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Sample records for networks redundancy learning

  1. Anti-pairing in learning of a neural network with redundant hidden units

    International Nuclear Information System (INIS)

    Kwon, Chulan; Kim, Hyong Kyun

    2005-01-01

    We study the statistical mechanics of learning from examples between the two-layered committee machines with different numbers of hidden units using the replica theory. The number M of hidden units of the student network is larger than the number M T of those of the target network called the teacher. We choose the networks to have binary synaptic weights, ±1, which makes it possible to compare the calculation with the Monte Carlo simulation. We propose an effective teacher as a virtual target network which has the same M hidden units as the student and gives identical outputs with those of the original teacher. This is a way of making a conjecture for a ground state of a thermodynamic system, given by the weights of the effective teacher in our study. We suppose that the weights on M T hidden units of the effective teacher are the same as those of the original teacher while those on M - M T redundant hidden units are composed of anti-pairs, {1, - 1}, with probability 1 - p in the limit p → 0. For p = 0 exact, there are no terms related to the effective teacher in the calculation, for the contributions of anti-pairs to outputs are exactly cancelled. In the limit p → 0, however, we find that the learnt weights of the student are actually equivalent to those of the suggested effective teacher, which is not possible from the calculation for p = 0. p plays the role of a symmetry breaking parameter for anti-pairing ordering, which is analogous to the magnetic field for the Ising model. A first-order phase transition is found to be signalled by breaking of symmetry in permuting hidden units. Above a critical number of examples, the student is shown to learn perfectly the effective teacher. Anti-pairing can be measured by a set of order parameters; zero in the permutation-symmetric phase and nonzero in the permutation symmetry breaking phase. Results from the Monte Carlo simulation are shown to be in good agreement with those from the replica calculation

  2. Self-Healing Networks: Redundancy and Structure

    Science.gov (United States)

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-01-01

    We introduce the concept of self-healing in the field of complex networks modelling; in particular, self-healing capabilities are implemented through distributed communication protocols that exploit redundant links to recover the connectivity of the system. We then analyze the effect of the level of redundancy on the resilience to multiple failures; in particular, we measure the fraction of nodes still served for increasing levels of network damages. Finally, we study the effects of redundancy under different connectivity patterns—from planar grids, to small-world, up to scale-free networks—on healing performances. Small-world topologies show that introducing some long-range connections in planar grids greatly enhances the resilience to multiple failures with performances comparable to the case of the most resilient (and least realistic) scale-free structures. Obvious applications of self-healing are in the important field of infrastructural networks like gas, power, water, oil distribution systems. PMID:24533065

  3. Designing Broadband Access Networks with Triple Redundancy

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Riaz, Muhammad Tahir; Knudsen, Thomas Phillip

    2005-01-01

    An architecture is proposed for designing broadband access networks, which offer triple redundancy to the end users, resulting in networks providing connectivity even in case of any two independent node or line failures. Two physically independent connections are offered by fiber, and the last...... provided by some wireless solution. Based on experience with planning Fiber To The Home, the architecture is designed to meet a number of demands, making it practicable and useful in realworld network planning. The proposed wired topology is planar, and suitable for being fitted onto the road network...

  4. Prioritising Redundant Network Component for HOWBAN Survivability Using FMEA

    Directory of Open Access Journals (Sweden)

    Cheong Loong Chan

    2017-01-01

    Full Text Available Deploying redundant component is the ubiquitous approach to improve the reliability and survivability of a hybrid optical wireless broadband access network (HOWBAN. Much work has been done to study the cost and impact of deploying redundant component in the network but no formal tools have been used to enable the evaluation and decision to prioritise the deployment of redundant facilities in the network. In this paper we show how FMEA (Failure Mode Effect and Analysis technique can be adapted to identify the critical segment in the network and prioritise the redundant component to be deployed to ensure network survivability. Our result showed that priority must be given to redundancy to mitigate grid power outage particularly in less developed countries which is poised for rapid expansion in broadband services.

  5. Network Gateway Technology: The Issue of Redundancy towards ...

    African Journals Online (AJOL)

    The Internet has provided advancement in the areas of network and networking facilities. Everyone connected to the Internet is concerned about two basic things: the availability of network services and the speed of the network. Network gateway redundancy technology falls within these categories and happens to be one of ...

  6. 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

  7. Optimization of robustness of interdependent network controllability by redundant design.

    Directory of Open Access Journals (Sweden)

    Zenghu Zhang

    Full Text Available Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy or DBS (degree based strategy for node backup and HDF(high degree first for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability.

  8. Maximization of learning speed in the motor cortex due to neuronal redundancy.

    Directory of Open Access Journals (Sweden)

    Ken Takiyama

    2012-01-01

    Full Text Available Many redundancies play functional roles in motor control and motor learning. For example, kinematic and muscle redundancies contribute to stabilizing posture and impedance control, respectively. Another redundancy is the number of neurons themselves; there are overwhelmingly more neurons than muscles, and many combinations of neural activation can generate identical muscle activity. The functional roles of this neuronal redundancy remains unknown. Analysis of a redundant neural network model makes it possible to investigate these functional roles while varying the number of model neurons and holding constant the number of output units. Our analysis reveals that learning speed reaches its maximum value if and only if the model includes sufficient neuronal redundancy. This analytical result does not depend on whether the distribution of the preferred direction is uniform or a skewed bimodal, both of which have been reported in neurophysiological studies. Neuronal redundancy maximizes learning speed, even if the neural network model includes recurrent connections, a nonlinear activation function, or nonlinear muscle units. Furthermore, our results do not rely on the shape of the generalization function. The results of this study suggest that one of the functional roles of neuronal redundancy is to maximize learning speed.

  9. Predicting genome-wide redundancy using machine learning

    Directory of Open Access Journals (Sweden)

    Shasha Dennis E

    2010-11-01

    Full Text Available Abstract Background Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here. Results Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1, suggesting that redundancy is stable over long evolutionary periods. Conclusions Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.

  10. Network Gateway Technology: The Issue of Redundancy towards ...

    African Journals Online (AJOL)

    Everyone connected to the Internet is concerned about two basic things: the availability of network services and the speed of the network. Network gateway redundancy technology falls within these categories and happens to be one of the newest technologies which only few companies, such as mobile companies and ...

  11. Coherent network detection of gravitational waves: the redundancy veto

    International Nuclear Information System (INIS)

    Wen Linqing; Schutz, Bernard F

    2005-01-01

    A network of gravitational wave detectors is called redundant if, given the direction to a source, the strain induced by a gravitational wave in one or more of the detectors can be fully expressed in terms of the strain induced in others in the network. Because gravitational waves have only two polarizations, any network of three or more differently oriented interferometers with similar observing bands is redundant. The three-armed LISA space interferometer has three outputs that are redundant at low frequencies. The two aligned LIGO interferometers at Hanford WA are redundant, and the LIGO detector at Livingston LA is nearly redundant with either of the Hanford detectors. Redundant networks have a powerful veto against spurious noise, a linear combination of the detector outputs that contains no gravitational wave signal. For LISA, this 'null' output is known as the Sagnac mode, and its use in discriminating between detector noise and a cosmological gravitational wave background is well understood. But the usefulness of the null veto for ground-based detector networks has been ignored until now. We show that it should make it possible to discriminate in a model-independent way between real gravitational waves and accidentally coincident non-Gaussian noise 'events' in redundant networks of two or more broadband detectors. It has been shown that with three detectors, the null output can even be used to locate the direction to the source, and then two other linear combinations of detector outputs give the optimal 'coherent' reconstruction of the two polarization components of the signal. We discuss briefly the implementation of such a detection strategy in realistic networks, where signals are weak, detector calibration is a significant uncertainty, and the various detectors may have different (but overlapping) observing bands

  12. On Planning of FTTH Access Networks with and without Redundancy

    DEFF Research Database (Denmark)

    Riaz, M. Tahir; Haraldsson, Gustav; Gutierrez Lopez, Jose Manuel

    2010-01-01

    This paper presents a planning analysis of FTTH access network with and without redundancy. Traditionally, access networks are planned only without redundancy, which is mainly due to lowering the cost of deployment. As fiber optics provide a huge amount of capacity, more and more services are being...... offered on a single fiber connection. As a single point of failure in fiber connection can cause multiple service deprivation therefore redundancy is very crucial. In this work, an automated planning model was used to test different scenarios of implementation. A cost estimation is presented in terms...... of digging and amount of fiber used. Three topologies, including the traditional one “tree topology”, were test with combination of various passive optical technologies....

  13. Probability Model for Data Redundancy Detection in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Suman Kumar

    2009-01-01

    Full Text Available Sensor networks are made of autonomous devices that are able to collect, store, process and share data with other devices. Large sensor networks are often redundant in the sense that the measurements of some nodes can be substituted by other nodes with a certain degree of confidence. This spatial correlation results in wastage of link bandwidth and energy. In this paper, a model for two associated Poisson processes, through which sensors are distributed in a plane, is derived. A probability condition is established for data redundancy among closely located sensor nodes. The model generates a spatial bivariate Poisson process whose parameters depend on the parameters of the two individual Poisson processes and on the distance between the associated points. The proposed model helps in building efficient algorithms for data dissemination in the sensor network. A numerical example is provided investigating the advantage of this model.

  14. Information theory and artificial grammar learning: inferring grammaticality from redundancy.

    Science.gov (United States)

    Jamieson, Randall K; Nevzorova, Uliana; Lee, Graham; Mewhort, D J K

    2016-03-01

    In artificial grammar learning experiments, participants study strings of letters constructed using a grammar and then sort novel grammatical test exemplars from novel ungrammatical ones. The ability to distinguish grammatical from ungrammatical strings is often taken as evidence that the participants have induced the rules of the grammar. We show that judgements of grammaticality are predicted by the local redundancy of the test strings, not by grammaticality itself. The prediction holds in a transfer test in which test strings involve different letters than the training strings. Local redundancy is usually confounded with grammaticality in stimuli widely used in the literature. The confounding explains why the ability to distinguish grammatical from ungrammatical strings has popularized the idea that participants have induced the rules of the grammar, when they have not. We discuss the judgement of grammaticality task in terms of attribute substitution and pattern goodness. When asked to judge grammaticality (an inaccessible attribute), participants answer an easier question about pattern goodness (an accessible attribute).

  15. Exploration of joint redundancy but not task space variability facilitates supervised motor learning.

    Science.gov (United States)

    Singh, Puneet; Jana, Sumitash; Ghosal, Ashitava; Murthy, Aditya

    2016-12-13

    The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.

  16. Learners misperceive the benefits of redundant text in multimedia learning.

    Science.gov (United States)

    Fenesi, Barbara; Kim, Joseph A

    2014-01-01

    Research on metacognition has consistently demonstrated that learners fail to endorse instructional designs that produce benefits to memory, and often prefer designs that actually impair comprehension. Unlike previous studies in which learners were only exposed to a single multimedia design, the current study used a within-subjects approach to examine whether exposure to both redundant text and non-redundant text multimedia presentations improved learners' metacognitive judgments about presentation styles that promote better understanding. A redundant text multimedia presentation containing narration paired with verbatim on-screen text (Redundant) was contrasted with two non-redundant text multimedia presentations: (1) narration paired with images and minimal text (Complementary) or (2) narration paired with minimal text (Sparse). Learners watched presentation pairs of either Redundant + Complementary, or Redundant + Sparse. Results demonstrate that Complementary and Sparse presentations produced highest overall performance on the final comprehension assessment, but the Redundant presentation produced highest perceived understanding and engagement ratings. These findings suggest that learners misperceive the benefits of redundant text, even after direct exposure to a non-redundant, effective presentation.

  17. Ethernet redundancy

    Energy Technology Data Exchange (ETDEWEB)

    Burak, K. [Invensys Process Systems, M/S C42-2B, 33 Commercial Street, Foxboro, MA 02035 (United States)

    2006-07-01

    We describe the Ethernet systems and their evolution: LAN Segmentation, DUAL networks, network loops, network redundancy and redundant network access. Ethernet (IEEE 802.3) is an open standard with no licensing fees and its specifications are freely available. As a result, it is the most popular data link protocol in use. It is important that the network be redundant and standard Ethernet protocols like RSTP (IEEE 802.1w) provide the fast network fault detection and recovery times that is required today. As Ethernet does continue to evolve, network redundancy is and will be a mixture of technology standards. So it is very important that both end-stations and networking devices be Ethernet (IEEE 802.3) compliant. Then when new technologies, such as the IEEE 802.1aq Shortest Path Bridging protocol, come to market they can be easily deployed in the network without worry.

  18. Ethernet redundancy

    International Nuclear Information System (INIS)

    Burak, K.

    2006-01-01

    We describe the Ethernet systems and their evolution: LAN Segmentation, DUAL networks, network loops, network redundancy and redundant network access. Ethernet (IEEE 802.3) is an open standard with no licensing fees and its specifications are freely available. As a result, it is the most popular data link protocol in use. It is important that the network be redundant and standard Ethernet protocols like RSTP (IEEE 802.1w) provide the fast network fault detection and recovery times that is required today. As Ethernet does continue to evolve, network redundancy is and will be a mixture of technology standards. So it is very important that both end-stations and networking devices be Ethernet (IEEE 802.3) compliant. Then when new technologies, such as the IEEE 802.1aq Shortest Path Bridging protocol, come to market they can be easily deployed in the network without worry

  19. A new way to improve the robustness of complex communication networks by allocating redundancy links

    International Nuclear Information System (INIS)

    Shi Chunhui; Zhuo Yue; Tang Jieying; Long Keping; Peng Yunfeng

    2012-01-01

    We investigate the robustness of complex communication networks on allocating redundancy links. The protecting key nodes (PKN) strategy is proposed to improve the robustness of complex communication networks against intentional attack. Our numerical simulations show that allocating a few redundant links among key nodes using the PKN strategy will significantly increase the robustness of scale-free complex networks. We have also theoretically proved and demonstrated the effectiveness of the PKN strategy. We expect that our work will help achieve a better understanding of communication networks. (paper)

  20. Risk-based replacement strategies for redundant deteriorating reinforced concrete pipe networks

    International Nuclear Information System (INIS)

    Adey, B.; Bernard, O.; Gerard, B.

    2003-01-01

    This paper gives an example of how predictive models of the deterioration of reinforced concrete pipes and the consequences of failure can be used to develop risk-based replacement strategies for redundant reinforced concrete pipe networks. It also shows how an accurate deterioration prediction can lead to a reduction of agency costs, and illustrates the limitation of the incremental intervention step algorithm. The main conclusion is that the use of predictive models, such as those developed by Oxand S.A., in the determination of replacement strategies for redundant reinforced concrete pipe networks can lead to a significant reduction in overall costs for the owner of the structure. (author)

  1. The effects of redundancy and information manipulation on traffic networks

    OpenAIRE

    Özel, Berk; Ozel, Berk

    2014-01-01

    Traffic congestion is one of the most frequently encountered problems in real life. It is not only a scientific concern of scholars, but also an inevitable issue for most of the individuals living in urban areas. Since every driver in traffic networks tries to minimize own journey length, and volume of the traffic prevents coordination between individuals, a cooperative behavior will not be provided spontaneously in order to decrease the total cost of the network and the time spent on traffic...

  2. Converging Redundant Sensor Network Information for Improved Building Control

    Energy Technology Data Exchange (ETDEWEB)

    Dale Tiller; D. Phil; Gregor Henze; Xin Guo

    2007-09-30

    This project investigated the development and application of sensor networks to enhance building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, but current sensor technology and control algorithms limit the effectiveness of these systems. For example, most of these systems rely on single monitoring points to detect occupancy, when more than one monitoring point could improve system performance. Phase I of the project focused on instrumentation and data collection. During the initial project phase, a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Phase II of the project demonstrated that a network of several sensors provides a more accurate measure of occupancy than is possible using systems based on single monitoring points. This phase also established that analysis algorithms could be applied to the sensor network data stream to improve the accuracy of system performance in energy management and security applications. In Phase III of the project, the sensor network from Phase I was complemented by a control strategy developed based on the results from the first two project phases: this controller was implemented in a small sample of work areas, and applied to lighting control. Two additional technologies were developed in the course of completing the project. A prototype web-based display that portrays the current status of each detector in a sensor network monitoring building occupancy was designed and implemented. A new capability that enables occupancy sensors in a sensor network to dynamically set the 'time delay' interval based on ongoing occupant behavior in the space was also designed and implemented.

  3. Redundancy reduction in wireless sensor networks via centrality metrics

    NARCIS (Netherlands)

    Mocanu, D.C.; Torres Vega, M.; Liotta, A.; Cui, P.; Aggarwal, C.; Zhou, Z.-H.; Tuzhilin, A.; Xiong, H.; Wu, X.

    2015-01-01

    The advances in wireless communications, together with the need of sensing and controlling various nature or human made systems in a large number of points (e.g. smart traffic control, environmental monitoring), lead to the emergence of Wireless Sensor Networks (WSN) as a powerful tool to fulfill

  4. A Dynamic Linear Hashing Method for Redundancy Management in Train Ethernet Consist Network

    Directory of Open Access Journals (Sweden)

    Xiaobo Nie

    2016-01-01

    Full Text Available Massive transportation systems like trains are considered critical systems because they use the communication network to control essential subsystems on board. Critical system requires zero recovery time when a failure occurs in a communication network. The newly published IEC62439-3 defines the high-availability seamless redundancy protocol, which fulfills this requirement and ensures no frame loss in the presence of an error. This paper adopts these for train Ethernet consist network. The challenge is management of the circulating frames, capable of dealing with real-time processing requirements, fast switching times, high throughout, and deterministic behavior. The main contribution of this paper is the in-depth analysis it makes of network parameters imposed by the application of the protocols to train control and monitoring system (TCMS and the redundant circulating frames discarding method based on a dynamic linear hashing, using the fastest method in order to resolve all the issues that are dealt with.

  5. CONVERGING REDUNDANT SENSOR NETWORK INFORMATION FOR IMPROVED BUILDING CONTROL

    Energy Technology Data Exchange (ETDEWEB)

    Dale K. Tiller; Gregor P. Henze

    2004-11-01

    Knowing how many people occupy a building, and where they are located, is a key component of building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, however, current sensor technology and control algorithms limit the effectiveness of both energy management and security systems. This topical report describes results from the first phase of a project to design, implement, validate, and prototype new technologies to monitor occupancy, control indoor environment services, and promote security in buildings. Phase I of the project focused on instrumentation and data collection. In this project phase a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Analysis tools based on Bayesian probability theory were applied to the occupancy data generated by the sensor network. The inference of primary importance is a probability distribution over the number of occupants and their locations in a building, given past and present sensor measurements. Inferences were computed for occupancy and its temporal persistence in individual offices as well as the persistence of sensor status. The raw sensor data were also used to calibrate the sensor belief network, including the occupancy transition matrix used in the Markov model, sensor sensitivity, and sensor failure models. This study shows that the belief network framework can be applied to the analysis of data streams from sensor networks, offering significant benefits to building operation compared to current practice.

  6. Learning Networks for Lifelong Learning

    OpenAIRE

    Sloep, Peter

    2009-01-01

    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.

  7. Synergy and redundancy in the Granger causal analysis of dynamical networks

    International Nuclear Information System (INIS)

    Stramaglia, Sebastiano; M Cortes, Jesus; Marinazzo, Daniele

    2014-01-01

    We analyze, by means of Granger causality (GC), the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. While we show that fully conditioned GC (CGC) is not affected by synergy, the pairwise analysis fails to prove synergetic effects. In cases when the number of samples is low, thus making the fully conditioned approach unfeasible, we show that partially conditioned GC (PCGC) is an effective approach if the set of conditioning variables is properly chosen. Here we consider two different strategies (based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences) for PCGC and show that, depending on the data structure, either one or the other might be equally valid. On the other hand, we observe that fully conditioned approaches do not work well in the presence of redundancy, thus suggesting the strategy of separating the pairwise links in two subsets: those corresponding to indirect connections of the CGC (which should thus be excluded) and links that can be ascribed to redundancy effects and, together with the results from the fully connected approach, provide a better description of the causality pattern in the presence of redundancy. Finally we apply these methods to two different real datasets. First, analyzing electrophysiological data from an epileptic brain, we show that synergetic effects are dominant just before seizure occurrences. Second, our analysis applied to gene expression time series from HeLa culture shows that the underlying regulatory networks are characterized by both redundancy and synergy. (paper)

  8. Quantifying the value of redundant measurements at GCOS Reference Upper-Air Network sites

    Directory of Open Access Journals (Sweden)

    F. Madonna

    2014-11-01

    Full Text Available The potential for measurement redundancy to reduce uncertainty in atmospheric variables has not been investigated comprehensively for climate observations. We evaluated the usefulness of entropy and mutual correlation concepts, as defined in information theory, for quantifying random uncertainty and redundancy in time series of the integrated water vapour (IWV and water vapour mixing ratio profiles provided by five highly instrumented GRUAN (GCOS, Global Climate Observing System, Reference Upper-Air Network stations in 2010–2012. Results show that the random uncertainties on the IWV measured with radiosondes, global positioning system, microwave and infrared radiometers, and Raman lidar measurements differed by less than 8%. Comparisons of time series of IWV content from ground-based remote sensing instruments with in situ soundings showed that microwave radiometers have the highest redundancy with the IWV time series measured by radiosondes and therefore the highest potential to reduce the random uncertainty of the radiosondes time series. Moreover, the random uncertainty of a time series from one instrument can be reduced by ~ 60% by constraining the measurements with those from another instrument. The best reduction of random uncertainty is achieved by conditioning Raman lidar measurements with microwave radiometer measurements. Specific instruments are recommended for atmospheric water vapour measurements at GRUAN sites. This approach can be applied to the study of redundant measurements for other climate variables.

  9. Networked professional learning

    NARCIS (Netherlands)

    Sloep, Peter

    2013-01-01

    Sloep, P. B. (2013). Networked professional learning. In A. Littlejohn, & A. Margaryan (Eds.), Technology-enhanced Professional Learning: Processes, Practices and Tools (pp. 97–108). London: Routledge.

  10. A Review of Multimedia Learning Principles: Split-Attention, Modality, and Redundancy Effects

    Directory of Open Access Journals (Sweden)

    Pelin YUKSEL ARSLAN

    2012-02-01

    Full Text Available This study aims to present a literature review on three principles of multimedia learningincluding split attention, modality, and redundancy effects with regard to their contribution to cognitiveload theory. According to cognitive load theory, information should be presented by considering excessiveload on working memory. The first principle states that attending to two distinct sources of informationmay impose a high cognitive load, and this process is referred to as the split-attention effect (Kalyuga,Chandler & Sweller, 1991, 1992. The second principle, Modality effect claims that on-screen text shouldbe presented in an auditory form instead of visually when designing a multimedia environment (Moreno &Mayer, 1999. Using more than one sensory mode augments forceful working memory that producesprogressive effects on learning. The third principle redundancy effect occurs when information presentedrepeatedly interferes with learning. This study provides guidance how to create more effective instructionwith multimedia materials for instructional designers.

  11. Learning contrast-invariant cancellation of redundant signals in neural systems.

    Directory of Open Access Journals (Sweden)

    Jorge F Mejias

    Full Text Available Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish.

  12. Minimizing Redundant Messages and Improving Search Efficiency under Highly Dynamic Mobile P2P Network

    Directory of Open Access Journals (Sweden)

    Ajay Arunachalam

    2016-02-01

    Full Text Available Resource Searching is one of the key functional tasks in large complex networks. With the P2P architecture, millions of peers connect together instantly building a communication pattern. Searching in mobile networks faces additional limitations and challenges. Flooding technique can cope up with the churn and searches aggressively by visiting almost all the nodes. But it exponentially increases the network traffic and thus does not scale well. Further the duplicated query messages consume extra battery power and network bandwidth. The blind flooding also suffers from long delay problem in P2P networks. In this paper, we propose optimal density based flooding resource discovery schemes. Our first model takes into account local graph topology information to supplement the resource discovery process while in our extended version we also consider the neighboring node topology information along with the local node information to further effectively use the mobile and network resources. Our proposed method reduces collision at the same time minimizes effect of redundant messages and failures. Overall the methods reduce network overhead, battery power consumption, query delay, routing load, MAC load and bandwidth usage while also achieving good success rate in comparison to the other techniques. We also perform a comprehensive analysis of the resource discovery schemes to verify the impact of varying node speed and different network conditions.

  13. Learning Networks Distributed Environment

    NARCIS (Netherlands)

    Martens, Harrie; Vogten, Hubert; Koper, Rob; Tattersall, Colin; Van Rosmalen, Peter; Sloep, Peter; Van Bruggen, Jan; Spoelstra, Howard

    2005-01-01

    Learning Networks Distributed Environment is a prototype of an architecture that allows the sharing and modification of learning materials through a number of transport protocols. The prototype implements a p2p protcol using JXTA.

  14. Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

    Directory of Open Access Journals (Sweden)

    Arthur Brady

    Full Text Available As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all. We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

  15. Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

    Science.gov (United States)

    Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J

    2009-01-01

    As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

  16. Remote Autonomous Sensor Networks: A Study in Redundancy and Life Cycle Costs

    Science.gov (United States)

    Ahlrichs, M.; Dotson, A.; Cenek, M.

    2017-12-01

    The remote nature of the United States and Canada border and their extreme seasonal shifts has made monitoring much of the area impossible using conventional monitoring techniques. Currently, the United States has large gaps in its ability to detect movement on an as-needed-basis in remote areas. The proposed autonomous sensor network aims to meet that need by developing a product that is low cost, robust, and can be deployed on an as-needed-basis for short term monitoring events. This is accomplished by identifying radio frequency disturbance and acoustic disturbance. This project aims to validate the proposed design and offer optimization strategies by conducting a redundancy model as well as performing a Life Cycle Assessment (LCA). The model will incorporate topological, meteorological, and land cover datasets to estimate sensor loss over a three-month period, ensuring that the remaining network does not have significant gaps in coverage which preclude being able to receive and transmit data. The LCA will investigate the materials used to create the sensor to generate an estimate of the total environmental energy that is utilized to create the network and offer alternative materials and distribution methods that can lower this cost. This platform can function as a stand-alone monitoring network or provide additional spatial and temporal resolution to existing monitoring networks. This study aims to create the framework to determine if a sensor's design and distribution is appropriate for the target environment. The incorporation of a LCA will seek to answer if the data a proposed sensor network will collect outweighs the environmental damage that will result from its deployment. Furthermore, as the arctic continues to thaw and economic development grows, the methodology described in paper will function as a guidance document to ensure that future sensor networks have a minimal impact on these pristine areas.

  17. Different-Level Simultaneous Minimization Scheme for Fault Tolerance of Redundant Manipulator Aided with Discrete-Time Recurrent Neural Network.

    Science.gov (United States)

    Jin, Long; Liao, Bolin; Liu, Mei; Xiao, Lin; Guo, Dongsheng; Yan, Xiaogang

    2017-01-01

    By incorporating the physical constraints in joint space, a different-level simultaneous minimization scheme, which takes both the robot kinematics and robot dynamics into account, is presented and investigated for fault-tolerant motion planning of redundant manipulator in this paper. The scheme is reformulated as a quadratic program (QP) with equality and bound constraints, which is then solved by a discrete-time recurrent neural network. Simulative verifications based on a six-link planar redundant robot manipulator substantiate the efficacy and accuracy of the presented acceleration fault-tolerant scheme, the resultant QP and the corresponding discrete-time recurrent neural network.

  18. 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...... a configuration of the discrete parents. We assume parameter independence and complete data. Further, to learn the structure of the network, the network score is deduced. We then develop a local master prior procedure, for deriving parameter priors in these networks. This procedure satisfies parameter...... 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....

  19. Redundancy and cooperativity in the mechanics of compositely crosslinked filamentous networks.

    Directory of Open Access Journals (Sweden)

    Moumita Das

    Full Text Available The cytoskeleton of living cells contains many types of crosslinkers. Some crosslinkers allow energy-free rotations between filaments and others do not. The mechanical interplay between these different crosslinkers is an open issue in cytoskeletal mechanics. Therefore, we develop a theoretical framework based on rigidity percolation to study a generic filamentous system containing both stretching and bond-bending forces to address this issue. The framework involves both analytical calculations via effective medium theory and numerical simulations on a percolating triangular lattice with very good agreement between both. We find that the introduction of angle-constraining crosslinkers to a semiflexible filamentous network with freely rotating crosslinks can cooperatively lower the onset of rigidity to the connectivity percolation threshold-a result argued for years but never before obtained via effective medium theory. This allows the system to ultimately attain rigidity at the lowest concentration of material possible. We further demonstrate that introducing angle-constraining crosslinks results in mechanical behaviour similar to just freely rotating crosslinked semflexible filaments, indicating redundancy and universality. Our results also impact upon collagen and fibrin networks in biological and bio-engineered tissues.

  20. A Network of Local and Redundant Gene Regulation Governs Arabidopsis Seed Maturation

    Science.gov (United States)

    To, Alexandra; Valon, Christiane; Savino, Gil; Guilleminot, Jocelyne; Devic, Martine; Giraudat, Jérôme; Parcy, François

    2006-01-01

    In Arabidopsis thaliana, four major regulators (ABSCISIC ACID INSENSITIVE3 [ABI3], FUSCA3 [FUS3], LEAFY COTYLEDON1 [LEC1], and LEC2) control most aspects of seed maturation, such as accumulation of storage compounds, cotyledon identity, acquisition of desiccation tolerance, and dormancy. The molecular basis for complex genetic interactions among these regulators is poorly understood. By analyzing ABI3 and FUS3 expression in various single, double, and triple maturation mutants, we have identified multiple regulatory links among all four genes. We found that one of the major roles of LEC2 was to upregulate FUS3 and ABI3. The lec2 mutation is responsible for a dramatic decrease in ABI3 and FUS3 expression, and most lec2 phenotypes can be rescued by ABI3 or FUS3 constitutive expression. In addition, ABI3 and FUS3 positively regulate themselves and each other, thereby forming feedback loops essential for their sustained and uniform expression in the embryo. Finally, LEC1 also positively regulates ABI3 and FUS3 in the cotyledons. Most of the genetic controls discovered were found to be local and redundant, explaining why they had previously been overlooked. This works establishes a genetic framework for seed maturation, organizing the key regulators of this process into a hierarchical network. In addition, it offers a molecular explanation for the puzzling variable features of lec2 mutant embryos. PMID:16731585

  1. 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…

  2. 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...... networks, spaces of algorithmic governance and more. The "boundaries in networked learning" section investigates frameworks of students' digital literacy practices, among other important frameworks in digital learning. Lastly, the "research in networked learning" section delves into new research methods...

  3. Simulating GPS radio signal to synchronize network--a new technique for redundant timing.

    Science.gov (United States)

    Shan, Qingxiao; Jun, Yang; Le Floch, Jean-Michel; Fan, Yaohui; Ivanov, Eugene N; Tobar, Michael E

    2014-07-01

    Currently, many distributed systems such as 3G mobile communications and power systems are time synchronized with a Global Positioning System (GPS) signal. If there is a GPS failure, it is difficult to realize redundant timing, and thus time-synchronized devices may fail. In this work, we develop time transfer by simulating GPS signals, which promises no extra modification to original GPS-synchronized devices. This is achieved by applying a simplified GPS simulator for synchronization purposes only. Navigation data are calculated based on a pre-assigned time at a fixed position. Pseudo-range data which describes the distance change between the space vehicle (SV) and users are calculated. Because real-time simulation requires heavy-duty computations, we use self-developed software optimized on a PC to generate data, and save the data onto memory disks while the simulator is operating. The radio signal generation is similar to the SV at an initial position, and the frequency synthesis of the simulator is locked to a pre-assigned time. A filtering group technique is used to simulate the signal transmission delay corresponding to the SV displacement. Each SV generates a digital baseband signal, where a unique identifying code is added to the signal and up-converted to generate the output radio signal at the centered frequency of 1575.42 MHz (L1 band). A prototype with a field-programmable gate array (FPGA) has been built and experiments have been conducted to prove that we can realize time transfer. The prototype has been applied to the CDMA network for a three-month long experiment. Its precision has been verified and can meet the requirements of most telecommunication systems.

  4. 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.

  5. Southern California Seismic Network: New Design and Implementation of Redundant and Reliable Real-time Data Acquisition Systems

    Science.gov (United States)

    Saleh, T.; Rico, H.; Solanki, K.; Hauksson, E.; Friberg, P.

    2005-12-01

    The Southern California Seismic Network (SCSN) handles more than 2500 high-data rate channels from more than 380 seismic stations distributed across southern California. These data are imported real-time from dataloggers, earthworm hubs, and partner networks. The SCSN also exports data to eight different partner networks. Both the imported and exported data are critical for emergency response and scientific research. Previous data acquisition systems were complex and difficult to operate, because they grew in an ad hoc fashion to meet the increasing needs for distributing real-time waveform data. To maximize reliability and redundancy, we apply best practices methods from computer science for implementing the software and hardware configurations for import, export, and acquisition of real-time seismic data. Our approach makes use of failover software designs, methods for dividing labor diligently amongst the network nodes, and state of the art networking redundancy technologies. To facilitate maintenance and daily operations we seek to provide some separation between major functions such as data import, export, acquisition, archiving, real-time processing, and alarming. As an example, we make waveform import and export functions independent by operating them on separate servers. Similarly, two independent servers provide waveform export, allowing data recipients to implement their own redundancy. The data import is handled differently by using one primary server and a live backup server. These data import servers, run fail-over software that allows automatic role switching in case of failure from primary to shadow. Similar to the classic earthworm design, all the acquired waveform data are broadcast onto a private network, which allows multiple machines to acquire and process the data. As we separate data import and export away from acquisition, we are also working on new approaches to separate real-time processing and rapid reliable archiving of real-time data

  6. 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

  7. 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.

  8. Networks of Learning

    Science.gov (United States)

    Bettencourt, Luis; Kaiser, David

    2004-03-01

    Based on an a historically documented example of scientific discovery - Feynman diagrams as the main calculational tool of theoretical high energy Physics - we map the time evolution of the social network of early adopters through in the US, UK, Japan and the USSR. The spread of the technique for total number of users in each region is then modelled in terms of epidemic models, highlighting parallel and divergent aspects of this analogy. We also show that transient social arrangements develop as the idea is introduced and learned, which later disappear as the technique becomes common knowledge. Such early transient is characterized by abnormally low connectivity distribution powers and by high clustering. This interesting early non-equilibrium stage of network evolution is captured by a new dynamical model for network evolution, which coincides in its long time limit with familiar preferential aggregation dynamics.

  9. Pigeons learn stimulus identity and stimulus relations when both serve as redundant, relevant cues during same-different discrimination training.

    Science.gov (United States)

    Gibson, Brett M; Wasserman, Edward A

    2003-01-01

    The authors taught pigeons to discriminate displays of 16 identical items from displays of 16 nonidentical items. Unlike most same-different discrimination studies--where only stimulus relations could serve a discriminative function--both the identity of the items and the relations among the items were discriminative features of the displays. The pigeons learned about both stimulus identity and stimulus relations when these 2 sources of information served as redundant, relevant cues. In tests of associative competition, identity cues exerted greater stimulus control than relational cues. These results suggest that the pigeon can respond to both specific stimuli and general relations in the environment.

  10. Comparative efficacy and safety of different circumcisions for patients with redundant prepuce or phimosis: A network meta-analysis.

    Science.gov (United States)

    Huang, Chuiguo; Song, Pan; Xu, Changbao; Wang, Ruofan; Wei, Lei; Zhao, Xinghua

    2017-07-01

    Phimosis and redundant prepuce are defined as the inability of the foreskin to be retracted behind the glans penis in uncircumcised males. To synthesize the evidence and provide the hierarchies of different circumcisions for phimosis and redundant prepuce, we performed an overall network meta-analysis (NMA) based on their comparative efficacy and safety. Electronic databases including PubMed, Embase, Wan Fang, VIP, CNKI and CBM database were researched from randomized controlled trials (RCTs) for redundant prepuce or phimosis. We conducted the direct and indirect comparisons by aggregate data drug information system (ADDIS) software. Moreover, consistency models were applied to assess the differences among the male circumcision practices, and the ranks based on probabilities of intervention for the different endpoints were performed. Node-splitting analysis was used to test inconsistency. Eighteen RCTs were included with 6179 participants. Compared with the conventional circumcision(CC), two new styles of circumcisions, the disposable circumcision suture device(DCSD) and Shang Ring circumcision(SRC), provided significantly shorter operation time[DCSD: standardized mean difference (SMD) = -20.60, 95% credible interval(CI) (-23.38, -17.82); SRC: SMD = -19.16, 95%CI (-21.86, -16.52)], shorter wound healing time [DCSD:SMD = -4.19, 95%CI (-8.24,-0.04); SRC: SMD = 4.55, 95%CI (1.62, 7.57); ] and better postoperative penile appearance [DCSD: odds ratios odds ratios (OR) = 11.42, 95%CI (3.60, 37.68); SRC: OR = 3.85,95%CI (1.29, 12.79)]. Additionally, DCSD showed a lower adverse events rate than other two treatments. However, no significant difference was shown in all surgeries for 24 h postoperative pain score. Node-splitting analysis showed that no significant inconsistency was existed (P > 0.05). Based on the results of NMA, DCSD may be a most effective and safest choice for phimosis and redundant prepuce. DCSD has the advantages of a shorter operation

  11. 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

  12. 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...

  13. Entropy Learning in Neural Network

    Directory of Open Access Journals (Sweden)

    Geok See Ng

    2017-12-01

    Full Text Available In this paper, entropy term is used in the learning phase of a neural network.  As learning progresses, more hidden nodes get into saturation.  The early creation of such hidden nodes may impair generalisation.  Hence entropy approach is proposed to dampen the early creation of such nodes.  The entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes.  At the end of learning, the less important nodes can then be eliminated to reduce the memory requirements of the neural network.

  14. Using Selective Redundancy and Testing to Optimize Learning from Multimedia Lessons

    OpenAIRE

    Yue, Carole Leigh

    2014-01-01

    Multimedia learning refers to learning from a combination of words and images. In the present dissertation, a multimedia lesson is defined as an animated, narrated educational video that depicts a scientific process--a format of instructional material becoming increasingly common in online, hybrid, and traditional classrooms. The overarching goal of the present research was to investigate how to optimize learning from multimedia lessons using two related theories of multimedia learning (the...

  15. Contingent factors affecting network learning

    OpenAIRE

    Peters, Linda D.; Pressey, Andrew D.; Johnston, Wesley J.

    2016-01-01

    To increase understanding of the impact of individuals on organizational learning processes, this paper explores the impact of individual cognition and action on the absorptive capacity process of the wider network. In particular this study shows how contingent factors such as social integration mechanisms and power relationships influence how network members engage in, and benefit from, learning. The use of cognitive consistency and sensemaking theory enables examination of how these conting...

  16. 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...... 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...

  17. On the performance of hybrid ARQ with incremental redundancy over amplify-and-forward dual-hop relay networks

    KAUST Repository

    Hadjtaieb, Amir

    2014-09-01

    In this paper, we consider a three node relay network comprising a source, a relay, and a destination. The source transmits the message to the destination using hybrid automatic repeat request (HARQ) with incremental redundancy (IR). The relay overhears the transmitted message, amplifies it using a variable gain amplifier, and then forwards the message to the destination. This latter combines both the source and the relay message and tries to decode the information. In case of decoding failure, the destination sends a negative acknowledgement. A new replica of the message containing new parity bits is then transmitted in the subsequent HARQ round. This process continues until successful decoding occurs at the destination or a maximum number M of rounds is reached. We study the performance of HARQ-IR over the considered relay channel from an information theoretic perspective. We derive for instance exact expressions and bounds for the information outage probability, the average number of transmissions, and the average transmission rate. The derived exact expressions are validated by Monte Carlo simulations.

  18. Embryonic maturation of epidermal Merkel cells is controlled by a redundant transcription factor network.

    Science.gov (United States)

    Perdigoto, Carolina N; Bardot, Evan S; Valdes, Victor J; Santoriello, Francis J; Ezhkova, Elena

    2014-12-01

    Merkel cell-neurite complexes are located in touch-sensitive areas of the mammalian skin and are involved in recognition of the texture and shape of objects. Merkel cells are essential for these tactile discriminations, as they generate action potentials in response to touch stimuli and induce the firing of innervating afferent nerves. It has been shown that Merkel cells originate from epidermal stem cells, but the cellular and molecular mechanisms of their development are largely unknown. In this study, we analyzed Merkel cell differentiation during development and found that it is a temporally regulated maturation process characterized by a sequential activation of Merkel cell-specific genes. We uncovered key transcription factors controlling this process and showed that the transcription factor Atoh1 is required for initial Merkel cell specification. The subsequent maturation steps of Merkel cell differentiation are controlled by cooperative function of the transcription factors Sox2 and Isl1, which physically interact and work to sustain Atoh1 expression. These findings reveal the presence of a robust transcriptional network required to produce functional Merkel cells that are required for tactile discrimination. © 2014. Published by The Company of Biologists Ltd.

  19. Effects of Modality and Redundancy Principles on the Learning and Attitude of a Computer-Based Music Theory Lesson among Jordanian Primary Pupils

    Science.gov (United States)

    Aldalalah, Osamah Ahmad; Fong, Soon Fook

    2010-01-01

    The purpose of this study was to investigate the effects of modality and redundancy principles on the attitude and learning of music theory among primary pupils of different aptitudes in Jordan. The lesson of music theory was developed in three different modes, audio and image (AI), text with image (TI) and audio with image and text (AIT). The…

  20. Clustered Data Management in Virtual Docker Networks Spanning Geo-Redundant Data Centers : A Performance Evaluation Study of Docker Networking

    OpenAIRE

    Alansari, Hayder

    2017-01-01

    Software containers in general and Docker in particular is becoming more popular both in software development and deployment. Software containers are intended to be a lightweight virtualization that provides the isolation of virtual machines with a performance that is close to native. Docker does not only provide virtual isolation but also virtual networking to connect the isolated containers in the desired way. Many alternatives exist when it comes to the virtual networking provided by Docke...

  1. 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.

  2. 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.

  3. Effects of Redundancy and Modality on the Situational Interest of Adult Learners in Multimedia Learning

    Science.gov (United States)

    Dousay, Tonia A.

    2016-01-01

    This study investigated the effects of two design principles as prescribed by the cognitive theory of multimedia learning on the situational interest of adult learners in a multimedia-based continuing education training program. One hundred and two adult learners employed by an emergency medical service were randomly assigned to one of three…

  4. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

    Science.gov (United States)

    Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

    2017-10-06

    Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

  5. 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…

  6. Building and Sustaining Learning Networks.

    OpenAIRE

    Bessant, John; Barnes, Justin; Morris, Mike; Kaplinsky, Raphael

    2003-01-01

    Research suggests that there are a number of potential advantages to learning in some form of network which include being able to benefit from other’s experience, being able to reduce the risks in experimentation, being able to engage in challenging reflection and in making use of peer group support. Examples of such configurations can be found in regional clusters, in sector groupings, in heterogeneous groups sharing a common topic of interest, in user groups concerned with le...

  7. Adaptive competitive learning neural networks

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2013-11-01

    Full Text Available In this paper, the adaptive competitive learning (ACL neural network algorithm is proposed. This neural network not only groups similar input feature vectors together but also determines the appropriate number of groups of these vectors. This algorithm uses a new proposed criterion referred to as the ACL criterion. This criterion evaluates different clustering structures produced by the ACL neural network for an input data set. Then, it selects the best clustering structure and the corresponding network architecture for this data set. The selected structure is composed of the minimum number of clusters that are compact and balanced in their sizes. The selected network architecture is efficient, in terms of its complexity, as it contains the minimum number of neurons. Synaptic weight vectors of these neurons represent well-separated, compact and balanced clusters in the input data set. The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters. Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.

  8. Collective Learning in Games through Social Networks

    NARCIS (Netherlands)

    Kosterman, S.; Gierasimczuk, N.; Armentano, M.G.; Monteserin, A.; Tang, J.; Yannibelli, V.

    2015-01-01

    This paper argues that combining social networks communication and games can positively influence the learning behavior of players. We propose a computational model that combines features of social network learning (communication) and game-based learning (strategy reinforcement). The focus is on

  9. 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…

  10. Learning Bayesian networks for discrete data

    KAUST Repository

    Liang, Faming; Zhang, Jian

    2009-01-01

    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

  11. Identifying Gatekeepers in Online Learning Networks

    Science.gov (United States)

    Gursakal, Necmi; Bozkurt, Aras

    2017-01-01

    The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

  12. Networks and learning in game theory

    NARCIS (Netherlands)

    Kets, W.

    2008-01-01

    This work concentrates on two topics, networks and game theory, and learning in games. The first part of this thesis looks at network games and the role of incomplete information in such games. It is assumed that players are located on a network and interact with their neighbors in the network.

  13. The Effects of Training, Modality, and Redundancy on the Development of a Historical Inquiry Strategy in a Multimedia Learning Environment

    Science.gov (United States)

    McNeill, Andrea L.; Doolittle, Peter E.; Hicks, David

    2009-01-01

    The purpose of this study was to assess the effects of training, modality, and redundancy on the participants' ability to apply and recall a historical inquiry strategy. An experimental research design was utilized with presentation mode as the independent variable and strategy application and strategy recall as the dependent variables. The…

  14. Co-Operative Learning and Development Networks.

    Science.gov (United States)

    Hodgson, V.; McConnell, D.

    1995-01-01

    Discusses the theory, nature, and benefits of cooperative learning. Considers the Cooperative Learning and Development Network (CLDN) trial in the JITOL (Just in Time Open Learning) project and examines the relationship between theories about cooperative learning and the reality of a group of professionals participating in a virtual cooperative…

  15. 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

  16. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    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 ...

  17. 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 learned...

  18. Learning-parameter adjustment in neural networks

    Science.gov (United States)

    Heskes, Tom M.; Kappen, Bert

    1992-06-01

    We present a learning-parameter adjustment algorithm, valid for a large class of learning rules in neural-network literature. The algorithm follows directly from a consideration of the statistics of the weights in the network. The characteristic behavior of the algorithm is calculated, both in a fixed and a changing environment. A simple example, Widrow-Hoff learning for statistical classification, serves as an illustration.

  19. 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...

  20. 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.

  1. 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.

  2. 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.

  3. Quantitative learning strategies based on word networks

    Science.gov (United States)

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

    2018-02-01

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

  4. Learning in innovation networks: Some simulation experiments

    Science.gov (United States)

    Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas

    2007-05-01

    According to the organizational learning literature, the greatest competitive advantage a firm has is its ability to learn. In this paper, a framework for modeling learning competence in firms is presented to improve the understanding of managing innovation. Firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms. In trying to vary and/or to stabilize their knowledge stocks by organizational learning, they attempt to adapt to environmental requirements while the market strongly selects on the results. The simulation experiments show the impact of different learning activities, underlining the importance of innovation and learning.

  5. 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.

  6. 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.

  7. A Modular Approach to Redundant Robot Control

    International Nuclear Information System (INIS)

    Anderson, R.J.

    1997-12-01

    This paper describes a modular approach for computing redundant robot kinematics. First some conventional redundant control methods are presented and shown to be 'passive control laws', i.e. they can be represented by a network consisting of passive elements. These networks are then put into modular form by applying scattering operator techniques. Additional subnetwork modules can then be added to further shape the motion. Modules for obstacle detection, joint limit avoidance, proximity sensing, and for imposing nonlinear velocity constraints are presented. The resulting redundant robot control system is modular, flexible and robust

  8. Analytical Redundancy Design for Aeroengine Sensor Fault Diagnostics Based on SROS-ELM

    Directory of Open Access Journals (Sweden)

    Jun Zhou

    2016-01-01

    Full Text Available Analytical redundancy technique is of great importance to guarantee the reliability and safety of aircraft engine system. In this paper, a machine learning based aeroengine sensor analytical redundancy technique is developed and verified through hardware-in-the-loop (HIL simulation. The modified online sequential extreme learning machine, selective updating regularized online sequential extreme learning machine (SROS-ELM, is employed to train the model online and estimate sensor measurements. It selectively updates the output weights of neural networks according to the prediction accuracy and the norm of output weight vector, tackles the problems of singularity and ill-posedness by regularization, and adopts a dual activation function in the hidden nodes combing neural and wavelet theory to enhance prediction capability. The experimental results verify the good generalization performance of SROS-ELM and show that the developed analytical redundancy technique for aeroengine sensor fault diagnosis based on SROS-ELM is effective and feasible.

  9. Edmodo social learning network for elementary school mathematics learning

    Science.gov (United States)

    Ariani, Y.; Helsa, Y.; Ahmad, S.; Prahmana, RCI

    2017-12-01

    A developed instructional media can be as printed media, visual media, audio media, and multimedia. The development of instructional media can also take advantage of technological development by utilizing Edmodo social network. This research aims to develop a digital classroom learning model using Edmodo social learning network for elementary school mathematics learning which is practical, valid and effective in order to improve the quality of learning activities. The result of this research showed that the prototype of mathematics learning device for elementary school students using Edmodo was in good category. There were 72% of students passed the assessment as a result of Edmodo learning. Edmodo has become a promising way to engage students in a collaborative learning process.

  10. Learning and coding in biological neural networks

    Science.gov (United States)

    Fiete, Ila Rani

    How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and

  11. 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.

  12. Social Networking Sites and Language Learning

    Science.gov (United States)

    Brick, Billy

    2011-01-01

    This article examines a study of seven learners who logged their experiences on the language leaning social networking site Livemocha over a period of three months. The features of the site are described and the likelihood of their future success is considered. The learners were introduced to the Social Networking Site (SNS) and asked to learn a…

  13. Adaptive Learning in Weighted Network Games

    NARCIS (Netherlands)

    Bayer, Péter; Herings, P. Jean-Jacques; Peeters, Ronald; Thuijsman, Frank

    2017-01-01

    This paper studies adaptive learning in the class of weighted network games. This class of games includes applications like research and development within interlinked firms, crime within social networks, the economics of pollution, and defense expenditures within allied nations. We show that for

  14. 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

  15. Logic Learning in Hopfield Networks

    OpenAIRE

    Sathasivam, Saratha; Abdullah, Wan Ahmad Tajuddin Wan

    2008-01-01

    Synaptic weights for neurons in logic programming can be calculated either by using Hebbian learning or by Wan Abdullah's method. In other words, Hebbian learning for governing events corresponding to some respective program clauses is equivalent with learning using Wan Abdullah's method for the same respective program clauses. In this paper we will evaluate experimentally the equivalence between these two types of learning through computer simulations.

  16. A robust cloud registration method based on redundant data reduction using backpropagation neural network and shift window

    Science.gov (United States)

    Xin, Meiting; Li, Bing; Yan, Xiao; Chen, Lei; Wei, Xiang

    2018-02-01

    A robust coarse-to-fine registration method based on the backpropagation (BP) neural network and shift window technology is proposed in this study. Specifically, there are three steps: coarse alignment between the model data and measured data, data simplification based on the BP neural network and point reservation in the contour region of point clouds, and fine registration with the reweighted iterative closest point algorithm. In the process of rough alignment, the initial rotation matrix and the translation vector between the two datasets are obtained. After performing subsequent simplification operations, the number of points can be reduced greatly. Therefore, the time and space complexity of the accurate registration can be significantly reduced. The experimental results show that the proposed method improves the computational efficiency without loss of accuracy.

  17. Functionality for learning networks: lessons learned from social web applications

    NARCIS (Netherlands)

    Berlanga, Adriana; Sloep, Peter; Brouns, Francis; Van Rosmalen, Peter; Bitter-Rijpkema, Marlies; Koper, Rob

    2007-01-01

    Berlanga, A. J., Sloep, P., Brouns, F., Van Rosmalen, P., Bitter-Rijpkema, M., & Koper, R. (2007). Functionality for learning networks: lessons learned from social web applications. Proceedings of the ePortfolio 2007 Conference. October, 18-19, 2007, Maastricht, The Netherlands. [See also

  18. 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.

  19. Media Presentation Mode, English Listening Comprehension and Cognitive Load in Ubiquitous Learning Environments: Modality Effect or Redundancy Effect?

    Science.gov (United States)

    Chang, Chi-Cheng; Lei, Hao; Tseng, Ju-Shih

    2011-01-01

    Although ubiquitous learning enhances students' access to learning materials, it is crucial to find out which media presentation modes produce the best results for English listening comprehension. The present study examined the effect of media presentation mode (sound and text versus sound) on English listening comprehension and cognitive load.…

  20. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    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......Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... 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...

  1. Machine Learning Topological Invariants with Neural Networks

    Science.gov (United States)

    Zhang, Pengfei; Shen, Huitao; Zhai, Hui

    2018-02-01

    In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

  2. SUSTAIN: a network model of category learning.

    Science.gov (United States)

    Love, Bradley C; Medin, Douglas L; Gureckis, Todd M

    2004-04-01

    SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

  3. 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.

  4. 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 fra......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...

  5. 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…

  6. 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....

  7. The TENCompetence Infrastructure: A Learning Network Implementation

    Science.gov (United States)

    Vogten, Hubert; Martens, Harrie; Lemmers, Ruud

    The TENCompetence project developed a first release of a Learning Network infrastructure to support individuals, groups and organisations in professional competence development. This infrastructure Learning Network infrastructure was released as open source to the community thereby allowing users and organisations to use and contribute to this development as they see fit. The infrastructure consists of client applications providing the user experience and server components that provide the services to these clients. These services implement the domain model (Koper 2006) by provisioning the entities of the domain model (see also Sect. 18.4) and henceforth will be referenced as domain entity services.

  8. Social Networks: Rational Learning and Information Aggregation

    Science.gov (United States)

    2009-09-01

    predecessor, Gale and Kariv (2003) who generalize the payoff equalization result of Bala and Goyal (1998) in connected social networks (discussed below...requires more notation. Using Bayes’ Rule and the assumption of equal priors on the state θ, we have that the social belief given by observing... Social Networks: Rational Learning and Information Aggregation by Ilan Lobel B.Sc., Pontif́ıcia Universidade Católica do Rio de Janeiro (2004

  9. Learning Transferable Features with Deep Adaptation Networks

    OpenAIRE

    Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

    2015-01-01

    Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

  10. Redundancy Elimination in DTN via ACK Mechanism

    Directory of Open Access Journals (Sweden)

    Xiqing Zhang

    2015-08-01

    Full Text Available The traditional routing protocols for delay tolerant networks (DTN usually take the strategy of spreading multiple copies of one message to the networks. When one copy reaches destination, the transmission of other copies not only waste the bandwidth but also deprive other messages of the opportunities for transmission. This paper brings up a mechanism to eliminate the redundant copies. By adding an acknowledge field to the packet header to delete redundant copies, it can degrade the network overhead while improve the delivery ratio. Simulation results confirm that the proposed method can improve the performance of epidemic and Spray and Wait routing protocol.

  11. Design of a Networked Learning Master Environment for Professionals

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone

    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 pri......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...

  12. 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.

  13. 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.

  14. Reinforcement learning account of network reciprocity.

    Science.gov (United States)

    Ezaki, Takahiro; Masuda, Naoki

    2017-01-01

    Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model) approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.

  15. Reinforcement learning account of network reciprocity.

    Directory of Open Access Journals (Sweden)

    Takahiro Ezaki

    Full Text Available Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.

  16. Learning State Space Dynamics in Recurrent Networks

    Science.gov (United States)

    Simard, Patrice Yvon

    Fully recurrent (asymmetrical) networks can be used to learn temporal trajectories. The network is unfolded in time, and backpropagation is used to train the weights. The presence of recurrent connections creates internal states in the system which vary as a function of time. The resulting dynamics can provide interesting additional computing power but learning is made more difficult by the existence of internal memories. This study first exhibits the properties of recurrent networks in terms of convergence when the internal states of the system are unknown. A new energy functional is provided to change the weights of the units in order to the control the stability of the fixed points of the network's dynamics. The power of the resultant algorithm is illustrated with the simulation of a content addressable memory. Next, the more general case of time trajectories on a recurrent network is studied. An application is proposed in which trajectories are generated to draw letters as a function of an input. In another application of recurrent systems, a neural network certain temporal properties observed in human callosally sectioned brains. Finally the proposed algorithm for stabilizing dynamics around fixed points is extended to one for stabilizing dynamics around time trajectories. Its effects are illustrated on a network which generates Lisajous curves.

  17. Networked Learning in 70001 Programs.

    Science.gov (United States)

    Fine, Marija Futchs

    The 7000l Training and Employment Institute offers self-paced instruction through the use of computers and audiovisual materials to young people to improve opportunities for success in the work force. In 1988, four sites were equipped with Apple stand-alone software in an integrated learning system that included courses in reading and math, test…

  18. Evolving autonomous learning in cognitive networks.

    Science.gov (United States)

    Sheneman, Leigh; Hintze, Arend

    2017-12-01

    There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.

  19. 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…

  20. 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…

  1. Learning to trust : network effects through time.

    NARCIS (Netherlands)

    Barrera, D.; Bunt, G. van de

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  2. Learning to trust: network effects through time

    NARCIS (Netherlands)

    Barrera, D.; van de Bunt, G

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  3. 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

  4. 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

  5. Researching Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

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

    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...

  6. 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.

  7. Factors that influence cooperation in networks for innovation and learning

    NARCIS (Netherlands)

    Sie, Rory; Bitter-Rijpkema, Marlies; Stoyanov, Slavi; Sloep, Peter

    2018-01-01

    Networked cooperation fails if the available partnerships remain opaque. A literature review and Delphi study uncovered the elements of a fruitful partnership. They relate to personality, diversity, cooperation, and management. Innovation networks and learning networks share the same cooperative

  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. PARTNERS IN LEARNING NETWORK FOR UKRAINIAN TEACHERS

    Directory of Open Access Journals (Sweden)

    K. Sereda

    2011-05-01

    Full Text Available The network «Partners in Learning Network» is presented in the article – the Ukrainian segment of global educational community. PILN is created with support of the Microsoft company for teachers who use information communication technology in their professional work. The PILN's purpose and value for Ukrainian teachers, for their professional dialogue and collaboration are described in the article. Functions of PILN's communities for teacher’s cooperation, the joint decision of questions and an exchange of ideas and of technique, teaching tools for increase of level of ICT introduction in educational process are described.

  10. Beyond redundancy how geographic redundancy can improve service availability and reliability of computer-based systems

    CERN Document Server

    Bauer, Eric; Eustace, Dan

    2012-01-01

    "While geographic redundancy can obviously be a huge benefit for disaster recovery, it is far less obvious what benefit is feasible and likely for more typical non-catastrophic hardware, software, and human failures. Georedundancy and Service Availability provides both a theoretical and practical treatment of the feasible and likely benefits of geographic redundancy for both service availability and service reliability. The text provides network/system planners, IS/IT operations folks, system architects, system engineers, developers, testers, and other industry practitioners with a general discussion about the capital expense/operating expense tradeoff that frames system redundancy and georedundancy"--

  11. Learning of N-layers neural network

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2005-01-01

    Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

  12. 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.

  13. 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.

  14. Characteristic imsets for learning Bayesian network structure

    Czech Academy of Sciences Publication Activity Database

    Hemmecke, R.; Lindner, S.; Studený, Milan

    2012-01-01

    Roč. 53, č. 9 (2012), s. 1336-1349 ISSN 0888-613X R&D Projects: GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * essential graph * standard imset * characteristic imset * LP relaxation of a polytope Subject RIV: BA - General Mathematics Impact factor: 1.729, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/studeny-0382596.pdf

  15. Learning Methods for Radial Basis Functions Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Kudová, Petra

    2005-01-01

    Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005

  16. Fastest learning in small-world neural networks

    International Nuclear Information System (INIS)

    Simard, D.; Nadeau, L.; Kroeger, H.

    2005-01-01

    We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition

  17. THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

    OpenAIRE

    Mohd Ishak Bin Ismail; Ruzaini Bin Abdullah Arshah

    2016-01-01

    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 stude...

  18. 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...

  19. Palpebral redundancy from hypothyroidism.

    Science.gov (United States)

    Wortsman, J; Wavak, P

    1980-01-01

    A patient is described with disabling palpebral edema. Primary hypothyroidism had been previously diagnosed and treated. Testing of thyroid function revealed persistence of the hypothyroidism. Treatment with L-thyroxine produced normalization of the biochemical parameters and resolution of palpebral edema. The search for hypothyrodism in patients with palpebral redundancy is emphasized.

  20. Threshold Learning Dynamics in Social Networks

    Science.gov (United States)

    González-Avella, Juan Carlos; Eguíluz, Victor M.; Marsili, Matteo; Vega-Redondo, Fernado; San Miguel, Maxi

    2011-01-01

    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 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. PMID:21637714

  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. 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

  3. Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

    OpenAIRE

    Poell, R.F.; Moorsel, M.A.A.H. van

    1996-01-01

    This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of learning groups in organisations. Four theoretical types of learning projects are distinguished. Four different approaches to the learning climate of work groups are compared to the approach offered by t...

  4. 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

  5. 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.

  6. Ensemble Network Architecture for Deep Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Xi-liang Chen

    2018-01-01

    Full Text Available The popular deep Q learning algorithm is known to be instability because of the Q-value’s shake and overestimation action values under certain conditions. These issues tend to adversely affect their performance. In this paper, we develop the ensemble network architecture for deep reinforcement learning which is based on value function approximation. The temporal ensemble stabilizes the training process by reducing the variance of target approximation error and the ensemble of target values reduces the overestimate and makes better performance by estimating more accurate Q-value. Our results show that this architecture leads to statistically significant better value evaluation and more stable and better performance on several classical control tasks at OpenAI Gym environment.

  7. 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...... is set at maximum, KES corresponds to the greedy equivalence search algorithm (GES). When greediness is kept at minimum, we prove that under mild assumptions KES asymptotically returns any inclusion optimal BN with nonzero probability. Experimental results for both synthetic and real data are reported...

  8. 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.

  9. 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

  10. Learning Bayesian Networks with Incomplete Data by Augmentation

    OpenAIRE

    Adel, Tameem; de Campos, Cassio P.

    2016-01-01

    We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...

  11. Exploring Practice-Research Networks for Critical Professional Learning

    Science.gov (United States)

    Appleby, Yvon; Hillier, Yvonne

    2012-01-01

    This paper discusses the contribution that practice-research networks can make to support critical professional development in the Learning and Skills sector in England. By practice-research networks we mean groups or networks which maintain a connection between research and professional practice. These networks stem from the philosophy of…

  12. Robust Learning of High-dimensional Biological Networks with Bayesian Networks

    Science.gov (United States)

    Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

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

  13. New designing of E-Learning systems with using network learning

    OpenAIRE

    Malayeri, Amin Daneshmand; Abdollahi, Jalal

    2010-01-01

    One of the most applied learning in virtual spaces is using E-Learning systems. Some E-Learning methodologies has been introduced, but the main subject is the most positive feedback from E-Learning systems. In this paper, we introduce a new methodology of E-Learning systems entitle "Network Learning" with review of another aspects of E-Learning systems. Also, we present benefits and advantages of using these systems in educating and fast learning programs. Network Learning can be programmable...

  14. Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

    NARCIS (Netherlands)

    Poell, R.F.; Moorsel, M.A.A.H. van

    1996-01-01

    This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of

  15. 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

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

    Science.gov (United States)

    Sudo, Akihito; Sato, Akihiro; Hasegawa, Osamu

    2009-06-01

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

  17. Cooperative Learning for Distributed In-Network Traffic Classification

    Science.gov (United States)

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

    2017-04-01

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

  18. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Directory of Open Access Journals (Sweden)

    Chernoded Andrey

    2017-01-01

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

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

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Sinclair, Christine

    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...

  20. Social networks as ICT collaborative and supportive learning media ...

    African Journals Online (AJOL)

    ... ICT collaborative and supportive learning media utilisation within the Nigerian educational system. The concept of ICT was concisely explained vis-à-vis the social network concept, theory and collaborative and supportive learning media utilisation. Different types of social network are highlighted among which Facebook, ...

  1. 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,…

  2. 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…

  3. Dialogue, Language and Identity: Critical Issues for Networked Management Learning

    Science.gov (United States)

    Ferreday, Debra; Hodgson, Vivien; Jones, Chris

    2006-01-01

    This paper draws on the work of Mikhail Bakhtin and Norman Fairclough to show how dialogue is central to the construction of identity in networked management learning. The paper is based on a case study of a networked management learning course in higher education and attempts to illustrate how participants negotiate issues of difference,…

  4. "Getting Practical" and the National Network of Science Learning Centres

    Science.gov (United States)

    Chapman, Georgina; Langley, Mark; Skilling, Gus; Walker, John

    2011-01-01

    The national network of Science Learning Centres is a co-ordinating partner in the Getting Practical--Improving Practical Work in Science programme. The principle of training provision for the "Getting Practical" programme is a cascade model. Regional trainers employed by the national network of Science Learning Centres trained the cohort of local…

  5. 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:

  6. 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...

  7. Multi-modal Social Networks: A MRF Learning Approach

    Science.gov (United States)

    2016-06-20

    Network forensics: random infection vs spreading epidemic , Proceedings of ACM Sigmetrics. 11-JUN-12, London, UK. : , TOTAL: 4 06/09/2016 Received Paper...Multi-modal Social Networks A MRF Learning Approach The work primarily focused on two lines of research. 1. We propose new greedy algorithms...Box 12211 Research Triangle Park, NC 27709-2211 social networks , learning and inference REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT

  8. Social networks and performance in distributed learning communities

    OpenAIRE

    Cadima, Rita; Ojeda Rodríguez, Jordi; Monguet Fierro, José María

    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 study we analyse two distributed learning communities' social networks in order to understand how characteristics of the social structure can enhance s...

  9. On-line learning in radial basis functions networks

    OpenAIRE

    Freeman, Jason; Saad, David

    1997-01-01

    An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...

  10. 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...

  11. Do Convolutional Neural Networks Learn Class Hierarchy?

    Science.gov (United States)

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

    2018-01-01

    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.

  12. 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.

  13. 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...

  14. Stochastic Online Learning in Dynamic Networks under Unknown Models

    Science.gov (United States)

    2016-08-02

    The key is to develop online learning strategies at each individual node. Specifically, through local information exchange with its neighbors, each...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning...Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for

  15. 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…

  16. Neural redundancy applied to the parity space for signal validation

    International Nuclear Information System (INIS)

    Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu; Martinez, Aquilino Senra

    2005-01-01

    The objective of signal validation is to provide more reliable information from the plant sensor data The method presented in this work introduces the concept of neural redundancy and applies it to the space parity method [1] to overcome an inherent deficiency of this method - the determination of the best estimative of the redundant measures when they are inconsistent. The concept of neural redundancy consists on the calculation of a redundancy through neural networks based on the time series of the own state variable. Therefore, neural networks, dynamically trained with the time series, will estimate the current value of the own measure, which will be used as referee of the redundant measures in the parity space. For this purpose the neural network should have the capacity to supply the neural redundancy in real time and with maximum error corresponding to the group deviation. The historical series should be enough to allow the estimate of the next value, during transients and at the same time, it should be optimized to facilitate the retraining of the neural network to each acquisition. In order to have the capacity to reproduce the tendency of the time series even under accident condition, the dynamic training of the neural network privileges the recent points of the time series. The tests accomplished with simulated data of a nuclear plant, demonstrated that this method applied on the parity space method improves the signal validation process. (author)

  17. Neural redundancy applied to the parity space for signal validation

    Energy Technology Data Exchange (ETDEWEB)

    Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)]. E-mail: cmnap@ien.gov.br; Martinez, Aquilino Senra [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia]. E-mail: aquilino@lmp.br

    2005-07-01

    The objective of signal validation is to provide more reliable information from the plant sensor data The method presented in this work introduces the concept of neural redundancy and applies it to the space parity method [1] to overcome an inherent deficiency of this method - the determination of the best estimative of the redundant measures when they are inconsistent. The concept of neural redundancy consists on the calculation of a redundancy through neural networks based on the time series of the own state variable. Therefore, neural networks, dynamically trained with the time series, will estimate the current value of the own measure, which will be used as referee of the redundant measures in the parity space. For this purpose the neural network should have the capacity to supply the neural redundancy in real time and with maximum error corresponding to the group deviation. The historical series should be enough to allow the estimate of the next value, during transients and at the same time, it should be optimized to facilitate the retraining of the neural network to each acquisition. In order to have the capacity to reproduce the tendency of the time series even under accident condition, the dynamic training of the neural network privileges the recent points of the time series. The tests accomplished with simulated data of a nuclear plant, demonstrated that this method applied on the parity space method improves the signal validation process. (author)

  18. Distributed redundancy and robustness in complex systems

    KAUST Repository

    Randles, Martin

    2011-03-01

    The uptake and increasing prevalence of Web 2.0 applications, promoting new large-scale and complex systems such as Cloud computing and the emerging Internet of Services/Things, requires tools and techniques to analyse and model methods to ensure the robustness of these new systems. This paper reports on assessing and improving complex system resilience using distributed redundancy, termed degeneracy in biological systems, to endow large-scale complicated computer systems with the same robustness that emerges in complex biological and natural systems. However, in order to promote an evolutionary approach, through emergent self-organisation, it is necessary to specify the systems in an \\'open-ended\\' manner where not all states of the system are prescribed at design-time. In particular an observer system is used to select robust topologies, within system components, based on a measurement of the first non-zero Eigen value in the Laplacian spectrum of the components\\' network graphs; also known as the algebraic connectivity. It is shown, through experimentation on a simulation, that increasing the average algebraic connectivity across the components, in a network, leads to an increase in the variety of individual components termed distributed redundancy; the capacity for structurally distinct components to perform an identical function in a particular context. The results are applied to a specific application where active clustering of like services is used to aid load balancing in a highly distributed network. Using the described procedure is shown to improve performance and distribute redundancy. © 2010 Elsevier Inc.

  19. Motion control of musculoskeletal systems with redundancy.

    Science.gov (United States)

    Park, Hyunjoo; Durand, Dominique M

    2008-12-01

    Motion control of musculoskeletal systems for functional electrical stimulation (FES) is a challenging problem due to the inherent complexity of the systems. These include being highly nonlinear, strongly coupled, time-varying, time-delayed, and redundant. The redundancy in particular makes it difficult to find an inverse model of the system for control purposes. We have developed a control system for multiple input multiple output (MIMO) redundant musculoskeletal systems with little prior information. The proposed method separates the steady-state properties from the dynamic properties. The dynamic control uses a steady-state inverse model and is implemented with both a PID controller for disturbance rejection and an artificial neural network (ANN) feedforward controller for fast trajectory tracking. A mechanism to control the sum of the muscle excitation levels is also included. To test the performance of the proposed control system, a two degree of freedom ankle-subtalar joint model with eight muscles was used. The simulation results show that separation of steady-state and dynamic control allow small output tracking errors for different reference trajectories such as pseudo-step, sinusoidal and filtered random signals. The proposed control method also demonstrated robustness against system parameter and controller parameter variations. A possible application of this control algorithm is FES control using multiple contact cuff electrodes where mathematical modeling is not feasible and the redundancy makes the control of dynamic movement difficult.

  20. On Redundancy in Describing Linguistic Systems

    Directory of Open Access Journals (Sweden)

    Vladimir Borissov Pericliev

    2015-12-01

    Full Text Available On Redundancy in Describing Linguistic Systems The notion of system of linguistic elements figures prominently in most post-Saussurian linguistics up to the present. A “system” is the network of the contrastive (or, distinctive features each element in the system bears to the remaining elements. The meaning (valeur of each element in the system is the set of features that are necessary and jointly sufficient to distinguish this element from all others. The paper addresses the problems of “redundancy”, i.e. the occurrence of features that are not strictly necessary in describing an element in a system. Redundancy is shown to smuggle into the description of linguistic systems, this infelicitous practice illustrated with some examples from the literature (e.g. the classical phonemic analysis of Russian by Cherry, Halle, and Jakobson, 1953. The logic and psychology of the occurrence of redundancy are briefly sketched and it is shown that, in addition to some other problems, redundancy leads to a huge and unresolvable ambiguity of descriptions of linguistic systems (the Buridan’s ass problem.

  1. 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.

  2. 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.......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....

  3. 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.

  4. Observing the continental-scale carbon balance: assessment of sampling complementarity and redundancy in a terrestrial assimilation system by means of quantitative network design

    OpenAIRE

    Kaminski, T.; Rayner, P. J.; Vossbeck, M.; Scholze, M.; Koffi, E.

    2012-01-01

    This paper investigates the relationship between the heterogeneity of the terrestrial carbon cycle and the optimal design of observing networks to constrain it. We combine the methods of quantitative network design and carbon-cycle data assimilation to a hierarchy of increasingly heterogeneous descriptions of the European terrestrial biosphere as indicated by increasing diversity of plant functional types. We employ three types of observat...

  5. 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.

  6. Sharing the cost of redundant items

    DEFF Research Database (Denmark)

    Hougaard, Jens Leth; Moulin, Hervé

    2014-01-01

    We ask how to share the cost of finitely many public goods (items) among users with different needs: some smaller subsets of items are enough to serve the needs of each user, yet the cost of all items must be covered, even if this entails inefficiently paying for redundant items. Typical examples...... are network connectivity problems when an existing (possibly inefficient) network must be maintained. We axiomatize a family cost ratios based on simple liability indices, one for each agent and for each item, measuring the relative worth of this item across agents, and generating cost allocation rules...... additive in costs....

  7. Software engineering : redundancy is key

    NARCIS (Netherlands)

    Brand, van den M.G.J.; Groote, J.F.

    2015-01-01

    Software engineers are humans and so they make lots of mistakes. Typically 1 out of 10 to 100 tasks go wrong. The only way to avoid these mistakes is to introduce redundancy in the software engineering process. This article is a plea to consciously introduce several levels of redundancy for each

  8. Trophic redundancy reduces vulnerability to extinction cascades.

    Science.gov (United States)

    Sanders, Dirk; Thébault, Elisa; Kehoe, Rachel; Frank van Veen, F J

    2018-03-06

    Current species extinction rates are at unprecedentedly high levels. While human activities can be the direct cause of some extinctions, it is becoming increasingly clear that species extinctions themselves can be the cause of further extinctions, since species affect each other through the network of ecological interactions among them. There is concern that the simplification of ecosystems, due to the loss of species and ecological interactions, increases their vulnerability to such secondary extinctions. It is predicted that more complex food webs will be less vulnerable to secondary extinctions due to greater trophic redundancy that can buffer against the effects of species loss. Here, we demonstrate in a field experiment with replicated plant-insect communities, that the probability of secondary extinctions is indeed smaller in food webs that include trophic redundancy. Harvesting one species of parasitoid wasp led to secondary extinctions of other, indirectly linked, species at the same trophic level. This effect was markedly stronger in simple communities than for the same species within a more complex food web. We show that this is due to functional redundancy in the more complex food webs and confirm this mechanism with a food web simulation model by highlighting the importance of the presence and strength of trophic links providing redundancy to those links that were lost. Our results demonstrate that biodiversity loss, leading to a reduction in redundant interactions, can increase the vulnerability of ecosystems to secondary extinctions, which, when they occur, can then lead to further simplification and run-away extinction cascades. Copyright © 2018 the Author(s). Published by PNAS.

  9. 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

  10. 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…

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

    Directory of Open Access Journals (Sweden)

    Xiaoyong Yan

    Full Text Available 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.

  12. 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.

  13. Redundant arrays of IDE drives

    Energy Technology Data Exchange (ETDEWEB)

    D.A. Sanders et al.

    2002-01-02

    The authors report tests of redundant arrays of IDE disk drives for use in offline high energy physics data analysis. Parts costs of total systems using commodity EIDE disks are now at the $4000 per Terabyte level. Disk storage prices have now decreased to the point where they equal the cost per Terabyte of Storage Technology tape silos. The disks, however, offer far better granularity; even small institutions can afford to deploy systems. The tests include reports on software RAID-5 systems running under Linux 2.4 using Promise Ultra 100{trademark} disk controllers. RAID-5 protects data in case of a single disk failure by providing parity bits. Tape backup is not required. Journaling file systems are used to allow rapid recovery from crashes. The data analysis strategy is to encapsulate data and CPU processing power. Analysis for a particular part of a data set takes place on the PC where the data resides. The network is only used to put results together. They explore three methods of moving data between sites; internet transfers, not pluggable IDE disks in FireWire cases, and DVD-R disks.

  14. Redundant arrays of IDE drives

    International Nuclear Information System (INIS)

    Sanders, D.A.

    2002-01-01

    The authors report tests of redundant arrays of IDE disk drives for use in offline high energy physics data analysis. Parts costs of total systems using commodity EIDE disks are now at the $4000 per Terabyte level. Disk storage prices have now decreased to the point where they equal the cost per Terabyte of Storage Technology tape silos. The disks, however, offer far better granularity; even small institutions can afford to deploy systems. The tests include reports on software RAID-5 systems running under Linux 2.4 using Promise Ultra 100trademark disk controllers. RAID-5 protects data in case of a single disk failure by providing parity bits. Tape backup is not required. Journaling file systems are used to allow rapid recovery from crashes. The data analysis strategy is to encapsulate data and CPU processing power. Analysis for a particular part of a data set takes place on the PC where the data resides. The network is only used to put results together. They explore three methods of moving data between sites; internet transfers, not pluggable IDE disks in FireWire cases, and DVD-R disks

  15. Using machine learning, neural networks and statistics to predict bankruptcy

    NARCIS (Netherlands)

    Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.

    1997-01-01

    Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear

  16. The Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    . 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...

  17. Stochastic sensitivity analysis and Langevin simulation for neural network learning

    International Nuclear Information System (INIS)

    Koda, Masato

    1997-01-01

    A comprehensive theoretical framework is proposed for the learning of a class of gradient-type neural networks with an additive Gaussian white noise process. The study is based on stochastic sensitivity analysis techniques, and formal expressions are obtained for stochastic learning laws in terms of functional derivative sensitivity coefficients. The present method, based on Langevin simulation techniques, uses only the internal states of the network and ubiquitous noise to compute the learning information inherent in the stochastic correlation between noise signals and the performance functional. In particular, the method does not require the solution of adjoint equations of the back-propagation type. Thus, the present algorithm has the potential for efficiently learning network weights with significantly fewer computations. Application to an unfolded multi-layered network is described, and the results are compared with those obtained by using a back-propagation method

  18. Personal Profiles: Enhancing Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter; Fetter, Sibren

    2009-01-01

    Berlanga, A. J., Bitter-Rijpkema, M., Brouns, F., Sloep, P. B., & Fetter, S. (2011). Personal Profiles: Enhancing Social Interaction in Learning Networks. International Journal of Web Based Communities, 7(1), 66-82.

  19. Learning Initiatives for Network Economies in Asia (LIRNEasia ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Learning Initiatives for Network Economies in Asia (LIRNEasia) : Building Capacity in ICT Policy ... LIRNEasia seeks to build capacity for evidence-based interventions in the public policy process by persons attuned to the ... Project status.

  20. Thermodynamic efficiency of learning a rule in neural networks

    Science.gov (United States)

    Goldt, Sebastian; Seifert, Udo

    2017-11-01

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

  1. Learning Local Components to Understand Large Bayesian Networks

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

    2009-01-01

    (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......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...... 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....

  2. Learning and forgetting on asymmetric, diluted neural networks

    International Nuclear Information System (INIS)

    Derrida, B.; Nadal, J.P.

    1987-01-01

    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

  3. 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…

  4. Social Media and Social Networking Applications for Teaching and Learning

    Science.gov (United States)

    Yeo, Michelle Mei Ling

    2014-01-01

    This paper aims to better understand the experiences of the youth and the educators with the tapping of social media like YouTube videos and the social networking application of Facebook for teaching and learning. This paper is interested in appropriating the benefits of leveraging of social media and networking applications like YouTube and…

  5. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    structures, protein–protein interaction networks, social interactions, the Internet, and so on can be described by complex networks [1–5]. Recent developments in the understanding of complex networks has led to deeper insights about their origin and other properties [1–5]. One common realization that emerges from these ...

  6. Cortical electrophysiological network dynamics of feedback learning

    NARCIS (Netherlands)

    Cohen, M.X.; Wilmes, K.A.; van de Vijver, I.

    2011-01-01

    Understanding the neurophysiological mechanisms of learning is important for both fundamental and clinical neuroscience. We present a neurophysiologically inspired framework for understanding cortical mechanisms of feedback-guided learning. This framework is based on dynamic changes in systems-level

  7. One size does not fit all: older adults benefit from redundant text in multimedia instruction

    OpenAIRE

    Fenesi, Barbara; Vandermorris, Susan; Kim, Joseph A.; Shore, David I.; Heisz, Jennifer J.

    2015-01-01

    The multimedia design of presentations typically ignores that younger and older adults have varying cognitive strengths and weaknesses. We examined whether differential instructional design may enhance learning in these populations. Younger and older participants viewed one of three computer-based presentations: Audio only (narration), Redundant (audio narration with redundant text), or Complementary (audio narration with non–redundant text and images). Younger participants learned better ...

  8. Networking for English Literature Class: Cooperative Learning in Chinese Context

    Science.gov (United States)

    Li, Huiyin

    2017-01-01

    This action research was conducted to investigate the efficacy of networking, an adjusted cooperative learning method employed in an English literature class for non-English majors in China. Questionnaire was administered online anonymously to college students after a 14-week cooperative learning in literature class in a Chinese university, aiming…

  9. Informal Learning and Identity Formation in Online Social Networks

    Science.gov (United States)

    Greenhow, Christine; Robelia, Beth

    2009-01-01

    All students today are increasingly expected to develop technological fluency, digital citizenship, and other twenty-first century competencies despite wide variability in the quality of learning opportunities schools provide. Social network sites (SNSs) available via the internet may provide promising contexts for learning to supplement…

  10. 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…

  11. 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

  12. 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…

  13. A Newton-type neural network learning algorithm

    International Nuclear Information System (INIS)

    Ivanov, V.V.; Puzynin, I.V.; Purehvdorzh, B.

    1993-01-01

    First- and second-order learning methods for feed-forward multilayer networks are considered. A Newton-type algorithm is proposed and compared with the common back-propagation algorithm. It is shown that the proposed algorithm provides better learning quality. Some recommendations for their usage are given. 11 refs.; 1 fig.; 1 tab

  14. A Comparison of Routing Protocol for WSNs: Redundancy Based Approach A Comparison of Routing Protocol for WSNs: Redundancy Based Approach

    Directory of Open Access Journals (Sweden)

    Anand Prakash

    2014-03-01

    Full Text Available Wireless Sensor Networks (WSNs with their dynamic applications gained a tremendous attention of researchers. Constant monitoring of critical situations attracted researchers to utilize WSNs at vast platforms. The main focus in WSNs is to enhance network localization as much as one could, for efficient and optimal utilization of resources. Different approaches based upon redundancy are proposed for optimum functionality. Localization is always related with redundancy of sensor nodes deployed at remote areas for constant and fault tolerant monitoring. In this work, we propose a comparison of classic flooding and the gossip protocol for homogenous networks which enhances stability and throughput quiet significantly.  

  15. Self-teaching neural network learns difficult reactor control problem

    International Nuclear Information System (INIS)

    Jouse, W.C.

    1989-01-01

    A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits

  16. 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.

  17. 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…

  18. Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes.

    Directory of Open Access Journals (Sweden)

    Emre Guney

    Full Text Available Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO analysis highlighted the role of functional diversity for such diseases.

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

    Science.gov (United States)

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

    2017-01-01

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

  20. Deep learning with convolutional neural network in radiology.

    Science.gov (United States)

    Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

    2018-04-01

    Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

  1. 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.

  2. 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…

  3. 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...

  4. 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. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  5. QSAR modelling using combined simple competitive learning networks and RBF neural networks.

    Science.gov (United States)

    Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

    2018-04-01

    The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

  6. Rapid learning in visual cortical networks.

    Science.gov (United States)

    Wang, Ye; Dragoi, Valentin

    2015-08-26

    Although changes in brain activity during learning have been extensively examined at the single neuron level, the coding strategies employed by cell populations remain mysterious. We examined cell populations in macaque area V4 during a rapid form of perceptual learning that emerges within tens of minutes. Multiple single units and LFP responses were recorded as monkeys improved their performance in an image discrimination task. We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity. More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning. These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning.

  7. 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.

  8. Quantum redundancies and local realism

    International Nuclear Information System (INIS)

    Horodecki, R.; Horodecki, P.

    1994-01-01

    The basic properties of quantum redundancies are presented. The previous definitions of the informationally coherent quantum (ICQ) system are generalized in terms of the redundancies. The ICQ systems are also considered in the context of local realism in terms of the information integrity factor η. The classical region η≤qslant[1]/[2] for the two classes of mixed, nonfactorizable states admitting the local hidden variable model is found. ((orig.))

  9. Deep Learning Neural Networks in Cybersecurity - Managing Malware with AI

    OpenAIRE

    Rayle, Keith

    2017-01-01

    There’s a lot of talk about the benefits of deep learning (neural networks) and how it’s the new electricity that will power us into the future. Medical diagnosis, computer vision and speech recognition are all examples of use-cases where neural networks are being applied in our everyday business environment. This begs the question…what are the uses of neural-network applications for cyber security? How does the AI process work when applying neural networks to detect malicious software bombar...

  10. 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.

  11. Lessons Learned from the Young Breast Cancer Survivorship Network.

    Science.gov (United States)

    Gisiger-Camata, Silvia; Nolan, Timiya S; Vo, Jacqueline B; Bail, Jennifer R; Lewis, Kayla A; Meneses, Karen

    2017-11-30

    The Young Breast Cancer Survivors Network (Network) is an academic and community-based partnership dedicated to education, support, and networking. The Network used a multi-pronged approach via monthly support and networking, annual education seminars, website networking, and individual survivor consultation. Formative and summative evaluations were conducted using group survey and individual survivor interviews for monthly gatherings, annual education meetings, and individual consultation. Google Analytics was applied to evaluate website use. The Network began with 4 initial partnerships and grew to 38 in the period from 2011 to 2017. During this 5-year period, 5 annual meetings (598 attendees), 23 support and networking meetings (373), and 115 individual survivor consultations were conducted. The Network website had nearly 12,000 individual users and more than 25,000 page views. Lessons learned include active community engagement, survivor empowerment, capacity building, social media outreach, and network sustainability. The 5-year experiences with the Network demonstrated that a regional program dedicated to the education, support, networking, and needs of young breast cancer survivors and their families can become a vital part of cancer survivorship services in a community. Strong community support, engagement, and encouragement were vital components to sustain the program.

  12. Phonological Networks and New Word Learning

    Science.gov (United States)

    Service, Elisabet

    2006-01-01

    The first report of a connection between vocabulary learning and phonological short-term memory was published in 1988 (Baddeley, Papagno, & Vallar, 1988). At that time, both Susan Gathercole and I were involved in longitudinal studies, investigating the relation between nonword repetition and language learning. We both found a connection. Now,…

  13. 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…

  14. "Follow" Me: Networked Professional Learning for Teachers

    Science.gov (United States)

    Holmes, Kathryn; Preston, Greg; Shaw, Kylie; Buchanan, Rachel

    2013-01-01

    Effective professional learning for teachers is fundamental for any school system aiming to make transformative and sustainable change to teacher practice. This paper investigates the efficacy of Twitter as a medium for teachers to participate in professional learning by analysing the tweets of 30 influential users of the popular medium. We find…

  15. 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. Copyright © 2015 Cognitive Science Society, Inc.

  16. 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.

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

    Science.gov (United States)

    Lecluijze, Suzanne Elisabeth; de Haan, Mariëtte; Ünlüsoy, Asli

    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 this study compared the…

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

    OpenAIRE

    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 which they reassess their held thoughts and make sense of their experiences together with others.

  19. 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

  20. Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Vidnerová, Petra

    2009-01-01

    Roč. 1, č. 2 (2009), s. 49-57 ISSN 2005-4262 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network * RBF networks * regularization * learning Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJGDC/vol2_no1/5.pdf

  1. Information filtering based on corrected redundancy-eliminating mass diffusion.

    Science.gov (United States)

    Zhu, Xuzhen; Yang, Yujie; Chen, Guilin; Medo, Matus; Tian, Hui; Cai, Shi-Min

    2017-01-01

    Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.

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

    Science.gov (United States)

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

    2018-01-01

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

  3. Teachers’ motives for learning in networks : costs, rewards and community interest

    NARCIS (Netherlands)

    van den Beemt, A.A.J.; Ketelaar, E.; Diepstraten, I.; de Laat, M.

    2018-01-01

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

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

    DEFF Research Database (Denmark)

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

    2010-01-01

    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...... which enables students to develop Networked Society competencies and maintain progression in the learning process also during the online periods. Additionally we suggest that our model contributes to the innovation of a networked society's design for learning....... 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......' acquisition of the subject matter within a time limit and at a learning quality that support their deep learning process during a subsequent period of on-line study work. In the process of moving from theory to application the model passes through three stages: 1) Conceptual modelling; 2) Orchestration, and 3...

  5. Learning-induced pattern classification in a chaotic neural network

    International Nuclear Information System (INIS)

    Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki

    2012-01-01

    In this Letter, we propose a Hebbian learning rule with passive forgetting (HLRPF) for use in a chaotic neural network (CNN). We then define the indices based on the Euclidean distance to investigate the evolution of the weights in a simplified way. Numerical simulations demonstrate that, under suitable external stimulations, the CNN with the proposed HLRPF acts as a fuzzy-like pattern classifier that performs much better than an ordinary CNN. The results imply relationship between learning and recognition. -- Highlights: ► Proposing a Hebbian learning rule with passive forgetting (HLRPF). ► Defining indices to investigate the evolution of the weights simply. ► The chaotic neural network with HLRPF acts as a fuzzy-like pattern classifier. ► The pattern classifier ability of the network is improved much.

  6. Lifelong learning networks for sustainable regional development

    NARCIS (Netherlands)

    De Kraker, Joop; Cörvers, Ron; Ruelle, Christine; Valkering, Pieter

    2010-01-01

    Sustainable regional development is a participatory, multi-actor process, involving a diversity of societal stakeholders, administrators, policy makers, practitioners and scientific experts. In this process, mutual and collective learning plays a major role as participants have to exchange and

  7. Community and Social Network Sites as Technology Enhanced Learning Environments

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Christiansen, Ellen

    2008-01-01

    This paper examines the affordance of the Danish social networking site Mingler.dk for peer-to-peer learning and development. With inspiration from different theoretical frameworks, the authors argue how learning and development in such social online systems can be conceptualised and analysed....... Theoretically the paper defines development in accordance with Vygotsky's concept of the zone of proximal development, and learning in accordance with Wenger's concept of communities of practice. The authors suggest analysing the learning and development taking place on Mingler.dk by using these concepts...... supplemented by the notion of horizontal learning adopted from Engestrm and Wenger. Their analysis shows how horizontal learning happens by crossing boundaries between several sites of engagement, and how the actors' multiple membership enables the community members to draw on a vast amount of resources from...

  8. 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. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

  9. Network Enabled - Unresolved Residual Analysis and Learning (NEURAL)

    Science.gov (United States)

    Temple, D.; Poole, M.; Camp, M.

    Since the advent of modern computational capacity, machine learning algorithms and techniques have served as a method through which to solve numerous challenging problems. However, for machine learning methods to be effective and robust, sufficient data sets must be available; specifically, in the space domain, these are generally difficult to acquire. Rapidly evolving commercial space-situational awareness companies boast the capability to collect hundreds of thousands nightly observations of resident space objects (RSOs) using a ground-based optical sensor network. This provides the ability to maintain custody of and characterize thousands of objects persistently. With this information available, novel deep learning techniques can be implemented. The technique discussed in this paper utilizes deep learning to make distinctions between nightly data collects with and without maneuvers. Implementation of these techniques will allow the data collected from optical ground-based networks to enable well informed and timely the space domain decision making.

  10. Deep learning classification in asteroseismology using an improved neural network

    DEFF Research Database (Denmark)

    Hon, Marc; Stello, Dennis; Yu, Jie

    2018-01-01

    Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary state of oscillating red giants into red giant branch stars and helium-core burning stars by recognizing visual features in their asteroseismic...... frequency spectra. We elaborate further on the deep learning method by developing an improved convolutional neural network classifier. To make our method useful for current and future space missions such as K2, TESS, and PLATO, we train classifiers that are able to classify the evolutionary states of lower...

  11. 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.

  12. Social Networking Sites and Addiction: Ten Lessons Learned

    OpenAIRE

    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 onl...

  13. Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

    OpenAIRE

    Shen, Li; Lin, Zhouchen; Huang, Qingming

    2015-01-01

    Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015...

  14. Overcoming uncertainty for within-network relational machine learning

    OpenAIRE

    Pfeiffer, Joseph J.

    2015-01-01

    People increasingly communicate through email and social networks to maintain friendships and conduct business, as well as share online content such as pictures, videos and products. Relational machine learning (RML) utilizes a set of observed attributes and network structure to predict corresponding labels for items; for example, to predict individuals engaged in securities fraud, we can utilize phone calls and workplace information to make joint predictions over the individuals. However, in...

  15. SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

    Science.gov (United States)

    Zenke, Friedemann; Ganguli, Surya

    2018-04-13

    A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

  16. 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.

  17. 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.

  18. 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.

  19. "FORCE" learning in recurrent neural networks as data assimilation

    Science.gov (United States)

    Duane, Gregory S.

    2017-12-01

    It is shown that the "FORCE" algorithm for learning in arbitrarily connected networks of simple neuronal units can be cast as a Kalman Filter, with a particular state-dependent form for the background error covariances. The resulting interpretation has implications for initialization of the learning algorithm, leads to an extension to include interactions between the weight updates for different neurons, and can represent relationships within groups of multiple target output signals.

  20. Learning and Generalisation in Neural Networks with Local Preprocessing

    OpenAIRE

    Kutsia, Merab

    2007-01-01

    We study learning and generalisation ability of a specific two-layer feed-forward neural network and compare its properties to that of a simple perceptron. The input patterns are mapped nonlinearly onto a hidden layer, much larger than the input layer, and this mapping is either fixed or may result from an unsupervised learning process. Such preprocessing of initially uncorrelated random patterns results in the correlated patterns in the hidden layer. The hidden-to-output mapping of the net...

  1. Learning Based on CC1 and CC4 Neural Networks

    OpenAIRE

    Kak, Subhash

    2017-01-01

    We propose that a general learning system should have three kinds of agents corresponding to sensory, short-term, and long-term memory that implicitly will facilitate context-free and context-sensitive aspects of learning. These three agents perform mututally complementary functions that capture aspects of the human cognition system. We investigate the use of CC1 and CC4 networks for use as models of short-term and sensory memory.

  2. The Role of Electronic Learning Technology in Networks Systems

    International Nuclear Information System (INIS)

    Abd ELhamid, A.; Ayad, N.M.A.; Fouad, Y.; Abdelkader, T.

    2016-01-01

    Recently, Electronic Learning Technology (ELT) has been widely spread as one of the new technologies in the world through using Information and Communication Technology (ICT). One of the strategies of ELT is Simulation, for instance Military and Medical simulations that are used to avoid risks and reduce Costs. A wireless communication network refers to any network not physically connected by cables, which enables the desired convenience and mobility for the user. Wireless communication networks have been useful in areas such as commerce, education and defense. According to the nature of a particular application, they can be used in home-based and industrial systems or in commercial and military environments. Historically, Mobile Ad-hoc Networks (MANET) have primarily been used for tactical military network related applications to improve battlefield communications/ survivability. MANET is a collection of wireless nodes that can dynamically be set up anywhere and anytime without using any pre-existing network infrastructure. Mobility in wireless networks basically refers to nodes changing its point of attachment to the network. Also, how the end terminals can move, there are many mobility models described the movement of nodes, many researchers use the Random Way point Mobility Model (RWPM). In this paper, a Graphical User Interface (GUI) for RWPM simulation is introduced as a proposal to be used through ELT Project. In the research area of computer and communications networks, simulation is a very useful technique for the behavior of networks

  3. 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.

  4. Language, Learning, and Identity in Social Networking Sites for Language Learning: The Case of Busuu

    Science.gov (United States)

    Alvarez Valencia, Jose Aldemar

    2014-01-01

    Recent progress in the discipline of computer applications such as the advent of web-based communication, afforded by the Web 2.0, has paved the way for novel applications in language learning, namely, social networking. Social networking has challenged the area of Computer Mediated Communication (CMC) to expand its research palette in order to…

  5. Learning about knowledge: A complex network approach

    International Nuclear Information System (INIS)

    Fontoura Costa, Luciano da

    2006-01-01

    An approach to modeling knowledge acquisition in terms of walks along complex networks is described. Each subset of knowledge is represented as a node, and relations between such knowledge are expressed as edges. Two types of edges are considered, corresponding to free and conditional transitions. The latter case implies that a node can only be reached after visiting previously a set of nodes (the required conditions). The process of knowledge acquisition can then be simulated by considering the number of nodes visited as a single agent moves along the network, starting from its lowest layer. It is shown that hierarchical networks--i.e., networks composed of successive interconnected layers--are related to compositions of the prerequisite relationships between the nodes. In order to avoid deadlocks--i.e., unreachable nodes--the subnetwork in each layer is assumed to be a connected component. Several configurations of such hierarchical knowledge networks are simulated and the performance of the moving agent quantified in terms of the percentage of visited nodes after each movement. The Barabasi-Albert and random models are considered for the layer and interconnecting subnetworks. Although all subnetworks in each realization have the same number of nodes, several interconnectivities, defined by the average node degree of the interconnection networks, have been considered. Two visiting strategies are investigated: random choice among the existing edges and preferential choice to so far untracked edges. A series of interesting results are obtained, including the identification of a series of plateaus of knowledge stagnation in the case of the preferential movement strategy in the presence of conditional edges

  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. Noise-driven manifestation of learning in mature neural networks

    International Nuclear Information System (INIS)

    Monterola, Christopher; Saloma, Caesar

    2002-01-01

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

  9. Impact of censoring on learning Bayesian networks in survival modelling.

    Science.gov (United States)

    Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

    2009-11-01

    Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from

  10. Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks

    OpenAIRE

    Airola, Rasmus; Hager, Kristoffer

    2017-01-01

    The use of machine learning and specifically neural networks is a growing trend in software development, and has grown immensely in the last couple of years in the light of an increasing need to handle big data and large information flows. Machine learning has a broad area of application, such as human-computer interaction, predicting stock prices, real-time translation, and self driving vehicles. Large companies such as Microsoft and Google have already implemented machine learning in some o...

  11. Experiment in Collaborative Learning Network for Enhanced ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    ... process and results of collaborative networking in a particular region and on a specific theme. They will share knowledge in the form of thematic information, best practices, policy analysis, practical methodologies and tools, online courses and seminars, coaching and mentoring, face-to-face exchanges, and workshops.

  12. 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…

  13. 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…

  14. Supervised learning in spiking neural networks with FORCE training.

    Science.gov (United States)

    Nicola, Wilten; Clopath, Claudia

    2017-12-20

    Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.

  15. 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.

  16. Sparse dictionary learning of resting state fMRI networks.

    Science.gov (United States)

    Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

    2012-07-02

    Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

  17. Learning Reproducibility with a Yearly Networking Contest

    KAUST Repository

    Canini, Marco; Crowcroft, Jon

    2017-01-01

    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

  18. Virtual learning networks for sustainable development

    NARCIS (Netherlands)

    De Kraker, Joop; Cörvers, Ron

    2010-01-01

    Sustainable development is a participatory, multi-actor process. In this process, learning plays a major role as participants have to exchange and integrate a diversity of perspectives and types of knowledge and expertise in order to arrive at innovative, jointly supported solutions. Virtual

  19. Learning Networks for Lifelong Competence Development

    NARCIS (Netherlands)

    Koper, Rob

    2006-01-01

    Contribution to Prolearn Summerschool, 7-6-2006; Bled; Slovenia. Slides of the lecture and the 'user questions' we produced in the workshop. The task in the workshop was to identify learning questions that a user could have for the TENCompetence system. These questions should be a) hard to answer

  20. Reflections and challenges in Networked Learning

    DEFF Research Database (Denmark)

    Bonderup Dohn, Nina; Sime, Julie-Ann; Cranmer, Susan

    2018-01-01

    with a short presentation of each of the chapters. This leads us to identify broader themes which point out significant perspectives and challenges for future research and practice. Among these are social justice, criticality, mobility, new forms of openness and learning in the public arena (all leading themes...

  1. Differential theory of learning for efficient neural network pattern recognition

    Science.gov (United States)

    Hampshire, John B., II; Vijaya Kumar, Bhagavatula

    1993-09-01

    We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

  2. Ad Hoc Transient Groups: Instruments for Awareness in Learning Networks

    NARCIS (Netherlands)

    Fetter, Sibren; Rajagopal, Kamakshi; Berlanga, Adriana; Sloep, Peter

    2011-01-01

    Fetter, S., Rajagopal, K., Berlanga, A. J., & Sloep, P. B. (2011). Ad Hoc Transient Groups: Instruments for Awareness in Learning Networks. In W. Reinhardt, T. D. Ullmann, P. Scott, V. Pammer, O. Conlan, & A. J. Berlanga (Eds.), Proceedings of the 1st European Workshop on Awareness and Reflection in

  3. 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...

  4. 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.

  5. 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.

  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

  7. 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…

  8. Networking and distance learning for teachers: A classification of possibilities

    NARCIS (Netherlands)

    Collis, Betty

    1995-01-01

    Computer based communication technologies, or what could be more conveniently called networking, are bringing new possibilities into teacher education in many different ways. As with distance education more generally they can facilitate flexibility in time and place of learning, but the range of

  9. 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.

  10. Home-School Links: Networking the Learning Community.

    Science.gov (United States)

    1996

    The topic of networking the learning community with home-school links is addressed in four papers: "Internet Access via School: Expectations of Students and Parents" (Roy Crotty); "The School Library as Community Information Gateway" (Megan Perry); "Rural Access to the Internet" (Ken Eustace); and "NetDay '96:…

  11. 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…

  12. Recommending Learning Activities in Social Network Using Data Mining Algorithms

    Science.gov (United States)

    Mahnane, Lamia

    2017-01-01

    In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). "NSN-AP-CF" processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the…

  13. Learner Views about Cooperative Learning in Social Learning Networks

    Science.gov (United States)

    Cankaya, Serkan; Yunkul, Eyup

    2018-01-01

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

  14. 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.

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

    International Nuclear Information System (INIS)

    Bornholdt, S.

    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

  16. 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. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Redundancy Effect on Retention of Vocabulary Words Using Multimedia Presentation

    Science.gov (United States)

    Samur, Yavuz

    2012-01-01

    This study was designed to examine the effect of the redundancy principle in a multimedia presentation constructed for foreign language vocabulary learning on undergraduate students' retention. The underlying hypothesis of this study is that when the students are exposed to the material in multiple ways through animation, concurrent narration,…

  18. Students' Feedback of mDPBL Approach and the Learning Impact towards Computer Networks Teaching and Learning

    Science.gov (United States)

    Winarno, Sri; Muthu, Kalaiarasi Sonai; Ling, Lew Sook

    2018-01-01

    This study presents students' feedback and learning impact on design and development of a multimedia learning in Direct Problem-Based Learning approach (mDPBL) for Computer Networks in Dian Nuswantoro University, Indonesia. This study examined the usefulness, contents and navigation of the multimedia learning as well as learning impacts towards…

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

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00218873; The ATLAS collaboration; Toler, Wesley; Vamosi, Ralf; Bogado Garcia, Joaquin Ignacio

    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...

  20. Learning in neural networks based on a generalized fluctuation theorem

    Science.gov (United States)

    Hayakawa, Takashi; Aoyagi, Toshio

    2015-11-01

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

  1. Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback.

    Science.gov (United States)

    Koush, Yury; Meskaldji, Djalel-E; Pichon, Swann; Rey, Gwladys; Rieger, Sebastian W; Linden, David E J; Van De Ville, Dimitri; Vuilleumier, Patrik; Scharnowski, Frank

    2017-02-01

    Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity-neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top-down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self-regulated the top-down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity-based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity-based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  2. Some Learning Properties of Modular Network SOMs

    Science.gov (United States)

    Takeda, Manabu; Ikeda, Kazushi; Furukawa, Tetsuo

    The Modular Network Self-Organizing Map (mnSOM) is a generalization of the SOM, where each node represents a parametric function such as a multi-layer perceptron or another SOM. Since given datasets are, in general, fewer than nodes, some nodes never win in competition and have to update their parameters from the winners in the neighborhood. This is a process that can be regarded as interpolation. This study derives the interpolation curve between winners in simple cases and discusses the distribution of winners based on the neighborhood function.

  3. 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.

  4. 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

  5. Cost Vs. Redundancy in FTTH Access Networks

    DEFF Research Database (Denmark)

    Haraldsson, Gustav Helgi; Pedersen, Jens Myrup

    2006-01-01

    separate distribution nodes to NT's. The cost of the ear topology is kept down by reusing trenches making extra digging minimal. The results show however that the ear topology with the home-run method is not suitable compared to the tree topology due to the extra fibers needed. Further work could apply...

  6. Demystifying communication signal lost for network redundancy ...

    African Journals Online (AJOL)

    These studies report on the communication signal lost factors that were analyzed and supported by evidences on coverage analysis activities for Automatic Meter Reading (AMR) systems. We have categorized the influential signal lost factors into four core elements that were concluded based on our field measurement ...

  7. 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…

  8. 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.

  9. 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.

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

    DEFF Research Database (Denmark)

    Ulhøi, John Parm; Madsen, Henning

    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...

  11. Learning Negotiation Policies Using IB3 and Bayesian Networks

    Science.gov (United States)

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

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

  12. 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.

  13. Redundancy in Nigerian Business Organizations: Alternatives (Pp ...

    African Journals Online (AJOL)

    FIRST LADY

    Redundancy in Nigerian Business Organizations: Alternatives (Pp. ... When business downturns ... The galloping pace of information technologies is a harbinger of profound ... Redundant staff in public departments can also be retained as.

  14. Dictionary Networking in an LSP Learning Context

    DEFF Research Database (Denmark)

    Nielsen, Sandro

    2007-01-01

    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...... 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......-lexicographic environment, i.e. what happens outside the dictionary when users write or translate texts, and relate these findings to the lexicographic environment represented by the theoretical basis and the dictionary itself. Nielsen (2006) gives a preliminary discussion of monolingual accounting dictionaries for EFL...

  15. Learning by Knowledge Networking across Cultures

    DEFF Research Database (Denmark)

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

    2005-01-01

    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......) 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...... 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. Redundant measurements for controlling errors

    International Nuclear Information System (INIS)

    Ehinger, M.H.; Crawford, J.M.; Madeen, M.L.

    1979-07-01

    Current federal regulations for nuclear materials control require consideration of operating data as part of the quality control program and limits of error propagation. Recent work at the BNFP has revealed that operating data are subject to a number of measurement problems which are very difficult to detect and even more difficult to correct in a timely manner. Thus error estimates based on operational data reflect those problems. During the FY 1978 and FY 1979 R and D demonstration runs at the BNFP, redundant measurement techniques were shown to be effective in detecting these problems to allow corrective action. The net effect is a reduction in measurement errors and a significant increase in measurement sensitivity. Results show that normal operation process control measurements, in conjunction with routine accountability measurements, are sensitive problem indicators when incorporated in a redundant measurement program

  17. Functional networks inference from rule-based machine learning models.

    Science.gov (United States)

    Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

    2016-01-01

    Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

  18. Repetitive motion planning and control of redundant robot manipulators

    CERN Document Server

    Zhang, Yunong

    2013-01-01

    Repetitive Motion Planning and Control of Redundant Robot Manipulators presents four typical motion planning schemes based on optimization techniques, including the fundamental RMP scheme and its extensions. These schemes are unified as quadratic programs (QPs), which are solved by neural networks or numerical algorithms. The RMP schemes are demonstrated effectively by the simulation results based on various robotic models; the experiments applying the fundamental RMP scheme to a physical robot manipulator are also presented. As the schemes and the corresponding solvers presented in the book have solved the non-repetitive motion problems existing in redundant robot manipulators, it is of particular use in applying theoretical research based on the quadratic program for redundant robot manipulators in industrial situations. This book will be a valuable reference work for engineers, researchers, advanced undergraduate and graduate students in robotics fields. Yunong Zhang is a professor at The School of Informa...

  19. 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

  20. 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.

  1. 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.

  2. Validating module network learning algorithms using simulated data.

    Science.gov (United States)

    Michoel, Tom; Maere, Steven; Bonnet, Eric; Joshi, Anagha; Saeys, Yvan; Van den Bulcke, Tim; Van Leemput, Koenraad; van Remortel, Piet; Kuiper, Martin; Marchal, Kathleen; Van de Peer, Yves

    2007-05-03

    In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network

  3. 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%.

  4. Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.

    Science.gov (United States)

    Covi, Erika; Brivio, Stefano; Serb, Alexander; Prodromakis, Themis; Fanciulli, Marco; Spiga, Sabina

    2016-01-01

    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 HfO 2 -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 images are displayed and it is robust to a device-to-device variability of up to ±30%.

  5. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

    Science.gov (United States)

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

  6. 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.

  7. Learning spectrum's selection in OLAM network for analysis cement samples

    International Nuclear Information System (INIS)

    Huang Ning; Wang Peng; Tang Daiquan; Hu Renlan

    2010-01-01

    It uses OLAM artificial neural network to analyze the samples of cement raw material. Two kinds of spectrums are used for network learning: pure-element spectrum and mix-element spectrum. The output of pure-element method can be used to construct a simulate spectrum, which can be compared with the original spectrum and judge the shift of spectrum; the mix-element method can store more message and correct the matrix effect, but the multicollinearity among spectrums can cause some side effect to the results. (authors)

  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......Bayesian networks with mixtures of truncated exponentials (MTEs) are gaining popularity as a flexible modelling framework for hybrid domains. MTEs support efficient and exact inference algorithms, but estimating an MTE from data has turned out to be a difficult task. Current methods suffer from...

  9. 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

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

    Science.gov (United States)

    Noor, Ahmed K. (Compiler)

    2003-01-01

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

  11. Self-learning Monte Carlo with deep neural networks

    Science.gov (United States)

    Shen, Huitao; Liu, Junwei; Fu, Liang

    2018-05-01

    The self-learning Monte Carlo (SLMC) method is a general algorithm to speedup MC simulations. Its efficiency has been demonstrated in various systems by introducing an effective model to propose global moves in the configuration space. In this paper, we show that deep neural networks can be naturally incorporated into SLMC, and without any prior knowledge can learn the original model accurately and efficiently. Demonstrated in quantum impurity models, we reduce the complexity for a local update from O (β2) in Hirsch-Fye algorithm to O (β lnβ ) , which is a significant speedup especially for systems at low temperatures.

  12. Music Learning with Long Short Term Memory Networks

    OpenAIRE

    Colombo, Florian François

    2015-01-01

    Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the highly complicated works of J.S. Bach. However, how our brain is able to store and produce these very long temporal sequences is still an open question. Long short-term memory (LSTM) artificial neural networks have been shown to be efficient in sequence learning tasks thanks to their inherent ability to bridge long time lags between input events and their target signals. Here, I investigate the po...

  13. Timing control by redundant inhibitory neuronal circuits

    Energy Technology Data Exchange (ETDEWEB)

    Tristan, I., E-mail: itristan@ucsd.edu; Rulkov, N. F.; Huerta, R.; Rabinovich, M. [BioCircuits Institute, University of California, San Diego, La Jolla, California 92093-0402 (United States)

    2014-03-15

    Rhythms and timing control of sequential activity in the brain is fundamental to cognition and behavior. Although experimental and theoretical studies support the understanding that neuronal circuits are intrinsically capable of generating different time intervals, the dynamical origin of the phenomenon of functionally dependent timing control is still unclear. Here, we consider a new mechanism that is related to the multi-neuronal cooperative dynamics in inhibitory brain motifs consisting of a few clusters. It is shown that redundancy and diversity of neurons within each cluster enhances the sensitivity of the timing control with the level of neuronal excitation of the whole network. The generality of the mechanism is shown to work on two different neuronal models: a conductance-based model and a map-based model.

  14. Timing control by redundant inhibitory neuronal circuits

    International Nuclear Information System (INIS)

    Tristan, I.; Rulkov, N. F.; Huerta, R.; Rabinovich, M.

    2014-01-01

    Rhythms and timing control of sequential activity in the brain is fundamental to cognition and behavior. Although experimental and theoretical studies support the understanding that neuronal circuits are intrinsically capable of generating different time intervals, the dynamical origin of the phenomenon of functionally dependent timing control is still unclear. Here, we consider a new mechanism that is related to the multi-neuronal cooperative dynamics in inhibitory brain motifs consisting of a few clusters. It is shown that redundancy and diversity of neurons within each cluster enhances the sensitivity of the timing control with the level of neuronal excitation of the whole network. The generality of the mechanism is shown to work on two different neuronal models: a conductance-based model and a map-based model

  15. 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.

  16. 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.

  17. 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?

  18. Neural network representation and learning of mappings and their derivatives

    Science.gov (United States)

    White, Halbert; Hornik, Kurt; Stinchcombe, Maxwell; Gallant, A. Ronald

    1991-01-01

    Discussed here are recent theorems proving that artificial neural networks are capable of approximating an arbitrary mapping and its derivatives as accurately as desired. This fact forms the basis for further results establishing the learnability of the desired approximations, using results from non-parametric statistics. These results have potential applications in robotics, chaotic dynamics, control, and sensitivity analysis. An example involving learning the transfer function and its derivatives for a chaotic map is discussed.

  19. Learning gene regulatory networks from only positive and unlabeled data

    Directory of Open Access Journals (Sweden)

    Elkan Charles

    2010-05-01

    Full Text Available Abstract Background Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact. Results A recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement. Conclusions Compared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.

  20. 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.

  1. Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.

    Science.gov (United States)

    Sohn, Insoo; Liu, Huaping; Ansari, Nirwan

    2015-01-01

    An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.

  2. Performance of hybrid-ARQ with incremental redundancy over relay channels

    KAUST Repository

    Chelli, Ali; Alouini, Mohamed-Slim

    2012-01-01

    In this paper, we consider a relay network consisting of a source, a relay, and a destination. The source transmits a message to the destination using hybrid automatic repeat request (HARQ) with incremental redundancy (IR). The relay overhears

  3. Bifurcation and category learning in network models of oscillating cortex

    Science.gov (United States)

    Baird, Bill

    1990-06-01

    A genetic model of oscillating cortex, which assumes “minimal” coupling justified by known anatomy, is shown to function as an associative memory, using previously developed theory. The network has explicit excitatory neurons with local inhibitory interneuron feedback that forms a set of nonlinear oscillators coupled only by long-range excitatory connections. Using a local Hebb-like learning rule for primary and higher-order synapses at the ends of the long-range connections, the system learns to store the kinds of oscillation amplitude patterns observed in olfactory and visual cortex. In olfaction, these patterns “emerge” during respiration by a pattern forming phase transition which we characterize in the model as a multiple Hopf bifurcation. We argue that these bifurcations play an important role in the operation of real digital computers and neural networks, and we use bifurcation theory to derive learning rules which analytically guarantee CAM storage of continuous periodic sequences-capacity: N/2 Fourier components for an N-node network-no “spurious” attractors.

  4. 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…

  5. 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

  6. 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."

  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......-construct knowledge using a wide range of resources for meaning making and expression of ideas. These outcomes were, however, contingent on the interplay of teacher understanding of the nature of science inquiry and school provision of an effective technological infrastructure and support for flexible curriculum...... 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...

  8. A stochastic learning algorithm for layered neural networks

    International Nuclear Information System (INIS)

    Bartlett, E.B.; Uhrig, R.E.

    1992-01-01

    The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given

  9. Empirical Models of Social Learning in a Large, Evolving Network.

    Directory of Open Access Journals (Sweden)

    Ayşe Başar Bener

    Full Text Available This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1 attraction homophily causes individuals to form ties on the basis of attribute similarity, 2 aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3 social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.

  10. A smart-pixel holographic competitive learning network

    Science.gov (United States)

    Slagle, Timothy Michael

    Neural networks are adaptive classifiers which modify their decision boundaries based on feedback from externally- or internally-generated error signals. Optics is an attractive technology for neural network implementation because it offers the possibility of parallel, nearly instantaneous computation of the weighted neuron inputs by the propagation of light through the optical system. Using current optical device technology, system performance levels of 3 × 1011 connection updates per second can be achieved. This thesis presents an architecture for an optical competitive learning network which offers advantages over previous optical implementations, including smart-pixel-based optical neurons, phase- conjugate self-alignment of a single neuron plane, and high-density, parallel-access weight storage, interconnection, and learning in a volume hologram. The competitive learning algorithm with modifications for optical implementation is described, and algorithm simulations are performed for an example problem. The optical competitive learning architecture is then introduced. The optical system is simulated using the ``beamprop'' algorithm at the level of light propagating through the system components, and results showing competitive learning operation in agreement with the algorithm simulations are presented. The optical competitive learning requires a non-linear, non-local ``winner-take-all'' (WTA) neuron function. Custom-designed smart-pixel WTA neuron arrays were fabricated using CMOS VLSI/liquid crystal technology. Results of laboratory tests of the WTA arrays' switching characteristics, time response, and uniformity are then presented. The system uses a phase-conjugate mirror to write the self-aligning interconnection weight holograms, and energy gain is required from the reflection to minimize erasure of the existing weights. An experimental system for characterizing the PCM response is described. Useful gains of 20 were obtained with a polarization

  11. Innovating Design for Learning in the Networked Society

    DEFF Research Database (Denmark)

    Levinsen, Karin Tweddell; Nielsen, Janni

    2012-01-01

    The transition from the industrial to the knowledge or networked society has, together with the worldwide digitalization and e-permeation of our social, political and economic lives, brought challenges to the educational systems. The changes call for new key competences in terms of self-initiated......The transition from the industrial to the knowledge or networked society has, together with the worldwide digitalization and e-permeation of our social, political and economic lives, brought challenges to the educational systems. The changes call for new key competences in terms of self......-initiated and lifelong learning and digital literacy. At the same time, the implementation of new public management in educational institutions put pressure on students’ available time for studying and the qualitative outcome of learning processes. These conditions give birth to emerging tensions at the organizational...... in their practice are students who are (if at all) only familiar with the curriculum at a surface level and who expect the teachers to present digested versions of the curriculum. This chapter presents a design for teaching and learning approach in the shape of a design for learning model that aims to scaffold...

  12. Networked learning in, for, and with the world

    DEFF Research Database (Denmark)

    Nørgård, Rikke Toft; Mor, Yishay; Bengtsen, Søren Smedegaard

    2018-01-01

    With the so-called ‘Mode 3’ university as overarching framework (Barnett, 2004; Bengtsen & Nørgård, 2016; Barnett & Bengtsen, 2017; Nørgård, Olesen & Toft-Nielsen, 2018) this chapter considers how traditional forms of and formats for teaching and learning within higher education can be rethought,......’ in higher education. In the following sections, we will describe these transformations of university being, before considering some of the new challenges, opportunities, and potentials of teaching and learning in and through hybrid networks in the Mode 3 institution......., opportunities, and potentials to the teaching and learning that takes place at the university. Through history, and across different present national contexts and cultures, the ‘being’ of the university and its livelihood and mandate has changed (Wright, 2016; Barnett, 2018). Through these transformations where......, reconfigured, and redesigned in order to facilitate valuable, meaningful and relevant hybrid networked learning in, for, and with the world. What it means to ‘be’ a university is changing and the university is a ‘being’ that in itself is changing (Barnett, 2011), something also offering challenges...

  13. Deep learning for steganalysis via convolutional neural networks

    Science.gov (United States)

    Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu

    2015-03-01

    Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.

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

    Science.gov (United States)

    Lassnig, Mario; Toler, Wesley; Vamosi, Ralf; Bogado, Joaquin; ATLAS Collaboration

    2017-10-01

    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 networkaware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

  15. SIMULATION MODEL FOR DESIGN SUPPORT OF INFOCOMM REDUNDANT SYSTEMS

    Directory of Open Access Journals (Sweden)

    V. A. Bogatyrev

    2016-09-01

    Full Text Available Subject of Research. The paper deals with the effectiveness of multipath transfer of request copies through the network and their redundant service without the use of laborious analytical modeling. The model and support tools for the design of highly reliable distributed systems based on simulation modeling have been created. Method. The effectiveness of many variants of service organization and delivery through the network to the query servers is formed and analyzed. Options for providing redundant service and delivery via the network to the servers of request copies are also considered. The choice of variants for the distribution and service of requests is carried out taking into account the criticality of queries to the time of their stay in the system. The request is considered successful if at least one of its copies is accurately delivered to the working server, ready to service the request received through a network, if it is fulfilled in the set time. Efficiency analysis of the redundant transmission and service of requests is based on the model built in AnyLogic 7 simulation environment. Main Results. Simulation experiments based on the proposed models have shown the effectiveness of redundant transmission of copies of queries (packets to the servers in the cluster through multiple paths with redundant service of request copies by a group of servers in the cluster. It is shown that this solution allows increasing the probability of exact execution of at least one copy of the request within the required time. We have carried out efficiency evaluation of destruction of outdated request copies in the queues of network nodes and the cluster. We have analyzed options for network implementation of multipath transfer of request copies to the servers in the cluster over disjoint paths, possibly different according to the number of their constituent nodes. Practical Relevance. The proposed simulation models can be used when selecting the optimal

  16. Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

    Science.gov (United States)

    Kulkarni, Shruti R; Rajendran, Bipin

    2018-07-01

    We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Kok, Illasha; Beter, Petra; Esterhuizen, Hennie

    2018-01-01

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

  18. Home and away : learning in and learning from organisational networks in Europe

    NARCIS (Netherlands)

    Docherty, P.; Huzzard, T.; Leede, J. de

    2003-01-01

    This report is a comparative analysis of the various learning networks established within the Innoflex Project. The report recaps on the central argument underpinning Innoflex, namely that traditional ways of organising workplaces and traditional styles of management cannot achieve the commitment,

  19. 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.

  20. Continuous Learning of a Multilayered Network Topology in a Video Camera Network

    Directory of Open Access Journals (Sweden)

    Zou Xiaotao

    2009-01-01

    Full Text Available Abstract A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. Unlike existing methods that used discrete events or appearance information to infer the network topology at a single level, this paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level is analyzed both in simulation and in real-life experiments and compared with previous approaches.

  1. Continuous Learning of a Multilayered Network Topology in a Video Camera Network

    Directory of Open Access Journals (Sweden)

    Xiaotao Zou

    2009-01-01

    Full Text Available A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. Unlike existing methods that used discrete events or appearance information to infer the network topology at a single level, this paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level is analyzed both in simulation and in real-life experiments and compared with previous approaches.

  2. 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

  3. A fully connected network of Bernoulli units with correlation learning

    Science.gov (United States)

    Dente, J. A.; Vilela Mendes, R.

    1996-02-01

    Biological evidence suggests that pattern recognition and associative memory in the mammalian nervous system operates through the establishment of spatio-temporal patterns of activity and not by the evolution towards an equilibrium point as in attractor neural networks. Information is carried by the space-time correlation of the activity intensities rather than by the details of individual neuron signals. Furthermore the fast recognition times that are achieved with relatively slow biological neurons seem to be associated to the chaotic nature of the basal nervous activity. To copy the biology hardware may not be technologically sound, but to look for inspiration in the efficient biological information processing methods is an idea that deserves consideration. Inspired by the mechanisms at work in the mammalian olfactory system we study a network where, in the absence of external inputs, the units have a dynamics of the Bernoulli shift type. When an external signal is presented, the pattern of excitation bursts depends on the learning history of the network. Association and pattern identification in the network operates by the selection, by the external stimulus, of distinct invariant measures in the chaotic system. The simplicity of the node dynamics, that is chosen, allows a reasonable analytical control of the network behavior.

  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. Construction of Neural Networks for Realization of Localized Deep Learning

    Directory of Open Access Journals (Sweden)

    Charles K. Chui

    2018-05-01

    Full Text Available The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis and online advertisement, speech and handwriting recognition, computer vision and natural language processing, time series forecasting, and search engines. However, theoretical development of deep learning is still at its infancy. The objective of this paper is to introduce a deep neural network (also called deep-net approach to localized manifold learning, with each hidden layer endowed with a specific learning task. For the purpose of illustrations, we only focus on deep-nets with three hidden layers, with the first layer for dimensionality reduction, the second layer for bias reduction, and the third layer for variance reduction. A feedback component is also designed to deal with outliers. The main theoretical result in this paper is the order O(m-2s/(2s+d of approximation of the regression function with regularity s, in terms of the number m of sample points, where the (unknown manifold dimension d replaces the dimension D of the sampling (Euclidean space for shallow nets.

  6. 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.

  7. Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

    Science.gov (United States)

    Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

    2018-03-01

    Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.

  8. High precision redundant robotic manipulator

    International Nuclear Information System (INIS)

    Young, K.K.D.

    1998-01-01

    A high precision redundant robotic manipulator for overcoming contents imposed by obstacles or imposed by a highly congested work space is disclosed. One embodiment of the manipulator has four degrees of freedom and another embodiment has seven degrees of freedom. Each of the embodiments utilize a first selective compliant assembly robot arm (SCARA) configuration to provide high stiffness in the vertical plane, a second SCARA configuration to provide high stiffness in the horizontal plane. The seven degree of freedom embodiment also utilizes kinematic redundancy to provide the capability of avoiding obstacles that lie between the base of the manipulator and the end effector or link of the manipulator. These additional three degrees of freedom are added at the wrist link of the manipulator to provide pitch, yaw and roll. The seven degrees of freedom embodiment uses one revolute point per degree of freedom. For each of the revolute joints, a harmonic gear coupled to an electric motor is introduced, and together with properly designed based servo controllers provide an end point repeatability of less than 10 microns. 3 figs

  9. 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…

  10. 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…

  11. The Application of S7-400H redundant PLC in I and C system for waterworks in nuclear power plant

    International Nuclear Information System (INIS)

    Pang Yuxiang

    2013-01-01

    This paper introduces Siemens S7-400H redundant PLC which is employed to implement monitor and control systems for waterworks in nuclear power plant. It focuses on the configuration and realization of the redundant system. The safety and reliability of the system is improved by using redundant CPU, network and server. (authors)

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

    Science.gov (United States)

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

    2017-03-01

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

  13. 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...... learn generic models for network entities in the form of automata templates. As is characteristic for SRL techniques, the abstraction level aorded by learning generic templates enables one to apply the learned model to new domains. A main benet of learning models based on nite automata lies in the fact...

  14. Self-Learning Power Control in Wireless Sensor Networks.

    Science.gov (United States)

    Chincoli, Michele; Liotta, Antonio

    2018-01-27

    Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.

  15. 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.

  16. Convolutional neural network with transfer learning for rice type classification

    Science.gov (United States)

    Patel, Vaibhav Amit; Joshi, Manjunath V.

    2018-04-01

    Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

  17. 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.

  18. 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,…

  19. Forecasting financial asset processes: stochastic dynamics via learning neural networks.

    Science.gov (United States)

    Giebel, S; Rainer, M

    2010-01-01

    Models for financial asset dynamics usually take into account their inherent unpredictable nature by including a suitable stochastic component into their process. Unknown (forward) values of financial assets (at a given time in the future) are usually estimated as expectations of the stochastic asset under a suitable risk-neutral measure. This estimation requires the stochastic model to be calibrated to some history of sufficient length in the past. Apart from inherent limitations, due to the stochastic nature of the process, the predictive power is also limited by the simplifying assumptions of the common calibration methods, such as maximum likelihood estimation and regression methods, performed often without weights on the historic time series, or with static weights only. Here we propose a novel method of "intelligent" calibration, using learning neural networks in order to dynamically adapt the parameters of the stochastic model. Hence we have a stochastic process with time dependent parameters, the dynamics of the parameters being themselves learned continuously by a neural network. The back propagation in training the previous weights is limited to a certain memory length (in the examples we consider 10 previous business days), which is similar to the maximal time lag of autoregressive processes. We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts for the EURTRY and EUR-HUF exchange rates each.

  20. Information filtering based on corrected redundancy-eliminating mass diffusion.

    Directory of Open Access Journals (Sweden)

    Xuzhen Zhu

    Full Text Available Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.

  1. 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.

  2. 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…

  3. Relabeling exchange method (REM) for learning in neural networks

    Science.gov (United States)

    Wu, Wen; Mammone, Richard J.

    1994-02-01

    The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.

  4. 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

  5. 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.

  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. 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.

  8. . Redundancy and blocking in the spatial domain: A connectionist model

    Directory of Open Access Journals (Sweden)

    I. P. L. Mc Laren

    2002-01-01

    Full Text Available How can the observations of spatial blocking (Rodrigo, Chamizo, McLaren & Mackintosh, 1997 and cue redundancy (O’Keefe and Conway, 1978 be reconciled within the framework provided by an error-correcting, connectionist account of spatial navigation? I show that an implementation of McLaren’s (1995 better beta model can serve this purpose, and examine some of the implications for spatial learning and memory.

  9. One Size Does Not Fit All: Older Adults Benefit From Redundant Text in Multimedia Instruction

    Directory of Open Access Journals (Sweden)

    Barbara eFenesi

    2015-07-01

    Full Text Available The multimedia design of presentations typically ignores that younger and older adults have varying cognitive strengths and weaknesses. We examined whether differential instructional design may enhance learning in these populations. Younger and older participants viewed one of three computer-based presentations: Audio only (narration, Redundant (audio narration with redundant text, or Complementary (audio narration with non–redundant text and images. Younger participants learned better when audio narration was paired with relevant images compared to when audio narration was paired with redundant text. However, older participants learned best when audio narration was paired with redundant text. Younger adults, who presumably have a higher working memory capacity, appear to benefit more from complementary information that may drive deeper conceptual processing. In contrast, older adults learn better from presentations that support redundant coding across modalities, which may help mitigate the effects of age-related decline in working memory capacity. Additionally, several misconceptions of design quality appeared across age groups: both younger and older participants positively rated less effective designs. Findings suggest that one-size does not fit all, with older adults requiring unique multimedia design tailored to their cognitive abilities for effective learning.

  10. One size does not fit all: older adults benefit from redundant text in multimedia instruction.

    Science.gov (United States)

    Fenesi, Barbara; Vandermorris, Susan; Kim, Joseph A; Shore, David I; Heisz, Jennifer J

    2015-01-01

    The multimedia design of presentations typically ignores that younger and older adults have varying cognitive strengths and weaknesses. We examined whether differential instructional design may enhance learning in these populations. Younger and older participants viewed one of three computer-based presentations: Audio only (narration), Redundant (audio narration with redundant text), or Complementary (audio narration with non-redundant text and images). Younger participants learned better when audio narration was paired with relevant images compared to when audio narration was paired with redundant text. However, older participants learned best when audio narration was paired with redundant text. Younger adults, who presumably have a higher working memory capacity (WMC), appear to benefit more from complementary information that may drive deeper conceptual processing. In contrast, older adults learn better from presentations that support redundant coding across modalities, which may help mitigate the effects of age-related decline in WMC. Additionally, several misconceptions of design quality appeared across age groups: both younger and older participants positively rated less effective designs. Findings suggest that one-size does not fit all, with older adults requiring unique multimedia design tailored to their cognitive abilities for effective learning.

  11. Networking European Universities through e-learning (reviewed text)

    OpenAIRE

    Dlouhá, Jana

    2008-01-01

    Virtual Campus for a Sustainable Europe (VCSE) network has been selected to be part of the EC DG EAC Inventory of innovative good practice on education for sustainable development. The main purpose of the Inventory is to show concrete examples which have been implemented in the Member States under the concept of ESD in formal and non-formal learning contexts and which are at the forefront as regards innovative approaches. Projects/programmes selected as innovative good practice will be use...

  12. A geometric view on learning Bayesian network structures

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Vomlel, Jiří; Hemmecke, R.

    2010-01-01

    Roč. 51, č. 5 (2010), s. 578-586 ISSN 0888-613X. [PGM 2008] R&D Projects: GA AV ČR(CZ) IAA100750603; GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : learning Bayesian networks * standard imset * inclusion neighborhood * geometric neighborhood * GES algorithm Subject RIV: BA - General Mathematics Impact factor: 1.679, year: 2010 http://library.utia.cas.cz/separaty/2010/MTR/studeny-0342804. pdf

  13. Integration of social networks in the teaching and learning process

    Directory of Open Access Journals (Sweden)

    Cynthia Dedós Reyes

    2015-09-01

    Full Text Available In this research we explored the integration of social media in the process of learning and teaching, in a private higher education institution, in Puerto Rico. Attention was given to the perspectives of teachers and students. The participants —9 part-time teachers and 118 students— were selected based on availability. The results showed that teachers and students alike use social the network You Tube for academic purposes; and use Facebook, Twitter, and blogs for social purposes and entertainment. Results also revealed that there is no significant contrast between the perspectives of teachers and students digital immigrants.

  14. 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.

  15. Supervised learning of probability distributions by neural networks

    Science.gov (United States)

    Baum, Eric B.; Wilczek, Frank

    1988-01-01

    Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

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

    Science.gov (United States)

    Christiano Silva, Thiago; Raphael Amancio, Diego

    2013-03-01

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

  17. Mutual information and redundancy in spontaneous communication between cortical neurons.

    Science.gov (United States)

    Szczepanski, J; Arnold, M; Wajnryb, E; Amigó, J M; Sanchez-Vives, M V

    2011-03-01

    An important question in neural information processing is how neurons cooperate to transmit information. To study this question, we resort to the concept of redundancy in the information transmitted by a group of neurons and, at the same time, we introduce a novel concept for measuring cooperation between pairs of neurons called relative mutual information (RMI). Specifically, we studied these two parameters for spike trains generated by neighboring neurons from the primary visual cortex in the awake, freely moving rat. The spike trains studied here were spontaneously generated in the cortical network, in the absence of visual stimulation. Under these conditions, our analysis revealed that while the value of RMI oscillated slightly around an average value, the redundancy exhibited a behavior characterized by a higher variability. We conjecture that this combination of approximately constant RMI and greater variable redundancy makes information transmission more resistant to noise disturbances. Furthermore, the redundancy values suggest that neurons can cooperate in a flexible way during information transmission. This mostly occurs via a leading neuron with higher transmission rate or, less frequently, through the information rate of the whole group being higher than the sum of the individual information rates-in other words in a synergetic manner. The proposed method applies not only to the stationary, but also to locally stationary neural signals.

  18. 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.

  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. Improving Accessibility for Seniors in a Life-Long Learning Network: A Usability Study of Learning Websites

    Science.gov (United States)

    Gu, Xiaoqing; Ding, Rui; Fu, Shirong

    2011-01-01

    Senior citizens are comparatively vulnerable in accessing learning opportunities offered on the Internet due to usability problems in current web design. In an effort to build a senior-friendly learning web as a part of the Life-long Learning Network in Shanghai, usability studies of two websites currently available to Shanghai senior citizens…

  1. 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

  2. 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…

  3. Approximation Methods for Inference and Learning in Belief Networks: Progress and Future Directions

    National Research Council Canada - National Science Library

    Pazzan, Michael

    1997-01-01

    .... In this research project, we have investigated methods and implemented algorithms for efficiently making certain classes of inference in belief networks, and for automatically learning certain...

  4. 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

  5. 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…

  6. 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…

  7. 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,…

  8. A learning algorithm for oscillatory cellular neural networks.

    Science.gov (United States)

    Ho, C Y.; Kurokawa, H

    1999-07-01

    We present a cellular type oscillatory neural network for temporal segregation of stationary input patterns. The model comprises an array of locally connected neural oscillators with connections limited to a 4-connected neighborhood. The architecture is reminiscent of the well-known cellular neural network that consists of local connection for feature extraction. By means of a novel learning rule and an initialization scheme, global synchronization can be accomplished without incurring any erroneous synchrony among uncorrelated objects. Each oscillator comprises two mutually coupled neurons, and neurons share a piecewise-linear activation function characteristic. The dynamics of traditional oscillatory models is simplified by using only one plastic synapse, and the overall complexity for hardware implementation is reduced. Based on the connectedness of image segments, it is shown that global synchronization and desynchronization can be achieved by means of locally connected synapses, and this opens up a tremendous application potential for the proposed architecture. Furthermore, by using special grouping synapses it is demonstrated that temporal segregation of overlapping gray-level and color segments can also be achieved. Finally, simulation results show that the learning rule proposed circumvents the problem of component mismatches, and hence facilitates a large-scale integration.

  9. 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.

  10. Biosignals learning and synthesis using deep neural networks.

    Science.gov (United States)

    Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

    2017-09-25

    Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

  11. 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

  12. Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies.

    Science.gov (United States)

    Cohen, Raphael; Elhadad, Michael; Elhadad, Noémie

    2013-01-16

    The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining? We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. (a)For text mining, preprocessing the EHR corpus with fingerprinting yields

  13. Learning related modulation of functional retrieval networks in man.

    Science.gov (United States)

    Petersson, K M; Sandblom, J; Gisselgård, J; Ingvar, M

    2001-07-01

    The medial temporal lobe has been implicated in studies of episodic memory tasks involving spatio-temporal context and object-location conjunctions. We have previously demonstrated that an increased level of practice in a free-recall task parallels a decrease in the functional activity of several brain regions, including the medial temporal lobe, the prefrontal, the anterior cingulate, the anterior insular, and the posterior parietal cortices, that in concert demonstrate a move from elaborate controlled processing towards a higher degree of automaticity. Here we report data from two experiments that extend these initial observations. We used a similar experimental approach but probed for effects of retrieval paradigms and stimulus material. In the first experiment we investigated practice related changes during recognition of object-location conjunctions and in the second during free-recall of pseudo-words. Learning in a neural network is a dynamic consequence of information processing and network plasticity. The present and previous PET results indicate that practice can induce a learning related functional restructuring of information processing. Different adaptive processes likely subserve the functional re-organisation observed. These may in part be related to different demands for attentional and working memory processing. It appears that the role(s) of the prefrontal cortex and the medial temporal lobe in memory retrieval are complex, perhaps reflecting several different interacting processes or cognitive components. We suggest that an integrative interactive perspective on the role of the prefrontal and medial temporal lobe is necessary for an understanding of the processing significance of these regions in learning and memory. It appears necessary to develop elaborated and explicit computational models for prefrontal and medial temporal functions in order to derive detailed empirical predictions, and in combination with an efficient use and development of

  14. Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.

    Directory of Open Access Journals (Sweden)

    Xia Jiang

    Full Text Available The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS datasets, which involve millions of single nucleotide polymorphism (SNPs, where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects.We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer's dataset, we investigated the performance of MBS-IGain.When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer's dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is significant because we have increasingly

  15. Redundancy in Nigerian Business Organizations: Alternatives ...

    African Journals Online (AJOL)

    This theoretical discourse examined the incidence of work redundancy in Nigerian organizations as to offer alternative options. Certainly, some redundancy exercises may be necessary for the survival of the organizations but certain variables may influence employees' reactions to the exercises and thus influence the ...

  16. Increasing The Dexterity Of Redundant Robots

    Science.gov (United States)

    Seraji, Homayoun

    1990-01-01

    Redundant coordinates used to define additional tasks. Configuration control emerging as effective way to control motions of robot having more degrees of freedom than necessary to define trajectory of end effector and/or of object to be manipulated. Extra or redundant degrees of freedom used to give robot humanlike dexterity and versatility.

  17. Learning free energy landscapes using artificial neural networks.

    Science.gov (United States)

    Sidky, Hythem; Whitmer, Jonathan K

    2018-03-14

    Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.

  18. Learning free energy landscapes using artificial neural networks

    Science.gov (United States)

    Sidky, Hythem; Whitmer, Jonathan K.

    2018-03-01

    Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.

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

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

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

  20. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

    Directory of Open Access Journals (Sweden)

    Yuexiang Li

    2018-02-01

    Full Text Available Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1, lesion dermoscopic feature extraction (task 2 and lesion classification (task 3. A deep learning framework consisting of two fully convolutional residual networks (FCRN is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

  1. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network.

    Science.gov (United States)

    Li, Yuexiang; Shen, Linlin

    2018-02-11

    Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

  2. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

    Science.gov (United States)

    2018-01-01

    Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved. PMID:29439500

  3. Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning

    KAUST Repository

    Aljaafari, Nura

    2018-04-15

    The study and the analysis of marine ecosystems is a significant part of the marine science research. These systems are valuable resources for fisheries, improving water quality and can even be used in drugs production. The investigation of ichthyoplankton inhabiting these ecosystems is also an important research field. Ichthyoplankton are fish in their early stages of life. In this stage, the fish have relatively similar shape and are small in size. The currently used way of identifying them is not optimal. Marine scientists typically study such organisms by sending a team that collects samples from the sea which is then taken to the lab for further investigation. These samples need to be studied by an expert and usually end needing a DNA sequencing. This method is time-consuming and requires a high level of experience. The recent advances in AI have helped to solve and automate several difficult tasks which motivated us to develop a classification tool for ichthyoplankton. We show that using machine learning techniques, such as generative adversarial networks combined with transfer learning solves such a problem with high accuracy. We show that using traditional machine learning algorithms fails to solve it. We also give a general framework for creating a classification tool when the dataset used for training is a limited dataset. We aim to build a user-friendly tool that can be used by any user for the classification task and we aim to give a guide to the researchers so that they can follow in creating a classification tool.

  4. 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.

  5. Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

    Directory of Open Access Journals (Sweden)

    Wenhui Ma

    2017-06-01

    Full Text Available 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.

  6. 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.

  7. Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.

    Science.gov (United States)

    Walter, Florian; Röhrbein, Florian; Knoll, Alois

    2015-12-01

    The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. 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).

  9. Industrial plant electrical systems: Simplicity, reliability, cost savings, redundancies

    International Nuclear Information System (INIS)

    Silvestri, A.; Tommazzolli, F.; Pavia Univ.

    1992-01-01

    This article represents a compact but complete design and construction manual for industrial plant electrical systems. It is to be used by design engineers having prior knowledge of local power supply routes and voltages and regards principally the optimum choice of internal distribution systems which can be radial or single, double ringed or with various network configurations, and with single or multiple supplies, and many or few redundancies. After giving guidelines on the choosing of these options, the manual deals with problematics relevant to suitable cable sizing. A cost benefit benefit analysis method is suggested for the choice of the number of redundancies. Recommendations are given for the choice of transformers, motorized equipment, switch boards and circuit breakers. Reference is made to Italian electrical safety and building codes

  10. Reliability–redundancy allocation problem considering optimal redundancy strategy using parallel genetic algorithm

    International Nuclear Information System (INIS)

    Kim, Heungseob; Kim, Pansoo

    2017-01-01

    To maximize the reliability of a system, the traditional reliability–redundancy allocation problem (RRAP) determines the component reliability and level of redundancy for each subsystem. This paper proposes an advanced RRAP that also considers the optimal redundancy strategy, either active or cold standby. In addition, new examples are presented for it. Furthermore, the exact reliability function for a cold standby redundant subsystem with an imperfect detector/switch is suggested, and is expected to replace the previous approximating model that has been used in most related studies. A parallel genetic algorithm for solving the RRAP as a mixed-integer nonlinear programming model is presented, and its performance is compared with those of previous studies by using numerical examples on three benchmark problems. - Highlights: • Optimal strategy is proposed to solve reliability redundancy allocation problem. • The redundancy strategy uses parallel genetic algorithm. • Improved reliability function for a cold standby subsystem is suggested. • Proposed redundancy strategy enhances the system reliability.

  11. 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.…

  12. 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…

  13. 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…

  14. Enhancing Teaching and Learning Wi-Fi Networking Using Limited Resources to Undergraduates

    Science.gov (United States)

    Sarkar, Nurul I.

    2013-01-01

    Motivating students to learn Wi-Fi (wireless fidelity) wireless networking to undergraduate students is often difficult because many students find the subject rather technical and abstract when presented in traditional lecture format. This paper focuses on the teaching and learning aspects of Wi-Fi networking using limited hardware resources. It…

  15. Professional Online Presence and Learning Networks: Educating for Ethical Use of Social Media

    Science.gov (United States)

    Forbes, Dianne

    2017-01-01

    In a teacher education context, this study considers the use of social media for building a professional online presence and learning network. This article provides an overview of uses of social media in teacher education, presents a case study of key processes in relation to professional online presence and learning networks, and highlights…

  16. 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…

  17. Categorical Structure among Shared Features in Networks of Early-Learned Nouns

    Science.gov (United States)

    Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

    2009-01-01

    The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

  18. Policy learning and policy networks in theory and practice: The role of policy brokers in the Indonesian biodiesel policy network

    NARCIS (Netherlands)

    M. Howlett (Michael); Mukherjee, I. (Ishani); J.F.M. Koppenjan (Joop)

    2017-01-01

    textabstractThis paper examines how learning has been treated, generally, in policy network theories and what questions have been posed, and answered, about this phenomenon to date. We examine to what extent network characteristics and especially the presence of various types of brokers impede or

  19. 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.

  20. 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

  1. 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.

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

    Science.gov (United States)

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

    2014-12-01

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

  3. Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

    KAUST Repository

    Alharbi, Basma Mohammed; Zhang, Xiangliang

    2017-01-01

    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.

  4. 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.

  5. 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.

  6. Redundant correlation effect on personalized recommendation

    Science.gov (United States)

    Qiu, Tian; Han, Teng-Yue; Zhong, Li-Xin; Zhang, Zi-Ke; Chen, Guang

    2014-02-01

    The high-order redundant correlation effect is investigated for a hybrid algorithm of heat conduction and mass diffusion (HHM), through both heat conduction biased (HCB) and mass diffusion biased (MDB) correlation redundancy elimination processes. The HCB and MDB algorithms do not introduce any additional tunable parameters, but keep the simple character of the original HHM. Based on two empirical datasets, the Netflix and MovieLens, the HCB and MDB are found to show better recommendation accuracy for both the overall objects and the cold objects than the HHM algorithm. Our work suggests that properly eliminating the high-order redundant correlations can provide a simple and effective approach to accurate recommendation.

  7. Exploiting Redundancy in an OFDM SDR Receiver

    Directory of Open Access Journals (Sweden)

    Tomas Palenik

    2009-01-01

    Full Text Available Common OFDM system contains redundancy necessary to mitigate interblock interference and allows computationally effective single-tap frequency domain equalization in receiver. Assuming the system implements an outer error correcting code and channel state information is available in the receiver, we show that it is possible to understand the cyclic prefix insertion as a weak inner ECC encoding and exploit the introduced redundancy to slightly improve error performance of such a system. In this paper, an easy way to implement modification to an existing SDR OFDM receiver is presented. This modification enables the utilization of prefix redundancy, while preserving full compatibility with existing OFDM-based communication standards.

  8. Do technologies have politics? The new paradigm and pedagogy in networked learning

    OpenAIRE

    Jones, Chris

    2001-01-01

    This paper explores the relationships between the technologies deployed in networked and e-Learning and the pedagogies and politics associated with them. Networked learning and the related move to e-Learning are coincident with the globalisation, commodification and massification of Higher Education. It examines the hard and soft forms of technological determinism (TD) found in the current advocacy of technological futures for Higher Education. Hard TD claims that new technologies bring about...

  9. Smart Social Networking: 21st Century Teaching and Learning Skills

    Directory of Open Access Journals (Sweden)

    Helen B. Boholano

    2017-06-01

    Full Text Available Education in the 21st century highlights globalization and internationalization. Preservice teachers in the 21st century are technology savvy. To effectively engage and teach generation Z students, preservice teachers will help the educational system meet this requirement. The educational systems must be outfitted with a prerequisite of ICT resources both hardware and software, and curricula must be designed to promote a collaborative learner-centered environment to which students will relate and respond. This study determines the 21st century skills possessed by the pre-service teachers in terms of social networking. Pre-service teachers use computers in very advanced ways, but educators must remember that they still need guidance to use technology safely and effectively. Through social media the pre-service teachers can use a multitude of applications, including Web 2.0, for their projects. Smart social networking requires critical-thinking skills and the ability to integrate and evaluate real-world scenarios and authentic learning skills for validation.

  10. 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

  11. 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.

  12. A Telescopic Binary Learning Machine for Training Neural Networks.

    Science.gov (United States)

    Brunato, Mauro; Battiti, Roberto

    2017-03-01

    This paper proposes a new algorithm based on multiscale stochastic local search with binary representation for training neural networks [binary learning machine (BLM)]. We study the effects of neighborhood evaluation strategies, the effect of the number of bits per weight and that of the maximum weight range used for mapping binary strings to real values. Following this preliminary investigation, we propose a telescopic multiscale version of local search, where the number of bits is increased in an adaptive manner, leading to a faster search and to local minima of better quality. An analysis related to adapting the number of bits in a dynamic way is presented. The control on the number of bits, which happens in a natural manner in the proposed method, is effective to increase the generalization performance. The learning dynamics are discussed and validated on a highly nonlinear artificial problem and on real-world tasks in many application domains; BLM is finally applied to a problem requiring either feedforward or recurrent architectures for feedback control.

  13. Accelerating Innovation Through Coopetition: The Innovation Learning Network Experience.

    Science.gov (United States)

    McCarthy, Chris; Ford Carleton, Penny; Krumpholz, Elizabeth; Chow, Marilyn P

    Coopetition, the simultaneous pursuit of cooperation and competition, is a growing force in the innovation landscape. For some organizations, the primary mode of innovation continues to be deeply secretive and highly competitive, but for others, a new style of shared challenges, shared purpose, and shared development has become a superior, more efficient way of working to accelerate innovation capabilities and capacity. Over the last 2 decades, the literature base devoted to coopetition has gradually expanded. However, the field is still in its infancy. The majority of coopetition research is qualitative, primarily consisting of case studies. Few studies have addressed the nonprofit sector or service industries such as health care. The authors believe that this article may offer a unique perspective on coopetition in the context of a US-based national health care learning alliance designed to accelerate innovation, the Innovation Learning Network or ILN. The mission of the ILN is to "Share the joy and pain of innovation," accelerating innovation by sharing solutions, teaching techniques, and cultivating friendships. These 3 pillars (sharing, teaching, and cultivating) form the foundation for coopetition within the ILN. Through the lens of coopetition, we examine the experience of the ILN over the last 10 years and provide case examples that illustrate the benefits and challenges of coopetition in accelerating innovation in health care.

  14. Reinforcement learning techniques for controlling resources in power networks

    Science.gov (United States)

    Kowli, Anupama Sunil

    As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

  15. Memory and learning in a class of neural network models

    International Nuclear Information System (INIS)

    Wallace, D.J.

    1986-01-01

    The author discusses memory and learning properties of the neural network model now identified with Hopfield's work. The model, how it attempts to abstract some key features of the nervous system, and the sense in which learning and memory are identified in the model are described. A brief report is presented on the important role of phase transitions in the model and their implications for memory capacity. The results of numerical simulations obtained using the ICL Distributed Array Processors at Edinburgh are presented. A summary is presented on how the fraction of images which are perfectly stored, depends on the number of nodes and the number of nominal images which one attempts to store using the prescription in Hopfield's paper. Results are presented on the second phase transition in the model, which corresponds to almost total loss of storage capacity as the number of nominal images is increased. Results are given on the performance of a new iterative algorithm for exact storage of up to N images in an N node model

  16. Structure Learning and Statistical Estimation in Distribution Networks - Part I

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-02-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as those related to demand response, outage detection and management, and improved load-monitoring. In this two part paper, inspired by proliferation of the metering technology, we discuss estimation problems in structurally loopy but operationally radial distribution grids from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. In Part I, the objective is to learn the operational layout of the grid. Part II of this paper presents algorithms that estimate load statistics or line parameters in addition to learning the grid structure. Further, Part II discusses the problem of structure estimation for systems with incomplete measurement sets. 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.

  17. To Enhance Collaborative Learning and Practice Network Knowledge with a Virtualization Laboratory and Online Synchronous Discussion

    Science.gov (United States)

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

    2014-01-01

    Recently, various computer networking courses have included additional laboratory classes in order to enhance students' learning achievement. However, these classes need to establish a suitable laboratory where each student can connect network devices to configure and test functions within different network topologies. In this case, the Linux…

  18. 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...... benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling....

  19. 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…

  20. 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…

  1. 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…

  2. Network Training for a Boy with Learning Disabilities and Behaviours That Challenge

    Science.gov (United States)

    Cooper, Kate; McElwee, Jennifer

    2016-01-01

    Background: Network Training is an intervention that draws upon systemic ideas and behavioural principles to promote positive change in networks of support for people defined as having a learning disability. To date, there are no published case studies looking at the outcomes of Network Training. Materials and Methods: This study aimed to…

  3. 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…

  4. Effects of the ISIS Recommender System for Navigation Support in Self-Organised Learning Networks

    Science.gov (United States)

    Drachsler, Hendrik; Hummel, Hans; van den Berg, Bert; Eshuis, Jannes; Waterink, Wim; Nadolski, Rob; Berlanga, Adriana; Boers, Nanda; Koper, Rob

    2009-01-01

    The need to support users of the Internet with the selection of information is becoming more important. Learners in complex, self-organising Learning Networks have similar problems and need guidance to find and select most suitable learning activities, in order to attain their lifelong learning goals in the most efficient way. Several research…

  5. 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…

  6. Celebrating the Tenth Networked Learning Conference: Looking Back and Moving Forward

    DEFF Research Database (Denmark)

    de Laat, Maarten; Ryberg, Thomas

    2018-01-01

    , actor network theory), learning environments and social media (e.g. LMS, MOOC, Virtual Worlds, Twitter, Facebook), technologies (e.g. phone, laptop, tablet), methodology (e.g. quantitative, qualitative) and related research in the domain of e-learning (e-learning, CSCL, TEL). The findings are placed...

  7. Structuring and Regulating Collaborative Learning in Higher Education with Wireless Networks and Mobile Tools

    Science.gov (United States)

    Jarvela, Sanna; Naykki, Piia; Laru, Jari; Luokkanen, Tiina

    2007-01-01

    In our recent research we have explored possibilities to scaffold collaborative learning in higher education with wireless networks and mobile tools. The pedagogical ideas are grounded on concepts of collaborative learning, including the socially shared origin of cognition, as well as self-regulated learning theory. This paper presents our three…

  8. The Redundancy Effect on Retention and Transfer for Individuals with High Symptoms of ADHD

    Science.gov (United States)

    Brown, Victoria; Lewis, David; Toussaint, Mario

    2016-01-01

    The multimedia elements of text and audio need to be carefully integrated together to maximize the impact of those elements for learning in a multimedia environment. Redundancy information presented through audio and visual channels can inhibit learning for individuals diagnosed with ADHD, who may experience challenges in the processing of…

  9. Developing student engagement in networked teaching and learning practices through problem- and project-based learning approaches

    DEFF Research Database (Denmark)

    Andreasen, Lars Birch; Lerche Nielsen, Jørgen

    2012-01-01

    This paper focuses on how learner engagement can be facilitated through use of social media and communication technologies. The discussions are based on the Danish Master’s Programme of ICT and Learning (MIL), where students study in groups within a networked learning structure. The paper reflect...

  10. An Analysis of Social Network Websites for Language Learning: Implications for Teaching and Learning English as a Second Language

    Science.gov (United States)

    Liu, M.; Abe, K.; Cao, M. W.; Liu, S.; Ok, D. U.; Park, J.; Parrish, C.; Sardegna, V. G.

    2015-01-01

    Although educators are excited about the potential of social network sites for language learning (SNSLL), there is a lack of understanding of how SNSLL can be used to facilitate teaching and learning for English as Second language (ESL) instructors and students. The purpose of this study was to examine the affordances of four selected SNSLL…

  11. Image Registration Using Redundant Wavelet Transforms

    National Research Council Canada - National Science Library

    Brown, Richard

    2001-01-01

    .... In our research, we present a fundamentally new wavelet-based registration algorithm utilizing redundant transforms and a masking process to suppress the adverse effects of noise and improve processing efficiency...

  12. The Effects of Race Conditions When Implementing Single-Source Redundant Clock Trees in Triple Modular Redundant Synchronous Architectures

    Science.gov (United States)

    Berg, Melanie D.; Kim, Hak S.; Phan, Anthony M.; Seidleck, Christina M.; Label, Kenneth A.; Pellish, Jonathan A.; Campola, Michael J.

    2016-01-01

    We present the challenges that arise when using redundant clock domains due to their time-skew. Radiation data show that a singular clock domain provides an improved triple modular redundant (TMR) scheme over redundant clocks.

  13. Redundancy for electric motors in spacecraft applications

    Science.gov (United States)

    Smith, Robert J.; Flew, Alastair R.

    1986-01-01

    The parts of electric motors which should be duplicated in order to provide maximum reliability in spacecraft application are identified. Various common types of redundancy are described. The advantages and disadvantages of each are noted. The principal types are illustrated by reference to specific examples. For each example, constructional details, basic performance data and failure modes are described, together with a discussion of the suitability of particular redundancy techniques to motor types.

  14. Dynamic Control of Kinematically Redundant Robotic Manipulators

    Directory of Open Access Journals (Sweden)

    Erling Lunde

    1987-07-01

    Full Text Available Several methods for task space control of kinematically redundant manipulators have been proposed in the literature. Most of these methods are based on a kinematic analysis of the manipulator. In this paper we propose a control algorithm in which we are especially concerned with the manipulator dynamics. The algorithm is particularly well suited for the class of redundant manipulators consisting of a relatively small manipulator mounted on a larger positioning part.

  15. RELATION BETWEEN COOPERATION AND ORGANIZATIONAL LEARNING WITH THE COMPETITIVENESS IN AN INTERORGANIZATIONAL NETWORK

    Directory of Open Access Journals (Sweden)

    Paulo Cesar Zonta

    2015-05-01

    Full Text Available The study analyzed the relationship between cooperation and organizational learning with competitiveness in a small and medium enterprises (SME network, with business of the groups of the Commercial and Industrial Association of Chapecó (ACIC. The methodology used was quantitative, with the factorial analysis. Currently, ACIC has 14 groups and 236 SME´s nucleated, developing joint activities of economic and social sustainability in Chapecó. The theoretical study raised concepts already endorsed by the scientific community on interorganizational networks, competitiveness, cooperation and organizational learning. The results demonstrated that indicators related to cooperation and learning in horizontal networks are characterized as antecedents of competitiveness in organizational networks, and that there is a positive correlation between the constructs cooperation and organizational learning with competitiveness construct. The study confirms the belief that small businesses associated in networks can increase their competitiveness, thus contributing to regional development.

  16. Using Epistemic Network Analysis to understand core topics as planned learning objectives

    DEFF Research Database (Denmark)

    Allsopp, Benjamin Brink; Dreyøe, Jonas; Misfeldt, Morten

    Epistemic Network Analysis is a tool developed by the epistemic games group at the University of Wisconsin Madison for tracking the relations between concepts in students discourse (Shaffer 2017). In our current work we are applying this tool to learning objectives in teachers digital preparation....... The danish mathematics curriculum is organised in six competencies and three topics. In the recently implemented learning platforms teacher choose which of the mathematical competencies that serves as objective for a specific lesson or teaching sequence. Hence learning objectives for lessons and teaching...... sequences are defining a network of competencies, where two competencies are closely related of they often are part of the same learning objective or teaching sequence. We are currently using Epistemic Network Analysis to study these networks. In the poster we will include examples of different networks...

  17. Intuitive theories of information: beliefs about the value of redundancy.

    Science.gov (United States)

    Soll, J B

    1999-03-01

    In many situations, quantity estimates from multiple experts or diagnostic instruments must be collected and combined. Normatively, and all else equal, one should value information sources that are nonredundant, in the sense that correlation in forecast errors should be minimized. Past research on the preference for redundancy has been inconclusive. While some studies have suggested that people correctly place higher value on uncorrelated inputs when collecting estimates, others have shown that people either ignore correlation or, in some cases, even prefer it. The present experiments show that the preference for redundancy depends on one's intuitive theory of information. The most common intuitive theory identified is the Error Tradeoff Model (ETM), which explicitly distinguishes between measurement error and bias. According to ETM, measurement error can only be averaged out by consulting the same source multiple times (normatively false), and bias can only be averaged out by consulting different sources (normatively true). As a result, ETM leads people to prefer redundant estimates when the ratio of measurement error to bias is relatively high. Other participants favored different theories. Some adopted the normative model, while others were reluctant to mathematically average estimates from different sources in any circumstance. In a post hoc analysis, science majors were more likely than others to subscribe to the normative model. While tentative, this result lends insight into how intuitive theories might develop and also has potential ramifications for how statistical concepts such as correlation might best be learned and internalized. Copyright 1999 Academic Press.

  18. Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

    Science.gov (United States)

    Sadeghi, Zahra

    2016-09-01

    In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

  19. How Redundant Are Redundant Color Adjectives? An Efficiency-Based Analysis of Color Overspecification

    OpenAIRE

    Rubio-Fern?ndez, Paula

    2016-01-01

    Color adjectives tend to be used redundantly in referential communication. I propose that redundant color adjectives are often intended to exploit a color contrast in the visual context and hence facilitate object identification, despite not being necessary to establish unique reference. Two language-production experiments investigated two types of factors that may affect the use of redundant color adjectives: factors related to the efficiency of color in the visual context and factors relate...

  20. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

    Science.gov (United States)

    Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

    2017-04-10

    This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

  1. On the Use of Machine Learning for Identifying Botnet Network Traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    contemporary approaches use machine learning techniques for identifying malicious traffic. This paper presents a survey of contemporary botnet detection methods that rely on machine learning for identifying botnet network traffic. The paper provides a comprehensive overview on the existing scientific work thus...... contributing to the better understanding of capabilities, limitations and opportunities of using machine learning for identifying botnet traffic. Furthermore, the paper outlines possibilities for the future development of machine learning-based botnet detection systems....

  2. THE USE OF SOCIAL NETWORKS IN THE PROCESS OF LEARNING ENGLISH AS A SECOND LANGUAGE

    Directory of Open Access Journals (Sweden)

    Halyna I. Sotska

    2018-02-01

    Full Text Available In the recent decade many changes in the process of education took place because of the development of information and communication technologies. Online social groups tend to be used by teachers and students for formal (study and informal (personal communication purposes. An efficient teacher may turn social networks into an effective tool, encouraging students to communicate in the target language. With the help of social networks the teacher can activate students in the process of learning, create situations for better understanding and perceiving the material. The use of such approaches as blended learning, corporative learning and active learning helps make the classes more attractive and effective. Moreover, social networks can help in the development of students’ creativity, provision of feedback and cooperative learning. The article deals with the question of influence of Massive online open courses on effectiveness of the educational process for students who learn English as a second language.

  3. Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

    Science.gov (United States)

    Gilra, Aditya; Gerstner, Wulfram

    2017-11-27

    The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

  4. Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic System

    Directory of Open Access Journals (Sweden)

    A. A. Salama

    2015-03-01

    Full Text Available In this paper, we present a review of different recommender system algorithms that are utilized in social networks based e-Learning systems. Future research will include our proposed our e-Learning system that utilizes Recommender System and Social Network. Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache in [21, 22, 23] and Salama et al. in [24-66].The purpose of this paper is to utilize a neutrosophic set to analyze social networks data conducted through learning activities.

  5. Celebrating the Tenth Networked Learning Conference: Looking Back and Moving Forward

    DEFF Research Database (Denmark)

    de Laat, Maarten; Ryberg, Thomas

    2018-01-01

    conferences with the aim to describe some general trends and developments in networked learning research as they emerge and fade out over the years. In order to do so the authors use the proceedings of each networked learning conference (from 1998 till 2016) as a compiled dataset. This dataset forms a text...... corpus that has been analysed with Voyant tools (Sinclair and Rockwell 2016) specifically designed for analysing digital texts. Voyant tools are used to generate a set of word clouds (Cirrus) in order to visualise networked learning research-related terms that feature most frequently in each set...

  6. Network affordances through online learning: Increasing use and complexity.

    OpenAIRE

    Hajhashemi, Karim; Anderson, Neil; Jackson, Cliff; Caltabiano, Nerina

    2013-01-01

    Computers, mobile devices and the Internet have enabled a learning environment described as online learning or a variety of other terms such as e-learning. Researchers believe that online learning has become more complex due to learners' sharing and acquiring knowledge at a variety of remote locations, in a variety of modalities. However, advances in technology and the integration of ICT with teaching and learning settings have quickened the growth of online learning and importantly have chan...

  7. An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments

    Directory of Open Access Journals (Sweden)

    Xiong Luo

    2016-07-01

    Full Text Available With the recent emergence of wireless sensor networks (WSNs in the cloud computing environment, it is now possible to monitor and gather physical information via lots of sensor nodes to meet the requirements of cloud services. Generally, those sensor nodes collect data and send data to sink node where end-users can query all the information and achieve cloud applications. Currently, one of the main disadvantages in the sensor nodes is that they are with limited physical performance relating to less memory for storage and less source of power. Therefore, in order to avoid such limitation, it is necessary to develop an efficient data prediction method in WSN. To serve this purpose, by reducing the redundant data transmission between sensor nodes and sink node while maintaining the required acceptable errors, this article proposes an entropy-based learning scheme for data prediction through the use of kernel least mean square (KLMS algorithm. The proposed scheme called E-KLMS develops a mechanism to maintain the predicted data synchronous at both sides. Specifically, the kernel-based method is able to adjust the coefficients adaptively in accordance with every input, which will achieve a better performance with smaller prediction errors, while employing information entropy to remove these data which may cause relatively large errors. E-KLMS can effectively solve the tradeoff problem between prediction accuracy and computational efforts while greatly simplifying the training structure compared with some other data prediction approaches. What’s more, the kernel-based method and entropy technique could ensure the prediction effect by both improving the accuracy and reducing errors. Experiments with some real data sets have been carried out to validate the efficiency and effectiveness of E-KLMS learning scheme, and the experiment results show advantages of the our method in prediction accuracy and computational time.

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

    Science.gov (United States)

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

    2016-11-01

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

  9. Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

    Directory of Open Access Journals (Sweden)

    Alexandru D. Iordan

    2018-01-01

    Full Text Available Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA and 20 older adults (OA were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of

  10. Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

    Science.gov (United States)

    Ammenwerth, Elske; Hackl, Werner O

    2017-01-01

    Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

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

    Science.gov (United States)

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

    2017-08-01

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

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

    Science.gov (United States)

    Miller, Robert D.

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

  13. ANA, automatic natural learning of a semantic network

    International Nuclear Information System (INIS)

    Enguehard, Chantal

    1992-01-01

    The objective of this research thesis is the automatic extraction of terminology and the study of its automatic structuring in order to produce a semantic network. Such an operation is applied to text corpus representing knowledge on a specific field in order to select the relevant technical vocabulary regarding this field. Thus, the author developed a method and a software for the automatic acquisition of terminology items. The author first gives an overview of systems and methods of document indexing and of thesaurus elaboration, and a brief presentation of the state-of-the-art of learning. Then, he discusses some drawbacks of computer systems of natural language processing which are using large knowledge sources such as grammars and dictionaries. After a presentation of the adopted approach and of some hypotheses, the author defines objects and operators which are necessary for an easier data handling, presents the knowledge acquisition process, and finally precisely describes the system computerization. Some results are assessed and discussed, and limitations and perspectives are commented [fr

  14. Improving Protein Fold Recognition by Deep Learning Networks

    Science.gov (United States)

    Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

    2015-12-01

    For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl’s benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

  15. Improving Protein Fold Recognition by Deep Learning Networks.

    Science.gov (United States)

    Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

    2015-12-04

    For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

  16. On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

    Science.gov (United States)

    Schmitt, Michael

    2004-09-01

    We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

  17. A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Sho Fukuda

    2014-12-01

    Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

  18. Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks

    DEFF Research Database (Denmark)

    Paluszewski, Martin; Hamelryck, Thomas Wim

    2010-01-01

    Background Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations...

  19. Learning the Structure of Bayesian Network from Small Amount of Data

    Directory of Open Access Journals (Sweden)

    Bogdan COCU

    2009-12-01

    Full Text Available Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways to do this is using representation and reasoning withBayesian networks. Creation of a Bayesian network consists in two stages. First stage isto design the node structure and directed links between them. Choosing of a structurefor network can be done either through empirical developing by human experts orthrough machine learning algorithm. The second stage is completion of probabilitytables for each node. Using a machine learning method is useful, especially when wehave a big amount of leaning data. But in many fields the amount of data is small,incomplete and inconsistent. In this paper, we make a case study for choosing the bestlearning method for small amount of learning data. Means more experiments we dropconclusion of using existent methods for learning a network structure.

  20. Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory

    DEFF Research Database (Denmark)

    Lüders, Benno; Schläger, Mikkel; Korach, Aleksandra

    2017-01-01

    it easier to find unused memory location and therefor facilitates the evolution of continual learning networks. Our results suggest that augmenting evolving networks with an external memory component is not only a viable mechanism for adaptive behaviors in neuroevolution but also allows these networks...... a new task is learned. This paper takes a step in overcoming this limitation by building on the recently proposed Evolving Neural Turing Machine (ENTM) approach. In the ENTM, neural networks are augmented with an external memory component that they can write to and read from, which allows them to store...... associations quickly and over long periods of time. The results in this paper demonstrate that the ENTM is able to perform one-shot learning in reinforcement learning tasks without catastrophic forgetting of previously stored associations. Additionally, we introduce a new ENTM default jump mechanism that makes...