Lessons Learnt from and Sustainability of Adopting a Personal Learning Environment & Network (Ple&N)

ERIC Educational Resources Information Center

Tsui, Eric; Sabetzadeh, Farzad

2014-01-01

This paper describes the feedback from the configuration and deployment of a Personal Learning Environment & Network (PLE&N) tool to support peer-based social learning for university students and graduates. An extension of an earlier project in which a generic and PLE&N was deployed for all learners, the current PLE&N is a…

A Comparative Study on Cooperative Learning in Multimedia and Network Environment Used by English Majors between China Mainland and Taiwan

ERIC Educational Resources Information Center

Xue, Gong; Lingling, Liu

2018-01-01

This paper first based on the theory of cooperative learning research. It analyses the characteristics and advantages of cooperative learning under the multimedia network environment. And then take China Three Gorges University and Taiwan I-Shou University English major students for example, using questionnaires and interviews to investigate the…

Design Patterns for Learning and Assessment: Facilitating the Introduction of a Complex Simulation-Based Learning Environment into a Community of Instructors

NASA Astrophysics Data System (ADS)

Frezzo, Dennis C.; Behrens, John T.; Mislevy, Robert J.

2010-04-01

Simulation environments make it possible for science and engineering students to learn to interact with complex systems. Putting these capabilities to effective use for learning, and assessing learning, requires more than a simulation environment alone. It requires a conceptual framework for the knowledge, skills, and ways of thinking that are meant to be developed, in order to design activities that target these capabilities. The challenges of using simulation environments effectively are especially daunting in dispersed social systems. This article describes how these challenges were addressed in the context of the Cisco Networking Academies with a simulation tool for computer networks called Packet Tracer. The focus is on a conceptual support framework for instructors in over 9,000 institutions around the world for using Packet Tracer in instruction and assessment, by learning to create problem-solving scenarios that are at once tuned to the local needs of their students and consistent with the epistemic frame of "thinking like a network engineer." We describe a layered framework of tools and interfaces above the network simulator that supports the use of Packet Tracer in the distributed community of instructors and students.

Instruction of Computer Supported Collaborative Learning Environment and Students' Contribution Quality

ERIC Educational Resources Information Center

Akgün, Ergün; Akkoyunlu, Buket

2013-01-01

Along with the integration of network and communication innovations into education, those technology enriched learning environments gained importance both qualitatively and operationally. Using network and communication innovations in the education field, provides diffusion of information and global accessibility, and also allows physically…

Sustaining Teacher Control in a Blog-Based Personal Learning Environment

ERIC Educational Resources Information Center

Tomberg, Vladimir; Laanpere, Mart; Ley, Tobias; Normak, Peeter

2013-01-01

Various tools and services based on Web 2.0 (mainly blogs, wikis, social networking tools) are increasingly used in formal education to create personal learning environments, providing self-directed learners with more freedom, choice, and control over their learning. In such distributed and personalized learning environments, the traditional role…

Doctoral Students' Identity Positioning in Networked Learning Environments

ERIC Educational Resources Information Center

Koole, Marguerite; Stack, Sara

2016-01-01

In this study, the authors explored identity positioning as perceived by doctoral learners in online, networked-learning environments. The study examined two distance doctoral programs at a Canadian university. It was a qualitative study based on methodologies involving open coding and discourse analysis. The social positioning cycle, based on…

Virtual Learning Environments as Sociomaterial Agents in the Network of Teaching Practice

ERIC Educational Resources Information Center

Johannesen, Monica; Erstad, Ola; Habib, Laurence

2012-01-01

This article presents findings related to the sociomaterial agency of educators and their practice in Norwegian education. Using actor-network theory, we ask how Virtual Learning Environments (VLEs) negotiate the agency of educators and how they shape their teaching practice. Since the same kinds of VLE tools have been widely implemented…

Building a Virtual Learning Network for Teachers in a Suburban School District

ERIC Educational Resources Information Center

Kurtzworth-Keen, Kristin A.

2011-01-01

Emerging research indicates that learning management systems such as Moodle can function as virtual, collaborative environments, where collegial interactions promote professional learning opportunities. This study deployed a mixed methods design in order to describe and analyze teacher participation in a virtual learning network (VLN) that was…

ENERGY-NET (Energy, Environment and Society Learning Network): Enhancing opportunities for learning using an Earth systems science framework

NASA Astrophysics Data System (ADS)

Elliott, E. M.; Bain, D. J.; Divers, M. T.; Crowley, K. J.; Povis, K.; Scardina, A.; Steiner, M.

2012-12-01

We describe a newly funded collaborative NSF initiative, ENERGY-NET (Energy, Environment and Society Learning Network), that brings together the Carnegie Museum of Natural History (CMNH) with the Learning Science and Geoscience research strengths at the University of Pittsburgh. ENERGY-NET aims to create rich opportunities for participatory learning and public education in the arena of energy, the environment, and society using an Earth systems science framework. We build upon a long-established teen docent program at CMNH and to form Geoscience Squads comprised of underserved teens. Together, the ENERGY-NET team, including museum staff, experts in informal learning sciences, and geoscientists spanning career stage (undergraduates, graduate students, faculty) provides inquiry-based learning experiences guided by Earth systems science principles. Together, the team works with Geoscience Squads to design "Exploration Stations" for use with CMNH visitors that employ an Earth systems science framework to explore the intersecting lenses of energy, the environment, and society. The goals of ENERGY-NET are to: 1) Develop a rich set of experiential learning activities to enhance public knowledge about the complex dynamics between Energy, Environment, and Society for demonstration at CMNH; 2) Expand diversity in the geosciences workforce by mentoring underrepresented teens, providing authentic learning experiences in earth systems science and life skills, and providing networking opportunities with geoscientists; and 3) Institutionalize ENERGY-NET collaborations among geosciences expert, learning researchers, and museum staff to yield long-term improvements in public geoscience education and geoscience workforce recruiting.

The Comparison of Students' Satisfaction between Ubiquitous and Web-Based Learning Environments

ERIC Educational Resources Information Center

Virtanen, Mari Aulikki; Kääriäinen, Maria; Liikanen, Eeva; Haavisto, Elina

2017-01-01

Higher education is moving towards digitalized learning. The rapid development of technological resources, devices and wireless networks enables more flexible opportunities to study and learn in innovative learning environments. New technologies enable combining of authentic and virtual learning spaces and digital resources as multifunctional…

eLearning--Theories, Design, Software and Applications

ERIC Educational Resources Information Center

Ghislandi, Patrizia, Ed.

2012-01-01

Chapters in this book include: (1) New e-Learning Environments: e-Merging Networks in the Relational Society (Blanca C. Garcia); (2) Knowledge Building in E-Learning (Xinyu Zhang and Lu Yuhao); (3) E-Learning and Desired Learning Outcomes (Ralph Palliam); (4) Innovative E-Learning Solutions and Environments for Small and Medium Sized Companies…

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…

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)

The Collaboratory Notebook: A Networked Knowledge-Building Environment for Project Learning.

ERIC Educational Resources Information Center

O'Neill, D. Kevin; Gomez, Louis M.

The Collaboratory Notebook, developed as part of the Learning Through Collaborative Visualization Project (CoVis), is a networked, multimedia knowledge-building environment which has been designed to help students, teachers and scientists share inquiry over the boundaries of time and space. CoVis is an attempt to change the way that science is…

Elementary Students' Affective Variables in a Networked Learning Environment Supported by a Blog: A Case Study

ERIC Educational Resources Information Center

Allaire, Stéphane; Thériault, Pascale; Gagnon, Vincent; Lalancette, Evelyne

2013-01-01

This study documents to what extent writing on a blog in a networked learning environment could influence the affective variables of elementary-school students' writing. The framework is grounded more specifically in theory of self-determination (Deci & Ryan, 1985), relationship to writing (Chartrand & Prince, 2009) and the transactional…

Cross-Cultural Collisions in Cyberspace: Case Studies of International Legal Issues for Educators Working in Globally Networked Learning Environments

ERIC Educational Resources Information Center

Rife, Martine Courant

2010-01-01

This article explores some of the legal and law-related challenges educators face in designing, implementing, and sustaining globally networked learning environments (GNLEs) in the context of conflicting international laws on intellectual property and censorship/free speech. By discussing cases and areas involving such legal issues, the article…

Female Arab Students' Perceptions of Social Networks as an English Language Learning Environment

ERIC Educational Resources Information Center

Ellili-Cherif, Maha

2017-01-01

The purpose of this research was to investigate female Arab college students' use of and perceptions about social networking sites (SNSs) as an English language learning environment. A mixed methods approach was adopted for data collection and analysis. First, a questionnaire was used to explore the extent to which participants (n = 182) were…

Identifying Students' Difficulties When Learning Technical Skills via a Wireless Sensor Network

ERIC Educational Resources Information Center

Wang, Jingying; Wen, Ming-Lee; Jou, Min

2016-01-01

Practical training and actual application of acquired knowledge and techniques are crucial for the learning of technical skills. We established a wireless sensor network system (WSNS) based on the 5E learning cycle in a practical learning environment to improve students' reflective abilities and to reduce difficulties for the learning of technical…

Digital Identity Formation: Socially Being Real and Present on Digital Networks

ERIC Educational Resources Information Center

Bozkurt, Aras; Tu, Chih-Hsiung

2016-01-01

Social networks have become popular communication and interaction environments recently. As digital environments, so as ecosystems, they have potential in terms of networked learning as they fulfill some roles such as mediating an environment for digital identity formation and providing social and emotional presence. Based on this phenomenon, the…

Social Network Analysis in E-Learning Environments: A Preliminary Systematic Review

ERIC Educational Resources Information Center

Cela, Karina L.; Sicilia, Miguel Ángel; Sánchez, Salvador

2015-01-01

E-learning occupies an increasingly prominent place in education. It provides the learner with a rich virtual network where he or she can exchange ideas and information and create synergies through interactions with other members of the network, whether fellow learners or teachers. Social network analysis (SNA) has proven extremely powerful at…

Designing a Secure Exam Management System (SEMS) for M-Learning Environments

ERIC Educational Resources Information Center

Kaiiali, Mustafa; Ozkaya, Armagan; Altun, Halis; Haddad, Hatem; Alier, Marc

2016-01-01

M-learning has enhanced the e-learning by making the learning process learner-centered. However, enforcing exam security in open environments where each student has his/her own mobile/tablet device connected to a Wi-Fi network through which it is further connected to the Internet can be one of the most challenging tasks. In such environments,…

Mining Learning Social Networks for Cooperative Learning with Appropriate Learning Partners in a Problem-Based Learning Environment

ERIC Educational Resources Information Center

Chen, Chih-Ming; Chang, Chia-Cheng

2014-01-01

Many studies have identified web-based cooperative learning as an increasingly popular educational paradigm with potential to increase learner satisfaction and interactions. However, peer-to-peer interaction often suffers barriers owing to a failure to explore useful social interaction information in web-based cooperative learning environments.…

Networked Learning: Design Considerations for Online Instructors

ERIC Educational Resources Information Center

Czerkawski, Betul C.

2016-01-01

The considerable increase in web-based knowledge networks in the past two decades is strongly influencing learning environments. Learning entails information retrieval, use, communication, and production, and is strongly enriched by socially mediated discussions, debates, and collaborative activities. It is becoming critical for educators to…

Learning in neural networks based on a generalized fluctuation theorem

NASA Astrophysics Data System (ADS)

Hayakawa, Takashi; Aoyagi, Toshio

2015-11-01

Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

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.

Mobile robots exploration through cnn-based reinforcement learning.

PubMed

Tai, Lei; Liu, Ming

2016-01-01

Exploration in an unknown environment is an elemental application for mobile robots. In this paper, we outlined a reinforcement learning method aiming for solving the exploration problem in a corridor environment. The learning model took the depth image from an RGB-D sensor as the only input. The feature representation of the depth image was extracted through a pre-trained convolutional-neural-networks model. Based on the recent success of deep Q-network on artificial intelligence, the robot controller achieved the exploration and obstacle avoidance abilities in several different simulated environments. It is the first time that the reinforcement learning is used to build an exploration strategy for mobile robots through raw sensor information.

Learning by Doing Approach in the Internet Environment to Improve the Teaching Efficiency of Information Technology

NASA Astrophysics Data System (ADS)

Zhang, X.-S.; Xie, Hua

This paper presents a learning-by-doing method in the Internet environment to enhance the results of information technology education by experimental work in the classroom of colleges. In this research, an practical approach to apply the "learning by doing" paradigm in Internet-based learning, both for higher educational environments and life-long training systems, taking into account available computer and network resources, such as blogging, podcasting, social networks, wiki etc. We first introduce the different phases in the learning process, which aimed at showing to the readers that the importance of the learning by doing paradigm, which is not implemented in many Internet-based educational environments. Secondly, we give the concept of learning by doing in the different perfective. Then, we identify the most important trends in this field, and give a real practical case for the application of this approach. The results show that the attempt methods are much better than traditional teaching methods.

Functional brain networks for learning predictive statistics.

PubMed

Giorgio, Joseph; Karlaftis, Vasilis M; Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew; Kourtzi, Zoe

2017-08-18

Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the functional brain networks that mediate this type of statistical learning. Here, we test whether changes in the processing and connectivity of functional brain networks due to training relate to our ability to learn temporal regularities. By combining behavioral training and functional brain connectivity analysis, we demonstrate that individuals adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. Further, we show that individual learning of temporal structures relates to decision strategy. Our fMRI results demonstrate that learning-dependent changes in fMRI activation within and functional connectivity between brain networks relate to individual variability in strategy. In particular, extracting the exact sequence statistics (i.e., matching) relates to changes in brain networks known to be involved in memory and stimulus-response associations, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to changes in frontal and striatal networks. Thus, our findings provide evidence that dissociable brain networks mediate individual ability in learning behaviorally-relevant statistics. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Collaborative Model for Ubiquitous Learning Environments

ERIC Educational Resources Information Center

Barbosa, Jorge; Barbosa, Debora; Rabello, Solon

2016-01-01

Use of mobile devices and widespread adoption of wireless networks have enabled the emergence of Ubiquitous Computing. Application of this technology to improving education strategies gave rise to Ubiquitous e-Learning, also known as Ubiquitous Learning. There are several approaches to organizing ubiquitous learning environments, but most of them…

Perspectives on Advanced Learning Technologies and Learning Networks and Future Aerospace Workforce Environments

NASA Technical Reports Server (NTRS)

Noor, Ahmed K. (Compiler)

2003-01-01

An overview of the advanced learning technologies is given in this presentation along with a brief description of their impact on future aerospace workforce development. The presentation is divided into five parts (see Figure 1). In the first part, a brief historical account of the evolution of learning technologies is given. The second part describes the current learning activities. The third part describes some of the future aerospace systems, as examples of high-tech engineering systems, and lists their enabling technologies. The fourth part focuses on future aerospace research, learning and design environments. The fifth part lists the objectives of the workshop and some of the sources of information on learning technologies and learning networks.

Synchronized Pair Configuration in Virtualization-Based Lab for Learning Computer Networks

ERIC Educational Resources Information Center

Kongcharoen, Chaknarin; Hwang, Wu-Yuin; Ghinea, Gheorghita

2017-01-01

More studies are concentrating on using virtualization-based labs to facilitate computer or network learning concepts. Some benefits are lower hardware costs and greater flexibility in reconfiguring computer and network environments. However, few studies have investigated effective mechanisms for using virtualization fully for collaboration.…

Neural network applications in telecommunications

NASA Technical Reports Server (NTRS)

Alspector, Joshua

1994-01-01

Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

Lessons Learned and Lessons To Be Learned: An Overview of Innovative Network Learning Environments.

ERIC Educational Resources Information Center

Jacobson, Michael J.; Jacobson, Phoebe Chen

This paper provides an overview of five innovative projects involving network learning technologies in the United States: (1) the MicroObservatory Internet Telescope is a collection of small, high-quality, and low-maintenance telescopes operated by the Harvard-Smithsonian Center for Astrophysics (Massachusetts), which may be used remotely via the…

Discovery of Action Patterns and User Correlations in Task-Oriented Processes for Goal-Driven Learning Recommendation

ERIC Educational Resources Information Center

Zhou, Xiaokang; Chen, Jian; Wu, Bo; Jin, Qun

2014-01-01

With the high development of social networks, collaborations in a socialized web-based learning environment has become increasing important, which means people can learn through interactions and collaborations in communities across social networks. In this study, in order to support the enhanced collaborative learning, two important factors, user…

Bridging the Gap between Students and Computers: Supporting Activity Awareness for Network Collaborative Learning with GSM Network

ERIC Educational Resources Information Center

Liu, C.-C.; Tao, S.-Y.; Nee, J.-N.

2008-01-01

The internet has been widely used to promote collaborative learning among students. However, students do not always have access to the system, leading to doubt in the interaction among the students, and reducing the effectiveness of collaborative learning, since the web-based collaborative learning environment relies entirely on the availability…

Social Networks and Performance in Distributed Learning Communities

ERIC Educational Resources Information Center

Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

2012-01-01

Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

Connectionist Learning Procedures.

ERIC Educational Resources Information Center

Hinton, Geoffrey E.

A major goal of research on networks of neuron-like processing units is to discover efficient learning procedures that allow these networks to construct complex internal representations of their environment. The learning procedures must be capable of modifying the connection strengths in such a way that internal units which are not part of the…

Late Departures from Paper-Based to Supported Networked Learning in South Africa: Lessons Learned

ERIC Educational Resources Information Center

Kok, Illasha; Beter, Petra; Esterhuizen, Hennie

2018-01-01

Fragmented connectivity in South Africa is the dominant barrier for digitising initiatives. New insights surfaced when a university-based nursing programme introduced tablets within a supportive network learning environment. A qualitative, explorative design investigated adult nurses' experiences of the realities when moving from paper-based…

An Auto-Scoring Mechanism for Evaluating Problem-Solving Ability in a Web-Based Learning Environment

ERIC Educational Resources Information Center

Chiou, Chuang-Kai; Hwang, Gwo-Jen; Tseng, Judy C. R.

2009-01-01

The rapid development of computer and network technologies has attracted researchers to investigate strategies for and the effects of applying information technologies in learning activities; simultaneously, learning environments have been developed to record the learning portfolios of students seeking web information for problem-solving. Although…

The Relationship between the Learning Strategies and Learning Styles in a Hypermedia Environment.

ERIC Educational Resources Information Center

Liu, Min; Reed, W. Michael

Different learning strategies that are used by field-independent (FI) and field-dependent (FD) people in a hypermedia-assisted instructional setting were studied with 63 international college students for whom English was a second language. The treatment was a semantic network-based hypermedia-assisted language-learning environment to help…

Impacts and Characteristics of Computer-Based Science Inquiry Learning Environments for Precollege Students

ERIC Educational Resources Information Center

Donnelly, Dermot F.; Linn, Marcia C.; Ludvigsen, Sten

2014-01-01

The National Science Foundation-sponsored report "Fostering Learning in the Networked World" called for "a common, open platform to support communities of developers and learners in ways that enable both to take advantage of advances in the learning sciences." We review research on science inquiry learning environments (ILEs)…

Galileo Educational Network: Creating, Researching, and Supporting 21st Century Learning

ERIC Educational Resources Information Center

Friesen, Sharon

2009-01-01

School and classroom structures designed to meet the needs of the industrial past cannot "maintain the temperature required for sustaining life." Recent learning sciences research findings compel educators to invent new learning environments better suited to meet the demands of the 21st century. These new learning environments require…

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…

The Effect of Virtual versus Traditional Learning in Achieving Competency-Based Skills

ERIC Educational Resources Information Center

Mosalanejad, Leili; Shahsavari, Sakine; Sobhanian, Saeed; Dastpak, Mehdi

2012-01-01

Background: By rapid developing of the network technology, the internet-based learning methods are substituting the traditional classrooms making them expand to the virtual network learning environment. The purpose of this study was to determine the effectiveness of virtual systems on competency-based skills of first-year nursing students.…

Student Interaction and Community Building: An Evaluation of Social Networking in Online Learning Environments

ERIC Educational Resources Information Center

Cardona-Divale, Maria Victoria

2012-01-01

Learners often report difficulty maintaining social connectivity in online courses. Technology is quickly changing how people communicate, collaborate and learn using online social networking sites (SNSs). These sites have transformed education in a way that provides new learning opportunities when integrated with web 2.0 tools. Little research is…

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning.

PubMed

Brincat, Scott L; Miller, Earl K

2016-09-14

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with "internal" memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)-regions critical for sensory associations-of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11-27 Hz) oscillatory power and synchrony associated with "top-down" or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired "top-down" knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. Copyright © 2016 the authors 0270-6474/16/369739-16$15.00/0.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning

PubMed Central

Brincat, Scott L.

2016-01-01

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. SIGNIFICANCE STATEMENT As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired “top-down” knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. PMID:27629722

The GenTechnique Project: Developing an Open Environment for Learning Molecular Genetics.

ERIC Educational Resources Information Center

Calza, R. E.; Meade, J. T.

1998-01-01

The GenTechnique project at Washington State University uses a networked learning environment for molecular genetics learning. The project is developing courseware featuring animation, hyper-link controls, and interactive self-assessment exercises focusing on fundamental concepts. The first pilot course featured a Web-based module on DNA…

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…

Interaction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments

ERIC Educational Resources Information Center

Eagle, Michael; Hicks, Drew; Barnes, Tiffany

2015-01-01

Intelligent tutoring systems and computer aided learning environments aimed at developing problem solving produce large amounts of transactional data which make it a challenge for both researchers and educators to understand how students work within the environment. Researchers have modeled student-tutor interactions using complex networks in…

Physical and Psychosocial Environments Associated with Networked Classrooms

ERIC Educational Resources Information Center

Zandvliet, David B.; Fraser, Barry J.

2005-01-01

This article reports a study of the learning environments in computer networked classrooms. The study is unique in that it involved an evaluation of both the physical and psychosocial classroom environments in these computerised settings through the use of a combination of questionnaires and ergonomic evaluations. The study involved administering…

KDU E-Community Network.

ERIC Educational Resources Information Center

Jonhendro; Ching, Goh Bee; Wahab, Rohazna; Leng, Wang Meei; Aun, Jimmy Tan Lip; Yeoh, Eugene; Hock, Oon; Koo, W. K.

2001-01-01

Describes an education initiative developed by a company in Malaysia, the KDU, to implement a student-centered, teacher-facilitated, educational technology-enabled and knowledge-based learning environment. Explains the KDU e-Community Network that enables passive, interactive, collaborative, and constructivist learning for a variety of…

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.

Merging Social Networking Environments and Formal Learning Environments to Support and Facilitate Interprofessional Instruction

PubMed Central

King, Sharla; Greidanus, Elaine; Carbonaro, Michael; Drummond, Jane; Patterson, Steven

2009-01-01

This study describes the redesign of an interprofessional team development course for health science students. A theoretical model is hypothesized as a framework for the redesign process, consisting of two themes: 1) the increasing trend among post-secondary students to participate in social networking (e.g., Facebook, Second Life) and 2) the need for healthcare educators to provide interprofessional training that results in effective communities of practice and better patient care. The redesign focused on increasing the relevance of the course through the integration of custom-designed technology to facilitate social networking during their interprofessional education. Results suggest that students in an educationally structured social networking environment can be guided to join learning communities quickly and access course materials. More research and implementation work is required to effectively develop interprofessional health sciences communities in a combined face-to-face and on-line social networking context. PMID:20165519

Merging social networking environments and formal learning environments to support and facilitate interprofessional instruction.

PubMed

King, Sharla; Greidanus, Elaine; Carbonaro, Michael; Drummond, Jane; Patterson, Steven

2009-04-28

This study describes the redesign of an interprofessional team development course for health science students. A theoretical model is hypothesized as a framework for the redesign process, consisting of two themes: 1) the increasing trend among post-secondary students to participate in social networking (e.g., Facebook, Second Life) and 2) the need for healthcare educators to provide interprofessional training that results in effective communities of practice and better patient care. The redesign focused on increasing the relevance of the course through the integration of custom-designed technology to facilitate social networking during their interprofessional education. Results suggest that students in an educationally structured social networking environment can be guided to join learning communities quickly and access course materials. More research and implementation work is required to effectively develop interprofessional health sciences communities in a combined face-to-face and on-line social networking context.

Learning Boolean Networks in HepG2 cells using ToxCast High-Content Imaging Data (SOT annual meeting)

EPA Science Inventory

Cells adapt to their environment via homeostatic processes that are regulated by complex molecular networks. Our objective was to learn key elements of these networks in HepG2 cells using ToxCast High-content imaging (HCI) measurements taken over three time points (1, 24, and 72h...

Students' Attitudes towards Edmodo, a Social Learning Network: A Scale Development Study

ERIC Educational Resources Information Center

Yunkul, Eyup; Cankaya, Serkan

2017-01-01

Social Learning Networks (SLNs) are the developed forms of Social Network Sites (SNSs) adapted to educational environments, and they are used by quite a large population throughout the world. In addition, in related literature, there is no scale for the measurement of students' attitudes towards such sites. The purpose of this study was to develop…

Social Media as Avenue for Personal Learning for Educators: Personal Learning Networks Encourage Application of Knowledge and Skills

ERIC Educational Resources Information Center

Eller, Linda S.

2012-01-01

Social media sites furnish an online space for a community of practice to create relationships and trust, collaboration and connections, and a personal learning environment. Social networking sites, both public and private, have common elements: member profiles, groups, discussions, and forums. A community of practice brings participants together…

Transformational Leadership & Professional Development for Digitally Rich Learning Environments: A Case Study of the Galileo Educational Network.

ERIC Educational Resources Information Center

Jacobsen, Michele; Clifford, Pat; Friesen, Sharon

The Galileo Educational Network is an innovative educational reform initiative that brings learning to learners. Expert teachers work alongside teachers and students in schools to create new images of engaged learning, technology integration and professional development. This case study is based on the nine schools involved with Galileo in…

Supporting More Inclusive Learning with Social Networking: A Case Study of Blended Socialised Design Education

ERIC Educational Resources Information Center

Rodrigo, Russell; Nguyen, Tam

2013-01-01

This paper presents a qualitative case study of socialised blended learning, using a social network platform to investigate the level of literacies and interactions of students in a blended learning environment of traditional face-to-face design studio and online participatory teaching. Using student and staff feedback, the paper examines the use…

Towards representation of a perceptual color manifold using associative memory for color constancy.

PubMed

Seow, Ming-Jung; Asari, Vijayan K

2009-01-01

In this paper, we propose the concept of a manifold of color perception through empirical observation that the center-surround properties of images in a perceptually similar environment define a manifold in the high dimensional space. Such a manifold representation can be learned using a novel recurrent neural network based learning algorithm. Unlike the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete locations in the state space, the dynamics of the proposed learning algorithm represent memory as a nonlinear line of attraction. The region of convergence around the nonlinear line is defined by the statistical characteristics of the training data. This learned manifold can then be used as a basis for color correction of the images having different color perception to the learned color perception. Experimental results show that the proposed recurrent neural network learning algorithm is capable of color balance the lighting variations in images captured in different environments successfully.

Pupils' Views on an ICT-Based Learning Environment in Health Learning

ERIC Educational Resources Information Center

Räihä, Teija; Tossavainen, Kerttu; Enkenberg, Jorma; Turunen, Hannele

2014-01-01

This paper presents a study that examined pupils' views on an ICT-based learning environment in health learning. The study was a part of the wider European Network of Health Promoting Schools programme (ENHPS; since 2008, Schools for Health in Europe, SHE) in Finland, and particularly its sub-project, From Puijo to the World with Health Lunch,…

"Seamlessly" Learning Chinese: Contextual Meaning Making and Vocabulary Growth in a Seamless Chinese as a Second Language Learning Environment

ERIC Educational Resources Information Center

Wong, Lung-Hsiang; King, Ronnel B.; Chai, Ching Sing; Liu, May

2016-01-01

Second language learners are typically hampered by the lack of a natural environment to use the target language for authentic communication purpose (as a means for "learning by applying"). Thus, we propose MyCLOUD, a mobile-assisted seamless language learning approach that aims to nurture a second language social network that bridges…

Social Web Content Enhancement in a Distance Learning Environment: Intelligent Metadata Generation for Resources

ERIC Educational Resources Information Center

García-Floriano, Andrés; Ferreira-Santiago, Angel; Yáñez-Márquez, Cornelio; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Villuendas-Rey, Yenny

2017-01-01

Social networking potentially offers improved distance learning environments by enabling the exchange of resources between learners. The existence of properly classified content results in an enhanced distance learning experience in which appropriate materials can be retrieved efficiently; however, for this to happen, metadata needs to be present.…

Modeling and Intervening across Time in Scientific Inquiry Exploratory Learning Environment

ERIC Educational Resources Information Center

Ting, Choo-Yee; Phon-Amnuaisuk, Somnuk; Chong, Yen-Kuan

2008-01-01

This article aims at discussing how Dynamic Decision Network (DDN) can be employed to tackle the challenges in modeling temporally variable scientific inquiry skills and provision of adaptive pedagogical interventions in INQPRO, a scientific inquiry exploratory learning environment for learning O'level Physics. We begin with an overview of INQPRO…

The Teaching Voice on the Learning Platform: Seeking Classroom Climates within a Virtual Learning Environment

ERIC Educational Resources Information Center

Crook, Charles; Cluley, Robert

2009-01-01

University staff are now encouraged to supplement their classroom activity with computer-based tools and resources accessible through virtual learning environments (VLEs). Meanwhile, university students increasingly make recreational use of computer networks in the form of various social software applications. This paper explores tensions of…

The Impact of Social Media on Informal Learning in Museums

ERIC Educational Resources Information Center

Russo, Angelina; Watkins, Jerry; Groundwater-Smith, Susan

2009-01-01

This paper posits that social networking can take a central role in learning in informal environments such as museums, libraries and galleries. It argues that social media offers young people agency previously unavailable in informal learning environments in order to explore complex responses to and participation with cultural content. The paper…

A spiking neural network model of model-free reinforcement learning with high-dimensional sensory input and perceptual ambiguity.

PubMed

Nakano, Takashi; Otsuka, Makoto; Yoshimoto, Junichiro; Doya, Kenji

2015-01-01

A theoretical framework of reinforcement learning plays an important role in understanding action selection in animals. Spiking neural networks provide a theoretically grounded means to test computational hypotheses on neurally plausible algorithms of reinforcement learning through numerical simulation. However, most of these models cannot handle observations which are noisy, or occurred in the past, even though these are inevitable and constraining features of learning in real environments. This class of problem is formally known as partially observable reinforcement learning (PORL) problems. It provides a generalization of reinforcement learning to partially observable domains. In addition, observations in the real world tend to be rich and high-dimensional. In this work, we use a spiking neural network model to approximate the free energy of a restricted Boltzmann machine and apply it to the solution of PORL problems with high-dimensional observations. Our spiking network model solves maze tasks with perceptually ambiguous high-dimensional observations without knowledge of the true environment. An extended model with working memory also solves history-dependent tasks. The way spiking neural networks handle PORL problems may provide a glimpse into the underlying laws of neural information processing which can only be discovered through such a top-down approach.

A Spiking Neural Network Model of Model-Free Reinforcement Learning with High-Dimensional Sensory Input and Perceptual Ambiguity

PubMed Central

Nakano, Takashi; Otsuka, Makoto; Yoshimoto, Junichiro; Doya, Kenji

2015-01-01

A theoretical framework of reinforcement learning plays an important role in understanding action selection in animals. Spiking neural networks provide a theoretically grounded means to test computational hypotheses on neurally plausible algorithms of reinforcement learning through numerical simulation. However, most of these models cannot handle observations which are noisy, or occurred in the past, even though these are inevitable and constraining features of learning in real environments. This class of problem is formally known as partially observable reinforcement learning (PORL) problems. It provides a generalization of reinforcement learning to partially observable domains. In addition, observations in the real world tend to be rich and high-dimensional. In this work, we use a spiking neural network model to approximate the free energy of a restricted Boltzmann machine and apply it to the solution of PORL problems with high-dimensional observations. Our spiking network model solves maze tasks with perceptually ambiguous high-dimensional observations without knowledge of the true environment. An extended model with working memory also solves history-dependent tasks. The way spiking neural networks handle PORL problems may provide a glimpse into the underlying laws of neural information processing which can only be discovered through such a top-down approach. PMID:25734662

Distributed Learning. CAUSE Professional Paper Series, No. 14.

ERIC Educational Resources Information Center

Oblinger, Diana G.; Maruyama, Mark K.

This paper synthesizes current thought about the role of networking technologies in instruction and addresses the need for higher education to create affordable and flexible student-centered "distributed learning environments" employing networking technologies. First, relevant trends are identified in the areas of information volume, technology…

Computer Networks as Instructional and Collaborative Distance Learning Environments.

ERIC Educational Resources Information Center

Schrum, Lynne; Lamb, Theodore A.

1997-01-01

Reports on the early stages of a project at the U.S. Air Force Academy, in which the instructional applications of a networked classroom laboratory, an intranet, and the Internet are explored as well as the effectiveness and efficiency of groupware and computer networks as instructional environments. Presents the results of the first pilot tests.…

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…

Globally Networked Union Education and Labour Studies: The Past, Present and Future

ERIC Educational Resources Information Center

Taylor, Jeffery

2010-01-01

The literature on globally networked learning environments (GNLEs) has predominantly focused on research or classroom partnerships in higher education that usually involve traditional students enrolled in traditional degree programmes. However, the driving motivation behind GNLEs--learning in partnership across institutional and national…

Teaching an Interdisciplinary Graduate-Level Methods Course in an Openly-Networked Connected Learning Environment: A Glass Half-Full

ERIC Educational Resources Information Center

Secret, Mary; Bryant, Nita L.; Cummings, Cory R.

2017-01-01

Our paper describes the design and delivery of an online interdisciplinary social science research methods course (ISRM) for graduate students in sociology, education, social work, and public administration. Collaborative activities and learning took place in two types of computer-mediated learning environments: a closed Blackboard course…

Designing for Learning: Online Social Networks as a Classroom Environment

ERIC Educational Resources Information Center

Casey, Gail; Evans, Terry

2011-01-01

This paper deploys notions of emergence, connections, and designs for learning to conceptualize high school students' interactions when using online social media as a learning environment. It makes links to chaos and complexity theories and to fractal patterns as it reports on a part of the first author's action research study, conducted while she…

Facebook: An Online Environment for Learning of English in Institutions of Higher Education?

ERIC Educational Resources Information Center

Kabilan, Muhammad Kamarul; Ahmad, Norlida; Abidin, Mohamad Jafre Zainol

2010-01-01

Facebook (FB) is currently considered as the most popular platform for online social networking among university students. The purpose of this study is to investigate if university students consider FB as a useful and meaningful learning environment that could support, enhance and/or strengthen their learning of the English language. A survey was…

Cross Space: The Exploration of SNS-Based Writing Activities in a Multimodal Learning Environment

ERIC Educational Resources Information Center

Lee, Kwang-Soon; Kim, Bong-Gyu

2016-01-01

This study explores the positive learning effect of formulating English sentences via Social Network Service (SNS; "Kakao-Talk") on less proficient L2 university students' (LPSs') writing, when the application is utilized as a tool to link in and out-of class activities in a multimodal-learning environment. Its objective is also to…

Understanding the Context of Learning in an Online Social Network for Health Professionals' Informal Learning.

PubMed

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

2017-01-01

Online social networks (OSN) enable health professionals to learn informally, for example by sharing medical knowledge, or discussing practice management challenges and clinical issues. Understanding the learning context in OSN is necessary to get a complete picture of the learning process, in order to better support this type of learning. This study proposes critical contextual factors for understanding the learning context in OSN for health professionals, and demonstrates how these contextual factors can be used to analyse the learning context in a designated online learning environment for health professionals.

Networking: You Can't Do It Alone, But....

ERIC Educational Resources Information Center

Fley, JoAnn

1986-01-01

Discusses how networking provides an effective means of learning about one's environment and of gaining help and support in improving it. The process of networking is examined through case studies. The difference between networking and organizing is also discussed. (CT)

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.

Intelligent Sensing in Dynamic Environments Using Markov Decision Process

PubMed Central

Nanayakkara, Thrishantha; Halgamuge, Malka N.; Sridhar, Prasanna; Madni, Asad M.

2011-01-01

In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a sensor node to sense and transmit environmental events. A common method that is generally adopted in ad-hoc sensor networks is to periodically put the sensor nodes to sleep. The purpose of the learning algorithm is to couple the sensor’s sleeping behavior to the natural statistics of the environment hence that it can be in optimal harmony with changes in the environment, the sensors can sleep when steady environment and stay awake when turbulent environment. This paper presents theoretical and experimental validation of a reward based learning algorithm that can be implemented on an embedded sensor. The key contribution of the proposed approach is the design and implementation of a reward function that satisfies a trade-off between the above two mutually contradicting objectives, and a linear critic function to approximate the discounted sum of future rewards in order to perform policy learning. PMID:22346624

Schools as Environments for Social Learning--Shaping Mechanisms? Comparisons of Smaller and Larger Rural Schools in Norway

ERIC Educational Resources Information Center

Kvalsund, Rune

2004-01-01

This article analyses and compares the learning environment in smaller and bigger rural schools by focusing on the arenas of both formal and informal learning; the lessons and the recesses between. Relational patterns are both analysed using complete network data from 19 schools in four different municipalities in four Norwegian counties and by…

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)…

Program Helps Simulate Neural Networks

NASA Technical Reports Server (NTRS)

Villarreal, James; Mcintire, Gary

1993-01-01

Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

Large-Scale Networked Virtual Environments: Architecture and Applications

ERIC Educational Resources Information Center

Lamotte, Wim; Quax, Peter; Flerackers, Eddy

2008-01-01

Purpose: Scalability is an important research topic in the context of networked virtual environments (NVEs). This paper aims to describe the ALVIC (Architecture for Large-scale Virtual Interactive Communities) approach to NVE scalability. Design/methodology/approach: The setup and results from two case studies are shown: a 3-D learning environment…

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…

Use of Communication Resources in a Networked Collaborative Design Environment.

ERIC Educational Resources Information Center

Gay, Geri; Lentini, Marc

1995-01-01

Examines student use of a prototype networked collaborative design environment to support or augment learning about engineering design. Finds that students use the channels for a variety of activities to increase depth of communication, increase breadth of communication, and overcome technical difficulty. Suggests that students need multiple…

Communication Resource Use in a Networked Collaborative Design Environment.

ERIC Educational Resources Information Center

Gay, Geri; Lentini, Marc

The purpose of this exploratory study was to examine student use of a prototype networked collaborative design environment to support or augment learning about engineering design. The theoretical framework is based primarily on Vygotsky's social construction of knowledge and the belief that collaboration and communication are critical components…

Networked Teacher Professional Development: The Case of Globaloria

ERIC Educational Resources Information Center

Whitehouse, Pamela

2011-01-01

The purpose of this paper is to explore a teacher professional development program embedded in a networked learning environment, and to offer an emerging model and analytic matrix of 21st century teacher professional development. The Globaloria program is based on theories of learning by design and facilitates teachers and students as they create…

Social Networking: A Collaborative Open Educational Resource

ERIC Educational Resources Information Center

Toetenel, Lisette

2014-01-01

Studies undertaken since the introduction of Web 2.0 have focussed mainly on open educational resources (OERs) such as email, blogging and virtual learning environments. No consistent efforts have been undertaken to study the use of social networking sites as a tool for learning in the second language classroom. This study examined the use of…

On Location Learning: Authentic Applied Science with Networked Augmented Realities

ERIC Educational Resources Information Center

Rosenbaum, Eric; Klopfer, Eric; Perry, Judy

2007-01-01

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…

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.

Privacy-preserving backpropagation neural network learning.

PubMed

Chen, Tingting; Zhong, Sheng

2009-10-01

With the development of distributed computing environment , many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an important learning model, multilayer neural networks. We present a privacy-preserving two-party distributed algorithm of backpropagation which allows a neural network to be trained without requiring either party to reveal her data to the other. We provide complete correctness and security analysis of our algorithms. The effectiveness of our algorithms is verified by experiments on various real world data sets.

Biologically Inspired SNN for Robot Control.

PubMed

Nichols, Eric; McDaid, Liam J; Siddique, Nazmul

2013-02-01

This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. Information is routed through the network using facilitating dynamic synapses with short-term plasticity. Learning occurs through long-term synaptic plasticity which is implemented using the temporal difference learning rule to enable the robot to learn to associate the correct movement with the appropriate input conditions. The network self-organizes to provide memories of environments that the robot encounters. A Pioneer robot simulator with laser and sonar proximity sensors is used to verify the performance of the network with a wall-following task, and the results are presented.

Cooperation enhanced by indirect reciprocity in spatial prisoner's dilemma games for social P2P systems

NASA Astrophysics Data System (ADS)

Tian, Lin-Lin; Li, Ming-Chu; Wang, Zhen

2016-11-01

With the growing interest in social Peer-to-Peer (P2P) applications, relationships of individuals are further exploited to improve the performances of reputation systems. It is an on-going challenge to investigate how spatial reciprocity aids indirect reciprocity in sustaining cooperation in practical P2P environments. This paper describes the construction of an extended prisoner's dilemma game on square lattice networks with three strategies, i.e., defection, unconditional cooperation, and reciprocal cooperation. Reciprocators discriminate partners according to their reputations based on image scoring, where mistakes in judgment of reputations may occur. The independent structures of interaction and learning neighborhood are discussed, with respect to the situation in which learning environments differ from interaction networks. The simulation results have indicated that the incentive mechanism enhances cooperation better in structured peers than among a well-mixed population. Given the realistic condition of inaccurate reputation scores, defection is still successfully held down when the players interact and learn within the unified neighborhoods. Extensive simulations have further confirmed the positive impact of spatial structure on cooperation with different sizes of lattice neighborhoods. And similar conclusions can also be drawn on regular random networks and scale-free networks. Moreover, for the separated structures of the neighborhoods, the interaction network has a critical effect on the evolution dynamics of cooperation and learning environments only have weaker impacts on the process. Our findings further provide some insights concerning the evolution of collective behaviors in social systems.

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

A Learning System for Discriminating Variants of Malicious Network Traffic

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

Beaver, Justin M; Symons, Christopher T; Gillen, Rob

Modern computer network defense systems rely primarily on signature-based intrusion detection tools, which generate alerts when patterns that are pre-determined to be malicious are encountered in network data streams. Signatures are created reactively, and only after in-depth manual analysis of a network intrusion. There is little ability for signature-based detectors to identify intrusions that are new or even variants of an existing attack, and little ability to adapt the detectors to the patterns unique to a network environment. Due to these limitations, the need exists for network intrusion detection techniques that can more comprehensively address both known unknown networkbased attacksmore » and can be optimized for the target environment. This work describes a system that leverages machine learning to provide a network intrusion detection capability that analyzes behaviors in channels of communication between individual computers. Using examples of malicious and non-malicious traffic in the target environment, the system can be trained to discriminate between traffic types. The machine learning provides insight that would be difficult for a human to explicitly code as a signature because it evaluates many interdependent metrics simultaneously. With this approach, zero day detection is possible by focusing on similarity to known traffic types rather than mining for specific bit patterns or conditions. This also reduces the burden on organizations to account for all possible attack variant combinations through signatures. The approach is presented along with results from a third-party evaluation of its performance.« less

Evaluation of Deep Learning Representations of Spatial Storm Data

NASA Astrophysics Data System (ADS)

Gagne, D. J., II; Haupt, S. E.; Nychka, D. W.

2017-12-01

The spatial structure of a severe thunderstorm and its surrounding environment provide useful information about the potential for severe weather hazards, including tornadoes, hail, and high winds. Statistics computed over the area of a storm or from the pre-storm environment can provide descriptive information but fail to capture structural information. Because the storm environment is a complex, high-dimensional space, identifying methods to encode important spatial storm information in a low-dimensional form should aid analysis and prediction of storms by statistical and machine learning models. Principal component analysis (PCA), a more traditional approach, transforms high-dimensional data into a set of linearly uncorrelated, orthogonal components ordered by the amount of variance explained by each component. The burgeoning field of deep learning offers two potential approaches to this problem. Convolutional Neural Networks are a supervised learning method for transforming spatial data into a hierarchical set of feature maps that correspond with relevant combinations of spatial structures in the data. Generative Adversarial Networks (GANs) are an unsupervised deep learning model that uses two neural networks trained against each other to produce encoded representations of spatial data. These different spatial encoding methods were evaluated on the prediction of severe hail for a large set of storm patches extracted from the NCAR convection-allowing ensemble. Each storm patch contains information about storm structure and the near-storm environment. Logistic regression and random forest models were trained using the PCA and GAN encodings of the storm data and were compared against the predictions from a convolutional neural network. All methods showed skill over climatology at predicting the probability of severe hail. However, the verification scores among the methods were very similar and the predictions were highly correlated. Further evaluations are being performed to determine how the choice of input variables affects the results.

NETS

NASA Technical Reports Server (NTRS)

Baffes, Paul T.

1993-01-01

NETS development tool provides environment for simulation and development of neural networks - computer programs that "learn" from experience. Written in ANSI standard C, program allows user to generate C code for implementation of neural network.

Netlearning and Learning through Networks

ERIC Educational Resources Information Center

Wiberg, Mikael

2007-01-01

Traditional non-computerized learning environments are typically founded on an understanding of learning as acquiring silence for an effective "individual learning process". Recently, it has also been reported that the high expectations for the impact of computer-based technology on educational practice have not been realized. This paper…

Neural network system for purposeful behavior based on foveal visual preprocessor

NASA Astrophysics Data System (ADS)

Golovan, Alexander V.; Shevtsova, Natalia A.; Klepatch, Arkadi A.

1996-10-01

Biologically plausible model of the system with an adaptive behavior in a priori environment and resistant to impairment has been developed. The system consists of input, learning, and output subsystems. The first subsystems classifies input patterns presented as n-dimensional vectors in accordance with some associative rule. The second one being a neural network determines adaptive responses of the system to input patterns. Arranged neural groups coding possible input patterns and appropriate output responses are formed during learning by means of negative reinforcement. Output subsystem maps a neural network activity into the system behavior in the environment. The system developed has been studied by computer simulation imitating a collision-free motion of a mobile robot. After some learning period the system 'moves' along a road without collisions. It is shown that in spite of impairment of some neural network elements the system functions reliably after relearning. Foveal visual preprocessor model developed earlier has been tested to form a kind of visual input to the system.

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.

Digital Native and Digital Immigrant Use of Scholarly Network for Doctoral Learners

ERIC Educational Resources Information Center

Berman, Ronald; Hassell, Deliesha

2014-01-01

The Doctoral Community Network (DC) is a learner driven, scholarly community designed to help online doctoral learners successfully complete their dissertation and program of study. While digital natives grew up in an environment immersed in technology, digital immigrants adapted to this environment through their ability to learn and adjust to…

Coral-View: A Network-Based Design Environment for Collaborative Learning

ERIC Educational Resources Information Center

Sun, Chuen-Tsai; Lin, Sunny S. J.

2004-01-01

The vast majority of complex engineering tasks in today's business world are completed using a team-oriented approach. Therefore, teaching collaborative skills to university students can be viewed as a practical means of enhancing their employability. With these goals in mind, the authors developed a network environment that helps Taiwanese…

The Effects of a Social Learning Network on Students' Performances and Attitudes

ERIC Educational Resources Information Center

Durak, Gürhan; Cankaya, Serkan; Yunkul, Eyup; Ozturk, Gülcan

2017-01-01

Despite the widespread use of Social Learning Networks (SLNs), there is little research on the effectiveness of these sites in related literature. Therefore, there is a need for studies investigating but use of SLNs in educational environments and their effects on learners' academic achievements. In this study, the purpose was to investigate the…

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data

PubMed Central

Shah, Abhik; Woolf, Peter

2009-01-01

Summary In this paper, we introduce pebl, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing. PMID:20161541

Online Social Networks as Formal Learning Environments: Learner Experiences and Activities

ERIC Educational Resources Information Center

Veletsianos, George; Navarrete, Cesar C.

2012-01-01

While the potential of social networking sites to contribute to educational endeavors is highlighted by researchers and practitioners alike, empirical evidence on the use of such sites for formal online learning is scant. To fill this gap in the literature, we present a case study of learners' perspectives and experiences in an online course…

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…

Evaluation of an Interactive Case-Based Online Network (ICON) in a Problem Based Learning Environment

ERIC Educational Resources Information Center

Nathoo, Arif N.; Goldhoff, Patricia; Quattrochi, James J.

2005-01-01

Purpose: This study sought to assess the introduction of a web-based innovation in medical education that complements traditional problem-based learning curricula. Utilizing the case method as its fundamental educational approach, the Interactive Case-based Online Network (ICON) allows students to interact with each other, faculty and a virtual…

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

PubMed

Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

2017-12-20

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

Topic Models for Link Prediction in Document Networks

ERIC Educational Resources Information Center

Kataria, Saurabh

2012-01-01

Recent explosive growth of interconnected document collections such as citation networks, network of web pages, content generated by crowd-sourcing in collaborative environments, etc., has posed several challenging problems for data mining and machine learning community. One central problem in the domain of document networks is that of "link…

Putting ECD into Practice: The Interplay of Theory and Data in Evidence Models within a Digital Learning Environment

ERIC Educational Resources Information Center

Rupp, Andre A.; Levy, Roy; Dicerbo, Kristen E.; Sweet, Shauna J.; Crawford, Aaron V.; Calico, Tiago; Benson, Martin; Fay, Derek; Kunze, Katie L.; Mislevy, Robert J.; Behrens, John T.

2012-01-01

In this paper we describe the development and refinement of "evidence rules" and "measurement models" within the "evidence model" of the "evidence-centered design" (ECD) framework in the context of the "Packet Tracer" digital learning environment of the "Cisco Networking Academy." Using…

The Effect of Scaffolding Strategies for Inscriptions and Argumentation in a Science Cyberlearning Environment

ERIC Educational Resources Information Center

Kern, Cindy L.; Crippen, Kent J.

2017-01-01

Scientific inscriptions--graphs, diagrams, and data--and argumentation are integral to learning and communicating science and are common elements in cyberlearning environments--those involving the use of networked learning technologies. However, previous research has indicated that learners struggle to use inscriptions and when they engage in…

Weaving Web 2.0 and the Writing Process with Feminist Pedagogy

ERIC Educational Resources Information Center

Zhao, Ruijie

2010-01-01

This dissertation, as a theoretical study, focused on how Web 2.0 technology potentially helps students gain power, knowledge, and agency in the networked learning environment and how feminist pedagogy conceivably facilitates the implementation of Web 2.0 technology to produce an opportune learning environment. Primarily, this study used feminist…

Blended Learning Environments: Using Social Networking Sites to Enhance the First Year Experience

ERIC Educational Resources Information Center

McCarthy, Joshua

2010-01-01

This study explores blending virtual and physical learning environments to enhance the experience of first year by immersing students into university culture through social and academic interaction between peers. It reports on the progress made from 2008 to 2009 using an existing academic platform, the first year design elective course…

A network-based training environment: a medical image processing paradigm.

PubMed

Costaridou, L; Panayiotakis, G; Sakellaropoulos, P; Cavouras, D; Dimopoulos, J

1998-01-01

The capability of interactive multimedia and Internet technologies is investigated with respect to the implementation of a distance learning environment. The system is built according to a client-server architecture, based on the Internet infrastructure, composed of server nodes conceptually modelled as WWW sites. Sites are implemented by customization of available components. The environment integrates network-delivered interactive multimedia courses, network-based tutoring, SIG support, information databases of professional interest, as well as course and tutoring management. This capability has been demonstrated by means of an implemented system, validated with digital image processing content, specifically image enhancement. Image enhancement methods are theoretically described and applied to mammograms. Emphasis is given to the interactive presentation of the effects of algorithm parameters on images. The system end-user access depends on available bandwidth, so high-speed access can be achieved via LAN or local ISDN connections. Network based training offers new means of improved access and sharing of learning resources and expertise, as promising supplements in training.

Associative memory for online learning in noisy environments using self-organizing incremental neural network.

PubMed

Sudo, Akihito; Sato, Akihiro; Hasegawa, Osamu

2009-06-01

Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.

Matching Navy Recruiting Needs with Social Network Profiles Using Lexical Link Analysis. N1 FY10 Research Project

DTIC Science & Technology

2010-01-01

recruiting needs and candidate profiles – Link the features in context of dynamic social network environments, learn from on-going market...universities, companies, etc.) • Friends list fandom (fan of) , • Endorsements (supporter of) • Navy Enlisted Rating descriptions – Hard Cards...the samples into a validation and a learning set Set aside . the validation set. Use the learning set to match the recruit ratings with the

Robot Competence Development by Constructive Learning

NASA Astrophysics Data System (ADS)

Meng, Q.; Lee, M. H.; Hinde, C. J.

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system’s adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.

Robot Competence Development by Constructive Learning

NASA Astrophysics Data System (ADS)

Meng, Q.; Lee, M. H.; Hinde, C. J.

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system's adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.

Ombudsman for a day: a job rotation opportunity at the University Health Network.

PubMed

Rogers, Sharon; Bakas, Vasiliki

2007-01-01

When staff at a major Canadian teaching hospital were asked what they found meaningful and important in the working environment, they responded by identifying four specific areas: recognition, communication, workload management and learning environment. One area that was identified as a particularly good example of a successful "learning environment" strategy was the opportunity to participate in a job rotation in a department other than their own.

Anatomy and histology as socially networked learning environments: some preliminary findings.

PubMed

Hafferty, Frederic W; Castellani, Brian; Hafferty, Philip K; Pawlina, Wojciech

2013-09-01

An exploratory study to better understand the "networked" life of the medical school as a learning environment. In a recent academic year, the authors gathered data during two six-week blocks of a sequential histology and anatomy course at a U.S. medical college. An eight-item questionnaire captured different dimensions of student interactions. The student cohort/network was 48 first-year medical students. Using social network analysis (SNA), the authors focused on (1) the initial structure and the evolution of informal class networks over time, (2) how informal class networks compare to formal in-class small-group assignments in influencing student information gathering, and (3) how peer assignment of professionalism role model status is shaped more by informal than formal ties. In examining these latter two issues, the authors explored not only how formal group assignment persisted over time but also how it functioned to prevent the tendency for groupings based on gender or ethnicity. The study revealed an evolving dynamic between the formal small-group learning structure of the course blocks and the emergence of informal student networks. For example, whereas formal group membership did influence in-class questions and did prevent formation of groups of like gender and ethnicity, outside-class questions and professionalism were influenced more by informal group ties where gender and, to a much lesser extent, ethnicity influence student information gathering. The richness of these preliminary findings suggests that SNA may be a useful tool in examining an array of medical student learning encounters.

Collaborative Learning Processes in an Asynchronous Environment: An Analysis through Discourse and Social Networks

ERIC Educational Resources Information Center

Tirado, Ramon; Aguaded, Ignacio; Hernando, Angel

2011-01-01

This article analyses an experience in collaborative learning in an asynchronous writing environment through discussion forums on a WebCt platform of the University of Huelva's virtual campus, and was part of an innovative teaching project in 2007-08. The main objectives are to describe the processes of collaborative knowledge construction and the…

Two Frameworks for Preparing Teachers for the Shift from Local to Global Educational Environments

ERIC Educational Resources Information Center

Craig, Barbara; Stevens, Ken

2012-01-01

The research outlined in this paper is based on the convergence of two conceptual frameworks that guide the transfer of knowledge and skills from traditional teacher education, which focused on teaching in single classrooms, to open networked learning environments that include both inter-institutional teaching and learning and local and global…

Learning and Digital Environment of Dance--The Case of Greek Traditional Dance in Youtube

ERIC Educational Resources Information Center

Gratsiouni, Dimitra; Koutsouba, Maria; Venetsanou, Foteini; Tyrovola, Vasiliki

2016-01-01

The incorporation of Information and Communication Technologies (ICT) in education has changed the educational procedures through the creation and use of new teaching and learning environments with the use of computers and network applications that afford new dimensions to distance education. In turn, these emerging and in progress technologies,…

Developing Information Technology Fluency in College Students: An Investigation of Learning Environments and Learner Characteristics

ERIC Educational Resources Information Center

Sardone, Nancy B.

2011-01-01

The confluence of powerful technologies of computers and network connectivity has brought explosive growth to the field of Information Technology (IT). The problem presented in this study is whether the type of learning environment where IT concepts are taught to undergraduates has a relationship to the development of IT fluency and course…

Experiences and Challenges of International Students in Technology-Rich Learning Environments

ERIC Educational Resources Information Center

Habib, Laurence; Johannesen, Monica; Øgrim, Leikny

2014-01-01

This article presents a study of international students and their use of technology in a Scandinavian institution of Higher Education. A special emphasis is placed on patterns of use of a virtual learning environment (VLE) that is available to all the study programmes at the institution. Actor-Network Theory (ANT) is used as a theoretical approach…

Developing Built Environment Programs in Local Health Departments: Lessons Learned From a Nationwide Mentoring Program

PubMed Central

Rube, Kate; Veatch, Maggie; Huang, Katy; Lent, Megan; Goldstein, Gail P.; Lee, Karen K.

2014-01-01

Local health departments (LHDs) have a key role to play in developing built environment policies and programs to encourage physical activity and combat obesity and related chronic diseases. However, information to guide LHDs’ effective engagement in this arena is lacking. During 2011–2012, the New York City Department of Health and Mental Hygiene (DOHMH) facilitated a built environment peer mentoring program for 14 LHDs nationwide. Program objectives included supporting LHDs in their efforts to achieve built environment goals, offering examples from DOHMH’s built environment work to guide LHDs, and building a healthy built environment learning network. We share lessons learned that can guide LHDs in developing successful healthy built environment agendas. PMID:24625166

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.

Diversity Networking Reception

NASA Astrophysics Data System (ADS)

2014-03-01

Join us at the APS Diversity Reception to relax, network with colleagues, and learn about programs and initiatives for women, underrepresented minorities, and LGBT physicists. You'll have a great time meeting friends in a supportive environment and making connections.

ACOT Classroom Networks: Today and Tomorrow. ACOT Report #5.

ERIC Educational Resources Information Center

Knapp, Linda

The Apple Classrooms of Tomorrow (ACOT) research project provides classroom sites with equipment, ongoing support, and training, enabling educators to discover the potential of networked learning environments. ACOT networks link together technology from Apple IIe computers and Image Writer printers, to Macintosh II systems, synthesizers, laserdisc…

Investigating student communities with network analysis of interactions in a physics learning center

NASA Astrophysics Data System (ADS)

Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

2012-06-01

Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

Competition-Based Learning: A Model for the Integration of Competitions with Project-Based Learning Using Open Source LMS

ERIC Educational Resources Information Center

Issa, Ghassan; Hussain, Shakir M.; Al-Bahadili, Hussein

2014-01-01

In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning (CBL) is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning (PBL) and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as…

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.

Modulation for emergent networks: serotonin and dopamine.

PubMed

Weng, Juyang; Paslaski, Stephen; Daly, James; VanDam, Courtland; Brown, Jacob

2013-05-01

In autonomous learning, value-sensitive experiences can improve the efficiency of learning. A learning network needs be motivated so that the limited computational resources and the limited lifetime are devoted to events that are of high value for the agent to compete in its environment. The neuromodulatory system of the brain is mainly responsible for developing such a motivation system. Although reinforcement learning has been extensively studied, many existing models are symbolic whose internal nodes or modules have preset meanings. Neural networks have been used to automatically generate internal emergent representations. However, modeling an emergent motivational system for neural networks is still a great challenge. By emergent, we mean that the internal representations emerge autonomously through interactions with the external environments. This work proposes a generic emergent modulatory system for emergent networks, which includes two subsystems - the serotonin system and the dopamine system. The former signals a large class of stimuli that are intrinsically aversive (e.g., stress or pain). The latter signals a large class of stimuli that are intrinsically appetitive (e.g., pleasure or sweet). We experimented with this motivational system for two settings. The first is a visual recognition setting to investigate how such a system can learn through interactions with a teacher, who does not directly give answers, but only punishments and rewards. The second is a setting for wandering in the presence of a friend and a foe. Copyright © 2012 Elsevier Ltd. All rights reserved.

The Best of All Worlds: Immersive Interfaces for Art Education in Virtual and Real World Teaching and Learning Environments

ERIC Educational Resources Information Center

Grenfell, Janette

2013-01-01

Selected ubiquitous technologies encourage collaborative participation between higher education students and educators within a virtual socially networked e-learning landscape. Multiple modes of teaching and learning, ranging from real world experiences, to text and digital images accessed within the Deakin studies online learning management…

Immersive Environments - A Connectivist Approach

NASA Astrophysics Data System (ADS)

Loureiro, Ana; Bettencourt, Teresa

We are conducting a research project with the aim of achieving better and more efficient ways to facilitate teaching and learning in Higher Level Education. We have chosen virtual environments, with particular emphasis to Second Life® platform augmented by web 2.0 tools, to develop the study. The Second Life® environment has some interesting characteristics that captured our attention, it is immersive; it is a real world simulator; it is a social network; it allows real time communication, cooperation, collaboration and interaction; it is a safe and controlled environment. We specifically chose tools from web 2.0 that enable sharing and collaborative way of learning. Through understanding the characteristics of this learning environment, we believe that immersive learning along with other virtual tools can be integrated in today's pedagogical practices.

Managing Digital Learning Environments: Student Teachers' Perception on the Social Networking Services Use in Writing Courses in Teacher Education

ERIC Educational Resources Information Center

Prasojo, Lantip Diat; Habibi, Akhmad; Mukminin, Amirul; Muhaimin; Taridi, Muhammad; Ikhsan; Saudagar, Ferdiaz

2017-01-01

Limited studies have been conducted to examine how effective and what impacts dealing with students' learning experiences as well as the problems faced by the students. This study focused on English student teachers' experiences on the advantages and problems faced in using Social Networking Services (SNS) in English as Foreign Language (EFL)…

Intelligent deflection routing in buffer-less networks.

PubMed

Haeri, Soroush; Trajković, Ljiljana

2015-02-01

Deflection routing is employed to ameliorate packet loss caused by contention in buffer-less architectures such as optical burst-switched networks. The main goal of deflection routing is to successfully deflect a packet based only on a limited knowledge that network nodes possess about their environment. In this paper, we present a framework that introduces intelligence to deflection routing (iDef). iDef decouples the design of the signaling infrastructure from the underlying learning algorithm. It consists of a signaling and a decision-making module. Signaling module implements a feedback management protocol while the decision-making module implements a reinforcement learning algorithm. We also propose several learning-based deflection routing protocols, implement them in iDef using the ns-3 network simulator, and compare their performance.

A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement learning.

PubMed

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu

2007-01-01

This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, "GNP with Reinforcement Learning (GNPRL)" which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.

A Learning Framework for Winner-Take-All Networks with Stochastic Synapses.

PubMed

Mostafa, Hesham; Cauwenberghs, Gert

2018-06-01

Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this letter, we show that a biologically motivated model based on multilayer winner-take-all circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task and a semisupervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.

Social Networking Sites as Communication, Interaction, and Learning Environments: Perceptions and Preferences of Distance Education Students

ERIC Educational Resources Information Center

Bozkurt, Aras; Karadeniz, Abdulkadir; Kocdar, Serpil

2017-01-01

The advent of Web 2.0 technologies transformed online networks into interactive spaces in which user-generated content has become the core material. With the possibilities that emerged from Web 2.0, social networking sites became very popular. The capability of social networking sites promises opportunities for communication and interaction,…

Path integration of head direction: updating a packet of neural activity at the correct speed using neuronal time constants.

PubMed

Walters, D M; Stringer, S M

2010-07-01

A key question in understanding the neural basis of path integration is how individual, spatially responsive, neurons may self-organize into networks that can, through learning, integrate velocity signals to update a continuous representation of location within an environment. It is of vital importance that this internal representation of position is updated at the correct speed, and in real time, to accurately reflect the motion of the animal. In this article, we present a biologically plausible model of velocity path integration of head direction that can solve this problem using neuronal time constants to effect natural time delays, over which associations can be learned through associative Hebbian learning rules. The model comprises a linked continuous attractor network and competitive network. In simulation, we show that the same model is able to learn two different speeds of rotation when implemented with two different values for the time constant, and without the need to alter any other model parameters. The proposed model could be extended to path integration of place in the environment, and path integration of spatial view.

Teaching Network Security in a Virtual Learning Environment

ERIC Educational Resources Information Center

Bergstrom, Laura; Grahn, Kaj J.; Karlstrom, Krister; Pulkkis, Goran; Astrom, Peik

2004-01-01

This article presents a virtual course with the topic network security. The course has been produced by Arcada Polytechnic as a part of the production team Computer Networks, Telecommunication and Telecommunication Systems in the Finnish Virtual Polytechnic. The article begins with an introduction to the evolution of the information security…

Characteristics of Effective Networking Environments.

ERIC Educational Resources Information Center

Kaye, Judith C.

This document chronicles a project called Model Nets, which studies the characteristics of computer networks that have a positive impact on K-12 learning. Los Alamos National Laboratory undertook the study so that their recommendations could help federal agencies wisely fund networking projects in an era when the national imperative has driven…

Supporting Effective Collaboration: Using a Rearview Mirror to Look Forward

ERIC Educational Resources Information Center

McManus, Margaret M.; Aiken, Robert M.

2016-01-01

Our original research, to design and develop an Intelligent Collaborative Learning System (ICLS), yielded the creation of a Group Leader Tutor software system which utilizes a Collaborative Skills Network to monitor students working collaboratively in a networked environment. The Collaborative Skills Network was a conceptualization of…

Noise-Driven Manifestation of Learning in Mature Neural Networks

NASA Astrophysics Data System (ADS)

Monterola, Christopher; Saloma, Caesar

2002-10-01

We show that the generalization capability of a mature thresholding neural network to process above-threshold disturbances in a noise-free environment is extended to subthreshold disturbances by ambient noise without retraining. The ability to benefit from noise is intrinsic and does not have to be learned separately. Nonlinear dependence of sensitivity with noise strength is significantly narrower than in individual threshold systems. Noise has a minimal effect on network performance for above-threshold signals. We resolve two seemingly contradictory responses of trained networks to noise-their ability to benefit from its presence and their robustness against noisy strong disturbances.

Guidelines for Network Security in the Learning Environment.

ERIC Educational Resources Information Center

Littman, Marlyn Kemper

1996-01-01

Explores security challenges and practical approaches to safeguarding school networks against invasion. Highlights include security problems; computer viruses; privacy assaults; Internet invasions; building a security policy; authentication; passwords; encryption; firewalls; and acceptable use policies. (Author/LRW)

The impact of poverty on the development of brain networks

PubMed Central

Lipina, Sebastián J.; Posner, Michael I.

2012-01-01

Although the study of brain development in non-human animals is an old one, recent imaging methods have allowed non-invasive studies of the gray and white matter of the human brain over the lifespan. Classic animal studies show clearly that impoverished environments reduce cortical gray matter in relation to complex environments and cognitive and imaging studies in humans suggest which networks may be most influenced by poverty. Studies have been clear in showing the plasticity of many brain systems, but whether sensitivity to learning differs over the lifespan and for which networks is still unclear. A major task for current research is a successful integration of these methods to understand how development and learning shape the neural networks underlying achievements in literacy, numeracy, and attention. This paper seeks to foster further integration by reviewing the current state of knowledge relating brain changes to behavior and indicating possible future directions. PMID:22912613

Deep imitation learning for 3D navigation tasks.

PubMed

Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina

2018-01-01

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

Implementation of an ICT-Based Learning Environment in a Nutrition Health Project

ERIC Educational Resources Information Center

Raiha, Teija; Tossavainen, Kerttu; Enkenberg, Jorma; Turunen, Hannele

2012-01-01

Purpose: The purpose of this study was to investigate the views of school staff on a nutrition health project implemented via an ICT-based learning environment in a secondary school (7th to 9th grades). Design/methodology/approach: The study was a part of the wider European Network for Health Promoting Schools programme (ENHPS; since 2008, Schools…

Encrypted Objects and Decryption Processes: Problem-Solving with Functions in a Learning Environment Based on Cryptography

ERIC Educational Resources Information Center

White, Tobin

2009-01-01

This paper introduces an applied problem-solving task, set in the context of cryptography and embedded in a network of computer-based tools. This designed learning environment engaged students in a series of collaborative problem-solving activities intended to introduce the topic of functions through a set of linked representations. In a…

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.…

Investigating the Potential of Computer Environments for the Teaching and Learning of Functions: A Double Analysis from Two Research Traditions

ERIC Educational Resources Information Center

Lagrange, Jean-Baptiste; Psycharis, Giorgos

2014-01-01

The general goal of this paper is to explore the potential of computer environments for the teaching and learning of functions. To address this, different theoretical frameworks and corresponding research traditions are available. In this study, we aim to network different frameworks by following a "double analysis" method to analyse two…

A Novel Learning Environment: Case Study of the Pan African e-Network Project

ERIC Educational Resources Information Center

Nanda, Silima; Saxena, Ashlesh

2013-01-01

The constructivist form of learning creates such an environment where the learners are not only active but they become actors' i.e members and contributors of the social and information space without taking into consideration the geographic boundaries. Such an innovative form of distance education was initiated in India in the year 2007 and it was…

Human-level control through deep reinforcement learning.

PubMed

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-26

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Human-level control through deep reinforcement learning

NASA Astrophysics Data System (ADS)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-01

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

1991-01-01

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

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

Ondrej Linda; Todd Vollmer; Jason Wright

Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrainedmore » computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.« less

Flexible Learning in a Workplace Model: Blended a Motivation to a Lifelong Learner in a Social Network Environment

ERIC Educational Resources Information Center

Na-songkhla, Jaitip

2011-01-01

This paper presents a model of learning in a workplace, in which an online course provides flexibility for staff to learn at their convenient hours. A motivation was brought into an account of the success of learning in a workplace program, based upon Behaviorist learning approach--an online mentor and an accumulated learning activities score was…

Advanced Learning Technologies and Learning Networks and Their Impact on Future Aerospace Workforce

NASA Technical Reports Server (NTRS)

Noor, Ahmed K. (Compiler)

2003-01-01

This document contains the proceedings of the training workshop on Advanced Learning Technologies and Learning Networks and their impact on Future Aerospace Workforce. The workshop was held at the Peninsula Workforce Development Center, Hampton, Virginia, April 2 3, 2003. The workshop was jointly sponsored by Old Dominion University and NASA. Workshop attendees came from NASA, other government agencies, industry, and universities. The objectives of the workshop were to: 1) provide broad overviews of the diverse activities related to advanced learning technologies and learning environments, and 2) identify future directions for research that have high potential for aerospace workforce development. Eighteen half-hour overviewtype presentations were made at the workshop.

A neural network prototyping package within IRAF

NASA Technical Reports Server (NTRS)

Bazell, D.; Bankman, I.

1992-01-01

We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do.

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,…

Afterschool Programs: Inspiring Students with a Connected Learning Approach

ERIC Educational Resources Information Center

Afterschool Alliance, 2015

2015-01-01

Afterschool programs have been among the pioneers in applying a connected learning approach-creating a learning environment for students that builds on their interests; introduces them to new passions; provides mentors and a supportive peer network; and links this engagement to academics, careers and civic participation. This report, discusses the…

Ontology-Based Multimedia Authoring Tool for Adaptive E-Learning

ERIC Educational Resources Information Center

Deng, Lawrence Y.; Keh, Huan-Chao; Liu, Yi-Jen

2010-01-01

More video streaming technologies supporting distance learning systems are becoming popular among distributed network environments. In this paper, the authors develop a multimedia authoring tool for adaptive e-learning by using characterization of extended media streaming technologies. The distributed approach is based on an ontology-based model.…

Remote Labs and Game-Based Learning for Process Control

ERIC Educational Resources Information Center

Zualkernan, Imran A.; Husseini, Ghaleb A.; Loughlin, Kevin F.; Mohebzada, Jamshaid G.; El Gaml, Moataz

2013-01-01

Social networking platforms and computer games represent a natural informal learning environment for the current generation of learners in higher education. This paper explores the use of game-based learning in the context of an undergraduate chemical engineering remote laboratory. Specifically, students are allowed to manipulate chemical…

Influence of Learning Styles on Social Structures in Online Learning Environments

ERIC Educational Resources Information Center

Cela, Karina; Sicilia, Miguel-Ángel; Sánchez-Alonso, Salvador

2016-01-01

In e-learning settings, the interactions of students with one another, with the course content and with the instructors generate a considerable amount of information that may be useful for understanding how people learn online. The objective of the present research was to use social network analysis to explore the social structure of an e-learning…

Synchronous Writing Environments: Real-Time Interaction in Cyberspace (Technology Tidbits).

ERIC Educational Resources Information Center

Anderson-Inman, Lynne; And Others

1996-01-01

Discusses three types of synchronous writing environments, each offering teachers and students a vehicle for using electronic text to promote literacy-based learning communities: classroom collaboration, networked notetaking, and virtual communities. (SR)

The Application of Social Networking Sites (SNSs) in e-Learning and Online Education Environments: A Review of Publications in SSCI-Indexed Journals from 2004 to 2013

ERIC Educational Resources Information Center

Tsai, Chia-Wen; Shen, Pei-Di; Chiang, Yi-Chun

2013-01-01

In this paper, the authors reviewed the empirical studies on social networking sites (SNSs), especially those focused on adopting SNSs for students' learning, published in SSCI journals from 2004 to 2013. It was found that the number of articles has significantly increased, particularly after 2009. Among the 76 published papers, most studies were…

Parsing learning in networks using brain-machine interfaces.

PubMed

Orsborn, Amy L; Pesaran, Bijan

2017-10-01

Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

NASA Astrophysics Data System (ADS)

Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

2013-03-01

Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

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.

A Network Neuroscience of Human Learning: Potential To Inform Quantitative Theories of Brain and Behavior

PubMed Central

Bassett, Danielle S.; Mattar, Marcelo G.

2017-01-01

Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior. PMID:28259554

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior.

PubMed

Bassett, Danielle S; Mattar, Marcelo G

2017-04-01

Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Use of Virtual Learning Environment (VLE) and Social Network Site (SNS) Hosted Forums in Higher Education: A Preliminary Examination

ERIC Educational Resources Information Center

Hollyhead, Andrew; Edwards, David J.; Holt, Gary D.

2012-01-01

Grounded theory is used to examine the role and application of both educator-led and student-led forums within a virtual learning environment (VLE) of a higher education institution (HEI). The study reports experiences and perceptions of academics in two faculties (business and technology) in the HEI who use both asynchronous VLE forums and social…

Is There a Mobile Social Presence?

ERIC Educational Resources Information Center

Tu, Chih-Hsiung; McIsaac, Marina; Sujo-Montes, Laura; Armfield, Shadow

2012-01-01

Mobile learning environments are human networks that afford the opportunity to participate in creative endeavors, social networking, organize/reorganize social contents, and manage social acts at anytime, anywhere through mobile technologies. Social acts that elicit identities, develop awareness, cement relationships, ensure connections, and…

Experimental comparisons of face-to-face and anonymous real-time team competition in a networked gaming learning environment.

PubMed

Yu, Fu-Yun; Han, Chialing; Chan, Tak-Wai

2008-08-01

This study investigates the impact of anonymous, computerized, synchronized team competition on students' motivation, satisfaction, and interpersonal relationships. Sixty-eight fourth-graders participated in this study. A synchronous gaming learning system was developed to have dyads compete against each other in answering multiple-choice questions set in accordance with the school curriculum in two conditions (face-to-face and anonymous). The results showed that students who were exposed to the anonymous team competition condition responded significantly more positively than those in the face-to-face condition in terms of motivation and satisfaction at the 0.050 and 0.056 levels respectively. Although further studies regarding the effects of anonymous interaction in a networked gaming learning environment are imperative, the positive effects detected in this preliminary study indicate that anonymity is a viable feature for mitigating the negative effects that competition may inflict on motivation and satisfaction as reported in traditional face-to-face environments.

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.

Component Exchange Community: A Model of Utilizing Research Components to Foster International Collaboration

ERIC Educational Resources Information Center

Deng, Yi-Chan; Lin, Taiyu; Kinshuk; Chan, Tak-Wai

2006-01-01

"One-to-one" technology enhanced learning research refers to the design and investigation of learning environments and learning activities where every learner is equipped with at least one portable computing device enabled by wireless capability. G1:1 is an international research community coordinated by a network of laboratories conducting…

Use of Web 2.0 Technologies to Enhance Learning Experiences in Alternative School Settings

ERIC Educational Resources Information Center

Karahan, Engin; Roehrig, Gillian

2016-01-01

As the learning paradigms are shifting to include various forms of digital technologies such as synchronous, asynchronous, and interactive methods, social networking technologies have been introduced to the educational settings in order to increase the quality of learning environments. The literature suggests that effective application of these…

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…

Effects of Group Reflection Variations in Project-Based Learning Integrated in a Web 2.0 Learning Space

ERIC Educational Resources Information Center

Kim, Paul; Hong, Ji-Seong; Bonk, Curtis; Lim, Gloria

2011-01-01

A Web 2.0 environment that is coupled with emerging multimodal interaction tools can have considerable influence on team learning outcomes. Today, technologies supporting social networking, collective intelligence, emotional interaction, and virtual communication are introducing new forms of collaboration that are profoundly impacting education.…

Adaptive control of nonlinear system using online error minimum neural networks.

PubMed

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models

PubMed Central

Najnin, Shamima; Banerjee, Bonny

2018-01-01

Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The “novel words to novel objects” language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task. PMID:29441027

ENERGY-NET (Energy, Environment and Society Learning Network): Best Practices to Enhance Informal Geoscience Learning

NASA Astrophysics Data System (ADS)

Rossi, R.; Elliott, E. M.; Bain, D.; Crowley, K. J.; Steiner, M. A.; Divers, M. T.; Hopkins, K. G.; Giarratani, L.; Gilmore, M. E.

2014-12-01

While energy links all living and non-living systems, the integration of energy, the environment, and society is often not clearly represented in 9 - 12 classrooms and informal learning venues. However, objective public learning that integrates these components is essential for improving public environmental literacy. ENERGY-NET (Energy, Environment and Society Learning Network) is a National Science Foundation funded initiative that uses an Earth Systems Science framework to guide experimental learning for high school students and to improve public learning opportunities regarding the energy-environment-society nexus in a Museum setting. One of the primary objectives of the ENERGY-NET project is to develop a rich set of experimental learning activities that are presented as exhibits at the Carnegie Museum of Natural History in Pittsburgh, Pennsylvania (USA). Here we detail the evolution of the ENERGY-NET exhibit building process and the subsequent evolution of exhibit content over the past three years. While preliminary plans included the development of five "exploration stations" (i.e., traveling activity carts) per calendar year, the opportunity arose to create a single, larger topical exhibit per semester, which was assumed to have a greater impact on museum visitors. Evaluative assessments conducted to date reveal important practices to be incorporated into ongoing exhibit development: 1) Undergraduate mentors and teen exhibit developers should receive additional content training to allow richer exhibit materials. 2) The development process should be distributed over as long a time period as possible and emphasize iteration. This project can serve as a model for other collaborations between geoscience departments and museums. In particular, these practices may streamline development of public presentations and increase the effectiveness of experimental learning activities.

Voices from Networked Classrooms.

ERIC Educational Resources Information Center

Brownlee-Conyers, Jean; Kraber, Brenda

1996-01-01

In 1994, the Glenview (Illinois) Public Schools created three technology-rich educational environments (TREEs) that use alternative teaching and learning methods through networked communication technologies. Each setting consists of three teachers and about 75 heterogeneously grouped students (ages 9-12) who work collaboratively to solve problems…

Networked Instructional Chemistry: Using Technology To Teach Chemistry

NASA Astrophysics Data System (ADS)

Smith, Stanley; Stovall, Iris

1996-10-01

Networked multimedia microcomputers provide new ways to help students learn chemistry and to help instructors manage the learning environment. This technology is used to replace some traditional laboratory work, collect on-line experimental data, enhance lectures and quiz sections with multimedia presentations, provide prelaboratory training for beginning nonchemistry- major organic laboratory, provide electronic homework for organic chemistry students, give graduate students access to real NMR data for analysis, and provide access to molecular modeling tools. The integration of all of these activities into an active learning environment is made possible by a client-server network of hundreds of computers. This requires not only instructional software but also classroom and course management software, computers, networking, and room management. Combining computer-based work with traditional course material is made possible with software management tools that allow the instructor to monitor the progress of each student and make available an on-line gradebook so students can see their grades and class standing. This client-server based system extends the capabilities of the earlier mainframe-based PLATO system, which was used for instructional computing. This paper outlines the components of a technology center used to support over 5,000 students per semester.

Ecologically relevant neurobehavioral assessment of the development of threat learning

PubMed Central

Mouly, Anne-Marie

2016-01-01

As altricial infants gradually transition to adults, their proximate environment changes. In three short weeks, pups transition from a small world with the caregiver and siblings to a complex milieu rich in dangers as their environment expands. Such contrasting environments require different learning abilities and lead to distinct responses throughout development. Here, we will review some of the learned fear conditioned responses to threats in rats during their ontogeny, including behavioral and physiological measures that permit the assessment of learning and its supporting neurobiology from infancy through adulthood. In adulthood, odor–shock conditioning produces robust fear learning to the odor that depends upon the amygdala and related circuitry. Paradoxically, this conditioning in young pups fails to support fear learning and supports approach learning to the odor previously paired with shock. This approach learning is mediated by the infant attachment network that does not include the amygdala. During the age range when pups transition from the infant to the adult circuit (10–15 d old), pups have access to both networks: odor–shock conditioning in maternal presence uses the attachment circuit but the adult amygdala-dependent circuit when alone. However, throughout development (as young as 5 d old) the attachment associated learning can be overridden and amygdala-dependent fear learning supported, if the mother expresses fear in the presence of the pup. This social modulation of the fear permits the expression of defense reactions in life threatening situations informed by the caregiver but prevents the learning of the caregiver itself as a threat. PMID:27634146

Ecologically relevant neurobehavioral assessment of the development of threat learning.

PubMed

Boulanger Bertolus, Julie; Mouly, Anne-Marie; Sullivan, Regina M

2016-10-01

As altricial infants gradually transition to adults, their proximate environment changes. In three short weeks, pups transition from a small world with the caregiver and siblings to a complex milieu rich in dangers as their environment expands. Such contrasting environments require different learning abilities and lead to distinct responses throughout development. Here, we will review some of the learned fear conditioned responses to threats in rats during their ontogeny, including behavioral and physiological measures that permit the assessment of learning and its supporting neurobiology from infancy through adulthood. In adulthood, odor-shock conditioning produces robust fear learning to the odor that depends upon the amygdala and related circuitry. Paradoxically, this conditioning in young pups fails to support fear learning and supports approach learning to the odor previously paired with shock. This approach learning is mediated by the infant attachment network that does not include the amygdala. During the age range when pups transition from the infant to the adult circuit (10-15 d old), pups have access to both networks: odor-shock conditioning in maternal presence uses the attachment circuit but the adult amygdala-dependent circuit when alone. However, throughout development (as young as 5 d old) the attachment associated learning can be overridden and amygdala-dependent fear learning supported, if the mother expresses fear in the presence of the pup. This social modulation of the fear permits the expression of defense reactions in life threatening situations informed by the caregiver but prevents the learning of the caregiver itself as a threat. © 2016 Boulanger Bertolus et al.; Published by Cold Spring Harbor Laboratory Press.

Establishing a virtual learning environment: a nursing experience.

PubMed

Wood, Anya; McPhee, Carolyn

2011-11-01

The use of virtual worlds has exploded in popularity, but getting started may not be easy. In this article, the authors, members of the corporate nursing education team at University Health Network, outline their experience with incorporating virtual technology into their learning environment. Over a period of several months, a virtual hospital, including two nursing units, was created in Second Life®, allowing more than 500 nurses to role-play in a safe environment without the fear of making a mistake. This experience has provided valuable insight into the best ways to develop and learn in a virtual environment. The authors discuss the challenges of installing and building the Second Life® platform and provide guidelines for preparing users and suggestions for crafting educational activities. This article provides a starting point for organizations planning to incorporate virtual worlds into their learning environment. Copyright 2011, SLACK Incorporated.

Students' Network Project Activities in the Context of the Information Educational Medium of Higher Education Institution

ERIC Educational Resources Information Center

Samerkhanova, Elvira K.; Krupoderova, Elena P.; Krupoderova, Klimentina R.; Bahtiyarova, Lyudmila N.; Ponachugin, Alexander V.

2016-01-01

The purpose of the research is justifying didactic possibilities of the use of network services for the organization of information for the learning environment of college, where students carry out their project activities, and where effective networking between students and teachers takes place. The authors consider didactic possibilities of…

Learning of embodied interaction dynamics with recurrent neural networks: some exploratory experiments.

PubMed

Oubbati, Mohamed; Kord, Bahram; Koprinkova-Hristova, Petia; Palm, Günther

2014-04-01

The new tendency of artificial intelligence suggests that intelligence must be seen as a result of the interaction between brains, bodies and environments. This view implies that designing sophisticated behaviour requires a primary focus on how agents are functionally coupled to their environments. Under this perspective, we present early results with the application of reservoir computing as an efficient tool to understand how behaviour emerges from interaction. Specifically, we present reservoir computing models, that are inspired by imitation learning designs, to extract the essential components of behaviour that results from agent-environment interaction dynamics. Experimental results using a mobile robot are reported to validate the learning architectures.

Learning of embodied interaction dynamics with recurrent neural networks: some exploratory experiments

NASA Astrophysics Data System (ADS)

Oubbati, Mohamed; Kord, Bahram; Koprinkova-Hristova, Petia; Palm, Günther

2014-04-01

The new tendency of artificial intelligence suggests that intelligence must be seen as a result of the interaction between brains, bodies and environments. This view implies that designing sophisticated behaviour requires a primary focus on how agents are functionally coupled to their environments. Under this perspective, we present early results with the application of reservoir computing as an efficient tool to understand how behaviour emerges from interaction. Specifically, we present reservoir computing models, that are inspired by imitation learning designs, to extract the essential components of behaviour that results from agent-environment interaction dynamics. Experimental results using a mobile robot are reported to validate the learning architectures.

Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning

PubMed Central

Zhang, Yue; Song, Bin; Zhang, Ying; Du, Xiaojiang; Guizani, Mohsen

2016-01-01

Emerging sensor networks (ESNs) are an inevitable trend with the development of the Internet of Things (IoT), and intend to connect almost every intelligent device. Therefore, it is critical to study resource allocation in such an environment, due to the concern of efficiency, especially when resources are limited. By viewing ESNs as multi-agent environments, we model them with an agent-based modelling (ABM) method and deal with resource allocation problems with market models, after describing users’ patterns. Reinforcement learning methods are introduced to estimate users’ patterns and verify the outcomes in our market models. Experimental results show the efficiency of our methods, which are also capable of guiding topology management. PMID:27916841

Sensory grammars for sensor networks

PubMed Central

Aloimonos, Yiannis

2009-01-01

One of the major goals of Ambient Intelligence and Smart Environments is to interpret human activity sensed by a variety of sensors. In order to develop useful technologies and a subsequent industry around smart environments, we need to proceed in a principled manner. This paper suggests that human activity can be expressed in a language. This is a special language with its own phonemes, its own morphemes (words) and its own syntax and it can be learned using machine learning techniques applied to gargantuan amounts of data collected by sensor networks. Developing such languages will create bridges between Ambient Intelligence and other disciplines. It will also provide a hierarchical structure that can lead to a successful industry. PMID:21897837

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.

Improving mathematics teaching and learning experiences for hard of hearing students with wireless technology-enhanced classrooms.

PubMed

Liu, Chen-Chung; Chou, Chien-Chia; Liu, Baw-Jhiune; Yang, Jui-Wen

2006-01-01

Hard of hearing students usually face more difficulties at school than other students. A classroom environment with wireless technology was implemented to explore whether wireless technology could enhance mathematics learning and teaching activities for a hearing teacher and her 7 hard of hearing students in a Taiwan junior high school. Experiments showed that the highly interactive communication through the wireless network increased student participation in learning activities. Students demonstrated more responses to the teacher and fewer distraction behaviors. Fewer mistakes were made in in-class course work because Tablet PCs provided students scaffolds. Students stated that the environment with wireless technology was desirable and said that they hoped to continue using the environment to learn mathematics.

Incorporation of perception-based information in robot learning using fuzzy reinforcement learning agents

NASA Astrophysics Data System (ADS)

Zhou, Changjiu; Meng, Qingchun; Guo, Zhongwen; Qu, Wiefen; Yin, Bo

2002-04-01

Robot learning in unstructured environments has been proved to be an extremely challenging problem, mainly because of many uncertainties always present in the real world. Human beings, on the other hand, seem to cope very well with uncertain and unpredictable environments, often relying on perception-based information. Furthermore, humans beings can also utilize perceptions to guide their learning on those parts of the perception-action space that are actually relevant to the task. Therefore, we conduct a research aimed at improving robot learning through the incorporation of both perception-based and measurement-based information. For this reason, a fuzzy reinforcement learning (FRL) agent is proposed in this paper. Based on a neural-fuzzy architecture, different kinds of information can be incorporated into the FRL agent to initialise its action network, critic network and evaluation feedback module so as to accelerate its learning. By making use of the global optimisation capability of GAs (genetic algorithms), a GA-based FRL (GAFRL) agent is presented to solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can perform a more effective global search. Different GAFRL agents are constructed and verified by using the simulation model of a physical biped robot. The simulation analysis shows that the biped learning rate for dynamic balance can be improved by incorporating perception-based information on biped balancing and walking evaluation. The biped robot can find its application in ocean exploration, detection or sea rescue activity, as well as military maritime activity.

Biologically inspired computation and learning in Sensorimotor Systems

NASA Astrophysics Data System (ADS)

Lee, Daniel D.; Seung, H. S.

2001-11-01

Networking systems presently lack the ability to intelligently process the rich multimedia content of the data traffic they carry. Endowing artificial systems with the ability to adapt to changing conditions requires algorithms that can rapidly learn from examples. We demonstrate the application of such learning algorithms on an inexpensive quadruped robot constructed to perform simple sensorimotor tasks. The robot learns to track a particular object by discovering the salient visual and auditory cues unique to that object. The system uses a convolutional neural network that automatically combines color, luminance, motion, and auditory information. The weights of the networks are adjusted using feedback from a teacher to reflect the reliability of the various input channels in the surrounding environment. Additionally, the robot is able to compensate for its own motion by adapting the parameters of a vestibular ocular reflex system.

PELS: A Noble Architecture and Framework for a Personal E-Learning System (PELS)

ERIC Educational Resources Information Center

Dewan, Jahangir; Chowdhury, Morshed; Batten, Lynn

2014-01-01

This article presents a personal e-learning system architecture in the context of a social network environment. The main objective of a personal e-learning system is to develop individual skills on a specific subject and share resources with peers. The authors' system architecture defines the organisation and management of a personal learning…

Dynamic and Interactive Mathematics Learning Environments: Opportunities and Challenges for Future Research

ERIC Educational Resources Information Center

Olive, John

2013-01-01

New networking and social interaction technologies offer new media for learning and teaching both inside and outside the classroom. How and what kind of learning may take place in these new media is the main focus of this paper. An integrative theoretical framework for investigating these questions is posed based on the Didactic Tetrahedron (Olive…

Online English Learning Using Internet for English-as-a-Foreign-Language Students.

ERIC Educational Resources Information Center

Wang, Lih-Ching Chen; Dalton, David W.

Learning to communicate in English is an essential tool to access many resources via worldwide networks in the global society. Like students from many other countries, students in Taiwan study English for years, but lack opportunities to practice. For English-as-a-Second-Language students, the World Wide Web provides a learning environment in…

Neural networks involved in learning lexical-semantic and syntactic information in a second language.

PubMed

Mueller, Jutta L; Rueschemeyer, Shirley-Ann; Ono, Kentaro; Sugiura, Motoaki; Sadato, Norihiro; Nakamura, Akinori

2014-01-01

The present study used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of language acquisition in a realistic learning environment. Japanese native speakers were trained in a miniature version of German prior to fMRI scanning. During scanning they listened to (1) familiar sentences, (2) sentences including a novel sentence structure, and (3) sentences containing a novel word while visual context provided referential information. Learning-related decreases of brain activation over time were found in a mainly left-hemispheric network comprising classical frontal and temporal language areas as well as parietal and subcortical regions and were largely overlapping for novel words and the novel sentence structure in initial stages of learning. Differences occurred at later stages of learning during which content-specific activation patterns in prefrontal, parietal and temporal cortices emerged. The results are taken as evidence for a domain-general network supporting the initial stages of language learning which dynamically adapts as learners become proficient.

Connectivism: A knowledge learning theory for the digital age?

PubMed

Goldie, John Gerard Scott

2016-10-01

The emergence of the internet, particularly Web 2.0 has provided access to the views and opinions of a wide range of individuals opening up opportunities for new forms of communication and knowledge formation. Previous ways of navigating and filtering available information are likely to prove ineffective in these new contexts. Connectivism is one of the most prominent of the network learning theories which have been developed for e-learning environments. It is beginning to be recognized by medical educators. This article aims to examine connectivism and its potential application. The conceptual framework and application of connectivism are presented along with an outline of the main criticisms. Its potential application in medical education is then considered. While connectivism provides a useful lens through which teaching and learning using digital technologies can be better understood and managed, further development and testing is required. There is unlikely to be a single theory that will explain learning in technological enabled networks. Educators have an important role to play in online network learning.

Interactional Coherence in Asynchronous Learning Networks: A Rhetorical Approach

ERIC Educational Resources Information Center

Potter, Andrew

2008-01-01

Numerous studies have affirmed the value of asynchronous online communication as a learning resource. Several investigations, however, have indicated that discussions in asynchronous environments are often neither interactive nor coherent. The research reported sought to develop an enhanced understanding of interactional coherence, argumentation,…

Connectivism: 21st Century's New Learning Theory

ERIC Educational Resources Information Center

Kropf, Dorothy C.

2013-01-01

Transformed into a large collaborative learning environment, the Internet is comprised of information reservoirs namely, (a) online classrooms, (b) social networks, and (c) virtual reality or simulated communities, to expeditiously create, reproduce, share, and deliver information into the hands of educators and students. Most importantly, the…

An Architecture for Case-Based Learning

ERIC Educational Resources Information Center

Cifuentes, Laurent; Mercer, Rene; Alverez, Omar; Bettati, Riccardo

2010-01-01

We report on the design, development, implementation, and evaluation of a case-based instructional environment designed for learning network engineering skills for cybersecurity. We describe the societal problem addressed, the theory-based solution, and the preliminary testing and evaluation of that solution. We identify an architecture for…

Enhancing User Support in Open Problem Solving Environments through Bayesian Network Inference Techniques

ERIC Educational Resources Information Center

Tselios, Nikolaos; Stoica, Adrian; Maragoudakis, Manolis; Avouris, Nikolaos; Komis, Vassilis

2006-01-01

During the last years, development of open learning environments that support effectively their users has been a challenge for the research community of educational technologies. The open interactive nature of these environments results in users experiencing difficulties in coping with the plethora of available functions, especially during their…

Vibration control of building structures using self-organizing and self-learning neural networks

NASA Astrophysics Data System (ADS)

Madan, Alok

2005-11-01

Past research in artificial intelligence establishes that artificial neural networks (ANN) are effective and efficient computational processors for performing a variety of tasks including pattern recognition, classification, associative recall, combinatorial problem solving, adaptive control, multi-sensor data fusion, noise filtering and data compression, modelling and forecasting. The paper presents a potentially feasible approach for training ANN in active control of earthquake-induced vibrations in building structures without the aid of teacher signals (i.e. target control forces). A counter-propagation neural network is trained to output the control forces that are required to reduce the structural vibrations in the absence of any feedback on the correctness of the output control forces (i.e. without any information on the errors in output activations of the network). The present study shows that, in principle, the counter-propagation network (CPN) can learn from the control environment to compute the required control forces without the supervision of a teacher (unsupervised learning). Simulated case studies are presented to demonstrate the feasibility of implementing the unsupervised learning approach in ANN for effective vibration control of structures under the influence of earthquake ground motions. The proposed learning methodology obviates the need for developing a mathematical model of structural dynamics or training a separate neural network to emulate the structural response for implementation in practice.

A Managerial Analysis of ATM in Facilitating Distance Education.

ERIC Educational Resources Information Center

Littman, Marlyn Kemper

In this paper, the fundamental characteristics and capabilities of ATM (Asynchronous Transfer Mode) networks in a distance learning environment are examined. Current and projected ATM applications are described, and issues and challenges associated with developing ATM networking solutions for instructional delivery are explored. Other topics…

Networked Professional Learning: Relating the Formal and the Informal

ERIC Educational Resources Information Center

Vaessen, Matthieu; van den Beemt, Antoine; de Laat, Maarten

2014-01-01

The increasing complexity of the workplace environment requires teachers and professionals in general to tap into their social networks, inside and outside circles of direct colleagues and collaborators, for finding appropriate knowledge and expertise. This collective process of sharing and constructing knowledge can be considered "networked…

Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data.

PubMed

Nakamura, Yoshihiro; Hasegawa, Osamu

2017-01-01

With the ongoing development and expansion of communication networks and sensors, massive amounts of data are continuously generated in real time from real environments. Beforehand, prediction of a distribution underlying such data is difficult; furthermore, the data include substantial amounts of noise. These factors make it difficult to estimate probability densities. To handle these issues and massive amounts of data, we propose a nonparametric density estimator that rapidly learns data online and has high robustness. Our approach is an extension of both kernel density estimation (KDE) and a self-organizing incremental neural network (SOINN); therefore, we call our approach KDESOINN. An SOINN provides a clustering method that learns about the given data as networks of prototype of data; more specifically, an SOINN can learn the distribution underlying the given data. Using this information, KDESOINN estimates the probability density function. The results of our experiments show that KDESOINN outperforms or achieves performance comparable to the current state-of-the-art approaches in terms of robustness, learning time, and accuracy.

Unsupervised learning of contextual constraints in neural networks for simultaneous visual processing of multiple objects

NASA Astrophysics Data System (ADS)

Marshall, Jonathan A.

1992-12-01

A simple self-organizing neural network model, called an EXIN network, that learns to process sensory information in a context-sensitive manner, is described. EXIN networks develop efficient representation structures for higher-level visual tasks such as segmentation, grouping, transparency, depth perception, and size perception. Exposure to a perceptual environment during a developmental period serves to configure the network to perform appropriate organization of sensory data. A new anti-Hebbian inhibitory learning rule permits superposition of multiple simultaneous neural activations (multiple winners), while maintaining contextual consistency constraints, instead of forcing winner-take-all pattern classifications. The activations can represent multiple patterns simultaneously and can represent uncertainty. The network performs parallel parsing, credit attribution, and simultaneous constraint satisfaction. EXIN networks can learn to represent multiple oriented edges even where they intersect and can learn to represent multiple transparently overlaid surfaces defined by stereo or motion cues. In the case of stereo transparency, the inhibitory learning implements both a uniqueness constraint and permits coactivation of cells representing multiple disparities at the same image location. Thus two or more disparities can be active simultaneously without interference. This behavior is analogous to that of Prazdny's stereo vision algorithm, with the bonus that each binocular point is assigned a unique disparity. In a large implementation, such a NN would also be able to represent effectively the disparities of a cloud of points at random depths, like human observers, and unlike Prazdny's method

Amplifying human ability through autonomics and machine learning in IMPACT

NASA Astrophysics Data System (ADS)

Dzieciuch, Iryna; Reeder, John; Gutzwiller, Robert; Gustafson, Eric; Coronado, Braulio; Martinez, Luis; Croft, Bryan; Lange, Douglas S.

2017-05-01

Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.

Web-Based Learning in the Computer-Aided Design Curriculum.

ERIC Educational Resources Information Center

Sung, Wen-Tsai; Ou, S. C.

2002-01-01

Applies principles of constructivism and virtual reality (VR) to computer-aided design (CAD) curriculum, particularly engineering, by integrating network, VR and CAD technologies into a Web-based learning environment that expands traditional two-dimensional computer graphics into a three-dimensional real-time simulation that enhances user…

The Role of Teacher's Initiation in Online Pedagogy

ERIC Educational Resources Information Center

Tsai, Chia-Wen

2012-01-01

Purpose: The author redesigned a course titled "Applied Information Technology: Networking" and applied online collaborative learning (CL) with initiation and self-regulated learning (SRL) to improve students' involvement in this course in an environment that is full of free online games, shopping websites, and social networking…

Team Learning on the Edge of Chaos

ERIC Educational Resources Information Center

Fisser, Sandra; Browaeys, Marie-Joelle

2010-01-01

Purpose: Organizations as complex networks aim to survive. The purpose of this paper is to provide an alternative perspective to current organizational challenges by considering team learning as key factor for surviving this turbulent environment. Design/methodology/approach: The dominating approach in this paper comes from the complexity…

Proceedings for the Annual Symposium and Exhibition on Situational Awareness in the Tactical Air Environment, (2nd), Held at Patuxent River, Maryland, on 3-4 June 1997

DTIC Science & Technology

1997-06-01

made based on a learning mechanism. Traditional statistical regression and neural network approaches offer some utility, but suffer from practical...Columbus, OH. Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill- based , and affective theories of learning outcomes to new...and Feature Effects 151 Enhanced Spatial State Feedback for Night Vision Goggle Displays 159 Statistical Network Applications of Decision Aiding for

Using deep learning in image hyper spectral segmentation, classification, and detection

NASA Astrophysics Data System (ADS)

Zhao, Xiuying; Su, Zhenyu

2018-02-01

Recent years have shown that deep learning neural networks are a valuable tool in the field of computer vision. Deep learning method can be used in applications like remote sensing such as Land cover Classification, Detection of Vehicle in Satellite Images, Hyper spectral Image classification. This paper addresses the use of the deep learning artificial neural network in Satellite image segmentation. Image segmentation plays an important role in image processing. The hue of the remote sensing image often has a large hue difference, which will result in the poor display of the images in the VR environment. Image segmentation is a pre processing technique applied to the original images and splits the image into many parts which have different hue to unify the color. Several computational models based on supervised, unsupervised, parametric, probabilistic region based image segmentation techniques have been proposed. Recently, one of the machine learning technique known as, deep learning with convolution neural network has been widely used for development of efficient and automatic image segmentation models. In this paper, we focus on study of deep neural convolution network and its variants for automatic image segmentation rather than traditional image segmentation strategies.

Media Literacy, Social Networking, and the Web 2.0 Environment for the K-12 Educator. Minding the Media: Critical Issues for Learning and Teaching. Volume 4

ERIC Educational Resources Information Center

de Abreu, Belinha S.

2011-01-01

This book, a resource for educators, uses the theme of media literacy as a lens through which to view and discuss social networking and Web 2.0 environments. There is ongoing and positive research on the participatory culture created by youth who are heavily involved in the new digital technologies, yet schools tend to avoid these mediums for fear…

Is It a Tool Suitable for Learning? A Critical Review of the Literature on Facebook as a Technology-Enhanced Learning Environment

ERIC Educational Resources Information Center

Manca, S.; Ranieri, M.

2013-01-01

Despite its continuing popularity as the social network site par excellence, the educational value of Facebook has not been fully determined, and results from the mainstream educational paradigms are contradictory, with some scholars emphasizing its pedagogical affordances (e.g., widening context of learning, mixing information and learning…

Learner Views about Cooperative Learning in Social Learning Networks

ERIC Educational Resources Information Center

Cankaya, Serkan; Yunkul, Eyup

2018-01-01

The purpose of this study was to reveal the attitudes and views of university students about the use of Edmodo as a cooperative learning environment. In the research process, the students were divided into groups of 4 or 5 within the scope of a course given in the department of Computer Education and Instructional Technology. For each group,…

Organisational adaptation in an activist network: social networks, leadership, and change in al-Muhajiroun.

PubMed

Kenney, Michael; Horgan, John; Horne, Cale; Vining, Peter; Carley, Kathleen M; Bigrigg, Michael W; Bloom, Mia; Braddock, Kurt

2013-09-01

Social networks are said to facilitate learning and adaptation by providing the connections through which network nodes (or agents) share information and experience. Yet, our understanding of how this process unfolds in real-world networks remains underdeveloped. This paper explores this gap through a case study of al-Muhajiroun, an activist network that continues to call for the establishment of an Islamic state in Britain despite being formally outlawed by British authorities. Drawing on organisation theory and social network analysis, we formulate three hypotheses regarding the learning capacity and social network properties of al-Muhajiroun (AM) and its successor groups. We then test these hypotheses using mixed methods. Our methods combine quantitative analysis of three agent-based networks in AM measured for structural properties that facilitate learning, including connectedness, betweenness centrality and eigenvector centrality, with qualitative analysis of interviews with AM activists focusing organisational adaptation and learning. The results of these analyses confirm that al-Muhajiroun activists respond to government pressure by changing their operations, including creating new platforms under different names and adjusting leadership roles among movement veterans to accommodate their spiritual leader's unwelcome exodus to Lebanon. Simple as they are effective, these adaptations have allowed al-Muhajiroun and its successor groups to continue their activism in an increasingly hostile environment. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

PubMed Central

Hartmann, Christoph; Lazar, Andreea; Nessler, Bernhard; Triesch, Jochen

2015-01-01

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms. PMID:26714277

An International Haze-Monitoring Network for Students.

ERIC Educational Resources Information Center

Mims, Forrest M.

1999-01-01

Describes the haze-monitoring program that was added to the protocols of the Global Learning and Observations to Benefit the Environment (GLOBE) Program. Finds that sun photometry provides a convenient means for allowing students to perform hands-on science while learning about various topics in history, electronics, algebra, statistics, graphing,…

Advances in Computer-Supported Learning

ERIC Educational Resources Information Center

Neto, Francisco; Brasileiro, Francisco

2007-01-01

The Internet and growth of computer networks have eliminated geographic barriers, creating an environment where education can be brought to a student no matter where that student may be. The success of distance learning programs and the availability of many Web-supported applications and multimedia resources have increased the effectiveness of…

Optimization of Educational Environment for Students

ERIC Educational Resources Information Center

Tausan, Liana

2015-01-01

The paradigm of adapting school to the learning necessities and possibilities of the student, characteristic for future systems of education and for contemporary type of educational system network requires a diversity of learning situations and experiences, built in accordance with the possibilities and the needs of all student categories, in…

Learning with Mobiles in Developing Countries: Technology, Language, and Literacy

ERIC Educational Resources Information Center

Traxler, John M.

2017-01-01

In the countries of the global South, the challenges of fixed infrastructure and environment, the apparent universality of mobile hardware, software and network technologies and the rhetoric of the global knowledge economy have slowed or impoverished the development of appropriate theoretical discourses to underpin learning with mobiles. This…

Learning with Security

ERIC Educational Resources Information Center

Jokela, Paivi; Karlsudd, Peter

2007-01-01

The current higher education, both distance education and traditional campus courses, relies more and more on modern information and communication technologies (ICT). The use of computer systems and networks results in a wide range of security issues that must be dealt with in order to create a safe learning environment. In this work, we study the…

Expanding Learning Opportunities with Transmedia Practices: "Inanimate Alice" as an Exemplar

ERIC Educational Resources Information Center

Fleming, Laura

2013-01-01

The proliferation of digital and networking technologies enables us to rethink, restructure, and redefine teaching and learning. Transmedia storytelling takes advantage of the rapid convergence of media and allows teachers and learners to participate in rich virtual (and physical) environments that have been shown to foster students' real…

Nature vs Nurture: Effects of Learning on Evolution

NASA Astrophysics Data System (ADS)

Nagrani, Nagina

In the field of Evolutionary Robotics, the design, development and application of artificial neural networks as controllers have derived their inspiration from biology. Biologists and artificial intelligence researchers are trying to understand the effects of neural network learning during the lifetime of the individuals on evolution of these individuals by qualitative and quantitative analyses. The conclusion of these analyses can help develop optimized artificial neural networks to perform any given task. The purpose of this thesis is to study the effects of learning on evolution. This has been done by applying Temporal Difference Reinforcement Learning methods to the evolution of Artificial Neural Tissue controller. The controller has been assigned the task to collect resources in a designated area in a simulated environment. The performance of the individuals is measured by the amount of resources collected. A comparison has been made between the results obtained by incorporating learning in evolution and evolution alone. The effects of learning parameters: learning rate, training period, discount rate, and policy on evolution have also been studied. It was observed that learning delays the performance of the evolving individuals over the generations. However, the non zero learning rate throughout the evolution process signifies natural selection preferring individuals possessing plasticity.

Computer network environment planning and analysis

NASA Technical Reports Server (NTRS)

Dalphin, John F.

1989-01-01

The GSFC Computer Network Environment provides a broadband RF cable between campus buildings and ethernet spines in buildings for the interlinking of Local Area Networks (LANs). This system provides terminal and computer linkage among host and user systems thereby providing E-mail services, file exchange capability, and certain distributed computing opportunities. The Environment is designed to be transparent and supports multiple protocols. Networking at Goddard has a short history and has been under coordinated control of a Network Steering Committee for slightly more than two years; network growth has been rapid with more than 1500 nodes currently addressed and greater expansion expected. A new RF cable system with a different topology is being installed during summer 1989; consideration of a fiber optics system for the future will begin soon. Summmer study was directed toward Network Steering Committee operation and planning plus consideration of Center Network Environment analysis and modeling. Biweekly Steering Committee meetings were attended to learn the background of the network and the concerns of those managing it. Suggestions for historical data gathering have been made to support future planning and modeling. Data Systems Dynamic Simulator, a simulation package developed at NASA and maintained at GSFC was studied as a possible modeling tool for the network environment. A modeling concept based on a hierarchical model was hypothesized for further development. Such a model would allow input of newly updated parameters and would provide an estimation of the behavior of the network.

Learning spatially coherent properties of the visual world in connectionist networks

NASA Astrophysics Data System (ADS)

Becker, Suzanna; Hinton, Geoffrey E.

1991-10-01

In the unsupervised learning paradigm, a network of neuron-like units is presented with an ensemble of input patterns from a structured environment, such as the visual world, and learns to represent the regularities in that input. The major goal in developing unsupervised learning algorithms is to find objective functions that characterize the quality of the network's representation without explicitly specifying the desired outputs of any of the units. The sort of objective functions considered cause a unit to become tuned to spatially coherent features of visual images (such as texture, depth, shading, and surface orientation), by learning to predict the outputs of other units which have spatially adjacent receptive fields. Simulations show that using an information-theoretic algorithm called IMAX, a network can be trained to represent depth by observing random dot stereograms of surfaces with continuously varying disparities. Once a layer of depth-tuned units has developed, subsequent layers are trained to perform surface interpolation of curved surfaces, by learning to predict the depth of one image region based on depth measurements in surrounding regions. An extension of the basic model allows a population of competing neurons to learn a distributed code for disparity, which naturally gives rise to a representation of discontinuities.

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning.

PubMed

Karuza, Elisabeth A; Thompson-Schill, Sharon L; Bassett, Danielle S

2016-08-01

A core question in cognitive science concerns how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. We connect here statistical learning approaches in cognitive science, which are rooted in the sensitivity of learners to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling-up quantitative approaches to learning at both the behavioral and neural levels. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

How to Involve Students in an Online Course: A Redesigned Online Pedagogy of Collaborative Learning and Self-Regulated Learning

ERIC Educational Resources Information Center

Tsai, Chia-Wen

2013-01-01

In an online course, students learn independently in the virtual environment without teacher's on-the-spot support. However, many students are addicted to the Internet which is filled with a plethora of shopping websites, online games, and social networks (e.g. Facebook). To help keep students focused on and involved in online or blended…

The OpenForest Portal as an Open Learning Ecosystem: Co-Developing in the Study of a Multidisciplinary Phenomenon in a Cultural Context

ERIC Educational Resources Information Center

Liljeström, Anu; Enkenberg, Jorma; Vanninen, Petteri; Vartiainen, Henriikka; Pöllänen, Sinikka

2014-01-01

This paper discusses the OpenForest portal and its related multidisciplinary learning project. The OpenForest portal is an open learning environment and ecosystem, in which students can participate in co-developing and co-creating practices. The aim of the OpenForest ecosystem is to create an extensive interactive network of diverse learning…

Computers as Media for Communication: Learning and Development in a Whole Earth Context.

ERIC Educational Resources Information Center

Levin, James A.

Educationally successful electronic network activities involving microcomputers and long-distance networks include a student newswire, joint social science projects, and joint science projects. A newswire activity, such as "The Computer Chronicles," can provide a wide range of audiences for writing, a functional environment for reading, and a…

Assessing the Academic Networked Environment: Strategies and Options.

ERIC Educational Resources Information Center

McClure, Charles R.; Lopata, Cynthia L.

Many people in higher education are looking to networked resources and services when formulating strategies for addressing the pursuits of learning, teaching, research, and community service. Sometimes it may be difficult to determine if users are seeing the same things or the "right" things. This manual provides a set of tools for…

Visual NNet: An Educational ANN's Simulation Environment Reusing Matlab Neural Networks Toolbox

ERIC Educational Resources Information Center

Garcia-Roselló, Emilio; González-Dacosta, Jacinto; Lado, Maria J.; Méndez, Arturo J.; Garcia Pérez-Schofield, Baltasar; Ferrer, Fátima

2011-01-01

Artificial Neural Networks (ANN's) are nowadays a common subject in different curricula of graduate and postgraduate studies. Due to the complex algorithms involved and the dynamic nature of ANN's, simulation software has been commonly used to teach this subject. This software has usually been developed specifically for learning purposes, because…

Graphing in Groups: Learning about Lines in a Collaborative Classroom Network Environment

ERIC Educational Resources Information Center

White, Tobin; Wallace, Matthew; Lai, Kevin

2012-01-01

This article presents a design experiment in which we explore new structures for classroom collaboration supported by a classroom network of handheld graphing calculators. We describe a design for small group investigations of linear functions and present findings from its implementation in three high school algebra classrooms. Our coding of the…

Social Networking: Keeping It Clean

ERIC Educational Resources Information Center

Waters, John K.

2011-01-01

The need to maintain an unpolluted learning environment is no easy task for schools and districts that have incorporated social networking sites into their educational life. The staff and teachers at Blaine High School in Minnesota's Anoka-Hennepin District 11 had been considering the pros and cons of establishing a school Facebook page when the…

K-12 Networking: Breaking Down the Walls of the Learning Environment.

ERIC Educational Resources Information Center

Epler, Doris, Ed.

Networks can benefit school libraries by: (1) offering multiple user access to information; (2) managing and distributing information and data; (3) allowing resources to be shared; (4) improving and enabling communications; (5) improving the management of resources; and (6) creating renewed interest in the library and its resources. As a result,…

Culture, Role and Group Work: A Social Network Analysis Perspective on an Online Collaborative Course

ERIC Educational Resources Information Center

Stepanyan, Karen; Mather, Richard; Dalrymple, Roger

2014-01-01

This paper discusses the patterns of network dynamics within a multicultural online collaborative learning environment. It analyses the interaction of participants (both students and facilitators) within a discussion board that was established as part of a 3-month online collaborative course. The study employs longitudinal probabilistic social…

Fast mapping rapidly integrates information into existing memory networks.

PubMed

Coutanche, Marc N; Thompson-Schill, Sharon L

2014-12-01

Successful learning involves integrating new material into existing memory networks. A learning procedure known as fast mapping (FM), thought to simulate the word-learning environment of children, has recently been linked to distinct neuroanatomical substrates in adults. This idea has suggested the (never-before tested) hypothesis that FM may promote rapid incorporation into cortical memory networks. We test this hypothesis here in 2 experiments. In our 1st experiment, we introduced 50 participants to 16 unfamiliar animals and names through FM or explicit encoding (EE) and tested participants on the training day, and again after sleep. Learning through EE produced strong declarative memories, without immediate lexical competition, as expected from slow-consolidation models. Learning through FM, however, led to almost immediate lexical competition, which continued to the next day. Additionally, the learned words began to prime related concepts on the day following FM (but not EE) training. In a 2nd experiment, we replicated the lexical integration results and determined that presenting an already-known item during learning was crucial for rapid integration through FM. The findings presented here indicate that learned items can be integrated into cortical memory networks at an accelerated rate through fast mapping. The retrieval of a related known concept, in order to infer the target of the FM question, is critical for this effect. PsycINFO Database Record (c) 2014 APA, all rights reserved.

LYDIAN: An Extensible Educational Animation Environment for Distributed Algorithms

ERIC Educational Resources Information Center

Koldehofe, Boris; Papatriantafilou, Marina; Tsigas, Philippas

2006-01-01

LYDIAN is an environment to support the teaching and learning of distributed algorithms. It provides a collection of distributed algorithms as well as continuous animations. Users can combine algorithms and animations with arbitrary network structures defining the interconnection and behavior of the distributed algorithm. Further, it facilitates…

Robust spatial memory maps in flickering neuronal networks: a topological model

NASA Astrophysics Data System (ADS)

Dabaghian, Yuri; Babichev, Andrey; Memoli, Facundo; Chowdhury, Samir; Rice University Collaboration; Ohio State University Collaboration

It is widely accepted that the hippocampal place cells provide a substrate of the neuronal representation of the environment--the ``cognitive map''. However, hippocampal network, as any other network in the brain is transient: thousands of hippocampal neurons die every day and the connections formed by these cells constantly change due to various forms of synaptic plasticity. What then explains the remarkable reliability of our spatial memories? We propose a computational approach to answering this question based on a couple of insights. First, we propose that the hippocampal cognitive map is fundamentally topological, and hence it is amenable to analysis by topological methods. We then apply several novel methods from homology theory, to understand how dynamic connections between cells influences the speed and reliability of spatial learning. We simulate the rat's exploratory movements through different environments and study how topological invariants of these environments arise in a network of simulated neurons with ``flickering'' connectivity. We find that despite transient connectivity the network of place cells produces a stable representation of the topology of the environment.

Toward Development of Distance Learning Environment in the Grid

ERIC Educational Resources Information Center

Li, Kuan-Ching; Tsai, Yin-Te; Tsai, Chuan-Ko

2008-01-01

In recent years, with the rapid development of communication and network technologies, distance learning has been popularized and it became one of the most well-known teaching methods, due to its practicability. Over the Internet, learners are free to access new knowledge without restrictions on time or location. However, current distance learning…

The Metadata Education and Research Information Commons (MERIC): A Collaborative Teaching and Research Initiative

ERIC Educational Resources Information Center

Vellucci, Sherry L.; Hsieh-Yee, Ingrid; Moen, William E.

2007-01-01

The networked environment forced a sea change in Library and Information Science (LIS) education. Most LIS programs offer a mixed-mode of instruction that integrates online learning materials with more traditional classroom pedagogical methods and faculty are now responsible for developing content and digital learning objects. The teaching commons…

Blended Identities: Identity Work, Equity and Marginalization in Blended Learning

ERIC Educational Resources Information Center

Heikoop, Will

2013-01-01

This article is a theoretical study of the self-presentation strategies employed by higher education students online; it examines student identity work via profile information and avatars in a blended learning environment delivered through social networking sites and virtual worlds. It argues that students are faced with difficult choices when…

The Effect of Centralization and Cohesion on the Social Construction of Knowledge in Discussion Forums

ERIC Educational Resources Information Center

Tirado, Ramón; Hernando, Ángel; Aguaded, José Ignacio

2015-01-01

Interactive relationships in online learning communities can influence the process and quality of knowledge building. The aim of this study is to empirically investigate the relationships between network structures and social knowledge building in an asynchronous writing environment through discussion forums in a learning management system. The…

The Effect of Centralization and Cohesion on the Social Construction of Knowledge in Discussion Forums

ERIC Educational Resources Information Center

Tirado, Ramon; Hernando, Angel; Aguaded, Jose Ignacio

2012-01-01

Interactive relationships in online learning communities can influence the process and quality of knowledge building. The aim of this study is to empirically investigate the relationships between network structures and social knowledge building in an asynchronous writing environment through discussion forums in a learning management system. The…

Easy Access: Auditing the System Network

ERIC Educational Resources Information Center

Wiech, Dean

2013-01-01

In today's electronic learning environment, access to appropriate systems and data is of the utmost importance to students, faculty, and staff. Without proper access to the school's internal systems, teachers could be prevented from logging on to an online learning system and students might be unable to submit course work to an online…

Demonstrating a Web-Design Technique in a Distance-Learning Environment

ERIC Educational Resources Information Center

Zdenek, Sean

2004-01-01

Objective: To lead a brief training session over a distance-learning network. Type of speech: Informative. Point value: 20% of course grade. Requirements: (a) References: Not specified; (b) Length: 15 minutes; (c) Visual aid: Yes; (d) Outline: No; (e) Prerequisite reading: Chapters 12-16, 18 (Bailey, 2002); (f) Additional requirements: None. This…

Making the Most of "External" Group Members in Blended and Online Environments

ERIC Educational Resources Information Center

Hernández-Nanclares, Núria; García-Muñiz, Ana S.; Rienties, Bart

2017-01-01

Although the importance of boundary spanning in blended and online learning is widely acknowledged, most educational research has ignored whether and how students learn from others outside their assigned group. One potential approach for understanding cross-boundary knowledge sharing is Social Network Analysis (SNA). In this article, we apply four…

Transforming the Undergraduate Research Experience through Sustained Mentoring: Creating a Strong Support Network and a Collaborative Learning Environment

ERIC Educational Resources Information Center

Camacho, Erika T.; Holmes, Raquell M.; Wirkus, Stephen A.

2015-01-01

This chapter describes how sustained mentoring together with rigorous collaborative learning and community building contributed to successful mathematical research and individual growth in the Applied Mathematical Sciences Summer Institute (AMSSI), a program that focused on women, underrepresented minorities, and individuals from small teaching…

A Critical Analysis of Hypermedia and Virtual Learning Environments.

ERIC Educational Resources Information Center

Oliver, Kevin M.

The use of hypermedia in education is supported by cognitive flexibility theory which indicates transfer of knowledge to real-world settings is improved when that material is learned in a case-based, associative network emphasizing complexity and links to related information. Hypermedia is further assumed to benefit education, because it resembles…

Analysing Students' Interactions through Social Presence and Social Network Metrics

ERIC Educational Resources Information Center

Martins da Silva, Vanessa Cristina; Siqueira, Sean Wolfgand Matsui

2016-01-01

In online learning environments, tutors have several problems to carry out their activities, such as evaluating the student, knowing the right way to guide each student, promoting discussions, and knowing the right time to interact or let students build knowledge alone. We consider scenarios in which teaching and learning occurs in online social…

Using Web 2.0 to Design Meaningful Language Learning Environments

ERIC Educational Resources Information Center

Feng, Jiuguang; Wang, Wei

2012-01-01

This article reports on an exploratory study that examines how social networks can be used in foreign language teaching and learning. Qualitative data including interviews, online observations, and students' responses to open-ended survey questions was collected. The data suggests that there are both advantages and challenges associated with using…

The Virtual School Library: Gateway to the Information Superhighway.

ERIC Educational Resources Information Center

Kuhlthau, Carol Collier, Ed.; And Others

This book is a compilation of 14 articles that present a wide range of perspectives on providing access to vast networks of information resources and enabling students to learn in an information-rich environment. The articles, arranged in four parts--overview of important technologies comprising the virtual library, learning in the electronic…

Understanding Social Learning Relations of International Students in a Large Classroom Using Social Network Analysis

ERIC Educational Resources Information Center

Rienties, Bart; Héliot, YingFei; Jindal-Snape, Divya

2013-01-01

A common assumption in higher education is that international students find it difficult to develop learning and friendship relations with host students. When students are placed in a student-centred environment, international students from different cultural backgrounds are "forced" to work together with other students, which allows…

Determining the Most Suitable E-Learning Delivery Mode for TUT Students

ERIC Educational Resources Information Center

Odunaike, Solomon Adeyemi; Chuene, Daniel

2011-01-01

Traditionally, in education and business environment, Information Technology has been seen as purely a support or operational tool. Advances in computing, information storage, software, and networking are all leading to new tools for teaching and learning and are also changing the paradigm for new initiative in the classroom teaching. The Internet…

Exploring the limits of learning: Segregation of information integration and response selection is required for learning a serial reversal task

PubMed Central

Zanutto, B. Silvano

2017-01-01

Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact raises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward. PMID:29077735

Learning Negotiation Policies Using IB3 and Bayesian Networks

NASA Astrophysics Data System (ADS)

Nalepa, Gislaine M.; Ávila, Bráulio C.; Enembreck, Fabrício; Scalabrin, Edson E.

This paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent's preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent's preferences.

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

PubMed Central

Mustapha, Ibrahim; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A.; Sali, Aduwati; Mohamad, Hafizal

2015-01-01

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

PubMed

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

Hexacopter trajectory control using a neural network

NASA Astrophysics Data System (ADS)

Artale, V.; Collotta, M.; Pau, G.; Ricciardello, A.

2013-10-01

The modern flight control systems are complex due to their non-linear nature. In fact, modern aerospace vehicles are expected to have non-conventional flight envelopes and, then, they must guarantee a high level of robustness and adaptability in order to operate in uncertain environments. Neural Networks (NN), with real-time learning capability, for flight control can be used in applications with manned or unmanned aerial vehicles. Indeed, using proven lower level control algorithms with adaptive elements that exhibit long term learning could help in achieving better adaptation performance while performing aggressive maneuvers. In this paper we show a mathematical modeling and a Neural Network for a hexacopter dynamics in order to develop proper methods for stabilization and trajectory control.

Learning to Argue in a Connected World: The Arc of Productive Disciplinary Engagement in a High School Academic Social Network

ERIC Educational Resources Information Center

Teske, Paul Robert-John

2014-01-01

Calls to virtually break down school walls through connected and blended learning environments are ubiquitous as of late as technologies in service of learning evolve and as schools are under pressure to change. Within the subject area of English Language Arts, there is a dearth of research or information on how to facilitate these new, digitally…

Topological Schemas of Cognitive Maps and Spatial Learning.

PubMed

Babichev, Andrey; Cheng, Sen; Dabaghian, Yuri A

2016-01-01

Spatial navigation in mammals is based on building a mental representation of their environment-a cognitive map. However, both the nature of this cognitive map and its underpinning in neural structures and activity remains vague. A key difficulty is that these maps are collective, emergent phenomena that cannot be reduced to a simple combination of inputs provided by individual neurons. In this paper we suggest computational frameworks for integrating the spiking signals of individual cells into a spatial map, which we call schemas. We provide examples of four schemas defined by different types of topological relations that may be neurophysiologically encoded in the brain and demonstrate that each schema provides its own large-scale characteristics of the environment-the schema integrals. Moreover, we find that, in all cases, these integrals are learned at a rate which is faster than the rate of complete training of neural networks. Thus, the proposed schema framework differentiates between the cognitive aspect of spatial learning and the physiological aspect at the neural network level.

Who Exactly Is the Moderator? A Consideration of Online Knowledge Management Network Moderation in Educational Organisations

ERIC Educational Resources Information Center

Gairin-Sallan, Joaquin; Rodriguez-Gomez, David; Armengol-Asparo, Carme

2010-01-01

In the knowledge society, the appearance and development of new networked working and learning environments is increasingly common. In the "Accelera" project, which is the basis for this paper, we have developed an online community of practice which enables experiences and knowledge to be shared between various educational agents, and…

"The Social Network" and the Legal Environment of Business: An Opportunity for Student-Centered Learning

ERIC Educational Resources Information Center

McGill, Shelley

2013-01-01

Aaron Sorkin has a passion for words--his signature movie and television scripts are fast talking, jargon laced, word pictures that are instantly recognizable. "The Social Network," Sorkin's 2011 Academy Award Winning movie about the founding of Facebook, Inc., offers more than just witty banter; it provides an ideal teaching platform for…

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

Interactive knowledge networks for interdisciplinary course navigation within Moodle.

PubMed

Scherl, Andre; Dethleffsen, Kathrin; Meyer, Michael

2012-12-01

Web-based hypermedia learning environments are widely used in modern education and seem particularly well suited for interdisciplinary learning. Previous work has identified guidance through these complex environments as a crucial problem of their acceptance and efficiency. We reasoned that map-based navigation might provide straightforward and effortless orientation. To achieve this, we developed a clickable and user-oriented concept map-based navigation plugin. This tool is implemented as an extension of Moodle, a widely used learning management system. It visualizes inner and interdisciplinary relations between learning objects and is generated dynamically depending on user set parameters and interactions. This plugin leaves the choice of navigation type to the user and supports direct guidance. Previously developed and evaluated face-to-face interdisciplinary learning materials bridging physiology and physics courses of a medical curriculum were integrated as learning objects, the relations of which were defined by metadata. Learning objects included text pages, self-assessments, videos, animations, and simulations. In a field study, we analyzed the effects of this learning environment on physiology and physics knowledge as well as the transfer ability of third-term medical students. Data were generated from pre- and posttest questionnaires and from tracking student navigation. Use of the hypermedia environment resulted in a significant increase of knowledge and transfer capability. Furthermore, the efficiency of learning was enhanced. We conclude that hypermedia environments based on Moodle and enriched by concept map-based navigation tools can significantly support interdisciplinary learning. Implementation of adaptivity may further strengthen this approach.

Appropriation of a Representational Tool in a Second-Language Classroom

ERIC Educational Resources Information Center

Wen, Yun; Looi, Chee-Kit; Chen, Wenli

2015-01-01

While the affordances of face-to-face and online environments have been studied somewhat extensively, there is relatively less research on how technology-mediated learning takes place across multiple media in the networked classroom environment where face-to-face and online interactions are intertwined, especially in the context of language…

Messenger in the Barn: Networking in a Learning Environment

ERIC Educational Resources Information Center

Rutter, Malcolm

2009-01-01

This case study describes the use of a synchronous communication application (MSN Messenger) in a large academic computing environment. It draws on data from interviews, questionnaires and student marks to examine the link between use of the application and success measured through module marks. The relationship is not simple. Total abstainers and…

Grounding statistical learning in context: The effects of learning and retrieval contexts on cross-situational word learning.

PubMed

Chen, Chi-Hsin; Yu, Chen

2017-06-01

Natural language environments usually provide structured contexts for learning. This study examined the effects of semantically themed contexts-in both learning and retrieval phases-on statistical word learning. Results from 2 experiments consistently showed that participants had higher performance in semantically themed learning contexts. In contrast, themed retrieval contexts did not affect performance. Our work suggests that word learners are sensitive to statistical regularities not just at the level of individual word-object co-occurrences but also at another level containing a whole network of associations among objects and their properties.

Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning.

PubMed

Chalmers, Eric; Contreras, Edgar Bermudez; Robertson, Brandon; Luczak, Artur; Gruber, Aaron

2017-04-17

The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-error learning. To be capable of efficient, long-term learning, RL agents should be able to apply knowledge gained in the past to new tasks they may encounter in the future. The ability to predict actions' consequences may facilitate such knowledge transfer. We consider here domains where an RL agent has access to two kinds of information: agent-centric information with constant semantics across tasks, and environment-centric information, which is necessary to solve the task, but with semantics that differ between tasks. For example, in robot navigation, environment-centric information may include the robot's geographic location, while agent-centric information may include sensor readings of various nearby obstacles. We propose that these situations provide an opportunity for a very natural style of knowledge transfer, in which the agent learns to predict actions' environmental consequences using agent-centric information. These predictions contain important information about the affordances and dangers present in a novel environment, and can effectively transfer knowledge from agent-centric to environment-centric learning systems. Using several example problems including spatial navigation and network routing, we show that our knowledge transfer approach can allow faster and lower cost learning than existing alternatives.

The Implementation of a Collaborative Action Research Programme for Developing Inclusive Practices: Social Learning in Small Internal Networks

ERIC Educational Resources Information Center

Angelides, Panayiotis; Georgiou, Renos; Kyriakou, Kyriaki

2008-01-01

The idea of inclusive education has featured very highly in the educational priorities of many educational systems. However, the same educational systems are very often criticised because of the difficulties of their teachers to respond to inclusive environments of learning, where all children, despite their abilities, receive equal opportunities…

Adapting Advanced Information Technology Network Training for Adults with Visual Impairments

ERIC Educational Resources Information Center

Armstrong, Helen L.; Murray, Iain D.

2010-01-01

This article describes an accessible e-learning environment that was designed to deliver advanced IT skills to legally blind students in preparation for employment. The aim was to convert industry-standard training materials in print into accessible formats and to deliver the learning materials in ways that are more suited to adult students with…

Using Web 2.0 Technology to Enhance, Scaffold and Assess Problem-Based Learning

ERIC Educational Resources Information Center

Hack, Catherine

2013-01-01

Web 2.0 technologies, such as social networks, wikis, blogs, and virtual worlds provide a platform for collaborative working, facilitating sharing of resources and joint document production. They can act as a stimulus to promote active learning and provide an engaging and interactive environment for students, and as such align with the philosophy…

Creating a Powerful Learning Environment with Networked Mobile Learning Devices

ERIC Educational Resources Information Center

Crawford, Valerie M.

2007-01-01

Highly mobile devices can make important information available to teachers in real-time, anywhere in the classroom, and in the form of easy-to-read graphical displays that support classroom decision making. By supporting such important teaching activities, we can create a high-performance classroom that supports teachers and the art of teaching,…

Improving Students' Educational Experience by Harnessing Digital Technology: elgg in the ODL Environment

ERIC Educational Resources Information Center

Tung, Lai Cheng

2013-01-01

Given the rising popularity of both open and distance learning (ODL) and social networking tools, it seems logical to merge and harness these two popular technologies with the goal of improving student educational experience. The integration seems to hold tremendous promise for the open and distance learning mode. To reduce the gap in the…

Guiding Principles for a Research Schools Network: Successes and Challenges

ERIC Educational Resources Information Center

Schwartz, Marc S.; Gerlach, Jeanne

2011-01-01

Building on J. Dewey's (1907) original work with the laboratory school, the College of Education and Health Professions at the University of Texas-Arlington is expanding the original concept to include partners throughout a school system and the community in order to support and advance learning in multiple learning environments. The goal is to…

Assessment of Learners' Attention to E-Learning by Monitoring Facial Expressions for Computer Network Courses

ERIC Educational Resources Information Center

Chen, Hong-Ren

2012-01-01

Recognition of students' facial expressions can be used to understand their level of attention. In a traditional classroom setting, teachers guide the classes and continuously monitor and engage the students to evaluate their understanding and progress. Given the current popularity of e-learning environments, it has become important to assess the…

Mobile Voting Tools for Creating Collaboration Environment and a New Educational Design of the University Lecture

ERIC Educational Resources Information Center

Titova, Svetlana

2014-01-01

Mobile devices can enhance learning experience in many ways: provide instant feedback and better diagnosis of learning problems; enhance learner autonomy; create mobile networking collaboration; help design enquiry-based activities based on augmented reality, geo-location awareness and video-capture. One of the main objectives of the international…

Comparing the Social Knowledge Construction Behavioral Patterns of Problem-Based Online Asynchronous Discussion in E/M-Learning Environments

ERIC Educational Resources Information Center

Lan, Yu-Feng; Tsai, Pei-Wei; Yang, Shih-Hsien; Hung, Chun-Ling

2012-01-01

In recent years, researchers have conducted various studies on applying wireless networking technology and mobile devices in education settings. However, research on behavioral patterns in learners' online asynchronous discussions with mobile devices is limited. The purposes of this study are to develop a mobile learning system, mobile interactive…

A Design and Development of Distance Learning Support Environment for Collaborative Problem Solving in Group Learners

ERIC Educational Resources Information Center

Nitta, Takuya; Takaoka, Ryo; Ahama, Shigeki; Shimokawa, Masayuki

2014-01-01

The competency and curriculum for human resource development in knowledge based society are proposed in each country. We think the keywords are "collaborative problem solving" and "effective use of ICT". In particular, the competency to perform the collaborative problem solving and learning with others on the network is…

Reflective Outcomes of Convergent and Divergent Group Tasking in the Online Learning Environment

ERIC Educational Resources Information Center

Hawkes, Mark

2007-01-01

Using collaborative critical reflection as an index, this study examines the asynchronous and face-to-face discourse of 28 suburban Chicago elementary teachers developing problem based learning (PBL) curriculum. Statistical analysis of the corpus produced by the 2 mediums shows that the asynchronous online network emerges as the medium of choice…

Dynamics of EEG functional connectivity during statistical learning.

PubMed

Tóth, Brigitta; Janacsek, Karolina; Takács, Ádám; Kóbor, Andrea; Zavecz, Zsófia; Nemeth, Dezso

2017-10-01

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning. Copyright © 2017 Elsevier Inc. All rights reserved.

A service based adaptive U-learning system using UX.

PubMed

Jeong, Hwa-Young; Yi, Gangman

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

A Service Based Adaptive U-Learning System Using UX

PubMed Central

Jeong, Hwa-Young

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques. PMID:25147832

Collaborative Visualization Project: shared-technology learning environments for science learning

NASA Astrophysics Data System (ADS)

Pea, Roy D.; Gomez, Louis M.

1993-01-01

Project-enhanced science learning (PESL) provides students with opportunities for `cognitive apprenticeships' in authentic scientific inquiry using computers for data-collection and analysis. Student teams work on projects with teacher guidance to develop and apply their understanding of science concepts and skills. We are applying advanced computing and communications technologies to augment and transform PESL at-a-distance (beyond the boundaries of the individual school), which is limited today to asynchronous, text-only networking and unsuitable for collaborative science learning involving shared access to multimedia resources such as data, graphs, tables, pictures, and audio-video communication. Our work creates user technology (a Collaborative Science Workbench providing PESL design support and shared synchronous document views, program, and data access; a Science Learning Resource Directory for easy access to resources including two-way video links to collaborators, mentors, museum exhibits, media-rich resources such as scientific visualization graphics), and refine enabling technologies (audiovisual and shared-data telephony, networking) for this PESL niche. We characterize participation scenarios for using these resources and we discuss national networked access to science education expertise.

Giving you every reason to SMiLE.

PubMed

Marsh, Wendy

2013-10-01

This article outlines the plans to develop a student midwife integrated learning environment (SMILE) which will focus upon the delivery of postnatal (PN) care to women and families whilst also creating an innovative clinical learning environment for students. The SMiLE PN hub (facilitated by student midwives, supervised by a qualified midwife/sign-off mentor) has been designed to fuse seamlessly with current PN provision and provide an alternative environment for women to access a wide range of PN services. This service proposal not only increases the amount of PN learning opportunities and experience of students but also provides a much needed hub of PN activity for women and their families. It provides drop-in breastfeeding support, signposting to additional support networks and scheduled daily parenting workshops, such as baby bathing and safe sleeping advice.

The use of video conferencing to develop a community of practice for preceptors located in rural and non traditional placement settings: an evaluation study.

PubMed

Zournazis, Helen E; Marlow, Annette H

2015-03-01

Support for nursing students in rural and non-traditional health environments within Tasmania is predominately undertaken by preceptors. It is recognised that preceptors who work within these environments, require support in their role and opportunities to communicate with academic staff within universities. Multiple methods of information distribution support and networking opportunities provide preceptors with flexible options to keep them abreast of the student learning process. This paper presents survey findings from preceptors in rural and non-traditional professional experience placement environments taken from a pilot project regarding the implementation of video conferencing forums for education and peer networking in Tasmania. The purpose of the evaluation was to establish whether video conferencing met the requirements of preceptors' understanding of learning and teaching requirements during students' professional experience placement. The findings reveal preceptors' workload pressures and the need for organisational support were key barriers that prevented preceptor participation. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

A Q-Learning-Based Delay-Aware Routing Algorithm to Extend the Lifetime of Underwater Sensor Networks.

PubMed

Jin, Zhigang; Ma, Yingying; Su, Yishan; Li, Shuo; Fu, Xiaomei

2017-07-19

Underwater sensor networks (UWSNs) have become a hot research topic because of their various aquatic applications. As the underwater sensor nodes are powered by built-in batteries which are difficult to replace, extending the network lifetime is a most urgent need. Due to the low and variable transmission speed of sound, the design of reliable routing algorithms for UWSNs is challenging. In this paper, we propose a Q-learning based delay-aware routing (QDAR) algorithm to extend the lifetime of underwater sensor networks. In QDAR, a data collection phase is designed to adapt to the dynamic environment. With the application of the Q-learning technique, QDAR can determine a global optimal next hop rather than a greedy one. We define an action-utility function in which residual energy and propagation delay are both considered for adequate routing decisions. Thus, the QDAR algorithm can extend the network lifetime by uniformly distributing the residual energy and provide lower end-to-end delay. The simulation results show that our protocol can yield nearly the same network lifetime, and can reduce the end-to-end delay by 20-25% compared with a classic lifetime-extended routing protocol (QELAR).

A Q-Learning-Based Delay-Aware Routing Algorithm to Extend the Lifetime of Underwater Sensor Networks

PubMed Central

Ma, Yingying; Su, Yishan; Li, Shuo; Fu, Xiaomei

2017-01-01

Underwater sensor networks (UWSNs) have become a hot research topic because of their various aquatic applications. As the underwater sensor nodes are powered by built-in batteries which are difficult to replace, extending the network lifetime is a most urgent need. Due to the low and variable transmission speed of sound, the design of reliable routing algorithms for UWSNs is challenging. In this paper, we propose a Q-learning based delay-aware routing (QDAR) algorithm to extend the lifetime of underwater sensor networks. In QDAR, a data collection phase is designed to adapt to the dynamic environment. With the application of the Q-learning technique, QDAR can determine a global optimal next hop rather than a greedy one. We define an action-utility function in which residual energy and propagation delay are both considered for adequate routing decisions. Thus, the QDAR algorithm can extend the network lifetime by uniformly distributing the residual energy and provide lower end-to-end delay. The simulation results show that our protocol can yield nearly the same network lifetime, and can reduce the end-to-end delay by 20–25% compared with a classic lifetime-extended routing protocol (QELAR). PMID:28753951

A Collaborative Knowledge Plane for Autonomic Networks

NASA Astrophysics Data System (ADS)

Mbaye, Maïssa; Krief, Francine

Autonomic networking aims to give network components self-managing capabilities. Several autonomic architectures have been proposed. Each of these architectures includes sort of a knowledge plane which is very important to mimic an autonomic behavior. Knowledge plane has a central role for self-functions by providing suitable knowledge to equipment and needs to learn new strategies for more accuracy.However, defining knowledge plane's architecture is still a challenge for researchers. Specially, defining the way cognitive supports interact each other in knowledge plane and implementing them. Decision making process depends on these interactions between reasoning and learning parts of knowledge plane. In this paper we propose a knowledge plane's architecture based on machine learning (inductive logic programming) paradigm and situated view to deal with distributed environment. This architecture is focused on two self-functions that include all other self-functions: self-adaptation and self-organization. Study cases are given and implemented.

Machine learning based Intelligent cognitive network using fog computing

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

2017-05-01

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

eLearning techniques supporting problem based learning in clinical simulation.

PubMed

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2005-08-01

This paper details the results of the first phase of a project using eLearning to support students' learning within a simulated environment. The locus was a purpose built clinical simulation laboratory (CSL) where the School's philosophy of problem based learning (PBL) was challenged through lecturers using traditional teaching methods. a student-centred, problem based approach to the acquisition of clinical skills that used high quality learning objects embedded within web pages, substituting for lecturers providing instruction and demonstration. This encouraged student nurses to explore, analyse and make decisions within the safety of a clinical simulation. Learning was facilitated through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that eLearning techniques can help students acquire clinical skills in the safety of a simulated environment within the context of a problem based learning curriculum.

Reinforcement learning in multidimensional environments relies on attention mechanisms.

PubMed

Niv, Yael; Daniel, Reka; Geana, Andra; Gershman, Samuel J; Leong, Yuan Chang; Radulescu, Angela; Wilson, Robert C

2015-05-27

In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this "representation learning" process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the "curse of dimensionality" in reinforcement learning. Copyright © 2015 the authors 0270-6474/15/358145-13$15.00/0.

Adopting Social Networking Sites (SNSs) as Interactive Communities among English Foreign Language (EFL) Learners in Writing: Opportunities and Challenges

ERIC Educational Resources Information Center

Razak, Norizan Abdul; Saeed, Murad; Ahmad, Zulkifli

2013-01-01

As most traditional classroom environments in English as Foreign Language (EFL) still restrict learners' collaboration and interaction in college writing classes, today, the majority of EFL learners are accessing Social Networking Sites (SNSs) as online communities of practice (CoPs) for adopting informal collaborative learning as a way of…

Research Capacity-Building with New Technologies within New Communities of Practice: Reflections on the First Year of the Teacher Education Research Network

ERIC Educational Resources Information Center

Fowler, Zoe; Stanley, Grant; Murray, Jean; Jones, Marion; McNamara, Olwen

2013-01-01

This article focuses on a virtual research environment (VRE) and how it facilitated the networking of teacher educators participating in an Economic and Social Research Council-funded research capacity-building project. Using the theoretical lenses of situated learning and socio-cultural approaches to literacy, participants' ways of engaging with…

Neural Models of Spatial Orientation in Novel Environments

DTIC Science & Technology

1994-01-01

tool use, the problem of self-organizing body -centered spatial representations for movement planning and spatial orientation, and the problem of...meeting of the American Association for the Advancement of Science, Boston, February, 1993. 23. Grossberg, S., annual Linnaeus Lecture, Uppsala...Congress on Neural Networks entitled --A self-organizing neural network for learning a body -centered invariant representa- tion of 3-D target

Neuromorphic Learning From Noisy Data

NASA Technical Reports Server (NTRS)

Merrill, Walter C.; Troudet, Terry

1993-01-01

Two reports present numerical study of performance of feedforward neural network trained by back-propagation algorithm in learning continuous-valued mappings from data corrupted by noise. Two types of noise considered: plant noise which affects dynamics of controlled process and data-processing noise, which occurs during analog processing and digital sampling of signals. Study performed with view toward use of neural networks as neurocontrollers to substitute for, or enhance, performances of human experts in controlling mechanical devices in presence of sensor and actuator noise and to enhance performances of more-conventional digital feedback electronic process controllers in noisy environments.

Multiagent cooperation and competition with deep reinforcement learning.

PubMed

Tampuu, Ardi; Matiisen, Tambet; Kodelja, Dorian; Kuzovkin, Ilya; Korjus, Kristjan; Aru, Juhan; Aru, Jaan; Vicente, Raul

2017-01-01

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments.

Transferring learning from faculty development to the classroom.

PubMed

Rock, Kim Z

2014-12-01

This study’s purpose was to better understand the transfer of learning by uncovering how various factors supported the integration of health information technology knowledge and skills gleaned from the Health Resources and Services Administration–funded faculty development programs into nursing education curricula. Through interviews with 20 participants from four programs, this study confirmed the importance of findings related to faculty, program, and work environment characteristics for supporting successful transfer of learning and substantiates a variety of other transfer-of-learning research. New or seldom discussed supportive individual characteristics were found, including leadership abilities, lifelong learning, ability to recognize limitations, persistence, creativity, and risk taking. The importance of networking, diversity of perspectives, postconference support, and teams in program designs were found to positively influence transfer. The variety of supportive factors and barriers in the participants’ work environments strengthens the assertions that transfer may be context dependent. Findings provided insight for recommendations to improve learning transfer. Copyright 2014, SLACK Incorporated.

Multiagent cooperation and competition with deep reinforcement learning

PubMed Central

Kodelja, Dorian; Kuzovkin, Ilya; Korjus, Kristjan; Aru, Juhan; Aru, Jaan; Vicente, Raul

2017-01-01

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments. PMID:28380078

Neural networks for function approximation in nonlinear control

NASA Technical Reports Server (NTRS)

Linse, Dennis J.; Stengel, Robert F.

1990-01-01

Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

CCML--Exchanging Marked-Up Documents in a Networked Writing Classroom.

ERIC Educational Resources Information Center

Ogata, Hiroaki; Yano, Yoneo; Wakita, Riko

1998-01-01

Describes an on-line mark-up-based composition learning environment system called CoCoA (Communicative Collection Assisting System). This system allows students and teachers to engage in marked-up documents via the Internet, and its environment is very similar to a real-world one in which people use pen and paper. CCML also facilitates teachers to…

Networking and Cooperation as School Improvement Elements

ERIC Educational Resources Information Center

Suárez-Guerrero, Cristóbal; Muñoz Moreno, José Luís

2017-01-01

The school is an enriched learning environment, but it is not the only educational environment. The educational mission of the school should take into account the school-family coordination as a feature of its social project. A great part of this bridge between school and family is based on dialogue through the participation of the family in the…

"Elven Elder LVL59 LFP/RB. Please PM Me": Immersion, Collaborative Tasks and Problem-Solving in Massively Multiplayer Online Games

ERIC Educational Resources Information Center

Voulgari, Iro; Komis, Vassilis

2010-01-01

Although there is strong evidence that massively multiplayer online games (MMOGs) constitute environments of social interactions and effective learning, we currently lack the tools for investigating the effectiveness of the social networks emerging as well as the cognitive aspects and knowledge acquisition such environments involve. Within this…

A Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP

PubMed Central

Balduzzi, David; Tononi, Giulio

2012-01-01

In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct motor output for each object. These tasks are learned through both supervised and unsupervised spike timing dependent plasticity (STDP). STDP is responsible for the strengthening (or weakening) of synapses in relation to pre- and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo. We utilize a variation of STDP learning, called burst-STDP, which is based on the notion that, since spikes are expensive in terms of energy consumption, then strong bursting activity carries more information than single (sparse) spikes. Furthermore, this learning algorithm takes advantage of homeostatic renormalization, which has been hypothesized to promote memory consolidation during NREM sleep. Using this learning rule, we design a spiking neural network architecture capable of object recognition, motion detection, attention towards important objects, and motor control outputs. We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. Most importantly, we show how this neural network is capable of performing these tasks using a simple leaky-integrate-and-fire (LIF) neuron model with binary synapses, making it fully compatible with state-of-the-art digital neuromorphic hardware designs. As such, the building blocks and learning rules presented in this paper appear promising for scalable fully neuromorphic systems to be implemented in hardware chips. PMID:22615855

Note-Taking Evaluation Using Network Illustrations Based on Term Co-Occurrence in a Blended Learning Environment

ERIC Educational Resources Information Center

Nakayama, Minoru; Mutsuura, Kouichi; Yamamoto, Hiroh

2016-01-01

Note contents taken by students during a blended learning course were evaluated, to improve the quality of university instruction. To conduct a quantitative comparison of the contents of all notes for effective instruction from lecturer to students to occur, the contents were mathematically compared and evaluated using two ways of summarizing the…

Design Concerns in the Engineering of Virtual Worlds for Learning

ERIC Educational Resources Information Center

Rapanotti, Lucia; Hall, Jon G.

2011-01-01

The convergence of 3D simulation and social networking into current multi-user virtual environments has opened the door to new forms of interaction for learning in order to complement the face-to-face and Web 2.0-based systems. Yet, despite a growing user community, design knowledge for virtual worlds remains patchy, particularly when it comes to…

Next Generation Online: Advancing Learning through Dynamic Design, Virtual and Web 2.0 Technologies, and Instructor "Attitude"

ERIC Educational Resources Information Center

O'Connor, Eileen

2013-01-01

With the advent of web 2.0 and virtual technologies and new understandings about learning within a global, networked environment, online course design has moved beyond the constraints of text readings, papers, and discussion boards. This next generation of online courses needs to dynamically and actively integrate the wide-ranging distribution of…

Conditions for Productive Learning in Networked Learning Environments: A Case Study from the VO@NET Project

ERIC Educational Resources Information Center

Ryberg, Thomas; Koottatep, Suporn; Pengchai, Petch; Dirckinck-Holmfeld, Lone

2006-01-01

In this article we bring together experiences from two international research projects: the Kaleidoscope ERT research collaboration and the VO@NET project. We do this by using a shared framework identified for cross-case analyses within the Kaleidoscope ERT to analyse a particular case in the VO@NET project, a training course called "Green…

Transfer of Training: The Role of Feedback in Supportive Social Networks

ERIC Educational Resources Information Center

Van den Bossche, Piet; Segers, Mien; Jansen, Niekie

2010-01-01

The transfer of training to the workplace often fails to occur. The authors argue that feedback generated within the work environment about the application of newly learned skills in the workplace helps to close the gap between the current performance and the desired goal of full application of what is learned during training. This study takes a…

Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms

PubMed Central

Daniel, Reka; Geana, Andra; Gershman, Samuel J.; Leong, Yuan Chang; Radulescu, Angela; Wilson, Robert C.

2015-01-01

In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this “representation learning” process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the “curse of dimensionality” in reinforcement learning. PMID:26019331

Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environments.

PubMed

Leong, Yuan Chang; Radulescu, Angela; Daniel, Reka; DeWoskin, Vivian; Niv, Yael

2017-01-18

Little is known about the relationship between attention and learning during decision making. Using eye tracking and multivariate pattern analysis of fMRI data, we measured participants' dimensional attention as they performed a trial-and-error learning task in which only one of three stimulus dimensions was relevant for reward at any given time. Analysis of participants' choices revealed that attention biased both value computation during choice and value update during learning. Value signals in the ventromedial prefrontal cortex and prediction errors in the striatum were similarly biased by attention. In turn, participants' focus of attention was dynamically modulated by ongoing learning. Attentional switches across dimensions correlated with activity in a frontoparietal attention network, which showed enhanced connectivity with the ventromedial prefrontal cortex between switches. Our results suggest a bidirectional interaction between attention and learning: attention constrains learning to relevant dimensions of the environment, while we learn what to attend to via trial and error. Copyright © 2017 Elsevier Inc. All rights reserved.

[Integration of the Internet into medical education].

PubMed

Taradi, Suncana Kukolja

2002-01-01

The Internet promises dramatic changes in the way we learn and teach, the way we interact as a society. Networked technologies introduce interactivity and multimedia into the educational process. The student of the 21st century will use his/her PC as a learning station, as a tutoring system, as an information provider and as a communication center. Therefore the passive classroom (teacher-centered teaching) will evolve into active studio learning (student-centered learning). This will be achieved by new teaching techniques and standards of quality. The role of the new generation of educators is to create exploratory learning environments that offer a wide range of views on many subject areas and encourage active lifelong learning. This will be achieved by 1) placing courseware on the web where it can be accessed by remote students and by 2) finding and reviewing teaching materials obtained from www for possible integration into the local lecture material. The paper suggests strategies for introducing medical educators to networked teaching.

Network-based stochastic competitive learning approach to disambiguation in collaborative networks.

PubMed

Christiano Silva, Thiago; Raphael Amancio, Diego

2013-03-01

Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods.

Network-based stochastic competitive learning approach to disambiguation in collaborative networks

NASA Astrophysics Data System (ADS)

Christiano Silva, Thiago; Raphael Amancio, Diego

2013-03-01

Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods.

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…

Disordered models of acquired dyslexia

NASA Astrophysics Data System (ADS)

Virasoro, M. A.

We show that certain specific correlations in the probability of errors observed in dyslexic patients that are normally explained by introducing additional complexity in the model for the reading process are typical of any Neural Network system that has learned to deal with a quasiregular environment. On the other hand we show that in Neural Networks the more regular behavior does not become naturally the default behavior.

Versatile RED-based buffer management mechanism for the efficient support of internet traffic

NASA Astrophysics Data System (ADS)

Nelissen, Jordi; De Cnodder, Stefaan

1999-11-01

This paper presents an evaluation of various GFR (Guaranteed Frame Rate) implementation proposals. By means of extensive simulations performed in different network environments we compare two ATM Forum example implementations, namely the `simple FIFO-based GFR.2 implementation' and the `per-VC threshold and scheduling implementation'. The lessons learned from this study are as well applicable to non-ATM network technologies.

The "Inverse Relationship" between Social Capital and Sport: A Qualitative Exploration of the Influence of Social Networks on the Development of Athletes

ERIC Educational Resources Information Center

Rosso, Edoardo G. F.

2015-01-01

Sport players' likelihood to fulfil their career expectations is influenced by both technical and non-technical aspects, including self-drive, self-confidence and access to high-quality coaching and to positive learning environments. Among other factors, belonging in the "right" social networks may help players to gain access to critical…

Radar signal categorization using a neural network

NASA Technical Reports Server (NTRS)

Anderson, James A.; Gately, Michael T.; Penz, P. Andrew; Collins, Dean R.

1991-01-01

Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.

The reality of virtual learning for nurses in the largest integrated health care system in the nation.

PubMed

Rick, Cathy; Kearns, Martha A; Thompson, Nancy A

2003-01-01

The health care network and hospital system within the Department of Veterans Affairs (VA), the Veterans Health Administration (VHA), provides employment to more than 56,000 nursing personnel and serves as clinical education site to countless other nursing and health professional students. Nurse administrators and educators are posed with the challenge of providing an environment in which each nurse is able to gain needed knowledge, learn new skills, and share and communicate this knowledge with other colleagues. The education of nurses improves the health status of veterans while also realizing individual professional enhancement. Regional and cultural diversity of the system present challenges to education, in both delivery and content. VHA's learning organizations, the Employee Education System and the Office of Special Projects, have maximized new technologies and information systems to provide innovative, virtual education opportunities, capitalizing on the benefits of informal and formal learning, thus moving VHA to the forefront in knowledge sharing and dissemination. The Virtual Learning Center, VA Knowledge Network, Learning Catalog, and VA Learning Online provide VHA's nurses with interactive, desktop virtual learning opportunities.

Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research

PubMed Central

Wiggins, Benjamin L.; Goodreau, Steven M.

2014-01-01

Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data. PMID:26086650

Synaptic State Matching: A Dynamical Architecture for Predictive Internal Representation and Feature Detection

PubMed Central

Tavazoie, Saeed

2013-01-01

Here we explore the possibility that a core function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single unifying computational framework. PMID:23991161

Transforming Science Teaching Environment for the 21st Century Primary School Pupils

ERIC Educational Resources Information Center

Sheikh Abdullah, Siti Hendon

2016-01-01

The transformation of technology in the 21st century has produced children who are technology savvy and exposed to the internet and social networking at a very young age. These children are already in our school system. Thus teachers too need to use technology and transform the learning environment to meet the requirements of these children. This…

The Relationship of Twitter Use to Students' Engagement and Academic Performance in Online Classes at an Urban Community College

ERIC Educational Resources Information Center

Hirsh, Orit S.

2012-01-01

Student success in online learning is strongly affected by the learner's social presence. There is evidence that not all students benefit from the online learning environment, as it limits social interaction between the students. The purpose of this study was to examine the impact of Twitter, a social network application, on online class…

A Pedagogy of Abundance or a Pedagogy to Support Human Beings? Participant Support on Massive Open Online Courses

ERIC Educational Resources Information Center

Kop, Rita; Fournier, Helene; Mak, John Sui Fai

2011-01-01

This paper examines how emergent technologies could influence the design of learning environments. It will pay particular attention to the roles of educators and learners in creating networked learning experiences on massive open online courses (MOOCs). The research shows that it is possible to move from a pedagogy of abundance to a pedagogy that…

Massive Multiplayer Online Gaming: A Research Framework for Military Training and Education

DTIC Science & Technology

2005-03-01

those required by a military transforming itself to operating under the concept of network centric warfare. The technologies and practice...learning. Simulations are popular in other business situations and management processes. Data files, video clips, and flowcharts might help learners...on nature of these environments is another key motivator. According to Randy Hinrich, Microsoft Research Group Research Manager for Learning

PlayPhysics: An Emotional Games Learning Environment for Teaching Physics

NASA Astrophysics Data System (ADS)

Muñoz, Karla; Kevitt, Paul Mc; Lunney, Tom; Noguez, Julieta; Neri, Luis

To ensure learning, game-based learning environments must incorporate assessment mechanisms, e.g. Intelligent Tutoring Systems (ITSs). ITSs are focused on recognising and influencing the learner's emotional or motivational states. This research focuses on designing and implementing an affective student model for intelligent gaming, which reasons about the learner's emotional state from cognitive and motivational variables using observable behaviour. A Probabilistic Relational Models (PRMs) approach is employed to derive Dynamic Bayesian Networks (DBNs). The model uses the Control-Value theory of 'achievement emotions' as a basis. A preliminary test was conducted to recognise the students' prospective-outcome emotions with results presented and discussed. PlayPhysics is an emotional games learning environment for teaching Physics. Once the affective student model proves effective it will be incorporated into PlayPhysics' architecture. The design, evaluation and postevaluation of PlayPhysics are also discussed. Future work will focus on evaluating the affective student model with a larger population of students, and on providing affective feedback.

TELMA: Technology-enhanced learning environment for minimally invasive surgery.

PubMed

Sánchez-González, Patricia; Burgos, Daniel; Oropesa, Ignacio; Romero, Vicente; Albacete, Antonio; Sánchez-Peralta, Luisa F; Noguera, José F; Sánchez-Margallo, Francisco M; Gómez, Enrique J

2013-06-01

Cognitive skills training for minimally invasive surgery has traditionally relied upon diverse tools, such as seminars or lectures. Web technologies for e-learning have been adopted to provide ubiquitous training and serve as structured repositories for the vast amount of laparoscopic video sources available. However, these technologies fail to offer such features as formative and summative evaluation, guided learning, or collaborative interaction between users. The "TELMA" environment is presented as a new technology-enhanced learning platform that increases the user's experience using a four-pillared architecture: (1) an authoring tool for the creation of didactic contents; (2) a learning content and knowledge management system that incorporates a modular and scalable system to capture, catalogue, search, and retrieve multimedia content; (3) an evaluation module that provides learning feedback to users; and (4) a professional network for collaborative learning between users. Face validation of the environment and the authoring tool are presented. Face validation of TELMA reveals the positive perception of surgeons regarding the implementation of TELMA and their willingness to use it as a cognitive skills training tool. Preliminary validation data also reflect the importance of providing an easy-to-use, functional authoring tool to create didactic content. The TELMA environment is currently installed and used at the Jesús Usón Minimally Invasive Surgery Centre and several other Spanish hospitals. Face validation results ascertain the acceptance and usefulness of this new minimally invasive surgery training environment. Copyright © 2013 Elsevier Inc. All rights reserved.

Growing a professional network to over 3000 members in less than 4 years: evaluation of InspireNet, British Columbia's virtual nursing health services research network.

PubMed

Frisch, Noreen; Atherton, Pat; Borycki, Elizabeth; Mickelson, Grace; Cordeiro, Jennifer; Novak Lauscher, Helen; Black, Agnes

2014-02-21

Use of Web 2.0 and social media technologies has become a new area of research among health professionals. Much of this work has focused on the use of technologies for health self-management and the ways technologies support communication between care providers and consumers. This paper addresses a new use of technology in providing a platform for health professionals to support professional development, increase knowledge utilization, and promote formal/informal professional communication. Specifically, we report on factors necessary to attract and sustain health professionals' use of a network designed to increase nurses' interest in and use of health services research and to support knowledge utilization activities in British Columbia, Canada. "InspireNet", a virtual professional network for health professionals, is a living laboratory permitting documentation of when and how professionals take up Web 2.0 and social media. Ongoing evaluation documents our experiences in establishing, operating, and evaluating this network. Overall evaluation methods included (1) tracking website use, (2) conducting two member surveys, and (3) soliciting member feedback through focus groups and interviews with those who participated in electronic communities of practice (eCoPs) and other stakeholders. These data have been used to learn about the types of support that seem relevant to network growth. Network growth exceeded all expectations. Members engaged with varying aspects of the network's virtual technologies, such as teams of professionals sharing a common interest, research teams conducting their work, and instructional webinars open to network members. Members used wikis, blogs, and discussion groups to support professional work, as well as a members' database with contact information and areas of interest. The database is accessed approximately 10 times per day. InspireNet public blog posts are accessed roughly 500 times each. At the time of writing, 21 research teams conduct their work virtually using the InspireNet platform; 10 topic-based Action Teams meet to address issues of mutual concern. Nursing and other health professionals, even those who rated themselves as computer literate, required significant mentoring and support in their efforts to adopt their practice to a virtual environment. There was a steep learning curve for professionals to learn to work in a virtual environment and to benefit from the available technologies. Virtual professional networks can be positioned to make a significant contribution to ongoing professional practice and to creating environments supportive of information sharing, mentoring, and learning across geographical boundaries. Nonetheless, creation of a Web 2.0 and social media platform is not sufficient, in and of itself, to attract or sustain a vibrant community of professionals interested in improving their practice. Essential support includes instruction in the use of Web-based activities and time management, a biweekly e-Newsletter, regular communication from leaders, and an annual face-to-face conference.

The brain as a system of nested but partially overlapping networks. Heuristic relevance of the model for brain physiology and pathology.

PubMed

Agnati, L F; Guidolin, D; Fuxe, K

2007-01-01

A new model of the brain organization is proposed. The model is based on the assumption that a global molecular network enmeshes the entire central nervous system. Thus, brain extra-cellular and intra-cellular molecular networks are proposed to communicate at the level of special plasma membrane regions (e.g., the lipid rafts) where horizontal molecular networks can represent input/output regions allowing the cell to have informational exchanges with the extracellular environment. Furthermore, some "pervasive signals" such as field potentials, pressure waves and thermal gradients that affect large parts of the brain cellular and molecular networks are discussed. Finally, at least two learning paradigms are analyzed taking into account the possible role of Volume Transmission: the so-called model of "temporal difference learning" and the "Turing B-unorganised machine". The relevance of this new view of brain organization for a deeper understanding of some neurophysiological and neuropathological aspects of its function is briefly discussed.

Social media for lifelong learning.

PubMed

Kind, Terry; Evans, Yolanda

2015-04-01

Learning is ongoing, and can be considered a social activity. In this paper we aim to provide a review of the use of social media for lifelong learning. We start by defining lifelong learning, drawing upon principles of continuous professional development and adult learning theory. We searched Embase and MEDLINE from 2004-2014 for search terms relevant to social media and learning. We describe examples of lifelong learners using social media in medical education and healthcare that have been reported in the peer-reviewed literature. Medical or other health professions students may have qualities consistent with being a lifelong learner, yet once individuals move beyond structured learning environments they will need to recognize their own gaps in knowledge and skills over time and be motivated to fill them, thereby incorporating lifelong learning principles into their day-to-day practice. Engagement with social media can parallel engagement in the learning process over time, to the extent that online social networking fosters feedback and collaboration. The use of social media and online networking platforms are a key way to continuously learn in today's information sharing society. Additional research is needed, particularly rigorous studies that extend beyond learner satisfaction to knowledge, behaviour change, and outcomes.

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

1993-01-01

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

From Lurker to Active Participant

NASA Astrophysics Data System (ADS)

Sloep, Peter; Kester, Liesbeth

For the purposes of this chapter and section, we conceive of a Learning Network as a particular kind of online social network that is designed to support non-formal learning in a particular domain. The ‘social’ implies that we will focus on interactions between people, the ‘non-formal’ that we will not assume the presence of cohorts, curricula, etc. A Learning Network thus becomes a rather haphazard collection of people who share an interest in a particular topic about which they want to further educate themselves professionally or privately. These people, we assume, do not know of each other’s existence. In actual fact this may be different, they may be accidental or even deliberate acquaintances, for instance if they decide to join as a group. However, for the case of the emergence of sociability, we’ll take the worst-case scenario of a collection of unconnected individuals. If sociability can be made to emerge in an environment that resembles a social desert, it always will, is the argument.

On learning navigation behaviors for small mobile robots with reservoir computing architectures.

PubMed

Antonelo, Eric Aislan; Schrauwen, Benjamin

2015-04-01

This paper proposes a general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) be fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior that can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on the examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using a hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors toward the goal.

Explaining technological change of wind power in China and the United States: Roles of energy policies, technological learning, and collaboration

NASA Astrophysics Data System (ADS)

Tang, Tian

The following dissertation explains how technological change of wind power, in terms of cost reduction and performance improvement, is achieved in China and the US through energy policies, technological learning, and collaboration. The objective of this dissertation is to understand how energy policies affect key actors in the power sector to promote renewable energy and achieve cost reductions for climate change mitigation in different institutional arrangements. The dissertation consists of three essays. The first essay examines the learning processes and technological change of wind power in China. I integrate collaboration and technological learning theories to model how wind technologies are acquired and diffused among various wind project participants in China through the Clean Development Mechanism (CDM)--an international carbon trade program, and empirically test whether different learning channels lead to cost reduction of wind power. Using pooled cross-sectional data of Chinese CDM wind projects and spatial econometric models, I find that a wind project developer's previous experience (learning-by-doing) and industrywide wind project experience (spillover effect) significantly reduce the costs of wind power. The spillover effect provides justification for subsidizing users of wind technologies so as to offset wind farm investors' incentive to free-ride on knowledge spillovers from other wind energy investors. The CDM has played such a role in China. Most importantly, this essay provides the first empirical evidence of "learning-by-interacting": CDM also drives wind power cost reduction and performance improvement by facilitating technology transfer through collaboration between foreign turbine manufacturers and local wind farm developers. The second essay extends this learning framework to the US wind power sector, where I examine how state energy policies, restructuring of the electricity market, and learning among actors in wind industry lead to performance improvement of wind farms. Unlike China, the restructuring of the US electricity market created heterogeneity in transmission network governance across regions. Thus, I add transmission network governance to my learning framework to test the impacts of different transmission network governance models. Using panel data of existing utility-scale wind farms in US during 2001-2012 and spatial models, I find that the performance of a wind project is improved through more collaboration among project participants (learning-by-interacting), and this improvement is even greater if the wind project is interconnected to a regional transmission network coordinated by an independent system operator or a regional transmission organization (ISO/RTO). In the third essay, I further explore how different transmission network governance models affect wind power integration through a comparative case study. I compare two regional transmission networks, which represent two major transmission network governance models in the US: the ISO/RTO-governance model and the non-RTO model. Using archival data and interviews with key network participants, I find that a centralized transmission network coordinated through an ISO/RTO is more effective in integrating wind power because it allows resource pooling and optimal allocating of the resources by the central network administrative agency (NAO). The case study also suggests an alternative path to improved network effectiveness for a less cohesive network, which is through more frequent resource exchange among subgroups within a large network. On top of that, this essay contributes to the network governance literature by providing empirical evidence on the coexistence of hierarchy, market, and collaboration in complex service delivery networks. These coordinating mechanisms complement each other to provide system flexibility and stability, particularly when the network operates in a turbulent environment with changes and uncertainties.

TH-A-12A-01: Medical Physicist's Role in Digital Information Security: Threats, Vulnerabilities and Best Practices

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

McDonald, K; Curran, B

I. Information Security Background (Speaker = Kevin McDonald) Evolution of Medical Devices Living and Working in a Hostile Environment Attack Motivations Attack Vectors Simple Safety Strategies Medical Device Security in the News Medical Devices and Vendors Summary II. Keeping Radiation Oncology IT Systems Secure (Speaker = Bruce Curran) Hardware Security Double-lock Requirements “Foreign” computer systems Portable Device Encryption Patient Data Storage System Requirements Network Configuration Isolating Critical Devices Isolating Clinical Networks Remote Access Considerations Software Applications / Configuration Passwords / Screen Savers Restricted Services / access Software Configuration Restriction Use of DNS to restrict accesse. Patches / Upgrades Awareness Intrusionmore » Prevention Intrusion Detection Threat Risk Analysis Conclusion Learning Objectives: Understanding how Hospital IT Requirements affect Radiation Oncology IT Systems. Illustrating sample practices for hardware, network, and software security. Discussing implementation of good IT security practices in radiation oncology. Understand overall risk and threats scenario in a networked environment.« less

Cloud E-Learning Service Strategies for Improving E-Learning Innovation Performance in a Fuzzy Environment by Using a New Hybrid Fuzzy Multiple Attribute Decision-Making Model

ERIC Educational Resources Information Center

Su, Chiu Hung; Tzeng, Gwo-Hshiung; Hu, Shu-Kung

2016-01-01

The purpose of this study was to address this problem by applying a new hybrid fuzzy multiple criteria decision-making model including (a) using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique to construct the fuzzy scope influential network relationship map (FSINRM) and determine the fuzzy influential weights of the…

SOLE: Applying Semantics and Social Web to Support Technology Enhanced Learning in Software Engineering

NASA Astrophysics Data System (ADS)

Colomo-Palacios, Ricardo; Jiménez-López, Diego; García-Crespo, Ángel; Blanco-Iglesias, Borja

eLearning educative processes are a challenge for educative institutions and education professionals. In an environment in which learning resources are being produced, catalogued and stored using innovative ways, SOLE provides a platform in which exam questions can be produced supported by Web 2.0 tools, catalogued and labeled via semantic web and stored and distributed using eLearning standards. This paper presents, SOLE, a social network of exam questions sharing particularized for Software Engineering domain, based on semantics and built using semantic web and eLearning standards, such as IMS Question and Test Interoperability specification 2.1.

Self-supervised ARTMAP.

PubMed

Amis, Gregory P; Carpenter, Gail A

2010-03-01

Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semi-supervised learning). In each case input patterns have a fixed number of features throughout training and testing. Human and machine learning contexts present additional opportunities for expanding incomplete knowledge from formal training, via self-directed learning that incorporates features not previously experienced. This article defines a new self-supervised learning paradigm to address these richer learning contexts, introducing a neural network called self-supervised ARTMAP. Self-supervised learning integrates knowledge from a teacher (labeled patterns with some features), knowledge from the environment (unlabeled patterns with more features), and knowledge from internal model activation (self-labeled patterns). Self-supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled patterns. A category selection function bases system predictions on known features, and distributed network activation scales unlabeled learning to prediction confidence. Slow distributed learning on unlabeled patterns focuses on novel features and confident predictions, defining classification boundaries that were ambiguous in the labeled patterns. Self-supervised ARTMAP improves test accuracy on illustrative low-dimensional problems and on high-dimensional benchmarks. Model code and benchmark data are available from: http://techlab.eu.edu/SSART/. Copyright 2009 Elsevier Ltd. All rights reserved.

An incremental approach to genetic-algorithms-based classification.

PubMed

Guan, Sheng-Uei; Zhu, Fangming

2005-04-01

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multiagent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an "integration" operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed.

A knowledge-based system with learning for computer communication network design

NASA Technical Reports Server (NTRS)

Pierre, Samuel; Hoang, Hai Hoc; Tropper-Hausen, Evelyne

1990-01-01

Computer communication network design is well-known as complex and hard. For that reason, the most effective methods used to solve it are heuristic. Weaknesses of these techniques are listed and a new approach based on artificial intelligence for solving this problem is presented. This approach is particularly recommended for large packet switched communication networks, in the sense that it permits a high degree of reliability and offers a very flexible environment dealing with many relevant design parameters such as link cost, link capacity, and message delay.

High fidelity wireless network evaluation for heterogeneous cognitive radio networks

NASA Astrophysics Data System (ADS)

Ding, Lei; Sagduyu, Yalin; Yackoski, Justin; Azimi-Sadjadi, Babak; Li, Jason; Levy, Renato; Melodia, Tammaso

2012-06-01

We present a high fidelity cognitive radio (CR) network emulation platform for wireless system tests, measure- ments, and validation. This versatile platform provides the configurable functionalities to control and repeat realistic physical channel effects in integrated space, air, and ground networks. We combine the advantages of scalable simulation environment with reliable hardware performance for high fidelity and repeatable evaluation of heterogeneous CR networks. This approach extends CR design only at device (software-defined-radio) or lower-level protocol (dynamic spectrum access) level to end-to-end cognitive networking, and facilitates low-cost deployment, development, and experimentation of new wireless network protocols and applications on frequency- agile programmable radios. Going beyond the channel emulator paradigm for point-to-point communications, we can support simultaneous transmissions by network-level emulation that allows realistic physical-layer inter- actions between diverse user classes, including secondary users, primary users, and adversarial jammers in CR networks. In particular, we can replay field tests in a lab environment with real radios perceiving and learning the dynamic environment thereby adapting for end-to-end goals over distributed spectrum coordination channels that replace the common control channel as a single point of failure. CR networks offer several dimensions of tunable actions including channel, power, rate, and route selection. The proposed network evaluation platform is fully programmable and can reliably evaluate the necessary cross-layer design solutions with configurable op- timization space by leveraging the hardware experiments to represent the realistic effects of physical channel, topology, mobility, and jamming on spectrum agility, situational awareness, and network resiliency. We also provide the flexibility to scale up the test environment by introducing virtual radios and establishing seamless signal-level interactions with real radios. This holistic wireless evaluation approach supports a large-scale, het- erogeneous, and dynamic CR network architecture and allows developing cross-layer network protocols under high fidelity, repeatable, and scalable wireless test scenarios suitable for heterogeneous space, air, and ground networks.

Managing clinical failure: a complex adaptive system perspective.

PubMed

Matthews, Jean I; Thomas, Paul T

2007-01-01

The purpose of this article is to explore the knowledge capture process at the clinical level. It aims to identify factors that enable or constrain learning. The study applies complex adaptive system thinking principles to reconcile learning within the NHS. The paper uses a qualitative exploratory study with an interpretative methodological stance set in a secondary care NHS Trust. Semi-structured interviews were conducted with healthcare practitioners and managers involved at both strategic and operational risk management processes. A network structure is revealed that exhibits the communication and interdependent working practices to support knowledge capture and adaptive learning. Collaborative multidisciplinary communities, whose values reflect local priorities and promote open dialogue and reflection, are featured. The main concern is that the characteristics of bureaucracy; rational-legal authority, a rule-based culture, hierarchical lines of communication and a centralised governance focus, are hindering clinical learning by generating barriers. Locally emergent collaborative processes are a key strategic resource to capture knowledge, potentially fostering an environment that could learn from failure and translate lessons between contexts. What must be addressed is that reporting mechanisms serve not only the governance objectives, but also supplement learning by highlighting the potential lessons in context. Managers must nurture a collaborative infrastructure using networks in a co-evolutionary manner. Their role is not to direct and design processes but to influence, support and create effective knowledge capture. Although the study only investigated one site the findings and conclusions may well translate to other trusts--such as the risk of not enabling a learning environment at clinical levels.

Central Limit Theorem: New SOCR Applet and Demonstration Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicholas; Sanchez, Juana

2008-01-01

Modern approaches for information technology based blended education utilize a variety of novel instructional, computational and network resources. Such attempts employ technology to deliver integrated, dynamically linked, interactive content and multi-faceted learning environments, which may facilitate student comprehension and information…

Network-Centric Operations Support: Lessons Learned, Status, and Way-Ahead

DTIC Science & Technology

2014-06-01

34 Information Sharing Environment (ISE) Presentation, Enterprise Architecture Conference, 2011 (http://goveaconference.com/Events/2011/Sessions/ Tuesday ...cgi-bin/GetTRDoc?AD=ADA525312) [35] Morris , Michael, et al. Widget and Mobile Technologies a Forcing Function for Acquisition Change: Paradigm Shift

Shared Governance.

ERIC Educational Resources Information Center

Mortenson, Robert A.

The MINK (Missouri, Iowa, Nebraska, and Kansas) Network Educational Resources Center is a regional, collaborative effort among Teacher Corps Projects and a model of shared governance to improve learning environments and understanding among teacher educators. The governance of this group comes from a Board of Directors comprised of project…

Learning To Live with Complexity.

ERIC Educational Resources Information Center

Dosa, Marta

Neither the design of information systems and networks nor the delivery of library services can claim true user centricity without an understanding of the multifaceted psychological environment of users and potential users. The complexity of the political process, social problems, challenges to scientific inquiry, entrepreneurship, and…

Stochastic Prediction and Feedback Control of Router Queue Size in a Virtual Network Environment

DTIC Science & Technology

2014-09-18

predictor equations, while the update equations for measurement can be thought of as corrector equations. 11 2.3.1.1 Predict Equations In the... Adaptive Filters and Self -Learning Systems. Springer London, 2005. [11] Zarchan, P., and Musoff, H. Fundamentals of Kalman filtering: A Practical...iv AFIT-ENG-T-14-S-10 Abstract Modern congestion and routing management algorithms work well for networks with static topologies and moderate

Application of Deep Learning of Multi-Temporal SENTINEL-1 Images for the Classification of Coastal Vegetation Zone of the Danube Delta

NASA Astrophysics Data System (ADS)

Niculescu, S.; Ienco, D.; Hanganu, J.

2018-04-01

Land cover is a fundamental variable for regional planning, as well as for the study and understanding of the environment. This work propose a multi-temporal approach relying on a fusion of radar multi-sensor data and information collected by the latest sensor (Sentinel-1) with a view to obtaining better results than traditional image processing techniques. The Danube Delta is the site for this work. The spatial approach relies on new spatial analysis technologies and methodologies: Deep Learning of multi-temporal Sentinel-1. We propose a deep learning network for image classification which exploits the multi-temporal characteristic of Sentinel-1 data. The model we employ is a Gated Recurrent Unit (GRU) Network, a recurrent neural network that explicitly takes into account the time dimension via a gated mechanism to perform the final prediction. The main quality of the GRU network is its ability to consider only the important part of the information coming from the temporal data discarding the irrelevant information via a forgetting mechanism. We propose to use such network structure to classify a series of images Sentinel-1 (20 Sentinel-1 images acquired between 9.10.2014 and 01.04.2016). The results are compared with results of the classification of Random Forest.

Collaborative Supervised Learning for Sensor Networks

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran

2011-01-01

Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

Projective simulation for artificial intelligence

NASA Astrophysics Data System (ADS)

Briegel, Hans J.; de Las Cuevas, Gemma

2012-05-01

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.

Projective simulation for artificial intelligence

PubMed Central

Briegel, Hans J.; De las Cuevas, Gemma

2012-01-01

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation. PMID:22590690

Clinical learning environment and supervision plus nurse teacher (CLES+T) scale: testing the psychometric characteristics of the Italian version.

PubMed

Tomietto, Marco; Saiani, Luisa; Palese, Alvisa; Cunico, Laura; Cicolini, Giancarlo; Watson, Paul; Saarikoski, Mikko

2012-01-01

A clinical learning environment is an "interactive network of forces within the clinical setting that influence the students' learning outcomes". International research indicates the Clinical Learning Environment and Supervision plus Nurse Teacher scale (CLES+T) as the gold standard to assess a good clinical learning environment. This study aims to evaluate the psychometric proprieties of CLES+T Italian version. 875 students attending the Bachelor in Nursing in 3 Universities in Italy participated in the study. Cronbach's alpha, item to total correlations, skewness and kurtosis were calculated; factor analysis was performed using Principal Axis Factoring and an oblique rotation method. Results showed a Cronbach's alpha of 0.95 of the scale and ranging from 0.80 to 0.96 among factors; all items verified item to total correlation and answers' variability criteria. Factor analysis showed a 7-factors model as explaining more than 67% of the variance, the higher variance was explained by the "pedagogical atmosphere" factor (37.61%). The nurse teacher factor in the Italian model is split into 3 sub-factors: theory-practice integration, cooperation with ward staff and relationship with mentor and student. These results enable an international debate concerning the theoretical structure of CLES+T and provide a reliable and valid tool for the comparison of supervisory models in guiding nursing students' clinical learning.

Deep learning for medical image segmentation - using the IBM TrueNorth neurosynaptic system

NASA Astrophysics Data System (ADS)

Moran, Steven; Gaonkar, Bilwaj; Whitehead, William; Wolk, Aidan; Macyszyn, Luke; Iyer, Subramanian S.

2018-03-01

Deep convolutional neural networks have found success in semantic image segmentation tasks in computer vision and medical imaging. These algorithms are executed on conventional von Neumann processor architectures or GPUs. This is suboptimal. Neuromorphic processors that replicate the structure of the brain are better-suited to train and execute deep learning models for image segmentation by relying on massively-parallel processing. However, given that they closely emulate the human brain, on-chip hardware and digital memory limitations also constrain them. Adapting deep learning models to execute image segmentation tasks on such chips, requires specialized training and validation. In this work, we demonstrate for the first-time, spinal image segmentation performed using a deep learning network implemented on neuromorphic hardware of the IBM TrueNorth Neurosynaptic System and validate the performance of our network by comparing it to human-generated segmentations of spinal vertebrae and disks. To achieve this on neuromorphic hardware, the training model constrains the coefficients of individual neurons to {-1,0,1} using the Energy Efficient Deep Neuromorphic (EEDN)1 networks training algorithm. Given the 1 million neurons and 256 million synapses, the scale and size of the neural network implemented by the IBM TrueNorth allows us to execute the requisite mapping between segmented images and non-uniform intensity MR images >20 times faster than on a GPU-accelerated network and using <0.1 W. This speed and efficiency implies that a trained neuromorphic chip can be deployed in intra-operative environments where real-time medical image segmentation is necessary.

The new and improved learning community at Johns Hopkins University School of Medicine resembles that at Hogwarts School of Witchcraft and Wizardry.

PubMed

Stewart, Rosalyn W; Barker, Allison R; Shochet, Robert B; Wright, Scott M

2007-05-01

In July 2005, a learning community was created at Johns Hopkins University School of Medicine (JHUSOM) to foster camaraderie, networking, advising, mentoring, professionalism, clinical skills, and scholarship--The Colleges. The cultural and structural changes that emerged with the creation of this program have resulted in JHUSOM bearing a resemblance to J. K. Rowling's fictional Hogwarts School of Witchcraft and Wizardry. This manuscript will describe the similarities between these two revered schools, and highlight the innovations and improvements made to JHUSOM's learning environment. The intense, stressful, and lengthy professional training required to achieve competency in the practice of medicine and in the practice of witchcraft (albeit fictional) have meaningful parallels. The supportive learning environment at these two schools should afford the next generation of graduates to have an even more enriching experience than those who have come before them.

Intelligent control based on fuzzy logic and neural net theory

NASA Technical Reports Server (NTRS)

Lee, Chuen-Chien

1991-01-01

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

Intelligent path loss prediction engine design using machine learning in the urban outdoor environment

NASA Astrophysics Data System (ADS)

Wang, Ruichen; Lu, Jingyang; Xu, Yiran; Shen, Dan; Chen, Genshe; Pham, Khanh; Blasch, Erik

2018-05-01

Due to the progressive expansion of public mobile networks and the dramatic growth of the number of wireless users in recent years, researchers are motivated to study the radio propagation in urban environments and develop reliable and fast path loss prediction models. During last decades, different types of propagation models are developed for urban scenario path loss predictions such as the Hata model and the COST 231 model. In this paper, the path loss prediction model is thoroughly investigated using machine learning approaches. Different non-linear feature selection methods are deployed and investigated to reduce the computational complexity. The simulation results are provided to demonstratethe validity of the machine learning based path loss prediction engine, which can correctly determine the signal propagation in a wireless urban setting.

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

PubMed Central

Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

2011-01-01

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569

Convolutional neural network based side attack explosive hazard detection in three dimensional voxel radar

NASA Astrophysics Data System (ADS)

Brockner, Blake; Veal, Charlie; Dowdy, Joshua; Anderson, Derek T.; Williams, Kathryn; Luke, Robert; Sheen, David

2018-04-01

The identification followed by avoidance or removal of explosive hazards in past and/or present conflict zones is a serious threat for both civilian and military personnel. This is a challenging task as variability exists with respect to the objects, their environment and emplacement context, to name a few factors. A goal is the development of automatic or human-in-the-loop sensor technologies that leverage signal processing, data fusion and machine learning. Herein, we explore the detection of side attack explosive hazards (SAEHs) in three dimensional voxel space radar via different shallow and deep convolutional neural network (CNN) architectures. Dimensionality reduction is performed by using multiple projected images versus the raw three dimensional voxel data, which leads to noteworthy savings in input size and associated network hyperparameters. Last, we explore the accuracy and interpretation of solutions learned via random versus intelligent network weight initialization. Experiments are provided on a U.S. Army data set collected over different times, weather conditions, target types and concealments. Preliminary results indicate that deep learning can perform as good as, if not better, than a skilled domain expert, even in light of limited training data with a class imbalance.

How Urban Youth Perceive Relationships Among School Environments, Social Networks, Self-Concept, and Substance Use.

PubMed

Dudovitz, Rebecca N; Perez-Aguilar, Giselle; Kim, Grace; Wong, Mitchell D; Chung, Paul J

2017-03-01

Studies suggest adolescent substance use aligns with academic and behavioral self-concept (whether teens think of themselves as good or bad students and as rule followers or rule breakers) as well as peer and adult social networks. Schools are an important context in which self-concept and social networks develop, but it remains unclear how school environments might be leveraged to promote healthy development and prevent substance use. We sought to describe how youth perceive the relationships among school environments, adolescent self-concept, social networks, and substance use. Semistructured interviews with 32 low-income minority youth (aged 17-22 years) who participated in a prior study, explored self-concept development, school environments, social networks, and substance use decisions. Recruitment was stratified by whether, during high school, they had healthy or unhealthy self-concept profiles and had engaged in or abstained from substance use. Youth described feeling labeled by peers and teachers and how these labels became incorporated into their self-concept. Teachers who made students feel noticed (eg, by learning students' names) and had high academic expectations reinforced healthy self-concepts. Academic tracking, extracurricular activities, and school norms determined potential friendship networks, grouping students either with well-behaving or misbehaving peers. Youth described peer groups, combined with their self-concept, shaping their substance use decisions. Affirming healthy aspects of their self-concept at key risk behavior decision points helped youth avoid substance use in the face of peer pressure. Youth narratives suggest school environments shape adolescent self-concept and adult and peer social networks, all of which impact substance use. Copyright © 2016 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.

How Urban Youth Perceive Relationships among School Environments, Social Networks, Self-Concept, and Substance Use

PubMed Central

Dudovitz, Rebecca N.; Perez-Aguilar, Giselle; Kim, Grace; Wong, Mitchell D.; Chung, Paul J.

2016-01-01

Objective Studies suggest adolescent substance use aligns with academic and behavioral self-concept (whether teens think of themselves as good or bad students and as rule followers or rule breakers) as well as peer and adult social networks. Schools are an important context in which self-concept and social networks develop, but it remains unclear how school environments might be leveraged to promote healthy development and prevent substance use. We sought to describe how youth perceive the relationships among school environments, adolescent self-concept, social networks, and substance use. Methods Semi-structured interviews with 32 low-income minority youth (ages 17-22) who participated in a prior study, explored self-concept development, school environments, social networks, and substance use decisions. Recruitment was stratified by whether, during high school, they had healthy or unhealthy self-concept profiles and had engaged in or abstained from substance use. Results Youth described feeling labeled by peers and teachers and how these labels became incorporated into their self-concept. Teachers who made students feel noticed (e.g., by learning students' names) and had high academic expectations reinforced healthy self-concepts. Academic tracking, extra-curricular activities, and school norms determined potential friendship networks, grouping students either with well-behaving or misbehaving peers. Youth described peer groups, combined with their self-concept, shaping their substance use decisions. Affirming healthy aspects of their self-concept at key risk behavior decision points helped youth avoid substance use in the face of peer pressure. Conclusions Youth narratives suggest school environments shape adolescent self-concept and adult and peer social networks, all of which impact substance use. PMID:28259338

Biomimetic Models for An Ecological Approach to Massively-Deployed Sensor Networks

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

2005-01-01

Promises of ubiquitous control of the physical environment by massively-deployed wireless sensor networks open avenues for new applications that will redefine the way we live and work. Due to small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors ubiquitous throughout our environment working in concert. Recent research has concentrated on developing techniques for performing relatively simple tasks with minimal energy expense, assuming some form of centralized control. Unfortunately, centralized control is not conducive to parallel activities and does not scale to massive size networks. Execution of simple tasks in sparse networks will not lead to the sophisticated applications predicted. We propose a new way of looking at massively-deployed sensor networks, motivated by lessons learned from the way biological ecosystems are organized. We demonstrate that in such a model, fully distributed data aggregation can be performed in a scalable fashion in massively deployed sensor networks, where motes operate on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects. We show that such architectures may be used to facilitate communication and synchronization in a fault-tolerant manner, while balancing workload and required energy expenditure throughout the network.

Joint Prior Learning for Visual Sensor Network Noisy Image Super-Resolution

PubMed Central

Yue, Bo; Wang, Shuang; Liang, Xuefeng; Jiao, Licheng; Xu, Caijin

2016-01-01

The visual sensor network (VSN), a new type of wireless sensor network composed of low-cost wireless camera nodes, is being applied for numerous complex visual analyses in wild environments, such as visual surveillance, object recognition, etc. However, the captured images/videos are often low resolution with noise. Such visual data cannot be directly delivered to the advanced visual analysis. In this paper, we propose a joint-prior image super-resolution (JPISR) method using expectation maximization (EM) algorithm to improve VSN image quality. Unlike conventional methods that only focus on upscaling images, JPISR alternatively solves upscaling mapping and denoising in the E-step and M-step. To meet the requirement of the M-step, we introduce a novel non-local group-sparsity image filtering method to learn the explicit prior and induce the geometric duality between images to learn the implicit prior. The EM algorithm inherently combines the explicit prior and implicit prior by joint learning. Moreover, JPISR does not rely on large external datasets for training, which is much more practical in a VSN. Extensive experiments show that JPISR outperforms five state-of-the-art methods in terms of both PSNR, SSIM and visual perception. PMID:26927114

Law of Large Numbers: The Theory, Applications and Technology-Based Education

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas; Gould, Robert

2009-01-01

Modern approaches for technology-based blended education utilize a variety of recently developed novel pedagogical, computational and network resources. Such attempts employ technology to deliver integrated, dynamically-linked, interactive-content and heterogeneous learning environments, which may improve student comprehension and information…

Home Learning, Technology, and Tomorrow's Workplace.

ERIC Educational Resources Information Center

Rieseberg, Rhonda L.

1995-01-01

Discusses characteristics and trends of home schools and workplaces. Use of computers and computer applications (CD-ROMS, interactive software, and networking) in home schooling provides a compatible environment for future home-based businesses and telecommuting trends. Sidebars include information on home schools on line; standardized test…

Memory Transformation Enhances Reinforcement Learning in Dynamic Environments.

PubMed

Santoro, Adam; Frankland, Paul W; Richards, Blake A

2016-11-30

Over the course of systems consolidation, there is a switch from a reliance on detailed episodic memories to generalized schematic memories. This switch is sometimes referred to as "memory transformation." Here we demonstrate a previously unappreciated benefit of memory transformation, namely, its ability to enhance reinforcement learning in a dynamic environment. We developed a neural network that is trained to find rewards in a foraging task where reward locations are continuously changing. The network can use memories for specific locations (episodic memories) and statistical patterns of locations (schematic memories) to guide its search. We find that switching from an episodic to a schematic strategy over time leads to enhanced performance due to the tendency for the reward location to be highly correlated with itself in the short-term, but regress to a stable distribution in the long-term. We also show that the statistics of the environment determine the optimal utilization of both types of memory. Our work recasts the theoretical question of why memory transformation occurs, shifting the focus from the avoidance of memory interference toward the enhancement of reinforcement learning across multiple timescales. As time passes, memories transform from a highly detailed state to a more gist-like state, in a process called "memory transformation." Theories of memory transformation speak to its advantages in terms of reducing memory interference, increasing memory robustness, and building models of the environment. However, the role of memory transformation from the perspective of an agent that continuously acts and receives reward in its environment is not well explored. In this work, we demonstrate a view of memory transformation that defines it as a way of optimizing behavior across multiple timescales. Copyright © 2016 the authors 0270-6474/16/3612228-15$15.00/0.

Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning

NASA Astrophysics Data System (ADS)

Zhao, Lei; Wang, Zengcai; Wang, Xiaojin; Qi, Yazhou; Liu, Qing; Zhang, Guoxin

2016-09-01

Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%.

Confidence and psychosis: a neuro-computational account of contingency learning disruption by NMDA blockade

PubMed Central

Vinckier, F; Gaillard, R; Palminteri, S; Rigoux, L; Salvador, A; Fornito, A; Adapa, R; Krebs, M O; Pessiglione, M; Fletcher, P C

2016-01-01

A state of pathological uncertainty about environmental regularities might represent a key step in the pathway to psychotic illness. Early psychosis can be investigated in healthy volunteers under ketamine, an NMDA receptor antagonist. Here, we explored the effects of ketamine on contingency learning using a placebo-controlled, double-blind, crossover design. During functional magnetic resonance imaging, participants performed an instrumental learning task, in which cue-outcome contingencies were probabilistic and reversed between blocks. Bayesian model comparison indicated that in such an unstable environment, reinforcement learning parameters are downregulated depending on confidence level, an adaptive mechanism that was specifically disrupted by ketamine administration. Drug effects were underpinned by altered neural activity in a fronto-parietal network, which reflected the confidence-based shift to exploitation of learned contingencies. Our findings suggest that an early characteristic of psychosis lies in a persistent doubt that undermines the stabilization of behavioral policy resulting in a failure to exploit regularities in the environment. PMID:26055423

Confidence and psychosis: a neuro-computational account of contingency learning disruption by NMDA blockade.

PubMed

Vinckier, F; Gaillard, R; Palminteri, S; Rigoux, L; Salvador, A; Fornito, A; Adapa, R; Krebs, M O; Pessiglione, M; Fletcher, P C

2016-07-01

A state of pathological uncertainty about environmental regularities might represent a key step in the pathway to psychotic illness. Early psychosis can be investigated in healthy volunteers under ketamine, an NMDA receptor antagonist. Here, we explored the effects of ketamine on contingency learning using a placebo-controlled, double-blind, crossover design. During functional magnetic resonance imaging, participants performed an instrumental learning task, in which cue-outcome contingencies were probabilistic and reversed between blocks. Bayesian model comparison indicated that in such an unstable environment, reinforcement learning parameters are downregulated depending on confidence level, an adaptive mechanism that was specifically disrupted by ketamine administration. Drug effects were underpinned by altered neural activity in a fronto-parietal network, which reflected the confidence-based shift to exploitation of learned contingencies. Our findings suggest that an early characteristic of psychosis lies in a persistent doubt that undermines the stabilization of behavioral policy resulting in a failure to exploit regularities in the environment.

Creating supportive nutrition environments for population health impact and health equity: an overview of the Nutrition and Obesity Policy Research and Evaluation Network's efforts.

PubMed

Blanck, Heidi M; Kim, Sonia A

2012-09-01

Childhood obesity is a major threat to individual health and society overall. Policies that support healthier food and beverage choices have been endorsed by many decision makers. These policies may reach a large proportion of the population or in some circumstances aim to reduce nutrition disparities to ensure health equity. The Nutrition and Obesity Policy Research and Evaluation Network (NOPREN) evaluates policy as a tool to improve food and beverage environments where Americans live, work, play, and learn. The network aspires to address research and evaluation gaps related to relevant policies, create standardized research tools, and help build the evidence base of effective policy solutions for childhood obesity prevention with a focus on reach, equity, cost effectiveness, and sustainability. Published by Elsevier Inc.

[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.

Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

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

Aziz, H. M. Abdul; Zhu, Feng; Ukkusuri, Satish V.

Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better atmore » higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO 2, NO x, VOC, PM 10) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.« less

SuperSchools: Education in the Information Age and Beyond.

ERIC Educational Resources Information Center

Ameritech Foundation, Chicago, IL.

This document discusses how improvements in the capabilities of the intelligent communications network are making new enhancements and advances available to educators, administrators, students, parents, and the community, focusing on the role of Ameritech. Modern technologies can create dynamic and appropriate learning environments for children…

ViNEL: A Virtual Networking Lab for Cyber Defense Education

ERIC Educational Resources Information Center

Reinicke, Bryan; Baker, Elizabeth; Toothman, Callie

2018-01-01

Professors teaching cyber security classes often face challenges when developing workshops for their students: How does one quickly and efficiently configure and deploy an operating system for a temporary learning/testing environment? Faculty teaching these classes spend countless hours installing, configuring and deploying multiple system…

Graduate Inquiry: Social Capital in Online Courses

ERIC Educational Resources Information Center

Mays, Thomas

2016-01-01

As colleges and universities increase their online course offerings, student social experiences in online learning environments require further examination, specifically for nonresidential students who may already be less integrated into college social networks. A social capital framework was used to guide this qualitative study of 17…

Developing Digital Portfolios for Childhood Education. Research Reports.

ERIC Educational Resources Information Center

Kankaanranta, Marja

This action research study developed, explored, and analyzed the use of digital portfolios as a multiperspective ecological assessment method in primary education learning environments in Finland. Participating in the study were kindergarten and primary school teachers who were challenged and encouraged to utilize networking and digital portfolios…

Dynamic gesture recognition using neural networks: a fundament for advanced interaction construction

NASA Astrophysics Data System (ADS)

Boehm, Klaus; Broll, Wolfgang; Sokolewicz, Michael A.

1994-04-01

Interaction in virtual reality environments is still a challenging task. Static hand posture recognition is currently the most common and widely used method for interaction using glove input devices. In order to improve the naturalness of interaction, and thereby decrease the user-interface learning time, there is a need to be able to recognize dynamic gestures. In this paper we describe our approach to overcoming the difficulties of dynamic gesture recognition (DGR) using neural networks. Backpropagation neural networks have already proven themselves to be appropriate and efficient for posture recognition. However, the extensive amount of data involved in DGR requires a different approach. Because of features such as topology preservation and automatic-learning, Kohonen Feature Maps are particularly suitable for the reduction of the high dimensional data space that is the result of a dynamic gesture, and are thus implemented for this task.

Vector-based navigation using grid-like representations in artificial agents.

PubMed

Banino, Andrea; Barry, Caswell; Uria, Benigno; Blundell, Charles; Lillicrap, Timothy; Mirowski, Piotr; Pritzel, Alexander; Chadwick, Martin J; Degris, Thomas; Modayil, Joseph; Wayne, Greg; Soyer, Hubert; Viola, Fabio; Zhang, Brian; Goroshin, Ross; Rabinowitz, Neil; Pascanu, Razvan; Beattie, Charlie; Petersen, Stig; Sadik, Amir; Gaffney, Stephen; King, Helen; Kavukcuoglu, Koray; Hassabis, Demis; Hadsell, Raia; Kumaran, Dharshan

2018-05-01

Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go 1,2 . Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning 3-5 failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex 6 . Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space 7,8 and is critical for integrating self-motion (path integration) 6,7,9 and planning direct trajectories to goals (vector-based navigation) 7,10,11 . Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types 12 . We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments-optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation 7,10,11 , demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments.

Aligning physical learning spaces with the curriculum: AMEE Guide No. 107.

PubMed

Nordquist, Jonas; Sundberg, Kristina; Laing, Andrew

2016-08-01

This Guide explores emerging issues on the alignment of learning spaces with the changing curriculum in medical education. As technology and new teaching methods have altered the nature of learning in medical education, it is necessary to re-think how physical learning spaces are aligned with the curriculum. The better alignment of learning spaces with the curriculum depends on more directly engaged leadership from faculty and the community of medical education for briefing the requirements for the design of all kinds of learning spaces. However, there is a lack of precedent and well-established processes as to how new kinds of learning spaces should be programmed. Such programmes are essential aspects of optimizing the intended experience of the curriculum. Faculty and the learning community need better tools and instruments to support their leadership role in briefing and programming. A Guide to critical concepts for exploring the alignment of curriculum and learning spaces is provided. The idea of a networked learning landscape is introduced as a way of assessing and evaluating the alignment of physical spaces to the emerging curriculum. The concept is used to explore how technology has widened the range of spaces and places in which learning happens as well as enabling new styles of learning. The networked learning landscaped is explored through four different scales within which learning is accommodated: the classroom, the building, the campus, and the city. High-level guidance on the process of briefing for the networked learning landscape is provided, to take into account the wider scale of learning spaces and the impact of technology. Key to a successful measurement process is argued to be the involvement of relevant academic stakeholders who can identify the strategic direction and purpose for the design of the learning environments in relation to the emerging demands of the curriculum.

A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load

NASA Astrophysics Data System (ADS)

Zhang, Wei; Li, Chuanhao; Peng, Gaoliang; Chen, Yuanhang; Zhang, Zhujun

2018-02-01

In recent years, intelligent fault diagnosis algorithms using machine learning technique have achieved much success. However, due to the fact that in real world industrial applications, the working load is changing all the time and noise from the working environment is inevitable, degradation of the performance of intelligent fault diagnosis methods is very serious. In this paper, a new model based on deep learning is proposed to address the problem. Our contributions of include: First, we proposed an end-to-end method that takes raw temporal signals as inputs and thus doesn't need any time consuming denoising preprocessing. The model can achieve pretty high accuracy under noisy environment. Second, the model does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working load is changed. To understand the proposed model, we will visualize the learned features, and try to analyze the reasons behind the high performance of the model.

Creating Successful Scientist-Teacher-Student Collaborations: Examples From the GLOBE Program

NASA Astrophysics Data System (ADS)

Geary, E.; Wright, E.; Yule, S.; Randolph, G.; Larsen, J.; Smith, D.

2007-12-01

Actively engaging students in research on the environment at local, regional, and globe scales is a primary objective of the GLOBE (Global Learning and Observations to Benefit the Environment) Program. During the past 18 months, GLOBE, an international education and science program in 109 countries and tens of thousands of schools worldwide, has been working with four NSF-funded Earth System Science Projects to involve K-12 students, teachers, and scientists in collaborative research investigations of Seasons and Biomes, the Carbon Cycle, Local and Extreme Environments, and Watersheds. This talk will discuss progress to date in each of these investigation areas and highlight successes and challenges in creating effective partnerships between diverse scientific and educational stakeholders. More specifically we will discuss lessons learned in the following areas: (a) mutual goal and responsibility setting, (b) resource allocation, (c) development of adaptable learning activities, tools, and services, (d) creation of scientist and school networks, and (e) development of evaluation metrics, all in support of student research.

An Intelligent System for Monitoring the Microgravity Environment Quality On-Board the International Space Station

NASA Technical Reports Server (NTRS)

Lin, Paul P.; Jules, Kenol

2002-01-01

An intelligent system for monitoring the microgravity environment quality on-board the International Space Station is presented. The monitoring system uses a new approach combining Kohonen's self-organizing feature map, learning vector quantization, and back propagation neural network to recognize and classify the known and unknown patterns. Finally, fuzzy logic is used to assess the level of confidence associated with each vibrating source activation detected by the system.

Social network size can influence linguistic malleability and the propagation of linguistic change.

PubMed

Lev-Ari, Shiri

2018-07-01

We learn language from our social environment, but the more sources we have, the less informative each source is, and therefore, the less weight we ascribe its input. According to this principle, people with larger social networks should give less weight to new incoming information, and should therefore be less susceptible to the influence of new speakers. This paper tests this prediction, and shows that speakers with smaller social networks indeed have more malleable linguistic representations. In particular, they are more likely to adjust their lexical boundary following exposure to a new speaker. Experiment 2 uses computational simulations to test whether this greater malleability could lead people with smaller social networks to be important for the propagation of linguistic change despite the fact that they interact with fewer people. The results indicate that when innovators were connected with people with smaller rather than larger social networks, the population exhibited greater and faster diffusion. Together these experiments show that the properties of people's social networks can influence individuals' learning and use as well as linguistic phenomena at the community level. Copyright © 2018 Elsevier B.V. All rights reserved.

On the way to the smart education in the cloud: The experience of using a virtual learning environment and webinars in educational and career guidance process

NASA Astrophysics Data System (ADS)

Lapshinsky, V. A.

2017-01-01

The article is devoted to the consideration of issues of functionality and application of educational portal as virtual learning environments and webinars as SaaS services. Examples of their use in educational and vocational guidance processes are presented. The prospects of transition from portal VLE to SaaS and cloud services are marked. Portal www.valinfo.ru with original learning management system has been used in the educational process since 2003 in the National Research Nuclear University MEPhI and in the Peoples' Friendship University of Russia. Supported courses: Computer Science, Computer Workshop, Networks, Information Technology, The Introduction to Nano-Engineer, Nanotechnology and Nanomaterials etc. For webinars as SaaS services, used the "virtual classroom," kindly provided by WebSoft Company.

Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

PubMed

Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

2015-01-01

Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

Coalition readiness management system preliminary interoperability experiment (CReaMS PIE)

NASA Astrophysics Data System (ADS)

Clark, Peter; Ryan, Peter; Zalcman, Lucien; Robbie, Andrew

2003-09-01

The United States Navy (USN) has initiated the Coalition Readiness Management System (CReaMS) Initiative to enhance coalition warfighting readiness through advancing development of a team interoperability training and combined mission rehearsal capability. It integrates evolving cognitive team learning principles and processes with advanced technology innovations to produce an effective and efficient team learning environment. The JOint Air Navy Networking Environment (JOANNE) forms the Australian component of CReaMS. The ultimate goal is to link Australian Defence simulation systems with the USN Battle Force Tactical Training (BFTT) system to demonstrate and achieve coalition level warfare training in a synthetic battlespace. This paper discusses the initial Preliminary Interoperability Experiment (PIE) involving USN and Australian Defence establishments.

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning

PubMed Central

Turk-Browne, Nicholas B.; Botvinick, Matthew M.; Norman, Kenneth A.

2017-01-01

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway—the pathway connecting entorhinal cortex directly to region CA1—was able to support statistical learning, while the trisynaptic pathway—connecting entorhinal cortex to CA1 through dentate gyrus and CA3—learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872368

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning.

PubMed

Schapiro, Anna C; Turk-Browne, Nicholas B; Botvinick, Matthew M; Norman, Kenneth A

2017-01-05

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway-the pathway connecting entorhinal cortex directly to region CA1-was able to support statistical learning, while the trisynaptic pathway-connecting entorhinal cortex to CA1 through dentate gyrus and CA3-learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

The Five Central Psychological Challenges Facing Effective Mobile Learning

ERIC Educational Resources Information Center

Terras, Melody M.; Ramsay, Judith

2012-01-01

Web 2.0 technology not only offers the opportunity of massively parallel interconnected networks that support the provision of information and communication anytime and anywhere but also offers immense opportunities for collaboration and sharing of user-generated content. This information-rich environment may support both formal and informal…

Factors Affecting Information Seeking and Evaluation in a Distributed Learning Environment

ERIC Educational Resources Information Center

Lee, Jae-Shin; Cho, Hichang

2011-01-01

The purpose of this study was to identify and analyze the processes of seeking information online and evaluating this information. We hypothesized that individuals' social network, in-out group categorization, and cultural proclivity would influence their online information-seeking behavior. Also, we tested whether individuals differentiated…

Networked Interactive Video for Group Training

ERIC Educational Resources Information Center

Eary, John

2008-01-01

The National Computing Centre (NCC) has developed an interactive video training system for the Scottish Police College to help train police supervisory officers in crowd control at major spectator events, such as football matches. This approach involves technology-enhanced training in a group-learning environment, and may have significant impact…

After High School: The Status of Youth with Emotional and Behavioral Disorders.

ERIC Educational Resources Information Center

Walker, Rhonda; Bunsen, Teresa D.

1995-01-01

This literature review examined the current status of young adults with emotional/behavioral disorders (EBD) two to five years after leaving high school, in employment, residential environment, social and interpersonal networks, dependency, and learned helplessness. Suggestions for school-implemented community adjustment programs are offered. (DB)

The Arts 3D VLE Metaverse as a Network of Imagination

ERIC Educational Resources Information Center

Rauch, Ulrich; Cohodas, Marvin; Wang, Tim

2009-01-01

Ulrich Rauch, Marvin Cohodas, and Tim Wang describe the Arts Metaverse, a Croquet-based virtual learning environment under development at the University of British Columbia. The Arts Metaverse allows three-dimensional virtual reconstruction of important artifacts and sites of classical, ancient, and indigenous American art, thereby allowing…

Preservice Teachers' Learning among Students with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Power, Anne; Costley, Debra

2014-01-01

This article reports on a collaborative venture between Autism Spectrum Australia and the University of Western Sydney, New South Wales, Australia. The Social Club network was formed for children and adolescents to provide structured opportunities for positive peer interactions in safe, stimulating and nonjudgmental environments. The Social Clubs…

MOOs for Teaching and Learning.

ERIC Educational Resources Information Center

Furst-Bowe, Julie

1996-01-01

Discusses the use of MOOs (Multi-User Dimension/Dungeon Object Oriented), text-based virtual reality environments, in education. Highlights include connecting to a network; exploring several MOOs to determine which is most appropriate; and familiarizing students with the MOO's interaction and behavior policies, as well as how to operate in the…

Giving Life to Data: University-Community Partnerships in Addressing HIV and AIDS through Building Digital Archives

ERIC Educational Resources Information Center

de Lange, Naydene; Mnisi, Thoko; Mitchell, Claudia; Park, Eun G.

2010-01-01

The partnerships, especially university-community partnerships, that sustain globally networked learning environments often face challenges in mobilizing research to empower local communities to effect change. This article examines these challenges by describing a university-community partnership involving researchers and graduate students in…

Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer

NASA Astrophysics Data System (ADS)

Ruske, Simon; Topping, David O.; Foot, Virginia E.; Kaye, Paul H.; Stanley, Warren R.; Crawford, Ian; Morse, Andrew P.; Gallagher, Martin W.

2017-03-01

Characterisation of bioaerosols has important implications within environment and public health sectors. Recent developments in ultraviolet light-induced fluorescence (UV-LIF) detectors such as the Wideband Integrated Bioaerosol Spectrometer (WIBS) and the newly introduced Multiparameter Bioaerosol Spectrometer (MBS) have allowed for the real-time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal spores and pollen.This new generation of instruments has enabled ever larger data sets to be compiled with the aim of studying more complex environments. In real world data sets, particularly those from an urban environment, the population may be dominated by non-biological fluorescent interferents, bringing into question the accuracy of measurements of quantities such as concentrations. It is therefore imperative that we validate the performance of different algorithms which can be used for the task of classification.For unsupervised learning we tested hierarchical agglomerative clustering with various different linkages. For supervised learning, 11 methods were tested, including decision trees, ensemble methods (random forests, gradient boosting and AdaBoost), two implementations for support vector machines (libsvm and liblinear) and Gaussian methods (Gaussian naïve Bayesian, quadratic and linear discriminant analysis, the k-nearest neighbours algorithm and artificial neural networks).The methods were applied to two different data sets produced using the new MBS, which provides multichannel UV-LIF fluorescence signatures for single airborne biological particles. The first data set contained mixed PSLs and the second contained a variety of laboratory-generated aerosol.Clustering in general performs slightly worse than the supervised learning methods, correctly classifying, at best, only 67. 6 and 91. 1 % for the two data sets respectively. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 82. 8 and 98. 27 % of the testing data, respectively, across the two data sets.A possible alternative to gradient boosting is neural networks. We do however note that this method requires much more user input than the other methods, and we suggest that further research should be conducted using this method, especially using parallelised hardware such as the GPU, which would allow for larger networks to be trained, which could possibly yield better results.We also saw that some methods, such as clustering, failed to utilise the additional shape information provided by the instrument, whilst for others, such as the decision trees, ensemble methods and neural networks, improved performance could be attained with the inclusion of such information.

A multi-agent intelligent environment for medical knowledge.

PubMed

Vicari, Rosa M; Flores, Cecilia D; Silvestre, André M; Seixas, Louise J; Ladeira, Marcelo; Coelho, Helder

2003-03-01

AMPLIA is a multi-agent intelligent learning environment designed to support training of diagnostic reasoning and modelling of domains with complex and uncertain knowledge. AMPLIA focuses on the medical area. It is a system that deals with uncertainty under the Bayesian network approach, where learner-modelling tasks will consist of creating a Bayesian network for a problem the system will present. The construction of a network involves qualitative and quantitative aspects. The qualitative part concerns the network topology, that is, causal relations among the domain variables. After it is ready, the quantitative part is specified. It is composed of the distribution of conditional probability of the variables represented. A negotiation process (managed by an intelligent MediatorAgent) will treat the differences of topology and probability distribution between the model the learner built and the one built-in in the system. That negotiation process occurs between the agents that represent the expert knowledge domain (DomainAgent) and the agent that represents the learner knowledge (LearnerAgent).

eLoom and Flatland: specification, simulation and visualization engines for the study of arbitrary hierarchical neural architectures.

PubMed

Caudell, Thomas P; Xiao, Yunhai; Healy, Michael J

2003-01-01

eLoom is an open source graph simulation software tool, developed at the University of New Mexico (UNM), that enables users to specify and simulate neural network models. Its specification language and libraries enables users to construct and simulate arbitrary, potentially hierarchical network structures on serial and parallel processing systems. In addition, eLoom is integrated with UNM's Flatland, an open source virtual environments development tool to provide real-time visualizations of the network structure and activity. Visualization is a useful method for understanding both learning and computation in artificial neural networks. Through 3D animated pictorially representations of the state and flow of information in the network, a better understanding of network functionality is achieved. ART-1, LAPART-II, MLP, and SOM neural networks are presented to illustrate eLoom and Flatland's capabilities.

Comparative Studies of Prediction Strategies for Solar X-ray Time Series

NASA Astrophysics Data System (ADS)

Muranushi, T.; Hattori, T.; Jin, Q.; Hishinuma, T.; Tominaga, M.; Nakagawa, K.; Fujiwara, Y.; Nakamura, T.; Sakaue, T.; Takahashi, T.; Seki, D.; Namekata, K.; Tei, A.; Ban, M.; Kawamura, A. D.; Hada-Muranushi, Y.; Asai, A.; Nemoto, S.; Shibata, K.

2016-12-01

Crucial virtues for operational space weather forecast are real-timeforecast ability, forecast precision and customizability to userneeds. The recent development of deep-learning makes it veryattractive to space weather, because (1) it learns gradually incomingdata, (2) it exhibits superior accuracy over conventional algorithmsin many fields, and (3) it makes the customization of the forecasteasier because it accepts raw images.However, the best deep-learning applications are only attainable bycareful human designers that understands both the mechanism of deeplearning and the application field. Therefore, we need to foster youngresearchers to enter the field of machine-learning aided forecast. So,we have held a seminar every Monday with undergraduate and graduatestudents from May to August 2016.We will review the current status of space weather science and theautomated real-time space weather forecast engine UFCORIN. Then, weintroduce the deep-learning space weather forecast environments wehave set up using Python and Chainer on students' laptop computers.We have started from simple image classification neural network, thenimplemented space-weather neural network that predicts future X-rayflux of the Sun based on the past X-ray lightcurve and magnetic fieldline-of-sight images.In order to perform each forecast faster, we have focused on simplelightcurve-to-lightcurve forecast, and performed comparative surveysby changing following parameters: The size and topology of the neural network Batchsize Neural network hyperparameters such as learning rates to optimize the preduction accuracy, and time for prediction.We have found how to design compact, fast but accurate neural networkto perform forecast. Our forecasters can perform predictionexperiment for four-year timespan in a few minutes, and achieveslog-scale errors of the order of 1. Our studies is ongoing, and inour talk we will review our progress till December.

Robust visual tracking via multiscale deep sparse networks

NASA Astrophysics Data System (ADS)

Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

2017-04-01

In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

NASA Astrophysics Data System (ADS)

Vaccaro, James M.; Yaworsky, Paul S.

1999-06-01

Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.

Design and implementation of a random neural network routing engine.

PubMed

Kocak, T; Seeber, J; Terzioglu, H

2003-01-01

Random neural network (RNN) is an analytically tractable spiked neural network model that has been implemented in software for a wide range of applications for over a decade. This paper presents the hardware implementation of the RNN model. Recently, cognitive packet networks (CPN) is proposed as an alternative packet network architecture where there is no routing table, instead the RNN based reinforcement learning is used to route packets. Particularly, we describe implementation details for the RNN based routing engine of a CPN network processor chip: the smart packet processor (SPP). The SPP is a dual port device that stores, modifies, and interprets the defining characteristics of multiple RNN models. In addition to hardware design improvements over the software implementation such as the dual access memory, output calculation step, and reduced output calculation module, this paper introduces a major modification to the reinforcement learning algorithm used in the original CPN specification such that the number of weight terms are reduced from 2n/sup 2/ to 2n. This not only yields significant memory savings, but it also simplifies the calculations for the steady state probabilities (neuron outputs in RNN). Simulations have been conducted to confirm the proper functionality for the isolated SPP design as well as for the multiple SPP's in a networked environment.

Automatic Classification of volcano-seismic events based on Deep Neural Networks.

NASA Astrophysics Data System (ADS)

Titos Luzón, M.; Bueno Rodriguez, A.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

2017-12-01

Seismic monitoring of active volcanoes is a popular remote sensing technique to detect seismic activity, often associated to energy exchanges between the volcano and the environment. As a result, seismographs register a wide range of volcano-seismic signals that reflect the nature and underlying physics of volcanic processes. Machine learning and signal processing techniques provide an appropriate framework to analyze such data. In this research, we propose a new classification framework for seismic events based on deep neural networks. Deep neural networks are composed by multiple processing layers, and can discover intrinsic patterns from the data itself. Internal parameters can be initialized using a greedy unsupervised pre-training stage, leading to an efficient training of fully connected architectures. We aim to determine the robustness of these architectures as classifiers of seven different types of seismic events recorded at "Volcán de Fuego" (Colima, Mexico). Two deep neural networks with different pre-training strategies are studied: stacked denoising autoencoder and deep belief networks. Results are compared to existing machine learning algorithms (SVM, Random Forest, Multilayer Perceptron). We used 5 LPC coefficients over three non-overlapping segments as training features in order to characterize temporal evolution, avoid redundancy and encode the signal, regardless of its duration. Experimental results show that deep architectures can classify seismic events with higher accuracy than classical algorithms, attaining up to 92% recognition accuracy. Pre-training initialization helps these models to detect events that occur simultaneously in time (such explosions and rockfalls), increase robustness against noisy inputs, and provide better generalization. These results demonstrate deep neural networks are robust classifiers, and can be deployed in real-environments to monitor the seismicity of restless volcanoes.

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.

NASA Astrophysics Data System (ADS)

Knosp, B.; Neely, S.; Zimdars, P.; Mills, B.; Vance, N.

2007-12-01

The Microwave Limb Sounder (MLS) Science Computing Facility (SCF) stores over 50 terabytes of data, has over 240 computer processing hosts, and 64 users from around the world. These resources are spread over three primary geographical locations - the Jet Propulsion Laboratory (JPL), Raytheon RIS, and New Mexico Institute of Mining and Technology (NMT). A need for a grid network system was identified and defined to solve the problem of users competing for finite, and increasingly scarce, MLS SCF computing resources. Using Sun's Grid Engine software, a grid network was successfully created in a development environment that connected the JPL and Raytheon sites, established master and slave hosts, and demonstrated that transfer queues for jobs can work among multiple clusters in the same grid network. This poster will first describe MLS SCF resources and the lessons that were learned in the design and development phase of this project. It will then go on to discuss the test environment and plans for deployment by highlighting benchmarks and user experiences.

LGBT Roundtable Discussion: Meet-up and Mentoring Discussion

NASA Astrophysics Data System (ADS)

2014-03-01

The LGBT+ Physicists group welcomes those who identify as gender sexual minorities, as LGBTQQIAAP+, or as allies to participate in a round-table discussion on mentoring physicists. The session will provide an opportunity to learn and discuss successful mentoring strategies at different career stages for physicists in all environments, including academia, industry, etc. Attendees are encouraged to attend a social event to follow the panel to continue to network. Allies are especially welcome at this event to learn how to support and mentor LGBT+ physicists.

Adaptivity in Agent-Based Routing for Data Networks

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Kirshner, Sergey; Merz, Chris J.; Turner, Kagan

2000-01-01

Adaptivity, both of the individual agents and of the interaction structure among the agents, seems indispensable for scaling up multi-agent systems (MAS s) in noisy environments. One important consideration in designing adaptive agents is choosing their action spaces to be as amenable as possible to machine learning techniques, especially to reinforcement learning (RL) techniques. One important way to have the interaction structure connecting agents itself be adaptive is to have the intentions and/or actions of the agents be in the input spaces of the other agents, much as in Stackelberg games. We consider both kinds of adaptivity in the design of a MAS to control network packet routing. We demonstrate on the OPNET event-driven network simulator the perhaps surprising fact that simply changing the action space of the agents to be better suited to RL can result in very large improvements in their potential performance: at their best settings, our learning-amenable router agents achieve throughputs up to three and one half times better than that of the standard Bellman-Ford routing algorithm, even when the Bellman-Ford protocol traffic is maintained. We then demonstrate that much of that potential improvement can be realized by having the agents learn their settings when the agent interaction structure is itself adaptive.

The Power of Large Scale Partnerships to Increase Climate Awareness and Literacy Around the World

NASA Astrophysics Data System (ADS)

Murphy, T.; Andersen, T. J.; Wegner, K.

2016-12-01

The Global Learning and Observations to Benefit the Environment (GLOBE) Program is an international science and education program that connects a network of communities around the world and gives them the opportunity to participate in data collection and the scientific process, and contribute meaningfully to our understanding of the Earth system and global environment. In the last few years, there has been an infusion of energy in the program as a result of a change to a more community focus. GLOBE was one of the first attempts at a citizen science program at the K-12 level proposed on a global scale. An initial ramp-up of the program was followed by the establishment of a network of partners in countries and within the U.S. One hundred and seventeen countries have participated in the program since its establishment in 1994. These countries are divided into six regions: Africa (23 countries); Asia and Pacific (18); Europe and Eurasia (41); Latin America and Caribbean (20); Near East and North Africa (13); and North America (2). The community within these regions has reached a maturity level that allows it to organize its own science campaigns ranging from aerosols to phenology…all of which increase awareness of climate issues. In addition, some countries within the regions have established science fairs, GLOBE proved to be the impetus for these fairs. The program's partnership network provides students and teachers with a platform for learning about climate issues in their local and global environment, as well as providing scientists with a network to organize data collection and analysis campaigns. Within the U.S., over 130 educational organizations (universities, science museums, nature centers) are members of a partner network divided into six geographical areas: Northwest; Midwest; Northeast and Mid-Atlantic; Southeast; Southwest; and Pacific. For the first time ever, the U.S. held GLOBE science fairs with considerable input and support from the community, the U.S. Partner Forum members, and U.S. Country Coordinator. GLOBE students exhibited their research and learned about climate issues at these fairs. GLOBE has evolved in 20 years and its strength is the community of partners that has helped moved climate literacy forward on a global scale.

Applying the Neurodynamics of Emotional Circular Causalities in Psychosocial and Cognitive Therapy using Multi-Sensory Environments: An ORBDE Case Study Analysis.

PubMed

Ryan, Janice

2017-10-01

This exploratory, evidence-based practice research study focuses on presenting a plausible mesoscopic brain dynamics hypothesis for the benefits of treating clients with psychosocial and cognitive challenges using a mindful therapeutic approach and multi-sensory environments. After an extensive neuroscientific review of the therapeutic benefits of mindfulness, a multi-sensory environment is presented as a window of therapeutic opportunity to more quickly and efficiently facilitate the neurobiological experience of becoming more mindful or conscious of self and environment. The complementary relationship between the default mode network and the executive attention network is offered as a neurobiological hypothesis that could explain positive occupational engagement pattern shifts in a case study video of a hospice client with advanced dementia during multi-sensory environment treatment. Orbital Decomposition is used for a video analysis that shows a significant behavioral pattern shift consistent with dampening of the perceptual system attractors that contribute to negative emotional circular causalities in a variety of client populations. This treatment approach may also prove to be valuable for any person who has developed circular causalities due to feelings of isolation, victimization, or abuse. A case is made for broader applications of this intervention that may positively influence perception during the information transfer and processing of hippocampal learning. Future research is called for to determine if positive affective, interpersonal, and occupational engagement pattern shifts during treatment are related to the improved default mode network-executive attention network synchrony characteristic of increased mindfulness.

Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

NASA Technical Reports Server (NTRS)

Ferreria, Paulo; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale

2017-01-01

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

Combining advanced networked technology and pedagogical methods to improve collaborative distance learning.

PubMed

Staccini, Pascal; Dufour, Jean-Charles; Raps, Hervé; Fieschi, Marius

2005-01-01

Making educational material be available on a network cannot be reduced to merely implementing hypermedia and interactive resources on a server. A pedagogical schema has to be defined to guide students for learning and to provide teachers with guidelines to prepare valuable and upgradeable resources. Components of a learning environment, as well as interactions between students and other roles such as author, tutor and manager, can be deduced from cognitive foundations of learning, such as the constructivist approach. Scripting the way a student will to navigate among information nodes and interact with tools to build his/her own knowledge can be a good way of deducing the features of the graphic interface related to the management of the objects. We defined a typology of pedagogical resources, their data model and their logic of use. We implemented a generic and web-based authoring and publishing platform (called J@LON for Join And Learn On the Net) within an object-oriented and open-source programming environment (called Zope) embedding a content management system (called Plone). Workflow features have been used to mark the progress of students and to trace the life cycle of resources shared by the teaching staff. The platform integrated advanced on line authoring features to create interactive exercises and support live courses diffusion. The platform engine has been generalized to the whole curriculum of medical studies in our faculty; it also supports an international master of risk management in health care and will be extent to all other continuous training diploma.

Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

NASA Technical Reports Server (NTRS)

Ferreria, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

2017-01-01

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

An Expedient Study on Back-Propagation (BPN) Neural Networks for Modeling Automated Evaluation of the Answers and Progress of Deaf Students' That Possess Basic Knowledge of the English Language and Computer Skills

NASA Astrophysics Data System (ADS)

Vrettaros, John; Vouros, George; Drigas, Athanasios S.

This article studies the expediency of using neural networks technology and the development of back-propagation networks (BPN) models for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the developed neural models is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the percentage measurement of the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process.

Environmental engineering education enhancement

NASA Astrophysics Data System (ADS)

Caporali, E.

2012-04-01

Since higher education plays a central role in the development of both human beings and modern societies, enhancing social, cultural and economic development, active citizenship, ethical values and expertises for a sustainable growth, environment respectful, the European Commission promotes a wide range of programmes. Among the EC programmes, the TEMPUS - Trans European Mobility Programme for University Studies, with the support of the DG EAC of the European Commission, has contributed to many aspects of general interest for higher education. Curricula harmonization, LifeLong Learning Programme development, ICT use, quality assessment, accreditation, innovation learning methods, growth of networks of institutions trusting each other, are the focused aspects. Such a solid cooperation framework is surely among the main outcomes of the TEMPUS Projects leaded by the University of Firenze UNIFI (Italy), DEREC - Development of Environment and Resources Engineering Curriculum (2005-2008), and its spin-off DEREL - Development of Environment and Resources Engineering Learning (2010-2013), and VICES - Videoconferencing Educational Services (2009-2012). DEREC and DEREL TEMPUS projects, through the co-operation of Universities in Italy, Austria, Germany, Greece, Macedonia, Albania and Serbia, are aimed at the development of first and second level curricula in "Environment and Resources Engineering" at the Ss. Cyril and Methodius University - UKIM Skopje (MK). In the DEREC Project the conditions for offering a joint degree title in the field of Environmental Engineering between UNIFI and UKIM Skopje were fulfilled and a shared educational programme leading to the mutual recognition of degree titles was defined. The DEREL project, as logical continuation of DEREC, is aimed to introduce a new, up-to-date, postgraduate second level curriculum in Environment and Resources Engineering at UKIM Skopje, University of Novi Sad (RS) and Polytechnic University of Tirana (AL). following the criteria and conditions for setting up a Joint Postgraduate Degree. A second objective foreseen the implementation of a sustainable regional network aimed at: offering lifelong learning seminars for environment and resources engineering education and training of interested stakeholders; organizing workshops focused on strengthening the links in the knowledge triangle: environment education-innovation-research, with participation of postgraduate students, public services, enterprises and NGO's. The strength of the knowledge triangle implies new educational requirements, stimulated by innovative telecommunication technologies together with novel educational materials and methodologies, and lead the development of distance learning environment. In order to provide the basis of distance learning environments based on video conferencing systems and the methodology of blended learning courses, the TEMPUS Project VICES - Videoconferencing Educational Services (2009-2012) was carried out by UNIFI with the cooperation of consortium members which includes Universities in Italy, Belgium, Hungary, Macedonia, Albania and Serbia. Within ViCES, a case study implemented in the framework of DEREC project, confirmed the positive impacts of videoconference systems within the educational context, i.e. intensification of cooperation among different education and research institutions; sharing for students and teachers of educational expertise and methods with foreign colleagues; sharing experiences and case studies as well as objectives and results in the framework of both education and research activities.

CyberCIEGE Scenario Illustrating Software Integrity Issues and Management of Air-Gapped Networks in a Military Environment

DTIC Science & Technology

2005-12-01

courses in which students learn programming techniques using gaming software models . As part of the effort, teaching modules and entire courses will......assets. The United States Navy recently implemented new policies to restrict service personnel’s use of commercial, web- based email applications in an

Quality through Networking--From Reactive Administration to Proactive Cooperation

ERIC Educational Resources Information Center

Karttu, Petri; Kiilunen, Liisa; Laulajainen, Tiina

2012-01-01

The Language Centre functions as an independent institute within the University of Helsinki. It provides services to all the faculties and units of the university by offering language services and doing its share in creating an international learning environment. Together, the Language Centre and the faculties have one goal in common, namely, a…

A Dialogic Action Perspective on Open Collective Inquiry in Online Forums

ERIC Educational Resources Information Center

Jung, Yusun

2012-01-01

In today's networked environment, online forums emerge as a popular form of social structures that have greater opportunities for learning in various organizational contexts. A plethora of studies have investigated the phenomenon to identify antecedent of its success, such as individual characteristics and organizational structure. However,…

Culturally Sensitive IS Teaching: Lessons Learned to Manage Motivation Issues

ERIC Educational Resources Information Center

Chen, Wenshin

2011-01-01

This paper seeks to raise awareness of culturally sensitive teaching that is largely overlooked in the IS teaching community. In a global, networked environment commonly faced by the contemporary business or academic world, it is imperative to prepare future IT professionals with adequate cultural understanding of such a multicultural environment…

Past, Present, and Future Trends in Teaching Clinical Skills through Web-Based Learning Environments

ERIC Educational Resources Information Center

Coe Regan, Jo Ann R.; Youn, Eric J.

2008-01-01

Distance education in social work has grown significantly due to the use of interactive television and computer networks. Given the recent developments in delivering distance education utilizing Web-based technology, this article presents a literature review focused on identifying generational trends in the development of Web-based learning…

Collection Directions: The Evolution of Library Collections and Collecting

ERIC Educational Resources Information Center

Dempsey, Lorcan; Malpas, Constance; Lavoie, Brian

2014-01-01

This article takes a broad view of the evolution of collecting behaviors in a network environment and suggests some future directions based on various simple models. The authors look at the changing dynamics of print collections, at the greater engagement with research and learning behaviors, and at trends in scholarly communication. The goal is…

Using Social Media to Improve Student-Instructor Communication in an Online Learning Environment

ERIC Educational Resources Information Center

Guo, Rong; Shen, Yide; Li, Lei

2018-01-01

The lack of effective faculty-student interaction has been identified as a main contributor to the high dropout rate in online education. For this paper, the authors conducted an empirical study using a social networking tool, specifically Facebook, to improve student-instructor communication and student performance in an online learning…

The Organization of the Distance Teaching Sub-System in an Open University.

ERIC Educational Resources Information Center

Chacon, Fabio J.

The problem of finding an adequate organization for the distance teaching subsystem in the Open University of Venezuela (Universidad Nacional Abierta) is analyzed. Problems facing this subsystem concern: communications with the headquarters and within the learning centers network, interaction with the environment in order to create a favorable…

An ANN That Applies Pragmatic Decision on Texts.

ERIC Educational Resources Information Center

Aretoulaki, Maria; Tsujii, Jun-ichi

A computer-based artificial neural network (ANN) that learns to classify sentences in a text as important or unimportant is described. The program is designed to select the sentences that are important enough to be included in composition of an abstract of the text. The ANN is embedded in a conventional symbolic environment consisting of…

Adding Interactivity to a Non-Interative Class

ERIC Educational Resources Information Center

Rogers, Gary; Krichen, Jack

2004-01-01

The IT 3050 course at Capella University is an introduction to fundamental computer networking. This course is one of the required courses in the Bachelor of Science in Information Technology program. In order to provide a more enriched learning environment for learners, Capella has significantly modified this class (and others) by infusing it…

Science and Math in the Library Media Center Using GLOBE.

ERIC Educational Resources Information Center

Aquino, Teresa L.; Levine, Elissa R.

2003-01-01

Describes the Global Learning and Observations to Benefit the Environment (GLOBE) program which helps school library media specialists and science and math teachers bring earth science, math, information literacy, information technology, and student inquiry into the classroom. Discusses use of the Internet to create a global network to study the…

Text-Based MOOing in Educational Practice: Experiences of Disinhibition

ERIC Educational Resources Information Center

Chester, Andrea

2006-01-01

Purpose: The purpose of this paper is to describe educational MOOs--MUD, object-oriented (text-based, network-accessible virtual environments) and explore how teaching and learning in such a context impacts on students' inhibitions. Design/methodology/approach: Students enrolled in a course on the psychology of cyberspace interacted for 12 weeks…

The Development of Virtual Educational Environments to Support Inter-School Collaboration

ERIC Educational Resources Information Center

Stevens, Ken

2007-01-01

The introduction of inter-school electronic networks has added a new dimension to education in Canada that has many implications for students who attend schools in rural communities. Collaborative internet-based teaching and learning and the creation of virtual classes within regional intranets now complement traditional on-site instruction in…

Cloud Computing in Support of Applied Learning: A Baseline Study of Infrastructure Design at Southern Polytechnic State University

ERIC Educational Resources Information Center

Conn, Samuel S.; Reichgelt, Han

2013-01-01

Cloud computing represents an architecture and paradigm of computing designed to deliver infrastructure, platforms, and software as constructible computing resources on demand to networked users. As campuses are challenged to better accommodate academic needs for applications and computing environments, cloud computing can provide an accommodating…

Cyber Operations Virtual Environment

DTIC Science & Technology

2010-09-01

automated system affects reliance on that system (e.g., Dzindolet, Peterson , Pomranky, Pierce, & Beck, 2003; Lee & Moray, 1994; Lee & See, 2004...described a need for instruction to enable interactive, realistic training ( Hershey , 2008): Network Warfare and Operations Distributed Training...knowledge or needs beyond this shallow level (Beck, Stern, & Haugsjaa, 1996 ). The immediate feedback model employed in behaviorist learning has

One for All: Maintaining a Single Schedule Database for Large Development Projects

NASA Technical Reports Server (NTRS)

Hilscher, R.; Howerton, G.

1999-01-01

Efficiently maintaining and controlling a single schedule database in an Integrated Product Team environment is a significant challenge. It's accomplished effectively with the right combination of tools, skills, strategy, creativity, and teamwork. We'll share our lessons learned maintaining a 20,000 plus task network on a 36 month project.

Synaptic connectivity and spatial memory: a topological approach

NASA Astrophysics Data System (ADS)

Milton, Russell; Babichev, Andrey; Dabaghian, Yuri

2015-03-01

In the hippocampus, a network of place cells generates a cognitive map of space, in which each cell is responsive to a particular area of the environment - its place field. The peak response of each cell and the size of each place field have considerable variability. Experimental evidence suggests that place cells encode a topological map of space that serves as a basis of spatial memory and spatial awareness. Using a computational model based on Persistent Homology Theory we demonstrate that if the parameters of the place cells spiking activity fall inside of the physiological range, the network correctly encodes the topological features of the environment. We next introduce parameters of synaptic connectivity into the model and demonstrate that failures in synapses that detect coincident neuronal activity lead to spatial learning deficiencies similar to the ones that are observed in rodent models of neurodegenerative diseases. Moreover, we show that these learning deficiencies may be mitigated by increasing the number of active cells and/or by increasing their firing rate, suggesting the existence of a compensatory mechanism inherent to the cognitive map.

GrDHP: a general utility function representation for dual heuristic dynamic programming.

PubMed

Ni, Zhen; He, Haibo; Zhao, Dongbin; Xu, Xin; Prokhorov, Danil V

2015-03-01

A general utility function representation is proposed to provide the required derivable and adjustable utility function for the dual heuristic dynamic programming (DHP) design. Goal representation DHP (GrDHP) is presented with a goal network being on top of the traditional DHP design. This goal network provides a general mapping between the system states and the derivatives of the utility function. With this proposed architecture, we can obtain the required derivatives of the utility function directly from the goal network. In addition, instead of a fixed predefined utility function in literature, we conduct an online learning process for the goal network so that the derivatives of the utility function can be adaptively tuned over time. We provide the control performance of both the proposed GrDHP and the traditional DHP approaches under the same environment and parameter settings. The statistical simulation results and the snapshot of the system variables are presented to demonstrate the improved learning and controlling performance. We also apply both approaches to a power system example to further demonstrate the control capabilities of the GrDHP approach.

A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks.

PubMed

Lin, Yun; Wang, Chao; Wang, Jiaxing; Dou, Zheng

2016-10-12

Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks. To address this problem, in this paper a new initialization mechanism is proposed to establish a communication link and set up a sensor network without adopting spectrum holes to convey control information. Specifically, firstly a transmission channel model for analyzing the maximum accessible capacity for three different polices in a fading environment is discussed. Secondly, a hybrid spectrum access algorithm based on a reinforcement learning model is proposed for the power allocation problem of both the transmission channel and the control channel. Finally, extensive simulations have been conducted and simulation results show that this new algorithm provides a significant improvement in terms of the tradeoff between the control channel reliability and the efficiency of the transmission channel.

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

PubMed

Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwairie; Khalid, Marzuki

2015-05-01

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

Identifying Corresponding Patches in SAR and Optical Images With a Pseudo-Siamese CNN

NASA Astrophysics Data System (ADS)

Hughes, Lloyd H.; Schmitt, Michael; Mou, Lichao; Wang, Yuanyuan; Zhu, Xiao Xiang

2018-05-01

In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross-entropy, we achieve a one-hot indication if two patches correspond or not. The network is trained and tested on an automatically generated dataset that is based on a deterministic alignment of SAR and optical imagery via previously reconstructed and subsequently co-registered 3D point clouds. The satellite images, from which the patches comprising our dataset are extracted, show a complex urban scene containing many elevated objects (i.e. buildings), thus providing one of the most difficult experimental environments. The achieved results show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development towards a generalized multi-sensor key-point matching procedure. Index Terms-synthetic aperture radar (SAR), optical imagery, data fusion, deep learning, convolutional neural networks (CNN), image matching, deep matching

A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks

PubMed Central

Lin, Yun; Wang, Chao; Wang, Jiaxing; Dou, Zheng

2016-01-01

Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks. To address this problem, in this paper a new initialization mechanism is proposed to establish a communication link and set up a sensor network without adopting spectrum holes to convey control information. Specifically, firstly a transmission channel model for analyzing the maximum accessible capacity for three different polices in a fading environment is discussed. Secondly, a hybrid spectrum access algorithm based on a reinforcement learning model is proposed for the power allocation problem of both the transmission channel and the control channel. Finally, extensive simulations have been conducted and simulation results show that this new algorithm provides a significant improvement in terms of the tradeoff between the control channel reliability and the efficiency of the transmission channel. PMID:27754316

Machine learning molecular dynamics for the simulation of infrared spectra.

PubMed

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

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

Exercise environment for Introduction to Cyber Technologies class. This software is essentially a collection of short scripts, configuration files, and small executables that form the exercise component of the Sandia Cyber Technologies Academy's Introduction to Cyber Technologies class. It builds upon other open-source technologies, such as Debian Linux and minimega, to provide comprehensive Linux and networking exercises that make learning these topics exciting and fun. Sample exercises: a pre-built set of home directories the student must navigate through to learn about privilege escalation, the creation of a virtual network playground designed to teach the student about the resiliency of themore » Internet, and a two-hour Capture the Flag challenge for the final lesson. There are approximately thirty (30) exercises included for the students to complete as part of the course.« less

Q-Learning and p-persistent CSMA based rendezvous protocol for cognitive radio networks operating with shared spectrum activity

NASA Astrophysics Data System (ADS)

Watson, Clifton L.; Biswas, Subir

2014-06-01

With an increasing demand for spectrum, dynamic spectrum access (DSA) has been proposed as viable means for providing the flexibility and greater access to spectrum necessary to meet this demand. Within the DSA concept, unlicensed secondary users temporarily "borrow" or access licensed spectrum, while respecting the licensed primary user's rights to that spectrum. As key enablers for DSA, cognitive radios (CRs) are based on software-defined radios which allow them to sense, learn, and adapt to the spectrum environment. These radios can operate independently and rapidly switch channels. Thus, the initial setup and maintenance of cognitive radio networks are dependent upon the ability of CR nodes to find each other, in a process known as rendezvous, and create a link on a common channel for the exchange of data and control information. In this paper, we propose a novel rendezvous protocol, known as QLP, which is based on Q-learning and the p-persistent CSMA protocol. With the QLP protocol, CR nodes learn which channels are best for rendezvous and thus adapt their behavior to visit those channels more frequently. We demonstrate through simulation that the QLP protocol provides a rendevous capability for DSA environments with different dynamics of PU activity, while attempting to achieve the following performance goals: (1) minimize the average time-to-rendezvous, (2) maximize system throughput, (3) minimize primary user interference, and (4) minimize collisions among CR nodes.

Analyzing Enterprise Networks Needs: Action Research from the Mechatronics Sector

NASA Astrophysics Data System (ADS)

Cagnazzo, Luca; Taticchi, Paolo; Bidini, Gianni; Baglieri, Enzo

New business models and theories are developing nowadays towards collaborative environments direction, and many new tools in sustaining companies involved in these organizations are emerging. Among them, a plethora of methodologies to analyze their needs are already developed for single companies. Few academic works are available about Enterprise Networks (ENs) need analysis. This paper presents the learning from an action research (AR) in the mechatronics sector: AR has been used in order to experience the issue of evaluating network needs and therefore define, develop, and test a complete framework for network evaluation. Reflection on the story in the light of the experience and the theory is presented, as well as extrapolation to a broader context and articulation of usable knowledge.

Online adaptation and over-trial learning in macaque visuomotor control.

PubMed

Braun, Daniel A; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

2011-01-01

When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning.

Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

PubMed Central

Braun, Daniel A.; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

2011-01-01

When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning. PMID:21720526

Multiple effect of social influence on cooperation in interdependent network games.

PubMed

Jiang, Luo-Luo; Li, Wen-Jing; Wang, Zhen

2015-10-01

The social influence exists widely in the human society, where individual decision-making process (from congressional election to electronic commerce) may be affected by the attitude and behavior of others belonging to different social networks. Here, we couple the snowdrift (SD) game and the prisoner's dilemma (PD) game on two interdependent networks, where strategies in both games are associated by social influence to mimick the majority rule. More accurately, individuals' strategies updating refers to social learning (based on payoff difference) and above-mentioned social influence (related with environment of interdependent group), which is controlled by social influence strength s. Setting s = 0 decouples the networks and returns the traditional network game; while its increase involves the interactions between networks. By means of numerous Monte Carlo simulations, we find that such a mechanism brings multiple influence to the evolution of cooperation. Small s leads to unequal cooperation level in both games, because social learning is still the main updating rule for most players. Though intermediate and large s guarantees the synchronized evolution of strategy pairs, cooperation finally dies out and reaches a completely dominance in both cases. Interestingly, these observations are attributed to the expansion of cooperation clusters. Our work may provide a new understanding to the emergence of cooperation in intercorrelated social systems.

Multiple effect of social influence on cooperation in interdependent network games

NASA Astrophysics Data System (ADS)

Jiang, Luo-Luo; Li, Wen-Jing; Wang, Zhen

2015-10-01

The social influence exists widely in the human society, where individual decision-making process (from congressional election to electronic commerce) may be affected by the attitude and behavior of others belonging to different social networks. Here, we couple the snowdrift (SD) game and the prisoner’s dilemma (PD) game on two interdependent networks, where strategies in both games are associated by social influence to mimick the majority rule. More accurately, individuals’ strategies updating refers to social learning (based on payoff difference) and above-mentioned social influence (related with environment of interdependent group), which is controlled by social influence strength s. Setting s = 0 decouples the networks and returns the traditional network game; while its increase involves the interactions between networks. By means of numerous Monte Carlo simulations, we find that such a mechanism brings multiple influence to the evolution of cooperation. Small s leads to unequal cooperation level in both games, because social learning is still the main updating rule for most players. Though intermediate and large s guarantees the synchronized evolution of strategy pairs, cooperation finally dies out and reaches a completely dominance in both cases. Interestingly, these observations are attributed to the expansion of cooperation clusters. Our work may provide a new understanding to the emergence of cooperation in intercorrelated social systems.

Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

PubMed

Lin, Chuan-Kai

2005-04-01

A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.

Experiments on Adaptive Techniques for Host-Based Intrusion Detection

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

DRAELOS, TIMOTHY J.; COLLINS, MICHAEL J.; DUGGAN, DAVID P.

2001-09-01

This research explores four experiments of adaptive host-based intrusion detection (ID) techniques in an attempt to develop systems that can detect novel exploits. The technique considered to have the most potential is adaptive critic designs (ACDs) because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Preliminary results of ID using an ACD, an Elman recurrent neural network, and a statistical anomaly detection technique demonstrate an ability to learn to distinguish between clean and exploit data. We used the Solaris Basic Security Module (BSM) as a data source and performed considerablemore » preprocessing on the raw data. A detection approach called generalized signature-based ID is recommended as a middle ground between signature-based ID, which has an inability to detect novel exploits, and anomaly detection, which detects too many events including events that are not exploits. The primary results of the ID experiments demonstrate the use of custom data for generalized signature-based intrusion detection and the ability of neural network-based systems to learn in this application environment.« less

Security of social network credentials for accessing course portal: Users' experience

NASA Astrophysics Data System (ADS)

Katuk, Norliza; Fong, Choo Sok; Chun, Koo Lee

2015-12-01

Social login (SL) has recently emerged as a solution for single sign-on (SSO) within the web and mobile environments. It allows users to use their existing social network credentials (SNC) to login to third party web applications without the need to create a new identity in the intended applications' database. Although it has been used by many web application providers, its' applicability in accessing learning materials is not yet fully investigated. Hence, this research aims to explore users' (i.e., instructors' and students') perception and experience on the security of SL for accessing learning contents. A course portal was developed for students at a higher learning institution and it provides two types of user authentications (i) traditional user authentication, and (ii) SL facility. Users comprised instructors and students evaluated the login facility of the course portal through a controlled lab experimental study following the within-subject design. The participants provided their feedback in terms of the security of SL for accessing learning contents. The study revealed that users preferred to use SL over the traditional authentication, however, they concerned on the security of SL and their privacy.

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.

Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

NASA Astrophysics Data System (ADS)

Meng, Qinggang; Lee, M. H.

2007-03-01

Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.

A neural network-based exploratory learning and motor planning system for co-robots

PubMed Central

Galbraith, Byron V.; Guenther, Frank H.; Versace, Massimiliano

2015-01-01

Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or “learning by doing,” an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object. PMID:26257640

Learning in the model space for cognitive fault diagnosis.

PubMed

Chen, Huanhuan; Tino, Peter; Rodan, Ali; Yao, Xin

2014-01-01

The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework.

Relative optical navigation around small bodies via Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Law, Andrew M.

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

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

A neural network-based exploratory learning and motor planning system for co-robots.

PubMed

Galbraith, Byron V; Guenther, Frank H; Versace, Massimiliano

2015-01-01

Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

Mobile learning in resource-constrained environments: a case study of medical education.

PubMed

Pimmer, Christoph; Linxen, Sebastian; Gröhbiel, Urs; Jha, Anil Kumar; Burg, Günter

2013-05-01

The achievement of the millennium development goals may be facilitated by the use of information and communication technology in medical and health education. This study intended to explore the use and impact of educational technology in medical education in resource-constrained environments. A multiple case study was conducted in two Nepalese teaching hospitals. The data were analysed using activity theory as an analytical basis. There was little evidence for formal e-learning, but the findings indicate that students and residents adopted mobile technologies, such as mobile phones and small laptops, as cultural tools for surprisingly rich 'informal' learning in a very short time. These tools allowed learners to enhance (a) situated learning, by immediately connecting virtual information sources to their situated experiences; (b) cross-contextual learning by documenting situated experiences in the form of images and videos and re-using the material for later reflection and discussion and (c) engagement with educational content in social network communities. By placing the students and residents at the centre of the new learning activities, this development has begun to affect the overall educational system. Leveraging these tools is closely linked to the development of broad media literacy, including awareness of ethical and privacy issues.

Event-driven contrastive divergence for spiking neuromorphic systems.

PubMed

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems

PubMed Central

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2014-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality. PMID:24574952

Convolutional neural network-based classification system design with compressed wireless sensor network images.

PubMed

Ahn, Jungmo; Park, JaeYeon; Park, Donghwan; Paek, Jeongyeup; Ko, JeongGil

2018-01-01

With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.

An adaptive neural swarm approach for intrusion defense in ad hoc networks

NASA Astrophysics Data System (ADS)

Cannady, James

2011-06-01

Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical applications due to the flexibility and extensibility of the technology. While these networks possess numerous advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the autonomic processes of biological systems. Each component of the network recognizes activity in its local environment and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network. The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network attacks.

Online access and motivation of tutors of health professions higher education.

PubMed

Monaco, Federico; Sarli, Leopoldo; Guasconi, Massimo; Alfieri, Emanuela

2016-11-22

The case study of PUNTOZERO as an open web lab for activities, research and support to 5 Master's courses for the health professions is described. A virtual learning environment integrated in a much wider network including social networks and open resources was experimented on for five Master's Courses for the health professions at the University of Parma. A social learning approach might be applied by the engagement of motivated and skilled tutors. This is not only needed for the improvement and integration of the digital and collaborative dimension in higher education, but it aims to introduce issues and biases of emerging e-health and online networking dimensions for future healthcare professionals. Elements of e-readiness to train tutors and improve their digital skills and e-moderation approaches are evident. This emerged during an online and asynchronous interview with two tutors out of the four that were involved, by the use of a wiki where interviewer and informants could both read and add contents and comments.

A neural learning classifier system with self-adaptive constructivism for mobile robot control.

PubMed

Hurst, Jacob; Bull, Larry

2006-01-01

For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

Building online learning communities in a graduate dental hygiene program.

PubMed

Rogo, Ellen J; Portillo, Karen M

2014-08-01

The literature abounds with research related to building online communities in a single course; however, limited evidence is available on this phenomenon from a program perspective. The intent of this qualitative case study inquiry was to explore student experiences in a graduate dental hygiene program contributing or impeding the development and sustainability of online learning communities. Approval from the IRB was received. A purposive sampling technique was used to recruit participants from a stratification of students and graduates. A total of 17 participants completed semi-structured interviews. Data analysis was completed through 2 rounds - 1 for coding responses and 1 to construct categories of experiences. The participants' collective definition of an online learning community was a complex synergistic network of interconnected people who create positive energy. The findings indicated the development of this network began during the program orientation and was beneficial for building a foundation for the community. Students felt socially connected and supported by the network. Course design was another important category for participation in weekly discussions and group activities. Instructors were viewed as active participants in the community, offering helpful feedback and being a facilitator in discussions. Experiences impeding the development of online learning communities related to the poor performance of peers and instructors. Specific categories of experiences supported and impeded the development of online learning communities related to the program itself, course design, students and faculty. These factors are important to consider in order to maximize student learning potential in this environment. Copyright © 2014 The American Dental Hygienists’ Association.

Obtaining Global Picture From Single Point Observations by Combining Data Assimilation and Machine Learning Tools

NASA Astrophysics Data System (ADS)

Shprits, Y.; Zhelavskaya, I. S.; Kellerman, A. C.; Spasojevic, M.; Kondrashov, D. A.; Ghil, M.; Aseev, N.; Castillo Tibocha, A. M.; Cervantes Villa, J. S.; Kletzing, C.; Kurth, W. S.

2017-12-01

Increasing volume of satellite measurements requires deployment of new tools that can utilize such vast amount of data. Satellite measurements are usually limited to a single location in space, which complicates the data analysis geared towards reproducing the global state of the space environment. In this study we show how measurements can be combined by means of data assimilation and how machine learning can help analyze large amounts of data and can help develop global models that are trained on single point measurement. Data Assimilation: Manual analysis of the satellite measurements is a challenging task, while automated analysis is complicated by the fact that measurements are given at various locations in space, have different instrumental errors, and often vary by orders of magnitude. We show results of the long term reanalysis of radiation belt measurements along with fully operational real-time predictions using data assimilative VERB code. Machine Learning: We present application of the machine learning tools for the analysis of NASA Van Allen Probes upper-hybrid frequency measurements. Using the obtained data set we train a new global predictive neural network. The results for the Van Allen Probes based neural network are compared with historical IMAGE satellite observations. We also show examples of predictions of geomagnetic indices using neural networks. Combination of machine learning and data assimilation: We discuss how data assimilation tools and machine learning tools can be combine so that physics-based insight into the dynamics of the particular system can be combined with empirical knowledge of it's non-linear behavior.

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2018-02-01

This paper proposes a combined Virtual Reference Feedback Tuning-Q-learning model-free control approach, which tunes nonlinear static state feedback controllers to achieve output model reference tracking in an optimal control framework. The novel iterative Batch Fitted Q-learning strategy uses two neural networks to represent the value function (critic) and the controller (actor), and it is referred to as a mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach. Learning convergence of the Q-learning schemes generally depends, among other settings, on the efficient exploration of the state-action space. Handcrafting test signals for efficient exploration is difficult even for input-output stable unknown processes. Virtual Reference Feedback Tuning can ensure an initial stabilizing controller to be learned from few input-output data and it can be next used to collect substantially more input-state data in a controlled mode, in a constrained environment, by compensating the process dynamics. This data is used to learn significantly superior nonlinear state feedback neural networks controllers for model reference tracking, using the proposed Batch Fitted Q-learning iterative tuning strategy, motivating the original combination of the two techniques. The mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach is experimentally validated for water level control of a multi input-multi output nonlinear constrained coupled two-tank system. Discussions on the observed control behavior are offered. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Learning for intelligent mobile robots

NASA Astrophysics Data System (ADS)

Hall, Ernest L.; Liao, Xiaoqun; Alhaj Ali, Souma M.

2003-10-01

Unlike intelligent industrial robots which often work in a structured factory setting, intelligent mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. However, such machines have many potential applications in medicine, defense, industry and even the home that make their study important. Sensors such as vision are needed. However, in many applications some form of learning is also required. The purpose of this paper is to present a discussion of recent technical advances in learning for intelligent mobile robots. During the past 20 years, the use of intelligent industrial robots that are equipped not only with motion control systems but also with sensors such as cameras, laser scanners, or tactile sensors that permit adaptation to a changing environment has increased dramatically. However, relatively little has been done concerning learning. Adaptive and robust control permits one to achieve point to point and controlled path operation in a changing environment. This problem can be solved with a learning control. In the unstructured environment, the terrain and consequently the load on the robot"s motors are constantly changing. Learning the parameters of a proportional, integral and derivative controller (PID) and artificial neural network provides an adaptive and robust control. Learning may also be used for path following. Simulations that include learning may be conducted to see if a robot can learn its way through a cluttered array of obstacles. If a situation is performed repetitively, then learning can also be used in the actual application. To reach an even higher degree of autonomous operation, a new level of learning is required. Recently learning theories such as the adaptive critic have been proposed. In this type of learning a critic provides a grade to the controller of an action module such as a robot. The creative control process is used that is "beyond the adaptive critic." A mathematical model of the creative control process is presented that illustrates the use for mobile robots. Examples from a variety of intelligent mobile robot applications are also presented. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots that could lead to many applications.

An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

PubMed Central

Jiang, Jiefeng; Beck, Jeffrey; Heller, Katherine; Egner, Tobias

2015-01-01

The anterior cingulate and lateral prefrontal cortices have been implicated in implementing context-appropriate attentional control, but the learning mechanisms underlying our ability to flexibly adapt the control settings to changing environments remain poorly understood. Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate. Using model-based functional magnetic resonance imaging, we demonstrate that volatility of control demand is estimated by the anterior insula, which in turn optimizes the prediction of forthcoming demand in the caudate nucleus. The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices. These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences. PMID:26391305

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