WorldWideScience

Sample records for k-16 networked learning

  1. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter

    2011-01-01

    Sloep, P. B. (2011). Learning Networks, Networked Learning. Presentation at Annual Assembly of the European Society for the Systemic Innovation of Education - ESSIE. May, 27, 2011, Leuven, Belgium: Open University in the Netherlands.

  2. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  3. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  4. Technology Development, Implementation, and Assessment: K-16 Pre-Service, In-Service, and Distance Learning Initiatives

    Science.gov (United States)

    Petersen, Richard

    1998-01-01

    This summer 22 kindergarten through 8th grade teachers attended a 3-week Teacher Enhancement Institute (TEI) at NASA Langley Research Center. TEI is funded by NASA Education Division and is a collaborative effort between NASA Langley's Office of Education and Christopher Newport University. Selected teacher teams were drawn from Langley's 5-state precollege service region, which includes Kentucky, North Carolina, South Carolina, Virginia, and West Virginia. The goal of TEI was for teachers to learn aeronautics and the broad application of science and technology through a problem-based learning (PBL) strategy. PBL is an instructional method using a real world problem, also known as an ill-structured problem, as the context for an in-depth investigation. Most real life problems are ill-structured, as are all the really important social, political and scientific problems. The teachers were immediately immersed in an ill-structured problem to design a communication strategy for the White House Commission on Aviation Safety and Security to educate and disseminate aviation information to the general public. Specifically, the communication strategy was to focus on aeronautics principles, technology and design associated with US general aviation revitalization and aviation safety programs. The presented problem addressed NASA's strategic outcome to widely communicate the content, relevancy and excitement of its missions and discoveries to the general population. Further, the PBL scenario addressed the technological challenges being taken up by NASA to revolutionize air travel and the way in which aircraft are designed, built, and operated. It also addressed getting people and freight safely and efficiently to any location in the world at a reasonable cost. With a "real" need-to-know problem facing them, the teachers set out to gather information and to better understand the problem using inquiry-based and scientific methods. The learning in this aeronautics scenario was

  5. Technology Development, Implementation, and Assessment: K-16 Pre-Service, In-Service, and Distance Learning Initiatives

    Science.gov (United States)

    Petersen, Richard

    1998-01-01

    This summer 22 kindergarten through 8th grade teachers attended a 3-week Teacher Enhancement Institute (TEI) at NASA Langley Research Center. TEI is funded by NASA Education Division and is a collaborative effort between NASA Langley's Office of Education and Christopher Newport University. Selected teacher teams were drawn from Langley's 5-state precollege service region, which includes Kentucky, North Carolina, South Carolina, Virginia, and West Virginia. The goal of TEI was for teachers to learn aeronautics and the broad application of science and technology through a problem-based learning (PBL) strategy. PBL is an instructional method using a real world problem, also known as an ill-structured problem, as the context for an in-depth investigation. Most real life problems are ill-structured, as are all the really important social, political and scientific problems. The teachers were immediately immersed in an ill-structured problem to design a communication strategy for the White House Commission on Aviation Safety and Security to educate and disseminate aviation information to the general public. Specifically, the communication strategy was to focus on aeronautics principles, technology and design associated with US general aviation revitalization and aviation safety programs. The presented problem addressed NASA's strategic outcome to widely communicate the content, relevancy and excitement of its missions and discoveries to the general population. Further, the PBL scenario addressed the technological challenges being taken up by NASA to revolutionize air travel and the way in which aircraft are designed, built, and operated. It also addressed getting people and freight safely and efficiently to any location in the world at a reasonable cost. With a "real" need-to-know problem facing them, the teachers set out to gather information and to better understand the problem using inquiry-based and scientific methods. The learning in this aeronautics scenario was

  6. Terra in K-16 formal education settings

    Science.gov (United States)

    Chambers, L. H.; Fischer, J. D.; Lewis, P. M.; Moore, S. W.; Oots, P. C.; Rogerson, T. M.; Hitke, K. M.; Riebeek, H.

    2009-12-01

    Since it began, the Terra mission has had an active presence in formal education at the K-16 level. This educational presence was provided through the S’COOL project for the first five years of the mission, joined by the MY NASA DATA project for the second five years. The Students’ Cloud Observations On-Line (S’COOL) Project, begun in 1997 under the auspices of the Clouds and the Earth’s Radiant Energy System (CERES) project, seeks to motivate students across the entire K-12 spectrum to learn science basics and how they tie in to a larger picture. Beginning early on, college level participants have also participated in the project, both in science classes and in science education coursework. The project uses the connection to an on-going NASA science investigation as a powerful motivator for student observations, analysis and learning, and has reached around the globe as shown in the world map. This poster will review the impact that Terra, through S’COOL, has made in formal education over the last decade. The MY NASA DATA Project began in 2004 under the NASA Research, Education and Applications Solutions Network (REASoN). A 5-year REASoN grant enabled the creation of an extensive website which wraps easily accessible Earth science data - including Terra parameters from CERES (involving MODIS data fusion), MISR, and MOPITT (an example for carbon monoxide is given in the graph, with dark areas indicating high CO levels) - with explanatory material written at the middle school level, and an extensive collection of peer-reviewed lesson plans. The MY NASA DATA site has a rapidly growing user-base and was recently adopted by a number of NASA Earth Science missions, in addition to Terra, as a formal education arm of their Education and Public Outreach efforts. This poster will summarize the contributions that Terra, through MY NASA DATA, has made to formal education since 2004.

  7. Learning Networks for Lifelong Learning

    OpenAIRE

    Koper, Rob

    2008-01-01

    Presentation in a seminar organized by Christopher Hoadley at Penn State University, October 2004.Contains general introduction into the Learning Network Programme and a demonstration of the Netlogo Simulation of a Learning Network.

  8. Constructive neural network learning

    OpenAIRE

    Lin, Shaobo; Zeng, Jinshan; Zhang, Xiaoqin

    2016-01-01

    In this paper, we aim at developing scalable neural network-type learning systems. Motivated by the idea of "constructive neural networks" in approximation theory, we focus on "constructing" rather than "training" feed-forward neural networks (FNNs) for learning, and propose a novel FNNs learning system called the constructive feed-forward neural network (CFN). Theoretically, we prove that the proposed method not only overcomes the classical saturation problem for FNN approximation, but also ...

  9. Learning network representations

    Science.gov (United States)

    Moyano, Luis G.

    2017-02-01

    In this review I present several representation learning methods, and discuss the latest advancements with emphasis in applications to network science. Representation learning is a set of techniques that has the goal of efficiently mapping data structures into convenient latent spaces. Either for dimensionality reduction or for gaining semantic content, this type of feature embeddings has demonstrated to be useful, for example, for node classification or link prediction tasks, among many other relevant applications to networks. I provide a description of the state-of-the-art of network representation learning as well as a detailed account of the connections with other fields of study such as continuous word embeddings and deep learning architectures. Finally, I provide a broad view of several applications of these techniques to networks in various domains.

  10. Learning to Generate Networks

    CERN Document Server

    Atwood, James

    2014-01-01

    The recent explosion in social network data has stimulated interest in probabilistic models of networks. Such models are appealing because they are empirically grounded; in contrast to more traditional network models, their parameters are estimated from data, and the models are evaluated on how well they represent the data. The exponential random graph model (ERGM, or, alternatively $p^*$) is currently the dominant framework for probabilistic network modeling. Despite their popularity, ERGMs suffer from a very serious flaw: near degeneracy. Briefly, an ERGM fit to a network or set of networks often ends up generating networks that look nothing at all like the training data. It is deeply troubling that the most likely model will generate instances that look nothing like data, and this calls the validity of models into question. In this work, we seek to address the general problem of learning to generate networks that do look like data. This is a large, challenging problem. To gain an understanding, we decompos...

  11. Learning Analytics for Networked Learning Models

    Science.gov (United States)

    Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

    2014-01-01

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

  12. Networked Learning in Networks: infrastructures for social learning & distributed innovation

    NARCIS (Netherlands)

    Sloep, Peter

    2011-01-01

    Sloep, P. B. (2011, 1-3 September). Networked Learning in Networks: infrastructures for social learning & distributed innovation. Presentation at the Third International Conference on Software, Services and Semantic Technologies (S3T 2011), Bourgas, Bulgaria.

  13. Learning conditional Gaussian networks

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given...... independence, parameter modularity and likelihood equivalence. Bayes factors to be used in model search are introduced. Finally the methods derived are illustrated by a simple example....

  14. Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Sloep, Peter

    2009-01-01

    The original publication is available from www.springerlink.com. Sloep, P. (2009). Social Interaction in Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp 13-15). Berlin, Germany: Springer Verlag.

  15. Learning Networks for Professional Development & Lifelong Learning

    NARCIS (Netherlands)

    Brouns, Francis; Sloep, Peter

    2009-01-01

    Brouns, F., & Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation of the Learning Network Programme for a Korean delegation of Chonnam National University and Dankook University (researchers dr. Jeeheon Ryu and dr. Minjeong Kim and a Group of PhD and

  16. Research, Boundaries, and Policy in Networked Learning

    DEFF Research Database (Denmark)

    This book presents cutting-edge, peer reviewed research on networked learning organized by three themes: policy in networked learning, researching networked learning, and boundaries in networked learning. The "policy in networked learning" section explores networked learning in relation to policy...

  17. Learning Networks for Professional Development & Lifelong Learning

    NARCIS (Netherlands)

    Sloep, Peter

    2009-01-01

    Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation at a NeLLL seminar with Etienne Wenger held at the Open Universiteit Nederland. September, 10, 2009, Heerlen, The Netherlands.

  18. Redes de aprendizaje, aprendizaje en red Learning Networks, Networked Learning

    Directory of Open Access Journals (Sweden)

    Peter Sloep

    2011-10-01

    Full Text Available Las redes de aprendizaje (Learning Networks son redes sociales en línea mediante las cuales los participantes comparten información y colaboran para crear conocimiento. De esta manera, estas redes enriquecen la experiencia de aprendizaje en cualquier contexto de aprendizaje, ya sea de educación formal (en escuelas o universidades o educación no-formal (formación profesional. Aunque el concepto de aprendizaje en red suscita el interés de diferentes actores del ámbito educativo, aún existen muchos interrogantes sobre cómo debe diseñarse el aprendizaje en red para facilitar adecuadamente la educación y la formación. El artículo toma este interrogante como punto de partida, y posteriormente aborda cuestiones como la dinámica de la evolución de las redes de aprendizaje, la importancia de fomentar la confianza entre los participantes y el papel central que desempeña el perfil de usuario en la construcción de la confianza, así como el apoyo entre compañeros. Además, se elabora el proceso de diseño de una red de aprendizaje, y se describe un ejemplo en el contexto universitario. Basándonos en la investigación que actualmente se lleva a cabo en nuestro propio centro y en otros lugares, el capítulo concluye con una visión del futuro de las redes de aprendizaje.Learning Networks are on-line social networks through which users share knowledge with each other and jointly develop new knowledge. This way, Learning Networks may enrich the experience of formal, school-based learning and form a viable setting for professional development. Although networked learning enjoys an increasing interest, many questions remain on how exactly learning in such networked contexts can contribute to successful education and training. Put differently, how should networked learning be designed best to facilitate education and training? Taking this as its point of departure, the chapter addresses such issues as the dynamic evolution of Learning Networks

  19. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin

    2014-01-01

    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  20. Workplace Learning in Informal Networks

    Science.gov (United States)

    Milligan, Colin; Littlejohn, Allison; Margaryan, Anoush

    2014-01-01

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

  1. Associative learning in biochemical networks

    OpenAIRE

    Ghandi, Nikhil; Ashkenasy, Gonen; Tannenbaum, Emmanuel

    2007-01-01

    We develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. In our model, two types of replicating molecules, denoted A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted GA and GB, respectively. It is also assu...

  2. Subspace learning of neural networks

    CERN Document Server

    Cheng Lv, Jian; Zhou, Jiliu

    2010-01-01

    PrefaceChapter 1. Introduction1.1 Introduction1.1.1 Linear Neural Networks1.1.2 Subspace Learning1.2 Subspace Learning Algorithms1.2.1 PCA Learning Algorithms1.2.2 MCA Learning Algorithms1.2.3 ICA Learning Algorithms1.3 Methods for Convergence Analysis1.3.1 SDT Method1.3.2 DCT Method1.3.3 DDT Method1.4 Block Algorithms1.5 Simulation Data Set and Notation1.6 ConclusionsChapter 2. PCA Learning Algorithms with Constants Learning Rates2.1 Oja's PCA Learning Algorithms2.1.1 The Algorithms2.1.2 Convergence Issue2.2 Invariant Sets2.2.1 Properties of Invariant Sets2.2.2 Conditions for Invariant Sets2.

  3. Fundamental Concepts for Environmental Management Education (K-16).

    Science.gov (United States)

    Roth, Robert Earl

    This study was undertaken to develop a taxonomy of conceptual objectives for use in planning programs of instruction related to environmental management education (K-16) and to determine whether or not biases exist among persons representative of selected disciplines. Survey techniques to obtain and validate concepts appropriate for environmental…

  4. Language Choice & Global Learning Networks

    Directory of Open Access Journals (Sweden)

    Dennis Sayers

    1995-05-01

    Full Text Available How can other languages be used in conjunction with English to further intercultural and multilingual learning when teachers and students participate in computer-based global learning networks? Two portraits are presented of multilingual activities in the Orillas and I*EARN learning networks, and are discussed as examples of the principal modalities of communication employed in networking projects between distant classes. Next, an important historical precedent --the social controversy which accompanied the introduction of telephone technology at the end of the last century-- is examined in terms of its implications for language choice in contemporary classroom telecomputing projects. Finally, recommendations are offered to guide decision making concerning the role of language choice in promoting collaborative critical inquiry.

  5. Modular, Hierarchical Learning By Artificial Neural Networks

    Science.gov (United States)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

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

  6. Network mechanisms of intentional learning.

    Science.gov (United States)

    Hampshire, Adam; Hellyer, Peter J; Parkin, Beth; Hiebert, Nole; MacDonald, Penny; Owen, Adrian M; Leech, Robert; Rowe, James

    2016-02-15

    The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus-response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.

  7. Learning Python network programming

    CERN Document Server

    Sarker, M O Faruque

    2015-01-01

    If you're a Python developer or a system administrator with Python experience and you're looking to take your first steps in network programming, then this book is for you. Basic knowledge of Python is assumed.

  8. Changing Conditions for Networked Learning?

    DEFF Research Database (Denmark)

    Ryberg, Thomas

    2011-01-01

    in describing the novel pedagogical potentials of these new technologies and practices (e.g. in debates around virtual learning environments versus personal learning environment). Likewise, I shall briefly discuss the notions of ‘digital natives’ or ‘the net generation’ from a critical perspective......In this talk I should like to initially take a critical look at popular ideas and discourses related to web 2.0, social technologies and learning. I argue that many of the pedagogical ideals particularly associated with web 2.0 have a longer history and background, which is often forgotten...... of social technologies. I argue that we are seeing the emergence of new architectures and scales of participation, collaboration and networking e.g. through interesting formations of learning networks at different levels of scale, for different purposes and often bridging boundaries such as formal...

  9. Designing Learning Networks for Lifelong Learners

    NARCIS (Netherlands)

    Koper, Rob

    2005-01-01

    Koper, R. (2005). Designing Learning Networks for Lifelong Learners. In: Koper, R. & Tattersall, C., Learning Design: A Handbook on Modelling and Delivering Networked Education and Training (pp. 239-252). Berlin-Heidelberg: Springer Verlag.

  10. Blending Formal and Informal Learning Networks for Online Learning

    Science.gov (United States)

    Czerkawski, Betül C.

    2016-01-01

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

  11. Collective Learning: Theoretical Perspectives and Ways To Support Networked Learning.

    Science.gov (United States)

    de Laat, Maarten; Simons, Robert-Jan

    2002-01-01

    Reviews three types of collective learning networks, teams, and communities. Advocates learning communities as a powerful way to stimulate shared learning. Warns that, although technology enables networked learning, group dynamics are crucial and must be considered. Describes progressive inquiry and team roles as ways to support collective…

  12. Associative learning in biochemical networks.

    Science.gov (United States)

    Gandhi, Nikhil; Ashkenasy, Gonen; Tannenbaum, Emmanuel

    2007-11-07

    It has been recently suggested that there are likely generic features characterizing the emergence of systems constructed from the self-organization of self-replicating agents acting under one or more selection pressures. Therefore, structures and behaviors at one length scale may be used to infer analogous structures and behaviors at other length scales. Motivated by this suggestion, we seek to characterize various "animate" behaviors in biochemical networks, and the influence that these behaviors have on genomic evolution. Specifically, in this paper, we develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. Associative learning, also known as conditioning, is believed to be a powerful learning process at work in the brain (associative learning is essentially "learning by analogy"). In our model, two types of replicating molecules, denoted as A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted as G(A) and G(B), respectively. It is also assumed that A and B can covalently link, and that the conjugated molecule can be stimulated by either the G(A) or G(B) growth factors (and can be degraded). We show that, if the chemostat is stimulated by both growth factors for a certain time, followed by a time gap during which the chemostat is not stimulated at all, and if the chemostat is then stimulated again by only one of the growth factors, then there will be a transient increase in the number of molecules activated by the other growth factor. Therefore, the chemostat bears the imprint of earlier, simultaneous stimulation with both growth factors, which is indicative of associative learning. It is interesting to note that the dynamics of our model is consistent with certain aspects of

  13. Benefits of Cooperative Learning in Weblog Networks

    Science.gov (United States)

    Wang, Jenny; Fang, Yuehchiu

    2005-01-01

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

  14. The Integration of Personal Learning Environments & Open Network Learning Environments

    Science.gov (United States)

    Tu, Chih-Hsiung; Sujo-Montes, Laura; Yen, Cherng-Jyh; Chan, Junn-Yih; Blocher, Michael

    2012-01-01

    Learning management systems traditionally provide structures to guide online learners to achieve their learning goals. Web 2.0 technology empowers learners to create, share, and organize their personal learning environments in open network environments; and allows learners to engage in social networking and collaborating activities. Advanced…

  15. The Integration of Personal Learning Environments & Open Network Learning Environments

    Science.gov (United States)

    Tu, Chih-Hsiung; Sujo-Montes, Laura; Yen, Cherng-Jyh; Chan, Junn-Yih; Blocher, Michael

    2012-01-01

    Learning management systems traditionally provide structures to guide online learners to achieve their learning goals. Web 2.0 technology empowers learners to create, share, and organize their personal learning environments in open network environments; and allows learners to engage in social networking and collaborating activities. Advanced…

  16. a Heterosynaptic Learning Rule for Neural Networks

    Science.gov (United States)

    Emmert-Streib, Frank

    In this article we introduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean learning time increases with the number of patterns to be learned polynomially, indicating efficient learning.

  17. Learning Processes of Layered Neural Networks

    OpenAIRE

    Fujiki, Sumiyoshi; FUJIKI, Nahomi, M.

    1995-01-01

    A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward neural network, and a learning equation similar to that of the Boltzmann machine algorithm is obtained. By applying a mean field approximation to the same stochastic feed-forward neural network, a deterministic analog feed-forward network is obtained and the back-propagation learning rule is re-derived.

  18. Learning Algorithms of Multilayer Neural Networks

    OpenAIRE

    Fujiki, Sumiyoshi; FUJIKI, Nahomi, M.

    1996-01-01

    A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward multilayer neural network, with far interlayer synaptic connections, and we obtain a learning rule similar to that of the Boltzmann machine on the same multilayer structure. By applying a mean field approximation to the stochastic feed-forward neural network, the generalized error back-propagation learning rule is derived for a deterministic analog feed-forward multilayer network with the far interlay...

  19. Open Educational Resources in Learning Networks

    NARCIS (Netherlands)

    Sloep, Peter

    2009-01-01

    Sloep, P. B. (2009). Open Educational Resources in Learning Networks. Keynote presentation at the 23rd ICDE World Conference on Open Learning and Distance Education 2009. June, 5-7, 2009, Maastricht, The Netherlands.

  20. Meta-Learning Evolutionary Artificial Neural Networks

    OpenAIRE

    Abraham, Ajith

    2004-01-01

    In this paper, we present MLEANN (Meta-Learning Evolutionary Artificial Neural Network), an automatic computational framework for the adaptive optimization of artificial neural networks wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conventionally designed artificial neural networks for function approximation problems. To evaluate the compara...

  1. Structure learning for Bayesian networks as models of biological networks.

    Science.gov (United States)

    Larjo, Antti; Shmulevich, Ilya; Lähdesmäki, Harri

    2013-01-01

    Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.

  2. Community-enhanced Network Representation Learning for Network Analysis

    CERN Document Server

    Tu, Cunchao; Zeng, Xiangkai; Liu, Zhiyuan; Sun, Maosong

    2016-01-01

    Network representation learning (NRL) aims to build low-dimensional vectors for vertices in a network. Most existing NRL methods focus on learning representations from local context of vertices (such as their neighbors). Nevertheless, vertices in many complex networks also exhibit significant global patterns widely known as communities. It's a common sense that vertices in the same community tend to connect densely, and usually share common attributes. These patterns are expected to improve NRL and benefit relevant evaluation tasks, such as link prediction and vertex classification. In this work, we propose a novel NRL model by introducing community information of vertices to learn more discriminative network representations, named as Community-enhanced Network Representation Learning (CNRL). CNRL simultaneously detects community distribution of each vertex and learns embeddings of both vertices and communities. In this way, we can obtain more informative representation of a vertex accompanying with its commu...

  3. Network anomaly detection a machine learning perspective

    CERN Document Server

    Bhattacharyya, Dhruba Kumar

    2013-01-01

    With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents mach

  4. Towards a Pattern Language for Networked Learning

    NARCIS (Netherlands)

    Goodyear, Peter; Avgeriou, Paris; Baggetun, Rune; Bartoluzzi, Sonia; Retalis, Simeon; Ronteltap, Frans; Rusman, Ellen

    2004-01-01

    The work of designing a useful, convivial networked learning environment is complex and demanding. People new to designing for networked learning face a number of major challenges when they try to draw on the experience of others – whether that experience is shared informally, in the everyday langua

  5. Learning the Structural Vocabulary of a Network.

    Science.gov (United States)

    Navlakha, Saket

    2017-02-01

    Networks have become instrumental in deciphering how information is processed and transferred within systems in almost every scientific field today. Nearly all network analyses, however, have relied on humans to devise structural features of networks believed to be most discriminative for an application. We present a framework for comparing and classifying networks without human-crafted features using deep learning. After training, autoencoders contain hidden units that encode a robust structural vocabulary for succinctly describing graphs. We use this feature vocabulary to tackle several network mining problems and find improved predictive performance versus many popular features used today. These problems include uncovering growth mechanisms driving the evolution of networks, predicting protein network fragility, and identifying environmental niches for metabolic networks. Deep learning offers a principled approach for mining complex networks and tackling graph-theoretic problems.

  6. Learning Multiple Tasks with Deep Relationship Networks

    OpenAIRE

    Long, Mingsheng; Wang, Jianmin

    2015-01-01

    Deep neural networks trained on large-scale dataset can learn transferable features that promote learning multiple tasks for inductive transfer and labeling mitigation. As deep features eventually transition from general to specific along the network, a fundamental problem is how to exploit the relationship structure across different tasks while accounting for the feature transferability in the task-specific layers. In this work, we propose a novel Deep Relationship Network (DRN) architecture...

  7. Cool Science: Engaging Adult and K-16 Audiences in Climate Change Science

    Science.gov (United States)

    Lustick, D.; Lohmeier, J.; Chen, R. F.

    2012-12-01

    A team of educators and scientists from the University of Massachusetts Lowell and the University of Massachusetts Boston will report on an informal science learning research project using mass transit spaces in Lowell, MA. Cool Science (CS) uses advertising spaces on buses and terminals to engage the public with an Out of Home Multi-Media (OHMM) learning experience. K-16 classrooms throughout Massachusetts will submit original artwork that conveys a scientific concept central to understanding climate change. The best 6 works submitted will be printed and placed on every bus in the city over a 6 month period during the first half of 2013. CS aims to promote and evaluate learning about climate change science among the general adult public and k-16 students/teachers. Cool Science offers teachers an efficient and effective means of seamlessly bringing the study of climate change into classroom learning both within science and across disciplines. The products of this effort are then used to improve public engagement with the science of climate change in mass transit environments. Cool Science is an example of Science, Technology, Engineering, Art and Math education (STEAM). The goals of CS are: 1) Engage professors, teachers, and their respective students in a climate change science communication competition. 2) Run the winning 6 selected placards and posters throughout the LRTA. 3) Identify how different communities of risk among the riding public approach and understand climate change. 4) Identify the advantages and disadvantages of using buses as a context for research on informal science learning. 5) Determine the extent to which student artwork serves as a trusted source of information. As advances in technology allow for more scientific knowledge to be generated, the role of informal education to improve adult understanding of science has never been greater. We see the convergence of circumstances (ISE, climate change, OHMM, mobile technology) as an enormous

  8. Conditions for Productive Learning in Network Learning Environments

    DEFF Research Database (Denmark)

    Ponti, M.; Dirckinck-Holmfeld, Lone; Lindström, B.

    2004-01-01

    are designed without a deep understanding of the pedagogical, communicative and collaborative conditions embedded in networked learning. Despite the existence of good theoretical views pointing to a social understanding of learning, rather than a traditional individualistic and information processing approach......The Kaleidoscope1 Jointly Executed Integrating Research Project (JEIRP) on Conditions for Productive Networked Learning Environments is developing and elaborating conceptual understandings of Computer Supported Collaborative Learning (CSCL) emphasizing the use of cross-cultural comparative......: Pedagogical design and the dialectics of the digital artefacts, the concept of collaboration, ethics/trust, identity and the role of scaffolding of networked learning environments.   The JEIRP is motivated by the fact that many networked learning environments in various European educational settings...

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

    Science.gov (United States)

    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.

  10. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    Kiran M Kolwankar; Quansheng Ren; Areejit Samal; Jürgen Jost

    2011-11-01

    We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution. Then we study the dynamics of the structure of a formal neural network. For properly chosen input signals, there exists a steady state with a residual network. We compare the motif profile of such a network with that of the real neural network of . elegans and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting network is robust under variations of model parameters.

  11. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    as in the previous Bayesian approaches and in addition allows learning of node specific link properties similar to that in the modularity objective. We employ a new relaxation method for efficient inference in these generative models that allows us to learn the behavior of very large networks. We compare the link...... such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure...... prediction performance of the learning based approaches and other widely used link prediction approaches in 14 networks ranging from medium size to large networks with more than a million nodes. While link prediction is typically well above chance for all networks, we find that the learning based mixed...

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

    National Research Council Canada - National Science Library

    Flaschberger, Edith; Gugglberger, Lisa; Dietscher, Christina

    2013-01-01

    ... (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...

  13. Stochastic Variational Learning in Recurrent Spiking Networks

    Directory of Open Access Journals (Sweden)

    Danilo eJimenez Rezende

    2014-04-01

    Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  14. Stochastic variational learning in recurrent spiking networks.

    Science.gov (United States)

    Jimenez Rezende, Danilo; Gerstner, Wulfram

    2014-01-01

    The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  15. ONR K 16 Engineering Pipeline :Engineering Success in STEM Project

    Science.gov (United States)

    2016-10-19

    learning ( PBL ). Last, teachers participated in the project for different lengths of time (Kaimuki teachers had three years with the ESS, and 12 others had...individuals talked about how their experiences in the project increased their confidence with problem-based learning ( PBL ). As one person declared, "The...other thing that I felt confident about was we just had a 3-day workshop doing the PBL and the PBL is exactly the same as the EDP and I sat there for 3

  16. Learning dynamic Bayesian networks with mixed variables

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learn....... An automated procedure for specifying prior distributions for the parameters in a dynamic Bayesian network is presented. It is a simple extension of the procedure for the ordinary Bayesian networks. Finally the W¨olfer?s sunspot numbers are analyzed....

  17. Network Learning and Innovation in SME Formal Networks

    Directory of Open Access Journals (Sweden)

    Jivka Deiters

    2013-02-01

    Full Text Available The driver for this paper is the need to better understand the potential for learning and innovation that networks canprovide especially for small and medium sized enterprises (SMEs which comprise by far the majority of enterprises in the food sector. With the challenges the food sector is facing in the near future, learning and innovation or more focused, as it is being discussed in the paper, ‘learning for innovation’ are not just opportunities but pre‐conditions for the sustainability of the sector. Network initiatives that could provide appropriate support involve social interaction and knowledge exchange, learning, competence development, and coordination (organization and management of implementation. The analysis identifies case studies in any of these orientations which serve different stages of the innovation process: invention and implementation. The variety of network case studies cover networks linked to a focus group for training, research, orconsulting, networks dealing with focused market oriented product or process development, promotional networks, and networks for open exchange and social networking.

  18. Deep learning in neural networks: an overview.

    Science.gov (United States)

    Schmidhuber, Jürgen

    2015-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

  19. Design of a Networked Learning Master Environment for Professionals

    DEFF Research Database (Denmark)

    2010-01-01

    The paper is presenting the overall learning design of MIL (Master in ICT and Learning). The learning design is integrating a number of principles: 1. Principles of problem and project based learning 2. Networked learning / learning in communities of practice. The paper will discuss how...... these principles interact productively in the design of a networked learning environment for professionals....

  20. Training on Call: The Open Learning Network.

    Science.gov (United States)

    Garside, Susan

    1996-01-01

    Queensland (Australia) Open Learning Network delivered training to telephone industry workers using audiographic conferencing. Ways to overcome the lack of face-to-face interaction were devised, and the presenter used oral cues more extensively. (SK)

  1. Learning Bayesian networks for discrete data

    KAUST Repository

    Liang, Faming

    2009-02-01

    Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

  2. Rethinking the learning of belief network probabilities

    Energy Technology Data Exchange (ETDEWEB)

    Musick, R.

    1996-03-01

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

  3. Designing spaces for the networked learning landscape.

    Science.gov (United States)

    Nordquist, Jonas; Laing, Andrew

    2015-04-01

    The concept of the learning landscape is used to explore the range of learning environments needed at multiple scales to better align with changes in the medical education curriculum. Four key scales that correspond to important types of learning spaces are identified: the classroom, the building, the campus and the city. "In-between" spaces are identified as growing in importance given changing patterns of learning and the use of information technology. Technology is altering how learning takes place in a wider variety of types of spaces as it is interwoven into every aspect of learning. An approach to planning learning environments which recognizes the need to think of networks of learning spaces connected across multiple scales is proposed. The focus is shifted from singular spaces to networks of inter-connected virtual and digital environments. A schematic model comprising the networked learning landscape, intended as a guide to planning that emphasizes relationships between the changing curriculum and its alignment with learning environments at multiple scales is proposed in this work. The need for higher levels of engagement of faculty, administrators and students in defining the briefs for the design of new kinds of medical education environments is highlighted.

  4. Collaborative learning agents supporting service network management

    NARCIS (Netherlands)

    Mulder, W.; Meijer, G.R.; Adriaans, P.W.

    2008-01-01

    Service oriented systems need to be maintained to keep the requested level of service. This is challenge in large grid- and saas based networks that are managed by numerous entities. This paper is about supporting multi agent systems that operate in the network and support its management by learning

  5. Learning drifting concepts with neural networks

    NARCIS (Netherlands)

    Biehl, Michael; Schwarze, Holm

    1993-01-01

    The learning of time-dependent concepts with a neural network is studied analytically and numerically. The linearly separable target rule is represented by an N-vector, whose time dependence is modelled by a random or deterministic drift process. A single-layer network is trained online using differ

  6. Towards a Novel Networked Learning Environment.

    Science.gov (United States)

    Koutoumanos, Anastasios; Papaspyrou, Nikolaos; Retalis, Simeon; Maurer, Hermann; Skordalakis, Emmanuel

    This paper presents a novel Networked Learning Environment (Nov-NLE); system components include Hyper-G (a networked hypermedia system) and the Internet. The first section discusses problems with the conventional university teaching model and technology-based solutions to these problems. The requirements and design of Nov-NLE are covered in the…

  7. Collaborative learning agents supporting service network management

    NARCIS (Netherlands)

    Mulder, W.; Meijer, G.R.; Adriaans, P.W.

    2008-01-01

    Service oriented systems need to be maintained to keep the requested level of service. This is challenge in large grid- and saas based networks that are managed by numerous entities. This paper is about supporting multi agent systems that operate in the network and support its management by learning

  8. Electronic Social Networks, Teaching, and Learning

    Science.gov (United States)

    Pidduck, Anne Banks

    2010-01-01

    This paper explores the relationship between electronic social networks, teaching, and learning. Previous studies have shown a strong positive correlation between student engagement and learning. By extending this work to engage instructors and add an electronic component, our study shows possible teaching improvement as well. In particular,…

  9. Realizing Wisdom Theory in Complex Learning Networks

    Science.gov (United States)

    Kok, Ayse

    2009-01-01

    The word "wisdom" is rarely seen in contemporary technology and learning discourse. This conceptual paper aims to provide some clear principles that answer the question: How can we establish wisdom in complex learning networks? By considering the nature of contemporary calls for wisdom the paper provides a metatheoretial framework to evaluate the…

  10. A Design Model for Lifelong Learning Networks

    Science.gov (United States)

    Koper, Rob; Giesbers, Bas; van Rosmalen, Peter; Sloep, Peter; van Bruggen, Jan; Tattersall, Colin; Vogten, Hubert; Brouns, Francis

    2005-01-01

    The provision of lifelong learning facilities is considered to be a major new direction for higher and distance teaching educational institutes catering for the demands of industry and society. ICT networks will in future support seamless, ubiquitous access to lifelong learning facilities at home, at work, in schools and universities. This implies…

  11. Implementation of an infrastructure for networked learning

    DEFF Research Database (Denmark)

    Nyvang, Tom; Bygholm, Ann

    2011-01-01

    What are the conditions under which institutional actors decide upon Information and Communication Technology strategies for networked learning purposes? The question is discussed within the frame of a case study of the decision process during a shift from one learning platform to another...

  12. Social Networks: Rational Learning and Information Aggregation

    Science.gov (United States)

    2009-09-01

    else who lived at 109. I dedicate this dissertation to the four people who make my life wonderful. My parents, Robert and Lucia , have showered me...Steger A., “Observational Learning in Random Networks,” chapter in “Learning Theory”, Springer Berlin / Heidelberg, 2007. [35] Montgomery J.D., “Social

  13. Networked Learning: Design Considerations for Online Instructors

    Science.gov (United States)

    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…

  14. NASA Engineering Network Lessons Learned

    Data.gov (United States)

    National Aeronautics and Space Administration — The NASA Lessons Learned system provides access to official, reviewed lessons learned from NASA programs and projects. These lessons have been made available to the...

  15. Interconnecting Networks of Practice for Professional Learning

    Directory of Open Access Journals (Sweden)

    Julie Mackey

    2011-03-01

    Full Text Available The article explores the complementary connections between communities of practice and the ways in which individuals orchestrate their engagement with others to further their professional learning. It does so by reporting on part of a research project conducted in New Zealand on teachers’ online professional learning in a university graduate diploma program on ICT education. Evolving from social constructivist pedagogy for online professional development, the research describes how teachers create their own networks of practice as they blend online and offline interactions with fellow learners and workplace colleagues. Teachers’ perspectives of their professional learning activities challenge the way universities design formal online learning communities and highlight the potential for networked learning in the zones and intersections between professional practice and study.The article extends the concepts of Lave and Wenger’s (1991 communities of practice social theory of learning by considering the role participants play in determining their engagement and connections in and across boundaries between online learning communities and professional practice. It provides insights into the applicability of connectivist concepts for developing online pedagogies to promote socially networked learning and for emphasising the role of the learner in defining their learning pathways.

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

    Science.gov (United States)

    Jamaludin, Azilawati; Shaari, Imran

    2016-01-01

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

  17. Evolutionary epistemology and dynamical virtual learning networks.

    Science.gov (United States)

    Giani, Umberto

    2004-01-01

    This paper is an attempt to define the main features of a new educational model aimed at satisfying the needs of a rapidly changing society. The evolutionary epistemology paradigm of culture diffusion in human groups could be the conceptual ground for the development of this model. Multidimensionality, multi-disciplinarity, complexity, connectivity, critical thinking, creative thinking, constructivism, flexible learning, contextual learning, are the dimensions that should characterize distance learning models aimed at increasing the epistemological variability of learning communities. Two multimedia educational software, Dynamic Knowledge Networks (DKN) and Dynamic Virtual Learning Networks (DVLN) are described. These two complementary tools instantiate these dimensions, and were tested in almost 150 online courses. Even if the examples are framed in the medical context, the analysis of the shortcomings of the traditional educational systems and the proposed solutions can be applied to the vast majority of the educational contexts.

  18. Learning Networks of Stochastic Differential Equations

    CERN Document Server

    Bento, José; Montanari, Andrea

    2010-01-01

    We consider linear models for stochastic dynamics. To any such model can be associated a network (namely a directed graph) describing which degrees of freedom interact under the dynamics. We tackle the problem of learning such a network from observation of the system trajectory over a time interval $T$. We analyze the $\\ell_1$-regularized least squares algorithm and, in the setting in which the underlying network is sparse, we prove performance guarantees that are \\emph{uniform in the sampling rate} as long as this is sufficiently high. This result substantiates the notion of a well defined `time complexity' for the network inference problem.

  19. Distance learning, problem based learning and dynamic knowledge networks.

    Science.gov (United States)

    Giani, U; Martone, P

    1998-06-01

    This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.

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

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

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

  1. A Decomposition Algorithm for Learning Bayesian Network Structures from Data

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Cordero Hernandez, Jorge

    2008-01-01

    It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

  2. Personalizing Access to Learning Networks

    DEFF Research Database (Denmark)

    Dolog, Peter; Simon, Bernd; Nejdl, Wolfgang

    2008-01-01

    In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address...... in this framework, enabling personalized access to federated learning repositories with a vast number of learning offers. Our infrastructure includes personalization strategies both at the query and the query results level. Query rewriting is based on learning and language preferences; rule-based and ranking......-based personalization improves these results further. Rule-based reasoning techniques are supported by formal ontologies we have developed based on standard information models for learning domains; ranking-based recommendations are supported through ensuring minimal sets of predicates appearing in query results. Our...

  3. Sparse neural networks with large learning diversity

    CERN Document Server

    Gripon, Vincent

    2011-01-01

    Coded recurrent neural networks with three levels of sparsity are introduced. The first level is related to the size of messages, much smaller than the number of available neurons. The second one is provided by a particular coding rule, acting as a local constraint in the neural activity. The third one is a characteristic of the low final connection density of the network after the learning phase. Though the proposed network is very simple since it is based on binary neurons and binary connections, it is able to learn a large number of messages and recall them, even in presence of strong erasures. The performance of the network is assessed as a classifier and as an associative memory.

  4. Social learning in Learning Networks through peer support: research findings and pitfalls

    NARCIS (Netherlands)

    Brouns, Francis

    2012-01-01

    Brouns, F. (2012, 2-4 April). Social learning in Learning Networks through peer support: research findings and pitfalls. Presentation at the Eighth International Conference on Networked Learning 2012, Maastricht, The Netherlands.

  5. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Distributed Extreme Learning Machine for Nonlinear Learning over Network

    Directory of Open Access Journals (Sweden)

    Songyan Huang

    2015-02-01

    Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

  7. Supervised Learning in Multilayer Spiking Neural Networks

    CERN Document Server

    Sporea, Ioana

    2012-01-01

    The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.

  8. Optimal learning paths in information networks.

    Science.gov (United States)

    Rodi, G C; Loreto, V; Servedio, V D P; Tria, F

    2015-06-01

    Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances.

  9. On local optima in learning bayesian networks

    DEFF Research Database (Denmark)

    Dalgaard, Jens; Kocka, Tomas; Pena, Jose

    2003-01-01

    This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness...

  10. Social Networking Services in E-Learning

    Science.gov (United States)

    Weber, Peter; Rothe, Hannes

    2016-01-01

    This paper is a report on the findings of a study conducted on the use of the social networking service NING in a cross-location e-learning setting named "Net Economy." We describe how we implemented NING as a fundamental part of the setting through a special phase concept and team building approach. With the help of user statistics, we…

  11. Learning in Networks for Sustainable Development

    NARCIS (Netherlands)

    Lansu, Angelique; Boon, Jo; Sloep, Peter; Van Dam-Mieras, Rietje

    2010-01-01

    The didactic model of remote internships described in this study provides the flexibility needed to support networked learners, i.e. to facilitate the development and subsequent assessment of their competences. The heterogeneity of the participants (students, employers, tutors) in the learning

  12. Unraveling networked learning initiatives: an analytic framework

    NARCIS (Netherlands)

    Rusman, Ellen; Prinsen, Fleur; Vermeulen, Marjan

    2016-01-01

    Networked learning happens naturally within the social systems of which we are all part. However, in certain circumstances individuals may want to actively take initiative to initiate interaction with others they are not yet regularly in exchange with. This may be the case when external influences a

  13. Social Networking Sites as a Learning Tool

    Science.gov (United States)

    Sanchez-Casado, Noelia; Cegarra Navarro, Juan Gabriel; Wensley, Anthony; Tomaseti-Solano, Eva

    2016-01-01

    Purpose: Over the past few years, social networking sites (SNSs) have become very useful for firms, allowing companies to manage the customer-brand relationships. In this context, SNSs can be considered as a learning tool because of the brand knowledge that customers develop from these relationships. Because of the fact that knowledge in…

  14. Learning chaotic attractors by neural networks

    NARCIS (Netherlands)

    Bakker, R; Schouten, JC; Giles, CL; Takens, F; van den Bleek, CM

    2000-01-01

    An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single measured time series. During training, the algorithm learns to short-term predict the time series. At the same time a criterion, developed by Diks, van Zwet, Takens, and de Goede (1996) is monitored th

  15. Learning in Networks for Sustainable Development

    NARCIS (Netherlands)

    Lansu, Angelique; Boon, Jo; Sloep, Peter; Van Dam-Mieras, Rietje

    2010-01-01

    The didactic model of remote internships described in this study provides the flexibility needed to support networked learners, i.e. to facilitate the development and subsequent assessment of their competences. The heterogeneity of the participants (students, employers, tutors) in the learning netwo

  16. LEARNING ALGORITHM OF STAGE CONTROL NBP NETWORK

    Institute of Scientific and Technical Information of China (English)

    Yan Lixiang; Qin Zheng

    2003-01-01

    This letter analyzes the reasons why the known Neural Back Promulgation (NBP)network learning algorithm has slower speed and greater sample error. Based on the analysis and experiment, the training group descending Enhanced Combination Algorithm (ECA) is proposed.The analysis of the generalized property and sample error shows that the ECA can heighten the study speed and reduce individual error.

  17. Social Networking Services in E-Learning

    Science.gov (United States)

    Weber, Peter; Rothe, Hannes

    2016-01-01

    This paper is a report on the findings of a study conducted on the use of the social networking service NING in a cross-location e-learning setting named "Net Economy." We describe how we implemented NING as a fundamental part of the setting through a special phase concept and team building approach. With the help of user statistics, we…

  18. Portability and networked learning environments

    NARCIS (Netherlands)

    Collis, B.A.; De Diana, I.P.F.

    1994-01-01

    Abstract The portability of educational software is defined as the likelihood of software usage, with or without adaptation, in an educational environment different from that for which it was originally designed and produced. Barriers and research relevant to the portability of electronic learning r

  19. Learning Bayesian Networks from Correlated Data

    Science.gov (United States)

    Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

    2016-05-01

    Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

  20. Learning Bayesian Networks from Correlated Data.

    Science.gov (United States)

    Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H; Perls, Thomas T; Sebastiani, Paola

    2016-05-05

    Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

  1. Learning the pseudoinverse solution to network weights.

    Science.gov (United States)

    Tapson, J; van Schaik, A

    2013-09-01

    The last decade has seen the parallel emergence in computational neuroscience and machine learning of neural network structures which spread the input signal randomly to a higher dimensional space; perform a nonlinear activation; and then solve for a regression or classification output by means of a mathematical pseudoinverse operation. In the field of neuromorphic engineering, these methods are increasingly popular for synthesizing biologically plausible neural networks, but the "learning method"-computation of the pseudoinverse by singular value decomposition-is problematic both for biological plausibility and because it is not an online or an adaptive method. We present an online or incremental method of computing the pseudoinverse precisely, which we argue is biologically plausible as a learning method, and which can be made adaptable for non-stationary data streams. The method is significantly more memory-efficient than the conventional computation of pseudoinverses by singular value decomposition. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. Learning networks and communication skills

    Directory of Open Access Journals (Sweden)

    Kerry Musselbrook

    2000-12-01

    Full Text Available The increase in student numbers in further and higher education over the last decade has been dramatic, placing greater pressures on academic staff in terms of contact hours. At the same time public funding of universities has decreased. Furthermore, the current pace of technological innovation and change and the fact that there are fewer jobs for life with clear pathways for progression mean that more of us need to be engaged in learning throughout our lives in order to remain competitive in the job-market. That is the reality of lifelong learning. Students are consequently demanding (especially as they are having to meet more of the costs of education themselves a more flexible learning framework. This framework should be able to accommodate all types of learners - part-time, mature, remote and disabled students. The revised Disability Discrimination Act, which came into force in October 1999, only temporarily excludes education from its remit and has already challenged university practices. (Another JlSC-funded initiative, Disability Information Systems in Higher Education, addresses just this issue: http://www.disinhe.ac.uk. All this is set against a backdrop of the government's stated vision for a more inclusive, less elitist education system with opportunities for all, and the requirement for a professional and accountable community of university teachers.

  3. Collaborative Supervised Learning for Sensor Networks

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    Bottrup, Pernille

    2005-01-01

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

  5. Dictionary Networking in an LSP Learning Context

    DEFF Research Database (Denmark)

    Nielsen, Sandro

    2007-01-01

    Dictionaries have long been an indispensable part of learning the factual and linguistic content of a subject-field, i.e. the relevant LSP. Both teachers and students refer to and use printed and electronic specialised dictionaries as tools when teaching and learning the structure, terminology...... accounting dictionaries linked to each other so that users can move from one dictionary to another. The network was designed to assist in learning native-language accounting terminology and usage and foreign-language accounting terminology and usage. Accounting students need to acquire knowledge about...... text production, but discusses an individual dictionary for a particular function. It is shown that in a general context of learning accounting and its relevant LSP with a view to writing or translating financial reporting texts, the modern theory of dictionary functions provides a good theoretical...

  6. Representational Distance Learning for Deep Neural Networks.

    Science.gov (United States)

    McClure, Patrick; Kriegeskorte, Nikolaus

    2016-01-01

    Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains.

  7. Improved Time Complexities for Learning Boolean Networks

    Directory of Open Access Journals (Sweden)

    Chee Keong Kwoh

    2013-09-01

    Full Text Available Existing algorithms for learning Boolean networks (BNs have time complexities of at least O(N · n0:7(k+1, where n is the number of variables, N is the number of samples and k is the number of inputs in Boolean functions. Some recent studies propose more efficient methods with O(N · n2 time complexities. However, these methods can only be used to learn monotonic BNs, and their performances are not satisfactory when the sample size is small. In this paper, we mathematically prove that OR/AND BNs, where the variables are related with logical OR/AND operations, can be found with the time complexity of O(k·(N+ logn·n2, if there are enough noiseless training samples randomly generated from a uniform distribution. We also demonstrate that our method can successfully learn most BNs, whose variables are not related with exclusive OR and Boolean equality operations, with the same order of time complexity for learning OR/AND BNs, indicating our method has good efficiency for learning general BNs other than monotonic BNs. When the datasets are noisy, our method can still successfully identify most BNs with the same efficiency. When compared with two existing methods with the same settings, our method achieves a better comprehensive performance than both of them, especially for small training sample sizes. More importantly, our method can be used to learn all BNs. However, of the two methods that are compared, one can only be used to learn monotonic BNs, and the other one has a much worse time complexity than our method. In conclusion, our results demonstrate that Boolean networks can be learned with improved time complexities.

  8. Reconstructing Causal Biological Networks through Active Learning.

    Directory of Open Access Journals (Sweden)

    Hyunghoon Cho

    Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

  9. Researching Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    2014-01-01

    and final section draws attention to a growing topic of interest within networked learning: that of networked learning in informal practices. In addition, we provide a reflection on the theories, methods and settings featured in the networked learning research of the chapters. We conclude the introduction...... by discussing four main themes that have emerged from our reading of the chapters and which we believe are important in taking forward the theory of networked learning. They are as follows: practice as epistemology; the coupling of learning contexts (the relationship and connection of learning contexts...

  10. Machine Learning for ATLAS DDM Network Metrics

    CERN Document Server

    Lassnig, Mario; The ATLAS collaboration; Vamosi, Ralf

    2016-01-01

    The increasing volume of physics data is posing a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from our ongoing automation efforts. First, we describe our framework for distributed data management and network metrics, automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

  11. Learning and coordinating in a multilayer network

    CERN Document Server

    Lugo, Haydee

    2014-01-01

    We introduce a two layer network model for social coordination incorporating two relevant ingredients: a) different networks of interaction to learn and to obtain a payoff , and b) decision making processes based both on social and strategic motivations. Two populations of agents are distributed in two layers with intralayer learning processes and playing interlayer a coordination game. We find that the skepticism about the wisdom of crowd and the local connectivity are the driving forces to accomplish full coordination of the two populations, while polarized coordinated layers are only possible for all-to-all interactions. Local interactions also allow for full coordination in the socially efficient Pareto-dominant strategy in spite of being the riskier one.

  12. File list: His.PSC.05.H4K16ac.AllCell [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available His.PSC.05.H4K16ac.AllCell mm9 Histone H4K16ac Pluripotent stem cell SRX298193,SRX2...12326,SRX212325,SRX298194 http://dbarchive.biosciencedbc.jp/kyushu-u/mm9/assembled/His.PSC.05.H4K16ac.AllCell.bed ...

  13. Complex Learning in Bio-plausible Memristive Networks

    OpenAIRE

    Deng, Lei; Li, Guoqi; Deng, Ning; Dong WANG; Zhang, Ziyang; He, Wei; Li, Huanglong; Pei, Jing; Shi, Luping

    2015-01-01

    The emerging memristor-based neuromorphic engineering promises an efficient computing paradigm. However, the lack of both internal dynamics in the previous feedforward memristive networks and efficient learning algorithms in recurrent networks, fundamentally limits the learning ability of existing systems. In this work, we propose a framework to support complex learning functions by introducing dedicated learning algorithms to a bio-plausible recurrent memristive network with internal dynamic...

  14. Learning in Neural Networks: VLSI Implementation Strategies

    Science.gov (United States)

    Duong, Tuan Anh

    1995-01-01

    Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

  15. Learning by Knowledge Networking across Cultures

    DEFF Research Database (Denmark)

    Wangel, Arne; Stærdahl, Jens; Bransholm Pedersen, Kirsten

    2005-01-01

    some of the obstacles into resources for knowledge sharing. However, students have stressed their positive experience of cross-cultural communication. While a joint course of three week duration by itself may involve only limited cross-cultural learning, serving primarily as an introduction to a long...... in the field, negotiate and agree on the analysis, and sustain the exchange of knowledge, possibly through virtual peer-to-peer networking....

  16. Conditions for Productive Learning in Network Learning Environments

    DEFF Research Database (Denmark)

    Ponti, M.; Dirckinck-Holmfeld, Lone; Lindström, B.

    2004-01-01

    approaches of case studies in different concrete higher educational settings and existing practices. The analyses are based in a socio-cultural approach in a broad sense (Engestrøm (1987), Wenger (1998), Dirckinck-Holmfeld and Fibiger (2002)) and are concerned with the following aspects and objects of study......, these ideas seem to have had very little impact both among designers and within the higher education community. The perspective of the theoretical work is therefore to inform design. Design understood as ?taking the system, its user, and the context all together? (Winograd 1996, xvi). This process implies......: Pedagogical design and the dialectics of the digital artefacts, the concept of collaboration, ethics/trust, identity and the role of scaffolding of networked learning environments.   The JEIRP is motivated by the fact that many networked learning environments in various European educational settings...

  17. Machine learning for identifying botnet network traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2013-01-01

    . Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

  18. The evaluation of the National Learning Network

    Directory of Open Access Journals (Sweden)

    Andrew S. Thomas Caven-Atack

    2000-12-01

    Full Text Available The National Learning Network (NLN is part of a response to the expectation that the further education (FE sector within England will grow steadily over the next three years to fulfil the Department for Employment and Education's requirement to widen participation in this area of education. The NLN is just one of the initiatives aimed at (though not exclusive to new students from non-traditional, disadvantaged and previously excluded groups, and is expected to bring the student population in FE to over four million. The Further Education Funding Council (FEFC has noted that traditional FE learning paradigms are not suitable for this level of participation and that increased levels of information and learning technology (ILT will need to be implemented to cope with these increased student numbers.

  19. Efficient learning strategy of Chinese characters based on network approach

    CERN Document Server

    Yan, Xiao-Yong; Di, Zengru; Havlin, Shlomo; Wu, Jinshan

    2013-01-01

    Based on network analysis of hierarchical structural relations among Chinese characters, we develop an efficient learning strategy of Chinese characters. We regard a more efficient learning method if one learns the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that takes into account both the weight of the nodes and the hierarchical structure of the network. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as...

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

    Science.gov (United States)

    McDonald, Joseph P.; Klein, Emily J.

    2003-01-01

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

  1. Learning as Issue Framing in Agricultural Innovation Networks

    Science.gov (United States)

    Tisenkopfs, Talis; Kunda, Ilona; Šumane, Sandra

    2014-01-01

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

  2. Threshold learning dynamics in social networks.

    Directory of Open Access Journals (Sweden)

    Juan Carlos González-Avella

    Full Text Available Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront external signals to the information gathered from their contacts. Economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist separated by sharp discontinuous transitions. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular, but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs.

  3. A Team Formation and Project-based Learning Support Service for Social Learning Networks

    NARCIS (Netherlands)

    Spoelstra, Howard; Van Rosmalen, Peter; Van de Vrie, Evert; Obreza, Matija; Sloep, Peter

    2014-01-01

    The Internet affords new approaches to learning. Geographically dispersed self-directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks

  4. ERT Conditions for Productive Learning in Networked Learning Environments: Leadership Report

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone

    This report provides a concluding account of the activities within the European Research Team: Conditions for Productive Learning in Networked Learning Environmentments......This report provides a concluding account of the activities within the European Research Team: Conditions for Productive Learning in Networked Learning Environmentments...

  5. A Team Formation and Project-based Learning Support Service for Social Learning Networks

    NARCIS (Netherlands)

    Spoelstra, Howard; Van Rosmalen, Peter; Van de Vrie, Evert; Obreza, Matija; Sloep, Peter

    2014-01-01

    The Internet affords new approaches to learning. Geographically dispersed self-directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks

  6. Linear programming for learning in neural networks

    Science.gov (United States)

    Raghavan, Raghu

    1991-08-01

    The authors have previously proposed a network of probabilistic cellular automata (PCAs) as part of an image recognition system designed to integrate model-based and data-driven approaches in a connectionist framework. The PCA arises from some natural requirements on the system which include incorporation of prior knowledge such as in inference rules, locality of inferences, and full parallelism. This network has been applied to recognize objects in both synthetic and in real data. This approach achieves recognition through the short-, rather than the long-time behavior of the dynamics of the PCA. In this paper, some methods are developed for learning the connection strengths by solving linear inequalities: the figures of merit are tendencies or directions of movement of the dynamical system. These 'dynamical' figures of merit result in inequality constraints on the connection strengths which are solved by linear (LP) or quadratic programs (QP). An algorithm is described for processing a large number of samples to determine weights for the PCA. The work may be regarded as either pointing out another application for constrained optimization, or as pointing out the need to extend the perceptron and similar methods for learning. The extension is needed because the neural network operates on a different principle from that for which the perceptron method was devised.

  7. Research and Development of a Positioning Service for Learning Networks for Lifelong Learning

    NARCIS (Netherlands)

    Kalz, Marco

    2006-01-01

    Kalz, M. (2006). Research and Development of a Positioning Service for Learning Networks for Lifelong Learning. Presentation given at the Doctoral Consortium of the First European Conference on Technology Enhanced Learning. October, 1-4, 2006, Crete.

  8. Bayesian network learning for natural hazard assessments

    Science.gov (United States)

    Vogel, Kristin

    2016-04-01

    Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables

  9. A Team Formation and Project-based Learning Support Service for Social Learning Networks

    OpenAIRE

    Spoelstra, Howard; Van Rosmalen, Peter; Van de Vrie, Evert; Obreza, Matija; Sloep, Peter

    2014-01-01

    The Internet affords new approaches to learning. Geographically dispersed self-directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks do not readily support effective, coherence-creating and motivating learning settings. It is argued that providing project-based learning opportunities and team formation services can help overcome...

  10. Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

    Science.gov (United States)

    2015-07-01

    Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data Guy Van den Broeck∗ and Karthika Mohan∗ and Arthur Choi and Adnan...We propose a family of efficient algorithms for learning the parameters of a Bayesian network from incomplete data. Our approach is based on recent...algorithms like EM (which require inference). 1 INTRODUCTION When learning the parameters of a Bayesian network from data with missing values, the

  11. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  12. Layered learning of soccer robot based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.

  13. Structure Learning in Power Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-01-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  14. WEB BASED LEARNING OF COMPUTER NETWORK COURSE

    Directory of Open Access Journals (Sweden)

    Hakan KAPTAN

    2004-04-01

    Full Text Available As a result of developing on Internet and computer fields, web based education becomes one of the area that many improving and research studies are done. In this study, web based education materials have been explained for multimedia animation and simulation aided Computer Networks course in Technical Education Faculties. Course content is formed by use of university course books, web based education materials and technology web pages of companies. Course content is formed by texts, pictures and figures to increase motivation of students and facilities of learning some topics are supported by animations. Furthermore to help working principles of routing algorithms and congestion control algorithms simulators are constructed in order to interactive learning

  15. Implementation of an infrastructure for networked learning

    DEFF Research Database (Denmark)

    Nyvang, Tom; Bygholm, Ann

    2011-01-01

    which is slow due to (too) many levels, lack of esthetic design, lack of coherence in practices and, generally, lack of relevant content. On the other hand, the predominant arguments for choosing the new system are more related to issues of operation, support, and management. We argue that the issues......What are the conditions under which institutional actors decide upon Information and Communication Technology strategies for networked learning purposes? The question is discussed within the frame of a case study of the decision process during a shift from one learning platform to another...... in an education at Aalborg University. The aim is to explicate and understand the multiplicity of issues involved and to point the possible ways of handling such decision processes. On the one hand, the analysis shows that the predominant reasons for deciding to change are dissatisfaction with the existing system...

  16. Learning Bayesian network structure with immune algorithm

    Institute of Scientific and Technical Information of China (English)

    Zhiqiang Cai; Shubin Si; Shudong Sun; Hongyan Dui

    2015-01-01

    Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network (BN), which is also necessary in promoting the application of BN. This pa-per proposes an immune algorithm based method (BN-IA) for the learning of the BN structure with the idea of vaccination. Further-more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Final y, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

  17. Graduate Employability: The Perspective of Social Network Learning

    Science.gov (United States)

    Chen, Yong

    2017-01-01

    This study provides a conceptual framework for understanding how the graduate acquire employability through the social network in the Chinese context, using insights from the social network theory. This paper builds a conceptual model of the relationship among social network, social network learning and the graduate employability, and uses…

  18. Learning Reproducibility with a Yearly Networking Contest

    KAUST Repository

    Canini, Marco

    2017-08-10

    Better reproducibility of networking research results is currently a major goal that the academic community is striving towards. This position paper makes the case that improving the extent and pervasiveness of reproducible research can be greatly fostered by organizing a yearly international contest. We argue that holding a contest undertaken by a plurality of students will have benefits that are two-fold. First, it will promote hands-on learning of skills that are helpful in producing artifacts at the replicable-research level. Second, it will advance the best practices regarding environments, testbeds, and tools that will aid the tasks of reproducibility evaluation committees by and large.

  19. Learning Bayesian Networks from Data by Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Learning Bayesian network is an NP-hard problem. When the number of variables is large, the process of searching optimal network structure could be very time consuming and tends to return a structure which is local optimal. The particle swarm optimization (PSO) was introduced to the problem of learning Bayesian networks and a novel structure learning algorithm using PSO was proposed. To search in directed acyclic graphs spaces efficiently, a discrete PSO algorithm especially for structure learning was proposed based on the characteristics of Bayesian networks. The results of experiments show that our PSO based algorithm is fast for convergence and can obtain better structures compared with genetic algorithm based algorithms.

  20. Navigation Support for Learners in Informal Learning Networks

    NARCIS (Netherlands)

    Drachsler, Hendrik

    2009-01-01

    Drachsler, H. (2009). Navigation Support for Learners in Informal Learning Networks. Unpublished doctoral thesis. Oktober, 16, 2009, Heerlen, The Netherlands: Open University of the Netherlands/CELSTEC.

  1. The role of social networks in students’ learning experiences

    OpenAIRE

    Liccardi, Ilaria; Ounnas, Asma; Pau, Reena; Massey, Elizabeth; Kinnumen, Paivi; Lewthwaite, Sarah; Midy, Marie-Anne; Sakar, Chandan

    2007-01-01

    The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in students’ learning experiences. The construction of students’ social networks, the evolution of these networks, and their effects on the students’ learning experi...

  2. Boltzmann learning of parameters in cellular neural networks

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    1992-01-01

    The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified...... by unsupervised adaptation of an image segmentation cellular network. The learning rule is applied to adaptive segmentation of satellite imagery...

  3. A Gaussian Mixed Model for Learning Discrete Bayesian Networks.

    Science.gov (United States)

    Balov, Nikolay

    2011-02-01

    In this paper we address the problem of learning discrete Bayesian networks from noisy data. Considered is a graphical model based on mixture of Gaussian distributions with categorical mixing structure coming from a discrete Bayesian network. The network learning is formulated as a Maximum Likelihood estimation problem and performed by employing an EM algorithm. The proposed approach is relevant to a variety of statistical problems for which Bayesian network models are suitable - from simple regression analysis to learning gene/protein regulatory networks from microarray data.

  4. Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

    CERN Document Server

    Hu, Fei

    2012-01-01

    Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

  5. The Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    Gleerup, Janne; Heilesen, Simon; Helms, Niels Henrik

    2014-01-01

    . The Design, Experience and Practice of Networked Learning will prove indispensable reading for researchers, teachers, consultants, and instructional designers in higher and continuing education; for those involved in staff and educational development, and for those studying post graduate qualifications......The chapters in this volume explore new and innovative ways of thinking about the nature of networked learning and its pedagogical values and beliefs. They pose a challenge to us to reflect on what we thought networked learning was 15 year ago, where it is today and where it is likely to be headed...... in learning and teaching. This, the second volume in the Springer Book Series on Researching Networked Learning, is based on a selection of papers presented at the 2012 Networked Learning Conference held in Maastricht, The Netherlands....

  6. Complex Learning in Bio-plausible Memristive Networks.

    Science.gov (United States)

    Deng, Lei; Li, Guoqi; Deng, Ning; Wang, Dong; Zhang, Ziyang; He, Wei; Li, Huanglong; Pei, Jing; Shi, Luping

    2015-06-19

    The emerging memristor-based neuromorphic engineering promises an efficient computing paradigm. However, the lack of both internal dynamics in the previous feedforward memristive networks and efficient learning algorithms in recurrent networks, fundamentally limits the learning ability of existing systems. In this work, we propose a framework to support complex learning functions by introducing dedicated learning algorithms to a bio-plausible recurrent memristive network with internal dynamics. We fabricate iron oxide memristor-based synapses, with well controllable plasticity and a wide dynamic range of excitatory/inhibitory connection weights, to build the network. To adaptively modify the synaptic weights, the comprehensive recursive least-squares (RLS) learning algorithm is introduced. Based on the proposed framework, the learning of various timing patterns and a complex spatiotemporal pattern of human motor is demonstrated. This work paves a new way to explore the brain-inspired complex learning in neuromorphic systems.

  7. Multi-agent reinforcement learning using modular neural network Q-learning algorithms

    Institute of Scientific and Technical Information of China (English)

    YANG Yin-xian; FANG Kai

    2005-01-01

    Reinforcement learning is an excellent approach which is used in artificial intelligence,automatic control, etc. However, ordinary reinforcement learning algorithm, such as Q-learning with lookup table cannot cope with extremely complex and dynamic environment due to the huge state space. To reduce the state space, modular neural network Q-learning algorithm is proposed, which combines Q-learning algorithm with neural network and module method. Forward feedback neural network, Elman neural network and radius-basis neural network are separately employed to construct such algorithm. It is revealed that Elman neural network Q-learning algorithm has the best performance under the condition that the same neural network training method, i.e. gradient descent error back-propagation algorithm is applied.

  8. Personality Type and Participation in Networked Learning Environments.

    Science.gov (United States)

    Ellis, Ainslie E.

    2003-01-01

    Discussion of networked learning environments and learner characteristics focuses on personality type as determined using the Myers Briggs Type Indicator. Investigates the relationship between personality type and student participation within a networked learning environment using asynchronous threaded discussion for a university course run both…

  9. Towards a Social Networks Model for Online Learning & Performance

    Science.gov (United States)

    Chung, Kon Shing Kenneth; Paredes, Walter Christian

    2015-01-01

    In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

  10. The Practices of Student Network as Cooperative Learning in Ethiopia

    Science.gov (United States)

    Reda, Weldemariam Nigusse; Hagos, Girmay Tsegay

    2015-01-01

    Student network is a teaching strategy introduced as cooperative learning to all educational levels above the upper primary schools (grade 5 and above) in Ethiopia. The study was, therefore, aimed at investigating to what extent the student network in Ethiopia is actually practiced in line with the principles of cooperative learning. Consequently,…

  11. Guidelines to foster interaction in online communities for Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Rusman, Ellen; Bitter-Rijpkema, Marlies; Sloep, Peter

    2009-01-01

    The original publication is available from www.springerlink.com. Berlanga, A., Rusman, E., Bitter-Rijpkema, M., & Sloep, P. B. (2009). Guidelines to foster interaction in online communities for Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp. 27-42).

  12. The Relationships Between Policy, Boundaries and Research in Networked Learning

    DEFF Research Database (Denmark)

    2016-01-01

    the books that include a selection of reworked and peer-reviewed papers from the conference. The 2014 Networked Learning Conference which was held in Edinburgh was characterised by animated dialogue on emergent influences affecting networked teaching and learning building on work established in earlier...

  13. Problems in the Deployment of Learning Networks In Small Organizations

    NARCIS (Netherlands)

    Shankle, Dean E.; Shankle, Jeremy P.

    2006-01-01

    Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria:

  14. Problems in the Deployment of Learning Networks In Small Organizations

    OpenAIRE

    Shankle, Dean E.; Shankle, Jeremy P.

    2006-01-01

    Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria: TENCompetence. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

  15. Towards a Social Networks Model for Online Learning & Performance

    Science.gov (United States)

    Chung, Kon Shing Kenneth; Paredes, Walter Christian

    2015-01-01

    In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

  16. Multi-Agent Reinforcement Learning and Adaptive Neural Networks.

    Science.gov (United States)

    2007-11-02

    learning method. The objective was to study the utility of reinforcement learning as an approach to complex decentralized control problems. The major...accomplishment was a detailed study of multi-agent reinforcement learning applied to a large-scale decentralized stochastic control problem. This study...included a very successful demonstration that a multi-agent reinforcement learning system using neural networks could learn high-performance

  17. Social learning in Learning Networks through peer support: research findings and pitfalls

    NARCIS (Netherlands)

    Brouns, Francis; Hsiao, Amy

    2012-01-01

    Brouns, F., & Hsiao, A. (2012). Social learning in Learning Networks through peer support: research findings and pitfalls. In V. Hodgson, C. Jones, M. de Laat, D. McConnell, T. Ryberg, & P. Sloep (Eds.), Proceedings of the Eighth International Conference on Networked Learning 2012 (pp. 18-25). April

  18. Researching Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    Hodgson, Vivien; de Laat, Maarten; McConnell, David;

    2014-01-01

    In the introductory chapter, we explore how networked learning has developed in recent years by summarising and discussing the research presented in the chapters of the book. The chapters are structured in three sections, each highlighting a particular aspect of practice. The first section focuse...... and spaces); the agency and active role of technology within networked learning; and the messy, often chaotic and always political nature of the design, experience and practice of networked learning....... on the relationship between design and its influence on how networked learning practices are implemented. The second section extends this discussion by raising the notion of experiencing networked learning practices. Here the expected and unexpected effects of design and its implementation are scrutinised. The third...

  19. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

    Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

  20. Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms

    OpenAIRE

    Abraham, Ajith

    2004-01-01

    Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex s...

  1. Robust Learning of Fixed-Structure Bayesian Networks

    OpenAIRE

    Diakonikolas, Ilias; Kane, Daniel; Stewart, Alistair

    2016-01-01

    We investigate the problem of learning Bayesian networks in an agnostic model where an $\\epsilon$-fraction of the samples are adversarially corrupted. Our agnostic learning model is similar to -- in fact, stronger than -- Huber's contamination model in robust statistics. In this work, we study the fully observable Bernoulli case where the structure of the network is given. Even in this basic setting, previous learning algorithms either run in exponential time or lose dimension-dependent facto...

  2. Using Social Networks to Create Powerful Learning Communities

    Science.gov (United States)

    Lenox, Marianne; Coleman, Maurice

    2010-01-01

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

  3. Structure of Small World Innovation Network and Learning Performance

    Directory of Open Access Journals (Sweden)

    Shuang Song

    2014-01-01

    Full Text Available This paper examines the differences of learning performance of 5 MNCs (multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality. We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter distance between the knowledge of the individual and the reality denotes a lower (higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

  4. Enhancing Formal E-Learning with Edutainment on Social Networks

    Science.gov (United States)

    Labus, A.; Despotovic-Zrakic, M.; Radenkovic, B.; Bogdanovic, Z.; Radenkovic, M.

    2015-01-01

    This paper reports on the investigation of the possibilities of enhancing the formal e-learning process by harnessing the potential of informal game-based learning on social networks. The goal of the research is to improve the outcomes of the formal learning process through the design and implementation of an educational game on a social network…

  5. An ART neural network model of discrimination shift learning

    NARCIS (Netherlands)

    Raijmakers, M.E.J.; Coffey, E.; Stevenson, C.; Winkel, J.; Berkeljon, A.; Taatgen, N.; van Rijn, H.

    2009-01-01

    We present an ART-based neural network model (adapted from [2]) of the development of discrimination-shift learning that models the trial-by-trial learning process in great detail. In agreement with the results of human participants (4-20 years of age) in [1] the model revealed two distinct learning

  6. Network Applications for Group-Based Learning: Is More Better?

    Science.gov (United States)

    Veen, Jan; Collis, Betty; Jones, Val

    2003-01-01

    Group-based learning is being introduced into many settings in higher education. Is this a sustainable development with respect to the resources required? Under what conditions can group-based learning be applied successfully in distance education and in increasingly flexible campus-based learning? Can networked support facilitate and enrich…

  7. Enhancing Formal E-Learning with Edutainment on Social Networks

    Science.gov (United States)

    Labus, A.; Despotovic-Zrakic, M.; Radenkovic, B.; Bogdanovic, Z.; Radenkovic, M.

    2015-01-01

    This paper reports on the investigation of the possibilities of enhancing the formal e-learning process by harnessing the potential of informal game-based learning on social networks. The goal of the research is to improve the outcomes of the formal learning process through the design and implementation of an educational game on a social network…

  8. EduCamp Colombia: Social Networked Learning for Teacher Training

    Directory of Open Access Journals (Sweden)

    Diego Ernesto Leal Fonseca

    2011-03-01

    Full Text Available This paper describes a learning experience called EduCamp, which was launched by the Ministry of Education of Colombia in 2007, based on emerging concepts such as e-Learning 2.0, connectivism, and personal learning environments. An EduCamp proposes an unstructured collective learning experience, which intends to make palpable the possibilities of social software tools in learning and interaction processes while demonstrating face-to-face organizational forms that reflect social networked learning ideas. The experience opens new perspectives for the design of technology training workshops and for the development of lifelong learning experiences.

  9. Do Convolutional Neural Networks Learn Class Hierarchy?

    Science.gov (United States)

    Alsallakh, Bilal; Jourabloo, Amin; Ye, Mao; Liu, Xiaoming; Ren, Liu

    2017-08-29

    Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.

  10. Evolution of individual versus social learning on social networks.

    Science.gov (United States)

    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.

  11. Theoretical framework on selected core issues on conditions for productive learning in networked learning environments

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone; Svendsen, Brian Møller; Ponti, Marisa

    The report documents and summarises the elements and dimensions that have been identified to describe and analyse the case studies collected in the Kaleidoscope Jointly Executed Integrating Research Project (JEIRP) on Conditions for productive learning in network learning environments....

  12. Theoretical framework on selected core issues on conditions for productive learning in networked learning environments

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone; Svendsen, Brian Møller; Ponti, Marisa;

    The report documents and summarises the elements and dimensions that have been identified to describe and analyse the case studies collected in the Kaleidoscope Jointly Executed Integrating Research Project (JEIRP) on Conditions for productive learning in network learning environments....

  13. Analysing Content and Patterns of Interaction for Improving the Learning Design of Networked Learning Environments

    Science.gov (United States)

    Haya, Pablo A.; Daems, Oliver; Malzahn, Nils; Castellanos, Jorge; Hoppe, Heinz Ulrich

    2015-01-01

    Learning Analytics constitutes a key tool for supporting Learning Design and teacher-led inquiry into student learning. In this paper, we demonstrate how a Social Learning Analytics toolkit can combine social network analysis and content analysis for supporting a global and formal teacher inquiry. This toolkit not only supports teachers in…

  14. Intra-Organizational Learning Networks within Knowledge-Intensive Learning Environments

    Science.gov (United States)

    Skerlavaj, M.; Dimovski, V.; Mrvar, A.; Pahor, M.

    2010-01-01

    Organizational learning contributes to organizational performance. One research question that remains inadequately explained is how learning occurs. Can it be explained by using the acquisition or participation perspectives? Or is there a need for some other view? This paper suggests that learning networks form an important learning environment…

  15. Analysing Content and Patterns of Interaction for Improving the Learning Design of Networked Learning Environments

    Science.gov (United States)

    Haya, Pablo A.; Daems, Oliver; Malzahn, Nils; Castellanos, Jorge; Hoppe, Heinz Ulrich

    2015-01-01

    Learning Analytics constitutes a key tool for supporting Learning Design and teacher-led inquiry into student learning. In this paper, we demonstrate how a Social Learning Analytics toolkit can combine social network analysis and content analysis for supporting a global and formal teacher inquiry. This toolkit not only supports teachers in…

  16. Distributed Reinforcement Learning Approach for Vehicular Ad Hoc Networks

    Science.gov (United States)

    Wu, Celimuge; Kumekawa, Kazuya; Kato, Toshihiko

    In Vehicular Ad hoc Networks (VANETs), general purpose ad hoc routing protocols such as AODV cannot work efficiently due to the frequent changes in network topology caused by vehicle movement. This paper proposes a VANET routing protocol QLAODV (Q-Learning AODV) which suits unicast applications in high mobility scenarios. QLAODV is a distributed reinforcement learning routing protocol, which uses a Q-Learning algorithm to infer network state information and uses unicast control packets to check the path availability in a real time manner in order to allow Q-Learning to work efficiently in a highly dynamic network environment. QLAODV is favored by its dynamic route change mechanism, which makes it capable of reacting quickly to network topology changes. We present an analysis of the performance of QLAODV by simulation using different mobility models. The simulation results show that QLAODV can efficiently handle unicast applications in VANETs.

  17. Learning without local minima in radial basis function networks.

    Science.gov (United States)

    Bianchini, M; Frasconi, P; Gori, M

    1995-01-01

    Learning from examples plays a central role in artificial neural networks. The success of many learning schemes is not guaranteed, however, since algorithms like backpropagation may get stuck in local minima, thus providing suboptimal solutions. For feedforward networks, optimal learning can be achieved provided that certain conditions on the network and the learning environment are met. This principle is investigated for the case of networks using radial basis functions (RBF). It is assumed that the patterns of the learning environment are separable by hyperspheres. In that case, we prove that the attached cost function is local minima free with respect to all the weights. This provides us with some theoretical foundations for a massive application of RBF in pattern recognition.

  18. THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

    Directory of Open Access Journals (Sweden)

    Mohd Ishak Bin Ismail

    2016-02-01

    Full Text Available Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by students in higher learning to communicate to each other, to carry out academic collaboration and sharing resources. Learning through social networking sites is based on the social interaction which learning are emphasizing on students, real world resources, active students` participation, diversity of learning resources and the use of digital tools to deliver meaningful learning. Many studies found the positive, neutral and negative impact of social networking sites on academic performance. Thus, this study will determine the relationship between Facebook usage and academic achievement. Also, it will investigate the association of social capital and academic collaboration to Facebook usage.

  19. Learning OpenStack networking (Neutron)

    CERN Document Server

    Denton, James

    2014-01-01

    If you are an OpenStack-based cloud operator with experience in OpenStack Compute and nova-network but are new to Neutron networking, then this book is for you. Some networking experience is recommended, and a physical network infrastructure is required to provide connectivity to instances and other network resources configured in the book.

  20. Structural constraints on learning in the neural network.

    Science.gov (United States)

    Martinez, Clarisa A; Wang, Chunji

    2015-11-01

    Recent research suggests the brain can learn almost any brain-computer interface (BCI) configuration; however, contrasting behavioral evidence from structural learning theory argues that previous experience facilitates, or impedes, future learning. A study by Sadtler and colleagues (Nature 512: 423-426, 2014) used BCI to demonstrate that neural network structural characteristics constrain learning, a finding that might also provide insight into how the brain responds to and recovers after injury.

  1. Dynamics of learning near singularities in layered networks.

    Science.gov (United States)

    Wei, Haikun; Zhang, Jun; Cousseau, Florent; Ozeki, Tomoko; Amari, Shun-Ichi

    2008-03-01

    We explicitly analyze the trajectories of learning near singularities in hierarchical networks, such as multilayer perceptrons and radial basis function networks, which include permutation symmetry of hidden nodes, and show their general properties. Such symmetry induces singularities in their parameter space, where the Fisher information matrix degenerates and odd learning behaviors, especially the existence of plateaus in gradient descent learning, arise due to the geometric structure of singularity. We plot dynamic vector fields to demonstrate the universal trajectories of learning near singularities. The singularity induces two types of plateaus, the on-singularity plateau and the near-singularity plateau, depending on the stability of the singularity and the initial parameters of learning. The results presented in this letter are universally applicable to a wide class of hierarchical models. Detailed stability analysis of the dynamics of learning in radial basis function networks and multilayer perceptrons will be presented in separate work.

  2. Learning oncogenetic networks by reducing to mixed integer linear programming.

    Science.gov (United States)

    Shahrabi Farahani, Hossein; Lagergren, Jens

    2013-01-01

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

  3. Learning Networks: connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning

    NARCIS (Netherlands)

    Koper, Rob; Sloep, Peter

    2003-01-01

    Koper, E.J.R., Sloep, P.B. (2002) Learning Networks connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning. RTD Programma into Learning Technologies 2003-2008. More is different… Heerlen, Nederland: Open Universiteit Neder

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

    Science.gov (United States)

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

    2016-01-01

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

  5. An Introduction of E-learning based on Social Networks

    Directory of Open Access Journals (Sweden)

    K.M. Aslam Uddin

    2015-03-01

    Full Text Available Social networks contribute a good portion of Internet traffic nowadays and thus attract tremendous research interests. Among social networking services, Facebook has become most popular for communication with familiar and also with unfamiliar persons. Many people at different levels use social network. The impact of the use of social network on students is very high. We have conducted a survey on various students from several universities of Bangladesh.The result has revealed that most of the students use Internet for social networking rather than studying. Our point of view is this part of information. Our concern is to propose an option where students can study from several pages regarding learning with the all other facilities of social network. It will help a student to learn through the use of social network.

  6. Shared learning in supply networks: evidence from an emerging market supply network

    NARCIS (Netherlands)

    K.J. Mason (Katy); I. Oshri (Ilan); S. Leek

    2009-01-01

    textabstractPurpose – Firms face the challenge of developing learning capabilities that enable them to work as part of an effective business network. While an extensive literature examines learning capabilities within the firm, little attention has been given to shared learning that occurs between n

  7. Efficient learning strategy of Chinese characters based on network approach.

    Science.gov (United States)

    Yan, Xiaoyong; Fan, Ying; Di, Zengru; Havlin, Shlomo; Wu, Jinshan

    2013-01-01

    We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

  8. A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

    Science.gov (United States)

    Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

    2015-12-01

    Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

  9. Parsing learning in networks using brain-machine interfaces.

    Science.gov (United States)

    Orsborn, Amy L; Pesaran, Bijan

    2017-08-24

    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. Published by Elsevier Ltd.

  10. Learning ambiguous functions by neural networks

    CERN Document Server

    Ligeiro, Rui

    2013-01-01

    It is not, in general, possible to have access to all variables that determine the behavior of a system. Having identified a number of variables whose values can be accessed, there may still be hidden variables which influence the dynamics of the system. The result is model ambiguity in the sense that, for the same (or very similar) input values, different objective outputs should have been obtained. In addition, the degree of ambiguity may vary widely across the whole range of input values. Thus, to evaluate the accuracy of a model it is of utmost importance to create a method to obtain the degree of reliability of each output result. In this paper we present such a scheme composed of two coupled artificial neural networks: the first one being responsible for outputting the predicted value, whereas the other evaluates the reliability of the output, which is learned from the error values of the first one. As an illustration, the scheme is applied to a model for tracking slopes in a straw chamber and to a cred...

  11. Navigation Support for Learners in Informal Learning Networks

    NARCIS (Netherlands)

    Drachsler, Hendrik

    2009-01-01

    Drachsler, H. (2009). Navigation Support for Learners in Informal Learning Networks. October, 16, 2009, Heerlen, The Netherlands: Open University of the Netherlands, CELSTEC. SIKS Dissertation Series No. 2009-37. ISBN 9789079447312.

  12. Tweetstorming PLNs: Using Twitter to Brainstorm about Personal Learning Networks

    NARCIS (Netherlands)

    Sie, Rory; Boursinou, Eleni; Rajagopal, Kamakshi; Pataraia, Nino

    2012-01-01

    Sie, R., Boursinou, E., Rajagopal, K., & Pataraia, N. (2011). Tweetstorming PLNs: Using Twitter to Brainstorm about Personal Learning Networks. In Proceedings of The PLE Conference 2011. July, 10-12, 2011, Southampton, UK.

  13. The Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    Gleerup, Janne; Heilesen, Simon; Helms, Niels Henrik

    2014-01-01

    . The Design, Experience and Practice of Networked Learning will prove indispensable reading for researchers, teachers, consultants, and instructional designers in higher and continuing education; for those involved in staff and educational development, and for those studying post graduate qualifications...

  14. Proceedings of the Eighth International Conference on Networked Learning 2012

    NARCIS (Netherlands)

    Hodgson, Vivien; Jones, Chris; De Laat, Maarten; McConnell, David; Ryberg, Thomas; Sloep, Peter

    2012-01-01

    Hodgson, V., Jones, C., De Laat, M., McConnell, D., Ryberg, T., & Sloep, P. B. (Eds.) (2012). Proceedings of the Eighth International Conference on Networked Learning 2012. April, 2-4, 2012, Maastricht, The Netherlands.

  15. Tweetstorming PLNs: Using Twitter to Brainstorm about Personal Learning Networks

    NARCIS (Netherlands)

    Sie, Rory; Boursinou, Eleni; Rajagopal, Kamakshi; Pataraia, Nino

    2012-01-01

    Sie, R., Boursinou, E., Rajagopal, K., & Pataraia, N. (2011). Tweetstorming PLNs: Using Twitter to Brainstorm about Personal Learning Networks. In Proceedings of The PLE Conference 2011. July, 10-12, 2011, Southampton, UK.

  16. One pass learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2016-01-01

    Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance. Copyright

  17. The Contribution of Social Networks to Individual Learning in Service Organizations

    Science.gov (United States)

    Poell, Rob F.; Van der Krogt, Ferd J.

    2007-01-01

    This study investigates how social networks in service organizations contribute to employee learning. Two specific types of social network seem especially relevant to individual learning: first, the service network, where employees carry out and improve their work, which may lead to learning; and second, the learning network, where employees…

  18. Stochastic margin-based structure learning of Bayesian network classifiers.

    Science.gov (United States)

    Pernkopf, Franz; Wohlmayr, Michael

    2013-02-01

    The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers. Furthermore, greedy hill-climbing and simulated annealing search heuristics are applied to determine the classifier structures. In the experiments, we demonstrate the advantages of maximum margin optimized Bayesian network structures in terms of classification performance compared to traditionally used discriminative structure learning methods. Stochastic simulated annealing requires less score evaluations than greedy heuristics. Additionally, we compare generative and discriminative parameter learning on both generatively and discriminatively structured Bayesian network classifiers. Margin-optimized Bayesian network classifiers achieve similar classification performance as support vector machines. Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.

  19. Learning Local Components to Understand Large Bayesian Networks

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

    2009-01-01

    Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... (domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

  20. Information theoretic derivation of network architecture and learning algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Jones, R.D.; Barnes, C.W.; Lee, Y.C.; Mead, W.C.

    1991-01-01

    Using variational techniques, we derive a feedforward network architecture that minimizes a least squares cost function with the soft constraint that the mutual information between input and output be maximized. This permits optimum generalization for a given accuracy. A set of learning algorithms are also obtained. The network and learning algorithms are tested on a set of test problems which emphasize time series prediction. 6 refs., 1 fig.

  1. Learning and forgetting on asymmetric, diluted neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Derrida, B.; Nadal, J.P.

    1987-12-01

    It is possible to construct diluted asymmetric models of neural networks for which the dynamics can be calculated exactly. The authors test several learning schemes, in particular, models for which the values of the synapses remain bounded and depend on the history. Our analytical results on the relative efficiencies of the various learning schemes are qualitatively similar to the corresponding ones obtained numerically on fully connected symmetric networks.

  2. Review of Learning in ICT-enabled Networks and Communities

    OpenAIRE

    ALA-MUTKA Kirsti Maria

    2009-01-01

    This report is part of a project launched by IPTS with DG Education and Culture to study the innovations for learning, which are emerging in the new collaborative and informal settings enabled by ICT. The report gathers and analyses evidence from learning opportunities that are emerging in ICT-enabled networks and communities. In these new virtual spaces, participation is motivated by an interest to a topic, by creative production and by search for social connection. Online networks and commu...

  3. Exploring dynamic mechanisms of learning networks for resource conservation

    Directory of Open Access Journals (Sweden)

    Petr Matous

    2015-06-01

    Full Text Available The importance of networks for social-ecological processes has been recognized in the literature; however, existing studies have not sufficiently addressed the dynamic nature of networks. Using data on the social learning networks of 265 farmers in Ethiopia for 2011 and 2012 and stochastic actor-oriented modeling, we explain the mechanisms of network evolution and soil conservation. The farmers' preferences for information exchange within the same social groups support the creation of interactive, clustered, nonhierarchical structures within the evolving learning networks, which contributed to the diffusion of the practice of composting. The introduced methods can be applied to determine whether and how social networks can be used to facilitate environmental interventions in various contexts.

  4. Learning algorithms for feedforward networks based on finite samples

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M.; Iyengar, S.S.

    1994-09-01

    Two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by feedforward networks, are discussed. The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted to concept learning problems.

  5. Networked Environments that Create Hybrid Spaces for Learning Science

    Science.gov (United States)

    Otrel-Cass, Kathrin; Khoo, Elaine; Cowie, Bronwen

    2014-01-01

    Networked learning environments that embed the essence of the Community of Inquiry (CoI) framework utilise pedagogies that encourage dialogic practices. This can be of significance for classroom teaching across all curriculum areas. In science education, networked environments are thought to support student investigations of scientific problems,…

  6. Using Recurrent Neural Network for Learning Expressive Ontologies

    OpenAIRE

    Petrucci, Giulio; Ghidini, Chiara; Rospocher, Marco

    2016-01-01

    Recently, Neural Networks have been proven extremely effective in many natural language processing tasks such as sentiment analysis, question answering, or machine translation. Aiming to exploit such advantages in the Ontology Learning process, in this technical report we present a detailed description of a Recurrent Neural Network based system to be used to pursue such goal.

  7. Implementing e-network-supported inquiry learning in science

    DEFF Research Database (Denmark)

    Williams, John; Cowie, Bronwen; Khoo, Elaine

    2013-01-01

    The successful implementation of electronically networked (e-networked) tools to support an inquiry-learning approach in secondary science classrooms is dependent on a range of factors spread between teachers, schools, and students. The teacher must have a clear understanding of the nature of inq...

  8. The Local Area Network and the Cooperative Learning Principle.

    Science.gov (United States)

    Sloan, Fred A.; Koohang, Alex A.

    1991-01-01

    Discussion of the advantages of local area networks (LANs) focuses on their use for successful cooperative learning. Individual and group assessment of success are discussed, effects on academic and affective achievement are considered, and computer-assisted instruction (CAI) programs to use with networking are suggested. (19 references) (LRW)

  9. Local Area Networks and the Learning Lab of the Future.

    Science.gov (United States)

    Ebersole, Dennis C.

    1987-01-01

    Considers educational applications of local area computer networks and discusses industry standards for design established by the International Standards Organization (ISO) and Institute of Electrical and Electronic Engineers (IEEE). A futuristic view of a learning laboratory using a local area network is presented. (Author/LRW)

  10. Social Software: Participants' Experience Using Social Networking for Learning

    Science.gov (United States)

    Batchelder, Cecil W.

    2010-01-01

    Social networking tools used in learning provides instructional design with tools for transformative change in education. This study focused on defining the meanings and essences of social networking through the lived common experiences of 7 college students. The problem of the study was a lack of learner voice in understanding the value of social…

  11. Analog neural network for support vector machine learning.

    Science.gov (United States)

    Perfetti, Renzo; Ricci, Elisa

    2006-07-01

    An analog neural network for support vector machine learning is proposed, based on a partially dual formulation of the quadratic programming problem. It results in a simpler circuit implementation with respect to existing neural solutions for the same application. The effectiveness of the proposed network is shown through some computer simulations concerning benchmark problems.

  12. Using Recurrent Neural Network for Learning Expressive Ontologies

    OpenAIRE

    Petrucci, Giulio; Ghidini, Chiara; Rospocher, Marco

    2016-01-01

    Recently, Neural Networks have been proven extremely effective in many natural language processing tasks such as sentiment analysis, question answering, or machine translation. Aiming to exploit such advantages in the Ontology Learning process, in this technical report we present a detailed description of a Recurrent Neural Network based system to be used to pursue such goal.

  13. Nurturing Global Collaboration and Networked Learning in Higher Education

    Science.gov (United States)

    Cronin, Catherine; Cochrane, Thomas; Gordon, Averill

    2016-01-01

    We consider the principles of communities of practice (CoP) and networked learning in higher education, illustrated with a case study. iCollab has grown from an international community of practice connecting students and lecturers in seven modules across seven higher education institutions in six countries, to a global network supporting the…

  14. Social Software: Participants' Experience Using Social Networking for Learning

    Science.gov (United States)

    Batchelder, Cecil W.

    2010-01-01

    Social networking tools used in learning provides instructional design with tools for transformative change in education. This study focused on defining the meanings and essences of social networking through the lived common experiences of 7 college students. The problem of the study was a lack of learner voice in understanding the value of social…

  15. Innovative Professional Development: Expanding Your Professional Learning Network

    Science.gov (United States)

    Perez, Lisa

    2012-01-01

    To assume the role of technology leaders and information literacy specialists in their schools, librarians need access to the most current information. And, they do this by helping each other. There are many definitions, but professional learning networks (PLNs) involve sharing work-related ideas with a network of colleagues via various digital…

  16. Novel Newton's learning algorithm of neural networks

    Institute of Scientific and Technical Information of China (English)

    Long Ning; Zhang Fengli

    2006-01-01

    Newton's learning algorithm of NN is presented and realized. In theory, the convergence rate of learning algorithm of NN based on Newton's method must be faster than BP's and other learning algorithms, because the gradient method is linearly convergent while Newton's method has second order convergence rate.The fast computing algorithm of Hesse matrix of the cost function of NN is proposed and it is the theory basis of the improvement of Newton's learning algorithm. Simulation results show that the convergence rate of Newton's learning algorithm is high and apparently faster than the traditional BP method's, and the robustness of Newton's learning algorithm is also better than BP method's.

  17. Social Networks and Performance in Distributed Learning Communities

    Science.gov (United States)

    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…

  18. The Unexpected Connection: Serendipity and Human Mediation in Networked Learning

    Science.gov (United States)

    Kop, Rita

    2012-01-01

    Major changes on the Web in recent years have contributed to an abundance of information for people to harness in their learning. Emerging technologies have instigated the need for critical literacies to support learners on open online networks in the mastering of critical information gathering during their learning journeys. This paper will argue…

  19. Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter

    2009-01-01

    Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning

  20. Language Learning through Social Networks: Perceptions and Reality

    Science.gov (United States)

    Lin, Chin-Hsi; Warschauer, Mark; Blake, Robert

    2016-01-01

    Language Learning Social Network Sites (LLSNSs) have attracted millions of users around the world. However, little is known about how people participate in these sites and what they learn from them. This study investigated learners' attitudes, usage, and progress in a major LLSNS through a survey of 4,174 as well as 20 individual case studies. The…

  1. Social Networks and Performance in Distributed Learning Communities

    Science.gov (United States)

    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…

  2. Student Learning Networks on Residential Field Courses: Does Size Matter?

    Science.gov (United States)

    Langan, A. Mark; Cullen, W. Rod; Shuker, David M.

    2008-01-01

    This article describes learner and tutor reports of a learning network that formed during the completion of investigative projects on a residential field course. Staff and students recorded project-related interactions, who they were with and how long they lasted over four phases during the field course. An enquiry based learning format challenged…

  3. Research and Development of a Positioning Service for Learning Networks for Lifelong Learning

    NARCIS (Netherlands)

    Kalz, Marco

    2006-01-01

    Kalz, M. (2006). Research and Development of a Positioning Service for Learning Networks for Lifelong Learning. In K. Maillet & R. Klamma (Eds.). Proceedings of the Doctoral Consortium of the First European Conference on Technology Enhanced Learning (pp. 18-25). October, 1-4, 2006, Crete, Greece.

  4. A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks

    NARCIS (Netherlands)

    De Jong, Tim; Fuertes, Alba; Schmeits, Tally; Specht, Marcus; Koper, Rob

    2008-01-01

    De Jong, T., Fuertes, A., Schmeits, T., Specht, M., & Koper, R. (2009). A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks. In D. Goh (Ed.), Multiplatform E-Learning Systems and Technologies: Mobile Devices for Ubiquitous ICT-Based Education (pp. 1-1

  5. Facilitating participation:From the EML web site to the Learning Network for Learning Design

    NARCIS (Netherlands)

    Hummel, Hans; Tattersall, Colin; Burgos, Daniel; Brouns, Francis; Kurvers, Hub; Koper, Rob

    2004-01-01

    Please refer to original publication: Hummel, H., Tattersall, C., Burgos, D., Brouns, F., Kurvers, H., & Koper, R. (2005). Facilitating participation: From the EML website to the Learning Network for Learning Design. Interactive Learning Environments,13(1-2), 55-69

  6. The Mobile Learning Network: Getting Serious about Games Technologies for Learning

    Science.gov (United States)

    Petley, Rebecca; Parker, Guy; Attewell, Jill

    2011-01-01

    The Mobile Learning Network currently in its third year, is a unique collaborative initiative encouraging and enabling the introduction of mobile learning in English post-14 education. The programme, funded jointly by the Learning and Skills Council and participating colleges and schools and supported by LSN has involved nearly 40,000 learners and…

  7. A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks

    NARCIS (Netherlands)

    De Jong, Tim; Fuertes, Alba; Schmeits, Tally; Specht, Marcus; Koper, Rob

    2008-01-01

    De Jong, T., Fuertes, A., Schmeits, T., Specht, M., & Koper, R. (2009). A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks. In D. Goh (Ed.), Multiplatform E-Learning Systems and Technologies: Mobile Devices for Ubiquitous ICT-Based Education (pp.

  8. Evolving neural networks with iterative learning scheme for associative memory

    CERN Document Server

    Fujita, Sh

    1995-01-01

    A locally iterative learning (LIL) rule is adapted to a model of the associative memory based on the evolving recurrent-type neural networks composed of growing neurons. There exist extremely different scale parameters of time, the individual learning time and the generation in evolution. This model allows us definite investigation on the interaction between learning and evolution. And the reinforcement of the robustness against the noise is also achieved in the evolutional scheme.

  9. Predicting Networked Strategic Behavior via Machine Learning and Game Theory

    Science.gov (United States)

    2015-01-13

    Report: Predicting Networked Strategic Behavior via Machine Learning and Game Theory The views, opinions and/or findings contained in this report...2211 machine learning, game theory , microeconomics, behavioral data REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR...Strategic Behavior via Machine Learning and Game Theory Report Title The funding for this project was used to develop basic models, methodology

  10. Functional networks underlying latent inhibition learning in the mouse brain

    OpenAIRE

    Puga, Frank; Barrett, Douglas W.; Bastida, Christel C.; Gonzalez-Lima, F.

    2007-01-01

    The present study reports the first comprehensive map of brain networks underlying latent inhibition learning and the first application of structural equation modeling to cytochrome oxidase data. In latent inhibition, repeated exposure to a stimulus results in a latent form of learning that inhibits subsequent associations with that stimulus. As neuronal energy demand to form learned associations changes, so does the induction of the respiratory enzyme cytochrome oxidase. Therefore, cytochrom...

  11. Dynamic Models of Appraisal Networks Explaining Collective Learning

    OpenAIRE

    Mei, Wenjun; Friedkin, Noah E.; Lewis, Kyle; Bullo, Francesco

    2016-01-01

    This paper proposes models of learning process in teams of individuals who collectively execute a sequence of tasks and whose actions are determined by individual skill levels and networks of interpersonal appraisals and influence. The closely-related proposed models have increasing complexity, starting with a centralized manager-based assignment and learning model, and finishing with a social model of interpersonal appraisal, assignments, learning, and influences. We show how rational optima...

  12. Learning, memory, and the role of neural network architecture.

    Science.gov (United States)

    Hermundstad, Ann M; Brown, Kevin S; Bassett, Danielle S; Carlson, Jean M

    2011-06-01

    The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

  13. Learning, memory, and the role of neural network architecture.

    Directory of Open Access Journals (Sweden)

    Ann M Hermundstad

    2011-06-01

    Full Text Available The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

  14. Biologically-inspired Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

    Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

  15. Biologically-inspired Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

    Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

  16. FPGA implementation of a pyramidal Weightless Neural Networks learning system.

    Science.gov (United States)

    Al-Alawi, Raida

    2003-08-01

    A hardware architecture of a Probabilistic Logic Neuron (PLN) is presented. The suggested model facilitates the on-chip learning of pyramidal Weightless Neural Networks using a modified probabilistic search reward/penalty training algorithm. The penalization strategy of the training algorithm depends on a predefined parameter called the probabilistic search interval. A complete Weightless Neural Network (WNN) learning system is modeled and implemented on Xilinx XC4005E Field Programmable Gate Array (FPGA), allowing its architecture to be configurable. Various experiments have been conducted to examine the feasibility and performance of the WNN learning system. Results show that the system has a fast convergence rate and good generalization ability.

  17. Nurturing global collaboration and networked learning in higher education

    Directory of Open Access Journals (Sweden)

    Catherine Cronin

    2016-03-01

    Full Text Available We consider the principles of communities of practice (CoP and networked learning in higher education, illustrated with a case study. iCollab has grown from an international community of practice connecting students and lecturers in seven modules across seven higher education institutions in six countries, to a global network supporting the exploration and evaluation of mobile web tools to engage in participatory curriculum development and supporting students in developing international collaboration and cooperation skills. This article explores the interplay of collaboration and cooperation, CoP and networked learning; describes how this interplay has operated in iCollab; and highlights opportunities and challenges of learning, teaching and interacting with students in networked publics in higher education.

  18. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  19. Teachers' Self-Initiated Professional Learning through Personal Learning Networks

    Science.gov (United States)

    Tour, Ekaterina

    2017-01-01

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

  20. Prespeech motor learning in a neural network using reinforcement.

    Science.gov (United States)

    Warlaumont, Anne S; Westermann, Gert; Buder, Eugene H; Oller, D Kimbrough

    2013-02-01

    Vocal motor development in infancy provides a crucial foundation for language development. Some significant early accomplishments include learning to control the process of phonation (the production of sound at the larynx) and learning to produce the sounds of one's language. Previous work has shown that social reinforcement shapes the kinds of vocalizations infants produce. We present a neural network model that provides an account of how vocal learning may be guided by reinforcement. The model consists of a self-organizing map that outputs to muscles of a realistic vocalization synthesizer. Vocalizations are spontaneously produced by the network. If a vocalization meets certain acoustic criteria, it is reinforced, and the weights are updated to make similar muscle activations increasingly likely to recur. We ran simulations of the model under various reinforcement criteria and tested the types of vocalizations it produced after learning in the different conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network's post-learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network's post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model's post-learning productions were more likely to resemble the English vowels and vice versa.

  1. Understanding Neural Networks for Machine Learning using Microsoft Neural Network Algorithm

    National Research Council Canada - National Science Library

    Nagesh Ramprasad

    2016-01-01

    .... In this research, focus is on the Microsoft Neural System Algorithm. The Microsoft Neural System Algorithm is a simple implementation of the adaptable and popular neural networks that are used in the machine learning...

  2. Hybrid Neural Network Architecture for On-Line Learning

    CERN Document Server

    Chen, Yuhua; Wang, Lei

    2008-01-01

    Approaches to machine intelligence based on brain models have stressed the use of neural networks for generalization. Here we propose the use of a hybrid neural network architecture that uses two kind of neural networks simultaneously: (i) a surface learning agent that quickly adapt to new modes of operation; and, (ii) a deep learning agent that is very accurate within a specific regime of operation. The two networks of the hybrid architecture perform complementary functions that improve the overall performance. The performance of the hybrid architecture has been compared with that of back-propagation perceptrons and the CC and FC networks for chaotic time-series prediction, the CATS benchmark test, and smooth function approximation. It has been shown that the hybrid architecture provides a superior performance based on the RMS error criterion.

  3. Networks that learn the precise timing of event sequences.

    Science.gov (United States)

    Veliz-Cuba, Alan; Shouval, Harel Z; Josić, Krešimir; Kilpatrick, Zachary P

    2015-12-01

    Neuronal circuits can learn and replay firing patterns evoked by sequences of sensory stimuli. After training, a brief cue can trigger a spatiotemporal pattern of neural activity similar to that evoked by a learned stimulus sequence. Network models show that such sequence learning can occur through the shaping of feedforward excitatory connectivity via long term plasticity. Previous models describe how event order can be learned, but they typically do not explain how precise timing can be recalled. We propose a mechanism for learning both the order and precise timing of event sequences. In our recurrent network model, long term plasticity leads to the learning of the sequence, while short term facilitation enables temporally precise replay of events. Learned synaptic weights between populations determine the time necessary for one population to activate another. Long term plasticity adjusts these weights so that the trained event times are matched during playback. While we chose short term facilitation as a time-tracking process, we also demonstrate that other mechanisms, such as spike rate adaptation, can fulfill this role. We also analyze the impact of trial-to-trial variability, showing how observational errors as well as neuronal noise result in variability in learned event times. The dynamics of the playback process determines how stochasticity is inherited in learned sequence timings. Future experiments that characterize such variability can therefore shed light on the neural mechanisms of sequence learning.

  4. Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

    Science.gov (United States)

    Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

    2016-09-14

    The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

  5. Distributed Extreme Learning Machine for Nonlinear Learning over Network

    OpenAIRE

    Songyan Huang; Chunguang Li

    2015-01-01

    Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN). To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN) with radial basis function (RBF) hidden neurons has the ability to approximate any continuous functions and, thus, may be used as...

  6. Learning Orthographic Structure With Sequential Generative Neural Networks.

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-04-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain.

  7. Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks.

    Science.gov (United States)

    Dosovitskiy, Alexey; Fischer, Philipp; Springenberg, Jost Tobias; Riedmiller, Martin; Brox, Thomas

    2016-09-01

    Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.

  8. Security, Privacy Awareness vs. Utilization of Social Networks and Mobile Apps for Learning: Students` Preparedness

    Directory of Open Access Journals (Sweden)

    Daniel Koloseni

    Full Text Available The application of social networks and mobile apps for learning can improve learning process drastically in Higher learning Institutions as it offers tools and capabilities that creates flexible and convenient learning environment. But the utilization of ...

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

    Science.gov (United States)

    Smith, Sue; Kempster, Steve; Barnes, Stewart

    2017-01-01

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

  10. Networking for Learning The role of Networking in a Lifelong Learner's Professional Development

    NARCIS (Netherlands)

    Rajagopal, Kamakshi

    2016-01-01

    This dissertation discusses the role the social activity of networking plays in lifelong learners’ professional and personal continuous development. The main hypothesis of this thesis is that networking is a learning strategy for lifelong learners, in which conversations are key activities through w

  11. What Online Networks Offer: Online Network Compositions and Online Learning Experiences of Three Ethnic Groups

    NARCIS (Netherlands)

    Lecluijze, Susanne Elisabeth; de Haan, M.J.; Ünlüsoy, A.

    2015-01-01

    This exploratory study examines ethno-cultural diversity in youth ́s narratives regarding their online learning experiences while also investigating how these narratives can be understood from the analysis of their online network structure and composition. Based on ego-network data of 79 respondents

  12. What Online Networks Offer: Online Network Compositions and Online Learning Experiences of Three Ethnic Groups

    NARCIS (Netherlands)

    Lecluijze, Susanne Elisabeth; de Haan, M.J.; Ünlüsoy, A.

    2015-01-01

    This exploratory study examines ethno-cultural diversity in youth ́s narratives regarding their online learning experiences while also investigating how these narratives can be understood from the analysis of their online network structure and composition. Based on ego-network data of 79 respondents

  13. The Applicability of Social Network Analysis to the Study of Networked Learning

    Science.gov (United States)

    Toikkanen, Tarmo; Lipponen, Lasse

    2011-01-01

    Studying networked learning (NL) by applying social network analysis (SNA) has gained popularity in recent years. However, it appears that in the context of NL the choice of SNA indices is very often dictated by using easily achievable SNA tools. Most studies in this field only involve a single group of students and utilise simple indices, such as…

  14. Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Hasan A. A. Al-Rawi

    2014-01-01

    Full Text Available Cognitive radio (CR enables unlicensed users (or secondary users, SUs to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs. Reinforcement learning (RL is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs’ network performance without significantly jeopardizing PUs’ network performance, specifically SUs’ interference to PUs.

  15. Reinforcement learning for routing in cognitive radio ad hoc networks.

    Science.gov (United States)

    Al-Rawi, Hasan A A; Yau, Kok-Lim Alvin; Mohamad, Hafizal; Ramli, Nordin; Hashim, Wahidah

    2014-01-01

    Cognitive radio (CR) enables unlicensed users (or secondary users, SUs) to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs). Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs' network performance without significantly jeopardizing PUs' network performance, specifically SUs' interference to PUs.

  16. Learning Topology and Dynamics of Large Recurrent Neural Networks

    Science.gov (United States)

    She, Yiyuan; He, Yuejia; Wu, Dapeng

    2014-11-01

    Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms. This paper studies how to identify all authentic connections and estimate system parameters of a recurrent network, given a sequence of node observations. This task becomes extremely challenging in modern network applications, because the available observations are usually very noisy and limited, and the associated dynamical system is strongly nonlinear. By formulating the problem as multivariate sparse sigmoidal regression, we develop simple-to-implement network learning algorithms, with rigorous convergence guarantee in theory, for a variety of sparsity-promoting penalty forms. A quantile variant of progressive recurrent network screening is proposed for efficient computation and allows for direct cardinality control of network topology in estimation. Moreover, we investigate recurrent network stability conditions in Lyapunov's sense, and integrate such stability constraints into sparse network learning. Experiments show excellent performance of the proposed algorithms in network topology identification and forecasting.

  17. Globally Networked Collaborative Learning in Industrial Design

    Science.gov (United States)

    Bohemia, Erik; Ghassan, Aysar

    2012-01-01

    This article explores project-based cross-cultural and cross-institutional learning. Using Web 2.0 technologies, this project involved more than 240 students and eighteen academic staff from seven international universities. The focus of this article relates to a project-based learning activity named "The Gift". At each institution the…

  18. Globally Networked Collaborative Learning in Industrial Design

    Science.gov (United States)

    Bohemia, Erik; Ghassan, Aysar

    2012-01-01

    This article explores project-based cross-cultural and cross-institutional learning. Using Web 2.0 technologies, this project involved more than 240 students and eighteen academic staff from seven international universities. The focus of this article relates to a project-based learning activity named "The Gift". At each institution the students…

  19. Globally Networked Collaborative Learning in Industrial Design

    Science.gov (United States)

    Bohemia, Erik; Ghassan, Aysar

    2012-01-01

    This article explores project-based cross-cultural and cross-institutional learning. Using Web 2.0 technologies, this project involved more than 240 students and eighteen academic staff from seven international universities. The focus of this article relates to a project-based learning activity named "The Gift". At each institution the…

  20. Delivery of E-Learning through Social Learning Networks

    Science.gov (United States)

    Dafoulas, Georgios A.; Shokri, Azam

    2014-01-01

    Over the past two decades policies and speculations have been evident about the importance of internet use including technologies in education and learning at all levels to individuals and societies. The purposes, theories and ways in which learning with technologies ought to be conceptualised and functionalised is generating an increased body of…

  1. Neural-network front ends in unsupervised learning.

    Science.gov (United States)

    Pedrycz, W; Waletzky, J

    1997-01-01

    Proposed is an idea of partial supervision realized in the form of a neural-network front end to the schemes of unsupervised learning (clustering). This neural network leads to an anisotropic nature of the induced feature space. The anisotropic property of the space provides us with some of its local deformation necessary to properly represent labeled data and enhance efficiency of the mechanisms of clustering to be exploited afterwards. The training of the network is completed based upon available labeled patterns-a referential form of the labeling gives rise to reinforcement learning. It is shown that the discussed approach is universal and can be utilized in conjunction with any clustering method. Experimental studies are concentrated on three main categories of unsupervised learning including FUZZY ISODATA, Kohonen self-organizing maps, and hierarchical clustering.

  2. Neural network models of learning and categorization in multigame experiments

    Directory of Open Access Journals (Sweden)

    Davide eMarchiori

    2011-12-01

    Full Text Available Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures. Moreover, the process of categorization of a game is implicitly carried out by these models, thus without the need of any external explicit theory of similarity between games. To validate our claims, we designed and ran two multigame experiments in which subjects faced, in random sequence, different instances of two completely mixed 2x2 games. Then, we tested on our experimental data two regret-driven neural network models, and compared their performance with that of other established models of learning and Nash equilibrium.

  3. Learning Equilibria with Partial Information in Decentralized Wireless Networks

    CERN Document Server

    Rose, Luca; Lasaulce, Samson; Debbah, Mérouane

    2011-01-01

    In this article, a survey of several important equilibrium concepts for decentralized networks is presented. The term decentralized is used here to refer to scenarios where decisions (e.g., choosing a power allocation policy) are taken autonomously by devices interacting with each other (e.g., through mutual interference). The iterative long-term interaction is characterized by stable points of the wireless network called equilibria. The interest in these equilibria stems from the relevance of network stability and the fact that they can be achieved by letting radio devices to repeatedly interact over time. To achieve these equilibria, several learning techniques, namely, the best response dynamics, fictitious play, smoothed fictitious play, reinforcement learning algorithms, and regret matching, are discussed in terms of information requirements and convergence properties. Most of the notions introduced here, for both equilibria and learning schemes, are illustrated by a simple case study, namely, an interfe...

  4. Two-layer networked learning control using self-learning fuzzy control algorithms

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Since the existing single-layer networked control systems have some inherent limitations and cannot effectively handle the problems associated with unreliable networks, a novel two-layer networked learning control system (NLCS) is proposed in this paper. Its lower layer has a number of local controllers that are operated independently, and its upper layer has a learning agent that communicates with the independent local controllers in the lower layer. To implement such a system, a packet-discard strategy is firstly developed to deal with network-induced delay and data packet loss. A cubic spline interpolator is then employed to compensate the lost data. Finally, the output of the learning agent based on a novel radial basis function neural network (RBFNN) is used to update the parameters of fuzzy controllers. A nonlinear heating, ventilation and air-conditioning (HVAC) system is used to demonstrate the feasibility and effectiveness of the proposed system.

  5. Fuzzy adaptive learning control network with sigmoid membership function

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    To get simpler operation in modified fuzzy adaptive learning control network (FALCON) in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing (SA) learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived;and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.

  6. Networked Learning and Network Science: Potential Applications to Health Professionals' Continuing Education and Development.

    Science.gov (United States)

    Margolis, Alvaro; Parboosingh, John

    2015-01-01

    Prior interpersonal relationships and interactivity among members of professional associations may impact the learning process in continuing medical education (CME). On the other hand, CME programs that encourage interactivity between participants may impact structures and behaviors in these professional associations. With the advent of information and communication technologies, new communication spaces have emerged that have the potential to enhance networked learning in national and international professional associations and increase the effectiveness of CME for health professionals. In this article, network science, based on the application of network theory and other theories, is proposed as an approach to better understand the contribution networking and interactivity between health professionals in professional communities make to their learning and adoption of new practices over time.

  7. Learning modular structures from network data and node variables

    CERN Document Server

    Azizi, Elham; Airoldi, Edoardo M

    2014-01-01

    A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, groups are termed modules, and the focus is on estimating the network structure among modules. However, estimation solely from node-specific variables can lead to spurious dependencies, and unverifiable structural assumptions are often used for regularization. Here, we propose an extended model that leverages direct observations about the network in addition to node-specific variables. By integrating complementary data types, we avoid the need for structural assumptions. We illustrate theoretical and practical significance of the model and develop a reversible-jump MCMC learning procedure for learning modules and model parameters. We demonstrate the method accuracy in predicting modular structur...

  8. Adult Learning in Alternative Food Networks

    Science.gov (United States)

    Etmanski, Catherine; Kajzer Mitchell, Ingrid

    2017-01-01

    This chapter describes the role small-scale organic farmers are playing as adult educators in alternative food networks and as leaders for food systems transformation. Findings are drawn from a survey of organic farmers in British Columbia, Western Canada.

  9. Threshold learning dynamics in social networks

    CERN Document Server

    González-Avella, J C; Marsili, M; Vega-Redondo, F; Miguel, M San

    2010-01-01

    Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take respect to an important issues, typically confront external signals to the information gathered from their contacts. Received economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally obse...

  10. The Use Of Social Networking Sites For Learning In Institutions Of Higher Learning

    Directory of Open Access Journals (Sweden)

    Mange Gladys Nkatha

    2015-08-01

    Full Text Available Abstract Institutions of higher learning are facing greater challenges to change and subjected to various transformations in the surrounding environment including technology. These challenge and motivate them to explore new ways to improve their teaching approaches. This study sought to investigate the use of social networking site in institutions of higher learning. To this end two objectives were formulated 1 to investigate the current state of the use of social networking sites by the students 2 investigate how social networking sites can be used to promote authentic learning in institutions of higher learning. The study adopted exploratory approach using descriptive survey design where a sample of 10 67 students were picked from Jomo Kenyatta University of Agriculture and Technology JKUAT main campus. The findings indicate the use of social networking sites is a viable option as the students are not only members of social networking sites but also that majority have access to the requisite technological devices. Additionally recommendations for ensuring authentic learning were presented. The researcher recommends the exploration of the leveraging of the existing social networking sites for learning in conjunction with key stakeholders.

  11. Template learning of cellular neural network using genetic programming.

    Science.gov (United States)

    Radwan, Elsayed; Tazaki, Eiichiro

    2004-08-01

    A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.

  12. Interconnecting Networks of Practice for Professional Learning

    OpenAIRE

    Julie Mackey; Terry Evans

    2011-01-01

    The article explores the complementary connections between communities of practice and the ways in which individuals orchestrate their engagement with others to further their professional learning. It does so by reporting on part of a research project conducted in New Zealand on teachers’ online professional learning in a university graduate diploma program on ICT education. Evolving from social constructivist pedagogy for online professional development, the research describes how teachers c...

  13. Machine learning using a higher order correlation network

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Y.C.; Doolen, G.; Chen, H.H.; Sun, G.Z.; Maxwell, T.; Lee, H.Y.

    1986-01-01

    A high-order correlation tensor formalism for neural networks is described. The model can simulate auto associative, heteroassociative, as well as multiassociative memory. For the autoassociative model, simulation results show a drastic increase in the memory capacity and speed over that of the standard Hopfield-like correlation matrix methods. The possibility of using multiassociative memory for a learning universal inference network is also discussed. 9 refs., 5 figs.

  14. Nurturing global collaboration and networked learning in higher education

    OpenAIRE

    Cronin, Catherine; Cochrane, Thomas; Gordon, Averill

    2016-01-01

    We consider the principles of communities of practice (CoP) and networked learning in higher education, illustrated with a case study. iCollab has grown from an international community of practice connecting students and lecturers in seven modules across seven higher education institutions in six countries, to a global network supporting the exploration and evaluation of mobile web tools to engage in participatory curriculum development and supporting students in developing international coll...

  15. Gamification of Learning Deactivates the Default Mode Network.

    Science.gov (United States)

    Howard-Jones, Paul A; Jay, Tim; Mason, Alice; Jones, Harvey

    2015-01-01

    We hypothesized that embedding educational learning in a game would improve learning outcomes, with increased engagement and recruitment of cognitive resources evidenced by increased activation of working memory network (WMN) and deactivation of default mode network (DMN) regions. In an fMRI study, we compared activity during periods of learning in three conditions that were increasingly game-like: Study-only (when periods of learning were followed by an exemplar question together with its correct answer), Self-quizzing (when periods of learning were followed by a multiple choice question in return for a fixed number of points) and Game-based (when, following each period of learning, participants competed with a peer to answer the question for escalating, uncertain rewards). DMN hubs deactivated as conditions became more game-like, alongside greater self-reported engagement and, in the Game-based condition, higher learning scores. These changes did not occur with any detectable increase in WMN activity. Additionally, ventral striatal activation was associated with responding to questions and receiving positive question feedback. Results support the significance of DMN deactivation for educational learning, and are aligned with recent evidence suggesting DMN and WMN activity may not always be anti-correlated.

  16. Gamification of learning deactivates the Default Mode Network

    Directory of Open Access Journals (Sweden)

    Paul Alexander Howard-Jones

    2016-01-01

    Full Text Available We hypothesised that embedding educational learning in a game would improve learning outcomes, with increased engagement and recruitment of cognitive resources evidenced by increased activation of working memory network (WMN and deactivation of Default Mode Network (DMN regions. In an fMRI study, we compared activity during periods of learning in three conditions that were increasingly game-like: Study-only (when periods of learning were followed by an exemplar question together with its correct answer, Self-quizzing (when periods of learning were followed by a multiple choice question in return for a fixed number of points and Game-based (when, following each period of learning, participants competed with a peer to answer the question for escalating, uncertain rewards. DMN hubs deactivated as conditions became more game-like, alongside greater self-reported engagement and, in the Game-based condition, higher learning scores. These changes did not occur with any detectable increase in WMN activity. Additionally, ventral striatal activation was associated with responding to questions and receiving positive question feedback. Results support the significance of DMN deactivation for educational learning, and are aligned with recent evidence suggesting DMN and WMN activity may not always be anti-correlated.

  17. Cellular computational networks--a scalable architecture for learning the dynamics of large networked systems.

    Science.gov (United States)

    Luitel, Bipul; Venayagamoorthy, Ganesh Kumar

    2014-02-01

    Neural networks for implementing large networked systems such as smart electric power grids consist of multiple inputs and outputs. Many outputs lead to a greater number of parameters to be adapted. Each additional variable increases the dimensionality of the problem and hence learning becomes a challenge. Cellular computational networks (CCNs) are a class of sparsely connected dynamic recurrent networks (DRNs). By proper selection of a set of input elements for each output variable in a given application, a DRN can be modified into a CCN which significantly reduces the complexity of the neural network and allows use of simple training methods for independent learning in each cell thus making it scalable. This article demonstrates this concept of developing a CCN using dimensionality reduction in a DRN for scalability and better performance. The concept has been analytically explained and empirically verified through application. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Orthogonal least squares learning algorithm for radial basis function networks

    Energy Technology Data Exchange (ETDEWEB)

    Chen, S.; Cowan, C.F.N.; Grant, P.M. (Dept. of Electrical Engineering, Univ. of Edinburgh, Mayfield Road, Edinburgh EH9 3JL, Scotland (GB))

    1991-03-01

    The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular value decomposition to solve for the weights of the network. Such a procedure has several drawbacks and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The paper proposes an alternative learning procedure based on the orthogonal least squares method. The procedure choose radial basis function centers one by one in a rational way until an adequate network has been constructed. The algorithm has the property that each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least squares learning strategy provides a simple and efficient means for fitting radial basis function networks, and this is illustrated using examples taken from two different signal processing applications.

  19. Orthogonal least squares learning algorithm for radial basis function networks.

    Science.gov (United States)

    Chen, S; Cowan, C N; Grant, P M

    1991-01-01

    The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular-value decomposition to solve for the weights of the network. Such a procedure has several drawbacks, and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The authors propose an alternative learning procedure based on the orthogonal least-squares method. The procedure chooses radial basis function centers one by one in a rational way until an adequate network has been constructed. In the algorithm, each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least-squares learning strategy provides a simple and efficient means for fitting radial basis function networks. This is illustrated using examples taken from two different signal processing applications.

  20. Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

    Science.gov (United States)

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

    Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

  1. A new learning method using prior information of neural networks

    Institute of Scientific and Technical Information of China (English)

    Lü Baiquan; Junichi Murata; Kotaro Hirasawa

    2004-01-01

    In this paper, we present a new learning method using prior information for three-layered neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their inter-relation of weight values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give an exact mathematical equation that describes the relation between weight values given by a set of data conveying prior information. Then we present a new learning method that trains a part of the weights and calculates the others by using these exact mathematical equations. In almost all cases, this method keeps prior information given by a mathematical structure exactly during the learning. In addition, a learning method using prior information expressed by inequality is also presented. In any case, the degree of freedom of networks (the number of adjustable weights) is appropriately limited in order to speed up the learning and ensure small errors. Numerical computer simulation results are provided to support the present approaches.

  2. Few-shot learning in deep networks through global prototyping.

    Science.gov (United States)

    Blaes, Sebastian; Burwick, Thomas

    2017-10-01

    Training a deep convolution neural network (CNN) to succeed in visual object classification usually requires a great number of examples. Here, starting from such a pre-learned CNN, we study the task of extending the network to classify additional categories on the basis of only few examples ("few-shot learning"). We find that a simple and fast prototype-based learning procedure in the global feature layers ("Global Prototype Learning", GPL) leads to some remarkably good classification results for a large portion of the new classes. It requires only up to ten examples for the new classes to reach a plateau in performance. To understand this few-shot learning performance resulting from GPL as well as the performance of the original network, we use the t-SNE method (Maaten and Hinton, 2008) to visualize clusters of object category examples. This reveals the strong connection between classification performance and data distribution and explains why some new categories only need few examples for learning while others resist good classification results even when trained with many more examples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Learning Languages: The Journal of the National Network for Early Language Learning, 1997-1998.

    Science.gov (United States)

    Learning Languages: The Journal of the National Network for Early Language Learning, 1998

    1998-01-01

    This document consists of the three issues of the journal "Learning Languages" published during volume year 3. These issues contain the following major articles: "A National Network for Early Language Learning (NNELL): A Brief History, 1987-1997;""Juguetes Fantasticos" (Mari Haas); "A Perspective on the Cultural…

  4. Using Social Networks to Enhance Teaching and Learning Experiences in Higher Learning Institutions

    Science.gov (United States)

    Balakrishnan, Vimala

    2014-01-01

    The paper first explores the factors that affect the use of social networks to enhance teaching and learning experiences among students and lecturers, using structured questionnaires prepared based on the Push-Pull-Mooring framework. A total of 455 students and lecturers from higher learning institutions in Malaysia participated in this study.…

  5. A Networked Learning Model for Construction of Personal Learning Environments in Seventh Grade Life Science

    Science.gov (United States)

    Drexler, Wendy

    2010-01-01

    The purpose of this design-based research case study was to apply a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. API widgets were…

  6. Assessment of Learning in Digital Interactive Social Networks: A Learning Analytics Approach

    Science.gov (United States)

    Wilson, Mark; Gochyyev, Perman; Scalise, Kathleen

    2016-01-01

    This paper summarizes initial field-test results from data analytics used in the work of the Assessment and Teaching of 21st Century Skills (ATC21S) project, on the "ICT Literacy--Learning in digital networks" learning progression. This project, sponsored by Cisco, Intel and Microsoft, aims to help educators around the world enable…

  7. Using Social Networks to Enhance Teaching and Learning Experiences in Higher Learning Institutions

    Science.gov (United States)

    Balakrishnan, Vimala

    2014-01-01

    The paper first explores the factors that affect the use of social networks to enhance teaching and learning experiences among students and lecturers, using structured questionnaires prepared based on the Push-Pull-Mooring framework. A total of 455 students and lecturers from higher learning institutions in Malaysia participated in this study.…

  8. Implementing e-network-supported inquiry learning in science

    DEFF Research Database (Denmark)

    Williams, John; Cowie, Bronwen; Khoo, Elaine

    2013-01-01

    The successful implementation of electronically networked (e-networked) tools to support an inquiry-learning approach in secondary science classrooms is dependent on a range of factors spread between teachers, schools, and students. The teacher must have a clear understanding of the nature...... of inquiry, the school must provide effective technological infrastructure and sympathetic curriculum parameters, and the students need to be carefully scaffolded to the point of engaging with the inquiry process. Within this study, e-networks supported students to exercise agency, collaborate, and co...

  9. Learning about knowledge: A complex network approach

    CERN Document Server

    Costa, L F

    2006-01-01

    This article describes an approach to modeling of knowledge acquisition in terms of complex networks and walks. Each subset of knowledge is represented as a node, and relationship between such knowledge are represented as edges. Two types of edges are considered, corresponding to logical equivalence and implication. Multiple conditional implications are also considered, implying that a node can only be reached after visiting previously a set of nodes (the conditions). It is shown that hierarchical networks, involving a series of interconnected layers containing a connected subnetwork, provides a simple and natural means for avoiding deadlocks, i.e. unreachable nodes. The process of knowledge acquisition can then be simulated by considering a single agent moving along the nodes and edges, starting from the lowest layer. Several configurations of such hierarchical knowledge networks are simulated and the performance of the agent quantified in terms of the percentage of visited nodes after each movement. The Bar...

  10. Markov Chain Monte Carlo Bayesian Learning for Neural Networks

    Science.gov (United States)

    Goodrich, Michael S.

    2011-01-01

    Conventional training methods for neural networks involve starting al a random location in the solution space of the network weights, navigating an error hyper surface to reach a minimum, and sometime stochastic based techniques (e.g., genetic algorithms) to avoid entrapment in a local minimum. It is further typically necessary to preprocess the data (e.g., normalization) to keep the training algorithm on course. Conversely, Bayesian based learning is an epistemological approach concerned with formally updating the plausibility of competing candidate hypotheses thereby obtaining a posterior distribution for the network weights conditioned on the available data and a prior distribution. In this paper, we developed a powerful methodology for estimating the full residual uncertainty in network weights and therefore network predictions by using a modified Jeffery's prior combined with a Metropolis Markov Chain Monte Carlo method.

  11. Application of Bayesian Network Learning Methods to Land Resource Evaluation

    Institute of Scientific and Technical Information of China (English)

    HUANG Jiejun; HE Xiaorong; WAN Youchuan

    2006-01-01

    Bayesian network has a powerful ability for reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to deal with prediction, classification and clustering. Firstly, this paper presented an overview of Bayesian network and its characteristics, and discussed how to learn a Bayesian network structure from given data, and then constructed a Bayesian network model for land resource evaluation with expert knowledge and the dataset. The experimental results based on the test dataset are that evaluation accuracy is 87.5%, and Kappa index is 0.826. All these prove the method is feasible and efficient, and indicate that Bayesian network is a promising approach for land resource evaluation.

  12. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    Energy Technology Data Exchange (ETDEWEB)

    Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  13. Dynamics of learning near singularities in radial basis function networks.

    Science.gov (United States)

    Wei, Haikun; Amari, Shun-Ichi

    2008-09-01

    The radial basis function (RBF) networks are one of the most widely used models for function approximation in the regression problem. In the learning paradigm, the best approximation is recursively or iteratively searched for based on observed data (teacher signals). One encounters difficulties in such a process when two component basis functions become identical, or when the magnitude of one component becomes null. In this case, the number of the components reduces by one, and then the reduced component recovers as the learning process proceeds further, provided such a component is necessary for the best approximation. Strange behaviors, especially the plateau phenomena, have been observed in dynamics of learning when such reduction occurs. There exist singularities in the space of parameters, and the above reduction takes place at the singular regions. This paper focuses on a detailed analysis of the dynamical behaviors of learning near the overlap and elimination singularities in RBF networks, based on the averaged learning equation that is applicable to both on-line and batch mode learning. We analyze the stability on the overlap singularity by solving the eigenvalues of the Hessian explicitly. Based on the stability analysis, we plot the analytical dynamic vector fields near the singularity, which are then compared to those real trajectories obtained by a numeric method. We also confirm the existence of the plateaus in both batch and on-line learning by simulation.

  14. A theoretical design for learning model addressing the networked society

    DEFF Research Database (Denmark)

    Levinsen, Karin; Nielsen, Janni; Sørensen, Birgitte Holm

    2010-01-01

    is continuously decreasing. We teach for deep learning but are confronted by students' cost-benefit strategies when they navigate through the study programme under time pressure. To meet these challenges a Design for Learning Model has been developed. The aim is to provide a scaffold that ensures students......The transition from the industrial to the networked society produces contradictions that challenges the educational system and force it to adapt to new conditions. In a Danish virtual Master in Information and Communication Technologies and Learning (MIL) these contradictions appear as a field...... of tension between time resources and the demand for educational quality. Our approach is based on constructivist and social constructivist traditions but we are required to measure students according to a list of learning goals. The size of curriculum is growing while the time available for learning...

  15. Learning Probabilistic Hierarchical Task Networks to Capture User Preferences

    CERN Document Server

    Li, Nan; Kambhampati, Subbarao; Yoon, Sungwook

    2010-01-01

    We propose automatically learning probabilistic Hierarchical Task Networks (pHTNs) in order to capture a user's preferences on plans, by observing only the user's behavior. HTNs are a common choice of representation for a variety of purposes in planning, including work on learning in planning. Our contributions are (a) learning structure and (b) representing preferences. In contrast, prior work employing HTNs considers learning method preconditions (instead of structure) and representing domain physics or search control knowledge (rather than preferences). Initially we will assume that the observed distribution of plans is an accurate representation of user preference, and then generalize to the situation where feasibility constraints frequently prevent the execution of preferred plans. In order to learn a distribution on plans we adapt an Expectation-Maximization (EM) technique from the discipline of (probabilistic) grammar induction, taking the perspective of task reductions as productions in a context-free...

  16. Neurodynamics of learning and network performance

    Science.gov (United States)

    Wilson, Charles L.; Blue, James L.; Omidvar, Omid M.

    1997-07-01

    A simple dynamic model of a neural network is presented. Using the dynamic model of a neural network, we improve the performance of a three-layer multilayer perceptron (MLP). The dynamic model of a MLP is used to make fundamental changes in the network optimization strategy. These changes are: neuron activation functions are used, which reduces the probability of singular Jacobians; successive regularization is used to constrain the volume of the weight space being minimized; Boltzmann pruning is used to constrain the dimension of the weight space; and prior class probabilities are used to normalize all error calculations, so that statistically significant samples of rare but important classes can be included without distortion of the error surface. All four of these changes are made in the inner loop of a conjugate gradient optimization iteration and are intended to simplify the training dynamics of the optimization. On handprinted digits and fingerprint classification problems, these modifications improve error-reject performance by factors between 2 and 4 and reduce network size by 40 to 60%.

  17. Will Learning Social Inclusion Assist Rural Networks

    Science.gov (United States)

    Marchant, Jillian

    2013-01-01

    Current research on social networks in some rural communities reports continuing demise despite efforts to build resilient communities. Several factors are identified as contributing to social decline including globalisation and rural social characteristics. Particular rural social characteristics, such as strong social bonds among members of…

  18. Will Learning Social Inclusion Assist Rural Networks

    Science.gov (United States)

    Marchant, Jillian

    2013-01-01

    Current research on social networks in some rural communities reports continuing demise despite efforts to build resilient communities. Several factors are identified as contributing to social decline including globalisation and rural social characteristics. Particular rural social characteristics, such as strong social bonds among members of…

  19. Understanding Knowledge Network, Learning and Connectivism

    Science.gov (United States)

    AlDahdouh, Alaa A.; Osório, António J.; Caires, Susana

    2015-01-01

    Behaviorism, Cognitivism, Constructivism and other growing theories such as Actor-Network and Connectivism are circulating in the educational field. For each, there are allies who stand behind research evidence and consistency of observation. Meantime, those existing theories dominate the field until the background is changed or new concrete…

  20. Machine learning for network-based malware detection

    DEFF Research Database (Denmark)

    Stevanovic, Matija

    and based on different, mutually complementary, principles of traffic analysis. The proposed approaches rely on machine learning algorithms (MLAs) for automated and resource-efficient identification of the patterns of malicious network traffic. We evaluated the proposed methods through extensive evaluations...

  1. Learning Networks and the Journey of "Becoming Doctor"

    Science.gov (United States)

    Barnacle, Robyn; Mewburn, Inger

    2010-01-01

    Scholars such as Kamler and Thompson argue that identity formation has a key role to play in doctoral learning, particularly the process of thesis writing. This article builds on these insights to address other sites in which scholarly identity is performed within doctoral candidature. Drawing on actor-network theory, the authors examine the role…

  2. A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge

    Institute of Scientific and Technical Information of China (English)

    徐俊刚; 赵越; 陈健; 韩超

    2015-01-01

    Learning structure from data is one of the most important fundamental tasks of Bayesian network research. Particularly, learning optional structure of Bayesian network is a non-deterministic polynomial-time (NP) hard problem. To solve this problem, many heuristic algorithms have been proposed, and some of them learn Bayesian network structure with the help of different types of prior knowledge. However, the existing algorithms have some restrictions on the prior knowledge, such as quality restriction and use restriction. This makes it difficult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into the Markov chain Monte Carlo (MCMC) algorithm and propose an algorithm called Constrained MCMC (C-MCMC) algorithm to learn the structure of the Bayesian network. Three types of prior knowledge are defined: existence of parent node, absence of parent node, and distribution knowledge including the conditional probability distribution (CPD) of edges and the probability distribution (PD) of nodes. All of these types of prior knowledge are easily used in this algorithm. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC.

  3. Implementation of an Adaptive Learning System Using a Bayesian Network

    Science.gov (United States)

    Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki

    2015-01-01

    An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…

  4. Evaluating Action Learning: A Critical Realist Complex Network Theory Approach

    Science.gov (United States)

    Burgoyne, John G.

    2010-01-01

    This largely theoretical paper will argue the case for the usefulness of applying network and complex adaptive systems theory to an understanding of action learning and the challenge it is evaluating. This approach, it will be argued, is particularly helpful in the context of improving capability in dealing with wicked problems spread around…

  5. Critical Facilities for Active Participation in Learning Networks

    NARCIS (Netherlands)

    Hummel, Hans; Tattersall, Colin; Burgos, Daniel; Brouns, Francis; Kurvers, Hub; Koper, Rob

    2005-01-01

    Please use the following citation: Hummel, H. G. K., Tattersall, C., Burgos, D., Brouns, F. M. R., Kurvers, H. J., & Koper, E. J. R. (2006). Critical facilities for active participation in learning networks. Int. J. Web Based Communities, 2, 1, 81-99. This article is an extended version (with

  6. Idea Management: Perspectives from Leadership, Learning, and Network Theory

    NARCIS (Netherlands)

    D. Deichmann (Dirk)

    2012-01-01

    textabstractIn this dissertation, we focus on how leadership styles, individual learning behaviors, and social network structures drive or inhibit organizational members to repeatedly generate and develop innovative ideas. Taking the idea management programs of three multinational companies as the r

  7. Social Capital Theory: Implications for Women's Networking and Learning

    Science.gov (United States)

    Alfred, Mary V.

    2009-01-01

    This chapter describes social capital theory as a framework for exploring women's networking and social capital resources. It presents the foundational assumptions of the theory, the benefits and risks of social capital engagement, a feminist critique of social capital, and the role of social capital in adult learning.

  8. Parameter learning in MTE networks using incomplete data

    DEFF Research Database (Denmark)

    Fernández, Antonio; Langseth, Helge; Nielsen, Thomas Dyhre

    a considerable computational burden as well as the inability to handle missing values in the training data. In this paper we describe an EM-based algorithm for learning the maximum likelihood parameters of an MTE network when confronted with incomplete data. In order to overcome the computational difficulties we...

  9. Regularized negative correlation learning for neural network ensembles.

    Science.gov (United States)

    Chen, Huanhuan; Yao, Xin

    2009-12-01

    Negative correlation learning (NCL) is a neural network ensemble learning algorithm that introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean square error (MSE) together with the correlation of the ensemble. This paper analyzes NCL and reveals that the training of NCL (when lambda = 1) corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This analysis explains the reason why NCL is prone to overfitting the noise in the training set. This paper also demonstrates that tuning the correlation parameter lambda in NCL by cross validation cannot overcome the overfitting problem. The paper analyzes this problem and proposes the regularized negative correlation learning (RNCL) algorithm which incorporates an additional regularization term for the whole ensemble. RNCL decomposes the ensemble's training objectives, including MSE and regularization, into a set of sub-objectives, and each sub-objective is implemented by an individual neural network. In this paper, we also provide a Bayesian interpretation for RNCL and provide an automatic algorithm to optimize regularization parameters based on Bayesian inference. The RNCL formulation is applicable to any nonlinear estimator minimizing the MSE. The experiments on synthetic as well as real-world data sets demonstrate that RNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set.

  10. Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter

    2009-01-01

    Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. (2009). Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring. Presentation at the IADIS international conference on Cognition and Exploratory in Digital Age (CELDA 2009). November, 20-22, 2009, Rome, Italy.

  11. Social Capital Theory: Implications for Women's Networking and Learning

    Science.gov (United States)

    Alfred, Mary V.

    2009-01-01

    This chapter describes social capital theory as a framework for exploring women's networking and social capital resources. It presents the foundational assumptions of the theory, the benefits and risks of social capital engagement, a feminist critique of social capital, and the role of social capital in adult learning.

  12. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    Science.gov (United States)

    Ye, Qiang

    2010-01-01

    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  13. Learning Networks and the Journey of "Becoming Doctor"

    Science.gov (United States)

    Barnacle, Robyn; Mewburn, Inger

    2010-01-01

    Scholars such as Kamler and Thompson argue that identity formation has a key role to play in doctoral learning, particularly the process of thesis writing. This article builds on these insights to address other sites in which scholarly identity is performed within doctoral candidature. Drawing on actor-network theory, the authors examine the role…

  14. On Location Learning: Authentic Applied Science with Networked Augmented Realities

    Science.gov (United States)

    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…

  15. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    Science.gov (United States)

    Ye, Qiang

    2010-01-01

    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  16. Bayesian Inference and Online Learning in Poisson Neuronal Networks.

    Science.gov (United States)

    Huang, Yanping; Rao, Rajesh P N

    2016-08-01

    Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

  17. Networked learning and reusable teaching resources

    Directory of Open Access Journals (Sweden)

    Serena Alvino

    2006-01-01

    Full Text Available Descrizione di uno studio rivolto ad individuare un set di metadati realmente efficaci nel supportare il progettista didattico nella ricerca e nel riuso di Learning Object (LO sulla base delle loro caratteristiche pedagogiche. Il lavoro si e’ sviluppato parzialmente nell’ambito del progetto VICE: Comunità Virtuali per l’Apprendimento.

  18. Probabilistic Universal Learning Networks and their Applications to Nonlinear Control Systems

    OpenAIRE

    1998-01-01

    Probabilistic Universal Learning Networks (PrULNs) are proposed, which are learning networks with a capability of dealing with stochastic signals. PrULNs are extensions of Universal Learning Networks (ULNs). ULNs form a superset of neural networks and were proposed to provide a universal framework for modeling and control of nonlinear large-scale complex systems. A generalized learning algorithm has been devised for ULNs which can also be used in a unified manner for almost all kinds of learn...

  19. Q-learning-based cross-layer Learning Engine design for cognitive radio network

    Science.gov (United States)

    Liu, Congbin; Jiang, Hong; Yang, Yanchao; Ma, Jinghui

    2013-03-01

    In cognitive radio (CR) networks, Learning Engine has considerable significance on dynamic spectrum access (DSA) and implementation of cognitive function. In this paper, a cross-layer learning engine design scheme is proposed by jointly considering physical-layer dynamic channel selection, modulation and coding scheme, data-link layer frame length in CR networks, with the purpose to maximize system throughput and simultaneously meet heterogeneous Quality of Service (QoS) requirements. The wireless fading channel is modeled as a continuous state space Markov decision process (MDP) and the licensed network activity is abstracted as a finite-state one. We introduce Q-learning algorithm to realize the function of learning from state space and adapt wireless environment. And meanwhile a large scale Qfunction approximator based on support vector machine (SVM) is employed to effectively reduce storage requirement and decrease the operation complexity. A cross-layer learning engine communication platform is realized by using Matlab simulator. the simulation results demonstrate that while lacking system prior knowledge, the learning engine can effectively achieve configuration function by system cross-layer learning approach, and furthermore, it can converge to the best—i.e., realize reconfiguration function in CR networks while meeting users' QoS.

  20. A RGD-Containing Oligopeptide (K)16GRGDSPC: A Novel Vector for Integrin-Mediated Targeted Gene Delivery

    Institute of Scientific and Technical Information of China (English)

    PAN Haitao; ZHENG Qixin; GUO Xiaodong; LIU Yong; LI Changwen; SONG Yulin

    2006-01-01

    A 23 amino acid, bifunctional integrin-targeted synthetic oligopeptide was evaluated for ex vivo gene delivery to rabbit bone marrow stromal cells (BMSCs). Synthesis of the peptide (K)16GRGDSPC was performed on a solid-phase batch peptide synthesizer. BMSCs were transfected with plasmid DNA coding for luciferase by (K)16GRGDSPC and the transfection efficiency was assayed. The influences of chloroquine and polyethyleneimine on the transfection efficiency were also examined. The target specificity of (K)16GRGDSPC to mediate exogenous gene into BMSCs was analyzed using cell attachment test and gene delivery inhibition test. The results showed that the transfection efficiency of the oligopeptide vector was lower than that of Lipofectamine. But in the presence of endosomal buffer chloroquine or endosomal disrupting agent polyethyleneimine, the transfection efficiency of the vector was greatly enhanced. In addition, RGD-containing peptides inhibited BMSCs' attachment to the 96-well plates pretreated with fibronectin or vitronectin and significantly decreased the transfection efficiency of the oligopeptide vector. These studies demonstrated that oligopeptide (K)16GRGDSPC was an ideal novel targeted non-viral gene delivery vector, which was easy to be synthesized, high efficient and low cytotoxicity. The vector could effectively deliver exogenous gene into rat BMSCs.

  1. Finite time convergent learning law for continuous neural networks.

    Science.gov (United States)

    Chairez, Isaac

    2014-02-01

    This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper.

  2. Machine learning of network metrics in ATLAS Distributed Data Management

    CERN Document Server

    Lassnig, Mario; The ATLAS collaboration

    2017-01-01

    The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our m...

  3. Learning Bayesian networks using genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Chen Fei; Wang Xiufeng; Rao Yimei

    2007-01-01

    A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not.Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.

  4. A Kohonen Network for Modeling Students' Learning Styles in Web 2.0 Collaborative Learning Systems

    Science.gov (United States)

    Zatarain-Cabada, Ramón; Barrón-Estrada, M. Lucia; Zepeda-Sánchez, Leopoldo; Sandoval, Guillermo; Osorio-Velazquez, J. Moises; Urias-Barrientos, J. E.

    The identification of the best learning style in an Intelligent Tutoring System must be considered essential as part of the success in the teaching process. In many implementations of automatic classifiers finding the right student learning style represents the hardest assignment. The reason is that most of the techniques work using expert groups or a set of questionnaires which define how the learning styles are assigned to students. This paper presents a novel approach for automatic learning styles classification using a Kohonen network. The approach is used by an author tool for building Intelligent Tutoring Systems running under a Web 2.0 collaborative learning platform. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the author tool.

  5. Social learning strategies in networked groups.

    Science.gov (United States)

    Wisdom, Thomas N; Song, Xianfeng; Goldstone, Robert L

    2013-01-01

    When making decisions, humans can observe many kinds of information about others' activities, but their effects on performance are not well understood. We investigated social learning strategies using a simple problem-solving task in which participants search a complex space, and each can view and imitate others' solutions. Results showed that participants combined multiple sources of information to guide learning, including payoffs of peers' solutions, popularity of solution elements among peers, similarity of peers' solutions to their own, and relative payoffs from individual exploration. Furthermore, performance was positively associated with imitation rates at both the individual and group levels. When peers' payoffs were hidden, popularity and similarity biases reversed, participants searched more broadly and randomly, and both quality and equity of exploration suffered. We conclude that when peers' solutions can be effectively compared, imitation does not simply permit scrounging, but it can also facilitate propagation of good solutions for further cumulative exploration.

  6. Dictionary Networking in an LSP Learning Context

    DEFF Research Database (Denmark)

    Nielsen, Sandro

    2007-01-01

    Dictionaries have long been an indispensable part of learning the factual and linguistic content of a subject-field, i.e. the relevant LSP. Both teachers and students refer to and use printed and electronic specialised dictionaries as tools when teaching and learning the structure, terminology...... and usage of a subject-field, particularly when they have to read, write or translate domain-specific texts. The modern theory of dictionary functions presented in Bergenholtz and Tarp (2002) opens up exciting new possibilities for theoretical and practical lexicography and encourages lexicographers...... to adopt a new way of thinking when planning and compiling dictionaries. The function of a dictionary is to assist a particular user group with specific characteristics in order to fulfil the complex needs that arise in a particular type of use-situation. This entails a study of the extra...

  7. Active Learning for Node Classification in Assortative and Disassortative Networks

    CERN Document Server

    Moore, Cristopher; Zhu, Yaojia; Rouquier, Jean-Baptiste; Lane, Terran

    2011-01-01

    In many real-world networks, nodes have class labels, attributes, or variables that affect the network's topology. If the topology of the network is known but the labels of the nodes are hidden, we would like to select a small subset of nodes such that, if we knew their labels, we could accurately predict the labels of all the other nodes. We develop an active learning algorithm for this problem which uses information-theoretic techniques to choose which nodes to explore. We test our algorithm on networks from three different domains: a social network, a network of English words that appear adjacently in a novel, and a marine food web. Our algorithm makes no initial assumptions about how the groups connect, and performs well even when faced with quite general types of network structure. In particular, we do not assume that nodes of the same class are more likely to be connected to each other---only that they connect to the rest of the network in similar ways.

  8. Earth Science Data and Applications for K-16 Education from the NASA Langley Atmospheric Science Data Center

    Science.gov (United States)

    Phelps, C. S.; Chambers, L. H.; Alston, E. J.; Moore, S. W.; Oots, P. C.

    2005-05-01

    NASA's Science Mission Directorate aims to stimulate public interest in Earth system science and to encourage young scholars to consider careers in science, technology, engineering and mathematics. NASA's Atmospheric Science Data Center (ASDC) at Langley Research Center houses over 700 data sets related to Earth's radiation budget, clouds, aerosols and tropospheric chemistry that are being produced to increase academic understanding of the natural and anthropogenic perturbations that influence global climate change. However, barriers still exist in the use of these actual satellite observations by educators in the classroom to supplement the educational process. Thus, NASA is sponsoring the "Mentoring and inquirY using NASA Data on Atmospheric and earth science for Teachers and Amateurs" (MY NASA DATA) project to systematically support educational activities by reducing the ASDC data holdings to `microsets' that can be easily accessible and explored by the K-16 educators and students. The microsets are available via Web site (http://mynasadata.larc.nasa.gov) with associated lesson plans, computer tools, data information pages, and a science glossary. A MY NASA DATA Live Access Server (LAS) has been populated with ASDC data such that users can create custom microsets online for desired time series, parameters and geographical regions. The LAS interface is suitable for novice to advanced users, teachers or students. The microsets may be visual representations of data or text output for spreadsheet analysis. Currently, over 148 parameters from the Clouds and the Earth's Radiant Energy System (CERES), Multi-angle Imaging SpectroRadiometer (MISR), Surface Radiation Budget (SRB), Tropospheric Ozone Residual (TOR) and the International Satellite Cloud Climatology Project (ISCCP) are available and provide important information on clouds, fluxes and cycles in the Earth system. Additionally, a MY NASA DATA OPeNDAP server has been established to facilitate file transfer of

  9. Students' Personal Networks in Virtual and Personal Learning Environments: A Case Study in Higher Education Using Learning Analytics Approach

    Science.gov (United States)

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

    2016-01-01

    The main objective of this paper is to analyse the effect of the affordances of a virtual learning environment and a personal learning environment (PLE) in the configuration of the students' personal networks in a higher education context. The results are discussed in light of the adaptation of the students to the learning network made up by two…

  10. Students' Personal Networks in Virtual and Personal Learning Environments: A Case Study in Higher Education Using Learning Analytics Approach

    Science.gov (United States)

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

    2016-01-01

    The main objective of this paper is to analyse the effect of the affordances of a virtual learning environment and a personal learning environment (PLE) in the configuration of the students' personal networks in a higher education context. The results are discussed in light of the adaptation of the students to the learning network made up by two…

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

    Science.gov (United States)

    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.

  12. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

    Directory of Open Access Journals (Sweden)

    Christian Jarvers

    2016-11-01

    Full Text Available Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e. tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN, which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that

  13. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

    Science.gov (United States)

    Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L.; Schulz, Andreas L.; Ohl, Frank W.; Lommerzheim, Marcel; Neumann, Heiko

    2016-01-01

    Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

  14. Introduction to spiking neural networks: Information processing, learning and applications.

    Science.gov (United States)

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

    The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

  15. Radial basis function networks and complexity regularization in function learning.

    Science.gov (United States)

    Krzyzak, A; Linder, T

    1998-01-01

    In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function network. Our approach differs from previous complexity regularization neural-network function learning schemes in that we operate with random covering numbers and l(1) metric entropy, making it possible to consider much broader families of activation functions, namely functions of bounded variation. Some constraints previously imposed on the network parameters are also eliminated this way. The network is trained by means of complexity regularization involving empirical risk minimization. Bounds on the expected risk in terms of the sample size are obtained for a large class of loss functions. Rates of convergence to the optimal loss are also derived.

  16. Elluminate Article: Learning Object Repository Network (LORNet

    Directory of Open Access Journals (Sweden)

    France Henri

    2005-07-01

    Full Text Available The publisher of IRRODL, The Canadian Institute of Distance Education Research (CIDER, is pleased to link here to a series of eight online seminars that took place over Spring 2005, using Elluminate live e-learning and collaborative solutions. These interactive CIDER Sessions disseminate research emanating from Canada's vibrant DE research community, and we feel these archived recordings are highly relevant to many in the international distance education research community. To access these sessions, you must first download FREE software. Visit http://www.elluminate.com/support/ to download this software.

  17. Commentary: Situated learning in the Network society

    Directory of Open Access Journals (Sweden)

    Rune Krumsvik

    2008-07-01

    Full Text Available There is a need to develop a broader view of knowledge for dealing with the way in which new digital trends influence the underlying conditions for schools, pedagogy and subjects. This short commentary article, based on my paper at the NVU-conference 2008, will therefore highlight whether a broader view of knowledge - situated learning, digital literacy and the digital revolution can generate new ways of how we perceive pedagogy within the new educational reform in Norway in particular and the digitized school in general. The focus is particularly angled towards the implications this may have for developing new practises for teachers and students.

  18. Fast Back Propagation Learning Using Optimization of Learning Rate for Pulsed Neural Networks

    Science.gov (United States)

    Yamamoto, Kenji; Koakutsu, Seiichi; Okamoto, Takashi; Hirata, Hironori

    Neural Networks (NN) are widely applied to information processing because of its nonlinear processing capability. Digital hardware implementation of NN seems to be effective in construction of NN systems in which real-time operation and much further wide applications are possible. However, the digital hardware implementation of analogue NN is very difficult because we have to fulfill the restrictions about circuit resource, such as circuit scale, arrangement, and wiring. A technique that uses pulsed neuron model instead of analogue neuron model as a method of solving this problem has been proposed, and its effectiveness has been confirmed. To construct Pulsed Neural Networks (PNN), Back Propagation (BP) learning has been proposed. However, BP learning takes much time to construct PNN compared with the learning of analogue NN. Therefore some method to speed up BP learning of PNN is necessary. In this paper, we propose a fast BP learning using optimization of learning rate for PNN. In the proposed method, the learning rate is optimized so as to speed up the learning at every learning epoch. To evaluate the proposed method, we apply it to some pattern recognition problems, such as XOR, 3-bits parity, and digit recognition. Results of computational experiments indicate the validity of the proposed method.

  19. Outsmarting neural networks: an alternative paradigm for machine learning

    Energy Technology Data Exchange (ETDEWEB)

    Protopopescu, V.; Rao, N.S.V.

    1996-10-01

    We address three problems in machine learning, namely: (i) function learning, (ii) regression estimation, and (iii) sensor fusion, in the Probably and Approximately Correct (PAC) framework. We show that, under certain conditions, one can reduce the three problems above to the regression estimation. The latter is usually tackled with artificial neural networks (ANNs) that satisfy the PAC criteria, but have high computational complexity. We propose several computationally efficient PAC alternatives to ANNs to solve the regression estimation. Thereby we also provide efficient PAC solutions to the function learning and sensor fusion problems. The approach is based on cross-fertilizing concepts and methods from statistical estimation, nonlinear algorithms, and the theory of computational complexity, and is designed as part of a new, coherent paradigm for machine learning.

  20. Are deep neural networks really learning relevant features?

    DEFF Research Database (Denmark)

    Kereliuk, Corey; Sturm, Bob L.; Larsen, Jan

    In recent years deep neural networks (DNNs) have become a popular choice for audio content analysis. This may be attributed to various factors including advancements in training algorithms, computational power, and the potential for DNNs to implicitly learn a set of feature detectors. We have...... recently re-examined two works \\cite{sigtiaimproved}\\cite{hamel2010learning} that consider DNNs for the task of music genre recognition (MGR). These papers conclude that frame-level features learned by DNNs offer an improvement over traditional, hand-crafted features such as Mel-frequency cepstrum...... leads one to question the degree to which the learned frame-level features are actually useful for MGR. We make available a reproducible software package allowing other researchers to completely duplicate our figures and results....

  1. Are deep neural networks really learning relevant features?

    DEFF Research Database (Denmark)

    Kereliuk, Corey; Sturm, Bob L.; Larsen, Jan

    In recent years deep neural networks (DNNs) have become a popular choice for audio content analysis. This may be attributed to various factors including advancements in training algorithms, computational power, and the potential for DNNs to implicitly learn a set of feature detectors. We have...... recently re-examined two works \\cite{sigtiaimproved}\\cite{hamel2010learning} that consider DNNs for the task of music genre recognition (MGR). These papers conclude that frame-level features learned by DNNs offer an improvement over traditional, hand-crafted features such as Mel-frequency cepstrum...... leads one to question the degree to which the learned frame-level features are actually useful for MGR. We make available a reproducible software package allowing other researchers to completely duplicate our figures and results....

  2. Learning anticipation via spiking networks: application to navigation control.

    Science.gov (United States)

    Arena, Paolo; Fortuna, Luigi; Frasca, Mattia; Patané, Luca

    2009-02-01

    In this paper, we introduce a network of spiking neurons devoted to navigation control. Three different examples, dealing with stimuli of increasing complexity, are investigated. In the first one, obstacle avoidance in a simulated robot is achieved through a network of spiking neurons. In the second example, a second layer is designed aiming to provide the robot with a target approaching system, making it able to move towards visual targets. Finally, a network of spiking neurons for navigation based on visual cues is introduced. In all cases, the robot was assumed to rely on some a priori known responses to low-level sensors (i.e., to contact sensors in the case of obstacles, to proximity target sensors in the case of visual targets, or to the visual target for navigation with visual cues). Based on their knowledge, the robot has to learn the response to high-level stimuli (i.e., range finder sensors or visual input). The biologically plausible paradigm of spike-timing-dependent plasticity (STDP) is included in the network to make the system able to learn high-level responses that guide navigation through a simple unstructured environment. The learning procedure is based on classical conditioning.

  3. High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers.

    Science.gov (United States)

    Linderman, Michael D; Athalye, Vivek; Meng, Teresa H; Asadi, Narges Bani; Bruggner, Robert; Nolan, Garry P

    2010-06-01

    Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20-50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations.

  4. Supervised dictionary learning for inferring concurrent brain networks.

    Science.gov (United States)

    Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

    2015-10-01

    Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

  5. SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

    Science.gov (United States)

    Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T

    2015-02-01

    Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks.

  6. Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

    Institute of Scientific and Technical Information of China (English)

    Gui-xia Liu; Wei Feng; Han Wang; Lei Liu; Chun-guang Zhou

    2009-01-01

    In the post-genomic biology era, the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system, and it has been a challenging task in bioinformatics. The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages, but how to determine the network structure and parameters is still important to be explored. This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network .The new algorithm is evaluated with the use of both simulated and yeast cell cycle data. The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.

  7. Social Networking Sites and Addiction: Ten Lessons Learned

    Science.gov (United States)

    Kuss, Daria J.; Griffiths, Mark D.

    2017-01-01

    Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided. PMID:28304359

  8. Social Networking Sites and Addiction: Ten Lessons Learned.

    Science.gov (United States)

    Kuss, Daria J; Griffiths, Mark D

    2017-03-17

    Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided.

  9. Social Networking Sites and Addiction: Ten Lessons Learned

    Directory of Open Access Journals (Sweden)

    Daria J. Kuss

    2017-03-01

    Full Text Available Online social networking sites (SNSs have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i social networking and social media use are not the same; (ii social networking is eclectic; (iii social networking is a way of being; (iv individuals can become addicted to using social networking sites; (v Facebook addiction is only one example of SNS addiction; (vi fear of missing out (FOMO may be part of SNS addiction; (vii smartphone addiction may be part of SNS addiction; (viii nomophobia may be part of SNS addiction; (ix there are sociodemographic differences in SNS addiction; and (x there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided.

  10. Analog memristive synapse in spiking networks implementing unsupervised learning

    Directory of Open Access Journals (Sweden)

    Erika Covi

    2016-10-01

    Full Text Available Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity. This implies a device able to change its resistance (synaptic strength, or weight upon proper electrical stimuli (synaptic activity and showing several stable resistive states throughout its dynamic range (analog behavior. Moreover, it should be able to perform spike timing dependent plasticity (STDP, an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%.

  11. Learning to play Go using recursive neural networks.

    Science.gov (United States)

    Wu, Lin; Baldi, Pierre

    2008-11-01

    Go is an ancient board game that poses unique opportunities and challenges for artificial intelligence. Currently, there are no computer Go programs that can play at the level of a good human player. However, the emergence of large repositories of games is opening the door for new machine learning approaches to address this challenge. Here we develop a machine learning approach to Go, and related board games, focusing primarily on the problem of learning a good evaluation function in a scalable way. Scalability is essential at multiple levels, from the library of local tactical patterns, to the integration of patterns across the board, to the size of the board itself. The system we propose is capable of automatically learning the propensity of local patterns from a library of games. Propensity and other local tactical information are fed into recursive neural networks, derived from a probabilistic Bayesian network architecture. The recursive neural networks in turn integrate local information across the board in all four cardinal directions and produce local outputs that represent local territory ownership probabilities. The aggregation of these probabilities provides an effective strategic evaluation function that is an estimate of the expected area at the end, or at various other stages, of the game. Local area targets for training can be derived from datasets of games played by human players. In this approach, while requiring a learning time proportional to N(4), skills learned on a board of size N(2) can easily be transferred to boards of other sizes. A system trained using only 9 x 9 amateur game data performs surprisingly well on a test set derived from 19 x 19 professional game data. Possible directions for further improvements are briefly discussed.

  12. Learning to Pivot with Adversarial Networks

    CERN Document Server

    Louppe, Gilles; Cranmer, Kyle

    2016-01-01

    Many inference problems involve data generation processes that are not uniquely specified or are uncertain in some way. In a scientific context, the presence of several plausible data generation processes is often associated to the presence of systematic uncertainties. Robust inference is possible if it is based on a pivot -- a quantity whose distribution is invariant to the unknown value of the (categorical or continuous) nuisance parameters that parametrizes this family of generation processes. In this work, we introduce a flexible training procedure based on adversarial networks for enforcing the pivotal property on a predictive model. We derive theoretical results showing that the proposed algorithm tends towards a minimax solution corresponding to a predictive model that is both optimal and independent of the nuisance parameters (if that models exists) or for which one can tune the trade-off between power and robustness. Finally, we demonstrate the effectiveness of this approach with a toy example and an...

  13. Analytical reasoning task reveals limits of social learning in networks.

    Science.gov (United States)

    Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

    2014-04-06

    Social learning-by observing and copying others-is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias', which limits their social learning to the output, rather than the process, of their peers' reasoning-even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

  14. Learning discriminative functional network features of schizophrenia

    Science.gov (United States)

    Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

    2017-03-01

    Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

  15. Learning by Knowledge Networking across Cultures

    DEFF Research Database (Denmark)

    Wangel, Arne; Stærdahl, Jens; Bransholm Pedersen, Kirsten

    2005-01-01

    ) and environmental impact assessment (EIA) in Malaysia 1998-2003 has sought to address these needs for new competences. Differences in educational background and the work culture of the participants have presented difficulties during these courses, in particular in terms of achieving a mixed team building to turn......Engineers and planners working in trans-national production and aid project interventions in Third World countries must be able to 're-invent' technological systems across cultures and plan and build the capacities of their counterparts. A series of joint courses on cleaner production (CP...... some of the obstacles into resources for knowledge sharing. However, students have stressed their positive experience of cross-cultural communication. While a joint course of three week duration by itself may involve only limited cross-cultural learning, serving primarily as an introduction to a long...

  16. Rethinking learning networks collaborative possibilities for a Deleuzian century

    CERN Document Server

    Kamp, Annelies

    2013-01-01

    In the face of today's complex policy challenges, various forms of 'joining-up' - networking, collaborating, partnering - have become key responses. However, institutions often fail to take advantage of the full benefits that joining-up offers. In this book, the author draws on ethnographic research into learning networks in post compulsory education and training in the state of Victoria, Australia, to explore why this might be the case and presents an argument for rethinking how joining-up works in practice. Throughout the book, Deleuzian concepts are engaged to forge a 'little complicating m

  17. The learning problem of multi-layer neural networks.

    Science.gov (United States)

    Ban, Jung-Chao; Chang, Chih-Hung

    2013-10-01

    This manuscript considers the learning problem of multi-layer neural networks (MNNs) with an activation function which comes from cellular neural networks. A systematic investigation of the partition of the parameter space is provided. Furthermore, the recursive formula of the transition matrix of an MNN is obtained. By implementing the well-developed tools in the symbolic dynamical systems, the topological entropy of an MNN can be computed explicitly. A novel phenomenon, the asymmetry of a topological diagram that was seen in Ban, Chang, Lin, and Lin (2009) [J. Differential Equations 246, pp. 552-580, 2009], is revealed.

  18. Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.

    Science.gov (United States)

    Chairez, Isaac

    2016-04-05

    This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.

  19. Learning Continuous Time Bayesian Network Classifiers Using MapReduce

    Directory of Open Access Journals (Sweden)

    Simone Villa

    2014-12-01

    Full Text Available Parameter and structural learning on continuous time Bayesian network classifiers are challenging tasks when you are dealing with big data. This paper describes an efficient scalable parallel algorithm for parameter and structural learning in the case of complete data using the MapReduce framework. Two popular instances of classifiers are analyzed, namely the continuous time naive Bayes and the continuous time tree augmented naive Bayes. Details of the proposed algorithm are presented using Hadoop, an open-source implementation of a distributed file system and the MapReduce framework for distributed data processing. Performance evaluation of the designed algorithm shows a robust parallel scaling.

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

    Science.gov (United States)

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

    2015-04-15

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

  1. Simultaneous perturbation learning rule for recurrent neural networks and its FPGA implementation.

    Science.gov (United States)

    Maeda, Yutaka; Wakamura, Masatoshi

    2005-11-01

    Recurrent neural networks have interesting properties and can handle dynamic information processing unlike ordinary feedforward neural networks. However, they are generally difficult to use because there is no convenient learning scheme. In this paper, a recursive learning scheme for recurrent neural networks using the simultaneous perturbation method is described. The detailed procedure of the scheme for recurrent neural networks is explained. Unlike ordinary correlation learning, this method is applicable to analog learning and the learning of oscillatory solutions of recurrent neural networks. Moreover, as a typical example of recurrent neural networks, we consider the hardware implementation of Hopfield neural networks using a field-programmable gate array (FPGA). The details of the implementation are described. Two examples of a Hopfield neural network system for analog and oscillatory targets are shown. These results show that the learning scheme proposed here is feasible.

  2. Machine learning based Intelligent cognitive network using fog computing

    Science.gov (United States)

    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.

  3. Fast learning of biased patterns in neural networks.

    Science.gov (United States)

    Wendemuth, A; Sherrington, D

    1993-09-01

    Usual neural network gradient descent training algorithms require training times of the same order as the number of neurons N if the patterns are biased. In this paper, modified algorithms are presented which require training times equal to those in unbiased cases which are of order 1. Exact convergence proofs are given. Gain parameters which produce minimal learning times in large networks are computed by replica methods. It is demonstrated how these modified algorithms are applied in order to produce four types of solutions to the learning problem: 1. A solution with all internal fields equal to the desired output, 2. The Adaline (or pseudo-inverse) solution, 3. The perceptron of optimal stability without threshold and 4. The perceptron of optimal stability with threshold.

  4. Cascaded VLSI Chips Help Neural Network To Learn

    Science.gov (United States)

    Duong, Tuan A.; Daud, Taher; Thakoor, Anilkumar P.

    1993-01-01

    Cascading provides 12-bit resolution needed for learning. Using conventional silicon chip fabrication technology of VLSI, fully connected architecture consisting of 32 wide-range, variable gain, sigmoidal neurons along one diagonal and 7-bit resolution, electrically programmable, synaptic 32 x 31 weight matrix implemented on neuron-synapse chip. To increase weight nominally from 7 to 13 bits, synapses on chip individually cascaded with respective synapses on another 32 x 32 matrix chip with 7-bit resolution synapses only (without neurons). Cascade correlation algorithm varies number of layers effectively connected into network; adds hidden layers one at a time during learning process in such way as to optimize overall number of neurons and complexity and configuration of network.

  5. A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

    Directory of Open Access Journals (Sweden)

    Satchidananda Dehuri

    2011-01-01

    dimensional-space where linear separability is possible. Moreover, the proposed HCFLNN combines the best attribute of particle swarm optimization (PSO, back propagation learning (BP learning, and functional link neural networks (FLNNs. The proposed method eliminates the need of hidden layer by expanding the input patterns using Chebyshev orthogonal polynomials. We have shown its effectiveness of classifying the unknown pattern using the publicly available datasets obtained from UCI repository. The computational results are then compared with functional link neural network (FLNN with a generic basis functions, PSO-based FLNN, and EFLN. From the comparative study, we observed that the performance of the HCFLNN outperforms FLNN, PSO-based FLNN, and EFLN in terms of classification accuracy.

  6. Neural network learning of optimal Kalman prediction and control

    CERN Document Server

    Linsker, Ralph

    2008-01-01

    Although there are many neural network (NN) algorithms for prediction and for control, and although methods for optimal estimation (including filtering and prediction) and for optimal control in linear systems were provided by Kalman in 1960 (with nonlinear extensions since then), there has been, to my knowledge, no NN algorithm that learns either Kalman prediction or Kalman control (apart from the special case of stationary control). Here we show how optimal Kalman prediction and control (KPC), as well as system identification, can be learned and executed by a recurrent neural network composed of linear-response nodes, using as input only a stream of noisy measurement data. The requirements of KPC appear to impose significant constraints on the allowed NN circuitry and signal flows. The NN architecture implied by these constraints bears certain resemblances to the local-circuit architecture of mammalian cerebral cortex. We discuss these resemblances, as well as caveats that limit our current ability to draw ...

  7. Exploring the Peer Interaction Effects on Learning Achievement in a Social Learning Platform Based on Social Network Analysis

    Science.gov (United States)

    Lin, Yu-Tzu; Chen, Ming-Puu; Chang, Chia-Hu; Chang, Pu-Chen

    2017-01-01

    The benefits of social learning have been recognized by existing research. To explore knowledge distribution in social learning and its effects on learning achievement, we developed a social learning platform and explored students' behaviors of peer interactions by the proposed algorithms based on social network analysis. An empirical study was…

  8. The Challenges to Connectivist Learning on Open Online Networks: Learning Experiences during a Massive Open Online Course

    Directory of Open Access Journals (Sweden)

    Rita Kop

    2011-03-01

    Full Text Available Self-directed learning on open online networks is now a possibility as communication and resources can be combined to create learning environments. But is it really? There are some challenges that might prevent learners from having a quality learning experience. This paper raises questions on levels of learner autonomy, presence, and critical literacies required in active connectivist learning.

  9. THE MACHINE LEARNING SYSTEM OF TELEPHONE NETWORKS MANAGEMENT

    Institute of Scientific and Technical Information of China (English)

    CaoLiming; ZhouQiang

    1996-01-01

    The problem of fault information process in telephone networks management system in AT & T in the US has been solved with step-wise learning approach.This method makes the information decrease step by step by means of merge and sort, classifies the information to several typical classes and establishes the knowledgebase (KB) eventually. If new fault information is inputted, we will call the knowledge in KB and predict the related faults which will happen.

  10. Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

    Science.gov (United States)

    2014-03-27

    encryption [37]. As an alternative to traditional classification approaches, machine learning (ML) algorithms (e.g., Naı̈ve Bayes) have successfully used...systems, and conducting physical security surveys of remote sites. Eliminating possible backdoor entry into a SCADA network can be a daunting task...notify the master of an issue. Furthermore, SCADA protocols generally lack authentication and encryption due to operating requirements and use of

  11. Practice of Connectivism As Learning Theory: Enhancing Learning Process Through Social Networking Site (Facebook

    Directory of Open Access Journals (Sweden)

    Fahriye Altınay Aksal

    2013-12-01

    Full Text Available The impact of the digital age within learning and social interaction has been growing rapidly. The realm of digital age and computer mediated communication requires reconsidering instruction based on collaborative interactive learning process and socio-contextual experience for learning. Social networking sites such as facebook can help create group space for digital dialogue to inform, question and challenge within a frame of connectivism as learning theory within the digital age. The aim of this study is to elaborate the practice of connectivism as learning theory in terms of internship course. Facebook group space provided social learning platform for dialogue and negotiation beside the classroom learning and teaching process in this study. The 35 internship students provided self-reports within a frame of this qualitative research. This showed how principles of theory practiced and how this theory and facebook group space contribute learning, selfleadership, decision making and reflection skills. As the research reflects a practice of new theory based on action research, learning is not individualistic attempt in the digital age as regards the debate on learning in digital age within a frame of connectivism

  12. Efficient Bayesian Learning in Social Networks with Gaussian Estimators

    CERN Document Server

    Mossel, Elchanan

    2010-01-01

    We propose a simple and efficient Bayesian model of iterative learning on social networks. This model is efficient in two senses: the process both results in an optimal belief, and can be carried out with modest computational resources for large networks. This result extends Condorcet's Jury Theorem to general social networks, while preserving rationality and computational feasibility. The model consists of a group of agents who belong to a social network, so that a pair of agents can observe each other's actions only if they are neighbors. We assume that the network is connected and that the agents have full knowledge of the structure of the network. The agents try to estimate some state of the world S (say, the price of oil a year from today). Each agent has a private measurement of S. This is modeled, for agent v, by a number S_v picked from a Gaussian distribution with mean S and standard deviation one. Accordingly, agent v's prior belief regarding S is a normal distribution with mean S_v and standard dev...

  13. Neural Network Machine Learning and Dimension Reduction for Data Visualization

    Science.gov (United States)

    Liles, Charles A.

    2014-01-01

    Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

  14. Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks.

    Science.gov (United States)

    Emary, E; Zawbaa, Hossam M; Grosan, Crina

    2017-01-10

    In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent's own experience and the current terrain of the search space. In order to achieve this, experience repository is built based on the neural network to map a set of agents' states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called experienced GWO (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various data sets demonstrate an advance of the EGWO over the original GWO and over other metaheuristics, such as genetic algorithms and particle swarm optimization.

  15. Learning Effectiveness of the NASA Digital Learning Network

    Science.gov (United States)

    Hix, Billy

    2005-01-01

    Student participation in actual investigations which develop inquiry and intellectual skills has long been regarded as an essential component of science instructions (Schwab, 1962; White, 1999). Such investigations give students an opportunity to appreciate the spirit of science and promote an understanding of the nature of science. However, classroom research conducted over the past 20 years describes science teaching as primarily teacher centered. Typical instruction consists of whole class, noninteractive activities in which individual seatwork has constituted the bulk of classroom interactions (Tobin and Gallagher, 1997). Students typically learn science from textbooks and lectures. Their main motivation is to do reasonably well on tests and examinations (Layman, 1999). During the past five years, infrastructure constraints have reduced to the point that many schools systems can now afford low cost, high quality video conferencing equipment (International Society for Technology in Education, 2003). This study investigates the use of interactive video conferencing vs. face to face interaction with hands-on, inquiry based activities. Some basic questions to be addressed are: How does the delivery method impact the students understanding of the goals of the experiment? Are students explanation of the strategies of experimentation different based on the method of instruction that was provided. Do students engaged in a workshop with the instructor in the room vs. an instructor over video conferencing have different perception of the understanding of the subject materials?

  16. Distributed reinforcement learning for adaptive and robust network intrusion response

    Science.gov (United States)

    Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel

    2015-07-01

    Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.

  17. Synthetic Modeling of Autonomous Learning with a Chaotic Neural Network

    Science.gov (United States)

    Funabashi, Masatoshi

    We investigate the possible role of intermittent chaotic dynamics called chaotic itinerancy, in interaction with nonsupervised learnings that reinforce and weaken the neural connection depending on the dynamics itself. We first performed hierarchical stability analysis of the Chaotic Neural Network model (CNN) according to the structure of invariant subspaces. Irregular transition between two attractor ruins with positive maximum Lyapunov exponent was triggered by the blowout bifurcation of the attractor spaces, and was associated with riddled basins structure. We secondly modeled two autonomous learnings, Hebbian learning and spike-timing-dependent plasticity (STDP) rule, and simulated the effect on the chaotic itinerancy state of CNN. Hebbian learning increased the residence time on attractor ruins, and produced novel attractors in the minimum higher-dimensional subspace. It also augmented the neuronal synchrony and established the uniform modularity in chaotic itinerancy. STDP rule reduced the residence time on attractor ruins, and brought a wide range of periodicity in emerged attractors, possibly including strange attractors. Both learning rules selectively destroyed and preserved the specific invariant subspaces, depending on the neuron synchrony of the subspace where the orbits are situated. Computational rationale of the autonomous learning is discussed in connectionist perspective.

  18. Social Networks as Learning Environments for Higher Education

    Directory of Open Access Journals (Sweden)

    J.A.Cortés

    2014-09-01

    Full Text Available Learning is considered as a social activity, a student does not learn only of the teacher and the textbook or only in the classroom, learn also from many other agents related to the media, peers and society in general. And since the explosion of the Internet, the information is within the reach of everyone, is there where the main area of opportunity in new technologies applied to education, as well as taking advantage of recent socialization trends that can be leveraged to improve not only informing of their daily practices, but rather as a tool that explore different branches of education research. One can foresee the future of higher education as a social learning environment, open and collaborative, where people construct knowledge in interaction with others, in a comprehensive manner. The mobility and ubiquity that provide mobile devices enable the connection from anywhere and at any time. In modern educational environments can be expected to facilitate mobile devices in the classroom expansion in digital environments, so that students and teachers can build the teaching-learning process collectively, this partial derivative results in the development of draft research approved by the CONADI in “Universidad Cooperativa de Colombia”, "Social Networks: A teaching strategy in learning environments in higher education."

  19. A Reinforcement Learning Model Using Neural Networks for Music Sight Reading Learning Problem

    CERN Document Server

    Yahya, Keyvan

    2010-01-01

    Music Sight Reading is a complex process that when it is occurred in the brain, some learning attributes would be emerged. Besides giving a model based on actor-critic method in the Reinforcement Learning, the agent is considered to have a neural network structure. We studied on where the sight reading process is happened and also a serious problem which is how the synaptic weights would be adjusted through the learning process. The model we offer here is a computational model on which an updated weights equation to fixing the weights is accompanied too.

  20. Moral learning in an integrated social and healthcare service network.

    Science.gov (United States)

    Visse, Merel; Widdershoven, Guy A M; Abma, Tineke A

    2012-09-01

    The traditional organizational boundaries between healthcare, social work, police and other non-profit organizations are fading and being replaced by new relational patterns among a variety of disciplines. Professionals work from their own history, role, values and relationships. It is often unclear who is responsible for what because this new network structure requires rules and procedures to be re-interpreted and re-negotiated. A new moral climate needs to be developed, particularly in the early stages of integrated services. Who should do what, with whom and why? Departing from a relational and hermeneutic perspective, this article shows that professionals in integrated service networks embark upon a moral learning process when starting to work together for the client's benefit. In this context, instrumental ways of thinking about responsibilities are actually counterproductive. Instead, professionals need to find out who they are in relation to other professionals, what core values they share and what responsibilities derive from these aspects. This article demonstrates moral learning by examining the case of an integrated social service network. The network's development and implementation were supported by responsive evaluation, enriched by insights of care ethics and hermeneutic ethics.

  1. BNFinder2: Faster Bayesian network learning and Bayesian classification.

    Science.gov (United States)

    Dojer, Norbert; Bednarz, Pawel; Podsiadlo, Agnieszka; Wilczynski, Bartek

    2013-08-15

    Bayesian Networks (BNs) are versatile probabilistic models applicable to many different biological phenomena. In biological applications the structure of the network is usually unknown and needs to be inferred from experimental data. BNFinder is a fast software implementation of an exact algorithm for finding the optimal structure of the network given a number of experimental observations. Its second version, presented in this article, represents a major improvement over the previous version. The improvements include (i) a parallelized learning algorithm leading to an order of magnitude speed-ups in BN structure learning time; (ii) inclusion of an additional scoring function based on mutual information criteria; (iii) possibility of choosing the resulting network specificity based on statistical criteria and (iv) a new module for classification by BNs, including cross-validation scheme and classifier quality measurements with receiver operator characteristic scores. BNFinder2 is implemented in python and freely available under the GNU general public license at the project Web site https://launchpad.net/bnfinder, together with a user's manual, introductory tutorial and supplementary methods.

  2. INVARIANT DESCRIPTOR LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    L. Chen

    2016-06-01

    Full Text Available In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.

  3. Are deep neural networks really learning relevant features?

    DEFF Research Database (Denmark)

    Kereliuk, Corey Mose; Larsen, Jan; Sturm, Bob L.

    In recent years deep neural networks (DNNs) have become a popular choice for audio content analysis. This may be attributed to various factors including advancements in training algorithms, computational power, and the potential for DNNs to implicitly learn a set of feature detectors. We have...... recently re-examined two works that consider DNNs for the task of music genre recognition (MGR). These papers conclude that frame-level features learned by DNNs offer an improvement over traditional, hand-crafted features such as Mel-frequency cepstrum coefficients (MFCCs). However, these conclusions were...... drawn based on training/testing using the GTZAN dataset, which is now known to contain several flaws including replicated observations and artists. We illustrate how considering these flaws dramatically changes the results, which leads one to question the degree to which the learned frame-level features...

  4. Invariant Descriptor Learning Using a Siamese Convolutional Neural Network

    Science.gov (United States)

    Chen, L.; Rottensteiner, F.; Heipke, C.

    2016-06-01

    In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.

  5. Evaluation of the Artificial Neural Network for Color Discrimination : Discrimination of Non-learned Colors

    OpenAIRE

    Tayagaki, Yasuko; Sekiya, Satoko; Sekine, Seishi; Ohkawa, Masashi

    2004-01-01

    Our research purpose is to build an artificial neural network with an excellent color discrimination capability like human being on a computer. In this study, we built the network, which was trained to learn 10 colors with different hues in the Munsell color system. Then, we examined the response of the trained network when the network was interrogated about 10 non-learned colors. The network showed a good color discrimination capability, close to that of human being.

  6. Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms.

    Science.gov (United States)

    Chen, Hsinchun

    1995-01-01

    Presents an overview of artificial-intelligence-based inductive learning techniques and their use in information science research. Three methods are discussed: the connectionist Hopfield network; the symbolic ID3/ID5R; evolution-based genetic algorithms. The knowledge representations and algorithms of these methods are examined in the context of…

  7. Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms.

    Science.gov (United States)

    Chen, Hsinchun

    1995-01-01

    Presents an overview of artificial-intelligence-based inductive learning techniques and their use in information science research. Three methods are discussed: the connectionist Hopfield network; the symbolic ID3/ID5R; evolution-based genetic algorithms. The knowledge representations and algorithms of these methods are examined in the context of…

  8. The Emergence of the Open Networked ``i-Learning'' Model

    Science.gov (United States)

    Elia, Gianluca

    The most significant forces that are changing the business world and the society behaviors in this beginning of the twenty-first century can be identified into the globalization of the economy, technological evolution and convergence, change of the workers' expectations, workplace diversity and mobility, and mostly, knowledge and learning as major organizational assets. But which type of ­learning dynamics must be nurtured and pursued within the organizations, today, in order to generate valuable knowledge and its effective applications? After a brief discussion on the main changes observable in management, ICT and society/workplace in the last years, this chapter aims to answer to this question, through the proposition of the “Π-shaped” profile (a new professional archetype for leading change), and through the discussion of the open networked “i-Learning” model (a new framework to “incubate” innovation in learning processes). Actually, the “i” stands for “innovation” (to highlight the nature of the impact on traditional ­learning model), but also it stands for “incubation” (to underline the urgency to have new environments in which incubating new professional profiles). Specifically, the main key characteristics at the basis of the innovation of the learning processes will be ­presented and described, by highlighting the managerial, technological and societal aspects of their nature. A set of operational guidelines will be also ­provided to ­activate and sustain the innovation process, so implementing changes in the strategic dimensions of the model. Finally, the “i-Learning Radar” is presented as an operational tool to design, communicate and control an “i-Learning experience”. This tool is represented by a radar diagram with six strategic dimensions of a ­learning initiative.

  9. Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

    CERN Document Server

    Grüning, André

    2011-01-01

    Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can be successfully applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons. We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons. While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understand...

  10. Where Have Network-based Self-learning Classes Gone?—Reflections&Expectations on the Employment of Network-based Self-learning Classes

    Institute of Scientific and Technical Information of China (English)

    吴雪茵

    2012-01-01

      To respond to the further development of college English reforms, many universities employed network-based self-learning classes to aid the traditional classroom teaching, especially in teaching listening, but as time went by, some universities gradually gave them up. The paper intends to reflect on the employment of network-based self-learning listening classes, analyz⁃ing the learning with and without its aid, and meanwhile introduce the need to re-employ it, and discuss how we can improve the network-based self-learning classes to help with students’ listening.

  11. Increasing Diversity in the K-16 Pipeline in Earth and Space Science

    Science.gov (United States)

    Walter, D.; Payne, L.

    2006-05-01

    We discuss the successes and challenges of a comprehensive program implemented at South Carolina State University (SCSU) that is intended to increase diversity in earth and space science. SCSU is a Historically Black College/University that has partnered with NASA and others over the past decade to develop activities that have largely concentrated on space science. We have effectively brought together scientists and educators to implement teacher training, K-12 student activities, public outreach and an undergraduate research program. Based on our space science experience we are applying the "lessons learned" to a new earth science program. Support has been provided by NASA MUCERPI (NNG04GD62G), NASA MU-SPIN (NNG04GC40A), NASA SERCH (NCC 5-607) and NASA's Science Mission Directorate (NRA NN-H-04-Z-YO-006-N).

  12. Bayesian Network Structure Learning Based On Rough Set and Mutual Information

    Directory of Open Access Journals (Sweden)

    Zuhong Feng

    2013-09-01

    Full Text Available Abstract In Bayesian network structure learning for incomplete data set, a common problem is too many attributes causing low efficiency and high computation complexity. In this paper, an algorithm of attribute reduction based on rough set is introduced. The algorithm can effectively reduce the dimension of attributes and quickly determine the network structure using mutual information for Bayesian network structure learning.

  13. Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

    Science.gov (United States)

    Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

    2012-01-01

    Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers

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

    Science.gov (United States)

    Álvarez Valencia, José Aldemar

    2016-01-01

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

  15. Personal Learning Network Clusters: A Comparison between Mathematics and Computer Science Students

    Science.gov (United States)

    Harding, Ansie; Engelbrecht, Johann

    2015-01-01

    "Personal learning environments" (PLEs) and "personal learning networks" (PLNs) are well-known concepts. A personal learning network "cluster" is a small group of people who regularly interact academically and whose PLNs have a non-empty intersection that includes all the other members. At university level PLN…

  16. Personal Learning Network Clusters: A Comparison between Mathematics and Computer Science Students

    Science.gov (United States)

    Harding, Ansie; Engelbrecht, Johann

    2015-01-01

    "Personal learning environments" (PLEs) and "personal learning networks" (PLNs) are well-known concepts. A personal learning network "cluster" is a small group of people who regularly interact academically and whose PLNs have a non-empty intersection that includes all the other members. At university level PLN…

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

    Science.gov (United States)

    Álvarez Valencia, José Aldemar

    2016-01-01

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

  18. Model of Learning Organizational Development of Primary School Network under the Office of Basic Education Commission

    Science.gov (United States)

    Sai-rat, Wipa; Tesaputa, Kowat; Sriampai, Anan

    2015-01-01

    The objectives of this study were 1) to study the current state of and problems with the Learning Organization of the Primary School Network, 2) to develop a Learning Organization Model for the Primary School Network, and 3) to study the findings of analyses conducted using the developed Learning Organization Model to determine how to develop the…

  19. Forming of Learning Set for Neural Networks in Problems of Losless Data Compression

    OpenAIRE

    Ivaskiv, Yuriy; Levchenko, Victor

    2008-01-01

    questions of forming of learning sets for artificial neural networks in problems of lossless data compression are considered. Methods of construction and use of learning sets are studied. The way of forming of learning set during training an artificial neural network on the data stream is offered.

  20. Universal learning network and its application to chaos control.

    Science.gov (United States)

    Hirasawa, K; Wang, X; Murata, J; Hu, J; Jin, C

    2000-03-01

    Universal Learning Networks (ULNs) are proposed and their application to chaos control is discussed. ULNs provide a generalized framework to model and control complex systems. They consist of a number of inter-connected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. Therefore, physical systems, which can be described by differential or difference equations and also their controllers, can be modeled in a unified way, and so ULNs may form a super set of neural networks and fuzzy neural networks. In order to optimize the ULNs, a generalized learning algorithm is derived, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. The derivatives are calculated by using forward or backward propagation schemes. These algorithms for calculating the derivatives are extended versions of Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL) of Williams in the sense that generalized node functions, generalized network connections with multi-branch of arbitrary time delays, generalized criterion functions and higher order derivatives can be deal with. As an application of ULNs, a chaos control method using maximum Lyapunov exponent of ULNs is proposed. Maximum Lyapunov exponent of ULNs can be formulated by using higher order derivatives of ULNs, and the parameters of ULNs can be adjusted so that the maximum Lyapunov exponent approaches the target value. From the simulation results, it has been shown that a fully connected ULN with three nodes is able to display chaotic behaviors.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  3. Deschooling Society? A Lifelong Learning Network for Sustainable Communities, Urban Regeneration and Environmental Technologies

    Directory of Open Access Journals (Sweden)

    John Blewitt

    2010-11-01

    Full Text Available The complexity and multifaceted nature of sustainable lifelong learning can be effectively addressed by a broad network of providers working co-operatively and collaboratively. Such a network involving the third, public and private sector bodies must realise the full potential of accredited flexible and blended formal learning, contextual opportunities offered by enablers of informal and non formal learning and the affordances derived from the various loose and open spaces that can make social learning effective. Such a conception informs the new Lifelong Learning Network Consortium on Sustainable Communities, Urban Regeneration and Environmental Technologies established and led by the Lifelong Learning Centre at Aston University. This paper offers a radical, reflective and political evaluation of its first year in development arguing that networked learning of this type could prefigure a new model for lifelong learning and sustainable education that renders the city itself a creative medium for transformative learning and sustainability.

  4. Dynamic functional brain networks involved in simple visual discrimination learning.

    Science.gov (United States)

    Fidalgo, Camino; Conejo, Nélida María; González-Pardo, Héctor; Arias, Jorge Luis

    2014-10-01

    Visual discrimination tasks have been widely used to evaluate many types of learning and memory processes. However, little is known about the brain regions involved at different stages of visual discrimination learning. We used cytochrome c oxidase histochemistry to evaluate changes in regional brain oxidative metabolism during visual discrimination learning in a water-T maze at different time points during training. As compared with control groups, the results of the present study reveal the gradual activation of cortical (prefrontal and temporal cortices) and subcortical brain regions (including the striatum and the hippocampus) associated to the mastery of a simple visual discrimination task. On the other hand, the brain regions involved and their functional interactions changed progressively over days of training. Regions associated with novelty, emotion, visuo-spatial orientation and motor aspects of the behavioral task seem to be relevant during the earlier phase of training, whereas a brain network comprising the prefrontal cortex was found along the whole learning process. This study highlights the relevance of functional interactions among brain regions to investigate learning and memory processes. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. Designing for Learning: Online Social Networks as a Classroom Environment

    Directory of Open Access Journals (Sweden)

    Gail Casey

    2011-11-01

    Full Text Available 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 was a teacher working in an Australian public high school and completing her PhD. The study investigates the use of a Ning online social network as a learning environment shared by seven classes, and it examines students’ reactions and online activity while using a range of social media and Web 2.0 tools.The authors use Graham Nuthall’s (2007 “lens on learning” to explore the social processes and culture of this shared online classroom. The paper uses his extensive body of research and analyses of classroom learning processes to conceptualize and analyze data throughout the action research cycle. It discusses the pedagogical implications that arise from the use of social media and, in so doing, challenges traditional models of teaching and learning.

  6. On-line learning algorithms for locally recurrent neural networks.

    Science.gov (United States)

    Campolucci, P; Uncini, A; Piazza, F; Rao, B D

    1999-01-01

    This paper focuses on on-line learning procedures for locally recurrent neural networks with emphasis on multilayer perceptron (MLP) with infinite impulse response (IIR) synapses and its variations which include generalized output and activation feedback multilayer networks (MLN's). We propose a new gradient-based procedure called recursive backpropagation (RBP) whose on-line version, causal recursive backpropagation (CRBP), presents some advantages with respect to the other on-line training methods. The new CRBP algorithm includes as particular cases backpropagation (BP), temporal backpropagation (TBP), backpropagation for sequences (BPS), Back-Tsoi algorithm among others, thereby providing a unifying view on gradient calculation techniques for recurrent networks with local feedback. The only learning method that has been proposed for locally recurrent networks with no architectural restriction is the one by Back and Tsoi. The proposed algorithm has better stability and higher speed of convergence with respect to the Back-Tsoi algorithm, which is supported by the theoretical development and confirmed by simulations. The computational complexity of the CRBP is comparable with that of the Back-Tsoi algorithm, e.g., less that a factor of 1.5 for usual architectures and parameter settings. The superior performance of the new algorithm, however, easily justifies this small increase in computational burden. In addition, the general paradigms of truncated BPTT and RTRL are applied to networks with local feedback and compared with the new CRBP method. The simulations show that CRBP exhibits similar performances and the detailed analysis of complexity reveals that CRBP is much simpler and easier to implement, e.g., CRBP is local in space and in time while RTRL is not local in space.

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

    Science.gov (United States)

    Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

    2017-01-01

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

  8. Elements of learning technologies designing of engineering networks heat

    Directory of Open Access Journals (Sweden)

    Sidorkina Irina G.

    2016-01-01

    Full Text Available Modern educational systems function as a medium fast analysis of shared information that defines them as analytical. The purpose of analytical information processing systems: working with distributed data on a global computer networks, mining and processing of semi structured information, knowledge. Existing mathematical and heuristic methods for the automated synthesis of electronic courses and their corresponding algorithms do not allow the full compliance of development realized in the form of adequate criteria for the totality of the properties distributed educational systems within acceptable time limits and characteristic. Therefore, the development of electronic educational applications must be accompanied by a variety of software support intelligent and adaptive functions. In addition, there is no theoretical justification for integrative aspects and their practical applications for intelligent and adaptive systems of designing distance learning courses. Currently, this type of problem may be considered as a potentially promising. The article presents the functionality of the e-learning course on the design engineering of thermal networks, process modeling in engineering networks with the solution of energy efficiency, detection of problem areas; identify the irrational layout of heaters and others.

  9. A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

    Directory of Open Access Journals (Sweden)

    Karim El-Laithy

    2011-01-01

    Full Text Available An integration of both the Hebbian-based and reinforcement learning (RL rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

  10. Distributed dictionary learning for sparse representation in sensor networks.

    Science.gov (United States)

    Liang, Junli; Zhang, Miaohua; Zeng, Xianyu; Yu, Guoyang

    2014-06-01

    This paper develops a distributed dictionary learning algorithm for sparse representation of the data distributed across nodes of sensor networks, where the sensitive or private data are stored or there is no fusion center or there exists a big data application. The main contributions of this paper are: 1) we decouple the combined dictionary atom update and nonzero coefficient revision procedure into two-stage operations to facilitate distributed computations, first updating the dictionary atom in terms of the eigenvalue decomposition of the sum of the residual (correlation) matrices across the nodes then implementing a local projection operation to obtain the related representation coefficients for each node; 2) we cast the aforementioned atom update problem as a set of decentralized optimization subproblems with consensus constraints. Then, we simplify the multiplier update for the symmetry undirected graphs in sensor networks and minimize the separable subproblems to attain the consistent estimates iteratively; and 3) dictionary atoms are typically constrained to be of unit norm in order to avoid the scaling ambiguity. We efficiently solve the resultant hidden convex subproblems by determining the optimal Lagrange multiplier. Some experiments are given to show that the proposed algorithm is an alternative distributed dictionary learning approach, and is suitable for the sensor network environment.

  11. A one-layer recurrent neural network for support vector machine learning.

    Science.gov (United States)

    Xia, Youshen; Wang, Jun

    2004-04-01

    This paper presents a one-layer recurrent neural network for support vector machine (SVM) learning in pattern classification and regression. The SVM learning problem is first converted into an equivalent formulation, and then a one-layer recurrent neural network for SVM learning is proposed. The proposed neural network is guaranteed to obtain the optimal solution of support vector classification and regression. Compared with the existing two-layer neural network for the SVM classification, the proposed neural network has a low complexity for implementation. Moreover, the proposed neural network can converge exponentially to the optimal solution of SVM learning. The rate of the exponential convergence can be made arbitrarily high by simply turning up a scaling parameter. Simulation examples based on benchmark problems are discussed to show the good performance of the proposed neural network for SVM learning.

  12. Research Notes ~ Development of a Defense Learning Network for the Canadian Department of National Defense

    Directory of Open Access Journals (Sweden)

    Dennis Margueratt

    2003-10-01

    Full Text Available The idea of an online learning network for members of the Canadian Department of National Defence (DND has surfaced several times over the past decade and a half, but has never reached the level of development seen in the current Defence Learning Network (DLN initiative. Past attempts at creating a learning network failed primarily because of the lack of a champion within DND’s senior leadership, and the ability of traditional residential learning to meet the training and education needs of the Department. Recently, however, the rising cost of residential learning, coupled with recognition of the benefits afforded by distance learning, particularly learning flexibility and the ability of learners to engaged in requisite learning at their home base rather than at dispersed locations across Canada, have greatly enhanced the attractiveness of distance learning as a viable learning delivery option.

  13. Chinese Restaurant Game - Part I: Theory of Learning with Negative Network Externality

    OpenAIRE

    Wang, Chih-Yu; Yan CHEN; Liu, K.J. Ray

    2011-01-01

    In a social network, agents are intelligent and have the capability to make decisions to maximize their utilities. They can either make wise decisions by taking advantages of other agents' experiences through learning, or make decisions earlier to avoid competitions from huge crowds. Both these two effects, social learning and negative network externality, play important roles in the decision process of an agent. While there are existing works on either social learning or negative network ext...

  14. A COMPOUND POISSON MODEL FOR LEARNING DISCRETE BAYESIAN NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Abdelaziz GHRIBI; Afif MASMOUDI

    2013-01-01

    We introduce here the concept of Bayesian networks, in compound Poisson model, which provides a graphical modeling framework that encodes the joint probability distribution for a set of random variables within a directed acyclic graph. We suggest an approach proposal which offers a new mixed implicit estimator. We show that the implicit approach applied in compound Poisson model is very attractive for its ability to understand data and does not require any prior information. A comparative study between learned estimates given by implicit and by standard Bayesian approaches is established. Under some conditions and based on minimal squared error calculations, we show that the mixed implicit estimator is better than the standard Bayesian and the maximum likelihood estimators. We illustrate our approach by considering a simulation study in the context of mobile communication networks.

  15. Detection of money laundering groups using supervised learning in networks

    CERN Document Server

    Savage, David; Chou, Pauline; Zhang, Xiuzhen; Yu, Xinghuo

    2016-01-01

    Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals, and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates th...

  16. Learning User Intention in Networked Mobile Robot Control

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Teleoperated networked robot often has unpredictable behaviors due to uncertain time delay from data transmission over Internet. The robot cannot accomplish the desired actions of the remote operator in time, which severely impairs reliability and efficiency of the robot system. This paper investigated a novel approach, learning user intention, to compensate the uncertain time delay with the autonomy of a mobile robot. The user intention to control and operate the robot was modeled and incrementally inferred based on Bayesian techniques so that the desired actions could be recognized and completed by the robot autonomously. Thus the networked robot is able to fulfill the task assigned without frequent interaction with the user, which decreases data transmission and improves the efficiency of the whole system. Experimental results show the validity and feasibility of the proposed method.

  17. Didactic Networks: A Proposal for e-learning Content Generation

    Directory of Open Access Journals (Sweden)

    F. Javier Del Alamo

    2010-12-01

    Full Text Available The Didactic Networks proposed in this paper are based on previous publications in the field of the RSR (Rhetorical-Semantic Relations. The RSR is a set of primitive relations used for building a specific kind of semantic networks for artificial intelligence applications on the web: the RSN (Rhetorical-Semantic Networks. We bring into focus the RSR application in the field of elearning, by defining Didactic Networks as a new set of semantic patterns oriented to the development of elearning applications. The different lines we offer in our research fall mainly into three levels: (1 The most basic one is in the field of computational linguistics and related to Logical Operations on RSR (RSR Inverses and plurals, RSR combinations, etc, once they have been created. The application of Walter Bosma's results regarding rhetorical distance application and treatment as semantic weighted networks is one of the important issues here. (2 In parallel, we have been working on the creation of a knowledge representation and storage model and data architecture capable of supporting the definition of knowledge networks based on RSR. (3 The third strategic line is in the meso-level, the formulation of a molecular structure of knowledge based on the most frequently used patterns. The main contribution at this level is the set of Fundamental Cognitive Networks (FCN as an application of Novak's mental maps proposal. This paper is part of this third intermediate level, and the Fundamental Didactic Networks (FDN are the result of the application of rhetorical theory procedures to the instructional theory. We have formulated a general set of RSR capable of building discourse, making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. The Instructional knowledge can then be elaborated in the same way. This network structure expressing the instructional knowledge in terms of RSR makes the objective of developing web-learning

  18. Fostering Social Capital in a Learning Network: Laying the Groundwork for a Peer-Support Service

    NARCIS (Netherlands)

    Fetter, Sibren; Berlanga, Adriana; Sloep, Peter

    2009-01-01

    Fetter, S., Berlanga, A. J., & Sloep, P. B. (2010). Fostering Social Capital in a Learning Network: Laying the Groundwork for a Peer-Support Service. International Journal of Learning Technology, 5(4), 388-400.

  19. Social Support System in Learning Network for lifelong learners: A Conceptual framework

    NARCIS (Netherlands)

    Nadeem, Danish; Stoyanov, Slavi; Koper, Rob

    2009-01-01

    Nadeem, D., Stoyanov, S., & Koper, R. (2009). Social support system in learning network for lifelong learners: A Conceptual framework [Special issue]. International Journal of Continuing Engineering Education and Life-Long Learning, 19(4/5/6), 337-351.

  20. Fostering Social Capital in a Learning Network: Laying the Groundwork for a Peer-Support Service

    NARCIS (Netherlands)

    Fetter, Sibren; Berlanga, Adriana; Sloep, Peter

    2009-01-01

    Fetter, S., Berlanga, A. J., & Sloep, P. B. (2010). Fostering Social Capital in a Learning Network: Laying the Groundwork for a Peer-Support Service. International Journal of Learning Technology, 5(4), 388-400.

  1. Research on Collective Learning Mechanism and Influencing Factors of Industrial Cluster Innovation Network

    Directory of Open Access Journals (Sweden)

    Lan Wang

    2013-02-01

    Full Text Available This study attempts to contribute to the cluster innovation literature by adding the collective learning perspective and propose an analytical framework on collective learning of cluster. Industrial cluster is viewed as a prevalent mode for technology innovation in knowledge-based economy. Collective learning outlines how local innovation network and spatial proximity between actors influence the sharing and creation of skills and knowledge in cluster. Firstly, this study discusses the structure and character of innovation network within industrial cluster. Secondly, it analyzes the collective learning mechanism of industrial cluster, which is involves in three dimensions: horizontal learning, vertical learning and multi-angle learning. Then, it focuses on some influencing factors of collective learning within innovation network. Finally, this study analyzes the role of global-local linkages in the dynamic capability of cluster innovation network.

  2. Socio-semantic Networks of Research Publications in the Learning Analytics Community

    NARCIS (Netherlands)

    Fazeli, Soude; Drachsler, Hendrik; Sloep, Peter

    2013-01-01

    Fazeli, S., Drachsler, H., & Sloep, P. B. (2013, April). Socio-semantic Networks of Research Publications in the Learning Analytics Community. Presentation at the Learning Analystic and Knowelege (LAK13), Leuven, Belgium.

  3. Positioning of Learners in Learning Networks with Content, Metadata and Ontologies

    NARCIS (Netherlands)

    Kalz, Marco; Van Bruggen, Jan; Rusman, Ellen; Giesbers, Bas; Koper, Rob

    2006-01-01

    Kalz, M, Van Bruggen, J., Rusmann, E., Giesbers, B., & Koper, R. (2007). Positioning of Learners in Learning Networks with Content-Analysis, Metadata and Ontologies. Interactive Learning Environments, 15, 191-200.

  4. Learning and generalization in radial basis function networks.

    Science.gov (United States)

    Freeman, J A; Saad, D

    1995-09-01

    The two-layer radial basis function network, with fixed centers of the basis functions, is analyzed within a stochastic training paradigm. Various definitions of generalization error are considered, and two such definitions are employed in deriving generic learning curves and generalization properties, both with and without a weight decay term. The generalization error is shown analytically to be related to the evidence and, via the evidence, to the prediction error and free energy. The generalization behavior is explored; the generic learning curve is found to be inversely proportional to the number of training pairs presented. Optimization of training is considered by minimizing the generalization error with respect to the free parameters of the training algorithms. Finally, the effect of the joint activations between hidden-layer units is examined and shown to speed training.

  5. The neighborhood MCMC sampler for learning Bayesian networks

    Science.gov (United States)

    Alyami, Salem A.; Azad, A. K. M.; Keith, Jonathan M.

    2016-07-01

    Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) structures. In this paper, we studied a recently proposed Markov chain Monte Carlo (MCMC) sampler, called the Neighbourhood sampler (NS), and examined how efficiently it can sample BNs when local maxima are present. We assume that a posterior distribution f(N,E|D) has been defined, where D represents data relevant to the inference, N and E are the sets of nodes and directed edges, respectively. We illustrate the new approach by sampling from such a distribution, and inferring BNs. The simulations conducted in this paper show that the new learning approach substantially avoids getting stuck in local modes of the distribution, and achieves a more rapid rate of convergence, compared to other common algorithms e.g. the MCMC Metropolis-Hastings sampler.

  6. Scholarly information discovery in the networked academic learning environment

    CERN Document Server

    Li, LiLi

    2014-01-01

    In the dynamic and interactive academic learning environment, students are required to have qualified information literacy competencies while critically reviewing print and electronic information. However, many undergraduates encounter difficulties in searching peer-reviewed information resources. Scholarly Information Discovery in the Networked Academic Learning Environment is a practical guide for students determined to improve their academic performance and career development in the digital age. Also written with academic instructors and librarians in mind who need to show their students how to access and search academic information resources and services, the book serves as a reference to promote information literacy instructions. This title consists of four parts, with chapters on the search for online and printed information via current academic information resources and services: part one examines understanding information and information literacy; part two looks at academic information delivery in the...

  7. Networked curricula: fostering transnational partnership in open and distance learning

    Directory of Open Access Journals (Sweden)

    María Luz Cacheiro-González

    2013-05-01

    Full Text Available Transnational Networked Curricula (TNC provides many benefits to the institutions that offer them as well as to the different stakeholders involved, not only the students but also the academics, the institutions as a whole, and the wider society. Supporting Higher Education Institutions in enhancing and implementing international networked practices in virtual campus building is the main aim of the NetCU project, which has been developed by the EADTU, in partnership with 14 member organizations, from 2009 to 2012. The project outcomes intend to facilitate the future set-up of networked curricula in Higher Education institutions and potentially lead to more transnational partnerships in Open and Distance Education (ODE and blended learning, showing challenges, obstacles and ways to overcome them. This paper presents the main products developed in the project, assesses its completeness and usage, and discusses on the challenges of curricula networking starting from the ideas and opinions shared in different stakeholders workshops organized under the NetCU project.

  8. Network Intrusion Detection System Based On Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Vipin Das

    2010-12-01

    Full Text Available Network and system security is of paramount importance in the present data communication environment. Hackers and intruders can create many successful attempts to cause the crash of the networks and web services by unauthorized intrusion. New threats and associated solutions to prevent these threats are emerging together with the secured system evolution. Intrusion Detection Systems (IDS are one of these solutions. The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviours of users, and then these behaviours will be considered an attack or a normal behaviour. We use Rough Set Theory (RST and Support Vector Machine (SVM to detect network intrusions. First, packets are captured from the network, RST is used to pre-process the data and reduce the dimensions. The features selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments compare the results with Principal Component Analysis (PCA and show RST and SVM schema could reduce the false positive rate and increase the accuracy.

  9. Satellite -Based Networks for U-Health & U-Learning

    Science.gov (United States)

    Graschew, G.; Roelofs, T. A.; Rakowsky, S.; Schlag, P. M.

    2008-08-01

    The use of modern Information and Communication Technologies (ICT) as enabling tools for healthcare services (eHealth) introduces new ways of creating ubiquitous access to high-level medical care for all, anytime and anywhere (uHealth). Satellite communication constitutes one of the most flexible methods of broadband communication offering high reliability and cost-effectiveness of connections meeting telemedicine communication requirements. Global networks and the use of computers for educational purposes stimulate and support the development of virtual universities for e-learning. Especially real-time interactive applications can play an important role in tailored and personalised services.

  10. Learning for Work and Professional Development: The Significance of Informal Learning Networks of Digital Media Industry Professionals

    Science.gov (United States)

    Campana, Joe

    2014-01-01

    Informal learning networks play a key role in the skill and professional development of professionals, working in micro-businesses within Australia's digital media industry, as they do not have access to learning and development or human resources sections that can assist in mapping their learning pathway. Professionals working in this environment…

  11. Learning for Work and Professional Development: The Significance of Informal Learning Networks of Digital Media Industry Professionals

    Science.gov (United States)

    Campana, Joe

    2014-01-01

    Informal learning networks play a key role in the skill and professional development of professionals, working in micro-businesses within Australia's digital media industry, as they do not have access to learning and development or human resources sections that can assist in mapping their learning pathway. Professionals working in this environment…

  12. Understanding the Construction of Personal Learning Networks to Support Non-Formal Workplace Learning of Training Professionals

    Science.gov (United States)

    Manning, Christin

    2013-01-01

    Workers in the 21st century workplace are faced with rapid and constant developments that place a heavy demand on them to continually learn beyond what the Human Resources and Training groups can meet. As a consequence, professionals must rely on non-formal learning approaches through the development of a personal learning network to keep…

  13. Design Guidelines for Collaboration and Participation with Examples from the LN4LD (Learning Network for Learning Design)

    NARCIS (Netherlands)

    Burgos, Daniel; Hummel, Hans; Tattersall, Colin; Brouns, Francis; Koper, Rob

    2007-01-01

    Burgos, D., Hummel, H. G. K., Tattersall, C., Brouns, F., & Koper, R. (2009). Design Guidelines for Collaboration and Participation with Examples from the LN4LD (Learning Network for Learning Design). In L. Lockyer, S. Bennett, S. Agostinho & B. Harper (Eds.), Handbook of Research on Learning Design

  14. Social Learning Networks: Build Mobile Learning Networks Based on Collaborative Services

    Science.gov (United States)

    Huang, Jeff J. S.; Yang, Stephen J. H.; Huang, Yueh-Min; Hsiao, Indy Y. T.

    2010-01-01

    Recently, the rising of Web 2.0 has made online community gradually become popular, like Facebook, blog, etc. As a result, the online knowledge sharing network formed by interpersonal interaction is now a major character of Web 2.0, and therefore, by this trend, we try to build up a collaborative service mechanism and further set up an analysis…

  15. Semantic Web, Reusable Learning Objects, Personal Learning Networks in Health: Key Pieces for Digital Health Literacy.

    Science.gov (United States)

    Konstantinidis, Stathis Th; Wharrad, Heather; Windle, Richard; Bamidis, Panagiotis D

    2017-01-01

    The knowledge existing in the World Wide Web is exponentially expanding, while continuous advancements in health sciences contribute to the creation of new knowledge. There are a lot of efforts trying to identify how the social connectivity can endorse patients' empowerment, while other studies look at the identification and the quality of online materials. However, emphasis has not been put on the big picture of connecting the existing resources with the patients "new habits" of learning through their own Personal Learning Networks. In this paper we propose a framework for empowering patients' digital health literacy adjusted to patients' currents needs by utilizing the contemporary way of learning through Personal Learning Networks, existing high quality learning resources and semantics technologies for interconnecting knowledge pieces. The framework based on the concept of knowledge maps for health as defined in this paper. Health Digital Literacy needs definitely further enhancement and the use of the proposed concept might lead to useful tools which enable use of understandable health trusted resources tailored to each person needs.

  16. Goals, Motivation for, and Outcomes of Personal Learning through Networks: Results of a Tweetstorm

    Science.gov (United States)

    Sie, Rory L. L.; Pataraia, Nino; Boursinou, Eleni; Rajagopal, Kamakshi; Margaryan, Anoush; Falconer, Isobel; Bitter-Rijpkema, Marlies; Littlejohn, Allison; Sloep, Peter B.

    2013-01-01

    Recent developments in the use of social media for learning have posed serious challenges for learners. The information overload that these online social tools create has changed the way learners learn and from whom they learn. An investigation of learners' goals, motivations and expected outcomes when using a personal learning network is…

  17. SME Innovation and Learning: The Role of Networks and Crisis Events

    Science.gov (United States)

    Saunders, Mark N. K.; Gray, David E; Goregaokar, Harshita

    2014-01-01

    Purpose: The purpose of this paper is to contribute to the literature on innovation and entrepreneurial learning by exploring how SMEs learn and innovate, how they use both formal and informal learning and in particular the role of networks and crisis events within their learning experience. Design/methodology/approach: Mixed method study,…

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

    Science.gov (United States)

    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…

  19. Goals, Motivation for, and Outcomes of Personal Learning through Networks: Results of a Tweetstorm

    Science.gov (United States)

    Sie, Rory L. L.; Pataraia, Nino; Boursinou, Eleni; Rajagopal, Kamakshi; Margaryan, Anoush; Falconer, Isobel; Bitter-Rijpkema, Marlies; Littlejohn, Allison; Sloep, Peter B.

    2013-01-01

    Recent developments in the use of social media for learning have posed serious challenges for learners. The information overload that these online social tools create has changed the way learners learn and from whom they learn. An investigation of learners' goals, motivations and expected outcomes when using a personal learning network is…

  20. The MOF chromobarrel domain controls genome-wide H4K16 acetylation and spreading of the MSL complex.

    Science.gov (United States)

    Conrad, Thomas; Cavalli, Florence M G; Holz, Herbert; Hallacli, Erinc; Kind, Jop; Ilik, Ibrahim; Vaquerizas, Juan M; Luscombe, Nicholas M; Akhtar, Asifa

    2012-03-13

    The histone H4 lysine 16 (H4K16)-specific acetyltransferase MOF is part of two distinct complexes involved in X chromosome dosage compensation and autosomal transcription regulation. Here we show that the MOF chromobarrel domain is essential for H4K16 acetylation throughout the Drosophila genome and is required for spreading of the male-specific lethal (MSL) complex on the X chromosome. The MOF chromobarrel domain directly interacts with nucleic acids and potentiates MOF's enzymatic activity after chromatin binding, making it a unique example of a chromo-like domain directly controlling acetylation activity in vivo. We also show that the Drosophila-specific N terminus of MOF has evolved to perform sex-specific functions. It modulates nucleosome binding and HAT activity and controls MSL complex assembly, thus regulating MOF function in dosage compensation. We propose that MOF has been especially tailored to achieve tight regulation of its enzymatic activity and enable its dual role on X and autosomes.

  1. PHF20 Readers Link Methylation of Histone H3K4 and p53 with H4K16 Acetylation

    Directory of Open Access Journals (Sweden)

    Brianna J. Klein

    2016-10-01

    Full Text Available PHF20 is a core component of the lysine acetyltransferase complex MOF (male absent on the first-NSL (non-specific lethal that generates the major epigenetic mark H4K16ac and is necessary for transcriptional regulation and DNA repair. The role of PHF20 in the complex remains elusive. Here, we report on functional coupling between methylation readers in PHF20. We show that the plant homeodomain (PHD finger of PHF20 recognizes dimethylated lysine 4 of histone H3 (H3K4me2 and represents an example of a native reader that selects for this modification. Biochemical and structural analyses help to explain this selectivity and the preference of Tudor2, another reader in PHF20, for dimethylated p53. Binding of the PHD finger to H3K4me2 is required for histone acetylation, accumulation of PHF20 at target genes, and transcriptional activation. Together, our findings establish a unique PHF20-mediated link between MOF histone acetyltransferase (HAT, p53, and H3K4me2, and suggest a model for rapid spreading of H4K16ac-enriched open chromatin.

  2. Mimicking Nature´s way of organizing in industry: a network learning perspective

    DEFF Research Database (Denmark)

    Ulhøi, John Parm; Madsen, Henning

    The purpose of this paper is to further advance existing theory on industrial ecology, organisational network and organisational learning in order to speed up the development towards increased environmental awareness and behavior. It is argued that such a perspective offers a unique opportunity...... to reconsider organisational learning as being both an internal as well as an external phenomenon. By bringing network learning into an existing interorganisational setting (such as industrial ecology) new potentials for increased learning emerge for the participating companies. The concept of network learning...

  3. Classification of CT brain images based on deep learning networks.

    Science.gov (United States)

    Gao, Xiaohong W; Hui, Rui; Tian, Zengmin

    2017-01-01

    While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3-5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information

  4. Adaptive learning with guaranteed stability for discrete-time recurrent neural networks

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15X15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.

  5. Learn the effective connectivity pattern of attention networks: a resting functional MRI and Bayesian network study

    Science.gov (United States)

    Li, Juan; Li, Rui; Yao, Li; Wu, Xia

    2011-03-01

    Task-based neuroimaging studies revealed that different attention operations were carried out by the functional interaction and cooperation between two attention systems: the dorsal attention network (DAN) and the ventral attention network (VAN), which were respectively involved in the "top-down" endogenous attention orienting and the "bottomup" exogenous attention reorienting process. Recent focused resting functional MRI (fMRI) studies found the two attention systems were inherently organized in the human brain regardless of whether or not the attention process were required, but how the two attention systems interact with each other in the absence of task is yet to be investigated. In this study, we first separated the DAN and VAN by applying the group independent component analysis (ICA) to the resting fMRI data acquired from 12 healthy young subjects, then used Gaussian Bayesian network (BN) learning approach to explore the plausible effective connectivity pattern of the two attention systems. It was found regions from the same attention network were strongly intra-dependent, and all the connections were located in the information flow from VAN to DAN, which suggested that an orderly functional interactions and information exchanges between the two attention networks existed in the intrinsic spontaneous brain activity, and the inherent connections might benefit the efficient cognitive process between DAN and VAN, such as the "top-down" and "bottom-up" reciprocal interaction when attention-related tasks were involved.

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

    Science.gov (United States)

    Miconi, Thomas

    2017-02-23

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

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

    Science.gov (United States)

    Fung, Wai-keung; Liu, Yun-hui

    2003-12-01

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

  8. Unsupervised learning in neural networks with short range synapses

    Science.gov (United States)

    Brunnet, L. G.; Agnes, E. J.; Mizusaki, B. E. P.; Erichsen, R., Jr.

    2013-01-01

    Different areas of the brain are involved in specific aspects of the information being processed both in learning and in memory formation. For example, the hippocampus is important in the consolidation of information from short-term memory to long-term memory, while emotional memory seems to be dealt by the amygdala. On the microscopic scale the underlying structures in these areas differ in the kind of neurons involved, in their connectivity, or in their clustering degree but, at this level, learning and memory are attributed to neuronal synapses mediated by longterm potentiation and long-term depression. In this work we explore the properties of a short range synaptic connection network, a nearest neighbor lattice composed mostly by excitatory neurons and a fraction of inhibitory ones. The mechanism of synaptic modification responsible for the emergence of memory is Spike-Timing-Dependent Plasticity (STDP), a Hebbian-like rule, where potentiation/depression is acquired when causal/non-causal spikes happen in a synapse involving two neurons. The system is intended to store and recognize memories associated to spatial external inputs presented as simple geometrical forms. The synaptic modifications are continuously applied to excitatory connections, including a homeostasis rule and STDP. In this work we explore the different scenarios under which a network with short range connections can accomplish the task of storing and recognizing simple connected patterns.

  9. Experimental design schemes for learning Boolean network models

    Science.gov (United States)

    Atias, Nir; Gershenzon, Michal; Labazin, Katia; Sharan, Roded

    2014-01-01

    Motivation: A holy grail of biological research is a working model of the cell. Current modeling frameworks, especially in the protein–protein interaction domain, are mostly topological in nature, calling for stronger and more expressive network models. One promising alternative is logic-based or Boolean network modeling, which was successfully applied to model signaling regulatory circuits in human. Learning such models requires observing the system under a sufficient number of different conditions. To date, the amount of measured data is the main bottleneck in learning informative Boolean models, underscoring the need for efficient experimental design strategies. Results: We developed novel design approaches that greedily select an experiment to be performed so as to maximize the difference or the entropy in the results it induces with respect to current best-fit models. Unique to our maximum difference approach is the ability to account for all (possibly exponential number of) Boolean models displaying high fit to the available data. We applied both approaches to simulated and real data from the EFGR and IL1 signaling systems in human. We demonstrate the utility of the developed strategies in substantially improving on a random selection approach. Our design schemes highlight the redundancy in these datasets, leading up to 11-fold savings in the number of experiments to be performed. Availability and implementation: Source code will be made available upon acceptance of the manuscript. Contact: roded@post.tau.ac.il PMID:25161232

  10. Learning a Markov Logic network for supervised gene regulatory network inference.

    Science.gov (United States)

    Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

    2013-09-12

    Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

  11. Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

    Science.gov (United States)

    Wenhui, Ma; Yu, Wang

    2017-06-01

    Learning evaluation is an effective method, which plays an important role in the network education evaluation system. But most of the current network learning evaluation methods still use traditional university education evaluation system, which do not take into account of web-based learning characteristics, and they are difficult to fit the rapid development of interuniversity collaborative learning based on network. Fuzzy comprehensive evaluation method is used to evaluate interuniversity collaborative learning based on the combination of fuzzy theory and analytic hierarchy process. Analytic hierarchy process is used to determine the weight of evaluation factors of each layer and to carry out the consistency check. According to the fuzzy comprehensive evaluation method, we establish interuniversity collaborative learning evaluation mathematical model. The proposed scheme provides a new thought for interuniversity collaborative learning evaluation based on network.

  12. Design Guidelines for Collaboration and Participation with Examples from the LN4LD (Learning Network for Learning Design)

    OpenAIRE

    2007-01-01

    Burgos, D., Hummel, H. G. K., Tattersall, C., Brouns, F., & Koper, R. (2009). Design Guidelines for Collaboration and Participation with Examples from the LN4LD (Learning Network for Learning Design). In L. Lockyer, S. Bennett, S. Agostinho & B. Harper (Eds.), Handbook of Research on Learning Design and Learning Objects: Issues, Applications and Technologies (Vol. 1, pp 373-389). Hershey, New York: Information Science Reference, IGI Global.

  13. The Learning of First and Second Person Pronouns in English: Network Models and Analysis.

    Science.gov (United States)

    Oshma-Takane, Yuriko; Takane, Hoshio; Shultz, Thomas R.

    1999-01-01

    Investigated young children's learning of the correct use of first and second person pronouns, using feed-forward neural networks. The study involved four computer simulations using the cascade-correlation (CC) learning algorithm. Results indicated that the CC networks could produce the correct pronouns without errors if children heard pronouns…

  14. In search of an adequate yet affordable tutor in online learning networks

    NARCIS (Netherlands)

    Sloep, Peter; Van Rosmalen, Peter; Kester, Liesbeth; Brouns, Francis; Koper, Rob

    2006-01-01

    Sloep, P., van Rosmalen, P., Kester, L., Brouns, F. M. R., & Koper, E. J. R. (2006). In search of an adequate yet affordable tutor in online learning networks. In search of an adequate yet affordable tutor in online learning networks. Presentation at the 6th IEEE International Conference on Advanced

  15. Social Networks, Communication Styles, and Learning Performance in a CSCL Community

    Science.gov (United States)

    Cho, Hichang; Gay, Geri; Davidson, Barry; Ingraffea, Anthony

    2007-01-01

    The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning…

  16. Foundation Year Students' Perceptions of Using Social Network Sites for Learning English in the Saudi Context

    Science.gov (United States)

    AlShoaibi, Rana; Shukri, Nadia

    2017-01-01

    The major aim of this study is to better understand the university students' perceptions and attitudes towards using social network sites for learning English as well as to identify if there is a difference between male and female university students in terms of using social networking sites for learning English inside and outside the classroom.…

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

    Science.gov (United States)

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

    2017-01-01

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

  18. The Role of Action Research in the Development of Learning Networks for Entrepreneurs

    Science.gov (United States)

    Brett, Valerie; Mullally, Martina; O'Gorman, Bill; Fuller-Love, Nerys

    2012-01-01

    Developing sustainable learning networks for entrepreneurs is the core objective of the Sustainable Learning Networks in Ireland and Wales (SLNIW) project. One research team drawn from the Centre for Enterprise Development and Regional Economy at Waterford Institute of Technology and the School of Management and Business from Aberystwyth…

  19. Prestige, Centrality, and Learning: A Social Network Analysis of an Online Class

    Science.gov (United States)

    Russo, Tracy C.; Koesten, Joy

    2005-01-01

    This study explored relations between social network characteristics in an online graduate class and two learning outcomes: affective and cognitive learning. The social network analysis data were compiled by entering the number of one-to-one postings sent by each student to each other student in a course web site discussion space into a specially…

  20. Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

    Science.gov (United States)

    Peng, Yefei

    2010-01-01

    An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

  1. ALADIN: The Adult Learning Documentation and Information Network. Directory of Members. Updated Version 2012

    Science.gov (United States)

    Krolak, Lisa

    2011-01-01

    ALADIN, the Adult Learning Documentation and Information Network, is a well-developed, well-defined and lasting follow-up initiative of CONFINTEA V (Fifth International Conference on Adult Education), which was held in 1997. This global network was brought to life by UIL and the efforts of many adult learning documentation and information centres.…

  2. Learning and Model-checking Networks of I/O Automata

    DEFF Research Database (Denmark)

    Mao, Hua; Jaeger, Manfred

    2012-01-01

    We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating nite probabilistic automata. Leveraging existing automata learning methods from the area of grammatical inference, we can le...

  3. Implementation of a Framework for Collaborative Social Networks in E-Learning

    Science.gov (United States)

    Maglajlic, Seid

    2016-01-01

    This paper describes the implementation of a framework for the construction and utilization of social networks in ELearning. These social networks aim to enhance collaboration between all E-Learning participants (i.e. both traineeto-trainee and trainee-to-tutor communication are targeted). E-Learning systems that include a so-called "social…

  4. The Networked Student Model for Construction of Personal Learning Environments: Balancing Teacher Control and Student Autonomy

    Science.gov (United States)

    Drexler, Wendy

    2010-01-01

    Principles of networked learning, constructivism, and connectivism inform the design of a test case through which secondary students construct personal learning environments for the purpose of independent inquiry. Emerging web applications and open educational resources are integrated to support a "Networked Student Model" that promotes…

  5. Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

    Science.gov (United States)

    Peng, Yefei

    2010-01-01

    An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

  6. The Networked Student Model for Construction of Personal Learning Environments: Balancing Teacher Control and Student Autonomy

    Science.gov (United States)

    Drexler, Wendy

    2010-01-01

    Principles of networked learning, constructivism, and connectivism inform the design of a test case through which secondary students construct personal learning environments for the purpose of independent inquiry. Emerging web applications and open educational resources are integrated to support a "Networked Student Model" that promotes…

  7. The US Fire Learning Network: Springing a Rigidity Trap through Multiscalar Collaborative Networks

    Directory of Open Access Journals (Sweden)

    William Hale. Butler

    2010-09-01

    Full Text Available Wildland fire management in the United States is caught in a rigidity trap, an inability to apply novelty and innovation in the midst of crisis. Despite wide recognition that public agencies should engage in ecological fire restoration, fire suppression still dominates planning and management, and restoration has failed to gain traction. The U.S. Fire Learning Network (FLN, a multiscalar collaborative endeavor established in 2002 by federal land management agencies and The Nature Conservancy, offers the potential to overcome barriers that inhibit restoration planning and management. By circulating people, planning products, and information among landscape- and regional-scale collaboratives, this network has facilitated the development and dissemination of innovative approaches to ecological fire restoration. Through experimentation and innovation generated in the network, the FLN has fostered change by influencing fire and land management plans as well as federal policy. We suggest that multiscalar collaborative planning networks such as the FLN can facilitate overcoming the rigidity traps that prevent resource management agencies from responding to complex cross-scalar problems.

  8. A survey on social networks to determine requirements for Learning Networks for professional development of university staff

    NARCIS (Netherlands)

    Brouns, Francis; Berlanga, Adriana; Fetter, Sibren; Bitter-Rijpkema, Marlies; Van Bruggen, Jan; Sloep, Peter

    2009-01-01

    Brouns, F., Berlanga, A. J., Fetter, S., Bitter-Rijpkema, M. E., Van Bruggen, J. M., & Sloep, P. B. (2011). A survey on social networks to determine requirements for Learning Networks for professional development of university staff. International Journal of Web Based Communities, 7(3), 298-311.

  9. Breast image feature learning with adaptive deconvolutional networks

    Science.gov (United States)

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  10. 学习信度网的结构%Learning Network Structure From Data

    Institute of Scientific and Technical Information of China (English)

    邢永康; 沈一栋

    2000-01-01

    A general approach to learn a network structure is to heuristically search the space of network structures for the one that best fits a given data set. The key to the search is a score function which evaluatos different network structures. In this paper,we give a detailed introduction to two representative score functions: Bde,MDL. We also discuss two widely used learning algorithms that apply those score functions to direct their search:hill climbing and simulated annealing. Finally, We briefly sketch an algorithm,SEM ,that can learn a network structure from incomplete data.

  11. Channel Decision in Cognitive Radio Enabled Sensor Networks: A Reinforcement Learning Approach

    Directory of Open Access Journals (Sweden)

    Joshua Abolarinwa

    2015-08-01

    Full Text Available Recent advancements in the field of cognitive radio technology have paved way for cognitive radio-based wireless sensor networks. This has been tipped to be the next generation sensor. Spectrum sensing and energy efficient channel access are two important operations in this network. In this paper, we propose the use of machine learning and decision making capability of reinforcement learning to address the problem of energy efficiency associated with channel access in cognitive radio aided sensor networks. A simple learning algorithm was developed to improve network parameters such as secondary user throughput, channel availability in relation to the sensing time. Comparing the results obtained from simulations with other channel access without intelligent learning such as random channel assignment and dynamic channel assignment, the learning algorithm produced better performance in terms of throughput, energy efficiency and other quality of service requirement of the network application.

  12. Metacognitive learning in a fully complex-valued radial basis function neural network.

    Science.gov (United States)

    Savitha, R; Suresh, S; Sundararajan, N

    2012-05-01

    Recent studies on human learning reveal that self-regulated learning in a metacognitive framework is the best strategy for efficient learning. As the machine learning algorithms are inspired by the principles of human learning, one needs to incorporate the concept of metacognition to develop efficient machine learning algorithms. In this letter we present a metacognitive learning framework that controls the learning process of a fully complex-valued radial basis function network and is referred to as a metacognitive fully complex-valued radial basis function (Mc-FCRBF) network. Mc-FCRBF has two components: a cognitive component containing the FC-RBF network and a metacognitive component, which regulates the learning process of FC-RBF. In every epoch, when a sample is presented to Mc-FCRBF, the metacognitive component decides what to learn, when to learn, and how to learn based on the knowledge acquired by the FC-RBF network and the new information contained in the sample. The Mc-FCRBF learning algorithm is described in detail, and both its approximation and classification abilities are evaluated using a set of benchmark and practical problems. Performance results indicate the superior approximation and classification performance of Mc-FCRBF compared to existing methods in the literature.

  13. Pedagogy framework design in social networked-based learning: Focus on children with learning difficulties

    Directory of Open Access Journals (Sweden)

    Samira Sadat Sajadi

    2014-09-01

    Full Text Available This paper presents an investigation on the theory of constructivism applicable for learners with learning difficulties, specifically learners with Attention Deficit Hyperactivity Disorder (ADHD. The primary objective of this paper is to determine whether a constructivist technology enhanced learning pedagogy could be used to help ADHD learners cope with their educational needs within a social-media learning environment. Preliminary work is stated here, in which we are seeking evidence to determine the viability of a constructivist approach for learners with ADHD. The novelty of this research lies in the proposals to support ADHD learners to overcome their weaknesses with appropriate pedagogically sound interventions. As a result, a framework has been designed to illuminate areas in which constructivist pedagogies require to address the limitations of ADHD learners. An analytical framework addressing the suitability of a constructivist learning for ADHD is developed from a combination of literature and expert advice from those involved in the education of learners with ADHD. This analytical framework is married to a new model of pedagogy, which the authors have derived from literature analysis. Future work will expand this model to develop a constructivist social network-based learning and eventually test it in specialist schools with ADHD learners.

  14. Towards a learning networked organisation: human capital, compatibility and usability in e-learning systems.

    Science.gov (United States)

    Ivergård, Toni; Hunt, Brian

    2005-03-01

    In all parts of organisations there flourish developments of different new subsystems in areas of knowledge and learning. Over recent decades, new systems for classification of jobs have emerged both at the level of organisations and at a macro-labour market level. Recent developments in job evaluation systems make it possible to cope with the new demands for equity at work (between, for example, genders, races, physical abilities). Other systems have emerged to describe job requirements in terms of skills, knowledge and competence. Systems for learning at work and web-based learning have created a demand for new ways to classify and to understand the process of learning. Often these new systems have been taken from other areas of the organisation not directly concerned with facilitating workplace learning. All these new systems are of course closely interrelated but, in most organisations, a major problem is the severe lack of cohesion and compatibility between the different subsystems. The aim of this paper is to propose a basis for how different human resource systems can be integrated into the business development of an organisation. We discuss this problem and develop proposals alternative to integrated macro-systems. A key element in our proposition is a structure for classification of knowledge and skill to be used in all parts of the process. This structure should be used as an added dimension or an overlay on all other subsystems of the total process. This will facilitate a continued use of all existing systems within different organisations. We develop Burge's (personal communication) model for learning to show that learning is not a successive linear process, but rather an iterative process. In this way we emphasise the need for greater involvement of learners in the development of learning systems towards increased usability in a networked system. This paper is divided into two parts which are closely related. The first part gives an overview of the

  15. Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning

    NARCIS (Netherlands)

    Villmann, T.; Biehl, M.; Villmann, A.; Saralajew, S.

    2017-01-01

    The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust b

  16. Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning

    NARCIS (Netherlands)

    Villmann, T.; Biehl, M.; Villmann, A.; Saralajew, S.

    2017-01-01

    The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust

  17. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    Science.gov (United States)

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Enhancing the Innovativeness of Food SMEs through the Management of Strategic Network Behavior and Network Learning Performance

    Directory of Open Access Journals (Sweden)

    Bianka Kühne

    2013-02-01

    Full Text Available The European project NetGrow stands for Enhancing the innovativeness of food SMEs through the management of strategic network behavior and network learning performance. It is a FP7 Cooperation project in the theme Food, Agriculture and Fisheries, and Biotechnology with an EC contribution of 3M €. The project has a duration of 4 years and will be finished in April 2014. The project coordinator is Prof. dr. Xavier Gellynck from Ghent University, Belgium. The aim of Netgrow is to enhance network learning leading to increased innovation, economic growth andsustainable competitive advantage for food small and medium enterprises (SMEs.

  19. Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

    KAUST Repository

    Alharbi, Basma Mohammed

    2017-02-07

    Location-Based Social Networks (LBSNs) capture individuals whereabouts for a large portion of the population. To utilize this data for user (location)-similarity based tasks, one must map the raw data into a low-dimensional uniform feature space. However, due to the nature of LBSNs, many users have sparse and incomplete check-ins. In this work, we propose to overcome this issue by leveraging the network of friends, when learning the new feature space. We first analyze the impact of friends on individuals\\'s mobility, and show that individuals trajectories are correlated with thoseof their friends and friends of friends (2-hop friends) in an online setting. Based on our observation, we propose a mixed-membership model that infers global mobility patterns from users\\' check-ins and their network of friends, without impairing the model\\'s complexity. Our proposed model infers global patterns and learns new representations for both usersand locations simultaneously. We evaluate the inferred patterns and compare the quality of the new user representation against baseline methods on a social link prediction problem.

  20. Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks.

    Science.gov (United States)

    Chen, S; Wu, Y; Luk, B L

    1999-01-01

    The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.

  1. A Bayesian Network Approach to Modeling Learning Progressions and Task Performance. CRESST Report 776

    Science.gov (United States)

    West, Patti; Rutstein, Daisy Wise; Mislevy, Robert J.; Liu, Junhui; Choi, Younyoung; Levy, Roy; Crawford, Aaron; DiCerbo, Kristen E.; Chappel, Kristina; Behrens, John T.

    2010-01-01

    A major issue in the study of learning progressions (LPs) is linking student performance on assessment tasks to the progressions. This report describes the challenges faced in making this linkage using Bayesian networks to model LPs in the field of computer networking. The ideas are illustrated with exemplar Bayesian networks built on Cisco…

  2. Social Networking Tools and Teacher Education Learning Communities: A Case Study

    Science.gov (United States)

    Poulin, Michael T.

    2014-01-01

    Social networking tools have become an integral part of a pre-service teacher's educational experience. As a result, the educational value of social networking tools in teacher preparation programs must be examined. The specific problem addressed in this study is that the role of social networking tools in teacher education learning communities…

  3. A Social Network Analysis of Teaching and Research Collaboration in a Teachers' Virtual Learning Community

    Science.gov (United States)

    Lin, Xiaofan; Hu, Xiaoyong; Hu, Qintai; Liu, Zhichun

    2016-01-01

    Analysing the structure of a social network can help us understand the key factors influencing interaction and collaboration in a virtual learning community (VLC). Here, we describe the mechanisms used in social network analysis (SNA) to analyse the social network structure of a VLC for teachers and discuss the relationship between face-to-face…

  4. Social Networking Tools and Teacher Education Learning Communities: A Case Study

    Science.gov (United States)

    Poulin, Michael T.

    2014-01-01

    Social networking tools have become an integral part of a pre-service teacher's educational experience. As a result, the educational value of social networking tools in teacher preparation programs must be examined. The specific problem addressed in this study is that the role of social networking tools in teacher education learning communities…

  5. A novel Bayesian learning method for information aggregation in modular neural networks

    DEFF Research Database (Denmark)

    Wang, Pan; Xu, Lida; Zhou, Shang-Ming;

    2010-01-01

    Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight ...

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

  8. Interprofessional practice and learning in a youth mental health service: A case study using network analysis.

    Science.gov (United States)

    Barnett, Tony; Hoang, Ha; Cross, Merylin; Bridgman, Heather

    2015-01-01

    Few studies have examined interprofessional practice (IPP) from a mental health service perspective. This study applied a mixed-method approach to examine the IPP and learning occurring in a youth mental health service in Tasmania, Australia. The aims of the study were to investigate the extent to which staff were networked, how collaboratively they practiced and supported student learning, and to elicit the organisation's strengths and opportunities regarding IPP and learning. Six data sets were collected: pre- and post-test readiness for interprofessional learning surveys, Social Network survey, organisational readiness for IPP and learning checklist, "talking wall" role clarification activity, and observations of participants working through a clinical case study. Participants (n = 19) were well-networked and demonstrated a patient-centred approach. Results confirmed participants' positive attitudes to IPP and learning and identified ways to strengthen the organisation's interprofessional capability. This mixed-method approach could assist others to investigate IPP and learning.

  9. Finding and Reusing Learning Materials with Multimedia Similarity Search and Social Networks

    Science.gov (United States)

    Little, Suzanne; Ferguson, Rebecca; Ruger, Stefan

    2012-01-01

    The authors describe how content-based multimedia search technologies can be used to help learners find new materials and learning pathways by identifying semantic relationships between educational resources in a social learning network. This helps users--both learners and educators--to explore and find material to support their learning aims.…

  10. Learning Styles and Student Attitudes toward Various Aspects of Network-based Instruction.

    Science.gov (United States)

    Federico, Pat-Anthony

    2000-01-01

    Describes a study conducted at the Naval Postgraduate School to determine student attitudes toward various aspects of network-based instruction. Discusses Internet technology; Web-based education; online learning; learning styles; and results from Kolb's Learning Style Inventory, the Hidden Figures Test, and a number of multivariate procedures.…

  11. Service-Learning Project in a First-Year Seminar: A Social Network Analysis

    Science.gov (United States)

    Teymuroglu, Zeynep

    2013-01-01

    Understanding the effects of a service-learning component on the classroom culture, socially and academically, brings a novel perspective to designing, executing, and assessing these types of active-learning projects. This paper evaluates the success of a service-learning project from a perspective of social networks by investigating the question:…

  12. Let's Face(book) It: Analyzing Interactions in Social Network Groups for Chemistry Learning

    Science.gov (United States)

    Rap, Shelley; Blonder, Ron

    2016-01-01

    We examined how social network (SN) groups contribute to the learning of chemistry. The main goal was to determine whether chemistry learning could occur in the group discourse. The emphasis was on groups of students in the 11th and 12th grades who learn chemistry in preparation for their final external examination. A total of 1118 discourse…

  13. Paradoxes of Social Networking in a Structured Web 2.0 Language Learning Community

    Science.gov (United States)

    Loiseau, Mathieu; Zourou, Katerina

    2012-01-01

    This paper critically inquires into social networking as a set of mechanisms and associated practices developed in a structured Web 2.0 language learning community. This type of community can be roughly described as learning spaces featuring (more or less) structured language learning resources displaying at least some notions of language learning…

  14. Service-Learning Project in a First-Year Seminar: A Social Network Analysis

    Science.gov (United States)

    Teymuroglu, Zeynep

    2013-01-01

    Understanding the effects of a service-learning component on the classroom culture, socially and academically, brings a novel perspective to designing, executing, and assessing these types of active-learning projects. This paper evaluates the success of a service-learning project from a perspective of social networks by investigating the question:…

  15. Finding and Reusing Learning Materials with Multimedia Similarity Search and Social Networks

    Science.gov (United States)

    Little, Suzanne; Ferguson, Rebecca; Ruger, Stefan

    2012-01-01

    The authors describe how content-based multimedia search technologies can be used to help learners find new materials and learning pathways by identifying semantic relationships between educational resources in a social learning network. This helps users--both learners and educators--to explore and find material to support their learning aims.…

  16. Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning

    Directory of Open Access Journals (Sweden)

    Ayoub Alsarhan

    2012-01-01

    Full Text Available In a cognitive wireless mesh network, licensed users (primary users, PUs may rent surplus spectrum to unlicensed users (secondary users, SUs for getting some revenue. For such spectrum sharing paradigm, maximizing the revenue is the key objective of the PUs while that of the SUs is to meet their requirements. These complex contradicting objectives are embedded in our reinforcement learning (RL model that is developed and implemented as shown in this paper. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. RL is used to extract the optimal control policy that maximizes the PUs’ profit continuously over time. The extracted policy is used by PUs to manage renting the spectrum to SUs and it helps PUs to adapt to the changing network conditions. Performance evaluation of the proposed spectrum trading approach shows that it is able to find the optimal size and price of spectrum for each primary user under different conditions. Moreover, the approach constitutes a framework for studying, synthesizing and optimizing other schemes. Another contribution is proposing a new distributed algorithm to manage spectrum sharing among PUs. In our scheme, PUs exchange channels dynamically based on the availability of neighbor’s idle channels. In our cooperative scheme, the objective of spectrum sharing is to maximize the total revenue and utilize spectrum efficiently. Compared to the poverty-line heuristic that does not consider the availability of unused spectrum, our scheme has the advantage of utilizing spectrum efficiently.

  17. Direction-dependent learning approach for radial basis function networks.

    Science.gov (United States)

    Singla, Puneet; Subbarao, Kamesh; Junkins, John L

    2007-01-01

    Direction-dependent scaling, shaping, and rotation of Gaussian basis functions are introduced for maximal trend sensing with minimal parameter representations for input output approximation. It is shown that shaping and rotation of the radial basis functions helps in reducing the total number of function units required to approximate any given input-output data, while improving accuracy. Several alternate formulations that enforce minimal parameterization of the most general radial basis functions are presented. A novel "directed graph" based algorithm is introduced to facilitate intelligent direction based learning and adaptation of the parameters appearing in the radial basis function network. Further, a parameter estimation algorithm is incorporated to establish starting estimates for the model parameters using multiple windows of the input-output data. The efficacy of direction-dependent shaping and rotation in function approximation is evaluated by modifying the minimal resource allocating network and considering different test examples. The examples are drawn from recent literature to benchmark the new algorithm versus existing methods.

  18. CosmoQuest Collaborative: Galvanizing a Dynamic Professional Learning Network

    Science.gov (United States)

    Cobb, Whitney; Bracey, Georgia; Buxner, Sanlyn; Gay, Pamela L.; Noel-Storr, Jacob; CosmoQuest Team

    2016-10-01

    The CosmoQuest Collaboration offers in-depth experiences to diverse audiences around the nation and the world through pioneering citizen science in a virtual research facility. An endeavor between universities, research institutes, and NASA centers, CosmoQuest brings together scientists, educators, researchers, programmers—and citizens of all ages—to explore and make sense of our solar system and beyond. Leveraging human networks to expand NASA science, scaffolded by an educational framework that inspires lifelong learners, CosmoQuest engages citizens in analyzing and interpreting real NASA data, inspiring questions and defining problems.The QuestionLinda Darling-Hammond calls for professional development to be: "focused on the learning and teaching of specific curriculum content [i.e. NGSS disciplinary core ideas]; organized around real problems of practice [i.e. NGSS science and engineering practices] … [and] connected to teachers' collaborative work in professional learning community...." (2012) In light of that, what is the unique role CosmoQuest's virtual research facility can offer NASA STEM education?A Few AnswersThe CosmoQuest Collaboration actively engages scientists in education, and educators (and learners) in science. CosmoQuest uses social channels to empower and expand NASA's learning community through a variety of media, including science and education-focused hangouts, virtual star parties, and social media. In addition to creating its own supportive, standards-aligned materials, CosmoQuest offers a hub for excellent resources and materials throughout NASA and the larger astronomy community.In support of CosmoQuest citizen science opportunities, CQ initiatives (Learning Space, S-ROSES, IDEASS, Educator Zone) will be leveraged and shared through the CQPLN. CosmoQuest can be present and alive in the awareness its growing learning community.Finally, to make the CosmoQuest PLN truly relevant, it aims to encourage partnerships between scientists

  19. Data Generators for Learning Systems Based on RBF Networks.

    Science.gov (United States)

    Robnik-Sikonja, Marko

    2016-05-01

    There are plenty of problems where the data available is scarce and expensive. We propose a generator of semiartificial data with similar properties to the original data, which enables the development and testing of different data mining algorithms and the optimization of their parameters. The generated data allow large-scale experimentation and simulations without danger of overfitting. The proposed generator is based on radial basis function networks, which learn sets of Gaussian kernels. These Gaussian kernels can be used in a generative mode to generate new data from the same distributions. To assess the quality of the generated data, we evaluated the statistical properties of the generated data, structural similarity, and predictive similarity using supervised and unsupervised learning techniques. To determine usability of the proposed generator we conducted a large scale evaluation using 51 data sets. The results show a considerable similarity between the original and generated data and indicate that the method can be useful in several development and simulation scenarios. We analyze possible improvements in the classification performance by adding different amounts of the generated data to the training set, performance on high-dimensional data sets, and conditions when the proposed approach is successful.

  20. Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

    Science.gov (United States)

    Pan, Yongping; Yu, Haoyong

    2017-06-01

    This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.