personalizing pharmacologic facilitation to each individual’s unique biology are also reviewed. At the molecular level, treatment with levodopa was shown to induce long-term potentiation-like and practice-dependent plasticity. Clinically, trials combining conventional therapy with levodopa in stroke survivors yielded statistically significant but clinically unconvincing outcomes because of limited personalization, standardization, and reproducibility. Robotic therapy can induce neuroplasticity by delivering intensive, reproducible, and functionally meaningful interventions that are objective enough for the rigors of research. Robotic therapy also provides an apt platform for virtual reality, which boosts learning by engaging reward circuits. The future of stroke rehabilitation should target distinct molecular, synaptic, and cortical sites through personalized multimodal treatments to maximize motor recovery. PMID:26829074

The impact of a faculty learning community on professional and personal development: the facilitator training program of the American Academy on Communication in Healthcare.

PubMed

Chou, Calvin L; Hirschmann, Krista; Fortin, Auguste H; Lichstein, Peter R

2014-07-01

Relationship-centered care attends to the entire network of human relationships essential to patient care. Few faculty development programs prepare faculty to teach principles and skills in relationship-centered care. One exception is the Facilitator Training Program (FTP), a 25-year-old training program of the American Academy on Communication in Healthcare. The authors surveyed FTP graduates to determine the efficacy of its curriculum and the most important elements for participants' learning. In 2007, surveys containing quantitative and narrative elements were distributed to 51 FTP graduates. Quantitative data were analyzed using descriptive statistics. The authors analyzed narratives using Burke's dramatistic pentad as a qualitative framework to delineate how interrelated themes interacted in the FTP. Forty-seven respondents (92%) identified two essential acts that happened in the program: an iterative learning process, leading to heightened personal awareness and group facilitation skills; and longevity of learning and effect on career. The structure of the program's learning community provided the scene, and the agents were the participants, who provided support and contributed to mutual success. Methods of developing skills in personal awareness, group facilitation, teaching, and feedback constituted agency. The purpose was to learn skills and to join a community to share common values. The FTP is a learning community that provided faculty with skills in principles of relationship-centered care. Four further features that describe elements of this successful faculty-based learning community are achievement of self-identified goals, distance learning modalities, opportunities to safely discuss workplace issues outside the workplace, and self-renewing membership.

Student Perceptions of Microblogging: Integrating Twitter with Blogging to Support Learning and Interaction

ERIC Educational Resources Information Center

Thoms, Brian

2012-01-01

Social networking technologies are used by millions of individuals around the globe to foster dialogue and share all types of information. It is therefore common to see that campuses abound with students embracing these technologies, sharing everything from personal experiences to general interests and current events with their immediate and…

Distance Learner Ecologies of the University of the West Indies Open Campus Program

ERIC Educational Resources Information Center

Beaubrun, Elizabeth

2012-01-01

This research project examined the learner ecologies of University of the West Indies (UWI) distance learning program participants in two countries within the regional university's network: Dominica, and Antigua and Barbuda. The descriptive study focused on a period of transition from dual-mode delivery (teleconference and in-person tutorial…

Online Grading of Calculations in General Chemistry Laboratory Write-Ups

ERIC Educational Resources Information Center

Silva, Alexsandra; Gonzales, Robert; Brennan, Daniel P.

2010-01-01

In the past, there were frequently complaints about the grading of laboratory reports in our laboratory chemistry courses. This article discussed the implementation of an online submission of laboratory acquired data using LON-CAPA (The Learning Online Network with Computer-Assisted Personalized Approach), which is an open source management and…

Identity, Context Collapse, and Facebook Use in Higher Education: Putting Presence and Privacy at Odds

ERIC Educational Resources Information Center

Dennen, Vanessa P.; Burner, Kerry J.

2017-01-01

This study examines university student's attitudes toward Facebook use, focusing specifically on how they feel about using a social network that encourages the performance of personal and social identity to support learning and interaction among classmates and instructors. Two surveys elicited student habits, preferences, and beliefs related to…

Learning Efficient Spatial-Temporal Gait Features with Deep Learning for Human Identification.

PubMed

Liu, Wu; Zhang, Cheng; Ma, Huadong; Li, Shuangqun

2018-02-06

The integration of the latest breakthroughs in bioinformatics technology from one side and artificial intelligence from another side, enables remarkable advances in the fields of intelligent security guard computational biology, healthcare, and so on. Among them, biometrics based automatic human identification is one of the most fundamental and significant research topic. Human gait, which is a biometric features with the unique capability, has gained significant attentions as the remarkable characteristics of remote accessed, robust and security in the biometrics based human identification. However, the existed methods cannot well handle the indistinctive inter-class differences and large intra-class variations of human gait in real-world situation. In this paper, we have developed an efficient spatial-temporal gait features with deep learning for human identification. First of all, we proposed a gait energy image (GEI) based Siamese neural network to automatically extract robust and discriminative spatial gait features for human identification. Furthermore, we exploit the deep 3-dimensional convolutional networks to learn the human gait convolutional 3D (C3D) as the temporal gait features. Finally, the GEI and C3D gait features are embedded into the null space by the Null Foley-Sammon Transform (NFST). In the new space, the spatial-temporal features are sufficiently combined with distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for pairs from different persons. Consequently, the experiments on the world's largest gait database show our framework impressively outperforms state-of-the-art methods.

How do general practice residents use social networking sites in asynchronous distance learning?

PubMed

Maisonneuve, Hubert; Chambe, Juliette; Lorenzo, Mathieu; Pelaccia, Thierry

2015-09-21

Blended learning environments - involving both face-to-face and remote interactions - make it easier to adapt learning programs to constraints such as residents' location and low teacher-student ratio. Social networking sites (SNS) such as Facebook®, while not originally intended to be used as learning environments, may be adapted for the distance-learning part of training programs. The purpose of our study was to explore the use of SNS for asynchronous distance learning in a blended learning environment as well as its influence on learners' face-to-face interactions. We conducted a qualitative study and carried out semi-structured interviews. We performed purposeful sampling for maximal variation to include eight general practice residents in 2(nd) and 3(rd) year training. A thematic analysis was performed. The social integration of SNS facilitates the engagement of users in their learning tasks. This may also stimulate students' interactions and group cohesion when members meet up in person. Most of the general practice residents who work in the blended learning environment we studied had a positive appraisal on their use of SNS. In particular, we report a positive impact on their engagement in learning and their participation in discussions during face-to-face instruction. Further studies are needed in order to evaluate the effectiveness of SNS in blended learning environments and the appropriation of SNS by teachers.

Development and Validation of Pre-Service Teachers' Personal Epistemologies of Teaching Scale (PT-PETS)

ERIC Educational Resources Information Center

Yu, Ji Hyun

2013-01-01

The Internet has changed not only how we conceptualize knowledge, but also how we learn in classroom. Knowledge is not any longer transmitted from experts to non-experts, but is constructed through communication, collaboration, and integration among a network of people. In this context, teachers are expected to facilitate student-centered learning…

What the Tech Is Going On? Social Media and Your Music Classroom

ERIC Educational Resources Information Center

Giebelhausen, Robin

2015-01-01

Social media is a dynamic tool capable of helping music teachers in various capacities. This article will explore two levels of involvement, including the personal learning network and the social classroom. When teachers use social media to its fullest potential in the music classroom, it allows for many new possibilities for the classroom,…

Challenges in Fibromyalgia Management: A Study of Anxiety, Depression, and Motivation Using Distance Learning and Social Networking

ERIC Educational Resources Information Center

Caines, Matthew J.

2010-01-01

Patients with fibromyalgia have difficulty managing symptoms (e.g., fatigue, chronic pain). The challenges in fibromyalgia management may vary from patient to patient, from painful physical exercise to pharmaceutical side-effects. Since the management of fibromyalgia greatly varies, there seems to be an individualist or personal component to…

First Steps Towards a University Social Network on Personal Learning Environments

ERIC Educational Resources Information Center

Marín-Diaz, Veronica; Vázquez Martínez, Ana Isabel; McMullin, Karen Josephine

2014-01-01

The evolution of the media and the Internet in education today is an unquestionable reality. At the university level, the use of Web 2.0 tools has become increasingly visible in the new resources that professors have been incorporating both into the classroom and into their research, reinforcing the methodological renewal that the implementation…

The Roles That Librarians and Libraries Play in Distance Education Settings

ERIC Educational Resources Information Center

Corbett, Amanda; Brown, Abbie

2015-01-01

This article explores the literature that focuses on the various roles librarians and libraries play in distance education settings. Learners visit libraries either in person or via networked computing technology to ask for help with their online courses. Questions range from how to upload a document with a learning management system, to how to…

PERSON-Personalized Expert Recommendation System for Optimized Nutrition.

PubMed

Chen, Chih-Han; Karvela, Maria; Sohbati, Mohammadreza; Shinawatra, Thaksin; Toumazou, Christofer

2018-02-01

The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connecting genetic information to personalized diets is the complexity of data and the scalability of the applied systems. Aiming to cross such barriers and provide direct applications, a personalized expert recommendation system for optimized nutrition is introduced in this paper, which performs direct to consumer personalized grocery product filtering and recommendation. Deep learning neural network model is applied to achieve automatic product categorization. The ability of scaling with unknown new data is achieved through the generalized representation of word embedding. Furthermore, the categorized products are filtered with a model based on individual genetic data with associated phenotypic information and a case study with databases from three different sources is carried out to confirm the system.

Students' perceptions of a blended learning experience in dental education.

PubMed

Varthis, S; Anderson, O R

2018-02-01

"Flipped" instructional sequencing is a new instructional method where online instruction precedes the group meeting, allowing for more sophisticated learning through discussion and critical thinking during the in-person class session; a novel approach studied in this research. The purpose of this study was to document dental students' perceptions of flipped-based blended learning and to apply a new method of displaying their perceptions based on Likert-scale data analysis using a network diagramming method known as an item correlation network diagram (ICND). In addition, this article aimed to encourage institutions or course directors to consider self-regulated learning and social constructivism as a theoretical framework when blended learning is incorporated in dental curricula. Twenty (second year) dental students at a Northeastern Regional Dental School in the United States participated in this study. A Likert scale was administered before and after the learning experience to obtain evidence of their perceptions of its quality and educational merits. Item correlation network diagrams, based on the intercorrelations amongst the responses to the Likert-scale items, were constructed to display students' changes in perceptions before and after the learning experience. Students reported positive perceptions of the blended learning, and the ICND analysis of their responses before and after the learning experience provided insights into their social (group-based) cognition about the learning experience. The ICNDs are considered evidence of social or group-based cognition, because they are constructed from evidence obtained using intercorrelations of the total group responses to the Likert-scale items. The students positively received blended learning in dental education, and the ICND analyses demonstrated marked changes in their social cognition of the learning experience based on the pre- and post-Likert survey data. Self-regulated learning and social constructivism are encouraged as useful theoretical frameworks for a blended learning approach. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Health rights pamphlets: critical literacy and inclusive citizenship, South Africa.

PubMed

Strecker, Morgan; Stuttaford, Maria; London, Leslie

2014-06-01

The Ottawa Charter recognizes the importance of strengthening community action for health and developing personal skills. At the same time, a rights-based approach to health includes the right to information, participation and accountability. The Learning Network for Health and Human Rights is a research and learning collaboration between Civil Society Organisations (CSOs) and universities in the Western Cape, South Africa. For the purposes of this article, a CSO is understood to be any organization that is outside of the state and private market sector. As part of a wider programme of action research, the learning network developed six pamphlets aimed at enhancing individual and collective skills to support action related to the implementation of the right to health. The research reported here analyses how the pamphlets, coupled with directed training, strengthened skills, promoted critical literacy and supported inclusive citizenship. Eighteen semi-structured interviews and eight focus groups were conducted with 59 participants from eight CSOs, their members, beneficiaries and communities. The success of the pamphlets was found to be attributed to the role they played in a wider training programme, requested by the CSOs and developed jointly by CSOs and university-based researchers. Community action on the right to health is contingent on personal as well as collective skills development. Understanding of the right to health and skills for participation and accountability were extended in breadth and depth, which enabled inclusive citizenship.

A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

2016-07-05

Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

CyberPsychological Computation on Social Community of Ubiquitous Learning.

PubMed

Zhou, Xuan; Dai, Genghui; Huang, Shuang; Sun, Xuemin; Hu, Feng; Hu, Hongzhi; Ivanović, Mirjana

2015-01-01

Under the modern network environment, ubiquitous learning has been a popular way for people to study knowledge, exchange ideas, and share skills in the cyberspace. Existing research findings indicate that the learners' initiative and community cohesion play vital roles in the social communities of ubiquitous learning, and therefore how to stimulate the learners' interest and participation willingness so as to improve their enjoyable experiences in the learning process should be the primary consideration on this issue. This paper aims to explore an effective method to monitor the learners' psychological reactions based on their behavioral features in cyberspace and therefore provide useful references for adjusting the strategies in the learning process. In doing so, this paper firstly analyzes the psychological assessment of the learners' situations as well as their typical behavioral patterns and then discusses the relationship between the learners' psychological reactions and their observable features in cyberspace. Finally, this paper puts forward a CyberPsychological computation method to estimate the learners' psychological states online. Considering the diversity of learners' habitual behaviors in the reactions to their psychological changes, a BP-GA neural network is proposed for the computation based on their personalized behavioral patterns.

CyberPsychological Computation on Social Community of Ubiquitous Learning

PubMed Central

Zhou, Xuan; Dai, Genghui; Huang, Shuang; Sun, Xuemin; Hu, Feng; Hu, Hongzhi; Ivanović, Mirjana

2015-01-01

Under the modern network environment, ubiquitous learning has been a popular way for people to study knowledge, exchange ideas, and share skills in the cyberspace. Existing research findings indicate that the learners' initiative and community cohesion play vital roles in the social communities of ubiquitous learning, and therefore how to stimulate the learners' interest and participation willingness so as to improve their enjoyable experiences in the learning process should be the primary consideration on this issue. This paper aims to explore an effective method to monitor the learners' psychological reactions based on their behavioral features in cyberspace and therefore provide useful references for adjusting the strategies in the learning process. In doing so, this paper firstly analyzes the psychological assessment of the learners' situations as well as their typical behavioral patterns and then discusses the relationship between the learners' psychological reactions and their observable features in cyberspace. Finally, this paper puts forward a CyberPsychological computation method to estimate the learners' psychological states online. Considering the diversity of learners' habitual behaviors in the reactions to their psychological changes, a BP-GA neural network is proposed for the computation based on their personalized behavioral patterns. PMID:26557846

Where do youth learn about suicides on the Internet, and what influence does this have on suicidal ideation?

PubMed

Dunlop, Sally M; More, Eian; Romer, Daniel

2011-10-01

Young people are susceptible to suicidal behavior as a result of learning about the suicidal behavior of others. This study was designed to determine whether Internet sites, such as online news and social networking websites, expose young people to suicide stories that might increase suicide ideation. We reinterviewed 719 young people ages 14 to 24 who had participated in a prior nationally representative survey. Respondents reported knowledge of persons they knew who had committed or attempted suicide as well as personal experiences of hopelessness and suicidal ideation on both occasions. On the second occasion one year later, they also reported use of various Internet platforms and how often they had been exposed to suicide stories on those sites, as well as from personal sources. Changes in ideation as a function of exposure to different sources of suicide stories were analyzed holding constant prior hopelessness and ideation. While traditional sources of information about suicide were most often cited (79% were from friends and family or newspapers), online sources were also quite common (59%). Social networking sites were frequently cited as sources, but these reports were not linked to increases in ideation. However, online discussion forums were both cited as sources and associated with increases in ideation. The Internet and especially social networking sites are important sources of suicide stories. However, discussion forums appear to be particularly associated with increases in suicidal ideation. Greater efforts should be undertaken to promote Internet sites directed to young people that enhance effective coping with hopelessness and suicidal ideation. © 2011 The Authors. Journal of Child Psychology and Psychiatry © 2011 Association for Child and Adolescent Mental Health.

Public Participation, Education, and Engagement in Drought Planning

NASA Astrophysics Data System (ADS)

Bathke, D. J.; Wall, N.; Haigh, T.; Smith, K. H.; Bernadt, T.

2014-12-01

Drought is a complex problem that typically goes beyond the capacity, resources, and jurisdiction of any single person, program, organization, political boundary, or sector. Thus, by nature, monitoring, planning for, and reducing drought risk must be a collaborative process. The National Drought Mitigation Center, in partnership with the National Integrated Drought Information System (NIDIS) Program Office and others, provides active engagement and education drought professionals, stakeholders, and the general public about managing drought-related risks through resilience planning, monitoring, and education. Using case studies, we discuss recruitment processes, network building, participation techniques, and educational methods as they pertain to a variety of unique audiences with distinct objectives. Examples include collaborative decision-making at a World Meteorological Organization conference; planning, and peer-learning among drought professionals in a community of practice; drought condition monitoring through citizen science networks; research and education dissemination with stakeholder groups; and informal learning activities for all ages. Finally, we conclude with evaluation methods, indicators of success, and lessons learned for increasing the effectiveness of our programs in increasing drought resilience.

An argument for mechanism-based statistical inference in cancer

PubMed Central

Ochs, Michael; Price, Nathan D.; Tomasetti, Cristian; Younes, Laurent

2015-01-01

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning bio-markers, metabolism, cell signaling, network inference and tumorigenesis. PMID:25381197

Educating Through Exploration: Emerging Evidence for Improved Learning Outcomes Using a New Theory of Digital Learning Design

NASA Astrophysics Data System (ADS)

Anbar, Ariel; Center for Education Through eXploration

2018-01-01

Advances in scientific visualization and public access to data have transformed science outreach and communication, but have yet to realize their potential impacts in the realm of education. Computer-based learning is a clear bridge between visualization and education that benefits students through adaptative personalization and enhanced access. Building this bridge requires close partnerships among scientists, technologists, and educators.The Infiniscope project fosters such partnerships to produce exploration-driven online learning experiences that teach basic science concepts using a combination of authentic space science narratives, data, and images, and a personalized guided inquiry approach. Infiniscope includes a web portal to host these digital learning experiences, as well as a teaching network of educators using and modifying these experiences. Infiniscope experiences are built around a new theory of digital learning design that we call “education through exploration” (ETX) developed during the creation of successful online, interactive science courses offered at ASU and other institutions. ETX builds on the research-based practices of active learning and guided inquiry to provide a set of design principles that aim to develop higher order thinking skills in addition to understanding of content. It is employed in these experiences by asking students to solve problems and actively discover relationships, supported by an intelligent tutoring system which provides immediate, personalized feedback and scaffolds scientific thinking and methods. The project is led by ASU’s School of Earth and Space Exploration working with learning designers in the Center for Education Through eXploration, with support from NASA’s Science Mission Directorate as part of the NASA Exploration Connection program.We will present an overview of ETX design, the Infinscope project, and emerging evidence of effectiveness.

Pleased to Tweet You: Making a Case for Twitter in the Classroom

ERIC Educational Resources Information Center

Messner, Kate

2009-01-01

As the ranks of educators on Twitter grow, more and more are heard about the importance of their "PLNs" (a term reportedly coined by educational technology guru David Warlick). A PLN, or Personal Learning Network, is a group of like-minded professionals with whom one can exchange ideas, advice, and resources. So why shouldn't students have PLNs of…

The Ethical and Practical Implications of Systems Architecture on Identity in Networked Learning: A Constructionist Perspective

ERIC Educational Resources Information Center

Koole, Marguerite L.; Parchoma, Gale

2012-01-01

Through relational dialogue, learners shape their identities by sharing information about the world and how they see themselves in it. As learners interact, they receive feedback from both the environment and other learners which, in turn, helps them assess and adjust their self-presentations. Although learners retain choice and personal agency,…

Social Capital, Team Efficacy and Team Potency: The Mediating Role of Team Learning Behaviors

ERIC Educational Resources Information Center

van Emmerik, Hetty; Jawahar, I. M.; Schreurs, Bert; de Cuyper, Nele

2011-01-01

Purpose: Drawing on social capital theory and self-identification theory, this study aims to examine the associations of two indicators of social capital, personal networks and deep-level similarity, with team capability measures of team efficacy and team potency. The central focus of the study is to be the hypothesized mediating role of team…

How Should the Higher Education Workforce Adapt to Advancements in Technology for Teaching and Learning?

ERIC Educational Resources Information Center

Kukulska-Hulme, Agnes

2012-01-01

In a time of change, higher education is in the position of having to adapt to external conditions created by widespread adoption of popular technologies such as social media, social networking services and mobile devices. For faculty members, there must be opportunities for concrete experiences capable of generating a personal conviction that a…

A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition.

PubMed

Saez, Yago; Baldominos, Alejandro; Isasi, Pedro

2016-12-30

Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing. Moreover, big data and machine learning are now cross-fertilizing each other in an approach called "deep learning", which consists of massive artificial neural networks able to detect complicated patterns from enormous amounts of input data to learn classification models. This work compares various state-of-the-art classification techniques for automatic cross-person activity recognition under different scenarios that vary widely in how much information is available for analysis. We have incorporated deep learning by using Google's TensorFlow framework. The data used in this study were acquired from PAMAP2 (Physical Activity Monitoring in the Ageing Population), a publicly available dataset containing physical activity data. To perform cross-person prediction, we used the leave-one-subject-out (LOSO) cross-validation technique. When working with large training sets, the best classifiers obtain very high average accuracies (e.g., 96% using extra randomized trees). However, when the data volume is drastically reduced (where available data are only 0.001% of the continuous data), deep neural networks performed the best, achieving 60% in overall prediction accuracy. We found that even when working with only approximately 22.67% of the full dataset, we can statistically obtain the same results as when working with the full dataset. This finding enables the design of more energy-efficient devices and facilitates cold starts and big data processing of physical activity records.

SortNet: learning to rank by a neural preference function.

PubMed

Rigutini, Leonardo; Papini, Tiziano; Maggini, Marco; Scarselli, Franco

2011-09-01

Relevance ranking consists in sorting a set of objects with respect to a given criterion. However, in personalized retrieval systems, the relevance criteria may usually vary among different users and may not be predefined. In this case, ranking algorithms that adapt their behavior from users' feedbacks must be devised. Two main approaches are proposed in the literature for learning to rank: the use of a scoring function, learned by examples, that evaluates a feature-based representation of each object yielding an absolute relevance score, a pairwise approach, where a preference function is learned to determine the object that has to be ranked first in a given pair. In this paper, we present a preference learning method for learning to rank. A neural network, the comparative neural network (CmpNN), is trained from examples to approximate the comparison function for a pair of objects. The CmpNN adopts a particular architecture designed to implement the symmetries naturally present in a preference function. The learned preference function can be embedded as the comparator into a classical sorting algorithm to provide a global ranking of a set of objects. To improve the ranking performances, an active-learning procedure is devised, that aims at selecting the most informative patterns in the training set. The proposed algorithm is evaluated on the LETOR dataset showing promising performances in comparison with other state-of-the-art algorithms.

Social networks as a tool for science communication and public engagement: focus on Twitter.

PubMed

López-Goñi, Ignacio; Sánchez-Angulo, Manuel

2018-02-01

Social networks have been used to teach and engage people about the importance of science. The integration of social networks in the daily routines of faculties and scientists is strongly recommended to increase their personal brand, improve their skills, enhance their visibility, share and communicate science to society, promote scientific culture, and even as a tool for teaching and learning. Here we review the use of Twitter in science and comment on our previous experience of using this social network as a platform for a Massive Online Open Course (MOOC) in Spain and Latin America. We propose to extend this strategy to a pan-European Microbiology MOOC in the near future. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Health 2.0-Lessons Learned: Social Networking With Patients for Health Promotion.

PubMed

Sharma, Suparna; Kilian, Reena; Leung, Fok-Han

2014-07-01

The advent of social networking as a major platform for human interaction has introduced a new dimension into the physician-patient relationship, known as Health 2.0. The concept of Health 2.0 is young and evolving; so far, it has meant the use of social media by health professionals and patients to personalize health care and promote health education. Social networking sites like Facebook and Twitter offer promising platforms for health care providers to engage patients. Despite the vast potential of Health 2.0, usage by health providers remains relatively low. Using a pilot study as an example, this commentary reviews the ways in which physicians can effectively harness the power of social networking to meaningfully engage their patients in primary prevention. © The Author(s) 2014.

Automated Depression Analysis Using Convolutional Neural Networks from Speech.

PubMed

He, Lang; Cao, Cui

2018-05-28

To help clinicians to efficiently diagnose the severity of a person's depression, the affective computing community and the artificial intelligence field have shown a growing interest in designing automated systems. The speech features have useful information for the diagnosis of depression. However, manually designing and domain knowledge are still important for the selection of the feature, which makes the process labor consuming and subjective. In recent years, deep-learned features based on neural networks have shown superior performance to hand-crafted features in various areas. In this paper, to overcome the difficulties mentioned above, we propose a combination of hand-crafted and deep-learned features which can effectively measure the severity of depression from speech. In the proposed method, Deep Convolutional Neural Networks (DCNN) are firstly built to learn deep-learned features from spectrograms and raw speech waveforms. Then we manually extract the state-of-the-art texture descriptors named median robust extended local binary patterns (MRELBP) from spectrograms. To capture the complementary information within the hand-crafted features and deep-learned features, we propose joint fine-tuning layers to combine the raw and spectrogram DCNN to boost the depression recognition performance. Moreover, to address the problems with small samples, a data augmentation method was proposed. Experiments conducted on AVEC2013 and AVEC2014 depression databases show that our approach is robust and effective for the diagnosis of depression when compared to state-of-the-art audio-based methods. Copyright © 2018. Published by Elsevier Inc.

Gaming, texting, learning? Teaching engineering ethics through students' lived experiences with technology.

PubMed

Voss, Georgina

2013-09-01

This paper examines how young peoples' lived experiences with personal technologies can be used to teach engineering ethics in a way which facilitates greater engagement with the subject. Engineering ethics can be challenging to teach: as a form of practical ethics, it is framed around future workplace experience in a professional setting which students are assumed to have no prior experience of. Yet the current generations of engineering students, who have been described as 'digital natives', do however have immersive personal experience with digital technologies; and experiential learning theory describes how students learn ethics more successfully when they can draw on personal experience which give context and meaning to abstract theories. This paper reviews current teaching practices in engineering ethics; and examines young people's engagement with technologies including cell phones, social networking sites, digital music and computer games to identify social and ethical elements of these practices which have relevance for the engineering ethics curricula. From this analysis three case studies are developed to illustrate how facets of the use of these technologies can be drawn on to teach topics including group work and communication; risk and safety; and engineering as social experimentation. Means for bridging personal experience and professional ethics when teaching these cases are discussed. The paper contributes to research and curriculum development in engineering ethics education, and to wider education research about methods of teaching 'the net generation'.

A safety mechanism for observational learning.

PubMed

Badets, Arnaud; Boutin, Arnaud; Michelet, Thomas

2018-04-01

This empirical article presents the first evidence of a "safety mechanism" based on an observational-learning paradigm. It is accepted that during observational learning, a person can use different strategies to learn a motor skill, but it is unknown whether the learner is able to circumvent the encoding of an uncompleted observed skill. In this study, participants were tested in a dyadic protocol in which an observer watched a participant practicing two different motor sequences during a learning phase. During this phase, one of the two motor sequences was interrupted by a stop signal that precluded motor learning. The results of the subsequent retention test revealed that both groups learned the two motor sequences, but only the physical practice group showed worse performance for the interrupted sequence. The observers were consequently able to use a safety strategy to learn both sequences equally. Our findings are discussed in light of the implications of the action observation network for sequence learning and the cognitive mechanisms of error-based observation.

Development of the Novel e-Learning System, "SPES NOVA" (Scalable Personality-Adapted Education System with Networking of Views and Activities)

ERIC Educational Resources Information Center

Takeuchi, Ken; Murakami, Manabu; Kato, Atsushi; Akiyama, Ryuichi; Honda, Hirotaka; Nozawa, Hajime; Sato, Ki-ichiro

2009-01-01

The Faculty of Industrial Science and Technology at Tokyo University of Science developed a two-campus system to produce well-trained engineers possessing both technical and humanistic traits. In their first year of study, students reside in dormitories in the natural setting of the Oshamambe campus located in Hokkaido, Japan. The education…

"Supporting Early Career Women in the Geosciences through Online Peer-Mentoring: Lessons from the Earth Science Women's Network (ESWN)"

NASA Astrophysics Data System (ADS)

Holloway, T.; Hastings, M. G.; Barnes, R. T.; Fischer, E. V.; Wiedinmyer, C.; Rodriguez, C.; Adams, M. S.; Marin-Spiotta, E.

2014-12-01

The Earth Science Women's Network (ESWN) is an international peer-mentoring organization with over 2000 members, dedicated to career development and community for women across the geosciences. Since its formation in 2002, ESWN has supported the growth of a more diverse scientific community through a combination of online and in-person networking activities. Lessons learned related to online networking and community-building will be presented. ESWN serves upper-level undergraduates, graduate students, professionals in a range of environmental fields, scientists working in federal and state governments, post-doctoral researchers, and academic faculty and scientists. Membership includes women working in over 50 countries, although the majority of ESWN members work in the U.S. ESWN increases retention of women in the geosciences by enabling and supporting professional person-to-person connections. This approach has been shown to reduce feelings of isolation among our members and help build professional support systems critical to career success. In early 2013 ESWN transitioned online activities to an advanced social networking platform that supports discussion threads, group formation, and individual messaging. Prior to that, on-line activities operated through a traditional list-serve, hosted by the National Center for Atmospheric Research (NCAR). The new web center, http://eswnonline.org, serves as the primary forum for members to build connections, seek advice, and share resources. For example, members share job announcements, discuss issues of work-life balance, and organize events at professional conferences. ESWN provides a platform for problem-based mentoring, drawing from the wisdom of colleagues across a range of career stages.

Video-based convolutional neural networks for activity recognition from robot-centric videos

NASA Astrophysics Data System (ADS)

Ryoo, M. S.; Matthies, Larry

2016-05-01

In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

Neural networks and the experience and cultivation of mind.

PubMed

Werbos, Paul J

2012-08-01

Hard core neural network research includes development of mathematical models of cognitive prediction and optimization aimed at dual use, both as models of what we see in brain circuits and behavior, and as useful general-purpose engineering technology. The pathway and principles now exist to let us someday replicate learning abilities as elevated as what we see in the brain of the mouse-but how can this help us today in understanding and maximizing the much greater potential of the human mind, as addressed by many schools of thought all over the world for centuries? This paper discusses how we might use what we have learned at a lower level to better illuminate key phenomena in first person and clinical human experience such as Freud's "psychic energy", the role of traumatic experience, the interpretation of dreams, creativity, the cultivation of sanity and sensitivity, and the biological foundations of language. Published by Elsevier Ltd.

Using convolutional neural networks to explore the microbiome.

PubMed

Reiman, Derek; Metwally, Ahmed; Yang Dai

2017-07-01

The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phenotype.

Computer technology applications in industrial and organizational psychology.

PubMed

Crespin, Timothy R; Austin, James T

2002-08-01

This article reviews computer applications developed and utilized by industrial-organizational (I-O) psychologists, both in practice and in research. A primary emphasis is on applications developed for Internet usage, because this "network of networks" changes the way I-O psychologists work. The review focuses on traditional and emerging topics in I-O psychology. The first topic involves information technology applications in measurement, defined broadly across levels of analysis (persons, groups, organizations) and domains (abilities, personality, attitudes). Discussion then focuses on individual learning at work, both in formal training and in coping with continual automation of work. A section on job analysis follows, illustrating the role of computers and the Internet in studying jobs. Shifting focus to the group level of analysis, we briefly review how information technology is being used to understand and support cooperative work. Finally, special emphasis is given to the emerging "third discipline" in I-O psychology research-computational modeling of behavioral events in organizations. Throughout this review, themes of innovation and dissemination underlie a continuum between research and practice. The review concludes by setting a framework for I-O psychology in a computerized and networked world.

Intrinsic Functional Connectivity in the Adult Brain and Success in Second-Language Learning.

PubMed

Chai, Xiaoqian J; Berken, Jonathan A; Barbeau, Elise B; Soles, Jennika; Callahan, Megan; Chen, Jen-Kai; Klein, Denise

2016-01-20

There is considerable variability in an individual's ability to acquire a second language (L2) during adulthood. Using resting-state fMRI data acquired before training in English speakers who underwent a 12 week intensive French immersion training course, we investigated whether individual differences in intrinsic resting-state functional connectivity relate to a person's ability to acquire an L2. We focused on two key aspects of language processing--lexical retrieval in spontaneous speech and reading speed--and computed whole-brain functional connectivity from two regions of interest in the language network, namely the left anterior insula/frontal operculum (AI/FO) and the visual word form area (VWFA). Connectivity between the left AI/FO and left posterior superior temporal gyrus (STG) and between the left AI/FO and dorsal anterior cingulate cortex correlated positively with improvement in L2 lexical retrieval in spontaneous speech. Connectivity between the VWFA and left mid-STG correlated positively with improvement in L2 reading speed. These findings are consistent with the different language functions subserved by subcomponents of the language network and suggest that the human capacity to learn an L2 can be predicted by an individual's intrinsic functional connectivity within the language network. Significance statement: There is considerable variability in second-language learning abilities during adulthood. We investigated whether individual differences in intrinsic functional connectivity in the adult brain relate to success in second-language learning, using resting-state functional magnetic resonance imaging in English speakers who underwent a 12 week intensive French immersion training course. We found that pretraining functional connectivity within two different language subnetworks correlated strongly with learning outcome in two different language skills: lexical retrieval in spontaneous speech and reading speed. Our results suggest that the human capacity to learn a second language can be predicted by an individual's intrinsic functional connectivity within the language network. Copyright © 2016 the authors 0270-6474/16/360755-07$15.00/0.

Emerging CAE technologies and their role in Future Ambient Intelligence Environments

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2011-03-01

Dramatic improvements are on the horizon in Computer Aided Engineering (CAE) and various simulation technologies. The improvements are due, in part, to the developments in a number of leading-edge technologies and their synergistic combinations/convergence. The technologies include ubiquitous, cloud, and petascale computing; ultra high-bandwidth networks, pervasive wireless communication; knowledge based engineering; networked immersive virtual environments and virtual worlds; novel human-computer interfaces; and powerful game engines and facilities. This paper describes the frontiers and emerging simulation technologies, and their role in the future virtual product creation and learning/training environments. The environments will be ambient intelligence environments, incorporating a synergistic combination of novel agent-supported visual simulations (with cognitive learning and understanding abilities); immersive 3D virtual world facilities; development chain management systems and facilities (incorporating a synergistic combination of intelligent engineering and management tools); nontraditional methods; intelligent, multimodal and human-like interfaces; and mobile wireless devices. The Virtual product creation environment will significantly enhance the productivity and will stimulate creativity and innovation in future global virtual collaborative enterprises. The facilities in the learning/training environment will provide timely, engaging, personalized/collaborative and tailored visual learning.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

PubMed

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

A mobile clinical e-portfolio for nursing and medical students, using wireless personal digital assistants (PDAs).

PubMed

Garrett, Bernard Mark; Jackson, Cathryn

2006-12-01

This paper outlines the development and evaluation of a wireless personal digital assistant (PDA) based clinical learning tool designed to promote professional reflection for health professionals. The "Clinical e-portfolio" was developed at the University of British Columbia School of Nursing to enable students immediately to access clinical expertise and resources remotely, and record their clinical experiences in a variety of media (text, audio and images). The PDA e-portfolio tool was developed to demonstrate the potential use of mobile networked technologies to support and improve clinical learning; promote reflective learning in practice; engage students in the process of knowledge translation; help contextualize and embed clinical knowledge whilst in the workplace; and to help prevent the isolation of students whilst engaged in supervised clinical practice. The mobile e-portfolio was developed to synchronise wirelessly with a user's personal Web based portfolio from any remote location where a cellular telephone signal or wireless (Wi-Fi) connection could be obtained. An evaluation of the tool was undertaken with nurse practitioner and medical students, revealing positive attitudes to the use of PDA based tools and portfolios, but limits to the use of the PDA portfolio due to the inherent interface restrictions of the PDA.

Embodied learning of a generative neural model for biological motion perception and inference

PubMed Central

Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V.

2015-01-01

Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons. PMID:26217215

Faculty development: if you build it, they will come.

PubMed

Steinert, Yvonne; Macdonald, Mary Ellen; Boillat, Miriam; Elizov, Michelle; Meterissian, Sarkis; Razack, Saleem; Ouellet, Marie-Noel; McLeod, Peter J

2010-09-01

The goals of this study were three-fold: to explore the reasons why some clinical teachers regularly attend centralised faculty development activities; to compare their responses with those of colleagues who do not attend, and to learn how we can make faculty development programmes more pertinent to teachers' needs. In 2008-2009, we conducted focus groups with 23 clinical teachers who had participated in faculty development activities on a regular basis in order to ascertain their perceptions of faculty development, reasons for participation, and perceived barriers against involvement. Thematic analysis and research team consensus guided the data interpretation. Reasons for regular participation included the perceptions that: faculty development enables personal and professional growth; learning and self-improvement are valued; workshop topics are viewed as relevant to teachers' needs; the opportunity to network with colleagues is appreciated, and initial positive experiences promote ongoing involvement. Barriers against participation mirrored those cited by non-attendees in an earlier study (e.g. volume of work, lack of time, logistical factors), but did not prevent participation. Suggestions for increasing participation included introducing a 'buddy system' for junior faculty members, an orientation workshop for new staff, and increased role-modelling and mentorship. The conceptualisation of faculty development as a means to achieve specific objectives and the desire for relevant programming that addresses current needs (i.e., expectancies), together with an appreciation of learning, self-improvement and networking with colleagues (i.e., values), were highlighted as reasons for participation by regular attendees. Medical educators should consider these 'lessons learned' in the design and delivery of faculty development offerings. They should also continue to explore the notion of faculty development as a social practice and the application of motivational theories that include expectancy-value constructs to personal and professional development.

Embodied learning of a generative neural model for biological motion perception and inference.

PubMed

Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V

2015-01-01

Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

Learning in networks: individual teacher learning versus organizational learning in a regional health-promoting schools network.

PubMed

Flaschberger, Edith; Gugglberger, Lisa; Dietscher, Christina

2013-12-01

To change a school into a health-promoting organization, organizational learning is required. The evaluation of an Austrian regional health-promoting schools network provides qualitative data on the views of the different stakeholders on learning in this network (steering group, network coordinator and representatives of the network schools; n = 26). Through thematic analysis and deep-structure analyses, the following three forms of learning in the network were identified: (A) individual learning through input offered by the network coordination, (B) individual learning between the network schools, i.e. through exchange between the representatives of different schools and (C) learning within the participating schools, i.e. organizational learning. Learning between (B) or within the participating schools (C) seems to be rare in the network; concepts of individual teacher learning are prevalent. Difficulties detected relating to the transfer of information from the network to the member schools included barriers to organizational learning such as the lack of collaboration, coordination and communication in the network schools, which might be effects of the school system in which the observed network is located. To ensure connectivity of the information offered by the network, more emphasis should be put on linking health promotion to school development and the core processes of schools.

Constrained dictionary learning and probabilistic hypergraph ranking for person re-identification

NASA Astrophysics Data System (ADS)

He, You; Wu, Song; Pu, Nan; Qian, Li; Xiao, Guoqiang

2018-04-01

Person re-identification is a fundamental and inevitable task in public security. In this paper, we propose a novel framework to improve the performance of this task. First, two different types of descriptors are extracted to represent a pedestrian: (1) appearance-based superpixel features, which are constituted mainly by conventional color features and extracted from the supepixel rather than a whole picture and (2) due to the limitation of discrimination of appearance features, the deep features extracted by feature fusion Network are also used. Second, a view invariant subspace is learned by dictionary learning constrained by the minimum negative sample (termed as DL-cMN) to reduce the noise in appearance-based superpixel feature domain. Then, we use deep features and sparse codes transformed by appearancebased features to establish the hyperedges respectively by k-nearest neighbor, rather than jointing different features simply. Finally, a final ranking is performed by probabilistic hypergraph ranking algorithm. Extensive experiments on three challenging datasets (VIPeR, PRID450S and CUHK01) demonstrate the advantages and effectiveness of our proposed algorithm.

Personal Competencies/Personalized Learning: Reflection on Instruction. A Peer-to-Peer Learning and Observation Tool

ERIC Educational Resources Information Center

Twyman, Janet; Redding, Sam

2015-01-01

This publication and its companion, "Personal Competencies/Personalized Learning: Lesson Plan Reflection Guide," were created in response to a request for further development of the practical application of personalized learning concepts by teachers. Personalized learning varies the time, place, and pace of learning for each student, and…

SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support

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

Thwaites, D; Holloway, L; Bailey, M

2015-06-15

Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction andmore » mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions, improved treatment quality and potential practice changes. The early indications from the distributed learning and radiomics pilots strengthen this. Improved routine patient data quality should strengthen such rapid learning systems.« less

Developing skilled doctor-patient communication in the workplace: a qualitative study of the experiences of trainees and clinical supervisors.

PubMed

Giroldi, Esther; Veldhuijzen, Wemke; Geelen, Kristel; Muris, Jean; Bareman, Frits; Bueving, Herman; van der Weijden, Trudy; van der Vleuten, Cees

2017-12-01

To inform the development of recommendations to facilitate learning of skilled doctor-patient communication in the workplace, this qualitative study explores experiences of trainees and supervisors regarding how trainees learn communication and how supervisors support trainees' learning in the workplace. We conducted a qualitative study in a general practice training setting, triangulating various sources of data to obtain a rich understanding of trainees and supervisors' experiences: three focus group discussions, five discussions during training sessions and five individual interviews. Thematic network analysis was performed during an iterative process of data collection and analysis. We identified a communication learning cycle consisting of six phases: impactful experience, change in frame of reference, identification of communication strategies, experimentation with strategies, evaluation of strategies and incorporation into personal repertoire. Supervisors supported trainees throughout this process by creating challenges, confronting trainees with their behaviour and helping them reflect on its underlying mechanisms, exploring and demonstrating communication strategies, giving concrete practice assignments, creating safety, exploring the effect of strategies and facilitating repeated practice and reflection. Based on the experiences of trainees and supervisors, we conclude that skilled communication involves the development of a personal communication repertoire from which learners are able to apply strategies that fit the context and their personal style. After further validation of our findings, it may be recommended to give learners concrete examples, opportunities for repeated practise and reflection on personal frames of reference and the effect of strategies, as well as space for authenticity and flexibility. In the workplace, the clinical supervisor is able to facilitate all these essential conditions to support his/her trainee in becoming a skilled communicator.

Organisational aspects and benchmarking of e-learning initiatives: a case study with South African community health workers.

PubMed

Reisach, Ulrike; Weilemann, Mitja

2016-06-01

South Africa desperately needs a comprehensive approach to fight HIV/AIDS. Education is crucial to reach this goal and Internet and e-learning could offer huge opportunities to broaden and deepen the knowledge basis. But due to the huge societal and digital divide between rich and poor areas, e-learning is difficult to realize in the townships. Community health workers often act as mediators and coaches for people seeking medical and personal help. They could give good advice regarding hygiene, nutrition, protection of family members in case of HIV/AIDS and finding legal ways to earn one's living if they were trained to do so. Therefore they need to have a broader general knowledge. Since learning opportunities in the townships are scarce, a system for e-learning has to be created in order to overcome the lack of experience with computers or the Internet and to enable them to implement a network of expertise. The article describes how the best international resources on basic medical knowledge, HIV/AIDS as well as on basic economic and entrepreneurial skills were benchmarked to be integrated into an e-learning system. After tests with community health workers, researchers developed recommendations on building a self-sustaining system for learning, including a network of expertise and best practice sharing. The article explains the opportunities and challenges for community health workers, which could provide information for other parts of the world with similar preconditions of rural poverty. © The Author(s) 2015.

Capitalizing on Social Media for Career Development.

PubMed

Escoffery, Cam; Kenzig, Melissa; Hyden, Christel; Hernandez, Kristen

2018-01-01

Social media is powerful and has effective tools for career advancement. Health promotion professionals at all stages of their career can employ social media to develop their profile, network with a range of colleagues, and learn about jobs and other career-enhancing opportunities. This article focuses on several social media resources, describes their key functions for career development, and offers strategies for effective use. Steps in using social media include creating a personal profile, sharing products such as newsletters or publications, and locating volunteer and job opportunities. Learning skills to use social media effectively is important to advancing careers and to the expansion of the public health workforce.

Permaculture in higher education: Teaching sustainability through action learning

NASA Astrophysics Data System (ADS)

Battisti, Bryce Thomas

This is a case study of the use of Action Learning (AL) theory to teach and confer degrees in Permaculture and other forms of sustainability at the newly formed Gaia University International (GUI). In Chapter Two I argue that GUI, as an institution of higher learning, is organized to provide support for learning. The goal of the university structure is to provide students, called Associates, with a vehicle for accumulation of credit towards a bachelor's degree. This organizational structure is necessary, but insufficient for AL because Associates need more than an organization to provide and coordinate their degree programs. In other words, just because the network of university structures are organized in ways that make AL possible and convenient, it does not necessarily follow that Action Learning will occur for any individual Associate. The support structures within GUI's degrees are discussed in Chapter Three. To a greater or lesser degree GUI provides support for personal learning among Associates as advisors and advisees with the goal of helping Associates complete and document the outcomes of world-change projects. The support structures are necessary, but not sufficient for AL because the personal learning process occurring for each Associate requires transformative reflection. Additionally, because Associates' attrition rate is very high, many Associates do not remain enrolled in GUI long enough to benefit from the support structures. At the simplest organizational level I discuss the reflection process conducted in the patterned interactions of assigned learning groups called Guilds (Chapter Four). These groups of Associates work to provide each other with the best possible environment for personal learning through reflection. As its Associates experience transformative reflection, GUI is able to help elevate the quality of world-change efforts in the Permaculture community. Provided the organizational and support structures are in place, this reflection process is both necessary and sufficient for AL. By this I mean that if transformative reflection is occurring in Guild meetings, and is supported by a system of advisors, reviewers and support people within a university organized to give credit for Action Learning, then Action Learning will occur for individual Associates.

Celestial data routing network

NASA Astrophysics Data System (ADS)

Bordetsky, Alex

2000-11-01

Imagine that information processing human-machine network is threatened in a particular part of the world. Suppose that an anticipated threat of physical attacks could lead to disruption of telecommunications network management infrastructure and access capabilities for small geographically distributed groups engaged in collaborative operations. Suppose that small group of astronauts are exploring the solar planet and need to quickly configure orbital information network to support their collaborative work and local communications. The critical need in both scenarios would be a set of low-cost means of small team celestial networking. To the geographically distributed mobile collaborating groups such means would allow to maintain collaborative multipoint work, set up orbital local area network, and provide orbital intranet communications. This would be accomplished by dynamically assembling the network enabling infrastructure of the small satellite based router, satellite based Codec, and set of satellite based intelligent management agents. Cooperating single function pico satellites, acting as agents and personal switching devices together would represent self-organizing intelligent orbital network of cooperating mobile management nodes. Cooperative behavior of the pico satellite based agents would be achieved by comprising a small orbital artificial neural network capable of learning and restructing the networking resources in response to the anticipated threat.

Next Generation School Districts: What Capacities Do Districts Need to Create and Sustain Schools That Are Ready to Deliver on Common Core?

ERIC Educational Resources Information Center

Lake, Robin; Hill, Paul T.; Maas, Tricia

2015-01-01

Every sector of the U.S. economy is working on ways to deliver services in a more customized manner. If all goes well, education is headed in the same direction. Personalized learning and globally benchmarked academic standards (a.k.a. Common Core) are the focus of most major school districts and charter school networks. Educators and parents know…

A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition

PubMed Central

Saez, Yago; Baldominos, Alejandro; Isasi, Pedro

2016-01-01

Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing. Moreover, big data and machine learning are now cross-fertilizing each other in an approach called “deep learning”, which consists of massive artificial neural networks able to detect complicated patterns from enormous amounts of input data to learn classification models. This work compares various state-of-the-art classification techniques for automatic cross-person activity recognition under different scenarios that vary widely in how much information is available for analysis. We have incorporated deep learning by using Google’s TensorFlow framework. The data used in this study were acquired from PAMAP2 (Physical Activity Monitoring in the Ageing Population), a publicly available dataset containing physical activity data. To perform cross-person prediction, we used the leave-one-subject-out (LOSO) cross-validation technique. When working with large training sets, the best classifiers obtain very high average accuracies (e.g., 96% using extra randomized trees). However, when the data volume is drastically reduced (where available data are only 0.001% of the continuous data), deep neural networks performed the best, achieving 60% in overall prediction accuracy. We found that even when working with only approximately 22.67% of the full dataset, we can statistically obtain the same results as when working with the full dataset. This finding enables the design of more energy-efficient devices and facilitates cold starts and big data processing of physical activity records. PMID:28042838

A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.

PubMed

Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J

2015-04-01

Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.

A mechanism of institutional isomorphism in referral networks among hospitals in Seoul, South Korea.

PubMed

Jung, Minsoo; Choi, Mankyu

2010-01-01

Hospitals engage in medical referral system relations voluntarily, by virtue of their own service capacities. These capacities include medical technology, equipment supply, and patient management, which are assessed individually by medical institutions in efforts to control costs and maintain efficiency in tertiary hospitals. This study assessed referral networks according to the institutional isomorphism theory of new economic sociology. As a result, the referral networks were shown to exhibit emergent structural hierarchy via cumulative clustering by established year and were not affected by attributive variables such as region, bed number, and year of establishment. In particular, the networks evidenced institutional isomorphism with certain central hospitals. As a consequence, personal indices were shown to decrease in accordance with its period, and only the structural index increased. Normative pressures cause organizations to become hierarchically homogenized, in accordance with the principle of organizational learning in specialized fields. Therefore, normative isomorphism on the basis of public domains should be considered an inherent factor in the development of referral networks.

Emerging Trends in Healthcare Adoption of Wireless Body Area Networks.

PubMed

Rangarajan, Anuradha

2016-01-01

Real-time personal health monitoring is gaining new ground with advances in wireless communications. Wireless body area networks (WBANs) provide a means for low-powered sensors, affixed either on the human body or in vivo, to communicate with each other and with external telecommunication networks. The healthcare benefits of WBANs include continuous monitoring of patient vitals, measuring postacute rehabilitation time, and improving quality of medical care provided in medical emergencies. This study sought to examine emerging trends in WBAN adoption in healthcare. To that end, a systematic literature survey was undertaken against the PubMed database. The search criteria focused on peer-reviewed articles that contained the keywords "wireless body area network" and "healthcare" or "wireless body area network" and "health care." A comprehensive review of these articles was performed to identify adoption dimensions, including underlying technology framework, healthcare subdomain, and applicable lessons-learned. This article benefits healthcare technology professionals by identifying gaps in implementation of current technology and highlighting opportunities for improving products and services.

Including the Learner in Personalized Learning. Connect: Making Learning Personal

ERIC Educational Resources Information Center

Rickabaugh, Jim

2015-01-01

This issue is in response to Janet Twyman's brief, "Competency-Based Education: Supporting Personalized Learning" in the "Connect: Making Learning Personal" series. The discussion in Twyman's brief stopped short of being explicit regarding the aspect of personalized learning that Wisconsin's education innovation lab, the…

Neural networks as a control methodology

NASA Technical Reports Server (NTRS)

Mccullough, Claire L.

1990-01-01

While conventional computers must be programmed in a logical fashion by a person who thoroughly understands the task to be performed, the motivation behind neural networks is to develop machines which can train themselves to perform tasks, using available information about desired system behavior and learning from experience. There are three goals of this fellowship program: (1) to evaluate various neural net methods and generate computer software to implement those deemed most promising on a personal computer equipped with Matlab; (2) to evaluate methods currently in the professional literature for system control using neural nets to choose those most applicable to control of flexible structures; and (3) to apply the control strategies chosen in (2) to a computer simulation of a test article, the Control Structures Interaction Suitcase Demonstrator, which is a portable system consisting of a small flexible beam driven by a torque motor and mounted on springs tuned to the first flexible mode of the beam. Results of each are discussed.

Aberrant Learning Achievement Detection Based on Person-Fit Statistics in Personalized e-Learning Systems

ERIC Educational Resources Information Center

Liu, Ming-Tsung; Yu, Pao-Ta

2011-01-01

A personalized e-learning service provides learning content to fit learners' individual differences. Learning achievements are influenced by cognitive as well as non-cognitive factors such as mood, motivation, interest, and personal styles. This paper proposes the Learning Caution Indexes (LCI) to detect aberrant learning patterns. The philosophy…

Variability in personality expression across contexts: a social network approach.

PubMed

Clifton, Allan

2014-04-01

The current research investigated how the contextual expression of personality differs across interpersonal relationships. Two related studies were conducted with college samples (Study 1: N = 52, 38 female; Study 2: N = 111, 72 female). Participants in each study completed a five-factor measure of personality and constructed a social network detailing their 30 most important relationships. Participants used a brief Five-Factor Model scale to rate their personality as they experience it when with each person in their social network. Multiple informants selected from each social network then rated the target participant's personality (Study 1: N = 227, Study 2: N = 777). Contextual personality ratings demonstrated incremental validity beyond standard global self-report in predicting specific informants' perceptions. Variability in these contextualized personality ratings was predicted by the position of the other individuals within the social network. Across both studies, participants reported being more extraverted and neurotic, and less conscientious, with more central members of their social networks. Dyadic social network-based assessments of personality provide incremental validity in understanding personality, revealing dynamic patterns of personality variability unobservable with standard assessment techniques. © 2013 Wiley Periodicals, Inc.

Online and in-person networking among women in the Earth Sciences Women's Network at www.ESWNonline.org

NASA Astrophysics Data System (ADS)

Kontak, R.; Adams, A. S.; De Boer, A. M.; Hastings, M. G.; Holloway, T.; Marin-Spiotta, E.; Steiner, A. L.; Wiedinmyer, C.

2012-12-01

The Earth Science Women's Network is an international peer-mentoring network of women in the Earth Sciences, many of whom are in the early stages of their careers. Membership is free and has grown through "word of mouth," and includes upper-level undergraduates, graduate students, professionals in a range of environmental fields, scientists working in public and private institutions. Our mission is to promote career development, build community, provide informal mentoring and support, and facilitate professional collaborations. Since 2002 we have accomplished this trough online networking, including over email and a listserv, on facebook, in-person networking events, and professional development workshops. Now in our 10th year, ESWN is debuting a new web-center that creates an online space exclusively for women in any discipline of the Earth (including planetary) sciences. ESWN members can connect and create an online community of support and encouragement for themselves as women in a demanding career. Many women in Earth Science fields feel isolated and are often the only woman in their department or work environments. ESWN is a place to meet others, discuss issues faced in creating work-life balance and professional success and share best practices through peer mentoring. Now on ESWN's new web-center, members can create and personalize their profiles and search for others in their field, nearby, or with similar interests. Online discussions in the members-only area can also be searched. Members can create groups for discussion or collaboration, with document sharing and password protection. Publicly, we can share gained knowledge with a broader audience, like lessons learned at our professional development workshops and collected recommendations from members. The new web center allows for more connectivity among other online platforms used by our members, including linked-in, facebook, and twitter. Built in Wordpress with a Buddpress members-only section, the new ESWN website is supported by AGU and a NSF ADVANCE grant.;

How to tap NASA-developed technology

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

Ruzic, N.

The National Aeronautics and Space Administration (NASA) space program's contribution to technology and the transfer of its achievements to industrial and consumer products is unprecedented. The process of transferring new technology suffers, however, partly because managers tend to ignore new technological markets unless new products solve their specific problems and partly because managers may not know the technology is available. NASA's Technology Utilization Branch has learned to initiate transfer, using a network of centers to dispense information on applications. NASA also has a large software library and computer programs, as well as teams to make person-to-person contacts. Examples of successfulmore » transfers have affected energy sources, building contruction, health, and safety. (DCK)« less

On Location Learning: Authentic Applied Science with Networked Augmented Realities

NASA Astrophysics Data System (ADS)

Rosenbaum, Eric; Klopfer, Eric; Perry, Judy

2007-02-01

The learning of science can be made more like the practice of science through authentic simulated experiences. We have created a networked handheld Augmented Reality environment that combines the authentic role-playing of Augmented Realities and the underlying models of Participatory Simulations. This game, known as Outbreak @ The Institute, is played across a university campus where players take on the roles of doctors, medical technicians, and public health experts to contain a disease outbreak. Players can interact with virtual characters and employ virtual diagnostic tests and medicines. They are challenged to identify the source and prevent the spread of an infectious disease that can spread among real and/or virtual characters according to an underlying model. In this paper, we report on data from three high school classes who played the game. We investigate students' perception of the authenticity of the game in terms of their personal embodiment in the game, their experience playing different roles, and their understanding of the dynamic model underlying the game.

Building Intrusion Detection with a Wireless Sensor Network

NASA Astrophysics Data System (ADS)

Wälchli, Markus; Braun, Torsten

This paper addresses the detection and reporting of abnormal building access with a wireless sensor network. A common office room, offering space for two working persons, has been monitored with ten sensor nodes and a base station. The task of the system is to report suspicious office occupation such as office searching by thieves. On the other hand, normal office occupation should not throw alarms. In order to save energy for communication, the system provides all nodes with some adaptive short-term memory. Thus, a set of sensor activation patterns can be temporarily learned. The local memory is implemented as an Adaptive Resonance Theory (ART) neural network. Unknown event patterns detected on sensor node level are reported to the base station, where the system-wide anomaly detection is performed. The anomaly detector is lightweight and completely self-learning. The system can be run autonomously or it could be used as a triggering system to turn on an additional high-resolution system on demand. Our building monitoring system has proven to work reliably in different evaluated scenarios. Communication costs of up to 90% could be saved compared to a threshold-based approach without local memory.

Advanced Networks in Dental Rich Online MEDiA (ANDROMEDA)

NASA Astrophysics Data System (ADS)

Elson, Bruce; Reynolds, Patricia; Amini, Ardavan; Burke, Ezra; Chapman, Craig

There is growing demand for dental education and training not only in terms of knowledge but also skills. This demand is driven by continuing professional development requirements in the more developed economies, personnel shortages and skills differences across the European Union (EU) accession states and more generally in the developing world. There is an excellent opportunity for the EU to meet this demand by developing an innovative online flexible learning platform (FLP). Current clinical online systems are restricted to the delivery of general, knowledge-based training with no easy method of personalization or delivery of skill-based training. The PHANTOM project, headed by Kings College London is developing haptic-based virtual reality training systems for clinical dental training. ANDROMEDA seeks to build on this and establish a Flexible Learning Platform that can integrate the haptic and sensor based training with rich media knowledge transfer, whilst using sophisticated technologies such as including service-orientated architecture (SOA), Semantic Web technologies, knowledge-based engineering, business intelligence (BI) and virtual worlds for personalization.

Disjointed Ways, Disunified Means: Learning From America’s Struggle to Build an Afghan Nation

DTIC Science & Technology

2012-05-01

unify- ing the intelligence community with a new National Intelligence Director, and creating a network-based information -sharing system. The...no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control... a monthly e-mail newsletter to update the national security community on the re- search of our analysts, recent and forthcoming publications, and

Learning Analytics for Networked Learning Models

ERIC Educational Resources Information Center

Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

2014-01-01

Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

Virtual working systems to support R&D groups

NASA Astrophysics Data System (ADS)

Dew, Peter M.; Leigh, Christine; Drew, Richard S.; Morris, David; Curson, Jayne

1995-03-01

The paper reports on the progress at Leeds University to build a Virtual Science Park (VSP) to enhance the University's ability to interact with industry, grow its applied research and workplace learning activities. The VSP exploits the advances in real time collaborative computing and networking to provide an environment that meets the objectives of physically based science parks without the need for the organizations to relocate. It provides an integrated set of services (e.g. virtual consultancy, workbased learning) built around a structured person- centered information model. This model supports the integration of tools for: (a) navigating around the information space; (b) browsing information stored within the VSP database; (c) communicating through a variety of Person-to-Person collaborative tools; and (d) the ability to the information stored in the VSP including the relationships to other information that support the underlying model. The paper gives an overview of a generic virtual working system based on X.500 directory services and the World-Wide Web that can be used to support the Virtual Science Park. Finally the paper discusses some of the research issues that need to be addressed to fully realize a Virtual Science Park.

Deep learning guided stroke management: a review of clinical applications.

PubMed

Feng, Rui; Badgeley, Marcus; Mocco, J; Oermann, Eric K

2018-04-01

Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess potential benefit by identifying areas of ischemia that have not yet experienced cellular death. The perfusion-diffusion mismatch, is used as a simple metric for potential benefit with timely intervention, yet penumbral patterns provide an inaccurate predictor of clinical outcome. Machine learning research in the form of deep learning (artificial intelligence) techniques using deep neural networks (DNNs) excel at working with complex inputs. The key areas where deep learning may be imminently applied to stroke management are image segmentation, automated featurization (radiomics), and multimodal prognostication. The application of convolutional neural networks, the family of DNN architectures designed to work with images, to stroke imaging data is a perfect match between a mature deep learning technique and a data type that is naturally suited to benefit from deep learning's strengths. These powerful tools have opened up exciting opportunities for data-driven stroke management for acute intervention and for guiding prognosis. Deep learning techniques are useful for the speed and power of results they can deliver and will become an increasingly standard tool in the modern stroke specialist's arsenal for delivering personalized medicine to patients with ischemic stroke. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

PubMed

López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

2015-04-15

Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

Bluetooth Low Energy Peripheral Android Health App for Educational and Interoperability Testing Purposes.

PubMed

Frohner, Matthias; Urbauer, Philipp; Sauermann, Stefan

2017-01-01

Based on recent telemonitoring activities in Austria for enabling integrated health care, the communication interfaces between personal health devices (e.g. blood pressure monitor) and personal health gateway devices (e.g. smartphone, routing received information to wide area networks) play an important role. In order to ease testing of the Bluetooth Low Energy interface functionality of the personal health gateway devices, a personal health device simulator was developed. Based on specifications from the Bluetooth SIG a XML software test configuration file structure is defined that declares the specific features of the personal health devices simulated. Using this configuration file, different scenarios are defined, e.g. send a single measurement result from a blood pressure reading or sending multiple (historic) weight scale readings. The simulator is intended to be used for educational purposes in lectures, where the number of physical personal health devices can be reduced and learning can be improved. It could be shown that this simulator assists the development process of mHealth applications by reducing the time needed for development and testing.

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

The Evolution of the Personal Networks of Novice Librarian Researchers

ERIC Educational Resources Information Center

Kennedy, Marie R.; Kennedy, David P.; Brancolini, Kristine R.

2017-01-01

This article describes for the first time the composition and structure of the personal networks of novice librarian researchers. We used social network analysis to observe if participating in the Institute for Research Design in Librarianship (IRDL) affected the development of the librarians' personal networks and how the networks changed over…

The Impacts of Network Centrality and Self-Regulation on an E-Learning Environment with the Support of Social Network Awareness

ERIC Educational Resources Information Center

Lin, Jian-Wei; Huang, Hsieh-Hong; Chuang, Yuh-Shy

2015-01-01

An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally,…

Using Machine Learning to Advance Personality Assessment and Theory.

PubMed

Bleidorn, Wiebke; Hopwood, Christopher James

2018-05-01

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

ISBP: Understanding the Security Rule of Users' Information-Sharing Behaviors in Partnership

PubMed Central

Wu, Hongchen; Wang, Xinjun

2016-01-01

The rapid growth of social network data has given rise to high security awareness among users, especially when they exchange and share their personal information. However, because users have different feelings about sharing their information, they are often puzzled about who their partners for exchanging information can be and what information they can share. Is it possible to assist users in forming a partnership network in which they can exchange and share information with little worry? We propose a modified information sharing behavior prediction (ISBP) model that can help in understanding the underlying rules by which users share their information with partners in light of three common aspects: what types of items users are likely to share, what characteristics of users make them likely to share information, and what features of users’ sharing behavior are easy to predict. This model is applied with machine learning techniques in WEKA to predict users’ decisions pertaining to information sharing behavior and form them into trustable partnership networks by learning their features. In the experiment section, by using two real-life datasets consisting of citizens’ sharing behavior, we identify the effect of highly sensitive requests on sharing behavior adjacent to individual variables: the younger participants’ partners are more difficult to predict than those of the older participants, whereas the partners of people who are not computer majors are easier to predict than those of people who are computer majors. Based on these findings, we believe that it is necessary and feasible to offer users personalized suggestions on information sharing decisions, and this is pioneering work that could benefit college researchers focusing on user-centric strategies and website owners who want to collect more user information without raising their privacy awareness or losing their trustworthiness. PMID:26950064

ISBP: Understanding the Security Rule of Users' Information-Sharing Behaviors in Partnership.

PubMed

Wu, Hongchen; Wang, Xinjun

2016-01-01

The rapid growth of social network data has given rise to high security awareness among users, especially when they exchange and share their personal information. However, because users have different feelings about sharing their information, they are often puzzled about who their partners for exchanging information can be and what information they can share. Is it possible to assist users in forming a partnership network in which they can exchange and share information with little worry? We propose a modified information sharing behavior prediction (ISBP) model that can help in understanding the underlying rules by which users share their information with partners in light of three common aspects: what types of items users are likely to share, what characteristics of users make them likely to share information, and what features of users' sharing behavior are easy to predict. This model is applied with machine learning techniques in WEKA to predict users' decisions pertaining to information sharing behavior and form them into trustable partnership networks by learning their features. In the experiment section, by using two real-life datasets consisting of citizens' sharing behavior, we identify the effect of highly sensitive requests on sharing behavior adjacent to individual variables: the younger participants' partners are more difficult to predict than those of the older participants, whereas the partners of people who are not computer majors are easier to predict than those of people who are computer majors. Based on these findings, we believe that it is necessary and feasible to offer users personalized suggestions on information sharing decisions, and this is pioneering work that could benefit college researchers focusing on user-centric strategies and website owners who want to collect more user information without raising their privacy awareness or losing their trustworthiness.

Teachers' Motives for Learning in Networks: Costs, Rewards and Community Interest

ERIC Educational Resources Information Center

van den Beemt, Antoine; Ketelaar, Evelien; Diepstraten, Isabelle; de Laat, Maarten

2018-01-01

Background: This paper discusses teachers' perspectives on learning networks and their motives for participating in these networks. Although it is widely held that teachers' learning may be developed through learning networks, not all teachers participate in such networks. Purpose: The theme of reciprocity, central to studies in the area of…

Up the ANTe: Understanding Entrepreneurial Leadership Learning through Actor-Network Theory

ERIC Educational Resources Information Center

Smith, Sue; Kempster, Steve; Barnes, Stewart

2017-01-01

This article explores the role of educators in supporting the development of entrepreneurial leadership learning by creating peer learning networks of owner-managers of small businesses. Using actor-network theory, the authors think through the process of constructing and maintaining a peer learning network (conceived of as an actor-network) and…

Intelligent Web-Based Learning System with Personalized Learning Path Guidance

ERIC Educational Resources Information Center

Chen, C. M.

2008-01-01

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths…

Changes in Personal Networks of Women in Residential and Outpatient Substance Abuse Treatment

PubMed Central

Min, Meeyoung O.; Tracy, Elizabeth M.; Kim, Hyunsoo; Park, Hyunyong; Jun, MinKyong; Brown, Suzanne; McCarty, Christopher; Laudet, Alexandre

2013-01-01

Changes in personal network composition, support and structure over 12 months were examined in 377 women from residential (n=119) and intensive outpatient substance abuse treatment (n=258) through face-to-face interviews utilizing computer based data collection. Personal networks of women who entered residential treatment had more substance users, more people with whom they had used alcohol and/or drugs, and fewer people from treatment programs or self- help groups than personal networks of women who entered intensive outpatient treatment. By 12 months post treatment intake, network composition improved for women in residential treatment; however, concrete support was still lower and substance users still more prevalent in their networks. Network composition of women in outpatient treatment remained largely the same over time. Both groups increased cohesiveness within the network over 12 months. Targeting interventions that support positive changes in personal networks may heighten positive long term outcomes for women entering treatment. PMID:23755971

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics

PubMed Central

Sinapayen, Lana; Ikegami, Takashi

2017-01-01

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle “Learning by Stimulation Avoidance” (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system. PMID:28158309

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

PubMed

Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

2017-01-01

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.

Modular, Hierarchical Learning By Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Baldi, Pierre F.; Toomarian, Nikzad

1996-01-01

Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

Intelligent Spectrum Handoff via Docitive Learning in Cognitive Radio Networks (CRNs)

DTIC Science & Technology

2017-03-01

loss rate. TABLE 8. PER Comparisons for Different Traffic Loads . Figure 42 shows the effect of traffic load on video PSNR. As we can see, the PSNR...REPORT DOCUMENTATION PAGE Form Approved OMB No . 0704-0188 The public reporting burden for this collection of information is estimated to average 1...other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a

Growing a National Learning Environments and Resources Network for Science, Mathematics, Engineering, and Technology Education: Current Issues and Opportunities for the NSDL Program; Open Linking in the Scholarly Information Environment Using the OpenURL Framework; The HeadLine Personal Information Environment: Evaluation Phase One.

ERIC Educational Resources Information Center

Zia, Lee L.; Van de Sompel, Herbert; Beit-Arie, Oren; Gambles, Anne

2001-01-01

Includes three articles that discuss the National Science Foundation's National Science, Mathematics, Engineering, and Technology Education Digital Library (NSDL) program; the OpenURL framework for open reference linking in the Web-based scholarly information environment; and HeadLine (Hybrid Electronic Access and Delivery in the Library Networked…

Imaging evidence for disturbances in multiple learning and memory systems in persons with autism spectrum disorders.

PubMed

Goh, Suzanne; Peterson, Bradley S

2012-03-01

The aim of this article is to review neuroimaging studies of autism spectrum disorders (ASD) that examine declarative, socio-emotional, and procedural learning and memory systems. We conducted a search of PubMed from 1996 to 2010 using the terms 'autism,''learning,''memory,' and 'neuroimaging.' We limited our review to studies correlating learning and memory function with neuroimaging features of the brain. The early literature supports the following preliminary hypotheses: (1) abnormalities of hippocampal subregions may contribute to autistic deficits in episodic and relational memory; (2) disturbances to an amygdala-based network (which may include the fusiform gyrus, superior temporal cortex, and mirror neuron system) may contribute to autistic deficits in socio-emotional learning and memory; and (3) abnormalities of the striatum may contribute to developmental dyspraxia in individuals with ASD. Characterizing the disturbances to learning and memory systems in ASD can inform our understanding of the neural bases of autistic behaviors and the phenotypic heterogeneity of ASD. © The Authors. Developmental Medicine & Child Neurology © 2012 Mac Keith Press.

A multi-label, semi-supervised classification approach applied to personality prediction in social media.

PubMed

Lima, Ana Carolina E S; de Castro, Leandro Nunes

2014-10-01

Social media allow web users to create and share content pertaining to different subjects, exposing their activities, opinions, feelings and thoughts. In this context, online social media has attracted the interest of data scientists seeking to understand behaviours and trends, whilst collecting statistics for social sites. One potential application for these data is personality prediction, which aims to understand a user's behaviour within social media. Traditional personality prediction relies on users' profiles, their status updates, the messages they post, etc. Here, a personality prediction system for social media data is introduced that differs from most approaches in the literature, in that it works with groups of texts, instead of single texts, and does not take users' profiles into account. Also, the proposed approach extracts meta-attributes from texts and does not work directly with the content of the messages. The set of possible personality traits is taken from the Big Five model and allows the problem to be characterised as a multi-label classification task. The problem is then transformed into a set of five binary classification problems and solved by means of a semi-supervised learning approach, due to the difficulty in annotating the massive amounts of data generated in social media. In our implementation, the proposed system was trained with three well-known machine-learning algorithms, namely a Naïve Bayes classifier, a Support Vector Machine, and a Multilayer Perceptron neural network. The system was applied to predict the personality of Tweets taken from three datasets available in the literature, and resulted in an approximately 83% accurate prediction, with some of the personality traits presenting better individual classification rates than others. Copyright © 2014 Elsevier Ltd. All rights reserved.

Assessment of Internet-based tele-medicine in Africa (the RAFT project).

PubMed

Bagayoko, Cheick Oumar; Müller, Henning; Geissbuhler, Antoine

2006-01-01

The objectives of this paper on the Réseau Afrique Francophone de Télémédecine (RAFT) project are the evaluation of feasibility, potential, problems and risks of an Internet-based tele-medicine network in developing countries of Africa. The RAFT project was started in Western African countries 5 years ago and has now extended to other regions of Africa as well (i.e. Madagascar, Rwanda). A project for the development of a national tele-medicine network in Mali was initiated in 2001, extended to Mauritania in 2002 and to Morocco in 2003. By 2006, a total of nine countries are connected. The entire technical infrastructure is based on Internet technologies for medical distance learning and tele-consultations. The results are a tele-medicine network that has been in productive use for over 5 years and has enabled various collaboration channels, including North-to-South (from Europe to Africa), South-to-South (within Africa), and South-to-North (from Africa to Europe) distance learning and tele-consultations, plus many personal exchanges between the participating hospitals and Universities. It has also unveiled a set of potential problems: (a) the limited importance of North-to-South collaborations when there are major differences in the available resources or the socio-cultural contexts between the collaborating parties; (b) the risk of an induced digital divide if the periphery of the health system in developing countries is not involved in the development of the network; and (c) the need for the development of local medical content management skills. Particularly point (c) is improved through the collaboration between the various countries as professionals from the medical and the computer science field are sharing courses and resources. Personal exchanges between partners in the project are frequent, and several persons received an education at one of the partner Universities. As conclusion, we can say that the identified risks have to be taken into account when designing large-scale tele-medicine projects in developing countries. These problems can be mitigated by fostering South-South collaboration channels, by the use of satellite-based Internet connectivity in remote areas, the appreciation of local knowledge and its publication on-line. The availability of such an infrastructure also facilitates the development of other projects, courses, and local content creation.

Recruitment and Retention of Volunteers in a Citizen Science Network to Detect Invasive Species on Private Lands

NASA Astrophysics Data System (ADS)

Andow, David A.; Borgida, Eugene; Hurley, Terrance M.; Williams, Allison L.

2016-10-01

Volunteer citizen monitoring is an increasingly important source of scientific data. We developed a volunteer program for early detection of new invasive species by private landowners on their own land. Early detection of an invasive species, however, subjects the landowner to the potentially costly risk of government intervention to control the invasive species. We hypothesized that an adult experiential learning module could increase recruitment and retention because private landowners could learn more about and understand the social benefits of early detection and more accurately gauge the level of personal risk. The experiential learning module emphasized group discussion and individual reflection of risks and benefits of volunteering and included interactions with experts and regulatory personnel. A population of woodland owners with >2 ha of managed oak woodland in central Minnesota were randomly assigned to recruitment treatments: (a) the experiential learning module or (b) a letter inviting their participation. The recruitment and retention rates and data quality were similar for the two methods. However, volunteers who experienced the learning module were more likely to recruit new volunteers than those who merely received an invitation letter. Thus the module may indirectly affect recruitment of new volunteers. The data collection was complex and required the volunteers to complete timely activities, yet the volunteers provided sufficiently high quality data that was useful to the organizers. Volunteers can collect complex data and are willing to assume personal risk to contribute to early detection of invasive species.

Recruitment and Retention of Volunteers in a Citizen Science Network to Detect Invasive Species on Private Lands.

PubMed

Andow, David A; Borgida, Eugene; Hurley, Terrance M; Williams, Allison L

2016-10-01

Volunteer citizen monitoring is an increasingly important source of scientific data. We developed a volunteer program for early detection of new invasive species by private landowners on their own land. Early detection of an invasive species, however, subjects the landowner to the potentially costly risk of government intervention to control the invasive species. We hypothesized that an adult experiential learning module could increase recruitment and retention because private landowners could learn more about and understand the social benefits of early detection and more accurately gauge the level of personal risk. The experiential learning module emphasized group discussion and individual reflection of risks and benefits of volunteering and included interactions with experts and regulatory personnel. A population of woodland owners with >2 ha of managed oak woodland in central Minnesota were randomly assigned to recruitment treatments: (a) the experiential learning module or (b) a letter inviting their participation. The recruitment and retention rates and data quality were similar for the two methods. However, volunteers who experienced the learning module were more likely to recruit new volunteers than those who merely received an invitation letter. Thus the module may indirectly affect recruitment of new volunteers. The data collection was complex and required the volunteers to complete timely activities, yet the volunteers provided sufficiently high quality data that was useful to the organizers. Volunteers can collect complex data and are willing to assume personal risk to contribute to early detection of invasive species.

Blending Formal and Informal Learning Networks for Online Learning

ERIC Educational Resources Information Center

Czerkawski, Betül C.

2016-01-01

With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

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

Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

DOE PAGES

Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy; ...

2018-01-24

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less

Task-Based Core-Periphery Organization of Human Brain Dynamics

PubMed Central

Bassett, Danielle S.; Wymbs, Nicholas F.; Rombach, M. Puck; Porter, Mason A.; Mucha, Peter J.; Grafton, Scott T.

2013-01-01

As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal core that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal periphery that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior. PMID:24086116

SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

PubMed

Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

2016-01-01

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

The salience network and human personality: Integrity of white matter tracts within anterior and posterior salience network relates to the self-directedness character trait.

PubMed

Prillwitz, Conrad; Rüber, Theodor; Reuter, Martin; Montag, Christian; Weber, Bernd; Elger, Christian E; Markett, Sebastian

2018-04-28

A prevailing topic in personality neuroscience is the question how personality traits are reflected in the brain. Functional and structural networks have been examined by functional and structural magnetic resonance imaging, however, the structural correlates of functionally defined networks have not been investigated in a personality context. By using the Temperament and Character Inventory (TCI) and Diffusion Tensor Imaging (DTI), the present study assesses in a sample of 116 healthy participants how personality traits proposed in the framework of the biopsychosocial theory on personality relate to white matter pathways delineated by functional network imaging. We show that the character trait self-directedness relates to the overall microstructural integrity of white matter tracts constituting the salience network as indicated by DTI-derived measures. Self-directedness has been proposed as the executive control component of personality and describes the tendency to stay focused on the attainment of long-term goals. The present finding corroborates the view of the salience network as an executive control network that serves maintenance of rules and task-sets to guide ongoing behavior. Copyright © 2018. Published by Elsevier B.V.

Maximum entropy methods for extracting the learned features of deep neural networks.

PubMed

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

Self-reported personality variability across the social network is associated with interpersonal dysfunction.

PubMed

Clifton, Allan; Kuper, Laura E

2011-04-01

We describe 2 studies (n=52 and n=82) examining variability in perceptions of personality using a social network methodology. Undergraduate participants completed self-report measures of personality and interpersonal dysfunction and then subsequently reported on their personalities with each of 30 members of their social networks. Results across the 2 studies found substantial variability in participants' perceived personalities within their social networks. Measures of interpersonal dysfunction were associated with the amount of variability in dyadic ratings of personality, specifically Agreeableness and Openness to Experience. Results suggest that personality variability across interpersonal contexts may be an important individual difference related to social behavior and dysfunction. © 2011 The Authors. Journal of Personality © 2011, Wiley Periodicals, Inc.

System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

PubMed

Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

2014-05-01

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

Changes in personal networks of women in residential and outpatient substance abuse treatment.

PubMed

Min, Meeyoung O; Tracy, Elizabeth M; Kim, Hyunsoo; Park, Hyunyong; Jun, Minkyoung; Brown, Suzanne; McCarty, Christopher; Laudet, Alexandre

2013-10-01

Changes in personal network composition, support and structure over 12 months were examined in 377 women from residential (n=119) and intensive outpatient substance abuse treatment (n=258) through face-to-face interviews utilizing computer based data collection. Personal networks of women who entered residential treatment had more substance users, more people with whom they had used alcohol and/or drugs, and fewer people from treatment programs or self- help groups than personal networks of women who entered intensive outpatient treatment. By 12 months post treatment intake, network composition improved for women in residential treatment; however, concrete support was still lower and substance users are still more prevalent in their networks. Network composition of women in outpatient treatment remained largely the same over time. Both groups increased cohesiveness within the network over 12 months. Targeting interventions that support positive changes in personal networks may heighten positive long term outcomes for women entering treatment. Copyright © 2013 Elsevier Inc. All rights reserved.

A smarter way to network.

PubMed

Cross, Rob; Thomas, Robert

2011-01-01

The adage "It's not what you know, it's who you know" is true. The right social network can have a huge impact on your success. But many people have misguided ideas about what makes a network strong: They believe the key is having a large circle filled with high-powered contacts. That's not the right approach, say Cross, of UVA's McIntire School of Commerce, and Thomas, of the Accenture Institute for High Performance. The authors, who have spent years researching how organizations can capitalize on employees' social networks, have seen that the happiest, highest-performing executives have a different kind of network: select but diverse, made up of high-quality relationships with people who come from varying spheres and from up and down the corporate ladder. Effective networks typically range in size from 12 to 18 people. They help managers learn, make decisions with less bias, and grow personally. Cross and Thomas have found that they include six critical kinds of connections: people who provide information, ideas, or expertise; formally and informally powerful people, who offer mentoring and political support; people who give developmental feedback; people who lend personal support; people who increase your sense of purpose or worth; and people who promote work/life balance. Moreover, the best kind of connections are "energizers"--positive, trustworthy individuals who enjoy other people and always see opportunities, even in challenging situations. If your network doesn't look like this, you can follow a four-step process to improve it. You'll need to identify who your connections are and what they offer you, back away from redundant and energy-draining connections, fill holes in your network with the right kind of people, and work to make the most of your contacts. Do this, and in due course, you'll have a network that steers the best opportunities, ideas, and talent your way.

Accelerating Learning By Neural Networks

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1992-01-01

Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.

Examining High-School Students' Preferences toward Learning Environments, Personal Beliefs and Concept Learning in Web-Based Contexts

ERIC Educational Resources Information Center

Yang, Fang-Ying; Chang, Cheng-Chieh

2009-01-01

The purpose of the study is to explore three kinds of personal affective traits among high-school students and their effects on web-based concept learning. The affective traits include personal preferences about web-based learning environments, personal epistemological beliefs, and beliefs about web-based learning. One hundred 11th graders…

Personal Learning Environments: A Solution for Self-Directed Learners

ERIC Educational Resources Information Center

Haworth, Ryan

2016-01-01

In this paper I discuss "personal learning environments" and their diverse benefits, uses, and implications for life-long learning. Personal Learning Environments (PLEs) are Web 2.0 and social media technologies that enable individual learners the ability to manage their own learning. Self-directed learning is explored as a foundation…

The Future of Personalized Learning for Students with Disabilities

ERIC Educational Resources Information Center

Worthen, Maria

2016-01-01

Personalized learning models can give each student differentiated learning experiences based on their needs, interests, and strengths, including students with disabilities. Personalized learning can pinpoint specific gaps in student learning, identify where a student is on his or her learning pathway, and provide the appropriate interventions to…

Network congestion control algorithm based on Actor-Critic reinforcement learning model

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2018-04-01

Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system.

PubMed

Chrysafiadi, Konstantina; Virvou, Maria

2013-12-01

In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner's knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner's knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.

The Interplay of Perceptions of the Learning Environment, Personality and Learning Strategies: A Study amongst International Business Studies Students

ERIC Educational Resources Information Center

Nijhuis, Jan; Segers, Mien; Gijselaers, Wim

2007-01-01

Previous research on students' learning strategies has examined the relationships between either perceptions of the learning environment or personality and learning strategies. The focus of this study was on the joint relationships between the students' perceptions of the learning environment, their personality, and the learning strategies they…

Cooperative Learning for Distributed In-Network Traffic Classification

NASA Astrophysics Data System (ADS)

Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

2017-04-01

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

node2vec: Scalable Feature Learning for Networks

PubMed Central

Grover, Aditya; Leskovec, Jure

2016-01-01

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node’s network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks. PMID:27853626

Patterns of thought: Population variation in the associations between large-scale network organisation and self-reported experiences at rest.

PubMed

Wang, Hao-Ting; Bzdok, Danilo; Margulies, Daniel; Craddock, Cameron; Milham, Michael; Jefferies, Elizabeth; Smallwood, Jonathan

2018-08-01

Contemporary cognitive neuroscience recognises unconstrained processing varies across individuals, describing variation in meaningful attributes, such as intelligence. It may also have links to patterns of on-going experience. This study examined whether dimensions of population variation in different modes of unconstrained processing can be described by the associations between patterns of neural activity and self-reports of experience during the same period. We selected 258 individuals from a publicly available data set who had measures of resting-state functional magnetic resonance imaging, and self-reports of experience during the scan. We used machine learning to determine patterns of association between the neural and self-reported data, finding variation along four dimensions. 'Purposeful' experiences were associated with lower connectivity - in particular default mode and limbic networks were less correlated with attention and sensorimotor networks. 'Emotional' experiences were associated with higher connectivity, especially between limbic and ventral attention networks. Experiences focused on themes of 'personal importance' were associated with reduced functional connectivity within attention and control systems. Finally, visual experiences were associated with stronger connectivity between visual and other networks, in particular the limbic system. Some of these patterns had contrasting links with cognitive function as assessed in a separate laboratory session - purposeful thinking was linked to greater intelligence and better abstract reasoning, while a focus on personal importance had the opposite relationship. Together these findings are consistent with an emerging literature on unconstrained states and also underlines that these states are heterogeneous, with distinct modes of population variation reflecting the interplay of different large-scale networks. Copyright © 2018 Elsevier Inc. All rights reserved.

Experiencing community psychology through community-based learning class projects: reflections from an American University in the Middle East.

PubMed

Amer, Mona M; Mohamed, Salma N; Ganzon, Vincent

2013-01-01

Many introductory community psychology courses do not incorporate community-based learning (CBL), and when they do, it is most often in the form of individualized volunteer hours. We present an alternative model for CBL in which the entire class collaborates on an experiential project that promotes community action. We believe that such an approach better embodies the values and methods of the discipline and has a more powerful impact on the students and stakeholders. It may be especially effective in developing countries that do not have an established network of service infrastructures; in such nations the onus is on the teachers and learners of community psychology to contribute to transformative change. In this article practical guidelines are provided by the instructor regarding how to structure and implement this CBL model. Additionally, two students describe how the CBL experience solidified their learning of course concepts and significantly impacted them personally.

Knowledge into action - supporting the implementation of evidence into practice in Scotland.

PubMed

Davies, Sandra; Herbert, Paul; Wales, Ann; Ritchie, Karen; Wilson, Suzanne; Dobie, Laura; Thain, Annette

2017-03-01

The knowledge into action model for NHS Scotland provides a framework for librarians and health care staff to support getting evidence into practice. Central to this model is the development of a network of knowledge brokers to facilitate identification, use, creation and sharing of knowledge. To translate the concepts described in the model into tangible activities with the intention of supporting better use of evidence in health care and subsequently improving patient outcomes. Four areas of activity were addressed by small working groups comprising knowledge services staff in local and national boards. The areas of activity were as follows: defining existing and required capabilities and developing learning opportunities for the knowledge broker network; establishing national search and summarising services; developing actionable knowledge tools; and supporting person-to-person knowledge sharing. This work presents the development of practical tools and support to translate a conceptual model for getting knowledge into action into a series of activities and outputs to support better use of evidence in health care and subsequently improved patient outcomes. © 2017 Health Libraries Group.

A New Approach to Personalization: Integrating E-Learning and M-Learning

ERIC Educational Resources Information Center

Nedungadi, Prema; Raman, Raghu

2012-01-01

Most personalized learning systems are designed for either personal computers (e-learning) or mobile devices (m-learning). Our research has resulted in a cloud-based adaptive learning system that incorporates mobile devices into a classroom setting. This system is fully integrated into the formative assessment process and, most importantly,…

How many music centers are in the brain?

PubMed

Altenmüller, E O

2001-06-01

When reviewing the literature on brain substrates of music processing, a puzzling variety of findings can be stated. The traditional view of a left-right dichotomy of brain organization--assuming that in contrast to language, music is primarily processed in the right hemisphere--was challenged 20 years ago, when the influence of music education on brain lateralization was demonstrated. Modern concepts emphasize the modular organization of music cognition. According to this viewpoint, different aspects of music are processed in different, although partly overlapping neuronal networks of both hemispheres. However, even when isolating a single "module," such as, for example, the perception of contours, the interindividual variance of brain substrates is enormous. To clarify the factors contributing to this variability, we conducted a longitudinal experiment comparing the effects of procedural versus explicit music teaching on brain networks. We demonstrated that cortical activation during music processing reflects the auditory "learning biography," the personal experiences accumulated over time. Listening to music, learning to play an instrument, formal instruction, and professional training result in multiple, in many instances multisensory, representations of music, which seem to be partly interchangeable and rapidly adaptive. In summary, as soon as we consider "real music" apart from laboratory experiments, we have to expect individually formed and quickly adaptive brain substrates, including widely distributed neuronal networks in both hemispheres.

Promotores' perspectives on a male-to-male peer network.

PubMed

Macia, Laura; Ruiz, Hector Camilo; Boyzo, Roberto; Documet, Patricia Isabel

2016-06-01

Little documentation exists about male community health workers (promotores) networks. The experiences of promotores can provide input on how to attract, train, supervise and maintain male promotores in CHW programs. We present the experience and perspectives of promotores who participated in a male promotores network assisting Latino immigrant men in an emerging Latino community. All promotores in this community-based participatory study received payment for work 10 hours a week. We conducted qualitative interviews with all promotores starting the program, after 5 and 13 months. Three main themes emerged: 1) Men decided to become promotores to help others, yet appreciated being paid. 2) Promotores' learning experience was ongoing and was facilitated by a cooperative dynamic among them. Learning how to listen was crucial for promotores 3) Promotores experienced difficulty separating their personal lives form their role as a promotor We conclude that paying promotores facilitates the fulfillment of their drive to serve the community. Enhancing listening abilities needs to be part of promotores' training curricula. Finally, it is advisable to build a project with many opportunities for promotores and project staff to share professional and non-professional time and discuss their challenges. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Elderly fall risk prediction based on a physiological profile approach using artificial neural networks.

PubMed

Razmara, Jafar; Zaboli, Mohammad Hassan; Hassankhani, Hadi

2016-11-01

Falls play a critical role in older people's life as it is an important source of morbidity and mortality in elders. In this article, elders fall risk is predicted based on a physiological profile approach using a multilayer neural network with back-propagation learning algorithm. The personal physiological profile of 200 elders was collected through a questionnaire and used as the experimental data for learning and testing the neural network. The profile contains a series of simple factors putting elders at risk for falls such as vision abilities, muscle forces, and some other daily activities and grouped into two sets: psychological factors and public factors. The experimental data were investigated to select factors with high impact using principal component analysis. The experimental results show an accuracy of ≈90 percent and ≈87.5 percent for fall prediction among the psychological and public factors, respectively. Furthermore, combining these two datasets yield an accuracy of ≈91 percent that is better than the accuracy of single datasets. The proposed method suggests a set of valid and reliable measurements that can be employed in a range of health care systems and physical therapy to distinguish people who are at risk for falls.

E-Learning Personalization Using Triple-Factor Approach in Standard-Based Education

NASA Astrophysics Data System (ADS)

Laksitowening, K. A.; Santoso, H. B.; Hasibuan, Z. A.

2017-01-01

E-Learning can be a tool in monitoring learning process and progress towards the targeted competency. Process and progress on every learner can be different one to another, since every learner may have different learning type. Learning type itself can be identified by taking into account learning style, motivation, and knowledge ability. This study explores personalization for learning type based on Triple-Factor Approach. Considering that factors in Triple-Factor Approach are dynamic, the personalization system needs to accommodate the changes that may occurs. Originated from the issue, this study proposed personalization that guides learner progression dynamically towards stages of their learning process. The personalization is implemented in the form of interventions that trigger learner to access learning contents and discussion forums more often as well as improve their level of knowledge ability based on their state of learning type.

Peer Learning Network: Implementing and Sustaining Cooperative Learning by Teacher Collaboration

ERIC Educational Resources Information Center

Miquel, Ester; Duran, David

2017-01-01

This article describes an in-service teachers', staff-development model "Peer Learning Network" and presents results about its efficiency. "Peer Learning Network" promotes three levels of peer learning simultaneously (among pupils, teachers, and schools). It supports pairs of teachers from several schools, who are linked…

New pathways to physics instruction: Blending a MOOC and in-person discussion to train physics graduate students and postdocs in evidence-based teaching

NASA Astrophysics Data System (ADS)

Goldberg, Bennett

A challenge facing physics education is how to encourage and support the adoption of evidence-based instructional practices that decades of physics education research has shown to be effective. Like many STEM departments, physics departments struggle to overcome the barriers of faculty knowledge, motivation and time; institutional cultures and reward systems; and disciplinary traditions. Research has demonstrated successful transformation of department-level approaches to instruction through local learning communities, in-house expertise, and department administrative support. In this talk, I will discuss how physics and other STEM departments can use a MOOC on evidence-based instruction together with in-person seminar discussions to create a learning community of graduate students and postdocs, and how such communities can affect departmental change in teaching and learning. Four university members of the 21-university network working to prepare future faculty to be both excellent researchers and excellent teachers collaborated on an NSF WIDER project to develop and deliver two massive open online courses (MOOCs) in evidence-based STEM instruction. A key innovation is a new blended mode of delivery where groups of participants engaged with the online content and then meet weekly in local learning communities to discuss content, communicate current experiences, and delve deeper into particular techniques of local interest. The MOOC team supported these so-called MOOC-Centered Learning Communities, or MCLCs, with detailed facilitator guides complete with synopses of online content, learning goals and suggested activities for in-person meetings, as well as virtual MCLC communities for sharing and feedback. In the initial run of the first MOOC, 40 MCLCs were created; in the second run this past fall, more than 80 MCLCs formed. Further, target audiences of STEM graduate students and postdocs completed at a 40-50% rate, indicating the value they place in building their knowledge in evidence-based instruction. We will present data on the impact of being in an MCLC on completion and learning outcomes, as well as data on departmental change in physics supported by MCLCs. Work supported by NSF DUE-1347605.

The World as Functional Learning Environment: An Intercultural Learning Network. Interactive Technology Laboratory Report #7.

ERIC Educational Resources Information Center

Cohen, Moshe; And Others

Electronic networks provide new opportunities to create functional learning environments which allow students in many different locations to carry out joint educational activities. A set of participant observation studies was conducted in the context of a cross-cultural, cross-language network called the Intercultural Learning Network in order to…

Developmental Learning Disorders: From Generic Interventions to Individualized Remediation

PubMed Central

Moreau, David; Waldie, Karen E.

2016-01-01

Developmental learning disorders affect many children, impairing their experience in the classroom and hindering many aspects of their life. Once a bleak sentence associated with life-long difficulties, several learning disorders can now be successfully alleviated, directly benefiting from promising interventions. In this review, we focus on two of the most prevalent learning disorders, dyslexia and attention-deficit/hyperactivity disorder (ADHD). Recent advances have refined our understanding of the specific neural networks that are altered in these disorders, yet questions remain regarding causal links between neural changes and behavioral improvements. After briefly reviewing the theoretical foundations of dyslexia and ADHD, we explore their distinct and shared characteristics, and discuss the comorbidity of the two disorders. We then examine current interventions, and consider the benefits of approaches that integrate remediation within other activities to encourage sustained motivation and improvements. Finally, we conclude with a reflection on the potential for remediation programs to be personalized by taking into account the specificities and demands of each individual. The effective remediation of learning disorders is critical to modern societies, especially considering the far-reaching ramifications of successful early interventions. PMID:26793160

Persons' various experiences of learning processes in patient education for osteoarthritis, a qualitative phenomenographic approach.

PubMed

Larsson, Ingalill; Sundén, Anne; Ekvall Hansson, Eva

2018-03-30

Patient education (PE) is a core treatment of osteoarthritis (OA) with the aim to increase persons' knowledge, self-efficacy, and empowerment. To describe person's various experiences of learning processes in PE for OA. Phenomenography. Semi-structured interviews were performed with the same persons, pre- (11) and post- (9) education. Various experiences on learning processes were found and were described in an outcome space. Achieving knowledge describes self-regulated learning and strongly relates to Control, which describes a high order cognitive learning skill, and minor to Confirm, which describes a cognitive learning skill based on recognition and application. Receiving knowledge describes the expectancy of learning regulated from the educator and strongly relates to Comply, which describes a low-order cognitive learning skill, and minor to Confirm. Different experiences of motivation and learning impact on persons' learning processes which, in turn, influence the persons' capability to accomplish self-efficacy and empowerment. The outcome space may serve as a basis for discussions between healthcare educators involved in PE to better understand what learning implies and to develop PE further.

Adaptive metric learning with deep neural networks for video-based facial expression recognition

NASA Astrophysics Data System (ADS)

Liu, Xiaofeng; Ge, Yubin; Yang, Chao; Jia, Ping

2018-01-01

Video-based facial expression recognition has become increasingly important for plenty of applications in the real world. Despite that numerous efforts have been made for the single sequence, how to balance the complex distribution of intra- and interclass variations well between sequences has remained a great difficulty in this area. We propose the adaptive (N+M)-tuplet clusters loss function and optimize it with the softmax loss simultaneously in the training phrase. The variations introduced by personal attributes are alleviated using the similarity measurements of multiple samples in the feature space with many fewer comparison times as conventional deep metric learning approaches, which enables the metric calculations for large data applications (e.g., videos). Both the spatial and temporal relations are well explored by a unified framework that consists of an Inception-ResNet network with long short term memory and the two fully connected layer branches structure. Our proposed method has been evaluated with three well-known databases, and the experimental results show that our method outperforms many state-of-the-art approaches.

Family and Friends: Which Types of Personal Relationships Go Together in a Network?

PubMed

Rözer, Jesper; Mollenhorst, Gerald; Poortman, Anne-Rigt

We examine the link between family and personal networks. Using arguments about meeting opportunities, competition and social influence, we hypothesise how the presence of specific types of family members (i.e., a partner, children, parents and siblings) and non-family members (i.e., friends, neighbours and colleagues) in the network mutually affect one another. In addition, we propose that-beyond their mere presence-the active role of family members in the network strongly affects the presence of non-family members in the network. Data from the third wave of the Survey on the Social Networks of the Dutch, collected in 2012 and 2013, show that active involvement is of key importance; more than merely having family members present in one's personal network, the active involvement of specific types of family members in the personal network is associated with having disproportionally more other family members and having somewhat fewer non-family members in the network.

Old boys' network in general practitioners' referral behavior?

PubMed

Hackl, Franz; Hummer, Michael; Pruckner, Gerald J

2015-09-01

We analyzed the impact of social networks on general practitioners' (GPs) referral behavior based on administrative panel data from 2,684,273 referrals to specialists made between 1998 and 2007. For the definition of social networks, we used information on the doctors' place and time of study and their hospital work history. We found that GPs referred more patients to specialists within their personal networks and that patients referred within a social network had fewer follow-up consultations and less inpatient days thereafter. The effects on patient outcomes (e.g. waiting periods, days in hospital) of referrals within personal networks and affinity-based networks differed. Specifically, whereas empirical evidence showed a concentration on high-quality specialists for referrals within the personal network, suggesting that referrals within personal networks overcome information asymmetry with respect to specialists' abilities, the empirical evidence for affinity-based networks was different and less clear. Same-gender networks tended to refer patients to low-quality specialists. Copyright © 2015 Elsevier B.V. All rights reserved.

Black Holes Traveling Exhibition: This Time, It's Personal.

NASA Astrophysics Data System (ADS)

Dussault, Mary E.; Braswell, E. L.; Sunbury, S.; Wasser, M.; Gould, R. R.

2012-01-01

How can you make a topic as abstract as black holes seem relevant to the life of the average museum visitor? In 2009, the Harvard-Smithsonian Center for Astrophysics developed a 2500 square foot interactive museum exhibition, "Black Holes: Space Warps & Time Twists,” with funding from the National Science Foundation and NASA. The exhibition has been visited by more than a quarter million museum-goers, and is about to open in its sixth venue at the Reuben H. Fleet Science Center in San Diego, California. We have found that encouraging visitors to adopt a custom black hole explorer's identity can help to make the science of black holes more accessible and meaningful. The Black Holes exhibition uses networked exhibit technology that serves to personalize the visitor experience, to support learning over time including beyond the gallery, and to provide a rich quantitative source of embedded evaluation data. Visitors entering the exhibition create their own bar-coded "Black Holes Explorer's Card” which they use throughout the exhibition to collect and record images, movies, their own predictions and conclusions, and other black hole artifacts. This digital database of personal discoveries grows as visitors navigate through the gallery, and an automated web-content authoring system creates a personalized online journal of their experience that they can access once they get home. We report here on new intriguing results gathered from data generated by 112,000 visitors across five different venues. For example, an initial review of the data reveals correlations between visitors’ black hole explorer identity choices and their engagement with the exhibition. We will also discuss correlations between learning gains and personalization.

Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

ERIC Educational Resources Information Center

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

PubMed

Lin, C T; Jou, C P

2000-01-01

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

Learning in a Network: A "Third Way" between School Learning and Workplace Learning?

ERIC Educational Resources Information Center

Bottrup, Pernille

2005-01-01

Purpose--The aim of this article is to examine network-based learning and discuss how participation in network can enhance organisational learning. Design/methodology/approach--In recent years, companies have increased their collaboration with other organisations, suppliers, customers, etc., in order to meet challenges from a globalised market.…

How Neural Networks Learn from Experience.

ERIC Educational Resources Information Center

Hinton, Geoffrey E.

1992-01-01

Discusses computational studies of learning in artificial neural networks and findings that may provide insights into the learning abilities of the human brain. Describes efforts to test theories about brain information processing, using artificial neural networks. Vignettes include information concerning how a neural network represents…

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

2017-10-01

Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Social networking and Internet use among pelvic floor patients: a multicenter survey.

PubMed

Mazloomdoost, Donna; Kanter, Gregory; Chan, Robert C; Deveaneau, Nicolette; Wyman, Allison M; Von Bargen, Emily C; Chaudhry, Zaid; Elshatanoufy, Solafa; Miranne, Jeannine M; Chu, Christine M; Pauls, Rachel N; Arya, Lily A; Antosh, Danielle D

2016-11-01

Internet resources are becoming increasingly important for patients seeking medical knowledge. It is imperative to understand patient use and preferences for using the Internet and social networking websites to optimize patient education. The purpose of this study was to evaluate social networking and Internet use among women with pelvic floor complaints to seek information for their conditions as well as describe the likelihood, preferences, and predictors of website usage. This was a cross-sectional, multicenter study of women presenting to clinical practices of 10 female pelvic medicine and reconstructive surgery fellowship programs across the United States, affiliated with the Fellows' Pelvic Research Network. New female patients presenting with pelvic floor complaints, including urinary incontinence, pelvic organ prolapse, and fecal incontinence were eligible. Participants completed a 24 item questionnaire designed by the authors to assess demographic information, general Internet use, preferences regarding social networking websites, referral patterns, and resources utilized to learn about their pelvic floor complaints. Internet use was quantified as high (≥4 times/wk), moderate (2-3 times/wk), or minimal (≤1 time/wk). Means were used for normally distributed data and medians for data not meeting this assumption. Fisher's exact and χ 2 tests were used to evaluate the associations between variables and Internet use. A total of 282 surveys were analyzed. The majority of participants, 83.3%, were white. The mean age was 55.8 years old. Referrals to urogynecology practices were most frequently from obstetrician/gynecologists (39.9%) and primary care providers (27.8%). Subjects were well distributed geographically, with the largest representation from the South (38.0%). Almost one third (29.9%) were most bothered by prolapse complaints, 22.0% by urgency urinary incontinence, 20.9% by stress urinary incontinence, 14.9% by urgency/frequency symptoms, and 4.1% by fecal incontinence. The majority, 75.0%, described high Internet use, whereas 8.5% moderately and 4.8% minimally used the Internet. Women most often used the Internet for personal motivations including medical research (76.4%), and 42.6% reported Google to be their primary search engine. Despite this, only 4.9% primarily used the Internet to learn about their pelvic floor condition, more commonly consulting an obstetrician-gynecologist for this information (39.4%). The majority (74.1%) held a social networking account, and 45.9% visited these daily. Nearly half, 41.7%, expressed the desire to use social networking websites to learn about their condition. Women <65 years old were significantly more likely to have high Internet use (83.4% vs 68.8%, P = .018) and to desire using social networking websites to learn about their pelvic floor complaint (P = .008). The presenting complaint was not associated with Internet use (P = .905) or the desire to use social networking websites to learn about pelvic floor disorders (P = .201). Women presenting to urogynecology practices have high Internet use and a desire to learn about their conditions via social networking websites. Despite this, obstetrician-gynecologists remain a common resource for information. Nonetheless, urogynecology practices and national organizations would likely benefit from increasing their Internet resources for patient education in pelvic floor disorders, though patients should be made aware of available resources. Copyright © 2016 Elsevier Inc. All rights reserved.

Model for disease dynamics of a waterborne pathogen on a random network.

PubMed

Li, Meili; Ma, Junling; van den Driessche, P

2015-10-01

A network epidemic SIWR model for cholera and other diseases that can be transmitted via the environment is developed and analyzed. The person-to-person contacts are modeled by a random contact network, and the contagious environment is modeled by an external node that connects to every individual. The model is adapted from the Miller network SIR model, and in the homogeneous mixing limit becomes the Tien and Earn deterministic cholera model without births and deaths. The dynamics of our model shows excellent agreement with stochastic simulations. The basic reproduction number [Formula: see text] is computed, and on a Poisson network shown to be the sum of the basic reproduction numbers of the person-to-person and person-to-water-to-person transmission pathways. However, on other networks, [Formula: see text] depends nonlinearly on the transmission along the two pathways. Type reproduction numbers are computed and quantify measures to control the disease. Equations giving the final epidemic size are obtained.

Quantitative learning strategies based on word networks

NASA Astrophysics Data System (ADS)

Zhao, Yue-Tian-Yi; Jia, Zi-Yang; Tang, Yong; Xiong, Jason Jie; Zhang, Yi-Cheng

2018-02-01

Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

A high-capacity model for one shot association learning in the brain

PubMed Central

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs. PMID:25426060

Adaptive categorization of ART networks in robot behavior learning using game-theoretic formulation.

PubMed

Fung, Wai-keung; Liu, Yun-hui

2003-12-01

Adaptive Resonance Theory (ART) networks are employed in robot behavior learning. Two of the difficulties in online robot behavior learning, namely, (1) exponential memory increases with time, (2) difficulty for operators to specify learning tasks accuracy and control learning attention before learning. In order to remedy the aforementioned difficulties, an adaptive categorization mechanism is introduced in ART networks for perceptual and action patterns categorization in this paper. A game-theoretic formulation of adaptive categorization for ART networks is proposed for vigilance parameter adaptation for category size control on the categories formed. The proposed vigilance parameter update rule can help improving categorization performance in the aspect of category number stability and solve the problem of selecting initial vigilance parameter prior to pattern categorization in traditional ART networks. Behavior learning using physical robot is conducted to demonstrate the effectiveness of the proposed adaptive categorization mechanism in ART networks.

A high-capacity model for one shot association learning in the brain.

PubMed

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs.

The Structural Underpinnings of Policy Learning: A Classroom Policy Simulation

NASA Astrophysics Data System (ADS)

Bird, Stephen

This paper investigates the relationship between the centrality of individual actors in a social network structure and their policy learning performance. In a dynamic comparable to real-world policy networks, results from a classroom simulation demonstrate a strong relationship between centrality in social learning networks and grade performance. Previous research indicates that social network centrality should have a positive effect on learning in other contexts and this link is tested in a policy learning context. Second, the distinction between collaborative learning versus information diffusion processes in policy learning is examined. Third, frequency of interaction is analyzed to determine whether consistent, frequent tics have a greater impact on the learning process. Finally, the data arc analyzed to determine if the benefits of centrality have limitations or thresholds when benefits no longer accrue. These results demonstrate the importance of network structure, and support a collaborative conceptualization of the policy learning process.

Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept.

PubMed

Jochems, Arthur; Deist, Timo M; van Soest, Johan; Eble, Michael; Bulens, Paul; Coucke, Philippe; Dries, Wim; Lambin, Philippe; Dekker, Andre

2016-12-01

One of the major hurdles in enabling personalized medicine is obtaining sufficient patient data to feed into predictive models. Combining data originating from multiple hospitals is difficult because of ethical, legal, political, and administrative barriers associated with data sharing. In order to avoid these issues, a distributed learning approach can be used. Distributed learning is defined as learning from data without the data leaving the hospital. Clinical data from 287 lung cancer patients, treated with curative intent with chemoradiation (CRT) or radiotherapy (RT) alone were collected from and stored in 5 different medical institutes (123 patients at MAASTRO (Netherlands, Dutch), 24 at Jessa (Belgium, Dutch), 34 at Liege (Belgium, Dutch and French), 48 at Aachen (Germany, German) and 58 at Eindhoven (Netherlands, Dutch)). A Bayesian network model is adapted for distributed learning (watch the animation: http://youtu.be/nQpqMIuHyOk). The model predicts dyspnea, which is a common side effect after radiotherapy treatment of lung cancer. We show that it is possible to use the distributed learning approach to train a Bayesian network model on patient data originating from multiple hospitals without these data leaving the individual hospital. The AUC of the model is 0.61 (95%CI, 0.51-0.70) on a 5-fold cross-validation and ranges from 0.59 to 0.71 on external validation sets. Distributed learning can allow the learning of predictive models on data originating from multiple hospitals while avoiding many of the data sharing barriers. Furthermore, the distributed learning approach can be used to extract and employ knowledge from routine patient data from multiple hospitals while being compliant to the various national and European privacy laws. Copyright © 2016 The Author(s). Published by Elsevier Ireland Ltd.. All rights reserved.

How Are Television Networks Involved in Distance Learning?

ERIC Educational Resources Information Center

Bucher, Katherine

1996-01-01

Reviews the involvement of various television networks in distance learning, including public broadcasting stations, Cable in the Classroom, Arts and Entertainment Network, Black Entertainment Television, C-SPAN, CNN (Cable News Network), The Discovery Channel, The Learning Channel, Mind Extension University, The Weather Channel, National Teacher…

Rethinking the learning of belief network probabilities

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

Musick, R.

Belief networks are a powerful tool for knowledge discovery that provide concise, understandable probabilistic models of data. There are methods grounded in probability theory to incrementally update the relationships described by the belief network when new information is seen, to perform complex inferences over any set of variables in the data, to incorporate domain expertise and prior knowledge into the model, and to automatically learn the model from data. This paper concentrates on part of the belief network induction problem, that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, the current practice of rotemore » learning the probabilities in belief networks can be significantly improved upon. We advance the idea of applying any learning algorithm to the task of conditional probability learning in belief networks, discuss potential benefits, and show results of applying neutral networks and other algorithms to a medium sized car insurance belief network. The results demonstrate from 10 to 100% improvements in model error rates over the current approaches.« less

Attitudes towards Social Networking and Sharing Behaviors among Consumers of Direct-to-Consumer Personal Genomics

PubMed Central

Lee, Sandra Soo-Jin; Vernez, Simone L.; Ormond, K.E.; Granovetter, Mark

2013-01-01

Little is known about how consumers of direct-to-consumer personal genetic services share personal genetic risk information. In an age of ubiquitous online networking and rapid development of social networking tools, understanding how consumers share personal genetic risk assessments is critical in the development of appropriate and effective policies. This exploratory study investigates how consumers share personal genetic information and attitudes towards social networking behaviors. Methods: Adult participants aged 23 to 72 years old who purchased direct-to-consumer genetic testing from a personal genomics company were administered a web-based survey regarding their sharing activities and social networking behaviors related to their personal genetic test results. Results: 80 participants completed the survey; of those, 45% shared results on Facebook and 50.9% reported meeting or reconnecting with more than 10 other individuals through the sharing of their personal genetic information. For help interpreting test results, 70.4% turned to Internet websites and online sources, compared to 22.7% who consulted their healthcare providers. Amongst participants, 51.8% reported that they believe the privacy of their personal genetic information would be breached in the future. Conclusion: Consumers actively utilize online social networking tools to help them share and interpret their personal genetic information. These findings suggest a need for careful consideration of policy recommendations in light of the current ambiguity of regulation and oversight of consumer initiated sharing activities. PMID:25562728

Attitudes towards Social Networking and Sharing Behaviors among Consumers of Direct-to-Consumer Personal Genomics.

PubMed

Lee, Sandra Soo-Jin; Vernez, Simone L; Ormond, K E; Granovetter, Mark

2013-10-14

Little is known about how consumers of direct-to-consumer personal genetic services share personal genetic risk information. In an age of ubiquitous online networking and rapid development of social networking tools, understanding how consumers share personal genetic risk assessments is critical in the development of appropriate and effective policies. This exploratory study investigates how consumers share personal genetic information and attitudes towards social networking behaviors. Adult participants aged 23 to 72 years old who purchased direct-to-consumer genetic testing from a personal genomics company were administered a web-based survey regarding their sharing activities and social networking behaviors related to their personal genetic test results. 80 participants completed the survey; of those, 45% shared results on Facebook and 50.9% reported meeting or reconnecting with more than 10 other individuals through the sharing of their personal genetic information. For help interpreting test results, 70.4% turned to Internet websites and online sources, compared to 22.7% who consulted their healthcare providers. Amongst participants, 51.8% reported that they believe the privacy of their personal genetic information would be breached in the future. Consumers actively utilize online social networking tools to help them share and interpret their personal genetic information. These findings suggest a need for careful consideration of policy recommendations in light of the current ambiguity of regulation and oversight of consumer initiated sharing activities.

Towards a Standards-Based Approach to E-Learning Personalization Using Reusable Learning Objects.

ERIC Educational Resources Information Center

Conlan, Owen; Dagger, Declan; Wade, Vincent

E-Learning systems that produce personalized course offerings for the learner are often expensive, both from a time and financial perspective, to develop and maintain. Learning content personalized to a learners' cognitive preferences has been shown to produce more effective learning, however many approaches to realizing this form of…

Underlying Processes of an Inverted Personalization Effect in Multimedia Learning – An Eye-Tracking Study

PubMed Central

Zander, Steffi; Wetzel, Stefanie; Kühl, Tim; Bertel, Sven

2017-01-01

One of the frequently examined design principles in multimedia learning is the personalization principle. Based on empirical evidence this principle states that using personalized messages in multimedia learning is more beneficial than using formal language (e.g., using ‘you’ instead of ‘the’). Although there is evidence that these slight changes in regard to the language style affect learning, motivation and the perceived cognitive load, it remains unclear, (1) whether the positive effects of personalized language can be transferred to all kinds of content of learning materials (e.g., specific potentially aversive health issues) and (2) which are the underlying processes (e.g., attention allocation) of the personalization effect. German university students (N = 37) learned symptoms and causes of cerebral hemorrhages either with a formal or a personalized version of the learning material. Analysis revealed comparable results to the few existing previous studies, indicating an inverted personalization effect for potentially aversive learning material. This effect was specifically revealed in regard to decreased average fixation duration and the number of fixations exclusively on the images in the personalized compared to the formal version. These results can be seen as indicators for an inverted effect of personalization on the level of visual attention. PMID:29326630

Lessons learned from use of social network strategy in HIV testing programs targeting African American men who have sex with men.

PubMed

McCree, Donna H; Millett, Gregorio; Baytop, Chanza; Royal, Scott; Ellen, Jonathan; Halkitis, Perry N; Kupprat, Sandra A; Gillen, Sara

2013-10-01

We report lessons derived from implementation of the Social Network Strategy (SNS) into existing HIV counseling, testing, and referral services targeting 18- to 64-year-old Black gay, bisexual, and other men who have sex with men (MSM). The SNS procedures used in this study were adapted from a Centers for Disease Control and Prevention-funded, 2-year demonstration project involving 9 community-based organizations (CBOs) in 7 cities. Under the SNS, HIV-positive and HIV-negative men at high risk for HIV (recruiters) were enlisted to identify and recruit persons from their social, sexual, or drug-using networks (network associates) for HIV testing. Sites maintained records of modified study protocols for ascertaining lessons learned. The study was conducted between April 2008 and May 2010 at CBOs in Washington, DC, and New York, New York, and at a health department in Baltimore, Maryland. Several common lessons regarding development of the plan, staffing, training, and use of incentives were identified across the sites. Collectively, these lessons indicate use of SNS is resource-intensive, requiring a detailed plan, dedicated staff, and continual input from clients and staff for successful implementation. SNS may provide a strategy for identifying and targeting clusters of high-risk Black MSM for HIV testing. Given the resources needed to implement the strategy, additional studies using an experimental design are needed to determine the cost-effectiveness of SNS compared with other testing strategies.

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Learning in Artificial Neural Systems

NASA Technical Reports Server (NTRS)

Matheus, Christopher J.; Hohensee, William E.

1987-01-01

This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

Personal networks of women in residential and outpatient substance abuse treatment

PubMed Central

Kim, HyunSoo; Tracy, Elizabeth; Brown, Suzanne; Jun, MinKyoung; Park, Hyunyong; Min, Meeyoung; McCarty, Chris

2015-01-01

This study compared compositional, social support, and structural characteristics of personal networks among women in residential (RT) and intensive outpatient (IOP) substance abuse treatment. The study sample included 377 women from inner-city substance use disorder treatment facilities. Respondents were asked about 25 personal network members known within the past 6 months, characteristics of each (relationship, substance use, types of support), and relationships between each network member. Differences between RT women and IOP women in personal network characteristics were identified using Chi-square and t-tests. Compared to IOP women, RT women had more substance users in their networks, more network members with whom they had used substances and fewer network members who provided social support. These findings suggest that women in residential treatment have specific network characteristics, not experienced by women in IOP, which may make them more vulnerable to relapse; they may therefore require interventions that target these specific network characteristics in order to reduce their vulnerability to relapse. PMID:27011762

Personal networks of women in residential and outpatient substance abuse treatment.

PubMed

Kim, HyunSoo; Tracy, Elizabeth; Brown, Suzanne; Jun, MinKyoung; Park, Hyunyong; Min, Meeyoung; McCarty, Chris

This study compared compositional, social support, and structural characteristics of personal networks among women in residential (RT) and intensive outpatient (IOP) substance abuse treatment. The study sample included 377 women from inner-city substance use disorder treatment facilities. Respondents were asked about 25 personal network members known within the past 6 months, characteristics of each (relationship, substance use, types of support), and relationships between each network member. Differences between RT women and IOP women in personal network characteristics were identified using Chi-square and t -tests. Compared to IOP women, RT women had more substance users in their networks, more network members with whom they had used substances and fewer network members who provided social support. These findings suggest that women in residential treatment have specific network characteristics, not experienced by women in IOP, which may make them more vulnerable to relapse; they may therefore require interventions that target these specific network characteristics in order to reduce their vulnerability to relapse.

Preparing culture change agents for academic medicine in a multi-institutional consortium: the C - change learning action network.

PubMed

Pololi, Linda H; Krupat, Edward; Schnell, Eugene R; Kern, David E

2013-01-01

Research suggests an ongoing need for change in the culture of academic medicine. This article describes the structure, activities and evaluation of a culture change project: the C - Change Learning Action Network (LAN) and its impact on participants. The LAN was developed to create the experience of a culture that would prepare participants to facilitate a culture in academic medicine that would be more collaborative, inclusive, relational, and that supports the humanity and vitality of faculty. Purposefully diverse faculty, leaders, and deans from 5 US medical schools convened in 2 1/2-day meetings biannually over 4 years. LAN meetings employed experiential, cognitive, and affective learning modes; innovative dialogue strategies; and reflective practice aimed at facilitating deep dialogue, relationship formation, collaboration, authenticity, and transformative learning to help members experience the desired culture. Robust aggregated qualitative and quantitative data collected from the 5 schools were used to inform and stimulate culture-change plans. Quantitative and qualitative evaluation methods were used. Participants indicated that a safe, supportive, inclusive, collaborative culture was established in LAN and highly valued. LAN members reported a deepened understanding of organizational change, new and valued interpersonal connections, increased motivation and resilience, new skills and approaches, increased self-awareness and personal growth, emotional connection to the issues of diversity and inclusion, and application of new learnings in their work. A carefully designed multi-institutional learning community can transform the way participants experience and view institutional culture. It can motivate and prepare them to be change agents in their own institutions. Copyright © 2013 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on CME, Association for Hospital Medical Education.

Investigating Patterns of Interaction in Networked Learning and Computer-Supported Collaborative Learning: A Role for Social Network Analysis

ERIC Educational Resources Information Center

de Laat, Maarten; Lally, Vic; Lipponen, Lasse; Simons, Robert-Jan

2007-01-01

The focus of this study is to explore the advances that Social Network Analysis (SNA) can bring, in combination with other methods, when studying Networked Learning/Computer-Supported Collaborative Learning (NL/CSCL). We present a general overview of how SNA is applied in NL/CSCL research; we then go on to illustrate how this research method can…

Sea ice classification using fast learning neural networks

NASA Technical Reports Server (NTRS)

Dawson, M. S.; Fung, A. K.; Manry, M. T.

1992-01-01

A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.

Galen Wagner, M.D., Ph.D. (1939-2016) as international mentor of young investigators in electrocardiology.

PubMed

Swenne, Cees A; Pahlm, Olle; Atwater, Brett D; Bacharova, Ljuba

This paper describes a substantial part of the international mentoring network of students and young investigators in electrocardiology that developed around Dr. Galen Wagner (1939-2016), including many experiences of his mentees and co-mentors. The paper is meant to stimulate thinking about international mentoring as a means to achieve important learning experiences and personal development of young investigators, to intensify international scientific cooperation, and to stimulate scientific production. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Personalized Learning: From Neurogenetics of Behaviors to Designing Optimal Language Training

PubMed Central

Wong, Patrick C. M.; Vuong, Loan; Liu, Kevin

2016-01-01

Variability in drug responsivity has prompted the development of Personalized Medicine, which has shown great promise in utilizing genotypic information to develop safer and more effective drug regimens for patients. Similarly, individual variability in learning outcomes has puzzled researchers who seek to create optimal learning environments for students. “Personalized Learning” seeks to identify genetic, neural and behavioral predictors of individual differences in learning and aims to use predictors to help create optimal teaching paradigms. Evidence for Personalized Learning can be observed by connecting research in pharmacogenomics, cognitive genetics and behavioral experiments across domains of learning, which provides a framework for conducting empirical studies from the laboratory to the classroom and holds promise for addressing learning effectiveness in the individual learners. Evidence can also be seen in the subdomain of speech learning, thus providing initial support for the applicability of Personalized Learning to language. PMID:27720749

Connectivism and Information Literacy: Moving from Learning Theory to Pedagogical Practice

ERIC Educational Resources Information Center

Transue, Beth M.

2013-01-01

Connectivism is an emerging learning theory positing that knowledge comprises networked relationships and that learning comprises the ability to successfully navigate through these networks. Successful pedagogical strategies involve the instructor helping students to identify, navigate, and evaluate information from their learning networks. Many…

Comparison between extreme learning machine and wavelet neural networks in data classification

NASA Astrophysics Data System (ADS)

Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

2017-03-01

Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

PubMed

Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

2017-11-13

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 < 0.001). The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Learning Strategy Preference and Personality Type: Are They Related?

ERIC Educational Resources Information Center

Conti, Gary J.; McNeil, Rita C.

2011-01-01

This study investigated the relationship of learning strategy preference to personality type. Learning strategy preference was identified with the "A"ssessing "T"he "L"earning Strategies of "A"dult"S" (ATLAS), and personality type was measured with the Myers-Briggs Type Indicator (MBTI). The…

Thermodynamic efficiency of learning a rule in neural networks

NASA Astrophysics Data System (ADS)

Goldt, Sebastian; Seifert, Udo

2017-11-01

Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

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

PubMed

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

2017-08-01

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

ICT-Based Learning Personalization Affordance in the Context of Implementation of Constructionist Learning Activities

ERIC Educational Resources Information Center

Ignatova, Natalija; Dagiene, Valentina; Kubilinskiene, Svetlana

2015-01-01

How to enable students to create a personalized learning environment? What are the criteria of evaluation of the ICT-based learning process personalization affordance? These questions are answered by conducting multiple case study research of the innovative ICT-based learning process in iTEC (Innovative Technologies for Engaging Classrooms)…

Concept Based Approach for Adaptive Personalized Course Learning System

ERIC Educational Resources Information Center

Salahli, Mehmet Ali; Özdemir, Muzaffer; Yasar, Cumali

2013-01-01

One of the most important factors for improving the personalization aspects of learning systems is to enable adaptive properties to them. The aim of the adaptive personalized learning system is to offer the most appropriate learning path and learning materials to learners by taking into account their profiles. In this paper, a new approach to…

HIV Transmission Dynamics Among Foreign-Born Persons in the United States.

PubMed

Valverde, Eduardo E; Oster, Alexandra M; Xu, Songli; Wertheim, Joel O; Hernandez, Angela L

2017-12-15

In the United States (US), foreign-born persons are disproportionately affected by HIV and differ epidemiologically from US-born persons with diagnosed HIV infection. Understanding HIV transmission dynamics among foreign-born persons is important to guide HIV prevention efforts for these populations. We conducted molecular transmission network analysis to describe HIV transmission dynamics among foreign-born persons with diagnosed HIV. Using HIV-1 polymerase nucleotide sequences reported to the US National HIV Surveillance System for persons with diagnosed HIV infection during 2001-2013, we constructed a genetic distance-based transmission network using HIV-TRACE and examined the birth region of potential transmission partners in this network. Of 77,686 people, 12,064 (16%) were foreign born. Overall, 28% of foreign-born persons linked to at least one other person in the transmission network. Of potential transmission partners, 62% were born in the United States, 31% were born in the same region as the foreign-born person, and 7% were born in another region of the world. Most transmission partners of male foreign-born persons (63%) were born in the United States, whereas most transmission partners of female foreign-borns (57%) were born in their same world region. These finding suggests that a majority of HIV infections among foreign-born persons in our network occurred after immigrating to the United States. Efforts to prevent HIV infection among foreign-born persons in the United States should include information of the transmission networks in which these individuals acquire or transmit HIV to develop more targeted HIV prevention interventions.

Learning about leadership - A personal account.

PubMed

Cheang, P P

2011-01-01

A personal account of learning about leadership. This article introduces the theory of power and influence, and aimed to report especially the personal reflection, emotional intelligence and learning about oneself that occurred on the way. Reading, group discussion and active reflection. Thoughts, reflections and learning were recorded regularly. The concept of leadership, influence tactics and emotional intelligence all have implications in workplace relationship management and ultimately leadership qualities. The issues discussed serves as food for thought for others. This is a genuine and very personal learning experience.

Renewal and change for health care executives.

PubMed

Burke, G C; Bice, M O

1991-01-01

Health care executives must consider renewal and change within their own lives if they are to breathe life into their own institutions. Yet numerous barriers to executive renewal exist, including time pressures, fatigue, cultural factors, and trustee attitudes. This essay discusses such barriers and suggests approaches that health care executives may consider for programming renewal into their careers. These include self-assessment for professional and personal goals, career or job change, process vs. outcome considerations, solitude, networking, lifelong education, surrounding oneself with change agents, business travel and sabbaticals, reading outside the field, physical exercise, mentoring, learning from failures, a sense of humor, spiritual reflection, and family and friends. Renewal is a continuous, lifelong process requiring constant learning. Individual executives would do well to develop a framework for renewal in their careers and organizations.

Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

DTIC Science & Technology

2014-03-27

BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS THESIS Jessica R. Werling, Captain, USAF AFIT-ENG-14-M-81 DEPARTMENT...subject to copyright protection in the United States. AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ...AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS Jessica R. Werling, B.S.C.S. Captain, USAF Approved

Improved Adjoint-Operator Learning For A Neural Network

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1995-01-01

Improved method of adjoint-operator learning reduces amount of computation and associated computational memory needed to make electronic neural network learn temporally varying pattern (e.g., to recognize moving object in image) in real time. Method extension of method described in "Adjoint-Operator Learning for a Neural Network" (NPO-18352).

Learning as Issue Framing in Agricultural Innovation Networks

ERIC Educational Resources Information Center

Tisenkopfs, Talis; Kunda, Ilona; Šumane, Sandra

2014-01-01

Purpose: Networks are increasingly viewed as entities of learning and innovation in agriculture. In this article we explore learning as issue framing in two agricultural innovation networks. Design/methodology/approach: We combine frame analysis and social learning theories to analyse the processes and factors contributing to frame convergence and…

Neuromorphic Optical Signal Processing and Image Understanding for Automated Target Recognition

DTIC Science & Technology

1989-12-01

34 Stochastic Learning Machine " Neuromorphic Target Identification * Cognitive Networks 3. Conclusions ..... ................ .. 12 4. Publications...16 5. References ...... ................... . 17 6. Appendices ....... .................. 18 I. Optoelectronic Neural Networks and...Learning Machines. II. Stochastic Optical Learning Machine. III. Learning Network for Extrapolation AccesFon For and Radar Target Identification

Transformative Learning: Personal Empowerment in Learning Mathematics

ERIC Educational Resources Information Center

Hassi, Marja-Liisa; Laursen, Sandra L.

2015-01-01

This article introduces the concept of personal empowerment as a form of transformative learning. It focuses on commonly ignored but enhancing elements of mathematics learning and argues that crucial personal resources can be essentially promoted by high engagement in mathematical problem solving, inquiry, and collaboration. This personal…

Optimizing targeted vaccination across cyber-physical networks: an empirically based mathematical simulation study.

PubMed

Mones, Enys; Stopczynski, Arkadiusz; Pentland, Alex 'Sandy'; Hupert, Nathaniel; Lehmann, Sune

2018-01-01

Targeted vaccination, whether to minimize the forward transmission of infectious diseases or their clinical impact, is one of the 'holy grails' of modern infectious disease outbreak response, yet it is difficult to achieve in practice due to the challenge of identifying optimal targets in real time. If interruption of disease transmission is the goal, targeting requires knowledge of underlying person-to-person contact networks. Digital communication networks may reflect not only virtual but also physical interactions that could result in disease transmission, but the precise overlap between these cyber and physical networks has never been empirically explored in real-life settings. Here, we study the digital communication activity of more than 500 individuals along with their person-to-person contacts at a 5-min temporal resolution. We then simulate different disease transmission scenarios on the person-to-person physical contact network to determine whether cyber communication networks can be harnessed to advance the goal of targeted vaccination for a disease spreading on the network of physical proximity. We show that individuals selected on the basis of their closeness centrality within cyber networks (what we call 'cyber-directed vaccination') can enhance vaccination campaigns against diseases with short-range (but not full-range) modes of transmission. © 2018 The Author(s).

The network approach and interventions to prevent HIV among injection drug users.

PubMed Central

Neaigus, A

1998-01-01

OBJECTIVE: To review human immunodeficiency virus (HIV) risk reduction interventions among injecting drug users (IDUs) that have adopted a network approach. METHODS: The design and outcomes of selected network-based interventions among IDUs are reviewed using the network concepts of the dyad (two-person relationship), the personal risk network (an index person and all of his or her relationship), and the "sociometric" network (the complete set of relations between people in a population) and community. RESULTS: In a dyad intervention among HIV-serodiscordant couples, many of which included IDUs, there were no HIV seroconversions. Participants in personal risk network interventions were more likely to reduce drug risks and in some of these interventions, sexual risks, than were participants in individual-based interventions. Sociometric network interventions reached more IDUs and may be more cost-effective than individual-based interventions. CONCLUSION: Network-based HIV risk reduction interventions among IDUs, and others at risk for HIV, hold promise and should be encouraged. PMID:9722819

The centrality of affective instability and identity in Borderline Personality Disorder: Evidence from network analysis.

PubMed

Richetin, Juliette; Preti, Emanuele; Costantini, Giulio; De Panfilis, Chiara

2017-01-01

We argue that the series of traits characterizing Borderline Personality Disorder samples do not weigh equally. In this regard, we believe that network approaches employed recently in Personality and Psychopathology research to provide information about the differential relationships among symptoms would be useful to test our claim. To our knowledge, this approach has never been applied to personality disorders. We applied network analysis to the nine Borderline Personality Disorder traits to explore their relationships in two samples drawn from university students and clinical populations (N = 1317 and N = 96, respectively). We used the Fused Graphical Lasso, a technique that allows estimating networks from different populations separately while considering their similarities and differences. Moreover, we examined centrality indices to determine the relative importance of each symptom in each network. The general structure of the two networks was very similar in the two samples, although some differences were detected. Results indicate the centrality of mainly affective instability, identity, and effort to avoid abandonment aspects in Borderline Personality Disorder. Results are consistent with the new DSM Alternative Model for Personality Disorders. We discuss them in terms of implications for therapy.

The centrality of affective instability and identity in Borderline Personality Disorder: Evidence from network analysis

PubMed Central

Costantini, Giulio; De Panfilis, Chiara

2017-01-01

We argue that the series of traits characterizing Borderline Personality Disorder samples do not weigh equally. In this regard, we believe that network approaches employed recently in Personality and Psychopathology research to provide information about the differential relationships among symptoms would be useful to test our claim. To our knowledge, this approach has never been applied to personality disorders. We applied network analysis to the nine Borderline Personality Disorder traits to explore their relationships in two samples drawn from university students and clinical populations (N = 1317 and N = 96, respectively). We used the Fused Graphical Lasso, a technique that allows estimating networks from different populations separately while considering their similarities and differences. Moreover, we examined centrality indices to determine the relative importance of each symptom in each network. The general structure of the two networks was very similar in the two samples, although some differences were detected. Results indicate the centrality of mainly affective instability, identity, and effort to avoid abandonment aspects in Borderline Personality Disorder. Results are consistent with the new DSM Alternative Model for Personality Disorders. We discuss them in terms of implications for therapy. PMID:29040324

SISL and SIRL: Two knowledge dissemination models with leader nodes on cooperative learning networks

NASA Astrophysics Data System (ADS)

Li, Jingjing; Zhang, Yumei; Man, Jiayu; Zhou, Yun; Wu, Xiaojun

2017-02-01

Cooperative learning is one of the most effective teaching methods, which has been widely used. Students' mutual contact forms a cooperative learning network in this process. Our previous research demonstrated that the cooperative learning network has complex characteristics. This study aims to investigating the dynamic spreading process of the knowledge in the cooperative learning network and the inspiration of leaders in this process. To this end, complex network transmission dynamics theory is utilized to construct the knowledge dissemination model of a cooperative learning network. Based on the existing epidemic models, we propose a new susceptible-infected-susceptible-leader (SISL) model that considers both students' forgetting and leaders' inspiration, and a susceptible-infected-removed-leader (SIRL) model that considers students' interest in spreading and leaders' inspiration. The spreading threshold λcand its impact factors are analyzed. Then, numerical simulation and analysis are delivered to reveal the dynamic transmission mechanism of knowledge and leaders' role. This work is of great significance to cooperative learning theory and teaching practice. It also enriches the theory of complex network transmission dynamics.

Let's Celebrate Personalization: But Not Too Fast

ERIC Educational Resources Information Center

Tomlinson, Carol Ann

2017-01-01

The concept of personalization in learning appeals to many K-12 teachers and students weary of regimented, one-size-fits-all instruction. The in-vogue term personalization is used to refer to many different learning strategies and structures--from personal learning plans to greater student voice. Differentiation expert Carol Ann Tomlinson is…

Personality Traits, Learning and Academic Achievements

ERIC Educational Resources Information Center

Jensen, Mikael

2015-01-01

There has been an increased interest in personality traits (especially the five-factor model) in relation to education and learning over the last decade. Previous studies have shown a relation between personality traits and learning, and between personality traits and academic achievement. The latter is typically described in terms of Grade Point…

Investigating the Value of Personalization in a Mobile Learning System

ERIC Educational Resources Information Center

Kalloo, Vani; Mohan, Permanand

2015-01-01

This paper investigates the potential benefits of personalization in a mobile learning environment for high school students learning mathematics. Personalization was expected to benefit the students in two main ways. These are improving their performance in mathematics and making navigation of the application easier. Personalization was…

Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome

PubMed Central

Hannigan, Geoffrey D.; Duhaime, Melissa B.; Koutra, Danai

2018-01-01

Viruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks. PMID:29668682

Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome.

PubMed

Hannigan, Geoffrey D; Duhaime, Melissa B; Koutra, Danai; Schloss, Patrick D

2018-04-01

Viruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks.

Personalization vs. How People Learn

ERIC Educational Resources Information Center

Riley, Benjamin

2017-01-01

Riley asserts that some findings of cognitive science conflict with key principles of personalized learning--that students should control the content of their learning and that they should control the pace of their learning. A personalized approach is in conflict with the cognitive science principle that committing key facts in a discipline to…

Critical Review on Affect of Personality on Learning Styles

ERIC Educational Resources Information Center

Kamarulzaman, Wirawani

2012-01-01

This paper is intended to review the affect of personality on learning styles. Costa and McCrae's Five-Factor Model of Personality (The Big 5) is explored against Kolb Learning Styles. The Big 5 factors are extraversion, neuroticism, openness, agreeableness and conscientiousness, whereas Kolb Learning Styles are divergers, assimilators,…

Integration of classroom science performance assessment tasks by participants of the Wisconsin Performance Assessment Development Project (WPADP)

NASA Astrophysics Data System (ADS)

Tonnis, Dorothy Ann

The goals of this interpretive study were to examine selected Wisconsin science teachers' perceptions of teaching and learning science, to describe the scope of classroom performance assessment practices, and to gain an understanding of teachers' personal and professional experiences that influenced their belief systems of teaching, learning and assessment. The study was designed to answer the research questions: (1) How does the integration of performance assessment relate to the teachers' views of teaching and learning? (2) How are the selected teachers integrating performance assessment in their teaching? (3) What past personal and professional experiences have influenced teachers' attitudes and beliefs related to their classroom performance assessment practices? Purposeful sampling was used to select seven Wisconsin elementary, middle and high school science teachers who participated in the WPADP initiative from 1993-1995. Data collection methods included a Teaching Practices Inventory (TPI), semi-structured interviews, teacher developed portfolios, portfolio conferences, and classroom observations. Four themes and multiple categories emerged through data analysis to answer the research questions and to describe the results. Several conclusions were drawn from this research. First, science teachers who appeared to effectively integrate performance assessment, demonstrated transformational thinking in their attitudes and beliefs about teaching and learning science. In addition, these teachers viewed assessment and instructional practices as interdependent. Third, transformational teachers generally used well defined criteria to judge student work and made it public to the students. Transformational teachers provided students with real-world performance assessment tasks that were also learning events. Furthermore, student task responses informed the transformational teachers about effectiveness of instruction, students' complex thinking skills, quality of assessment instruments, students' creativity, and students' self-assessment skills. Finally, transformational teachers maintained integration of performance assessment practices through sustaining teacher support networks, engaging in professional development programs, and reflecting upon past personal and professional experiences related to teaching, learning and assessment. Salient conflicts overcome or minimized by transformational teachers include the conflict between assessment scoring and grading issues, validity and reliability concerns about the performance assessment tasks used, and the difficulty for teachers to consistently provide public criteria to students before task administration.

Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation

NASA Astrophysics Data System (ADS)

Karargyros, Alex; Syeda-Mahmood, Tanveer

2018-02-01

Deep learning networks are gaining popularity in many medical image analysis tasks due to their generalized ability to automatically extract relevant features from raw images. However, this can make the learning problem unnecessarily harder requiring network architectures of high complexity. In case of anomaly detection, in particular, there is often sufficient regional difference between the anomaly and the surrounding parenchyma that could be easily highlighted through bottom-up saliency operators. In this paper we propose a new hybrid deep learning network using a combination of raw image and such regional maps to more accurately learn the anomalies using simpler network architectures. Specifically, we modify a deep learning network called U-Net using both the raw and pre-segmented images as input to produce joint encoding (contraction) and expansion paths (decoding) in the U-Net. We present results of successfully delineating subdural and epidural hematomas in brain CT imaging and liver hemangioma in abdominal CT images using such network.

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks

PubMed Central

Miconi, Thomas

2017-01-01

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior. DOI: http://dx.doi.org/10.7554/eLife.20899.001 PMID:28230528

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

PubMed

Miconi, Thomas

2017-02-23

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

Neuronify: An Educational Simulator for Neural Circuits.

PubMed

Dragly, Svenn-Arne; Hobbi Mobarhan, Milad; Våvang Solbrå, Andreas; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel; Einevoll, Gaute T

2017-01-01

Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

Neuronify: An Educational Simulator for Neural Circuits

PubMed Central

Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne

2017-01-01

Abstract Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux). PMID:28321440

Statewide Work-Based Learning Intermediary Network: Fiscal Year 2014 Report

ERIC Educational Resources Information Center

Iowa Department of Education, 2014

2014-01-01

The Statewide Work-based Learning Intermediary Network Fiscal Year 2014 Report summarizes fiscal year 2014 (FY14) work-based learning activities of the 15 regional intermediary networks. This report includes activities which occurred between October 1, 2013, to June 30, 2014. It is notable that some intermediary regional networks have been in…

Networking for Teacher Learning: Toward a Theory of Effective Design.

ERIC Educational Resources Information Center

McDonald, Joseph P.; Klein, Emily J.

2003-01-01

Examines how teacher networks design for teacher learning, describing several dynamic tensions inherent in the designs of a sample of teacher networks and assessing the relationships of these tensions to teacher learning. The paper illustrates these design concepts with reference to the work of seven networks that aim to revamp teacher' knowledge…

Network reciprocity by coexisting learning and teaching strategies

NASA Astrophysics Data System (ADS)

Tanimoto, Jun; Brede, Markus; Yamauchi, Atsuo

2012-03-01

We propose a network reciprocity model in which an agent probabilistically adopts learning or teaching strategies. In the learning adaptation mechanism, an agent may copy a neighbor's strategy through Fermi pairwise comparison. The teaching adaptation mechanism involves an agent imposing its strategy on a neighbor. Our simulations reveal that the reciprocity is significantly affected by the frequency with which learning and teaching agents coexist in a network and by the structure of the network itself.

Influences of Formal Learning, Personal Learning Orientation, and Supportive Learning Environment on Informal Learning

ERIC Educational Resources Information Center

Choi, Woojae; Jacobs, Ronald L.

2011-01-01

While workplace learning includes formal and informal learning, the relationship between the two has been overlooked, because they have been viewed as separate entities. This study investigated the effects of formal learning, personal learning orientation, and supportive learning environment on informal learning among 203 middle managers in Korean…

Peer Apprenticeship Learning in Networked Learning Communities: The Diffusion of Epistemic Learning

ERIC Educational Resources Information Center

Jamaludin, Azilawati; Shaari, Imran

2016-01-01

This article discusses peer apprenticeship learning (PAL) as situated within networked learning communities (NLCs). The context revolves around the diffusion of technologically-mediated learning in Singapore schools, where teachers begin to implement inquiry-oriented learning, consistent with 21st century learning, among students. As these schools…

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

PubMed

Tran, Son N; d'Avila Garcez, Artur S

2018-02-01

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language-a set of logical rules that we call confidence rules-and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural-symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.

Deep neural network using color and synthesized three-dimensional shape for face recognition

NASA Astrophysics Data System (ADS)

Rhee, Seon-Min; Yoo, ByungIn; Han, Jae-Joon; Hwang, Wonjun

2017-03-01

We present an approach for face recognition using synthesized three-dimensional (3-D) shape information together with two-dimensional (2-D) color in a deep convolutional neural network (DCNN). As 3-D facial shape is hardly affected by the extrinsic 2-D texture changes caused by illumination, make-up, and occlusions, it could provide more reliable complementary features in harmony with the 2-D color feature in face recognition. Unlike other approaches that use 3-D shape information with the help of an additional depth sensor, our approach generates a personalized 3-D face model by using only face landmarks in the 2-D input image. Using the personalized 3-D face model, we generate a frontalized 2-D color facial image as well as 3-D facial images (e.g., a depth image and a normal image). In our DCNN, we first feed 2-D and 3-D facial images into independent convolutional layers, where the low-level kernels are successfully learned according to their own characteristics. Then, we merge them and feed into higher-level layers under a single deep neural network. Our proposed approach is evaluated with labeled faces in the wild dataset and the results show that the error rate of the verification rate at false acceptance rate 1% is improved by up to 32.1% compared with the baseline where only a 2-D color image is used.

Stakeholder engagement: a key component of integrating genomic information into electronic health records

PubMed Central

Hartzler, Andrea; McCarty, Catherine A.; Rasmussen, Luke V.; Williams, Marc S.; Brilliant, Murray; Bowton, Erica A.; Clayton, Ellen Wright; Faucett, William A.; Ferryman, Kadija; Field, Julie R.; Fullerton, Stephanie M.; Horowitz, Carol R.; Koenig, Barbara A.; McCormick, Jennifer B.; Ralston, James D.; Sanderson, Saskia C.; Smith, Maureen E.; Trinidad, Susan Brown

2014-01-01

Integrating genomic information into clinical care and the electronic health record can facilitate personalized medicine through genetically guided clinical decision support. Stakeholder involvement is critical to the success of these implementation efforts. Prior work on implementation of clinical information systems provides broad guidance to inform effective engagement strategies. We add to this evidence-based recommendations that are specific to issues at the intersection of genomics and the electronic health record. We describe stakeholder engagement strategies employed by the Electronic Medical Records and Genomics Network, a national consortium of US research institutions funded by the National Human Genome Research Institute to develop, disseminate, and apply approaches that combine genomic and electronic health record data. Through select examples drawn from sites of the Electronic Medical Records and Genomics Network, we illustrate a continuum of engagement strategies to inform genomic integration into commercial and homegrown electronic health records across a range of health-care settings. We frame engagement as activities to consult, involve, and partner with key stakeholder groups throughout specific phases of health information technology implementation. Our aim is to provide insights into engagement strategies to guide genomic integration based on our unique network experiences and lessons learned within the broader context of implementation research in biomedical informatics. On the basis of our collective experience, we describe key stakeholder practices, challenges, and considerations for successful genomic integration to support personalized medicine. PMID:24030437

Experiments on Learning by Back Propagation.

ERIC Educational Resources Information Center

Plaut, David C.; And Others

This paper describes further research on a learning procedure for layered networks of deterministic, neuron-like units, described by Rumelhart et al. The units, the way they are connected, the learning procedure, and the extension to iterative networks are presented. In one experiment, a network learns a set of filters, enabling it to discriminate…

Personal network structure and substance use in women by 12 months post treatment intake

PubMed Central

Tracy, Elizabeth M.; Min, Meeyoung O.; Park, Hyunyong; Jun, MinKyoung; Brown, Suzanne; Francis, Meredith W.

2015-01-01

Introduction Women with substance use disorders enter treatment with limited personal network resources and reduced recovery support. This study examined the impact of personal networks on substance use by 12 months post treatment intake. Methods Data were collected from 284 women who received substance abuse treatment. At six month follow up, composition, support availability and structure of personal networks were examined. Substance use was measured by women’s report of any use of alcohol or drugs. Hierarchical multivariate logistic regression was conducted to examine the contribution of personal network characteristics on substance use by 12 months post treatment intake. Results Higher numbers of substance using alters (network members) and more densely connected networks at six month follow-up were associated with an increased likelihood of substance use by 12 months post treatment intake. A greater number of isolates in women’s networks was associated with decreased odds of substance use. Women who did not use substances by 12 months post treatment intake had more non-users among their isolates at six months compared to those who used substances. No association was found between support availability and likelihood of substance use. Conclusions Both network composition and structure could be relevant foci for network interventions e.g. helping women change network composition by reducing substance users as well as increasing network connections. Isolates who are not substance users may be a particular strength to help women cultivate within their network to promote sustained sobriety post treatment. PMID:26712040

Does academic performance or personal growth share a stronger association with learning environment perception?

PubMed

Colbert-Getz, Jorie M; Tackett, Sean; Wright, Scott M; Shochet, Robert S

2016-08-28

This study was conducted to characterize the relative strength of associations of learning environment perception with academic performance and with personal growth. In 2012-2014 second and third year students at Johns Hopkins University School of Medicine completed a learning environment survey and personal growth scale. Hierarchical linear regression analysis was employed to determine if the proportion of variance in learning environment scores accounted for by personal growth was significantly larger than the proportion accounted for by academic performance (course/clerkship grades). The proportion of variance in learning environment scores accounted for by personal growth was larger than the proportion accounted for by academic performance in year 2 [R(2)Δ of 0.09, F(1,175) = 14.99,  p < .001] and year 3 [R(2)Δ of 0.28, F(1,169) = 76.80, p < .001]. Learning environment scores shared a small amount of variance with academic performance in years 2 and 3.  The amount of variance between learning environment scores and personal growth was small in year 2 and large in year 3. Since supportive learning environments are essential for medical education, future work must determine if enhancing personal growth prior to and during the clerkship year will increase learning environment perception.

Does academic performance or personal growth share a stronger association with learning environment perception?

PubMed Central

Tackett, Sean; Wright, Scott M.; Shochet, Robert S.

2016-01-01

Objectives This study was conducted to characterize the relative strength of associations of learning environment perception with academic performance and with personal growth. Methods In 2012-2014 second and third year students at Johns Hopkins University School of Medicine completed a learning environment survey and personal growth scale. Hierarchical linear regression analysis was employed to determine if the proportion of variance in learning environment scores accounted for by personal growth was significantly larger than the proportion accounted for by academic performance (course/clerkship grades). Results The proportion of variance in learning environment scores accounted for by personal growth was larger than the proportion accounted for by academic performance in year 2 [R2Δ of 0.09, F(1,175) = 14.99,  p < .001] and year 3 [R2Δ of 0.28, F(1,169) = 76.80, p < .001]. Learning environment scores shared a small amount of variance with academic performance in years 2 and 3.  The amount of variance between learning environment scores and personal growth was small in year 2 and large in year 3. Conclusions Since supportive learning environments are essential for medical education, future work must determine if enhancing personal growth prior to and during the clerkship year will increase learning environment perception. PMID:27570912

The effect of electronic networking on preservice elementary teachers' science teaching self-efficacy and attitude towards science teaching

NASA Astrophysics Data System (ADS)

Mathew, Nishi Mary

Preservice elementary teachers' science teaching efficacy and attitude towards science teaching are important determinants of whether and how they will teach science in their classrooms. Preservice teachers' understanding of science and science teaching experiences have an impact on their beliefs about their ability to teach science. This study had a quasi-experimental pretest-posttest control group design (N = 60). Preservice elementary teachers in this study were networked through the Internet (using e-mail, newsgroups, listserv, world wide web access and electronic mentoring) during their science methods class and student practicum. Electronic networking provides a social context in which to learn collaboratively, share and reflect upon science teaching experiences and practices, conduct tele-research effectively, and to meet the demands of student teaching through peer support. It was hoped that the activities over the electronic networks would provide them with positive and helpful science learning and teaching experiences. Self-efficacy was measured using a 23-item Likert scale instrument, the Science Teaching Efficacy Belief Instrument, Form-B (STEBI-B). Attitude towards science teaching was measured using the Revised Science Attitude Scale (RSAS). Analysis of covariance was used to analyze the data, with pretest scores as the covariate. Findings of this study revealed that prospective elementary teachers in the electronically networked group had better science teaching efficacy and personal science teaching efficacy as compared to the non-networked group of preservice elementary teachers. The science teaching outcome expectancy of prospective elementary teachers in the networked group was not greater than that of the prospective teachers in the non-networked group (at p < 0.05). Attitude towards science teaching was not significantly affected by networking. However, this is surmised to be related to the duration of the study. Information about the experiences of the participants in this study was also collected through interview, and inventories. Findings from the interview data revealed that prospective teachers benefited from the interactions with peers, science mentors, and science methods instructors during student teaching. Students who did not have access to computers noted that time was a constraint in the use of the electronic networks.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment

PubMed Central

Uddin, Raihan; Singh, Shiva M.

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning. PMID:29066959

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

PubMed

Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.

Hebbian based learning with winner-take-all for spiking neural networks

NASA Astrophysics Data System (ADS)

Gupta, Ankur; Long, Lyle

2009-03-01

Learning methods for spiking neural networks are not as well developed as the traditional neural networks that widely use back-propagation training. We propose and implement a Hebbian based learning method with winner-take-all competition for spiking neural networks. This approach is spike time dependent which makes it naturally well suited for a network of spiking neurons. Homeostasis with Hebbian learning is implemented which ensures stability and quicker learning. Homeostasis implies that the net sum of incoming weights associated with a neuron remains the same. Winner-take-all is also implemented for competitive learning between output neurons. We implemented this learning rule on a biologically based vision processing system that we are developing, and use layers of leaky integrate and fire neurons. The network when presented with 4 bars (or Gabor filters) of different orientation learns to recognize the bar orientations (or Gabor filters). After training, each output neuron learns to recognize a bar at specific orientation and responds by firing more vigorously to that bar and less vigorously to others. These neurons are found to have bell shaped tuning curves and are similar to the simple cells experimentally observed by Hubel and Wiesel in the striate cortex of cat and monkey.

"You See Yourself Like in a Mirror": The Effects of Internet-Mediated Personal Networks on Body Image and Eating Disorders.

PubMed

Pallotti, Francesca; Tubaro, Paola; Casilli, Antonio A; Valente, Thomas W

2018-09-01

Body image issues associated with eating disorders involve attitudinal and perceptual components: individuals' dissatisfaction with body shape or weight, and inability to assess body size correctly. While prior research has mainly explored social pressures produced by the media, fashion, and advertising industries, this paper focuses on the effects of personal networks on body image, particularly in the context of internet communities. We use data collected on a sample of participants to websites on eating disorders, and map their personal networks. We specify and estimate a model for the joint distribution of attitudinal and perceptual components of body image as a function of network-related characteristics and attributional factors. Supported by information gathered through in-depth interviews, the empirical estimates provide evidence that personal networks can be conducive to positive body image development, and that the influence of personal networks varies significantly by body size. We situate our discussion in current debates about the effects of computer-mediated and face-to-face communication networks on eating disorders and related behaviors.

Personalized Learning in Wisconsin: FLIGHT Academy. Connect: Making Learning Personal

ERIC Educational Resources Information Center

Taege, Jeffrey; Krauter, Krista; Lees, Jonathan

2015-01-01

This field report is the third in a series produced by the Center on Innovations in Learning's League of Innovators. The series describes, discusses, and analyzes policies and practices that enable personalization in education. Issues of the series will present either issue briefs or, like this one, field reports on lessons learned by…

Preservice Teachers' Perception and Use of Personal Learning Environments (PLEs)

ERIC Educational Resources Information Center

Sahin, Sami; Uluyol, Çelebi

2016-01-01

Personal learning environments (PLEs) are Web 2.0 tools and services by which users' access, construct, manage, and share educational contents in order to meet their learning needs. These environments enable users to manage their learning according to their own personal preferences. They further promote socialization and collaboration with their…

Personal Adult Learning Lab (Pall). Implications for Practice.

ERIC Educational Resources Information Center

Klippel, Judith A.; And Others

The Personal Adult Learning Lab was establsiehd at the Georgia Center for Continuing Education (GCCE) at the University of Georgia to serve self-directed adult learners and conduct research on self-directed learning. The lab allows adult learners to design, conduct, and evaluate their personal learning experiences while proceeding at their own…

Bidirectional extreme learning machine for regression problem and its learning effectiveness.

PubMed

Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

2012-09-01

It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.

The Network Structure of Human Personality According to the NEO-PI-R: Matching Network Community Structure to Factor Structure

PubMed Central

Goekoop, Rutger; Goekoop, Jaap G.; Scholte, H. Steven

2012-01-01

Introduction Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. Aim To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). Methods 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. Results At facet level, NCS showed a best match (96.2%) with a ‘confirmatory’ 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with ‘confirmatory’ 5-FS and ‘exploratory’ 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. Conclusion We present the first optimized network graph of personality traits according to the NEO-PI-R: a ‘Personality Web’. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network. PMID:23284713

The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

PubMed

Goekoop, Rutger; Goekoop, Jaap G; Scholte, H Steven

2012-01-01

Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. At facet level, NCS showed a best match (96.2%) with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

VLSI Implementation of Neuromorphic Learning Networks

DTIC Science & Technology

1993-03-31

AND DATES COVEREDFINAL/O1 AUG 90 TO 31 MAR 93 4. TITLE AND SUBTII1L S. FUNDING NUMBERS VLSI IMPLEMENTATION OF NEUROMORPHIC LEARNING NETWORKS (U) 6...Standard Form 298 (Rev 2-89) rtrfbc byv nN$I A Z’Si - 8 9- A* qip. COVER SHEET VLSI Implementation of Neuromorphic Learning Networks Contract Number... Neuromorphic Learning Networks Sponsored by Defense Advanced Research Projects Agency DARPA Order No. 7013 Monitored by AFOSR Under Contract No. F49620-90-C

Loneliness and social isolation among young and late middle-age adults: Associations with personal networks and social participation.

PubMed

Child, Stephanie T; Lawton, Leora

2017-11-24

Associations between social networks and loneliness or social isolation are well established among older adults. Yet, limited research examines personal networks and participation on perceived loneliness and social isolation as distinct experiences among younger adults. Accordingly, we explore relationships among objective and subjective measures of personal networks with loneliness and isolation, comparing a younger and older cohort. The UC Berkeley Social Networks Study offers unique cohort data on young (21-30 years old, n = 472) and late middle-age adults' (50-70 years old, n = 637) personal network characteristics, social participation, network satisfaction, relationship status, and days lonely and isolated via online survey or in-person interview. Negative binomial regression models were used to examine associations between social network characteristics, loneliness, and isolation by age group. Young adults reported twice as many days lonely and isolated than late middle-age adults, despite, paradoxically, having larger networks. For young adults, informal social participation and weekly religious attendance were associated with fewer days isolated. Among late middle-age adults, number of close kin and relationship status were associated with loneliness. Network satisfaction was associated with fewer days lonely or isolated among both age groups. Distinct network characteristics were associated with either loneliness or isolation for each cohort, suggesting network factors are independently associated with each outcome, and may fluctuate over time. Network satisfaction was associated with either loneliness or isolation among both cohorts, suggesting perceptions of social networks may be equally important as objective measures, and remain salient for loneliness and isolation throughout the life course.

Learning oncogenetic networks by reducing to mixed integer linear programming.

PubMed

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

ERIC Educational Resources Information Center

van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

2016-01-01

E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

The 3 R's of Learning Time: Rethink, Reshape, Reclaim

ERIC Educational Resources Information Center

Sackey, Shera Carter

2012-01-01

The Learning School Alliance is a network of schools collaborating about professional practice. The network embodies Learning Forward's purpose to advance effective job-embedded professional learning that leads to student outcomes. A key component of Learning Forward's Standards for Professional Learning is a focus on collaborative learning,…

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

PubMed Central

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-01-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting. PMID:25837826

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

PubMed

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-04-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting.

[Supporting the Love, Marriage, and Child-Rearing of Persons with Schizophrenia].

PubMed

Ikebuchi, Emi

2015-01-01

Persons with schizophrenia and their families have strong interests and hopes for love, marriage, pregnancy, and child-rearing. These experiences often lead to recovery from schizophrenia. There are many partners with schizophrenia who enjoy fruitful lives even with their disability. However, only some persons can enter into such lives in the real world in Japan and other countries. This leads persons with schizophrenia to develop a discouraged and disappointed attitude, and also causes professionals of mental health to develop indifference or pessimism about these issues. Schizophrenics are thought to have interests in love and sexual behavior just as strong as the general population. I discuss with my patients about these issues and working life early in the course of treatment. Because they lose their chance to learn adult behavior in social lives with peers due to the beginning of schizophrenia, they need an opportunity to participate in a social situation to learn knowledge and skills of dating and related behaviors, and systematic education such as psycho-education and social skills training should be provided. Continuing married life and child-rearing require more support from experts with rich experience and knowledge. Psychiatrists are required to participate in shared decision-making about medication during pregnancy and breast-feeding, as well as provide knowledge on the benefits and risks of antipsychotics. Net-working with the family, professionals of child welfare, and the community is necessary to support child-rearing. Urakawa Bethel's House was introduced as a pioneering concept to support love, marriage, and child-rearing. Finally, professionals' negative or indifferent attitudes toward these issues are discussed in the setting of treatment. I hope that professionals of mental health will think about these issues from the standpoints of persons with schizophrenia and their families.

Language Views on Social Networking Sites for Language Learning: The Case of Busuu

ERIC Educational Resources Information Center

Álvarez Valencia, José Aldemar

2016-01-01

Social networking has compelled the area of computer-assisted language learning (CALL) to expand its research palette and account for new virtual ecologies that afford language learning and socialization. This study focuses on Busuu, a social networking site for language learning (SNSLL), and analyzes the views of language that are enacted through…

User-Centered Indexing for Adaptive Information Access

NASA Technical Reports Server (NTRS)

Chen, James R.; Mathe, Nathalie

1996-01-01

We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Leading and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.

An Exploratory Analysis of Personality, Attitudes, and Study Skills on the Learning Curve within a Team-based Learning Environment

PubMed Central

Henry, Teague; Campbell, Ashley

2015-01-01

Objective. To examine factors that determine the interindividual variability of learning within a team-based learning environment. Methods. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students’ Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. Results. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. Conclusion. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course. PMID:25861101

An exploratory analysis of personality, attitudes, and study skills on the learning curve within a team-based learning environment.

PubMed

Persky, Adam M; Henry, Teague; Campbell, Ashley

2015-03-25

To examine factors that determine the interindividual variability of learning within a team-based learning environment. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students' Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course.

Differential use of learning strategies in first-year higher education: the impact of personality, academic motivation, and teaching strategies.

PubMed

Donche, Vincent; De Maeyer, Sven; Coertjens, Liesje; Van Daal, Tine; Van Petegem, Peter

2013-06-01

Although the evidence in support of the variability of students' learning strategies has expanded in recent years, less is known about the explanatory base of these individual differences in terms of the joint influences of personal and contextual characteristics. Previous studies have often investigated how student learning is associated with either personal or contextual factors. This study takes an integrative research perspective into account and examines the joint effects of personality, academic motivation, and teaching strategies on students' learning strategies in a same educational context in first-year higher education. In this study, 1,126 undergraduate students and 90 lecturers from eight professional bachelor programmes in a university college participated. Self-report measures were used to measure students' personality, academic motivation, and learning strategies. Students' processing and regulation strategies are mapped using the Inventory of Learning Styles. Key characteristics of more content-focused versus learning-focused teaching strategies were measured. Multivariate multi-level analysis was used to take the nested data structure and interrelatedness of learning strategies into account. Different personality traits (openness, conscientiousness, and neuroticism) and academic motivation (amotivation, autonomous, and controlled motivation) were found to be independently associated with student learning strategies. Besides these student characteristics, also teaching strategies were found to be directly associated with learning strategies. The study makes clear that the impact of teaching strategies on learning strategies in first-year higher education cannot be overlooked nor overinterpreted, due to the importance of students' personality and academic motivation which also partly explain why students learn the way they do. © 2013 The British Psychological Society.

Applying a social network analysis (SNA) approach to understanding radiologists' performance in reading mammograms

NASA Astrophysics Data System (ADS)

Tavakoli Taba, Seyedamir; Hossain, Liaquat; Heard, Robert; Brennan, Patrick; Lee, Warwick; Lewis, Sarah

2017-03-01

Rationale and objectives: Observer performance has been widely studied through examining the characteristics of individuals. Applying a systems perspective, while understanding of the system's output, requires a study of the interactions between observers. This research explains a mixed methods approach to applying a social network analysis (SNA), together with a more traditional approach of examining personal/ individual characteristics in understanding observer performance in mammography. Materials and Methods: Using social networks theories and measures in order to understand observer performance, we designed a social networks survey instrument for collecting personal and network data about observers involved in mammography performance studies. We present the results of a study by our group where 31 Australian breast radiologists originally reviewed 60 mammographic cases (comprising of 20 abnormal and 40 normal cases) and then completed an online questionnaire about their social networks and personal characteristics. A jackknife free response operating characteristic (JAFROC) method was used to measure performance of radiologists. JAFROC was tested against various personal and network measures to verify the theoretical model. Results: The results from this study suggest a strong association between social networks and observer performance for Australian radiologists. Network factors accounted for 48% of variance in observer performance, in comparison to 15.5% for the personal characteristics for this study group. Conclusion: This study suggest a strong new direction for research into improving observer performance. Future studies in observer performance should consider social networks' influence as part of their research paradigm, with equal or greater vigour than traditional constructs of personal characteristics.

Effects of nurses' personality traits and their environmental characteristics on their workplace learning and nursing competence.

PubMed

Takase, Miyuki; Yamamoto, Masako; Sato, Yoko

2018-04-01

A good fit between an individual's personality traits and job characteristics motivates employees, and thus enhances their work behavior. However, how nurses' personality traits and their environmental characteristics relate to nurses' engagement in workplace learning, which improves their competence, has not been investigated. The aim of this study was to investigate how nurses' personality traits, environmental characteristics, and workplace learning were related to nursing competence. A cross-sectional survey design was used. Questionnaires were distributed to 1167 Japanese registered nurses. Multiple regression analysis was used to examine the relationships between nurses' personality traits, the environmental characteristics, the nurses' engagement in workplace learning, and their competence. A total of 315 nurses returned questionnaires (i.e., a return rate of 27.0%). The results showed that both the personality traits (extraversion, conscientiousness, openness to experience) and environmental characteristics (autonomy at work and feedback given) were related to workplace learning and self-rated nursing competence. The results also showed that the relationship between extraversion (active, adventurous and ambitious dispositions of an individual) and self-rated nursing competence was moderated by environmental characteristics, and partially mediated by workplace learning. Positive personality traits, such as extraversion, conscientiousness, and openness to experience could enhance workplace learning and nursing competence. Moreover, environmental characteristics that allow nurses to express their personality traits have the potential to improve their learning and competence further. © 2017 Japan Academy of Nursing Science.

Using goal-directed design to create a novel system for improving chronic illness care.

PubMed

Fore, David; Goldenhar, Linda M; Margolis, Peter A; Seid, Michael

2013-10-29

A learning health system enables patients, clinicians, and researchers to work together to choose care based on the best evidence, drive discovery as a natural outgrowth of patient care, and ensure innovation, quality, safety, and value in health care; all in a more real-time fashion. Our paper describes how goal-directed design (GDD) methods were employed to understand the context and goals of potential participants in such a system as part of a design process to translate the concept of a learning health system into a prototype collaborative chronic care network (C3N), specifically for pediatric inflammatory bowel disease. Thirty-six one-on-one in-depth interviews and observations were conducted with patients (10/36, 28%), caregivers (10/36, 28%), physicians/researchers (10/36, 28%), and nurses (6/36, 17%) from a pediatric gastroenterology center participating in the ImproveCareNow network. GDD methods were used to determine the context and goals of participants. These same methods were used in conjunction with idealized design process techniques to help determine characteristics of a learning health system for this pediatric health care ecology. Research was conducted in a clinic and, in the case of some patients and caregivers, at home. Thematic analysis revealed 3 parent-child dyad personas (ie, representations of interviewees' behavior patterns, goals, skills, attitudes, and contextual information) that represented adaptation to a chronic illness over time. These were used as part of a design process to generate scenarios (potential interactions between personas and the learning health system under design) from which system requirements were derived. These scenarios in turn helped guide generation, prioritization, design, measurement, and implementation of approximately 100 prototype interventions consistent with the aim of C3N becoming a learning health network. GDD methods help ensure human goals and contexts inform the design of a network of health care interventions which reflect the shape and purpose of a C3N in pediatric chronic illness care. Developing online and in-person interventions according to well-documented context and motivations of participants increases the likelihood that a C3N will enable all participants to act in ways that achieve their goals with grace and dignity. GDD methods complemented quality-improvement methods to generate prototypes consistent with clinical and research aims, as well as the goals of patient disease management.

Ethics in actor networks, or: what Latour could learn from Darwin and Dewey.

PubMed

Waelbers, Katinka; Dorstewitz, Philipp

2014-03-01

In contemporary Science, Technology and Society (STS) studies, Bruno Latour's Actor Network Theory (ANT) is often used to study how social change arises from interaction between people and technologies. Though Latour's approach is rich in the sense of enabling scholars to appreciate the complexity of many relevant technological, environmental, and social factors in their studies, the approach is poor from an ethical point of view: the doings of things and people are couched in one and the same behaviorist (third person) vocabulary without giving due recognition to the ethical relevance of human intelligence, sympathy and reflection in making responsible choices. This article argues that two other naturalist projects, the non-teleological virtue ethics of Charles Darwin and the pragmatist instrumentalism of John Dewey can enrich ANT-based STS studies, both, in a descriptive and in a normative sense.

EPPS: Efficient and Privacy-Preserving Personal Health Information Sharing in Mobile Healthcare Social Networks

PubMed Central

Jiang, Shunrong; Zhu, Xiaoyan; Wang, Liangmin

2015-01-01

Mobile healthcare social networks (MHSNs) have emerged as a promising next-generation healthcare system, which will significantly improve the quality of life. However, there are many security and privacy concerns before personal health information (PHI) is shared with other parities. To ensure patients’ full control over their PHI, we propose a fine-grained and scalable data access control scheme based on attribute-based encryption (ABE). Besides, policies themselves for PHI sharing may be sensitive and may reveal information about underlying PHI or about data owners or recipients. In our scheme, we let each attribute contain an attribute name and its value and adopt the Bloom filter to efficiently check attributes before decryption. Thus, the data privacy and policy privacy can be preserved in our proposed scheme. Moreover, considering the fact that the computational cost grows with the complexity of the access policy and the limitation of the resource and energy in a smart phone, we outsource ABE decryption to the cloud while preventing the cloud from learning anything about the content and access policy. The security and performance analysis is carried out to demonstrate that our proposed scheme can achieve fine-grained access policies for PHI sharing in MHSNs. PMID:26404300

EPPS: Efficient and Privacy-Preserving Personal Health Information Sharing in Mobile Healthcare Social Networks.

PubMed

Jiang, Shunrong; Zhu, Xiaoyan; Wang, Liangmin

2015-09-03

Mobile healthcare social networks (MHSNs) have emerged as a promising next-generation healthcare system, which will significantly improve the quality of life. However, there are many security and privacy concerns before personal health information (PHI) is shared with other parities. To ensure patients' full control over their PHI, we propose a fine-grained and scalable data access control scheme based on attribute-based encryption (ABE). Besides, policies themselves for PHI sharing may be sensitive and may reveal information about underlying PHI or about data owners or recipients. In our scheme, we let each attribute contain an attribute name and its value and adopt the Bloom filter to efficiently check attributes before decryption. Thus, the data privacy and policy privacy can be preserved in our proposed scheme. Moreover, considering the fact that the computational cost grows with the complexity of the access policy and the limitation of the resource and energy in a smart phone, we outsource ABE decryption to the cloud while preventing the cloud from learning anything about the content and access policy. The security and performance analysis is carried out to demonstrate that our proposed scheme can achieve fine-grained access policies for PHI sharing in MHSNs.

Learning In networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1995-01-01

Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.

Learning from Scientific Texts: Personalizing the Text Increases Transfer Performance and Task Involvement

ERIC Educational Resources Information Center

Dutke, Stephan; Grefe, Anna Christina; Leopold, Claudia

2016-01-01

In an experiment with 65 high-school students, we tested the hypothesis that personalizing learning materials would increase students' learning performance and motivation to study the learning materials. Students studied either a 915-word standard text on the anatomy and functionality of the human eye or a personalized version of the same text in…

Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

NASA Astrophysics Data System (ADS)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.

PubMed

Hueso, Miguel; Vellido, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep Maria; Jonsson, Anders

2018-02-01

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.

Training strategy for convolutional neural networks in pedestrian gender classification

NASA Astrophysics Data System (ADS)

Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

2017-06-01

In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

A globally networked hybrid approach to public health capacity training for maternal health professionals in low and middle income countries.

PubMed

McIntosh, Scott; Pérez-Ramos, José G; David, Tamala; Demment, Margaret M; Avendaño, Esteban; Ossip, Deborah J; De Ver Dye, Timothy

2017-01-01

MundoComm is a current NIH-funded project for sustainable public health capacity building in community engagement and technological advances aimed at improving maternal health issues. Two to four teams are selected annually, each consisting of three healthcare professionals and one technical person from specific low and middle income countries (LMICs) including Costa Rica, Dominican Republic, Honduras, and other LMICs. MundoComm is a course with three parts: in-person workshops, online modules, and mentored community engagement development. Two annual 1-week on-site "short courses" convened in Costa Rica are supplemented with six monthly online training modules using the Moodle® online platform for e-learning, and mentored project development. The year-long course comprises over 20 topics divided into the six modules - each module further segmented into 4 week-long assignments, with readings and assigned tasks covering different aspects of community-engaged interventions. The content is peer reviewed by experts in the respective fields from University of Rochester, UCIMED in Costa Rica, and faculty from Costa Rica and the Dominican Republic who maintain regular contact with the trainees to mentor learning and project progress. The purpose of this paper is to report the first year results of the MundoComm project. Both quantitative and qualitative feedback (using online data capturing forms) assess baseline and post-training knowledge and skills in public health project strategies. The course currently has one team each in Costa Rica, the Dominican Republic, and Honduras for a total of 12 trainees. The course and modules include best practices in information and communication technologies (ICTs), ethical reviews, community engagement, evidence-based community interventions, and e-Health strategies. To maximize successful and culturally appropriate training approaches, the multi-media didactic presentations, flexible distance learning strategies, and the use of tablets for offline data collection are offered to trainees, and then feedback from trainees and other lessons learned aid in the refinement of subsequent curricular improvements. Through remark and discussion, the authors report on 1) the feasibility of using a globally networked learning environment (GNLE) plus workshop approach to public health capacity training and 2) the capacity of LMIC teams to complete the MundoComm trainings and produce ICT-based interventions to address a maternal health issue in their respective regions.

Prediction in Health Domain Using Bayesian Networks Optimization Based on Induction Learning Techniques

NASA Astrophysics Data System (ADS)

Felgaer, Pablo; Britos, Paola; García-Martínez, Ramón

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

Experiencing Rights within Positive, Person-Centred Support Networks of People with Intellectual Disability in Australia

ERIC Educational Resources Information Center

Hillman, A.; Donelly, M.; Whitaker, L.; Dew, A.; Stancliffe, R. J.; Knox, M.; Shelley, K.; Parmenter, T. R.

2012-01-01

Background: This research describes issues related to human rights as they arose within the everyday lives of people in nine personal support networks that included adult Australians with an intellectual disability (ID). Method: The research was part of a wider 3-year ethnographic study of nine personal support networks. A major criterion for…

Deep Learning in Label-free Cell Classification

PubMed Central

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-01-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells. PMID:26975219

Deep Learning in Label-free Cell Classification

NASA Astrophysics Data System (ADS)

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-03-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

Back-propagation learning of infinite-dimensional dynamical systems.

PubMed

Tokuda, Isao; Tokunaga, Ryuji; Aihara, Kazuyuki

2003-10-01

This paper presents numerical studies of applying back-propagation learning to a delayed recurrent neural network (DRNN). The DRNN is a continuous-time recurrent neural network having time delayed feedbacks and the back-propagation learning is to teach spatio-temporal dynamics to the DRNN. Since the time-delays make the dynamics of the DRNN infinite-dimensional, the learning algorithm and the learning capability of the DRNN are different from those of the ordinary recurrent neural network (ORNN) having no time-delays. First, two types of learning algorithms are developed for a class of DRNNs. Then, using chaotic signals generated from the Mackey-Glass equation and the Rössler equations, learning capability of the DRNN is examined. Comparing the learning algorithms, learning capability, and robustness against noise of the DRNN with those of the ORNN and time delay neural network, advantages as well as disadvantages of the DRNN are investigated.

The educational impact of the Specialty Care Access Network-Extension of Community Healthcare Outcomes program.

PubMed

Salgia, Reena J; Mullan, Patricia B; McCurdy, Heather; Sales, Anne; Moseley, Richard H; Su, Grace L

2014-11-01

With the aging hepatitis C cohort and increasing prevalence of fatty liver disease, the burden on primary care providers (PCPs) to care for patients with liver disease is growing. In response, the Veterans Administration implemented initiatives for primary care-specialty referral to increase PCP competency in complex disease management. The Specialty Care Access Network-Extension of Community Healthcare Outcomes (SCAN-ECHO) program initiative was designed to transfer subspecialty knowledge to PCPs through case-based distance learning combined with real-time consultation. There is limited information regarding the initiative's ability to engage PCPs to learn and influence their practice. We surveyed PCPs to determine the factors that led to their participation in this program and the educational impact of participation. Of 51 potential participants, 24 responded to an anonymous survey. More than 75% of respondents participated more than one time in a SCAN-ECHO clinic. Providers were motivated to participate by a desire to learn more about liver disease, to apply the knowledge gained to future patients, and to save their patients time traveling to another center for specialty consultation. Seventy-one percent responded that the didactic component and case-based discussion were equally important. It is important that participation changed clinical practice: 75% of providers indicated they had personally discussed the information they learned from the case presentations with their colleague(s), and 42% indicated they helped a colleague care for their patient with the knowledge learned during discussions of other participants' cases. This study shows that the SCAN-ECHO videoconferencing program between PCPs and specialists can educate providers in the delivery of specialty care from a distance and potentially improve healthcare delivery.

Neural network and letter recognition

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

Lee, Hue Yeon.

Neural net architectures and learning algorithms that recognize hand written 36 alphanumeric characters are studied. The thin line input patterns written in 32 x 32 binary array are used. The system is comprised of two major components, viz. a preprocessing unit and a Recognition unit. The preprocessing unit in turn consists of three layers of neurons; the U-layer, the V-layer, and the C-layer. The functions of the U-layer is to extract local features by template matching. The correlation between the detected local features are considered. Through correlating neurons in a plane with their neighboring neurons, the V-layer would thicken themore » on-cells or lines that are groups of on-cells of the previous layer. These two correlations would yield some deformation tolerance and some of the rotational tolerance of the system. The C-layer then compresses data through the Gabor transform. Pattern dependent choice of center and wavelengths of Gabor filters is the cause of shift and scale tolerance of the system. Three different learning schemes had been investigated in the recognition unit, namely; the error back propagation learning with hidden units, a simple perceptron learning, and a competitive learning. Their performances were analyzed and compared. Since sometimes the network fails to distinguish between two letters that are inherently similar, additional ambiguity resolving neural nets are introduced on top of the above main neural net. The two dimensional Fourier transform is used as the preprocessing and the perceptron is used as the recognition unit of the ambiguity resolver. One hundred different person's handwriting sets are collected. Some of these are used as the training sets and the remainders are used as the test sets.« less

QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms.

PubMed

Zwartjes, Ardjan; Havinga, Paul J M; Smit, Gerard J M; Hurink, Johann L

2016-10-01

In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

PubMed

Li, Can; Belkin, Daniel; Li, Yunning; Yan, Peng; Hu, Miao; Ge, Ning; Jiang, Hao; Montgomery, Eric; Lin, Peng; Wang, Zhongrui; Song, Wenhao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei

2018-06-19

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

Machine Learning and Quantum Mechanics

NASA Astrophysics Data System (ADS)

Chapline, George

The author has previously pointed out some similarities between selforganizing neural networks and quantum mechanics. These types of neural networks were originally conceived of as away of emulating the cognitive capabilities of the human brain. Recently extensions of these networks, collectively referred to as deep learning networks, have strengthened the connection between self-organizing neural networks and human cognitive capabilities. In this note we consider whether hardware quantum devices might be useful for emulating neural networks with human-like cognitive capabilities, or alternatively whether implementations of deep learning neural networks using conventional computers might lead to better algorithms for solving the many body Schrodinger equation.

Web-Based Learning Support System

NASA Astrophysics Data System (ADS)

Fan, Lisa

Web-based learning support system offers many benefits over traditional learning environments and has become very popular. The Web is a powerful environment for distributing information and delivering knowledge to an increasingly wide and diverse audience. Typical Web-based learning environments, such as Web-CT, Blackboard, include course content delivery tools, quiz modules, grade reporting systems, assignment submission components, etc. They are powerful integrated learning management systems (LMS) that support a number of activities performed by teachers and students during the learning process [1]. However, students who study a course on the Internet tend to be more heterogeneously distributed than those found in a traditional classroom situation. In order to achieve optimal efficiency in a learning process, an individual learner needs his or her own personalized assistance. For a web-based open and dynamic learning environment, personalized support for learners becomes more important. This chapter demonstrates how to realize personalized learning support in dynamic and heterogeneous learning environments by utilizing Adaptive Web technologies. It focuses on course personalization in terms of contents and teaching materials that is according to each student's needs and capabilities. An example of using Rough Set to analyze student personal information to assist students with effective learning and predict student performance is presented.

Distance Learning in a Multimedia Networks Project: Main Results.

ERIC Educational Resources Information Center

Ruokamo, Heli; Pohjolainen, Seppo

2000-01-01

Discusses a goal-oriented project, focused on open learning environments using computer networks, called Distance Learning in Multimedia Networks that was part of the Finnish Multimedia Program. Describes the combined efforts of Finnish telecommunications companies, content providers, publishing houses, hardware companies, and educational…

A common neural network differentially mediates direct and social fear learning.

PubMed

Lindström, Björn; Haaker, Jan; Olsson, Andreas

2018-02-15

Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning. Dynamic causal modeling combined with reinforcement learning modeling revealed that the amygdala and AI provided input to this network during direct and social learning, respectively. Furthermore, the AI gated learning signals based on surprise (associability), which were conveyed to the ACC, in both learning modalities. Our findings provide insights into the mechanisms underlying social fear learning, with implications for understanding common psychological dysfunctions, such as phobias and other anxiety disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Five Dispositions for Personalization

ERIC Educational Resources Information Center

Carter, Kim

2017-01-01

The author reviews various ways personalized learning has come to be interpreted and asserts that rather than requiring we create individualized learning plans for each student, true personalization requires that teachers give learners the tools, knowledge, skills, and dispositions to manage themselves and their learning environment. Teachers…

Intercorrelates of Postsecondary Students' Learning Styles and Personality Traits.

ERIC Educational Resources Information Center

Rothschild, Jacqueline; Piland, William E.

1994-01-01

Describes a study investigating the learning styles and personality types of community college and private university students. Identifies three broad types of learners (cooperative, independent, and competitive), suggesting significant correlations between experimenting personality types and learning style preferences. Discusses the role of…

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks.

PubMed

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-12-08

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the "small sample size" (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0-1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

PubMed Central

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-01-01

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system. PMID:25494350

Knowledgeable Lemurs Become More Central in Social Networks.

PubMed

Kulahci, Ipek G; Ghazanfar, Asif A; Rubenstein, Daniel I

2018-04-23

Strong relationships exist between social connections and information transmission [1-9], where individuals' network position plays a key role in whether or not they acquire novel information [2, 3, 5, 6]. The relationships between social connections and information acquisition may be bidirectional if learning novel information, in addition to being influenced by it, influences network position. Individuals who acquire information quickly and use it frequently may receive more affiliative behaviors [10, 11] and may thus have a central network position. However, the potential influence of learning on network centrality has not been theoretically or empirically addressed. To bridge this epistemic gap, we investigated whether ring-tailed lemurs' (Lemur catta) centrality in affiliation networks changed after they learned how to solve a novel foraging task. Lemurs who had frequently initiated interactions and approached conspecifics before the learning experiment were more likely to observe and learn the task solution. Comparing social networks before and after the learning experiment revealed that the frequently observed lemurs received more affiliative behaviors than they did before-they became more central after the experiment. This change persisted even after the task was removed and was not caused by the observed lemurs initiating more affiliative behaviors. Consequently, quantifying received and initiated interactions separately provides unique insights into the relationships between learning and centrality. While the factors that influence network position are not fully understood, our results suggest that individual differences in learning and becoming successful can play a major role in social centrality, especially when learning from others is advantageous. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Dynamic Three-Dimensional Network Visualization Program for Integration into CyberCIEGE and Other Network Visualization Scenarios

DTIC Science & Technology

2007-06-01

information flow involved in network attacks. This kind of information can be invaluable in learning how to best setup and defend computer networks...administrators, and those interested in learning about securing networks a way to conceptualize this complex system of computing. NTAV3D will provide a three...teaching with visual and other components can make learning more effective” (Baxley et al, 2006). A hyperbox (Alpern and Carter, 1991) is

Benefits of Cooperative Learning in Weblog Networks

ERIC Educational Resources Information Center

Wang, Jenny; Fang, Yuehchiu

2005-01-01

The purpose of this study was to explore the benefits of cooperative learning in weblog networks, focusing particularly on learning outcomes in college writing curriculum integrated with computer-mediated learning tool-weblog. The first section addressed the advantages of using weblogs in cooperative learning structure on teaching and learning.…

Investigating the Educational Value of Social Learning Networks: A Quantitative Analysis

ERIC Educational Resources Information Center

Dafoulas, Georgios; Shokri, Azam

2016-01-01

Purpose: The emergence of Education 2.0 enabled technology-enhanced learning, necessitating new pedagogical approaches, while e-learning has evolved into an instrumental pedagogy of collaboration through affordances of social media. Social learning networks and ubiquitous learning enabled individual and group learning through social engagement and…

An adult learning perspective on disability and microfinance: The case of Katureebe.

PubMed

Nuwagaba, Ephraim L; Rule, Peter N

2016-01-01

Despite Uganda's progress in promoting affirmative action for persons with disabilities and its strategy of using microfinance to fight poverty, access to microfinance services by persons with disabilities is still problematic due to barriers, characterised by discrepancies between policies and practices. Regarding education, the affirmative action in favour of learners with disabilities has not translated into actual learning opportunities due to personal and environmental barriers. The study on which this article is based investigated the non-formal and informal adult learning practices regarding microfinance that persons with disabilities engaged in. This article seeks to illuminate the barriers that a person with a visual impairment encountered while learning about and engaging with microfinance and the strategies that he developed to overcome them. This was a case study, framed within the social model of disability and critical research paradigm. Data were collected through in-depth interviews of a person with visual impairment and observations of the environment in which adult learning and engagement with Savings and Credit Cooperative Organisations (SACCOs) occurred. Findings indicate that the person with a visual disability faced barriers to learning about microfinance services. He experienced barriers in an integrated manner and developed strategies to overcome these barriers. The barriers and strategies are theorised using the social model of disability. The case of a person with visual impairment suggests that persons with disabilities face multiple barriers regarding microfinance, including social, psychological and educational. However, his own agency and attitudes were also of importance as they influenced his learning. Viewing these barriers as blockades can lead to non-participation in learning and engagement with microfinance whereas viewing them as surmountable hurdles can potentially motivate participants to succeed in learning about and engaging with microfinance.

An adult learning perspective on disability and microfinance: The case of Katureebe

PubMed Central

Nuwagaba, Ephraim L.

2016-01-01

Background Despite Uganda’s progress in promoting affirmative action for persons with disabilities and its strategy of using microfinance to fight poverty, access to microfinance services by persons with disabilities is still problematic due to barriers, characterised by discrepancies between policies and practices. Regarding education, the affirmative action in favour of learners with disabilities has not translated into actual learning opportunities due to personal and environmental barriers. Objectives The study on which this article is based investigated the non-formal and informal adult learning practices regarding microfinance that persons with disabilities engaged in. This article seeks to illuminate the barriers that a person with a visual impairment encountered while learning about and engaging with microfinance and the strategies that he developed to overcome them. Methods This was a case study, framed within the social model of disability and critical research paradigm. Data were collected through in-depth interviews of a person with visual impairment and observations of the environment in which adult learning and engagement with Savings and Credit Cooperative Organisations (SACCOs) occurred. Results Findings indicate that the person with a visual disability faced barriers to learning about microfinance services. He experienced barriers in an integrated manner and developed strategies to overcome these barriers. The barriers and strategies are theorised using the social model of disability. Conclusion The case of a person with visual impairment suggests that persons with disabilities face multiple barriers regarding microfinance, including social, psychological and educational. However, his own agency and attitudes were also of importance as they influenced his learning. Viewing these barriers as blockades can lead to non-participation in learning and engagement with microfinance whereas viewing them as surmountable hurdles can potentially motivate participants to succeed in learning about and engaging with microfinance. PMID:28730047

Strategies to overcome clinical, regulatory, and financial challenges in the implementation of personalized medicine.

PubMed

Tsimberidou, Apostolia M; Ringborg, Ulrik; Schilsky, Richard L

2013-01-01

This article highlights major developments over the last decade in personalized medicine in cancer. Emerging data from clinical studies demonstrate that the use of targeted agents in patients with targetable molecular aberrations improves clinical outcomes. Despite a surge of studies, however, significant gaps in knowledge remain, especially in identifying driver molecular aberrations in patients with multiple aberrations, understanding molecular networks that control carcinogenesis and metastasis, and most importantly, discovering effective targeted agents. Implementation of personalized medicine requires continued scientific and technological breakthroughs; standardization of tumor tissue acquisition and molecular testing; changes in oncology practice and regulatory standards for drug and device access and approval; modification of reimbursement policies by health care payers; and innovative ways to collect and analyze electronic patient information that are linked to prospective clinical registries and rapid learning systems. Informatics systems that integrate clinical, laboratory, radiologic, molecular, and economic data will improve clinical care and will provide infrastructure to enable clinical research. The initiative of the EurocanPlatform aims to overcome the challenges of implementing personalized medicine in Europe by sharing patients, biologic materials, and technological resources across borders. The EurocanPlatform establishes a complete translational cancer research program covering the drug development process and strengthening collaborations among academic centers, pharmaceutical companies, regulatory authorities, health technology assessment organizations, and health care systems. The CancerLinQ rapid learning system being developed by ASCO has the potential to revolutionize how all stakeholders in the cancer community assemble and use information obtained from patients treated in real-world settings to guide clinical practice, regulatory decisions, and health care payment policy.

Personalized E-Learning System Using Item Response Theory

ERIC Educational Resources Information Center

Chih-Ming, Chen; Lee, Hahn-Ming; Chen, Ya-Hui

2005-01-01

Personalized service is important on the Internet, especially in Web-based learning. Generally, most personalized systems consider learner preferences, interests, and browsing behaviors in providing personalized services. However, learner ability usually is neglected as an important factor in implementing personalization mechanisms. Besides, too…

S-CNN: Subcategory-aware convolutional networks for object detection.

PubMed

Chen, Tao; Lu, Shijian; Fan, Jiayuan

2017-09-26

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.

Multilabel user classification using the community structure of online networks

PubMed Central

Papadopoulos, Symeon; Kompatsiaris, Yiannis

2017-01-01

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user’s graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score. PMID:28278242

Multilabel user classification using the community structure of online networks.

PubMed

Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

2017-01-01

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

Personal support networks, social capital, and risk of relapse among individuals treated for substance use issues.

PubMed

Panebianco, Daria; Gallupe, Owen; Carrington, Peter J; Colozzi, Ivo

2016-01-01

The success of treatment for substance use issues varies with personal and social factors, including the composition and structure of the individual's personal support network. This paper describes the personal support networks and social capital of a sample of Italian adults after long-term residential therapeutic treatment for substance use issues, and analyses network correlates of post-treatment substance use (relapse). Using a social network analysis approach, data were obtained from structured interviews (90-120 min long) with 80 former clients of a large non-governmental therapeutic treatment agency in Italy providing voluntary residential treatments and rehabilitation services for substance use issues. Participants had concluded the program at least six months prior. Data were collected on socio-demographic variables, addiction history, current drug use status (drug-free or relapsed), and the composition and structure of personal support networks. Factors related to risk of relapse were assessed using bivariate and multivariate logistic regression models. A main goal of this study was to identify differences between the support network profiles of drug free and relapsed participants. Drug free participants had larger, less dense, more heterogeneous and reciprocal support networks, and more brokerage social capital than relapsed participants. Additionally, a lower risk of relapse was associated with higher socio-economic status, being married/cohabiting, and having network members with higher socio-economic status, who have greater occupational heterogeneity, and reciprocate support. Post-treatment relapse was found to be negatively associated with the socioeconomic status and occupational heterogeneity of ego's support network, reciprocity in the ties between ego and network members, and a support network in which the members are relatively loosely connected with one another (i.e., ego possesses "brokerage social capital"). These findings suggest the incorporation into therapeutic programming of interventions that address those aspects of clients' personal support networks. Copyright © 2015 Elsevier B.V. All rights reserved.

Advancing Personalized Learning through the Iterative Application of Innovation Science

ERIC Educational Resources Information Center

Redding, Sam; Twyman, Janet; Murphy, Marilyn

2016-01-01

The promise of personalized learning excites many educators, and schools are wondering how best to introduce it and how they know when they have achieved it. Rather than thinking of personalized learning as an "it" (i.e., a program that is either present or not), we might think of it as an approach to teaching and learning that has many…

The Application of Carl Rogers' Person-Centered Learning Theory to Web-Based Instruction.

ERIC Educational Resources Information Center

Miller, Christopher T.

This paper provides a review of literature that relates research on Carl Rogers' person-centered learning theory to Web-based learning. Based on the review of the literature, a set of criteria is described that can be used to determine how closely a Web-based course matches the different components of Rogers' person-centered learning theory. Using…

Development of the PRO-SDLS: A Measure of Self-Direction in Learning Based on the Personal Responsibility Orientation Model

ERIC Educational Resources Information Center

Stockdale, Susan L.; Brockett, Ralph G.

2011-01-01

The purpose of this study was to develop a reliable and valid instrument to measure self-directedness in learning among college students based on an operationalization of the personal responsibility orientation (PRO) model of self-direction in learning. The resultant 25-item Personal Responsibility Orientation to Self-Direction in Learning Scale…

Outcomes of a Self-Regulated Learning Curriculum Model

NASA Astrophysics Data System (ADS)

Peters-Burton, Erin E.

2015-10-01

The purpose of this study was to describe connections among students' views of nature of science in relation to the goals of a curriculum delivered in a unique setting, one where a researcher and two teachers collaborated to develop a course devoted to teaching students about how knowledge is built in science. Students proceeded through a cycle of self-regulated phases, forethought, performance, and self-reflection, during each segment of the curriculum: (a) independent research, (b) knowledge building in the discipline of science, and (c) a citizen science project. Student views were measured at the beginning and end of the course using epistemic network analysis. The pretest map reported student understanding of science as experimentation and indicated three clusters representing the durability of knowledge, empirical evidence, and habits of mind, which were loosely connected and represented knowledge generation as external to personal thinking. The posttest map displayed a broader understanding of scientific endeavors beyond experimentation, a shift toward personal knowledge generation, and indicated a larger number of connections among three more tightly oriented clusters: empirical evidence, habits of mind, and tentativeness. Implications include the potential to build curriculum that purposefully considers reinforcing cycles of learning of the nature of science in different contexts.

Nurses' perceptions of personal attributes required when working with people with a learning disability and an offending background: a qualitative study.

PubMed

Lovell, A; Bailey, J

2017-02-01

WHAT IS KNOWN ON THE SUBJECT?: Learning disability nursing in the area of people with a learning disability and an offending background has developed considerably over recent years, particularly since the publication of the Bradley (). There has been limited work into the competencies nurses require to work in this area, and even less about the personal attributes of learning disability nurses. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: Learning disability nursing's specific contribution to the care of this population lies in their knowledge of the interaction between the learning disability, an individual's, sometimes abusive, personal history and an understanding of the subsequent offending behaviour. The knowledge base of nurses working with people with learning disabilities and an offending background needs to reflect the changing service user group. This is particularly in relation to substance misuse, borderline personality disorder, and mental health and the way such factors inter-relate with the learning disability. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Further research is required into the relationship among decision making, risk taking or reluctance to do this, and the personal attributes required by nurses to work in secure learning disability care. Learning disability secure services are likely to continue to undergo change as circumstances alter and the offending population demonstrate greater complexity; nursing competencies and personal attributes need similarly to adapt to such changes. Mental health nursing has a great deal to contribute to effective working with this population, specifically with regard to developing strong relationships when concerns around borderline personality disorder or substance misuse are particularly in evidence. Aim To identify and discuss the personal attributes required by learning disability nurses to work effectively with people with an offending background in secure and community settings. Background This study was part of a larger research investigation into the nursing competencies required to work with people with an offending background. There are few existing studies examining the personal attributes necessary for working with this group. Design A qualitative study addressing the perceptions of nurses around the personal attributes required to work with people with learning disabilities and an offending background. Methods A semi-structured interview schedule was devised and constructed, and 39 individual interviews were subsequently undertaken with learning disability nurses working in high, medium, low secure and community settings. Data were collected over 1 year in 2010/11 and analysed using a structured thematic analysis supported by the software package MAXqda. Findings The thematic analysis produced three categories of personal attributes, named as looking deeper, achieving balance and connecting, each of which contained a further three sub-categories. Conclusion Nursing of those with a learning disability and an offending background continues to develop. The interplay among personal history, additional background factors, nurses' personal attributes and learning disability is critical for effective relationship building. © 2016 John Wiley & Sons Ltd.

Anticipation of Personal Genomics Data Enhances Interest and Learning Environment in Genomics and Molecular Biology Undergraduate Courses

PubMed Central

Weber, K. Scott; Jensen, Jamie L.; Johnson, Steven M.

2015-01-01

An important discussion at colleges is centered on determining more effective models for teaching undergraduates. As personalized genomics has become more common, we hypothesized it could be a valuable tool to make science education more hands on, personal, and engaging for college undergraduates. We hypothesized that providing students with personal genome testing kits would enhance the learning experience of students in two undergraduate courses at Brigham Young University: Advanced Molecular Biology and Genomics. These courses have an emphasis on personal genomics the last two weeks of the semester. Students taking these courses were given the option to receive personal genomics kits in 2014, whereas in 2015 they were not. Students sent their personal genomics samples in on their own and received the data after the course ended. We surveyed students in these courses before and after the two-week emphasis on personal genomics to collect data on whether anticipation of obtaining their own personal genomic data impacted undergraduate student learning. We also tested to see if specific personal genomic assignments improved the learning experience by analyzing the data from the undergraduate students who completed both the pre- and post-course surveys. Anticipation of personal genomic data significantly enhanced student interest and the learning environment based on the time students spent researching personal genomic material and their self-reported attitudes compared to those who did not anticipate getting their own data. Personal genomics homework assignments significantly enhanced the undergraduate student interest and learning based on the same criteria and a personal genomics quiz. We found that for the undergraduate students in both molecular biology and genomics courses, incorporation of personal genomic testing can be an effective educational tool in undergraduate science education. PMID:26241308

Anticipation of Personal Genomics Data Enhances Interest and Learning Environment in Genomics and Molecular Biology Undergraduate Courses.

PubMed

Weber, K Scott; Jensen, Jamie L; Johnson, Steven M

2015-01-01

An important discussion at colleges is centered on determining more effective models for teaching undergraduates. As personalized genomics has become more common, we hypothesized it could be a valuable tool to make science education more hands on, personal, and engaging for college undergraduates. We hypothesized that providing students with personal genome testing kits would enhance the learning experience of students in two undergraduate courses at Brigham Young University: Advanced Molecular Biology and Genomics. These courses have an emphasis on personal genomics the last two weeks of the semester. Students taking these courses were given the option to receive personal genomics kits in 2014, whereas in 2015 they were not. Students sent their personal genomics samples in on their own and received the data after the course ended. We surveyed students in these courses before and after the two-week emphasis on personal genomics to collect data on whether anticipation of obtaining their own personal genomic data impacted undergraduate student learning. We also tested to see if specific personal genomic assignments improved the learning experience by analyzing the data from the undergraduate students who completed both the pre- and post-course surveys. Anticipation of personal genomic data significantly enhanced student interest and the learning environment based on the time students spent researching personal genomic material and their self-reported attitudes compared to those who did not anticipate getting their own data. Personal genomics homework assignments significantly enhanced the undergraduate student interest and learning based on the same criteria and a personal genomics quiz. We found that for the undergraduate students in both molecular biology and genomics courses, incorporation of personal genomic testing can be an effective educational tool in undergraduate science education.

A conceptual framework related to ICT-AT competence development: The theoretical foundations of ENTELIS.

PubMed

Mavrou, Katerina; Hoogerwerf, Evert-Jan; Meletiou-Mavrotheris, Maria; Kärki, Anne; Sallinen, Merja

2015-01-01

This paper provides an overview of the construction of a conceptual framework regarding ICT-Assistive Technology (ICT-AT) competence development, designed to gain awareness of the elements involved and to facilitate the understanding and exchange among stakeholders of the ENTELIS (European Network for Technology Enhanced Learning in an Inclusive Society) project. The framework was designed based on the basic principles of Activity Theory, which however have been adapted and adjusted to the project's objectives. Hence, it includes a map of actors and other parameters functioning in a person surrounding "ecosystem", and it allows us to understand and map roles, expectations, barriers, as well as to devise solutions to tackle digital divide. Taking as a starting and central point the person and his/her wish to self-determination and fulfilment (quality of life) and the related needs, it provides a map of how the various concepts and variables interact within the theoretical and methodological perspective of the collection, description and assessment of experiences in ICT-AT education and competences development of persons with disabilities (PwD) of all ages. The conceptual framework represents two interacting learning activity systems: (a) the internal system of the end-user, which includes the end-user and his/her needs, the setting where learning takes place and the other actors involved, and (b) the external system, which embraces the internal system but also wider issues of policy and practice and experiences and 'actors' that contribute to the development and use of ICT and ICT-AT skills in all areas of life. The elements of these systems and their interaction provide the basis for analysing experiences and advancing knowledge relevant for bridging the digital divide.

The development of the Older Person's Nurse Fellowship: Education concept to delivery.

PubMed

Naughton, Corina; Hayes, Nicky; Zahran, Zainab; Norton, Christine; Lee, Geraldine; Fitzpatrick, Joanne M; Crawford, Mary; Tee, Stephen

2016-09-01

Preparing the nursing workforce to meet the challenges of an ageing population is a priority for many countries. The development of an Older Person's Nurse Fellowship (OPNF) programme for senior clinical nurses is an important innovation. This article describes the philosophical development, delivery and early evaluation of the OPNF. In 2014, Health Education England funded 24 senior clinical nurses to participate in the OPNF. The Fellowship was designed to build clinical leadership and innovation capability and develop a network of nurses to influence local and national strategy for older people's care. The Fellows selected were drawn from mental health (n=4), community/primary care (n=9) and acute care (n=11). The twelve month programme consisted of two Masters-level modules, delivered through study days and e-learning. The first cohort (n=12) commenced the course in November 2014 with a module designed to enhance clinical knowledge and skills. Evaluation data were collected from the first cohort using anonymous surveys (n=11) and focus group interviews (n=9). Descriptive statistics are presented for the quantitative data and common themes are described in the qualitative data. The overall satisfaction with the clinical module was high with a median score of 18/20 (range 17-20). Topics such as comprehensive geriatric assessment, frailty, pharmacology and cognitive assessment were regarded as highly relevant and most likely to result in a change to clinical practice. In the focus group interviews students discussed their learning experience in terms of: module specificity, peer-to-peer learning and using the OPNF as leverage for change. The OPNF is a timely innovation and a positive commitment to developing an academic pathway for senior nurses. It marks an important step in the future development of the older person's nursing workforce. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluating Career Development Resources: Lessons from the Earth Science Women's Network (ESWN)

NASA Astrophysics Data System (ADS)

Kogan, M.; Laursen, S. L.

2010-12-01

Retention of geoscientists throughout the professional pipeline is especially challenging in the case of groups that are already underrepresented in science, including racial minorities and women. The Earth Science Women’s Network (ESWN) is a professional network of early-career female geoscientists that provides its members with a variety of career resources, through both informal, online and in-person networking and formal career development workshops. The group’s members are of diverse nationalities and racial/ethnic backgrounds, of various age cohorts and career stages, but primarily graduate students, postdocs, and early-career researchers. With funding from an NSF ADVANCE grant to ESWN, we have conducted a detailed survey of ESWN members as part of an evaluation-with-research study that aims to determine the career needs of young geoscientists. The survey data provide information about members’ personal and professional situations, their professional development needs, and obstacles they face as young women scientists. ESWN members indicated a variety of areas of professional growth that would advance their scientific careers, but at all career stages, members chose expanding their professional networks as among their top career needs. Professional networking has established benefits for retention of people from groups underrepresented in science, including women: it introduces young scientists to career best practices and advancement opportunities, provides access to role models, and creates a sense of community. ESWN members strongly indicate that their professional networks benefited from their involvement with the Network. The community aspect of network-building is especially important for people from underrepresented groups, as they often feel alone due to the lack of role models. The intimate character of the ESWN discussion list greatly contributes to its members’ sense of community. Moreover, personal concerns and professional success are inextricably linked for women scientists, who still perform a disproportionate share of domestic and parenting duties, as our data show. ESWN members of all career stages cited work/life balance as among their top career obstacles. Here the intimate tone of ESWN discussion list proves helpful once again: women feel safe to exchange their experiences and suggestions for handling a variety of work/life dilemmas. Our data offer a snapshot of the population that is not well documented by researchers so far - young women scientists at various early-career stages, ranging from graduate students and postdocs to young faculty. We will offer a glimpse of their career needs and present the strategies that have enabled ESWN to provide them with relevant career resources through establishing a supportive community, as well as suggest future directions for the Network to develop. These lessons learned from ESWN should be helpful to all interested in supporting young scientists through critical career junctures.

Whole Person Learning: Embedding Ethical Enterprise Leadership in Business Education

ERIC Educational Resources Information Center

Carter, E. Vincent; Donohue, Mary

2012-01-01

This study introduces a collaborative business education curricular design known as "whole person learning." The post-financial crisis market environment requires business education to encompass curricular, commercial and community skills. Drawing on the Toronto based National Mentoring Program (NMP), "whole person learning"…

Personality, Organizational Orientations and Self-Reported Learning Outcomes

ERIC Educational Resources Information Center

Bamber, David; Castka, Pavel

2006-01-01

Purpose: To identify competencies connecting personality, organizational orientations and self-reported learning outcomes (as measured by concise Likert-type scales), for individuals who are learning for their organizations. Design/methodology/approach: Five concise factor scales were constructed to represent aspects of personality. Three further…

Reward-based training of recurrent neural networks for cognitive and value-based tasks

PubMed Central

Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

2017-01-01

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal’s internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task. DOI: http://dx.doi.org/10.7554/eLife.21492.001 PMID:28084991

The application of network teaching in applied optics teaching

NASA Astrophysics Data System (ADS)

Zhao, Huifu; Piao, Mingxu; Li, Lin; Liu, Dongmei

2017-08-01

Network technology has become a creative tool of changing human productivity, the rapid development of it has brought profound changes to our learning, working and life. Network technology has many advantages such as rich contents, various forms, convenient retrieval, timely communication and efficient combination of resources. Network information resources have become the new education resources, get more and more application in the education, has now become the teaching and learning tools. Network teaching enriches the teaching contents, changes teaching process from the traditional knowledge explanation into the new teaching process by establishing situation, independence and cooperation in the network technology platform. The teacher's role has shifted from teaching in classroom to how to guide students to learn better. Network environment only provides a good platform for the teaching, we can get a better teaching effect only by constantly improve the teaching content. Changchun university of science and technology introduced a BB teaching platform, on the platform, the whole optical classroom teaching and the classroom teaching can be improved. Teachers make assignments online, students learn independently offline or the group learned cooperatively, this expands the time and space of teaching. Teachers use hypertext form related knowledge of applied optics, rich cases and learning resources, set up the network interactive platform, homework submission system, message board, etc. The teaching platform simulated the learning interest of students and strengthens the interaction in the teaching.

Language Evolution by Iterated Learning with Bayesian Agents

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Kalish, Michael L.

2007-01-01

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…

Development of an Adaptive Learning System with Two Sources of Personalization Information

ERIC Educational Resources Information Center

Tseng, J. C. R.; Chu, H. C.; Hwang, G. J.; Tsai, C. C.

2008-01-01

Previous research of adaptive learning mainly focused on improving student learning achievements based only on single-source of personalization information, such as learning style, cognitive style or learning achievement. In this paper, an innovative adaptive learning approach is proposed by basing upon two main sources of personalization…

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

PubMed

Ness, Robert O; Sachs, Karen; Mallick, Parag; Vitek, Olga

2018-06-21

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

Students' Feedback of mDPBL Approach and the Learning Impact towards Computer Networks Teaching and Learning

ERIC Educational Resources Information Center

Winarno, Sri; Muthu, Kalaiarasi Sonai; Ling, Lew Sook

2018-01-01

This study presents students' feedback and learning impact on design and development of a multimedia learning in Direct Problem-Based Learning approach (mDPBL) for Computer Networks in Dian Nuswantoro University, Indonesia. This study examined the usefulness, contents and navigation of the multimedia learning as well as learning impacts towards…

Material Matters for Learning in Virtual Networks: A Case Study of a Professional Learning Programme Hosted in a Google+ Online Community

ERIC Educational Resources Information Center

Ackland, Aileen; Swinney, Ann

2015-01-01

In this paper, we draw on Actor-Network Theories (ANT) to explore how material components functioned to create gateways and barriers to a virtual learning network in the context of a professional development module in higher education. Students were practitioners engaged in family learning in different professional roles and contexts. The data…

Model of Learning Organizational Development of Primary School Network under the Office of Basic Education Commission

ERIC Educational Resources Information Center

Sai-rat, Wipa; Tesaputa, Kowat; Sriampai, Anan

2015-01-01

The objectives of this study were 1) to study the current state of and problems with the Learning Organization of the Primary School Network, 2) to develop a Learning Organization Model for the Primary School Network, and 3) to study the findings of analyses conducted using the developed Learning Organization Model to determine how to develop the…

Edmodo social learning network for elementary school mathematics learning

NASA Astrophysics Data System (ADS)

Ariani, Y.; Helsa, Y.; Ahmad, S.; Prahmana, RCI

2017-12-01

A developed instructional media can be as printed media, visual media, audio media, and multimedia. The development of instructional media can also take advantage of technological development by utilizing Edmodo social network. This research aims to develop a digital classroom learning model using Edmodo social learning network for elementary school mathematics learning which is practical, valid and effective in order to improve the quality of learning activities. The result of this research showed that the prototype of mathematics learning device for elementary school students using Edmodo was in good category. There were 72% of students passed the assessment as a result of Edmodo learning. Edmodo has become a promising way to engage students in a collaborative learning process.

Cooperative Education, Experiential Learning, and Personal Knowledge.

ERIC Educational Resources Information Center

Abrahamsson, Kenneth, Ed.

Cooperative education, experiential learning, and personal knowledge are addressed in nine conference papers. Kenneth Abrahamsson considers the nature of experiential learning, the recognition of prior learning, educational design and the assessment of quality, and policy and practice for integrating learning and experience. Harry Hienemann…

Neural-Network-Development Program

NASA Technical Reports Server (NTRS)

Phillips, Todd A.

1993-01-01

NETS, software tool for development and evaluation of neural networks, provides simulation of neural-network algorithms plus computing environment for development of such algorithms. Uses back-propagation learning method for all of networks it creates. Enables user to customize patterns of connections between layers of network. Also provides features for saving, during learning process, values of weights, providing more-precise control over learning process. Written in ANSI standard C language. Machine-independent version (MSC-21588) includes only code for command-line-interface version of NETS 3.0.

Learning and coding in biological neural networks

NASA Astrophysics Data System (ADS)

Fiete, Ila Rani

How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and theoretical results on the scalability of this rule show that learning with stochastic gradient ascent may be adequately fast to explain learning in the bird. Finally, we address the more general issue of the scalability of stochastic gradient learning on quadratic cost surfaces in linear systems, as a function of system size and task characteristics, by deriving analytical expressions for the learning curves.

Towards a Social Networks Model for Online Learning & Performance

ERIC Educational Resources Information Center

Chung, Kon Shing Kenneth; Paredes, Walter Christian

2015-01-01

In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

Social Networks, Communication Styles, and Learning Performance in a CSCL Community

ERIC Educational Resources Information Center

Cho, Hichang; Gay, Geri; Davidson, Barry; Ingraffea, Anthony

2007-01-01

The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning…

Networked Learning for Agricultural Extension: A Framework for Analysis and Two Cases

ERIC Educational Resources Information Center

Kelly, Nick; Bennett, John McLean; Starasts, Ann

2017-01-01

Purpose: This paper presents economic and pedagogical motivations for adopting information and communications technology (ICT)- mediated learning networks in agricultural education and extension. It proposes a framework for networked learning in agricultural extension and contributes a theoretical and case-based rationale for adopting the…

Learning Networks--Enabling Change through Community Action Research

ERIC Educational Resources Information Center

Bleach, Josephine

2016-01-01

Learning networks are a critical element of ethos of the community action research approach taken by the Early Learning Initiative at the National College of Ireland, a community-based educational initiative in the Dublin Docklands. Key criteria for networking, whether at local, national or international level, are the individual's and…

Neural networks for self-learning control systems

NASA Technical Reports Server (NTRS)

Nguyen, Derrick H.; Widrow, Bernard

1990-01-01

It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

Lifelong Learning in German Learning Cities/Regions

ERIC Educational Resources Information Center

Reghenzani-Kearns, Denise; Kearns, Peter

2012-01-01

This paper traces the policies and lessons learned from two consecutive German national programs aimed at developing learning cities/regions. Known as Learning Regions Promotion of Networks, this first program transitioned into the current program, Learning on Place. A case study chosen is from the Tolzer region where a network has self-sustained…

Leaking privacy and shadow profiles in online social networks.

PubMed

Garcia, David

2017-08-01

Social interaction and data integration in the digital society can affect the control that individuals have on their privacy. Social networking sites can access data from other services, including user contact lists where nonusers are listed too. Although most research on online privacy has focused on inference of personal information of users, this data integration poses the question of whether it is possible to predict personal information of nonusers. This article tests the shadow profile hypothesis, which postulates that the data given by the users of an online service predict personal information of nonusers. Using data from a disappeared social networking site, we perform a historical audit to evaluate whether personal data of nonusers could have been predicted with the personal data and contact lists shared by the users of the site. We analyze personal information of sexual orientation and relationship status, which follow regular mixing patterns in the social network. Going back in time over the growth of the network, we measure predictor performance as a function of network size and tendency of users to disclose their contact lists. This article presents robust evidence supporting the shadow profile hypothesis and reveals a multiplicative effect of network size and disclosure tendencies that accelerates the performance of predictors. These results call for new privacy paradigms that take into account the fact that individual privacy decisions do not happen in isolation and are mediated by the decisions of others.

A Self-Organizing Incremental Neural Network based on local distribution learning.

PubMed

Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

2016-12-01

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Online Learning of Genetic Network Programming and its Application to Prisoner’s Dilemma Game

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

A new evolutionary model with the network structure named Genetic Network Programming (GNP) has been proposed recently. GNP, that is, an expansion of GA and GP, represents solutions as a network structure and evolves it by using “offline learning (selection, mutation, crossover)”. GNP can memorize the past action sequences in the network flow, so it can deal with Partially Observable Markov Decision Process (POMDP) well. In this paper, in order to improve the ability of GNP, Q learning (an off-policy TD control algorithm) that is one of the famous online methods is introduced for online learning of GNP. Q learning is suitable for GNP because (1) in reinforcement learning, the rewards an agent will get in the future can be estimated, (2) TD control doesn’t need much memory and can learn quickly, and (3) off-policy is suitable in order to search for an optimal solution independently of the policy. Finally, in the simulations, online learning of GNP is applied to a player for “Prisoner’s dilemma game” and its ability for online adaptation is confirmed.

The Personal Social Networks of Resettled Bhutanese Refugees During Pregnancy in the United States: A Social Network Analysis.

PubMed

M Kingsbury, Diana; P Bhatta, Madhav; Castellani, Brian; Khanal, Aruna; Jefferis, Eric; S Hallam, Jeffery

2018-04-25

Women comprise 50% of the refugee population, 25% of whom are of reproductive age. Female refugees are at risk for experiencing significant hardships associated with the refugee experience, including after resettlement. For refugee women, the strength of their personal social networks can play an important role in mitigating the stress of resettlement and can be an influential source of support during specific health events, such as pregnancy. A personal social network analysis was conducted among 45 resettled Bhutanese refugee women who had given birth within the past 2 years in the Akron Metropolitan Area of Northeast Ohio. Data were collected using in-depth interviews conducted in Nepali over a 6-month period in 2016. Size, demographic characteristics of ties, frequency of communication, length of relationship, and strength of connection were the social network measures used to describe the personal networks of participants. A qualitative analysis was also conducted to assess what matters were commonly discussed within networks and how supportive participants perceived their networks to be. Overall, participants reported an average of 3 close personal connections during their pregnancy. The networks were comprised primarily of female family members whom the participant knew prior to resettlement in the U.S. Participants reported their networks as "very close" and perceived their connections to be supportive of them during their pregnancies. These results may be used to guide future research, as well as public health programming, that seeks to improve the pregnancy experiences of resettled refugee women.

Investigating the structure of semantic networks in low and high creative persons

PubMed Central

Kenett, Yoed N.; Anaki, David; Faust, Miriam

2014-01-01

According to Mednick's (1962) theory of individual differences in creativity, creative individuals appear to have a richer and more flexible associative network than less creative individuals. Thus, creative individuals are characterized by “flat” (broader associations) instead of “steep” (few, common associations) associational hierarchies. To study these differences, we implement a novel computational approach to the study of semantic networks, through the analysis of free associations. The core notion of our method is that concepts in the network are related to each other by their association correlations—overlap of similar associative responses (“association clouds”). We began by collecting a large sample of participants who underwent several creativity measurements and used a decision tree approach to divide the sample into low and high creative groups. Next, each group underwent a free association generation paradigm which allowed us to construct and analyze the semantic networks of both groups. Comparison of the semantic memory networks of persons with low creative ability and persons with high creative ability revealed differences between the two networks. The semantic memory network of persons with low creative ability seems to be more rigid, compared to the network of persons with high creative ability, in the sense that it is more spread out and breaks apart into more sub-parts. We discuss how our findings are in accord and extend Mednick's (1962) theory and the feasibility of using network science paradigms to investigate high level cognition. PMID:24959129

Facilitative Components of Collaborative Learning: A Review of Nine Health Research Networks.

PubMed

Leroy, Lisa; Rittner, Jessica Levin; Johnson, Karin E; Gerteis, Jessie; Miller, Therese

2017-02-01

Collaborative research networks are increasingly used as an effective mechanism for accelerating knowledge transfer into policy and practice. This paper explored the characteristics and collaborative learning approaches of nine health research networks. Semi-structured interviews with representatives from eight diverse US health services research networks conducted between November 2012 and January 2013 and program evaluation data from a ninth. The qualitative analysis assessed each network's purpose, duration, funding sources, governance structure, methods used to foster collaboration, and barriers and facilitators to collaborative learning. The authors reviewed detailed notes from the interviews to distill salient themes. Face-to-face meetings, intentional facilitation and communication, shared vision, trust among members and willingness to work together were key facilitators of collaborative learning. Competing priorities for members, limited funding and lack of long-term support and geographic dispersion were the main barriers to coordination and collaboration across research network members. The findings illustrate the importance of collaborative learning in research networks and the challenges to evaluating the success of research network functionality. Conducting readiness assessments and developing process and outcome evaluation metrics will advance the design and show the impact of collaborative research networks. Copyright © 2017 Longwoods Publishing.

Exploring Practice-Research Networks for Critical Professional Learning

ERIC Educational Resources Information Center

Appleby, Yvon; Hillier, Yvonne

2012-01-01

This paper discusses the contribution that practice-research networks can make to support critical professional development in the Learning and Skills sector in England. By practice-research networks we mean groups or networks which maintain a connection between research and professional practice. These networks stem from the philosophy of…

Graduate Employability: The Perspective of Social Network Learning

ERIC Educational Resources Information Center

Chen, Yong

2017-01-01

This study provides a conceptual framework for understanding how the graduate acquire employability through the social network in the Chinese context, using insights from the social network theory. This paper builds a conceptual model of the relationship among social network, social network learning and the graduate employability, and uses…

The Nature of Self-Directed Learning and Transformational Learning in Self-Managing Bipolar Disorder to Stay Well

ERIC Educational Resources Information Center

Francik, Wendy A.

2012-01-01

The purpose of the research was to explore the self-directed learning and transformational learning experiences among persons with bipolar disorder. A review of previous research pointed out how personal experiences with self-directed learning and transformational learning facilitated individuals' learning to manage HIV, Methicillan-resitant…

Invited Reaction: Influences of Formal Learning, Personal Learning Orientation, and Supportive Learning Environment on Informal Learning

ERIC Educational Resources Information Center

Cseh, Maria; Manikoth, Nisha N.

2011-01-01

As the authors of the preceding article (Choi and Jacobs, 2011) have noted, the workplace learning literature shows evidence of the complementary and integrated nature of formal and informal learning in the development of employee competencies. The importance of supportive learning environments in the workplace and of employees' personal learning…

Learning polynomial feedforward neural networks by genetic programming and backpropagation.

PubMed

Nikolaev, N Y; Iba, H

2003-01-01

This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search technique relying on the genetic programming paradigm, and second, further adjustment of the best discovered network weights by an especially derived backpropagation algorithm for higher order networks with polynomial activation functions. These two stages of the PFNN learning process enable us to identify networks with good training as well as generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.

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.

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

PubMed

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

Co-Operative Learning and Development Networks.

ERIC Educational Resources Information Center

Hodgson, V.; McConnell, D.

1995-01-01

Discusses the theory, nature, and benefits of cooperative learning. Considers the Cooperative Learning and Development Network (CLDN) trial in the JITOL (Just in Time Open Learning) project and examines the relationship between theories about cooperative learning and the reality of a group of professionals participating in a virtual cooperative…

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Personality and Second Language Learning. Language in Education: Theory and Practice, No. 12.

ERIC Educational Resources Information Center

Hodge, Virginia D.

This annotated reading list addresses the problem of the paucity of literature dealing specifically with the relationship between personality and language learning. There is no general theoretical model that encompasses personality theory, self-concept, ego development, learning theory, motivation, and body image as they relate to…

Tapping the Power of Personalized Learning: A Roadmap for School Leaders

ERIC Educational Resources Information Center

Rickabaugh, James

2016-01-01

In this powerful new book, James Rickabaugh, former superintendent and current director of the Institute for Personalized Learning (IPL), presents the groundbreaking results of the Institute's half-decade of research, development, and practice: a simple but powerful model for personalizing students' learning experiences by building their levels of…

A Path to the Future: Creating Accountability for Personalized Learning

ERIC Educational Resources Information Center

Hyslop, Anne; Mead, Sara

2015-01-01

A small but growing number of schools and districts across the country are experimenting with personalized learning, an innovation that customizes students' experiences to their individual needs and strengths. Through new kinds of environments, technologies, and ways to demonstrate their knowledge, personalized learning aims to meet students where…

Student Engagement: Key to Personalized Learning

ERIC Educational Resources Information Center

Ferlazzo, Larry

2017-01-01

Personalized learning has the potential to greatly improve student achievement--but realistic teachers know that any instructional strategy will only be effective if students are willing to do the work. That is why Larry Ferlazzo emphasizes the importance of weaving intrinsic motivation into every personalized learning classroom. Four key elements…

Modelling Diffusion of a Personalized Learning Framework

ERIC Educational Resources Information Center

Karmeshu; Raman, Raghu; Nedungadi, Prema

2012-01-01

A new modelling approach for diffusion of personalized learning as an educational process innovation in social group comprising adopter-teachers is proposed. An empirical analysis regarding the perception of 261 adopter-teachers from 18 schools in India about a particular personalized learning framework has been made. Based on this analysis,…

Prototype-Incorporated Emotional Neural Network.

PubMed

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

A Study of Multimedia Annotation of Web-Based Materials

ERIC Educational Resources Information Center

Hwang, Wu-Yuin; Wang, Chin-Yu; Sharples, Mike

2007-01-01

Web-based learning has become an important way to enhance learning and teaching, offering many learning opportunities. A limitation of current Web-based learning is the restricted ability of students to personalize and annotate the learning materials. Providing personalized tools and analyzing some types of learning behavior, such as students'…

DCS-Neural-Network Program for Aircraft Control and Testing

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Why the Personal Competencies Matter. Connect: Making Learning Personal

ERIC Educational Resources Information Center

Redding, Sam

2015-01-01

This issue in the "Connect" series is a field report that discusses how a student's personal competencies--cognitive, metacognitive, motivational, and social/emotional--propel learning and other forms of goal attainment. These personal competencies are personal to the individual in their shape, size, and effect, but they are enhanced by…

A Statewide Service Learning Network Ignites Teachers and Students.

ERIC Educational Resources Information Center

Monsour, Florence

Service learning, curriculum-linked community service, has proved remarkably effective in igniting students' desire to learn. In 1997, the Wisconsin Partnership in Service Learning was initiated as a cross-disciplinary, cross-institutional endeavor. Supported by a grant from Learn and Serve America, the partnership created a network throughout…

Scaffolding in Connectivist Mobile Learning Environment

ERIC Educational Resources Information Center

Ozan, Ozlem

2013-01-01

Social networks and mobile technologies are transforming learning ecology. In this changing learning environment, we find a variety of new learner needs. The aim of this study is to investigate how to provide scaffolding to the learners in connectivist mobile learning environment: (1) to learn in a networked environment; (2) to manage their…

Personalizing E-Learning.

ERIC Educational Resources Information Center

Bollet, Robert M.; Fallon, Santiago

2002-01-01

Discussion of the concepts of learning and training focuses on how to incorporate the whole brain in the learning process when personalizing electronic learning. Suggests that trainers will need to learn teaching strategies that embrace the right brain's need for time and space to examine, contemplate, integrate, and utilize information.…

Next-Generation Machine Learning for Biological Networks.

PubMed

Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J

2018-06-14

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Copyright © 2018 Elsevier Inc. All rights reserved.

Social network changes and life events across the life span: a meta-analysis.

PubMed

Wrzus, Cornelia; Hänel, Martha; Wagner, Jenny; Neyer, Franz J

2013-01-01

For researchers and practitioners interested in social relationships, the question remains as to how large social networks typically are, and how their size and composition change across adulthood. On the basis of predictions of socioemotional selectivity theory and social convoy theory, we conducted a meta-analysis on age-related social network changes and the effects of life events on social networks using 277 studies with 177,635 participants from adolescence to old age. Cross-sectional as well as longitudinal studies consistently showed that (a) the global social network increased up until young adulthood and then decreased steadily, (b) both the personal network and the friendship network decreased throughout adulthood, (c) the family network was stable in size from adolescence to old age, and (d) other networks with coworkers or neighbors were important only in specific age ranges. Studies focusing on life events that occur at specific ages, such as transition to parenthood, job entry, or widowhood, demonstrated network changes similar to such age-related network changes. Moderator analyses detected that the type of network assessment affected the reported size of global, personal, and family networks. Period effects on network sizes occurred for personal and friendship networks, which have decreased in size over the last 35 years. Together the findings are consistent with the view that a portion of normative, age-related social network changes are due to normative, age-related life events. We discuss how these patterns of normative social network development inform research in social, evolutionary, cultural, and personality psychology. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

"Getting Practical" and the National Network of Science Learning Centres

ERIC Educational Resources Information Center

Chapman, Georgina; Langley, Mark; Skilling, Gus; Walker, John

2011-01-01

The national network of Science Learning Centres is a co-ordinating partner in the Getting Practical--Improving Practical Work in Science programme. The principle of training provision for the "Getting Practical" programme is a cascade model. Regional trainers employed by the national network of Science Learning Centres trained the cohort of local…

The Practices of Student Network as Cooperative Learning in Ethiopia

ERIC Educational Resources Information Center

Reda, Weldemariam Nigusse; Hagos, Girmay Tsegay

2015-01-01

Student network is a teaching strategy introduced as cooperative learning to all educational levels above the upper primary schools (grade 5 and above) in Ethiopia. The study was, therefore, aimed at investigating to what extent the student network in Ethiopia is actually practiced in line with the principles of cooperative learning. Consequently,…

Hypermedia-Assisted Instruction and Second Language Learning: A Semantic-Network-Based Approach.

ERIC Educational Resources Information Center

Liu, Min

This literature review examines a hypermedia learning environment from a semantic network basis and the application of such an environment to second language learning. (A semantic network is defined as a conceptual representation of knowledge in human memory). The discussion is organized under the following headings and subheadings: (1) Advantages…

The STIN in the Tale: A Socio-Technical Interaction Perspective on Networked Learning

ERIC Educational Resources Information Center

Walker, Steve; Creanor, Linda

2009-01-01

In this paper, we go beyond what have been described as "mechanistic" accounts of e-learning to explore the complexity of relationships between people and technology as encountered in cases of networked learning. We introduce from the social informatics literature the concept of sociotechnical interaction networks which focus on the…

Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

ERIC Educational Resources Information Center

Peng, Yefei

2010-01-01

An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

Implementation of a Framework for Collaborative Social Networks in E-Learning

ERIC Educational Resources Information Center

Maglajlic, Seid

2016-01-01

This paper describes the implementation of a framework for the construction and utilization of social networks in ELearning. These social networks aim to enhance collaboration between all E-Learning participants (i.e. both traineeto-trainee and trainee-to-tutor communication are targeted). E-Learning systems that include a so-called "social…

The Role of Action Research in the Development of Learning Networks for Entrepreneurs

ERIC Educational Resources Information Center

Brett, Valerie; Mullally, Martina; O'Gorman, Bill; Fuller-Love, Nerys

2012-01-01

Developing sustainable learning networks for entrepreneurs is the core objective of the Sustainable Learning Networks in Ireland and Wales (SLNIW) project. One research team drawn from the Centre for Enterprise Development and Regional Economy at Waterford Institute of Technology and the School of Management and Business from Aberystwyth…

Enhancing Teaching and Learning Wi-Fi Networking Using Limited Resources to Undergraduates

ERIC Educational Resources Information Center

Sarkar, Nurul I.

2013-01-01

Motivating students to learn Wi-Fi (wireless fidelity) wireless networking to undergraduate students is often difficult because many students find the subject rather technical and abstract when presented in traditional lecture format. This paper focuses on the teaching and learning aspects of Wi-Fi networking using limited hardware resources. It…

Categorical Structure among Shared Features in Networks of Early-Learned Nouns

ERIC Educational Resources Information Center

Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

2009-01-01

The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

Stable architectures for deep neural networks

NASA Astrophysics Data System (ADS)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

Toolkits and Libraries for Deep Learning.

PubMed

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth

2017-08-01

Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

Deep learning for computational chemistry.

PubMed

Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

2017-06-15

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Deep learning for computational chemistry

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

Goh, Garrett B.; Hodas, Nathan O.; Vishnu, Abhinav

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. Inmore » this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.« less

A Neural Network Model of the Structure and Dynamics of Human Personality

ERIC Educational Resources Information Center

Read, Stephen J.; Monroe, Brian M.; Brownstein, Aaron L.; Yang, Yu; Chopra, Gurveen; Miller, Lynn C.

2010-01-01

We present a neural network model that aims to bridge the historical gap between dynamic and structural approaches to personality. The model integrates work on the structure of the trait lexicon, the neurobiology of personality, temperament, goal-based models of personality, and an evolutionary analysis of motives. It is organized in terms of two…

The Structure of Informal Social Networks of Persons with Profound Intellectual and Multiple Disabilities

ERIC Educational Resources Information Center

Kamstra, A.; van der Putten, A. A. J.; Vlaskamp, C.

2015-01-01

Background: Persons with less severe disabilities are able to express their needs and show initiatives in social contacts, persons with profound intellectual and multiple disabilities (PIMD), however, depend on others for this. This study analysed the structure of informal networks of persons with PIMD. Materials and Methods: Data concerning the…

Identifying Gatekeepers in Online Learning Networks

ERIC Educational Resources Information Center

Gursakal, Necmi; Bozkurt, Aras

2017-01-01

The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System

NASA Astrophysics Data System (ADS)

Nawir, Mukrimah; Amir, Amiza; Lynn, Ong Bi; Yaakob, Naimah; Badlishah Ahmad, R.

2018-05-01

The rapid growth of technologies might endanger them to various network attacks due to the nature of data which are frequently exchange their data through Internet and large-scale data that need to be handle. Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. Several issues regarding these available labelled network datasets are discussed in this paper. The aim of this paper to build a network anomaly detection system using machine learning algorithms that are efficient, effective and fast processing. The finding showed that AODE algorithm is performed well in term of accuracy and processing time for binary classification towards UNSW-NB15 dataset.

The challenge of social networking in the field of environment and health.

PubMed

van den Hazel, Peter; Keune, Hans; Randall, Scott; Yang, Aileen; Ludlow, David; Bartonova, Alena

2012-06-28

The fields of environment and health are both interdisciplinary and trans-disciplinary, and until recently had little engagement in social networking designed to cross disciplinary boundaries. The EU FP6 project HENVINET aimed to establish integrated social network and networking facilities for multiple stakeholders in environment and health. The underlying assumption is that increased social networking across disciplines and sectors will enhance the quality of both problem knowledge and problem solving, by facilitating interactions. Inter- and trans-disciplinary networks are considered useful for this purpose. This does not mean that such networks are easily organized, as openness to such cooperation and exchange is often difficult to ascertain. Different methods may enhance network building. Using a mixed method approach, a diversity of actions were used in order to investigate the main research question: which kind of social networking activities and structures can best support the objective of enhanced inter- and trans-disciplinary cooperation and exchange in the fields of environment and health. HENVINET applied interviews, a role playing session, a personal response system, a stakeholder workshop and a social networking portal as part of the process of building an interdisciplinary and trans-disciplinary network. The interviews provided support for the specification of requirements for an interdisciplinary and trans-disciplinary network. The role playing session, the personal response system and the stakeholder workshop were assessed as useful tools in forming such network, by increasing the awareness by different disciplines of other's positions. The social networking portal was particularly useful in delivering knowledge, but the role of the scientist in social networking is not yet clear. The main challenge in the field of environment and health is not so much a lack of scientific problem knowledge, but rather the ability to effectively communicate, share and use available knowledge for policy making. Structured social network facilities can be useful by policy makers to engage with the research community. It is beneficial for scientists to be able to integrate the perspective of policy makers in the research agenda, and to assist in co-production of policy-relevant information. A diversity of methods need to be applied for network building: according to the fit-for-purpose-principle. It is useful to know which combination of methods and in which time frame produces the best results.Networking projects such as HENVINET are created not only for the benefit of the network itself, but also because the applying of the different methods is a learning tool for future network building. Finally, it is clear that the importance of specialized professionals in enabling effective communication between different groups should not be underestimated.

The challenge of social networking in the field of environment and health

PubMed Central

2012-01-01

Background The fields of environment and health are both interdisciplinary and trans-disciplinary, and until recently had little engagement in social networking designed to cross disciplinary boundaries. The EU FP6 project HENVINET aimed to establish integrated social network and networking facilities for multiple stakeholders in environment and health. The underlying assumption is that increased social networking across disciplines and sectors will enhance the quality of both problem knowledge and problem solving, by facilitating interactions. Inter- and trans-disciplinary networks are considered useful for this purpose. This does not mean that such networks are easily organized, as openness to such cooperation and exchange is often difficult to ascertain. Methods Different methods may enhance network building. Using a mixed method approach, a diversity of actions were used in order to investigate the main research question: which kind of social networking activities and structures can best support the objective of enhanced inter- and trans-disciplinary cooperation and exchange in the fields of environment and health. HENVINET applied interviews, a role playing session, a personal response system, a stakeholder workshop and a social networking portal as part of the process of building an interdisciplinary and trans-disciplinary network. Results The interviews provided support for the specification of requirements for an interdisciplinary and trans-disciplinary network. The role playing session, the personal response system and the stakeholder workshop were assessed as useful tools in forming such network, by increasing the awareness by different disciplines of other’s positions. The social networking portal was particularly useful in delivering knowledge, but the role of the scientist in social networking is not yet clear. Conclusions The main challenge in the field of environment and health is not so much a lack of scientific problem knowledge, but rather the ability to effectively communicate, share and use available knowledge for policy making. Structured social network facilities can be useful by policy makers to engage with the research community. It is beneficial for scientists to be able to integrate the perspective of policy makers in the research agenda, and to assist in co-production of policy-relevant information. A diversity of methods need to be applied for network building: according to the fit-for-purpose-principle. It is useful to know which combination of methods and in which time frame produces the best results. Networking projects such as HENVINET are created not only for the benefit of the network itself, but also because the applying of the different methods is a learning tool for future network building. Finally, it is clear that the importance of specialized professionals in enabling effective communication between different groups should not be underestimated. PMID:22759497

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

PubMed

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

PubMed Central

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. PMID:28377709

Generalization of Clustering Coefficients to Signed Correlation Networks

PubMed Central

Costantini, Giulio; Perugini, Marco

2014-01-01

The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However, some applications of network analysis disregard negative edges, such as computing clustering coefficients. In this contribution, we illustrate the importance of the distinction between positive and negative edges in networks based on correlation matrices. The clustering coefficient is generalized to signed correlation networks: three new indices are introduced that take edge signs into account, each derived from an existing and widely used formula. The performances of the new indices are illustrated and compared with the performances of the unsigned indices, both on a signed simulated network and on a signed network based on actual personality psychology data. The results show that the new indices are more resistant to sample variations in correlation networks and therefore have higher convergence compared with the unsigned indices both in simulated networks and with real data. PMID:24586367

Self-efficacy and social networks after treatment for alcohol or drug dependence and major depression: disentangling person and time-level effects.

PubMed

Worley, Matthew J; Trim, Ryan S; Tate, Susan R; Roesch, Scott C; Myers, Mark G; Brown, Sandra A

2014-12-01

Proximal personal and environmental factors typically predict outcomes of treatment for alcohol or drug dependence (AODD), but longitudinal treatment studies have rarely examined these factors in adults with co-occurring psychiatric disorders. In adults with AODD and major depression, the aims of this study were to: (a) disaggregate person-and time-level components of network substance use and self-efficacy, (b) examine their prospective effects on posttreatment alcohol/drug use, and (c) examine whether residential environment moderated relations between these proximal factors and substance use outcomes. Veterans (N = 201) enrolled in a trial of group psychotherapy for AODD and independent MDD completed assessments every 3 months during 1 year of posttreatment follow-up. Outcome variables were percent days drinking (PDD) and using drugs (PDDRG). Proximal variables included abstinence self-efficacy and social network drinking and drug use. Self-efficacy and network substance use at the person-level prospectively predicted PDD (ps < .05) and PDDRG (ps < .05). Within-person, time-level effects of social networks predicted future PDD (ps < .05) but not PDDRG. Controlled environments moderated person-level social network effects (ps < .05), such that greater time in controlled settings attenuated the association between a heavier drinking/using network and posttreatment drinking and drug use. Both individual differences and time-specific fluctuations in proximal targets of psychosocial interventions are related to posttreatment substance use in adults with co-occurring AODD and MDD. More structured environmental settings appear to alleviate risk associated with social network substance use, and may be especially advised for those who have greater difficulty altering social networks during outpatient treatment.

Blended Learning in Personalized Assistive Learning Environments

ERIC Educational Resources Information Center

Marinagi, Catherine; Skourlas, Christos

2013-01-01

In this paper, the special needs/requirements of disabled students and cost-benefits for applying blended learning in Personalized Educational Learning Environments (PELE) in Higher Education are studied. The authors describe how blended learning can form an attractive and helpful framework for assisting Deaf and Hard-of-Hearing (D-HH) students to…

Web-Based Specialist Support for Spinal Cord Injury Person's Care: Lessons Learned.

PubMed

Della Mea, Vincenzo; Marin, Dario; Rosin, Claudio; Zampa, Agostino

2012-01-01

Persons with disability from spinal cord injury (SCI) are subject to high risk of pathological events and need a regular followup even after discharge from the rehabilitation hospital. To help in followup, we developed a web portal for providing online specialist as well as GP support to SCI persons. After a feasibility study with 13 subjects, the portal has been introduced in the regional healthcare network in order to make it compliant with current legal regulations on data protection, including smartcard authentication. Although a number of training courses have been made to introduce SCI persons to portal use (up to 50 users), the number of accesses remained very low. Reasons for that have been investigated by means of a questionnaire submitted to the initial feasibility study subjects and included the still easier use of telephone versus our web-based smartcard-authenticated portal, in particular, because online communications are still perceived as an unusual way of interacting with the doctor. To summarize, the overall project has been appreciated by the users, but when it is time to ask for help to, the specialist, it is still much easier to make a phone call.

Imagine All the People: How the Brain Creates and Uses Personality Models to Predict Behavior

PubMed Central

Hassabis, Demis; Spreng, R. Nathan; Rusu, Andrei A.; Robbins, Clifford A.; Mar, Raymond A.; Schacter, Daniel L.

2014-01-01

The behaviors of other people are often central to envisioning the future. The ability to accurately predict the thoughts and actions of others is essential for successful social interactions, with far-reaching consequences. Despite its importance, little is known about how the brain represents people in order to predict behavior. In this functional magnetic resonance imaging study, participants learned the unique personality of 4 protagonists and imagined how each would behave in different scenarios. The protagonists' personalities were composed of 2 traits: Agreeableness and Extraversion. Which protagonist was being imagined was accurately inferred based solely on activity patterns in the medial prefrontal cortex using multivariate pattern classification, providing novel evidence that brain activity can reveal whom someone is thinking about. Lateral temporal and posterior cingulate cortex discriminated between different degrees of agreeableness and extraversion, respectively. Functional connectivity analysis confirmed that regions associated with trait-processing and individual identities were functionally coupled. Activity during the imagination task, and revealed by functional connectivity, was consistent with the default network. Our results suggest that distinct regions code for personality traits, and that the brain combines these traits to represent individuals. The brain then uses this “personality model” to predict the behavior of others in novel situations. PMID:23463340

The research of "blind" spot in the LVQ network

NASA Astrophysics Data System (ADS)

Guo, Zhanjie; Nan, Shupo; Wang, Xiaoli

2017-04-01

Nowadays competitive neural network has been widely used in the pattern recognition, classification and other aspects, and show the great advantages compared with the traditional clustering methods. But the competitive neural networks still has inadequate in many aspects, and it needs to be further improved. Based on the learning Vector Quantization Network proposed by Learning Kohonen [1], this paper resolve the issue of the large training error, when there are "blind" spots in a network through the introduction of threshold value learning rules and finally programs the realization with Matlab.

Evolution of individual versus social learning on social networks

PubMed Central

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-01-01

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of ‘cultural models’ exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. PMID:25631568

Evolution of individual versus social learning on social networks.

PubMed

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-03-06

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Personal networks: a tool for gaining insight into the transmission of knowledge about food and medicinal plants among Tyrolean (Austrian) migrants in Australia, Brazil and Peru

PubMed Central

2014-01-01

Background Investigations into knowledge about food and medicinal plants in a certain geographic area or within a specific group are an important element of ethnobotanical research. This knowledge is context specific and dynamic due to changing ecological, social and economic circumstances. Migration processes affect food habits and the knowledge and use of medicinal plants as a result of adaptations that have to be made to new surroundings and changing environments. This study analyses and compares the different dynamics in the transmission of knowledge about food and medicinal plants among Tyrolean migrants in Australia, Brazil and Peru. Methods A social network approach was used to collect data on personal networks of knowledge about food and medicinal plants among Tyroleans who have migrated to Australia, Brazil and Peru and their descendants. A statistical analysis of the personal network maps and a qualitative analysis of the narratives were combined to provide insight into the process of transmitting knowledge about food and medicinal plants. Results 56 personal networks were identified in all (food: 30; medicinal plants: 26) across all the field sites studied here. In both sets of networks, the main source of knowledge is individual people (food: 71%; medicinal plants: 68%). The other sources mentioned are print and audiovisual media, organisations and institutions. Personal networks of food knowledge are larger than personal networks of medicinal plant knowledge in all areas of investigation. Relatives play a major role as transmitters of knowledge in both domains. Conclusions Human sources, especially relatives, play an important role in knowledge transmission in both domains. Reference was made to other sources as well, such as books, television, the internet, schools and restaurants. By taking a personal network approach, this study reveals the mode of transmission of knowledge about food and medicinal plants within a migrational context. PMID:24398225

Personal networks: a tool for gaining insight into the transmission of knowledge about food and medicinal plants among Tyrolean (Austrian) migrants in Australia, Brazil and Peru.

PubMed

Haselmair, Ruth; Pirker, Heidemarie; Kuhn, Elisabeth; Vogl, Christian R

2014-01-07

Investigations into knowledge about food and medicinal plants in a certain geographic area or within a specific group are an important element of ethnobotanical research. This knowledge is context specific and dynamic due to changing ecological, social and economic circumstances. Migration processes affect food habits and the knowledge and use of medicinal plants as a result of adaptations that have to be made to new surroundings and changing environments. This study analyses and compares the different dynamics in the transmission of knowledge about food and medicinal plants among Tyrolean migrants in Australia, Brazil and Peru. A social network approach was used to collect data on personal networks of knowledge about food and medicinal plants among Tyroleans who have migrated to Australia, Brazil and Peru and their descendants. A statistical analysis of the personal network maps and a qualitative analysis of the narratives were combined to provide insight into the process of transmitting knowledge about food and medicinal plants. 56 personal networks were identified in all (food: 30; medicinal plants: 26) across all the field sites studied here. In both sets of networks, the main source of knowledge is individual people (food: 71%; medicinal plants: 68%). The other sources mentioned are print and audiovisual media, organisations and institutions. Personal networks of food knowledge are larger than personal networks of medicinal plant knowledge in all areas of investigation. Relatives play a major role as transmitters of knowledge in both domains. Human sources, especially relatives, play an important role in knowledge transmission in both domains. Reference was made to other sources as well, such as books, television, the internet, schools and restaurants. By taking a personal network approach, this study reveals the mode of transmission of knowledge about food and medicinal plants within a migrational context.