An Attempt To Design Synchronous Collaborative Learning Environments for Peer Dyads on the World Wide Web.

ERIC Educational Resources Information Center

Lee, Fong-Lok; Liang, Steven; Chan, Tak-Wai

1999-01-01

Describes the design, implementation, and preliminary evaluation of three synchronous distributed learning prototype systems: Co-Working System, Working Along System, and Hybrid System. Each supports a particular style of interaction, referred to a socio-activity learning model, between members of student dyads (pairs). All systems were…

Multi-Agent Framework for Virtual Learning Spaces.

ERIC Educational Resources Information Center

Sheremetov, Leonid; Nunez, Gustavo

1999-01-01

Discussion of computer-supported collaborative learning, distributed artificial intelligence, and intelligent tutoring systems focuses on the concept of agents, and describes a virtual learning environment that has a multi-agent system. Describes a model of interactions in collaborative learning and discusses agents for Web-based virtual…

Parallel-distributed mobile robot simulator

NASA Astrophysics Data System (ADS)

Okada, Hiroyuki; Sekiguchi, Minoru; Watanabe, Nobuo

1996-06-01

The aim of this project is to achieve an autonomous learning and growth function based on active interaction with the real world. It should also be able to autonomically acquire knowledge about the context in which jobs take place, and how the jobs are executed. This article describes a parallel distributed movable robot system simulator with an autonomous learning and growth function. The autonomous learning and growth function which we are proposing is characterized by its ability to learn and grow through interaction with the real world. When the movable robot interacts with the real world, the system compares the virtual environment simulation with the interaction result in the real world. The system then improves the virtual environment to match the real-world result more closely. This the system learns and grows. It is very important that such a simulation is time- realistic. The parallel distributed movable robot simulator was developed to simulate the space of a movable robot system with an autonomous learning and growth function. The simulator constructs a virtual space faithful to the real world and also integrates the interfaces between the user, the actual movable robot and the virtual movable robot. Using an ultrafast CG (computer graphics) system (FUJITSU AG series), time-realistic 3D CG is displayed.

Ontology-Based Multimedia Authoring Tool for Adaptive E-Learning

ERIC Educational Resources Information Center

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

2010-01-01

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

A Framework for Open, Flexible and Distributed Learning.

ERIC Educational Resources Information Center

Khan, Badrul H.

Designing open, flexible distance learning systems on the World Wide Web requires thoughtful analysis and investigation combined with an understanding of both the Web's attributes and resources and the ways instructional design principles can be applied to tap the Web's potential. A framework for open, flexible, and distributed learning has been…

An Intelligent System for Document Retrieval in Distributed Office Environments.

ERIC Educational Resources Information Center

Mukhopadhyay, Uttam; And Others

1986-01-01

MINDS (Multiple Intelligent Node Document Servers) is a distributed system of knowledge-based query engines for efficiently retrieving multimedia documents in an office environment of distributed workstations. By learning document distribution patterns and user interests and preferences during system usage, it customizes document retrievals for…

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

PubMed

Jiang, Yizhang; Wu, Dongrui; Deng, Zhaohong; Qian, Pengjiang; Wang, Jun; Wang, Guanjin; Chung, Fu-Lai; Choi, Kup-Sze; Wang, Shitong

2017-12-01

Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

Developing Army Leaders: Lessons for Teaching Critical Thinking in Distributed, Resident, and Mixed-Delivery Venues

DTIC Science & Technology

2014-01-01

Based and Affective Theories of Learning Outcomes to New Methods of Training Evaluation,” Journal of Applied Psychology Monograph, Vol. 2, No. 2, 1993...officers. Thus, the Command and Staff General School offers non-resident alternatives for the Common Core: an advanced distributed learning (ADL...course delivered online and a course combining in-person instruction and distributed learning taught in The Army School System (TASS). This report

Web-Based Learning Support System

NASA Astrophysics Data System (ADS)

Fan, Lisa

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

Distributed Learning and Information Dynamics In Networked Autonomous Systems

DTIC Science & Technology

2015-11-20

2009 to June 30, 2015 4. TITLE AND SUBTITLE DISTRIBUTED LEARNING AND INFORMATION DYNAMICS IN NETWORKED AUTONOMOUS SYSTEMS 5a. CONTRACT NUMBER 5b...AUTONOMOUS SYSTEMS AFOSR Grant #FA9550–09–1–0538 PI: Eric Feron (current) Jeff S. Shamma (former) Georgia Institute of Technology Atlanta, GA 30332 1...Control. Design of event-based optimal remote estimation systems : We have proposed two new for- mulations to study the design of optimal remote

A Comparative Study of E-Learning System for Smart Education

ERIC Educational Resources Information Center

An, SangJin; Lee, Eunkyoung; Lee, YoungJun

2013-01-01

Korean government aims to implement SMART education nationwide, so it is planning many ways to provide digital learning contents. There are some ways of distributing digital contents, and each way has its own characteristics. Edunet is a nationwide system for providing educational resource. Cyber Home Learning System is a regional service which…

Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics.

PubMed

Yuan, Chengzhi; Licht, Stephen; He, Haibo

2017-09-26

In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

A Framework System for Intelligent Support in Open Distributed Learning Environments--A Look Back from 16 Years Later

ERIC Educational Resources Information Center

Hoppe, H. Ulrich

2016-01-01

The 1998 paper by Martin Mühlenbrock, Frank Tewissen, and myself introduced a multi-agent architecture and a component engineering approach for building open distributed learning environments to support group learning in different types of classroom settings. It took up prior work on "multiple student modeling" as a method to configure…

A Learning Management System Enhanced with Internet of Things Applications

ERIC Educational Resources Information Center

Mershad, Khaleel; Wakim, Pilar

2018-01-01

A breakthrough in the development of online learning occurred with the utilization of Learning Management Systems (LMS) as a tool for creating, distributing, tracking, and managing various types of educational and training material. Since the appearance of the first LMS, major technological enhancements transformed this tool into a powerful…

Mentoring in a Distributed Learning Social Work Program

ERIC Educational Resources Information Center

Jensen, Donna

2017-01-01

Students in alternative education programs often experience differential access to faculty, advisors, university support systems, and the supportive culture established by being on campus. This study is a descriptive-exploratory program evaluation of the distributed learning social work mentoring program at California State University, Chico. The…

Hybrid Multiagent System for Automatic Object Learning Classification

NASA Astrophysics Data System (ADS)

Gil, Ana; de La Prieta, Fernando; López, Vivian F.

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of learning object metadata, which provides learners in a web-based educational system with ubiquitous access to multiple distributed repositories. This article presents a hybrid agent-based architecture that enables the recovery of learning objects tagged in Learning Object Metadata (LOM) and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives.

Extended Relation Metadata for SCORM-Based Learning Content Management Systems

ERIC Educational Resources Information Center

Lu, Eric Jui-Lin; Horng, Gwoboa; Yu, Chia-Ssu; Chou, Ling-Ying

2010-01-01

To increase the interoperability and reusability of learning objects, Advanced Distributed Learning Initiative developed a model called Content Aggregation Model (CAM) to describe learning objects and express relationships between learning objects. However, the suggested relations defined in the CAM can only describe structure-oriented…

A digital protection system incorporating knowledge based learning

NASA Astrophysics Data System (ADS)

Watson, Karan; Russell, B. Don; McCall, Kurt

A digital system architecture used to diagnoses the operating state and health of electric distribution lines and to generate actions for line protection is presented. The architecture is described functionally and to a limited extent at the hardware level. This architecture incorporates multiple analysis and fault-detection techniques utilizing a variety of parameters. In addition, a knowledge-based decision maker, a long-term memory retention and recall scheme, and a learning environment are described. Preliminary laboratory implementations of the system elements have been completed. Enhanced protection for electric distribution feeders is provided by this system. Advantages of the system are enumerated.

A Distributed Intelligent E-Learning System

ERIC Educational Resources Information Center

Kristensen, Terje

2016-01-01

An E-learning system based on a multi-agent (MAS) architecture combined with the Dynamic Content Manager (DCM) model of E-learning, is presented. We discuss the benefits of using such a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA). This MAS architecture may also be used within…

Exploring Distributed Leadership for the Quality Management of Online Learning Environments

ERIC Educational Resources Information Center

Palmer, Stuart; Holt, Dale; Gosper, Maree; Sankey, Michael; Allan, Garry

2013-01-01

Online learning environments (OLEs) are complex information technology (IT) systems that intersect with many areas of university organisation. Distributed models of leadership have been proposed as appropriate for the good governance of OLEs. Based on theoretical and empirical research, a group of Australian universities proposed a framework for…

The Evolution of Electronic Pedagogy in an Outcome Based Learning Environment: Learning, Teaching, and the Culture of Technology at California's Newest University--CSU Monterey Bay.

ERIC Educational Resources Information Center

Baldwin, George

California State University Monterey Bay (CSUMB) is the newest university in the CSU system. CSUMB's vision statement distinguishes the institution from others in the system by promoting learning paradigms of Outcome Based Education (OBE) and communication technologies of distributed learning (DL). Faculty are committed to the experimental use of…

Observer-based distributed adaptive iterative learning control for linear multi-agent systems

NASA Astrophysics Data System (ADS)

Li, Jinsha; Liu, Sanyang; Li, Junmin

2017-10-01

This paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.

Governance and assessment in a widely distributed medical education program in Australia.

PubMed

Solarsh, Geoff; Lindley, Jennifer; Whyte, Gordon; Fahey, Michael; Walker, Amanda

2012-06-01

The learning objectives, curriculum content, and assessment standards for distributed medical education programs must be aligned across the health care systems and community contexts in which their students train. In this article, the authors describe their experiences at Monash University implementing a distributed medical education program at metropolitan, regional, and rural Australian sites and an offshore Malaysian site, using four different implementation models. Standardizing learning objectives, curriculum content, and assessment standards across all sites while allowing for site-specific implementation models created challenges for educational alignment. At the same time, this diversity created opportunities to customize the curriculum to fit a variety of settings and for innovations that have enriched the educational system as a whole.Developing these distributed medical education programs required a detailed review of Monash's learning objectives and curriculum content and their relevance to the four different sites. It also required a review of assessment methods to ensure an identical and equitable system of assessment for students at all sites. It additionally demanded changes to the systems of governance and the management of the educational program away from a centrally constructed and mandated curriculum to more collaborative approaches to curriculum design and implementation involving discipline leaders at multiple sites.Distributed medical education programs, like that at Monash, in which cohorts of students undertake the same curriculum in different contexts, provide potentially powerful research platforms to compare different pedagogical approaches to medical education and the impact of context on learning outcomes.

Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines.

PubMed

Vanli, Nuri Denizcan; Sayin, Muhammed O; Delibalta, Ibrahim; Kozat, Suleyman Serdar

2017-03-01

We study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data- and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data.

Transforming the Doctorate from Residential to Online: A Distributed PhD Learning Technologies

ERIC Educational Resources Information Center

Jones, Greg; Warren, Scott J.; Ennis-Cole, Demetria; Knezek, Gerald; Lin, Lin; Norris, Cathie

2014-01-01

This article discusses a systemic change that expanded the doctorate in Learning Technologies at the University of North Texas to include a distributed option, delivered primarily online. It provides an overview of the development process from concept to initial implementation. The article examines the specific differences that make the online…

Assessing the Application of Three-Dimensional Collaborative Technologies within an E-Learning Environment

ERIC Educational Resources Information Center

McArdle, Gavin; Bertolotto, Michela

2012-01-01

Today, the Internet plays a major role in distributing learning material within third level education. Multiple online facilities provide access to educational resources. While early systems relied on webpages, which acted as repositories for learning material, nowadays sophisticated online applications manage and deliver learning resources.…

2000 Worldwide Joint Lessons Learned Conference. Forging a Future Joint Lessons Learned System. (Joint Center for Lessons Learned Special Bulletin. Volume 3, Special Issue 1, January 2001)

DTIC Science & Technology

2001-01-01

Management System (JTIMS) followed, and generated spirited discussion regarding the respective roles of JTIMS and the JLLP. The discussion concluded...waiting for the Director, Joint Staff�s signature and should be in official distribution by January 2001. An update on the Joint Training Information

Automatic System for Producing and Distributing Lecture Recordings and Livestreams Using Opencast Matterhorn

ERIC Educational Resources Information Center

Jonach, Rafael; Ebner, Martin; Grigoriadis, Ypatios

2015-01-01

Lectures of courses at universities are increasingly being recorded and offered through various distribution channels to support students' learning activities. This research work aims to create an automatic system for producing and distributing high quality lecture recordings. Opencast Matterhorn is an open source platform for automated video…

Population-based learning of load balancing policies for a distributed computer system

NASA Technical Reports Server (NTRS)

Mehra, Pankaj; Wah, Benjamin W.

1993-01-01

Effective load-balancing policies use dynamic resource information to schedule tasks in a distributed computer system. We present a novel method for automatically learning such policies. At each site in our system, we use a comparator neural network to predict the relative speedup of an incoming task using only the resource-utilization patterns obtained prior to the task's arrival. Outputs of these comparator networks are broadcast periodically over the distributed system, and the resource schedulers at each site use these values to determine the best site for executing an incoming task. The delays incurred in propagating workload information and tasks from one site to another, as well as the dynamic and unpredictable nature of workloads in multiprogrammed multiprocessors, may cause the workload pattern at the time of execution to differ from patterns prevailing at the times of load-index computation and decision making. Our load-balancing policy accommodates this uncertainty by using certain tunable parameters. We present a population-based machine-learning algorithm that adjusts these parameters in order to achieve high average speedups with respect to local execution. Our results show that our load-balancing policy, when combined with the comparator neural network for workload characterization, is effective in exploiting idle resources in a distributed computer system.

Classroom Audio Distribution in the Postsecondary Setting: A Story of Universal Design for Learning

ERIC Educational Resources Information Center

Flagg-Williams, Joan B.; Bokhorst-Heng, Wendy D.

2016-01-01

Classroom Audio Distribution Systems (CADS) consist of amplification technology that enhances the teacher's, or sometimes the student's, vocal signal above the background noise in a classroom. Much research has supported the benefits of CADS for student learning, but most of it has focused on elementary school classrooms. This study investigated…

Student Assessment of an Electronic Learning System.

ERIC Educational Resources Information Center

Fissel, Mark Charles

1993-01-01

The Video Information System (VIS) permits the fiber-optic distribution of teaching media from a central resource facility to the classroom. Undergraduate students taking a Western civilization course that used VIS, reported that VIS helped their notetaking, made the textbook more understandable, and encouraged learning. (KS)

Second Language Writing Classification System Based on Word-Alignment Distribution

ERIC Educational Resources Information Center

Kotani, Katsunori; Yoshimi, Takehiko

2010-01-01

The present paper introduces an automatic classification system for assisting second language (L2) writing evaluation. This system, which classifies sentences written by L2 learners as either native speaker-like or learner-like sentences, is constructed by machine learning algorithms using word-alignment distributions as classification features…

Consensus-based distributed cooperative learning from closed-loop neural control systems.

PubMed

Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

2015-02-01

In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

Distributed Practicum Supervision in a Managed Learning Environment (MLE)

ERIC Educational Resources Information Center

Carter, David

2005-01-01

This evaluation-research feasibility study piloted the creation of a technology-mediated managed learning environment (MLE) involving the implementation of one of a new generation of instructionally driven management information systems (IMISs). The system, and supporting information and communications technology (ICT) was employed to support…

Temporal Patterns and Dynamics of E-Learning Usage in Medical Education

ERIC Educational Resources Information Center

Panzarasa, Pietro; Kujawski, Bernard; Hammond, Edward J.; Roberts, C. Michael

2016-01-01

Despite the increasing popularity of e-learning systems across a variety of educational programmes, there is relatively little understanding of how students and trainees distribute their learning efforts over time. This study aimed to analyse the usage patterns of an e-learning resource designed to support specialty training. Data were collected…

Strategies to improve learning of all students in a class

NASA Astrophysics Data System (ADS)

Suraishkumar, G. K.

2018-05-01

The statistical distribution of the student learning abilities in a typical undergraduate engineering class poses a significant challenge to simultaneously improve the learning of all the students in the class. With traditional instruction styles, the students with significantly high learning abilities are not satisfied due to a feeling of unfulfilled potential, and the students with significantly low learning abilities feel lost. To address the challenge in an undergraduate core/required course on 'transport phenomena in biological systems', a combination of learning strategies such as active learning including co-operative group learning, challenge exercises, and others were employed in a pro-advising context. The short-term and long-term impacts were evaluated through student course performances and input, respectively. The results show that it is possible to effectively address the challenge posed by the distribution of student learning abilities in a class.

Coaching the exploration and exploitation in active learning for interactive video retrieval.

PubMed

Wei, Xiao-Yong; Yang, Zhen-Qun

2013-03-01

Conventional active learning approaches for interactive video/image retrieval usually assume the query distribution is unknown, as it is difficult to estimate with only a limited number of labeled instances available. Thus, it is easy to put the system in a dilemma whether to explore the feature space in uncertain areas for a better understanding of the query distribution or to harvest in certain areas for more relevant instances. In this paper, we propose a novel approach called coached active learning that makes the query distribution predictable through training and, therefore, avoids the risk of searching on a completely unknown space. The estimated distribution, which provides a more global view of the feature space, can be used to schedule not only the timing but also the step sizes of the exploration and the exploitation in a principled way. The results of the experiments on a large-scale data set from TRECVID 2005-2009 validate the efficiency and effectiveness of our approach, which demonstrates an encouraging performance when facing domain-shift, outperforms eight conventional active learning methods, and shows superiority to six state-of-the-art interactive video retrieval systems.

ICCE/ICCAI 2000 Full & Short Papers (Collaborative Learning).

ERIC Educational Resources Information Center

2000

This document contains the full and short papers on collaborative learning from ICCE/ICCAI 2000 (International Conference on Computers in Education/International Conference on Computer-Assisted Instruction) covering the following topics: comparison of applying Internet to cooperative and traditional learning; a distributed backbone system for…

Collective learning for the emergence of social norms in networked multiagent systems.

PubMed

Yu, Chao; Zhang, Minjie; Ren, Fenghui

2014-12-01

Social norms such as social rules and conventions play a pivotal role in sustaining system order by regulating and controlling individual behaviors toward a global consensus in large-scale distributed systems. Systematic studies of efficient mechanisms that can facilitate the emergence of social norms enable us to build and design robust distributed systems, such as electronic institutions and norm-governed sensor networks. This paper studies the emergence of social norms via learning from repeated local interactions in networked multiagent systems. A collective learning framework, which imitates the opinion aggregation process in human decision making, is proposed to study the impact of agent local collective behaviors on the emergence of social norms in a number of different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action toward each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. Extensive experiments are carried out to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, influences of nonlearning agents, and so on. Experimental results reveal some significant insights into the manipulation and control of norm emergence in networked multiagent systems achieved through local collective behaviors.

The evolution of eLearning background, blends and blackboard....

PubMed

Sleator, Roy D

2010-01-01

This review of eLearning is divided into three sections: the first charts the evolution of eLearning from early correspondence courses to the current computer mediated approaches to distributed learning. The second section deals with the concept of blended learning; combining best practice in face-to-face and online learning. The final section focuses on current platform technologies in eLearning and outlines the strengths and weaknesses of learning management systems such as Blackboard.

Leadership for Nursing Work-Based Mobile Learning

ERIC Educational Resources Information Center

Fahlman, Dorothy

2016-01-01

This paper reflects on work-based mobile learning in the Canadian healthcare system for registered nurses' ongoing skills development and continuing professional development. It calls on distributed leadership to address the organizational contextual factors for making this mode of learning sustainable. [For the full proceedings, see ED571335.

Games as Distributed Teaching and Learning Systems

ERIC Educational Resources Information Center

Gee, Elisabeth; Gee, James Paul

2017-01-01

Background: Videogames and virtual worlds have frequently been studied as learning environments in isolation; that is, scholars have focused on understanding the features of games or virtual worlds as separate from or different than "real world" environments for learning. Although more recently, scholars have explored the teaching and…

Kees: a Practical Ict Solution for Rural Areas

NASA Astrophysics Data System (ADS)

Dai, Xiaoye; Tabirca, Sabin; Lenihan, Eamon

This paper introduces a practical e-learning system, identified as Knowledge Exchange E-learning System (abbr. KEES), for knowledge distribution in rural areas. Particularly, this paper is about providing a virtual teaching and learning environment for small holders in agriculture in those rural areas. E-learning is increasingly influencing the agricultural education (information and knowledge learning) in all forms and the current e-learning in agricultural education appears in informal and formal methods in many developed countries and some developing areas such as Asian Pacific regions. KEES is a solution to provide education services including other services of information distribution and knowledge sharing to local farmers, local institutes or local collection of farmers. The design of KEES is made to meet the needs of knowledge capacity building, experience sharing, skill upgrading, and information exchanging in agriculture for different conditions in rural areas. The system allows the online lecture/training materials to be distributed simultaneously with all multimedia resources through different file formats across different platforms. The teaching/training content can be contextless and broad, allowing for greater participation by more small holders, commercial farmers, extension workers, agriculturists, educators, and other agriculture-related experts. The relative inconsistency in content gives farmers more localised and useful knowledge. The framework of KEES has been designed to be a three-tier architecture logic workflow, which can configure the progressive approach for KEES to pass on and respond to different requests/communications between the client side and the server.

Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

PubMed

Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

2015-11-01

The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

Distributed reinforcement learning for adaptive and robust network intrusion response

NASA Astrophysics Data System (ADS)

Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel

2015-07-01

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

Distributed Problem-Based Learning in Social Economy: A Study of the Use of a Structured Method for Education.

ERIC Educational Resources Information Center

Bjorck, Ulric

Students' use of distributed Problem-Based Learning (dPBL) in university courses in social economy was studied. A sociocultural framework was used to analyze the actions of students focusing on their mastery of dPBL. The main data material consisted of messages written in an asynchronous conferencing system by 50 Swedish college students in 2…

Integration of advanced technologies to enhance problem-based learning over distance: Project TOUCH.

PubMed

Jacobs, Joshua; Caudell, Thomas; Wilks, David; Keep, Marcus F; Mitchell, Steven; Buchanan, Holly; Saland, Linda; Rosenheimer, Julie; Lozanoff, Beth K; Lozanoff, Scott; Saiki, Stanley; Alverson, Dale

2003-01-01

Distance education delivery has increased dramatically in recent years as a result of the rapid advancement of communication technology. The National Computational Science Alliance's Access Grid represents a significant advancement in communication technology with potential for distance medical education. The purpose of this study is to provide an overview of the TOUCH project (Telehealth Outreach for Unified Community Health; http://hsc.unm.edu/touch) with special emphasis on the process of problem-based learning case development for distribution over the Access Grid. The objective of the TOUCH project is to use emerging Internet-based technology to overcome geographic barriers for delivery of tutorial sessions to medical students pursuing rotations at remote sites. The TOUCH project also is aimed at developing a patient simulation engine and an immersive virtual reality environment to achieve a realistic health care scenario enhancing the learning experience. A traumatic head injury case is developed and distributed over the Access Grid as a demonstration of the TOUCH system. Project TOUCH serves as an example of a computer-based learning system for developing and implementing problem-based learning cases within the medical curriculum, but this system should be easily applied to other educational environments and disciplines involving functional and clinical anatomy. Future phases will explore PC versions of the TOUCH cases for increased distribution. Copyright 2003 Wiley-Liss, Inc.

Planning of distributed generation in distribution network based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Li, Jinze; Qu, Zhi; He, Xiaoyang; Jin, Xiaoming; Li, Tie; Wang, Mingkai; Han, Qiu; Gao, Ziji; Jiang, Feng

2018-02-01

Large-scale access of distributed power can improve the current environmental pressure, at the same time, increasing the complexity and uncertainty of overall distribution system. Rational planning of distributed power can effectively improve the system voltage level. To this point, the specific impact on distribution network power quality caused by the access of typical distributed power was analyzed and from the point of improving the learning factor and the inertia weight, an improved particle swarm optimization algorithm (IPSO) was proposed which could solve distributed generation planning for distribution network to improve the local and global search performance of the algorithm. Results show that the proposed method can well reduce the system network loss and improve the economic performance of system operation with distributed generation.

Simulation of noisy dynamical system by Deep Learning

NASA Astrophysics Data System (ADS)

Yeo, Kyongmin

2017-11-01

Deep learning has attracted huge attention due to its powerful representation capability. However, most of the studies on deep learning have been focused on visual analytics or language modeling and the capability of the deep learning in modeling dynamical systems is not well understood. In this study, we use a recurrent neural network to model noisy nonlinear dynamical systems. In particular, we use a long short-term memory (LSTM) network, which constructs internal nonlinear dynamics systems. We propose a cross-entropy loss with spatial ridge regularization to learn a non-stationary conditional probability distribution from a noisy nonlinear dynamical system. A Monte Carlo procedure to perform time-marching simulations by using the LSTM is presented. The behavior of the LSTM is studied by using noisy, forced Van der Pol oscillator and Ikeda equation.

Extending the ARIADNE Web-Based Learning Environment.

ERIC Educational Resources Information Center

Van Durm, Rafael; Duval, Erik; Verhoeven, Bart; Cardinaels, Kris; Olivie, Henk

One of the central notions of the ARIADNE learning platform is a share-and-reuse approach toward the development of digital course material. The ARIADNE infrastructure includes a distributed database called the Knowledge Pool System (KPS), which acts as a repository of pedagogical material, described with standardized IEEE LTSC Learning Object…

Organization of Distributed Adaptive Learning

ERIC Educational Resources Information Center

Vengerov, Alexander

2009-01-01

The growing sensitivity of various systems and parts of industry, society, and even everyday individual life leads to the increased volume of changes and needs for adaptation and learning. This creates a new situation where learning from being purely academic knowledge transfer procedure is becoming a ubiquitous always-on essential part of all…

The Impact of Microtechnology. A Case for Reassessing the Role of Computers in Learning.

ERIC Educational Resources Information Center

Alty, J. L.

1982-01-01

Reviews recent advances in microtechnology and describes the impact they will have on computer aided instruction and learning. It is suggested that distributed systems based on network technology will become widespread, and computer assisted guidance systems will be developed to assist new unskilled users. Eight references are given. (CHC)

The cerebellum: a neuronal learning machine?

NASA Technical Reports Server (NTRS)

Raymond, J. L.; Lisberger, S. G.; Mauk, M. D.

1996-01-01

Comparison of two seemingly quite different behaviors yields a surprisingly consistent picture of the role of the cerebellum in motor learning. Behavioral and physiological data about classical conditioning of the eyelid response and motor learning in the vestibulo-ocular reflex suggests that (i) plasticity is distributed between the cerebellar cortex and the deep cerebellar nuclei; (ii) the cerebellar cortex plays a special role in learning the timing of movement; and (iii) the cerebellar cortex guides learning in the deep nuclei, which may allow learning to be transferred from the cortex to the deep nuclei. Because many of the similarities in the data from the two systems typify general features of cerebellar organization, the cerebellar mechanisms of learning in these two systems may represent principles that apply to many motor systems.

Extending the IEEE-LTSA.

ERIC Educational Resources Information Center

Voskamp, Jorg; Hambach, Sybille

An Internet-based course management system has been under development at the Fraunhofer-Institute for Computer Graphics Rostock (Germany) for the past 5 years. It is used by experts for distributing their courses via the Internet and by students for learning with the material distributed. The "Course Management System for WWW--CMS-W3"…

Artificial intelligent e-learning architecture

NASA Astrophysics Data System (ADS)

Alharbi, Mafawez; Jemmali, Mahdi

2017-03-01

Many institutions and university has forced to use e learning, due to its ability to provide additional and flexible solutions for students and researchers. E-learning In the last decade have transported about the extreme changes in the distribution of education allowing learners to access multimedia course material at any time, from anywhere to suit their specific needs. In the form of e learning, instructors and learners live in different places and they do not engage in a classroom environment, but within virtual universe. Many researches have defined e learning based on their objectives. Therefore, there are small number of e-learning architecture have proposed in the literature. However, the proposed architecture has lack of embedding intelligent system in the architecture of e learning. This research argues that unexplored potential remains, as there is scope for e learning to be intelligent system. This research proposes e-learning architecture that incorporates intelligent system. There are intelligence components, which built into the architecture.

Heterogeneous distributed query processing: The DAVID system

NASA Technical Reports Server (NTRS)

Jacobs, Barry E.

1985-01-01

The objective of the Distributed Access View Integrated Database (DAVID) project is the development of an easy to use computer system with which NASA scientists, engineers and administrators can uniformly access distributed heterogeneous databases. Basically, DAVID will be a database management system that sits alongside already existing database and file management systems. Its function is to enable users to access the data in other languages and file systems without having to learn the data manipulation languages. Given here is an outline of a talk on the DAVID project and several charts.

Domain generality vs. modality specificity: The paradox of statistical learning

PubMed Central

Frost, Ram; Armstrong, Blair C.; Siegelman, Noam; Christiansen, Morten H.

2015-01-01

Statistical learning is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. Recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal, however, modality and stimulus specificity. An important question is, therefore, how and why a hypothesized domain-general learning mechanism systematically produces such effects. We offer a theoretical framework according to which statistical learning is not a unitary mechanism, but a set of domain-general computational principles, that operate in different modalities and therefore are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility. PMID:25631249

Architectures for Distributed and Complex M-Learning Systems: Applying Intelligent Technologies

ERIC Educational Resources Information Center

Caballe, Santi, Ed.; Xhafa, Fatos, Ed.; Daradoumis, Thanasis, Ed.; Juan, Angel A., Ed.

2009-01-01

Over the last decade, the needs of educational organizations have been changing in accordance with increasingly complex pedagogical models and with the technological evolution of e-learning environments with very dynamic teaching and learning requirements. This book explores state-of-the-art software architectures and platforms used to support…

Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.

PubMed

Liu, Weirong; Zhuang, Peng; Liang, Hao; Peng, Jun; Huang, Zhiwu; Weirong Liu; Peng Zhuang; Hao Liang; Jun Peng; Zhiwu Huang; Liu, Weirong; Liang, Hao; Peng, Jun; Zhuang, Peng; Huang, Zhiwu

2018-06-01

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

An Analysis of Our Cable Distribution System: Its Current and Future Capabilities.

ERIC Educational Resources Information Center

Clarke, Tobin de Leon

Three goals have been set for San Joaquin Delta College Learning Resource Center's cable distribution system: it is to be made useable, useful, and flexible. Presently the system consists of a microwave dish installed on one building which points to a relay station with approximately one and one half miles of cable pulled to various locations. A…

Moving beyond Blackboard: Using a Social Network as a Learning Management System

ERIC Educational Resources Information Center

Thacker, Christopher

2012-01-01

Web 2.0 is a paradigm of a participatory Internet, which has implications for the delivery of online courses. Instructors and students can now develop, distribute, and aggregate content through the use of third-party web applications, particularly social networking platforms, which combine to form a user-created learning management system (LMS).…

Aging Affects Acquisition and Reversal of Reward-Based Associative Learning

ERIC Educational Resources Information Center

Weiler, Julia A.; Bellebaum, Christian; Daum, Irene

2008-01-01

Reward-based associative learning is mediated by a distributed network of brain regions that are dependent on the dopaminergic system. Age-related changes in key regions of this system, the striatum and the prefrontal cortex, may adversely affect the ability to use reward information for the guidance of behavior. The present study investigated the…

Academics' Perceptions of the Impact of Internal Quality Assurance on Teaching and Learning

ERIC Educational Resources Information Center

Tavares, Orlanda; Sin, Cristina; Videira, Pedro; Amaral, Alberto

2017-01-01

Internal quality assurance systems are expected to improve the institutions' core mission of teaching and learning. Using data gathered through an online survey, distributed in 2014/2015, to the teaching staff of all Portuguese private and public higher education institutions, this paper examines the impact of internal quality assurance systems on…

Effect of reinforcement learning on coordination of multiangent systems

NASA Astrophysics Data System (ADS)

Bukkapatnam, Satish T. S.; Gao, Greg

2000-12-01

For effective coordination of distributed environments involving multiagent systems, learning ability of each agent in the environment plays a crucial role. In this paper, we develop a simple group learning method based on reinforcement, and study its effect on coordination through application to a supply chain procurement scenario involving a computer manufacturer. Here, all parties are represented by self-interested, autonomous agents, each capable of performing specific simple tasks. They negotiate with each other to perform complex tasks and thus coordinate supply chain procurement. Reinforcement learning is intended to enable each agent to reach a best negotiable price within a shortest possible time. Our simulations of the application scenario under different learning strategies reveals the positive effects of reinforcement learning on an agent's as well as the system's performance.

Center for the Built Environment: Research on Building HVAC Systems

Science.gov Websites

, and lessons learned. Underfloor Air Distribution (UFAD) Cooling Airflow Design Tool Developing simplified design tools for optimization of underfloor systems. Underfloor Air Distribution (UFAD) Cost Near-ZNE Buildings Setpoint Energy Savings Calculator UFAD Case Studies UFAD Cooling Design Tool UFAD

An overview of the EOSDIS V0 information management system: Lessons learned from the implementation of a distributed data system

NASA Technical Reports Server (NTRS)

Ryan, Patrick M.

1994-01-01

The EOSDIS Version 0 system, released in July, 1994, is a working prototype of a distributed data system. One of the purposes of the V0 project is to take several existing data systems and coordinate them into one system while maintaining the independent nature of the original systems. The project is a learning experience and the lessons are being passed on to the architects of the system which will distribute the data received from the planned EOS satellites. In the V0 system, the data resides on heterogeneous systems across the globe but users are presented with a single, integrated interface. This interface allows users to query the participating data centers based on a wide set of criteria. Because this system is a prototype, we used many novel approaches in trying to connect a diverse group of users with the huge amount of available data. Some of these methods worked and others did not. Now that V0 has been released to the public, we can look back at the design and implementation of the system and also consider some possible future directions for the next generation of EOSDIS.

Monitoring Distributed Systems: A Relational Approach.

DTIC Science & Technology

1982-12-01

relationship, and time. The first two have been are modeled directly in the relational model. The third is perhaps the most fundamental , for without the system ...of another, newly created file. The approach adopted here applies to object-based operatin systems , and will support capability addressing at the...in certainties. -- Francis Bacon, in The Advancement of Learning The thesis of this research is that monitoring distributed systems is fundamentally a

Challenges of Using CSCL in Open Distributed Learning.

ERIC Educational Resources Information Center

Nilsen, Anders Grov; Instefjord, Elen J.

As a compulsory part of the study in Pedagogical Information Science at the University of Bergen and Stord/Haugesund College (Norway) during the spring term of 1999, students participated in a distributed group activity that provided experience on distributed collaboration and use of online groupware systems. The group collaboration process was…

Event-Triggered Distributed Approximate Optimal State and Output Control of Affine Nonlinear Interconnected Systems.

PubMed

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-06-08

This paper presents an approximate optimal distributed control scheme for a known interconnected system composed of input affine nonlinear subsystems using event-triggered state and output feedback via a novel hybrid learning scheme. First, the cost function for the overall system is redefined as the sum of cost functions of individual subsystems. A distributed optimal control policy for the interconnected system is developed using the optimal value function of each subsystem. To generate the optimal control policy, forward-in-time, neural networks are employed to reconstruct the unknown optimal value function at each subsystem online. In order to retain the advantages of event-triggered feedback for an adaptive optimal controller, a novel hybrid learning scheme is proposed to reduce the convergence time for the learning algorithm. The development is based on the observation that, in the event-triggered feedback, the sampling instants are dynamic and results in variable interevent time. To relax the requirement of entire state measurements, an extended nonlinear observer is designed at each subsystem to recover the system internal states from the measurable feedback. Using a Lyapunov-based analysis, it is demonstrated that the system states and the observer errors remain locally uniformly ultimately bounded and the control policy converges to a neighborhood of the optimal policy. Simulation results are presented to demonstrate the performance of the developed controller.

Effectiveness of Learning Process Using "Web Technology" in the Distance Learning System

ERIC Educational Resources Information Center

Killedar, Manoj

2008-01-01

Web is a globally distributed, still highly personalized media for cost-effective delivery of multimedia information and services. Web is expected to have a strong impact on almost every aspect of how we learn. "Total Quality" is the totality of features, as perceived by the customers of the product or service. Totality of features…

Can Clicking Promote Learning?: Measuring Student Learning Performance Using Clickers in the Undergraduate Information Systems Class

ERIC Educational Resources Information Center

Rana, Nripendra P.; Dwivedi, Yogesh K.

2017-01-01

Purpose: The purpose of this paper is to explore the impact of factors such as attention, preparation, participation, feedback and engagement on the student learning performance. Design/methodology/approach: Students of an undergraduate business course of a British university took part in the survey. The survey questionnaire was distributed to…

A Closer Look at Split Visual Attention in System- and Self-Paced Instruction in Multimedia Learning

ERIC Educational Resources Information Center

Schmidt-Weigand, Florian; Kohnert, Alfred; Glowalla, Ulrich

2010-01-01

Two experiments examined visual attention distribution in learning from text and pictures. Participants watched a 16-step multimedia instruction on the formation of lightning. In Experiment 1 (N=90) the instruction was system-paced (fast, medium, slow pace), while it was self-paced in Experiment 2 (N=31). In both experiments the text modality was…

Event-Triggered Distributed Control of Nonlinear Interconnected Systems Using Online Reinforcement Learning With Exploration.

PubMed

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-09-07

In this paper, a distributed control scheme for an interconnected system composed of uncertain input affine nonlinear subsystems with event triggered state feedback is presented by using a novel hybrid learning scheme-based approximate dynamic programming with online exploration. First, an approximate solution to the Hamilton-Jacobi-Bellman equation is generated with event sampled neural network (NN) approximation and subsequently, a near optimal control policy for each subsystem is derived. Artificial NNs are utilized as function approximators to develop a suite of identifiers and learn the dynamics of each subsystem. The NN weight tuning rules for the identifier and event-triggering condition are derived using Lyapunov stability theory. Taking into account, the effects of NN approximation of system dynamics and boot-strapping, a novel NN weight update is presented to approximate the optimal value function. Finally, a novel strategy to incorporate exploration in online control framework, using identifiers, is introduced to reduce the overall cost at the expense of additional computations during the initial online learning phase. System states and the NN weight estimation errors are regulated and local uniformly ultimately bounded results are achieved. The analytical results are substantiated using simulation studies.

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

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

Jiang, Huaiguang; Zhang, Yingchen

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vectormore » regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.« less

The Pioneering Role of the Vaccine Safety Datalink Project (VSD) to Advance Collaborative Research and Distributed Data Networks

PubMed Central

Fahey, Kevin R.

2015-01-01

Introduction: Large-scale distributed data networks consisting of diverse stakeholders including providers, patients, and payers are changing health research in terms of methods, speed and efficiency. The Vaccine Safety Datalink (VSD) set the stage for expanded involvement of health plans in collaborative research. Expanding Surveillance Capacity and Progress Toward a Learning Health System: From an initial collaboration of four integrated health systems with fewer than 10 million covered lives to 16 diverse health plans with nearly 100 million lives now in the FDA Sentinel, the expanded engagement of health plan researchers has been essential to increase the value and impact of these efforts. The collaborative structure of the VSD established a pathway toward research efforts that successfully engage all stakeholders in a cohesive rather than competitive manner. The scientific expertise and methodology developed through the VSD such as rapid cycle analysis (RCA) to conduct near real-time safety surveillance allowed for the development of the expanded surveillance systems that now exist. Building on Success and Lessons Learned: These networks have learned from and built on the knowledge base and infrastructure created by the VSD investigators. This shared technical knowledge and experience expedited the development of systems like the FDA’s Mini-Sentinel and the Patient Centered Outcomes Research Institute (PCORI)’s PCORnet Conclusion: This narrative reviews the evolution of the VSD, its contribution to other collaborative research networks, longer-term sustainability of this type of distributed research, and how knowledge gained from the earlier efforts can contribute to a continually learning health system. PMID:26793736

Lessons Learned In Developing Multiple Distributed Planning Systems for the International Space Station

NASA Technical Reports Server (NTRS)

Maxwell, Theresa G.; McNair, Ann R. (Technical Monitor)

2002-01-01

The planning processes for the International Space Station (ISS) Program are quite complex. Detailed mission planning for ISS on-orbit operations is a distributed function. Pieces of the on-orbit plan are developed by multiple planning organizations, located around the world, based on their respective expertise and responsibilities. The "pieces" are then integrated to yield the final detailed plan that will be executed onboard the ISS. Previous space programs have not distributed the planning and scheduling functions to this extent. Major ISS planning organizations are currently located in the United States (at both the NASA Johnson Space Center (JSC) and NASA Marshall Space Flight Center (MSFC)), in Russia, in Europe, and in Japan. Software systems have been developed by each of these planning organizations to support their assigned planning and scheduling functions. Although there is some cooperative development and sharing of key software components, each planning system has been tailored to meet the unique requirements and operational environment of the facility in which it operates. However, all the systems must operate in a coordinated fashion in order to effectively and efficiently produce a single integrated plan of ISS operations, in accordance with the established planning processes. This paper addresses lessons learned during the development of these multiple distributed planning systems, from the perspective of the developer of one of the software systems. The lessons focus on the coordination required to allow the multiple systems to operate together, rather than on the problems associated with the development of any particular system. Included in the paper is a discussion of typical problems faced during the development and coordination process, such as incompatible development schedules, difficulties in defining system interfaces, technical coordination and funding for shared tools, continually evolving planning concepts/requirements, programmatic and budget issues, and external influences. Techniques that mitigated some of these problems will also be addressed, along with recommendations for any future programs involving the development of multiple planning and scheduling systems. Many of these lessons learned are not unique to the area of planning and scheduling systems, so may be applied to other distributed ground systems that must operate in concert to successfully support space mission operations.

Lessons Learned in Developing Multiple Distributed Planning Systems for the International Space Station

NASA Technical Reports Server (NTRS)

Maxwell, Theresa G.

2002-01-01

The planning processes for the International Space Station (ISS) Program are quite complex. Detailed mission planning for ISS on-orbit operations is a distributed function. Pieces of the on-orbit plan are developed by multiple planning organizations, located around the world, based on their respective expertise and responsibilities. The pieces are then integrated to yield the final detailed plan that will be executed onboard the ISS. Previous space programs have not distributed the planning and scheduling functions to this extent. Major ISS planning organizations are currently located in the United States (at both the NASA Johnson Space Center (JSC) and NASA Marshall Space Flight Center (MSFC)), in Russia, in Europe, and in Japan. Software systems have been developed by each of these planning organizations to support their assigned planning and scheduling functions. Although there is some cooperative development and sharing of key software components, each planning system has been tailored to meet the unique requirements and operational environment of the facility in which it operates. However, all the systems must operate in a coordinated fashion in order to effectively and efficiently produce a single integrated plan of ISS operations, in accordance with the established planning processes. This paper addresses lessons learned during the development of these multiple distributed planning systems, from the perspective of the developer of one of the software systems. The lessons focus on the coordination required to allow the multiple systems to operate together, rather than on the problems associated with the development of any particular system. Included in the paper is a discussion of typical problems faced during the development and coordination process, such as incompatible development schedules, difficulties in defining system interfaces, technical coordination and funding for shared tools, continually evolving planning concepts/requirements, programmatic and budget issues, and external influences. Techniques that mitigated some of these problems will also be addressed, along with recommendations for any future programs involving the development of multiple planning and scheduling systems. Many of these lessons learned are not unique to the area of planning and scheduling systems, so may be applied to other distributed ground systems that must operate in concert to successfully support space mission operations.

Evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments

NASA Astrophysics Data System (ADS)

Nelson, Kevin; Corbin, George; Blowers, Misty

2014-05-01

Machine learning is continuing to gain popularity due to its ability to solve problems that are difficult to model using conventional computer programming logic. Much of the current and past work has focused on algorithm development, data processing, and optimization. Lately, a subset of research has emerged which explores issues related to security. This research is gaining traction as systems employing these methods are being applied to both secure and adversarial environments. One of machine learning's biggest benefits, its data-driven versus logic-driven approach, is also a weakness if the data on which the models rely are corrupted. Adversaries could maliciously influence systems which address drift and data distribution changes using re-training and online learning. Our work is focused on exploring the resilience of various machine learning algorithms to these data-driven attacks. In this paper, we present our initial findings using Monte Carlo simulations, and statistical analysis, to explore the maximal achievable shift to a classification model, as well as the required amount of control over the data.

Learning Competencies in Action: Tenth Grade Students' Investment in Accumulating Human Capital under the Influence of the Upper Secondary Education System in Japan

ERIC Educational Resources Information Center

Ryogi, Matsuoka

2013-01-01

Kariya (2009) proposes a concept of learning competencies to understand how social reproduction occurs in the current context of Japanese society; he argues that students learning competencies are not equally distributed but shaped by their family background, a foundation of unequal socioeconomic inequality. While he contends that learning…

Proceedings of the Seventh International Symposium on Methodologies for Intelligent Systems (Poster Session)

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

Harber, K.S.

1993-05-01

This report contains the following papers: Implications in vivid logic; a self-learning bayesian expert system; a natural language generation system for a heterogeneous distributed database system; competence-switching'' managed by intelligent systems; strategy acquisition by an artificial neural network: Experiments in learning to play a stochastic game; viewpoints and selective inheritance in object-oriented modeling; multivariate discretization of continuous attributes for machine learning; utilization of the case-based reasoning method to resolve dynamic problems; formalization of an ontology of ceramic science in CLASSIC; linguistic tools for intelligent systems; an application of rough sets in knowledge synthesis; and a relational model for imprecise queries.more » These papers have been indexed separately.« less

Proceedings of the Seventh International Symposium on Methodologies for Intelligent Systems (Poster Session)

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

Harber, K.S.

1993-05-01

This report contains the following papers: Implications in vivid logic; a self-learning Bayesian Expert System; a natural language generation system for a heterogeneous distributed database system; ``competence-switching`` managed by intelligent systems; strategy acquisition by an artificial neural network: Experiments in learning to play a stochastic game; viewpoints and selective inheritance in object-oriented modeling; multivariate discretization of continuous attributes for machine learning; utilization of the case-based reasoning method to resolve dynamic problems; formalization of an ontology of ceramic science in CLASSIC; linguistic tools for intelligent systems; an application of rough sets in knowledge synthesis; and a relational model for imprecise queries.more » These papers have been indexed separately.« less

Q&A: Defining Internet Architecture for Learning.

ERIC Educational Resources Information Center

Hernandez-Ramos, Pedro

1999-01-01

Presents Pedro Hernandez-Ramos's thoughts on Educom's Instructional Management Systems (IMS), a global coalition of organizations working together to create standards for software development in distributed learning. Focuses on the organization's relevance to community colleges, the benefits of participation, why IMS is a global effort, and how…

Teaching and Learning Activity Sequencing System using Distributed Genetic Algorithms

NASA Astrophysics Data System (ADS)

Matsui, Tatsunori; Ishikawa, Tomotake; Okamoto, Toshio

The purpose of this study is development of a supporting system for teacher's design of lesson plan. Especially design of lesson plan which relates to the new subject "Information Study" is supported. In this study, we developed a system which generates teaching and learning activity sequences by interlinking lesson's activities corresponding to the various conditions according to the user's input. Because user's input is multiple information, there will be caused contradiction which the system should solve. This multiobjective optimization problem is resolved by Distributed Genetic Algorithms, in which some fitness functions are defined with reference models on lesson, thinking and teaching style. From results of various experiments, effectivity and validity of the proposed methods and reference models were verified; on the other hand, some future works on reference models and evaluation functions were also pointed out.

Automated Decomposition of Model-based Learning Problems

NASA Technical Reports Server (NTRS)

Williams, Brian C.; Millar, Bill

1996-01-01

A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of [\\em decompositional model-based learning (DML)], a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate.

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

PubMed Central

Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

2016-01-01

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment. PMID:27922592

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

NASA Astrophysics Data System (ADS)

Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

2016-12-01

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.

PubMed

Bradbury, Kyle; Saboo, Raghav; L Johnson, Timothy; Malof, Jordan M; Devarajan, Arjun; Zhang, Wuming; M Collins, Leslie; G Newell, Richard

2016-12-06

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures

PubMed Central

Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures.

PubMed

Rock, Adam J; Coventry, William L; Morgan, Methuen I; Loi, Natasha M

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

Multi Agent Systems with Symbiotic Learning and Evolution using GNP

NASA Astrophysics Data System (ADS)

Eguchi, Toru; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

Recently, various attempts relevant to Multi Agent Systems (MAS) which is one of the most promising systems based on Distributed Artificial Intelligence have been studied to control large and complicated systems efficiently. In these trends of MAS, Multi Agent Systems with Symbiotic Learning and Evolution named Masbiole has been proposed. In Masbiole, symbiotic phenomena among creatures are considered in the process of learning and evolution of MAS. So we can expect more flexible and sophisticated solutions than conventional MAS. In this paper, we apply Masbiole to Iterative Prisoner’s Dilemma Games (IPD Games) using Genetic Network Programming (GNP) which is a newly developed evolutionary computation method for constituting agents. Some characteristics of Masbiole using GNP in IPD Games are clarified.

A broadband multimedia TeleLearning system

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

Wang, Ruiping; Karmouch, A.

1996-12-31

In this paper we discuss a broadband multimedia TeleLearning system under development in the Multimedia Information Research Laboratory at the University of Ottawa. The system aims at providing a seamless environment for TeleLearning using the latest telecommunication and multimedia information processing technology. It basically consists of a media production center, a courseware author site, a courseware database, a courseware user site, and an on-line facilitator site. All these components are distributed over an ATM network and work together to offer a multimedia interactive courseware service. An MHEG-based model is exploited in designing the system architecture to achieve the real-time, interactive,more » and reusable information interchange through heterogeneous platforms. The system architecture, courseware processing strategies, courseware document models are presented.« less

Exciting Normal Distribution

ERIC Educational Resources Information Center

Fuchs, Karl Josef; Simonovits, Reinhard; Thaller, Bernd

2008-01-01

This paper describes a high school project where the mathematics teaching and learning software M@th Desktop (MD) based on the Computer Algebra System Mathematica was used for symbolical and numerical calculations and for visualisation. The mathematics teaching and learning software M@th Desktop 2.0 (MD) contains the modules Basics including tools…

Distributed Learning Environment: Major Functions, Implementation, and Continuous Improvement.

ERIC Educational Resources Information Center

Converso, Judith A.; Schaffer, Scott P.; Guerra, Ingrid J.

The content of this paper is based on a development plan currently in design for the U.S. Navy in conjunction with the Learning Systems Institute at Florida State University. Leading research (literature review) references and case study ("best practice") references are presented as supporting evidence for the results-oriented…

Phases and Patterns of Group Development in Virtual Learning Teams

ERIC Educational Resources Information Center

Yoon, Seung Won; Johnson, Scott D.

2008-01-01

With the advancement of Internet communication technologies, distributed work groups have great potential for remote collaboration and use of collective knowledge. Adopting the Complex Adaptive System (CAS) perspective (McGrath, Arrow, & Berdhal, "Personal Soc Psychol Rev" 4 (2000) 95), which views virtual learning teams as an adaptive and…

Adolescents' Perceptions of Chronic Self-Concept, Peer Relations, and Learning Conditions

ERIC Educational Resources Information Center

Liu, Weiping; Eckert, Thomas

2014-01-01

Based on Lewin's Field Theory, Bronfenbrenner's Bioecological Systems Theory and social network analysis, the authors collected data from 405 Chinese adolescents about their peer relations, chronic self-concept levels and learning condition variables through questionnaire distributing, and from their teachers about their annual average academic…

Architecture for an artificial immune system.

PubMed

Hofmeyr, S A; Forrest, S

2000-01-01

An artificial immune system (ARTIS) is described which incorporates many properties of natural immune systems, including diversity, distributed computation, error tolerance, dynamic learning and adaptation, and self-monitoring. ARTIS is a general framework for a distributed adaptive system and could, in principle, be applied to many domains. In this paper, ARTIS is applied to computer security in the form of a network intrusion detection system called LISYS. LISYS is described and shown to be effective at detecting intrusions, while maintaining low false positive rates. Finally, similarities and differences between ARTIS and Holland's classifier systems are discussed.

Multidimensional Learner Model In Intelligent Learning System

NASA Astrophysics Data System (ADS)

Deliyska, B.; Rozeva, A.

2009-11-01

The learner model in an intelligent learning system (ILS) has to ensure the personalization (individualization) and the adaptability of e-learning in an online learner-centered environment. ILS is a distributed e-learning system whose modules can be independent and located in different nodes (servers) on the Web. This kind of e-learning is achieved through the resources of the Semantic Web and is designed and developed around a course, group of courses or specialty. An essential part of ILS is learner model database which contains structured data about learner profile and temporal status in the learning process of one or more courses. In the paper a learner model position in ILS is considered and a relational database is designed from learner's domain ontology. Multidimensional modeling agent for the source database is designed and resultant learner data cube is presented. Agent's modules are proposed with corresponding algorithms and procedures. Multidimensional (OLAP) analysis guidelines on the resultant learner module for designing dynamic learning strategy have been highlighted.

Global adaptation in networks of selfish components: emergent associative memory at the system scale.

PubMed

Watson, Richard A; Mills, Rob; Buckley, C L

2011-01-01

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organize into structures that enhance global adaptation, efficiency, or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology, and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalization, and optimization are well understood. Such global functions within a single agent or organism are not wholly surprising, since the mechanisms (e.g., Hebbian learning) that create these neural organizations may be selected for this purpose; but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviors when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g., when they can influence which other agents they interact with), then, in adapting these inter-agent relationships to maximize their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviors as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalize by idealizing stored patterns and/or creating new combinations of subpatterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviors in the same sense, and by the same mechanism, as with the organizational principles familiar in connectionist models of organismic learning.

LDCM Ground System. Network Lesson Learned

NASA Technical Reports Server (NTRS)

Gal-Edd, Jonathan

2010-01-01

This slide presentation reviews the Landsat Data Continuity Mission (LDCM) and the lessons learned in implementing the network that was assembled to allow for the acquisition, archiving and distribution of the data from the Landsat mission. The objective of the LDCM is to continue the acquisition, archiving, and distribution of moderate-resolution multispectral imagery affording global, synoptic, and repetitive coverage of the earth's land surface at a scale where natural and human-induced changes can be detected, differentiated, characterized, and monitored over time. It includes a review of the ground network, including a block diagram of the ground network elements (GNE) and a review of the RF design and testing. Also included is a listing of the lessons learned.

A new distributed systems scheduling algorithm: a swarm intelligence approach

NASA Astrophysics Data System (ADS)

Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

2011-12-01

The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

Learning Probabilities From Random Observables in High Dimensions: The Maximum Entropy Distribution and Others

NASA Astrophysics Data System (ADS)

Obuchi, Tomoyuki; Cocco, Simona; Monasson, Rémi

2015-11-01

We consider the problem of learning a target probability distribution over a set of N binary variables from the knowledge of the expectation values (with this target distribution) of M observables, drawn uniformly at random. The space of all probability distributions compatible with these M expectation values within some fixed accuracy, called version space, is studied. We introduce a biased measure over the version space, which gives a boost increasing exponentially with the entropy of the distributions and with an arbitrary inverse `temperature' Γ . The choice of Γ allows us to interpolate smoothly between the unbiased measure over all distributions in the version space (Γ =0) and the pointwise measure concentrated at the maximum entropy distribution (Γ → ∞ ). Using the replica method we compute the volume of the version space and other quantities of interest, such as the distance R between the target distribution and the center-of-mass distribution over the version space, as functions of α =(log M)/N and Γ for large N. Phase transitions at critical values of α are found, corresponding to qualitative improvements in the learning of the target distribution and to the decrease of the distance R. However, for fixed α the distance R does not vary with Γ which means that the maximum entropy distribution is not closer to the target distribution than any other distribution compatible with the observable values. Our results are confirmed by Monte Carlo sampling of the version space for small system sizes (N≤ 10).

Fixed Point Learning Based Intelligent Traffic Control System

NASA Astrophysics Data System (ADS)

Zongyao, Wang; Cong, Sui; Cheng, Shao

2017-10-01

Fixed point learning has become an important tool to analyse large scale distributed system such as urban traffic network. This paper presents a fixed point learning based intelligence traffic network control system. The system applies convergence property of fixed point theorem to optimize the traffic flow density. The intelligence traffic control system achieves maximum road resources usage by averaging traffic flow density among the traffic network. The intelligence traffic network control system is built based on decentralized structure and intelligence cooperation. No central control is needed to manage the system. The proposed system is simple, effective and feasible for practical use. The performance of the system is tested via theoretical proof and simulations. The results demonstrate that the system can effectively solve the traffic congestion problem and increase the vehicles average speed. It also proves that the system is flexible, reliable and feasible for practical use.

Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech

PubMed Central

Huebner, Philip A.; Willits, Jon A.

2018-01-01

Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary “deep learning” approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge. We trained two types of recurrent neural networks (Simple Recurrent Network, and Long Short-Term Memory) to predict word sequences in a 5-million-word corpus of speech directed to children ages 0–3 years old, and assessed what semantic knowledge they acquired. We found that learned internal representations are encoding various abstract grammatical and semantic features that are useful for predicting word sequences. Assessing the organization of semantic knowledge in terms of the similarity structure, we found evidence of emergent categorical and hierarchical structure in both models. We found that the Long Short-term Memory (LSTM) and SRN are both learning very similar kinds of representations, but the LSTM achieved higher levels of performance on a quantitative evaluation. We also trained a non-recurrent neural network, Skip-gram, on the same input to compare our results to the state-of-the-art in machine learning. We found that Skip-gram achieves relatively similar performance to the LSTM, but is representing words more in terms of thematic compared to taxonomic relations, and we provide reasons why this might be the case. Our findings show that a learning system that derives abstract, distributed representations for the purpose of predicting sequential dependencies in naturalistic language may provide insight into emergence of many properties of the developing semantic system. PMID:29520243

An Exploration of Advanced Distributed Learning Service Success Measures for Social Policy

DTIC Science & Technology

2009-04-01

and global access: 20 “We believe that all the hype surrounding the capabilities of information technologies have led us to develop a dangerous...System and Navy eLearning should be sought and made available. In addition, three existing concepts, already being used in other Air Force arenas, are

A Method to Improve Learning Analysing Communication in Team Working

ERIC Educational Resources Information Center

Bermejo, Miren; Sanchez, Ana; Gutierrez, Julian; Perez, Tomas A.

2011-01-01

In recent years learning how to work in teams has become a common subject in higher education. Communication between student team members can be monitored using a bulletin board system, and hence, analyse individual and group role development. The composition and distribution of roles in a team are relevant characteristics that will considerably…

QSIA--A Web-Based Environment for Learning, Assessing and Knowledge Sharing in Communities

ERIC Educational Resources Information Center

Rafaeli, Sheizaf; Barak, Miri; Dan-Gur, Yuval; Toch, Eran

2004-01-01

This paper describes a Web-based and distributed system named QSIA that serves as an environment for learning, assessing and knowledge sharing. QSIA--Questions Sharing and Interactive Assignments--offers a unified infrastructure for developing, collecting, managing and sharing of knowledge items. QSIA enhances collaboration in authoring via online…

A Distributed System for Learning Programming On-Line

ERIC Educational Resources Information Center

Verdu, Elena; Regueras, Luisa M.; Verdu, Maria J.; Leal, Jose P.; de Castro, Juan P.; Queiros, Ricardo

2012-01-01

Several Web-based on-line judges or on-line programming trainers have been developed in order to allow students to train their programming skills. However, their pedagogical functionalities in the learning of programming have not been clearly defined. EduJudge is a project which aims to integrate the "UVA On-line Judge", an existing…

[Assessment of learning activities using streaming video for laboratory practice education: aiming for development of E-learning system that promotes self-learning].

PubMed

Takeda, Naohito; Takeuchi, Isao; Haruna, Mitsumasa

2007-12-01

In order to develop an e-learning system that promotes self-learning, lectures and basic operations in laboratory practice of chemistry were recorded and edited on DVD media, consisting of 8 streaming videos as learning materials. Twenty-six students wanted to watch the DVD, and answered the following questions after they had watched it: "Do you think the video would serve to encourage you to study independently in the laboratory practice?" Almost all students (95%) approved of its usefulness, and more than 60% of them watched the videos repeatedly in order to acquire deeper knowledge and skill of the experimental operations. More than 60% answered that the demonstration-experiment should be continued in the laboratory practice, in spite of distribution of the DVD media.

Strategies for Learners with Special Needs in Marketing and Distributive Education.

ERIC Educational Resources Information Center

Missouri Univ., Columbia. Missouri LINC.

This Vocational Instructional Management System (VIMS) module addresses general information related to the instructional/teaching strategies and cognitive/learning strategies for special needs students in marketing and distributive education. In addition, specific strategies are suggested as they relate to Access Skills objectives for some of the…

Self-supervised ARTMAP.

PubMed

Amis, Gregory P; Carpenter, Gail A

2010-03-01

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

Distance Learning: A Way of Life-Long Learning

DTIC Science & Technology

2005-09-01

promise of future benefits. 15. SUBJECT TERMS training, educational technology , distributed learning , distance learning , collaboration, online instruction...knowledge." - Aristotle Introduction Modern learning technology assumes various names: distance learning , distributed training, computer-based...training, web-based learning , or advanced distributed learning . No matter the name, the basic concept is using computer technology for instruction with no

Reframing clinical workplace learning using the theory of distributed cognition.

PubMed

Pimmer, Christoph; Pachler, Norbert; Genewein, Urs

2013-09-01

In medicine, knowledge is embodied and socially, temporally, spatially, and culturally distributed between actors and their environment. In addition, clinicians increasingly are using technology in their daily work to gain and share knowledge. Despite these characteristics, surprisingly few studies have incorporated the theory of distributed cognition (DCog), which emphasizes how cognition is distributed in a wider system in the form of multimodal representations (e.g., clinical images, speech, gazes, and gestures) between social actors (e.g., doctors and patients) in the physical environment (e.g., with technological instruments and computers). In this article, the authors provide an example of an interaction between medical actors. Using that example, they then introduce the important concepts of the DCog theory, identifying five characteristics of clinical representations-that they are interwoven, co-constructed, redundantly accessed, intersubjectively shared, and substantiated-and discuss their value for learning. By contrasting these DCog perspectives with studies from the field of medical education, the authors argue that researchers should focus future medical education scholarship on the ways in which medical actors use and connect speech, bodily movements (e.g., gestures), and the visual and haptic structures of their own bodies and of artifacts, such as technological instruments and computers, to construct complex, multimodal representations. They also argue that future scholarship should "zoom in" on detailed, moment-by-moment analysis and, at the same time, "zoom out" following the distribution of cognition through an overall system to develop a more integrated view of clinical workplace learning.

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

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

Thwaites, D; Holloway, L; Bailey, M

2015-06-15

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

EXTENDING THE REALM OF OPTIMIZATION FOR COMPLEX SYSTEMS: UNCERTAINTY, COMPETITION, AND DYNAMICS

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

Shanbhag, Uday V; Basar, Tamer; Meyn, Sean

Research reported addressed these topics: the development of analytical and algorithmic tools for distributed computation of Nash equilibria; synchronization in mean-field oscillator games, with an emphasis on learning and efficiency analysis; questions that combine learning and computation; questions including stochastic and mean-field games; modeling and control in the context of power markets.

Students' Involvement in Continuous Assessment Methodologies: A Case Study for a Distributed Information Systems Course

ERIC Educational Resources Information Center

Cano, M.-D.

2011-01-01

The creation of the new European Higher Education Area (EHEA), with the corresponding changes in the structure and content of university degrees, offers a great opportunity to review learning methodologies. This paper investigates the effect on students of moving from a traditional learning process, based on lectures and laboratory work, to an…

Technology-Mediated ELT Writing: Acceptance and Engagement in an Online Moodle Course

ERIC Educational Resources Information Center

Zyad, Hicham

2016-01-01

In the past fifteen years, Web 2.0 social networking technologies have ushered in a new era of information production, distribution and consumption with significant implications for language teaching and learning. An example of such technology is Moodle, which is a learning management system with several useful features that can transform the…

Knowledge Management System Model for Learning Organisations

ERIC Educational Resources Information Center

Amin, Yousif; Monamad, Roshayu

2017-01-01

Based on the literature of knowledge management (KM), this paper reports on the progress of developing a new knowledge management system (KMS) model with components architecture that are distributed over the widely-recognised socio-technical system (STS) aspects to guide developers for selecting the most applicable components to support their KM…

Aircraft adaptive learning control

NASA Technical Reports Server (NTRS)

Lee, P. S. T.; Vanlandingham, H. F.

1979-01-01

The optimal control theory of stochastic linear systems is discussed in terms of the advantages of distributed-control systems, and the control of randomly-sampled systems. An optimal solution to longitudinal control is derived and applied to the F-8 DFBW aircraft. A randomly-sampled linear process model with additive process and noise is developed.

How Emotions Affect Learning.

ERIC Educational Resources Information Center

Sylwester, Robert

1994-01-01

Studies show our emotional system is a complex, widely distributed, and error-prone system that defines our basic personality early in life and is quite resistant to change. This article describes our emotional system's major parts (the peptides that carry emotional information and the body and brain structures that activate and regulate emotions)…

Integrating labview into a distributed computing environment.

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

Kasemir, K. U.; Pieck, M.; Dalesio, L. R.

2001-01-01

Being easy to learn and well suited for a selfcontained desktop laboratory setup, many casual programmers prefer to use the National Instruments Lab-VIEW environment to develop their logic. An ActiveX interface is presented that allows integration into a plant-wide distributed environment based on the Experimental Physics and Industrial Control System (EPICS). This paper discusses the design decisions and provides performance information, especially considering requirements for the Spallation Neutron Source (SNS) diagnostics system.

Resource depletion promotes automatic processing: implications for distribution of practice.

PubMed

Scheel, Matthew H

2010-12-01

Recent models of cognition include two processing systems: an automatic system that relies on associative learning, intuition, and heuristics, and a controlled system that relies on deliberate consideration. Automatic processing requires fewer resources and is more likely when resources are depleted. This study showed that prolonged practice on a resource-depleting mental arithmetic task promoted automatic processing on a subsequent problem-solving task, as evidenced by faster responding and more errors. Distribution of practice effects (0, 60, 120, or 180 sec. between problems) on rigidity also disappeared when groups had equal time on resource-depleting tasks. These results suggest that distribution of practice effects is reducible to resource availability. The discussion includes implications for interpreting discrepancies in the traditional distribution of practice effect.

Learning in Artificial Neural Systems

NASA Technical Reports Server (NTRS)

Matheus, Christopher J.; Hohensee, William E.

1987-01-01

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

Videodisc Feasibility Study. An Evaluation of the Use of Videodisc as a Distribution Medium.

ERIC Educational Resources Information Center

France, Ralph

This study evaluated the practicality of using videodiscs to distribute the television programs that are part of the courses of the International University Consortium (IUC) for Telecommunications in Learning, a network of colleges and universities in partnership with public broadcasting stations and cable systems. Fifteen videodisc players, along…

Evaluating the Effects of Scripted Distributed Pair Programming on Student Performance and Participation

ERIC Educational Resources Information Center

Tsompanoudi, Despina; Satratzemi, Maya; Xinogalos, Stelios

2016-01-01

The results presented in this paper contribute to research on two different areas of teaching methods: distributed pair programming (DPP) and computer-supported collaborative learning (CSCL). An evaluation study of a DPP system that supports collaboration scripts was conducted over one semester of a computer science course. Seventy-four students…

Cavity approach to noisy learning in nonlinear perceptrons.

PubMed

Luo, P; Michael Wong, K Y

2001-12-01

We analyze the learning of noisy teacher-generated examples by nonlinear and differentiable student perceptrons using the cavity method. The generic activation of an example is a function of the cavity activation of the example, which is its activation in the perceptron that learns without the example. Mean-field equations for the macroscopic parameters and the stability condition yield results consistent with the replica method. When a single value of the cavity activation maps to multiple values of the generic activation, there is a competition in learning strategy between preferentially learning an example and sacrificing it in favor of the background adjustment. We find parameter regimes in which examples are learned preferentially or sacrificially, leading to a gap in the activation distribution. Full phase diagrams of this complex system are presented, and the theory predicts the existence of a phase transition from poor to good generalization states in the system. Simulation results confirm the theoretical predictions.

Neural priming in human frontal cortex: multiple forms of learning reduce demands on the prefrontal executive system.

PubMed

Race, Elizabeth A; Shanker, Shanti; Wagner, Anthony D

2009-09-01

Past experience is hypothesized to reduce computational demands in PFC by providing bottom-up predictive information that informs subsequent stimulus-action mapping. The present fMRI study measured cortical activity reductions ("neural priming"/"repetition suppression") during repeated stimulus classification to investigate the mechanisms through which learning from the past decreases demands on the prefrontal executive system. Manipulation of learning at three levels of representation-stimulus, decision, and response-revealed dissociable neural priming effects in distinct frontotemporal regions, supporting a multiprocess model of neural priming. Critically, three distinct patterns of neural priming were identified in lateral frontal cortex, indicating that frontal computational demands are reduced by three forms of learning: (a) cortical tuning of stimulus-specific representations, (b) retrieval of learned stimulus-decision mappings, and (c) retrieval of learned stimulus-response mappings. The topographic distribution of these neural priming effects suggests a rostrocaudal organization of executive function in lateral frontal cortex.

Technology and Its Use in Education: Present Roles and Future Prospects

ERIC Educational Resources Information Center

Courville, Keith

2011-01-01

(Purpose) This article describes two current trends in Educational Technology: distributed learning and electronic databases. (Findings) Topics addressed in this paper include: (1) distributed learning as a means of professional development; (2) distributed learning for content visualization; (3) usage of distributed learning for educational…

Designing Distributed Learning Environments with Intelligent Software Agents

ERIC Educational Resources Information Center

Lin, Fuhua, Ed.

2005-01-01

"Designing Distributed Learning Environments with Intelligent Software Agents" reports on the most recent advances in agent technologies for distributed learning. Chapters are devoted to the various aspects of intelligent software agents in distributed learning, including the methodological and technical issues on where and how intelligent agents…

Developing Learning Tool of Control System Engineering Using Matrix Laboratory Software Oriented on Industrial Needs

NASA Astrophysics Data System (ADS)

Isnur Haryudo, Subuh; Imam Agung, Achmad; Firmansyah, Rifqi

2018-04-01

The purpose of this research is to develop learning media of control technique using Matrix Laboratory software with industry requirement approach. Learning media serves as a tool for creating a better and effective teaching and learning situation because it can accelerate the learning process in order to enhance the quality of learning. Control Techniques using Matrix Laboratory software can enlarge the interest and attention of students, with real experience and can grow independent attitude. This research design refers to the use of research and development (R & D) methods that have been modified by multi-disciplinary team-based researchers. This research used Computer based learning method consisting of computer and Matrix Laboratory software which was integrated with props. Matrix Laboratory has the ability to visualize the theory and analysis of the Control System which is an integration of computing, visualization and programming which is easy to use. The result of this instructional media development is to use mathematical equations using Matrix Laboratory software on control system application with DC motor plant and PID (Proportional-Integral-Derivative). Considering that manufacturing in the field of Distributed Control systems (DCSs), Programmable Controllers (PLCs), and Microcontrollers (MCUs) use PID systems in production processes are widely used in industry.

The Lord of the Rings - Deep Learning Craters on the Moon and Other Bodies

NASA Astrophysics Data System (ADS)

Silburt, Ari; Ali-Dib, Mohamad; Zhu, Chenchong; Jackson, Alan; Valencia, Diana; Kissin, Yevgeni; Tamayo, Daniel; Menou, Kristen

2018-01-01

Crater detection has traditionally been done via manual inspection of images, leading to statistically significant disagreements between scientists for the Moon's crater distribution. In addition, there are millions of uncategorized craters on the Moon and other Solar System bodies that will never be classified by humans due to the time required to manually detect craters. I will show that a deep learning model trained on the near-side of the Moon can successfully reproduce the crater distribution on the far-side, as well as detect thousands of small, new craters that were previously uncharacterized. In addition, this Moon-trained model can be transferred to accurately classify craters on Mercury. It is therefore likely that this model can be extended to classify craters on all Solar System bodies with Digital Elevation Maps. This will facilitate, for the first time ever, a systematic, accurate, and reproducible study of the crater records throughout the Solar System.

DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2012-09-30

platform (HPC) was developed, called the HPC-Acoustic Data Accelerator, or HPC-ADA for short. The HPC-ADA was designed based on fielded systems [1-4...software (Detection cLassificaiton for MAchine learning - High Peformance Computing). The software package was designed to utilize parallel and...Sedna [7] and is designed using a parallel architecture2, allowing existing algorithms to distribute to the various processing nodes with minimal changes

Neural correlates of reward-based spatial learning in persons with cocaine dependence.

PubMed

Tau, Gregory Z; Marsh, Rachel; Wang, Zhishun; Torres-Sanchez, Tania; Graniello, Barbara; Hao, Xuejun; Xu, Dongrong; Packard, Mark G; Duan, Yunsuo; Kangarlu, Alayar; Martinez, Diana; Peterson, Bradley S

2014-02-01

Dysfunctional learning systems are thought to be central to the pathogenesis of and impair recovery from addictions. The functioning of the brain circuits for episodic memory or learning that support goal-directed behavior has not been studied previously in persons with cocaine dependence (CD). Thirteen abstinent CD and 13 healthy participants underwent MRI scanning while performing a task that requires the use of spatial cues to navigate a virtual-reality environment and find monetary rewards, allowing the functional assessment of the brain systems for spatial learning, a form of episodic memory. Whereas both groups performed similarly on the reward-based spatial learning task, we identified disturbances in brain regions involved in learning and reward in CD participants. In particular, CD was associated with impaired functioning of medial temporal lobe (MTL), a brain region that is crucial for spatial learning (and episodic memory) with concomitant recruitment of striatum (which normally participates in stimulus-response, or habit, learning), and prefrontal cortex. CD was also associated with enhanced sensitivity of the ventral striatum to unexpected rewards but not to expected rewards earned during spatial learning. We provide evidence that spatial learning in CD is characterized by disturbances in functioning of an MTL-based system for episodic memory and a striatum-based system for stimulus-response learning and reward. We have found additional abnormalities in distributed cortical regions. Consistent with findings from animal studies, we provide the first evidence in humans describing the disruptive effects of cocaine on the coordinated functioning of multiple neural systems for learning and memory.

Set size manipulations reveal the boundary conditions of perceptual ensemble learning.

PubMed

Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

2017-11-01

Recent evidence suggests that observers can grasp patterns of feature variations in the environment with surprising efficiency. During visual search tasks where all distractors are randomly drawn from a certain distribution rather than all being homogeneous, observers are capable of learning highly complex statistical properties of distractor sets. After only a few trials (learning phase), the statistical properties of distributions - mean, variance and crucially, shape - can be learned, and these representations affect search during a subsequent test phase (Chetverikov, Campana, & Kristjánsson, 2016). To assess the limits of such distribution learning, we varied the information available to observers about the underlying distractor distributions by manipulating set size during the learning phase in two experiments. We found that robust distribution learning only occurred for large set sizes. We also used set size to assess whether the learning of distribution properties makes search more efficient. The results reveal how a certain minimum of information is required for learning to occur, thereby delineating the boundary conditions of learning of statistical variation in the environment. However, the benefits of distribution learning for search efficiency remain unclear. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Two-Stage Multi-Agent Based Assessment Approach to Enhance Students' Learning Motivation through Negotiated Skills Assessment

ERIC Educational Resources Information Center

Chadli, Abdelhafid; Bendella, Fatima; Tranvouez, Erwan

2015-01-01

In this paper we present an Agent-based evaluation approach in a context of Multi-agent simulation learning systems. Our evaluation model is based on a two stage assessment approach: (1) a Distributed skill evaluation combining agents and fuzzy sets theory; and (2) a Negotiation based evaluation of students' performance during a training…

Application of a hierarchical structure stochastic learning automation

NASA Technical Reports Server (NTRS)

Neville, R. G.; Chrystall, M. S.; Mars, P.

1979-01-01

A hierarchical structure automaton was developed using a two state stochastic learning automato (SLA) in a time shared model. Application of the hierarchical SLA to systems with multidimensional, multimodal performance criteria is described. Results of experiments performed with the hierarchical SLA using a performance index with a superimposed noise component of ? or - delta distributed uniformly over the surface are discussed.

Experiences Integrating Transmission and Distribution Simulations for DERs with the Integrated Grid Modeling System (IGMS)

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

Palmintier, Bryan; Hale, Elaine; Hodge, Bri-Mathias

2016-08-11

This paper discusses the development of, approaches for, experiences with, and some results from a large-scale, high-performance-computer-based (HPC-based) co-simulation of electric power transmission and distribution systems using the Integrated Grid Modeling System (IGMS). IGMS was developed at the National Renewable Energy Laboratory (NREL) as a novel Independent System Operator (ISO)-to-appliance scale electric power system modeling platform that combines off-the-shelf tools to simultaneously model 100s to 1000s of distribution systems in co-simulation with detailed ISO markets, transmission power flows, and AGC-level reserve deployment. Lessons learned from the co-simulation architecture development are shared, along with a case study that explores the reactivemore » power impacts of PV inverter voltage support on the bulk power system.« less

Explorations on High Dimensional Landscapes: Spin Glasses and Deep Learning

NASA Astrophysics Data System (ADS)

Sagun, Levent

This thesis deals with understanding the structure of high-dimensional and non-convex energy landscapes. In particular, its focus is on the optimization of two classes of functions: homogeneous polynomials and loss functions that arise in machine learning. In the first part, the notion of complexity of a smooth, real-valued function is studied through its critical points. Existing theoretical results predict that certain random functions that are defined on high dimensional domains have a narrow band of values whose pre-image contains the bulk of its critical points. This section provides empirical evidence for convergence of gradient descent to local minima whose energies are near the predicted threshold justifying the existing asymptotic theory. Moreover, it is empirically shown that a similar phenomenon may hold for deep learning loss functions. Furthermore, there is a comparative analysis of gradient descent and its stochastic version showing that in high dimensional regimes the latter is a mere speedup. The next study focuses on the halting time of an algorithm at a given stopping condition. Given an algorithm, the normalized fluctuations of the halting time follow a distribution that remains unchanged even when the input data is sampled from a new distribution. Two qualitative classes are observed: a Gumbel-like distribution that appears in Google searches, human decision times, and spin glasses and a Gaussian-like distribution that appears in conjugate gradient method, deep learning with MNIST and random input data. Following the universality phenomenon, the Hessian of the loss functions of deep learning is studied. The spectrum is seen to be composed of two parts, the bulk which is concentrated around zero, and the edges which are scattered away from zero. Empirical evidence is presented for the bulk indicating how over-parametrized the system is, and for the edges that depend on the input data. Furthermore, an algorithm is proposed such that it would explore such large dimensional, degenerate landscapes to locate a solution with decent generalization properties. Finally, a demonstration of how the new method can explain the empirical success of some of the recent methods that have been proposed for distributed deep learning. In the second part, two applied machine learning problems are studied that are complementary to the machine learning problems of part I. First, US asylum applications cases are studied using random forests on the data of past twenty years. Using only features up to when the case opens, the algorithm can predict the outcome of the case with 80% accuracy. Next, a particular question and answer system has been studied. The questions are collected from Jeopardy! show and they fed to Google, then the results are parsed into a recurrent neural network to come up with a system that would outcome the answer to the original question. Close to 50% accuracy is achieved where human level benchmark is just a little above 60%.

Leader-Follower Output Synchronization of Linear Heterogeneous Systems With Active Leader Using Reinforcement Learning.

PubMed

Yang, Yongliang; Modares, Hamidreza; Wunsch, Donald C; Yin, Yixin

2018-06-01

This paper develops optimal control protocols for the distributed output synchronization problem of leader-follower multiagent systems with an active leader. Agents are assumed to be heterogeneous with different dynamics and dimensions. The desired trajectory is assumed to be preplanned and is generated by the leader. Other follower agents autonomously synchronize to the leader by interacting with each other using a communication network. The leader is assumed to be active in the sense that it has a nonzero control input so that it can act independently and update its control to keep the followers away from possible danger. A distributed observer is first designed to estimate the leader's state and generate the reference signal for each follower. Then, the output synchronization of leader-follower systems with an active leader is formulated as a distributed optimal tracking problem, and inhomogeneous algebraic Riccati equations (AREs) are derived to solve it. The resulting distributed optimal control protocols not only minimize the steady-state error but also optimize the transient response of the agents. An off-policy reinforcement learning algorithm is developed to solve the inhomogeneous AREs online in real time and without requiring any knowledge of the agents' dynamics. Finally, two simulation examples are conducted to illustrate the effectiveness of the proposed algorithm.

Distributed learning and multi-objectivity in traffic light control

NASA Astrophysics Data System (ADS)

Brys, Tim; Pham, Tong T.; Taylor, Matthew E.

2014-01-01

Traffic jams and suboptimal traffic flows are ubiquitous in modern societies, and they create enormous economic losses each year. Delays at traffic lights alone account for roughly 10% of all delays in US traffic. As most traffic light scheduling systems currently in use are static, set up by human experts rather than being adaptive, the interest in machine learning approaches to this problem has increased in recent years. Reinforcement learning (RL) approaches are often used in these studies, as they require little pre-existing knowledge about traffic flows. Distributed constraint optimisation approaches (DCOP) have also been shown to be successful, but are limited to cases where the traffic flows are known. The distributed coordination of exploration and exploitation (DCEE) framework was recently proposed to introduce learning in the DCOP framework. In this paper, we present a study of DCEE and RL techniques in a complex simulator, illustrating the particular advantages of each, comparing them against standard isolated traffic actuated signals. We analyse how learning and coordination behave under different traffic conditions, and discuss the multi-objective nature of the problem. Finally we evaluate several alternative reward signals in the best performing approach, some of these taking advantage of the correlation between the problem-inherent objectives to improve performance.

Playable Serious Games for Studying and Programming Computational STEM and Informatics Applications of Distributed and Parallel Computer Architectures

ERIC Educational Resources Information Center

Amenyo, John-Thones

2012-01-01

Carefully engineered playable games can serve as vehicles for students and practitioners to learn and explore the programming of advanced computer architectures to execute applications, such as high performance computing (HPC) and complex, inter-networked, distributed systems. The article presents families of playable games that are grounded in…

Effects of Distributed Practice on the Acquisition of Second Language English Syntax

ERIC Educational Resources Information Center

Bird, Steve

2010-01-01

A longitudinal study compared the effects of distributed and massed practice schedules on the learning of second language English syntax. Participants were taught distinctions in the tense and aspect systems of English at short and long practice intervals. They were then tested at short and long intervals. The results showed that distributed…

Learning from Listservs: Collaboration, Knowledge Exchange, and the Formation of Distributed Leadership for Farmers' Markets and the Food Movement

ERIC Educational Resources Information Center

Quintana, Maclovia; Morales, Alfonso

2015-01-01

Computer-mediated communications, in particular listservs, can be powerful tools for creating social change--namely, shifting our food system to a more healthy, just, and localised model. They do this by creating the conditions--collaborations, interaction, self-reflection, and personal empowerment--that cultivate distributed leadership. In this…

Hippocampus and Retrograde Amnesia in the Rat Model: A Modest Proposal for the Situation of Systems Consolidation

PubMed Central

Sutherland, Robert J.; Sparks, Fraser; Lehmann, Hugo

2010-01-01

The properties of retrograde amnesia after damage to the hippocampus have been explicated with some success using a rat model of human medial temporal lobe amnesia. We review the results of this experimental work with rats focusing on several areas of consensus in this growing literature. We evaluate the theoretically significant hypothesis that hippocampal retrograde amnesia normally exhibits a temporal gradient, affecting recent, but sparing remote memories. Surprisingly, the evidence does not provide much support for the idea that there is a lengthy process of systems consolidation following a learning episode. Instead, recent and remote memories tend to be equally affected. The extent of damage to the hippocampus is a significant factor in this work since it is likely that spared hippocampal tissue can support at least partial memory retrieval. With extensive hippocampal damage gradients are flat or, in the case of memory tasks with flavour/odour retrieval cues, the retrograde amnesia covers a period of about 1 – 3 days. There is consistent evidence that at the time of learning the hippocampus interferes with or overshadows memory acquisition by other systems. This contributes to the breadth and severity of retrograde amnesia relative to anterograde amnesia in the rat. The fact that multiple, distributed learning episodes can overcome this overshadowing is consistent with a parallel dual-store theory or a Distributed Reinstatement Theory in which each learning episode triggers a short period of memory replay that provides a brief hippocampal-dependent systems consolidation. PMID:20430043

Lessons Learned from the Design, Certification, and Operation of the Space Shuttle Integrated Main Propulsion System (IMPS)

NASA Technical Reports Server (NTRS)

Martinez, Hugo E.; Albright, John D.; D'Amico, Stephen J.; Brewer, John M.; Melcher, John C., IV

2011-01-01

The Space Shuttle Integrated Main Propulsion System (IMPS) consists of the External Tank (ET), Orbiter Main Propulsion System (MPS), and Space Shuttle Main Engines (SSMEs). The IMPS is tasked with the storage, conditioning, distribution, and combustion of cryogenic liquid hydrogen (LH2) and liquid oxygen (LO2) propellants to provide first and second stage thrust for achieving orbital velocity. The design, certification, and operation of the associated IMPS hardware have produced many lessons learned over the course of the Space Shuttle Program (SSP). A subset of these items will be discussed in this paper for consideration when designing, building, and operating future spacecraft propulsion systems. This paper will focus on lessons learned related to Orbiter MPS and is the first of a planned series to address the subject matter.

Adaptive functional systems: learning with chaos.

PubMed

Komarov, M A; Osipov, G V; Burtsev, M S

2010-12-01

We propose a new model of adaptive behavior that combines a winnerless competition principle and chaos to learn new functional systems. The model consists of a complex network of nonlinear dynamical elements producing sequences of goal-directed actions. Each element describes dynamics and activity of the functional system which is supposed to be a distributed set of interacting physiological elements such as nerve or muscle that cooperates to obtain certain goal at the level of the whole organism. During "normal" behavior, the dynamics of the system follows heteroclinic channels, but in the novel situation chaotic search is activated and a new channel leading to the target state is gradually created simulating the process of learning. The model was tested in single and multigoal environments and had demonstrated a good potential for generation of new adaptations. © 2010 American Institute of Physics.

Interaction between episodic and semantic memory networks in the acquisition and consolidation of novel spoken words.

PubMed

Takashima, Atsuko; Bakker, Iske; van Hell, Janet G; Janzen, Gabriele; McQueen, James M

2017-04-01

When a novel word is learned, its memory representation is thought to undergo a process of consolidation and integration. In this study, we tested whether the neural representations of novel words change as a function of consolidation by observing brain activation patterns just after learning and again after a delay of one week. Words learned with meanings were remembered better than those learned without meanings. Both episodic (hippocampus-dependent) and semantic (dependent on distributed neocortical areas) memory systems were utilised during recognition of the novel words. The extent to which the two systems were involved changed as a function of time and the amount of associated information, with more involvement of both systems for the meaningful words than for the form-only words after the one-week delay. These results suggest that the reason the meaningful words were remembered better is that their retrieval can benefit more from these two complementary memory systems. Copyright © 2016 Elsevier Inc. All rights reserved.

Web-based GIS for collaborative planning and public participation: an application to the strategic planning of wind farm sites.

PubMed

Simão, Ana; Densham, Paul J; Haklay, Mordechai Muki

2009-05-01

Spatial planning typically involves multiple stakeholders. To any specific planning problem, stakeholders often bring different levels of knowledge about the components of the problem and make assumptions, reflecting their individual experiences, that yield conflicting views about desirable planning outcomes. Consequently, stakeholders need to learn about the likely outcomes that result from their stated preferences; this learning can be supported through enhanced access to information, increased public participation in spatial decision-making and support for distributed collaboration amongst planners, stakeholders and the public. This paper presents a conceptual system framework for web-based GIS that supports public participation in collaborative planning. The framework combines an information area, a Multi-Criteria Spatial Decision Support System (MC-SDSS) and an argumentation map to support distributed and asynchronous collaboration in spatial planning. After analysing the novel aspects of this framework, the paper describes its implementation, as a proof of concept, in a system for Web-based Participatory Wind Energy Planning (WePWEP). Details are provided on the specific implementation of each of WePWEP's four tiers, including technical and structural aspects. Throughout the paper, particular emphasis is placed on the need to support user learning throughout the planning process.

Bayesian analysis of energy and count rate data for detection of low count rate radioactive sources

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

Klumpp, John

We propose a radiation detection system which generates its own discrete sampling distribution based on past measurements of background. The advantage to this approach is that it can take into account variations in background with respect to time, location, energy spectra, detector-specific characteristics (i.e. different efficiencies at different count rates and energies), etc. This would therefore be a 'machine learning' approach, in which the algorithm updates and improves its characterization of background over time. The system would have a 'learning mode,' in which it measures and analyzes background count rates, and a 'detection mode,' in which it compares measurements frommore » an unknown source against its unique background distribution. By characterizing and accounting for variations in the background, general purpose radiation detectors can be improved with little or no increase in cost. The statistical and computational techniques to perform this kind of analysis have already been developed. The necessary signal analysis can be accomplished using existing Bayesian algorithms which account for multiple channels, multiple detectors, and multiple time intervals. Furthermore, Bayesian machine-learning techniques have already been developed which, with trivial modifications, can generate appropriate decision thresholds based on the comparison of new measurements against a nonparametric sampling distribution. (authors)« less

Moodog: Tracking Student Activity in Online Course Management Systems

ERIC Educational Resources Information Center

Zhang, Hangjin; Almeroth, Kevin

2010-01-01

Many universities are currently using Course Management Systems (CMSes) to conduct online learning, for example, by distributing course materials or submitting homework assignments. However, most CMSes do not include comprehensive activity tracking and analysis capabilities. This paper describes a method to track students' online learning…

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Cognitive Training for Impaired Neural Systems in Neuropsychiatric Illness

PubMed Central

Vinogradov, Sophia; Fisher, Melissa; de Villers-Sidani, Etienne

2012-01-01

Neuropsychiatric illnesses are associated with dysfunction in distributed prefrontal neural systems that underlie perception, cognition, social interactions, emotion regulation, and motivation. The high degree of learning-dependent plasticity in these networks—combined with the availability of advanced computerized technology—suggests that we should be able to engineer very specific training programs that drive meaningful and enduring improvements in impaired neural systems relevant to neuropsychiatric illness. However, cognitive training approaches for mental and addictive disorders must take into account possible inherent limitations in the underlying brain ‘learning machinery' due to pathophysiology, must grapple with the presence of complex overlearned maladaptive patterns of neural functioning, and must find a way to ally with developmental and psychosocial factors that influence response to illness and to treatment. In this review, we briefly examine the current state of knowledge from studies of cognitive remediation in psychiatry and we highlight open questions. We then present a systems neuroscience rationale for successful cognitive training for neuropsychiatric illnesses, one that emphasizes the distributed nature of neural assemblies that support cognitive and affective processing, as well as their plasticity. It is based on the notion that, during successful learning, the brain represents the relevant perceptual and cognitive/affective inputs and action outputs with disproportionately larger and more coordinated populations of neurons that are distributed (and that are interacting) across multiple levels of processing and throughout multiple brain regions. This approach allows us to address limitations found in earlier research and to introduce important principles for the design and evaluation of the next generation of cognitive training for impaired neural systems. We summarize work to date using such neuroscience-informed methods and indicate some of the exciting future directions of this field. PMID:22048465

A case study in experiential learning: pharmaceutical cold chain management on wheels.

PubMed

Vesper, James; Kartoglu, Ümit; Bishara, Rafik; Reeves, Thomas

2010-01-01

People who handle and regulate temperature-sensitive pharmaceutical products require the knowledge and skills to ensure those products maintain quality, integrity, safety, and efficacy throughout their shelf life. People best acquire such knowledge and skills through "experiential learning" that involves working with other learners and experts. The World Health Organization developed a weeklong experiential learning event for participants so they could gain experience in how temperature-sensitive products are handled, stored, and distributed throughout the length of the distribution supply chain system. This experiential learning method enabled participants to visit, critically observe, discuss and report on the various components of the cold chain process. An emphasis was placed on team members working together to learn from one another and on several global expert mentors who were available to guide the learning, share their experiences, and respond to questions. The learning event, Pharmaceutical Cold Chain Management on Wheels, has been conducted once each year since 2008 in Turkey with participants from the global pharmaceutical industry, health care providers, national regulatory authorities, and suppliers/vendors. Observations made during the course showed that it was consistent with the principles of experiential and social learning theories. Questionnaires and focus groups provided evidence of the value of the learning event and ways to improve it. Reflecting the critical elements derived from experiential and social learning theories, five factors contributed to the success of this unique experiential learning event. These factors may also have relevance in other experiential learning courses and, potentially, for experiential e-learning events.

Distributional Language Learning: Mechanisms and Models of ategory Formation.

PubMed

Aslin, Richard N; Newport, Elissa L

2014-09-01

In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes---acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars---actually represent a single mechanism. Evidence in support of this single-mechanism hypothesis comes from a series of artificial grammar-learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic-category learning are summarized.

Dynamics of Learning in Cultured Neuronal Networks with Antagonists of Glutamate Receptors

PubMed Central

Li, Yanling; Zhou, Wei; Li, Xiangning; Zeng, Shaoqun; Luo, Qingming

2007-01-01

Cognitive dysfunction may result from abnormality of ionotropic glutamate receptors. Although various forms of synaptic plasticity in learning that rely on altering of glutamate receptors have been considered, the evidence is insufficient from an informatics view. Dynamics could reflect neuroinformatics encoding, including temporal pattern encoding, spatial pattern encoding, and energy distribution. Discovering informatics encoding is fundamental and crucial to understanding the working principle of the neural system. In this article, we analyzed the dynamic characteristics of response activities during learning training in cultured hippocampal networks under normal and abnormal conditions of ionotropic glutamate receptors, respectively. The rate, which is one of the temporal configurations, was decreased markedly by inhibition of α-amino-3-hydroxy-5-methylisoxazole-4-proprionic acid (AMPA) receptors. Moreover, the energy distribution in different characteristic frequencies was changed markedly by inhibition of AMPA receptors. Spatial configurations, including regularization, correlation, and synchrony, were changed significantly by inhibition of N-methyl-d-aspartate receptors. These results suggest that temporal pattern encoding and energy distribution of response activities in cultured hippocampal neuronal networks during learning training are modulated by AMPA receptors, whereas spatial pattern encoding of response activities is modulated by N-methyl-d-aspartate receptors. PMID:17766359

[Neurodynamic Bases of Imitation Learning and Episodic Memory].

PubMed

Tsukerman, V D

2016-01-01

In this review, three essentially important processes in development of cognitive behavior are considered: knowledge of a spatial environment by means of physical activity, coding and a call of an existential context of episodic memory and imitation learning based on the mirror neural mechanism. The data show that the parietal and frontal system of learning by imitation, allows the developing organism to seize skills of management and motive synergies in perisomatic space, to understand intentions and the purposes of observed actions of other individuals. At the same time a widely distributed parietal and frontal and entorhinal-hippocampal system mediates spatial knowledge and the solution of the navigation tasks important for creation of an existential context of episodic memory.

Computer-assisted learning in human and dental medicine.

PubMed

Höhne, S; Schumann, R R

2004-04-01

This article describes the development and application of new didactic methods for use in computer-assisted teaching and learning systems for training doctors and dentists. Taking the Meducase project as an example, didactic models and their technological implementation are explained, together with the limitations of imparting knowledge with the "new media". In addition, legal concepts for a progressive, pragmatic, and innovative distribution of knowledge to undergraduate students are presented. In conclusion, potential and visions for the wide use of electronic learning in the German and European universities in the future are discussed. Self-directed learning (SDL) is a key component in both undergraduate education and lifelong learning for medical practitioners. E-learning can already be used to promote SDL at undergraduate level. The Meducase project uses self-directed, constructive, case- and problem-oriented learning within a learning platform for medical and dental students. In the long run, e-learning programs can only be successful in education if there is consistent analysis and implementation of value-added factors and the development and use of media-didactic concepts matched to electronic learning. The use of innovative forms of licensing - open source licenses for software and similar licenses for content - facilitates continuous, free access to these programs for all students and teachers. These legal concepts offer the possibility of innovative knowledge distribution, quality assurance and standardization across specializations, university departments, and possibly even national borders.

Modelling the Effects of Principal Leadership and School Capacity on Teacher Professional Learning in Hong Kong Primary Schools

ERIC Educational Resources Information Center

Hallinger, Philip; Lu, Jiafang

2014-01-01

Over the past 30 years, school principals have been exhorted to articulate a clear vision as a key tool for stimulating the improvement of teaching and learning in their schools. Over the past decade, as school systems have sought to distribute leadership more broadly within schools, the same imperative has applied to middle-level leaders. Indeed,…

Water System Resiliency: Lessons from Boston's 2010 Water Emergency

NASA Astrophysics Data System (ADS)

Phillips, N.; Boston Urban Metabolism Ultra-Ex Team

2010-12-01

On May 1, 2010, a ten foot diameter water pipe, the sole pipe supplying potable water to 2.2 million residents of Greater Boston, burst. Categorized as a "catastrophic" leak by the Massachusetts Water Resources Authority, Governor Deval Patrick declared a State of Emergency, mobilizing local, state and federal disaster responses. By May 4, 2010, a boil-water order was lifted after the leak was fixed. This event has provided many lessons about the resiliency of municipal water system infrastructure, the level of human understanding of reliability and vulnerability of resource distribution systems, and the human capacity to adapt in short and longer terms to disturbances in resource distribution systems, and to learn. This talk will use a narrative of the events during May 2010 in Boston to explore the broader question of the nature of resilient resource distribution networks, and describe a heuristic, semi-quantitative model for resilient urban resource distribution networks, including water.

A data distribution strategy for the 1990s (files are not enough)

NASA Technical Reports Server (NTRS)

Tankenson, Mike; Wright, Steven

1993-01-01

Virtually all of the data distribution strategies being contemplated for the EOSDIS era revolve around the use of files. Most, if not all, mass storage technologies are based around the file model. However, files may be the wrong primary abstraction for supporting scientific users in the 1990s and beyond. Other abstractions more closely matching the respective scientific discipline of the end user may be more appropriate. JPL has built a unique multimission data distribution system based on a strategy of telemetry stream emulation to match the responsibilities of spacecraft team and ground data system operators supporting our nations suite of planetary probes. The current system, operational since 1989 and the launch of the Magellan spacecraft, is supporting over 200 users at 15 remote sites. This stream-oriented data distribution model can provide important lessons learned to builders of future data systems.

What might we learn from climate forecasts?

PubMed Central

Smith, Leonard A.

2002-01-01

Most climate models are large dynamical systems involving a million (or more) variables on big computers. Given that they are nonlinear and not perfect, what can we expect to learn from them about the earth's climate? How can we determine which aspects of their output might be useful and which are noise? And how should we distribute resources between making them “better,” estimating variables of true social and economic interest, and quantifying how good they are at the moment? Just as “chaos” prevents accurate weather forecasts, so model error precludes accurate forecasts of the distributions that define climate, yielding uncertainty of the second kind. Can we estimate the uncertainty in our uncertainty estimates? These questions are discussed. Ultimately, all uncertainty is quantified within a given modeling paradigm; our forecasts need never reflect the uncertainty in a physical system. PMID:11875200

Dissociating error-based and reinforcement-based loss functions during sensorimotor learning

PubMed Central

McGregor, Heather R.; Mohatarem, Ayman

2017-01-01

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback. PMID:28753634

Dissociating error-based and reinforcement-based loss functions during sensorimotor learning.

PubMed

Cashaback, Joshua G A; McGregor, Heather R; Mohatarem, Ayman; Gribble, Paul L

2017-07-01

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback.

ICCE/ICCAI 2000 Full & Short Papers (Intelligent Tutoring Systems).

ERIC Educational Resources Information Center

2000

This document contains the full and short papers on intelligent tutoring systems (ITS) from ICCE/ICCAI 2000 (International Conference on Computers in Education/International Conference on Computer-Assisted Instruction) covering the following topics: a framework for Internet-based distributed learning; a fuzzy-based assessment for the Perl tutoring…

Guidance Systems across Europe: Heritage, Change and the Art of Becoming

ERIC Educational Resources Information Center

Moreno da Fonseca, Pedro

2015-01-01

Guidance systems exist within learning, working and welfare cultures, which are upheld by prevailing institutions and stakeholders. Implementing a lifelong approach questions rooted codes and idiosyncrasies of the sectors across which guidance is distributed. To support individuals' careers, unlock their potential and increase their contribution…

Hydrogen Fuel Cell Analysis: Lessons Learned from Stationary Power Generation Final Report

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

Scott E. Grasman; John W. Sheffield; Fatih Dogan

2010-04-30

This study considered opportunities for hydrogen in stationary applications in order to make recommendations related to RD&D strategies that incorporate lessons learned and best practices from relevant national and international stationary power efforts, as well as cost and environmental modeling of pathways. The study analyzed the different strategies utilized in power generation systems and identified the different challenges and opportunities for producing and using hydrogen as an energy carrier. Specific objectives included both a synopsis/critical analysis of lessons learned from previous stationary power programs and recommendations for a strategy for hydrogen infrastructure deployment. This strategy incorporates all hydrogen pathways andmore » a combination of distributed power generating stations, and provides an overview of stationary power markets, benefits of hydrogen-based stationary power systems, and competitive and technological challenges. The motivation for this project was to identify the lessons learned from prior stationary power programs, including the most significant obstacles, how these obstacles have been approached, outcomes of the programs, and how this information can be used by the Hydrogen, Fuel Cells & Infrastructure Technologies Program to meet program objectives primarily related to hydrogen pathway technologies (production, storage, and delivery) and implementation of fuel cell technologies for distributed stationary power. In addition, the lessons learned address environmental and safety concerns, including codes and standards, and education of key stakeholders.« less

Statistical, Graphical, and Learning Methods for Sensing, Surveillance, and Navigation Systems

DTIC Science & Technology

2016-06-28

harsh propagation environments. Conventional filtering techniques fail to provide satisfactory performance in many important nonlinear or non...Gaussian scenarios. In addition, there is a lack of a unified methodology for the design and analysis of different filtering techniques. To address...these problems, we have proposed a new filtering methodology called belief condensation (BC) DISTRIBUTION A: Distribution approved for public release

Using machine learning and real-time workload assessment in a high-fidelity UAV simulation environment

NASA Astrophysics Data System (ADS)

Monfort, Samuel S.; Sibley, Ciara M.; Coyne, Joseph T.

2016-05-01

Future unmanned vehicle operations will see more responsibilities distributed among fewer pilots. Current systems typically involve a small team of operators maintaining control over a single aerial platform, but this arrangement results in a suboptimal configuration of operator resources to system demands. Rather than devoting the full-time attention of several operators to a single UAV, the goal should be to distribute the attention of several operators across several UAVs as needed. Under a distributed-responsibility system, operator task load would be continuously monitored, with new tasks assigned based on system needs and operator capabilities. The current paper sought to identify a set of metrics that could be used to assess workload unobtrusively and in near real-time to inform a dynamic tasking algorithm. To this end, we put 20 participants through a variable-difficulty multiple UAV management simulation. We identified a subset of candidate metrics from a larger pool of pupillary and behavioral measures. We then used these metrics as features in a machine learning algorithm to predict workload condition every 60 seconds. This procedure produced an overall classification accuracy of 78%. An automated tasker sensitive to fluctuations in operator workload could be used to efficiently delegate tasks for teams of UAV operators.

Learning and motivation in the human striatum.

PubMed

Shohamy, Daphna

2011-06-01

The past decade has seen a dramatic change in our understanding of the role of the striatum in behavior. Early perspectives emphasized a role for the striatum in habitual learning of stimulus-response associations and sequences of actions. Recent advances from human neuroimaging research suggest a broader role for the striatum in motivated learning. New findings demonstrate that the striatum represents multiple learning signals and highlight the contribution of the striatum across many cognitive domains and contexts. Recent findings also emphasize interactions between the striatum and other specialized brain systems for learning. Together, these findings suggest that the striatum contributes to a distributed network that learns to select actions based on their predicted value in order to optimize behavior. Copyright © 2011 Elsevier Ltd. All rights reserved.

Sparse distributed memory overview

NASA Technical Reports Server (NTRS)

Raugh, Mike

1990-01-01

The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan.

PubMed

Chen, Hong-Ren; Tseng, Hsiao-Fen

2012-08-01

Web-based e-learning is not restricted by time or place and can provide teachers with a learning environment that is flexible and convenient, enabling them to efficiently learn, quickly develop their professional expertise, and advance professionally. Many research reports on web-based e-learning have neglected the role of the teacher's perspective in the acceptance of using web-based e-learning systems for in-service education. We distributed questionnaires to 402 junior high school teachers in central Taiwan. This study used the Technology Acceptance Model (TAM) as our theoretical foundation and employed the Structure Equation Model (SEM) to examine factors that influenced intentions to use in-service training conducted through web-based e-learning. The results showed that motivation to use and Internet self-efficacy were significantly positively associated with behavioral intentions regarding the use of web-based e-learning for in-service training through the factors of perceived usefulness and perceived ease of use. The factor of computer anxiety had a significantly negative effect on behavioral intentions toward web-based e-learning in-service training through the factor of perceived ease of use. Perceived usefulness and motivation to use were the primary reasons for the acceptance by junior high school teachers of web-based e-learning systems for in-service training. Copyright © 2011 Elsevier Ltd. All rights reserved.

Model-free learning on robot kinematic chains using a nested multi-agent topology

NASA Astrophysics Data System (ADS)

Karigiannis, John N.; Tzafestas, Costas S.

2016-11-01

This paper proposes a model-free learning scheme for the developmental acquisition of robot kinematic control and dexterous manipulation skills. The approach is based on a nested-hierarchical multi-agent architecture that intuitively encapsulates the topology of robot kinematic chains, where the activity of each independent degree-of-freedom (DOF) is finally mapped onto a distinct agent. Each one of those agents progressively evolves a local kinematic control strategy in a game-theoretic sense, that is, based on a partial (local) view of the whole system topology, which is incrementally updated through a recursive communication process according to the nested-hierarchical topology. Learning is thus approached not through demonstration and training but through an autonomous self-exploration process. A fuzzy reinforcement learning scheme is employed within each agent to enable efficient exploration in a continuous state-action domain. This paper constitutes in fact a proof of concept, demonstrating that global dexterous manipulation skills can indeed evolve through such a distributed iterative learning of local agent sensorimotor mappings. The main motivation behind the development of such an incremental multi-agent topology is to enhance system modularity, to facilitate extensibility to more complex problem domains and to improve robustness with respect to structural variations including unpredictable internal failures. These attributes of the proposed system are assessed in this paper through numerical experiments in different robot manipulation task scenarios, involving both single and multi-robot kinematic chains. The generalisation capacity of the learning scheme is experimentally assessed and robustness properties of the multi-agent system are also evaluated with respect to unpredictable variations in the kinematic topology. Furthermore, these numerical experiments demonstrate the scalability properties of the proposed nested-hierarchical architecture, where new agents can be recursively added in the hierarchy to encapsulate individual active DOFs. The results presented in this paper demonstrate the feasibility of such a distributed multi-agent control framework, showing that the solutions which emerge are plausible and near-optimal. Numerical efficiency and computational cost issues are also discussed.

Health Worker Focused Distributed Simulation for Improving Capability of Health Systems in Liberia.

PubMed

Gale, Thomas C E; Chatterjee, Arunangsu; Mellor, Nicholas E; Allan, Richard J

2016-04-01

The main goal of this study was to produce an adaptable learning platform using virtual learning and distributed simulation, which can be used to train health care workers, across a wide geographical area, key safety messages regarding infection prevention control (IPC). A situationally responsive agile methodology, Scrum, was used to develop a distributed simulation module using short 1-week iterations and continuous synchronous plus asynchronous communication including end users and IPC experts. The module contained content related to standard IPC precautions (including handwashing techniques) and was structured into 3 distinct sections related to donning, doffing, and hazard perception training. Using Scrum methodology, we were able to link concepts applied to best practices in simulation-based medical education (deliberate practice, continuous feedback, self-assessment, and exposure to uncommon events), pedagogic principles related to adult learning (clear goals, contextual awareness, motivational features), and key learning outcomes regarding IPC, as a rapid response initiative to the Ebola outbreak in West Africa. Gamification approach has been used to map learning mechanics to enhance user engagement. The developed IPC module demonstrates how high-frequency, low-fidelity simulations can be rapidly designed using scrum-based agile methodology. Analytics incorporated into the tool can help demonstrate improved confidence and competence of health care workers who are treating patients within an Ebola virus disease outbreak region. These concepts could be used in a range of evolving disasters where rapid development and communication of key learning messages are required.

Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

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

Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

2017-05-17

A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severemore » voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.« less

Learning and dynamics in social systems. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

NASA Astrophysics Data System (ADS)

Dolfin, Marina

2016-03-01

The interesting novelty of the paper by Burini et al. [1] is that the authors present a survey and a new approach of collective learning based on suitable development of methods of the kinetic theory [2] and theoretical tools of evolutionary game theory [3]. Methods of statistical dynamics and kinetic theory lead naturally to stochastic and collective dynamics. Indeed, the authors propose the use of games where the state of the interacting entities is delivered by probability distributions.

45 CFR 2516.600 - How are funds for school-based service-learning programs distributed?

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 4 2010-10-01 2010-10-01 false How are funds for school-based service-learning... (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE SCHOOL-BASED SERVICE-LEARNING PROGRAMS Distribution of Funds § 2516.600 How are funds for school-based service-learning programs distributed? (a) Of...

45 CFR 2516.600 - How are funds for school-based service-learning programs distributed?

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 4 2011-10-01 2011-10-01 false How are funds for school-based service-learning... (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE SCHOOL-BASED SERVICE-LEARNING PROGRAMS Distribution of Funds § 2516.600 How are funds for school-based service-learning programs distributed? (a) Of...

45 CFR 2517.600 - How are funds for community-based service-learning programs distributed?

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 4 2010-10-01 2010-10-01 false How are funds for community-based service-learning... (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE COMMUNITY-BASED SERVICE-LEARNING PROGRAMS Distribution of Funds § 2517.600 How are funds for community-based service-learning programs distributed? All...

45 CFR 2517.600 - How are funds for community-based service-learning programs distributed?

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 4 2011-10-01 2011-10-01 false How are funds for community-based service-learning... (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE COMMUNITY-BASED SERVICE-LEARNING PROGRAMS Distribution of Funds § 2517.600 How are funds for community-based service-learning programs distributed? All...

Decomposed fuzzy systems and their application in direct adaptive fuzzy control.

PubMed

Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang

2014-10-01

In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.

Non-Bayesian Optical Inference Machines

NASA Astrophysics Data System (ADS)

Kadar, Ivan; Eichmann, George

1987-01-01

In a recent paper, Eichmann and Caulfield) presented a preliminary exposition of optical learning machines suited for use in expert systems. In this paper, we extend the previous ideas by introducing learning as a means of reinforcement by information gathering and reasoning with uncertainty in a non-Bayesian framework2. More specifically, the non-Bayesian approach allows the representation of total ignorance (not knowing) as opposed to assuming equally likely prior distributions.

An Analysis Of Personalized Learning Systems For Navy Training And Education Settings

DTIC Science & Technology

2016-12-01

of dedicated “schoolhouse” training and education among the services account for approximately $8.7 billion per year (Department of Defense [DOD...calls it customized learning) opportunities for the Air Force with the sole intent of reducing time-to- train , and thereby significantly reducing...technology to develop and distribute personalized, cost-effective, always available, high quality training and education to service members and DOD

Context-Based Intent Understanding for Autonomous Systems in Naval and Collaborative Robot Applications

DTIC Science & Technology

2013-10-29

COVERED (From - To) 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d...based on contextual information, 3) develop vision-based techniques for learning of contextual information, and detection and identification of...that takes into account many possible contexts. The probability distributions of these contexts will be learned from existing databases on common sense

Guidelines for developing distributed virtual environment applications

NASA Astrophysics Data System (ADS)

Stytz, Martin R.; Banks, Sheila B.

1998-08-01

We have conducted a variety of projects that served to investigate the limits of virtual environments and distributed virtual environment (DVE) technology for the military and medical professions. The projects include an application that allows the user to interactively explore a high-fidelity, dynamic scale model of the Solar System and a high-fidelity, photorealistic, rapidly reconfigurable aircraft simulator. Additional projects are a project for observing, analyzing, and understanding the activity in a military distributed virtual environment, a project to develop a distributed threat simulator for training Air Force pilots, a virtual spaceplane to determine user interface requirements for a planned military spaceplane system, and an automated wingman for use in supplementing or replacing human-controlled systems in a DVE. The last two projects are a virtual environment user interface framework; and a project for training hospital emergency department personnel. In the process of designing and assembling the DVE applications in support of these projects, we have developed rules of thumb and insights into assembling DVE applications and the environment itself. In this paper, we open with a brief review of the applications that were the source for our insights and then present the lessons learned as a result of these projects. The lessons we have learned fall primarily into five areas. These areas are requirements development, software architecture, human-computer interaction, graphical database modeling, and construction of computer-generated forces.

Virtual Collaborative Environments for System of Systems Engineering and Applications for ISAT

NASA Technical Reports Server (NTRS)

Dryer, David A.

2002-01-01

This paper describes an system of systems or metasystems approach and models developed to help prepare engineering organizations for distributed engineering environments. These changes in engineering enterprises include competition in increasingly global environments; new partnering opportunities caused by advances in information and communication technologies, and virtual collaboration issues associated with dispersed teams. To help address challenges and needs in this environment, a framework is proposed that can be customized and adapted for NASA to assist in improved engineering activities conducted in distributed, enhanced engineering environments. The approach is designed to prepare engineers for such distributed collaborative environments by learning and applying e-engineering methods and tools to a real-world engineering development scenario. The approach consists of two phases: an e-engineering basics phase and e-engineering application phase. The e-engineering basics phase addresses skills required for e-engineering. The e-engineering application phase applies these skills in a distributed collaborative environment to system development projects.

Drive Control Scheme of Electric Power Assisted Wheelchair Based on Neural Network Learning of Human Wheelchair Operation Characteristics

NASA Astrophysics Data System (ADS)

Tanohata, Naoki; Seki, Hirokazu

This paper describes a novel drive control scheme of electric power assisted wheelchairs based on neural network learning of human wheelchair operation characteristics. “Electric power assisted wheelchair” which enhances the drive force of the operator by employing electric motors is expected to be widely used as a mobility support system for elderly and disabled people. However, some handicapped people with paralysis of the muscles of one side of the body cannot maneuver the wheelchair as desired because of the difference in the right and left input force. Therefore, this study proposes a neural network learning system of such human wheelchair operation characteristics and a drive control scheme with variable distribution and assistance ratios. Some driving experiments will be performed to confirm the effectiveness of the proposed control system.

Creating and Nurturing Distributed Asynchronous Learning Environments.

ERIC Educational Resources Information Center

Kochtanek, Thomas R.; Hein, Karen K.

2000-01-01

Describes the evolution of a university course from a face-to-face experience to a Web-based asynchronous learning environment. Topics include cognition and learning; distance learning and distributed learning; student learning communities and the traditional classroom; the future as it relates to education and technology; collaborative student…

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

PubMed

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

2017-06-01

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

Learning classifier systems for single and multiple mobile robots in unstructured environments

NASA Astrophysics Data System (ADS)

Bay, John S.

1995-12-01

The learning classifier system (LCS) is a learning production system that generates behavioral rules via an underlying discovery mechanism. The LCS architecture operates similarly to a blackboard architecture; i.e., by posted-message communications. But in the LCS, the message board is wiped clean at every time interval, thereby requiring no persistent shared resource. In this paper, we adapt the LCS to the problem of mobile robot navigation in completely unstructured environments. We consider the model of the robot itself, including its sensor and actuator structures, to be part of this environment, in addition to the world-model that includes a goal and obstacles at unknown locations. This requires a robot to learn its own I/O characteristics in addition to solving its navigation problem, but results in a learning controller that is equally applicable, unaltered, in robots with a wide variety of kinematic structures and sensing capabilities. We show the effectiveness of this LCS-based controller through both simulation and experimental trials with a small robot. We then propose a new architecture, the Distributed Learning Classifier System (DLCS), which generalizes the message-passing behavior of the LCS from internal messages within a single agent to broadcast massages among multiple agents. This communications mode requires little bandwidth and is easily implemented with inexpensive, off-the-shelf hardware. The DLCS is shown to have potential application as a learning controller for multiple intelligent agents.

Learning from LANCE: Developing a Web Portal Infrastructure for NASA Earth Science Data (Invited)

NASA Astrophysics Data System (ADS)

Murphy, K. J.

2013-12-01

NASA developed the Land Atmosphere Near real-time Capability for EOS (LANCE) in response to a growing need for timely satellite observations by applications users, operational agencies and researchers. EOS capabilities originally intended for long-term Earth science research were modified to deliver satellite data products with sufficient latencies to meet the needs of the NRT user communities. LANCE products are primarily distributed as HDF data files for analysis, however novel capabilities for distribution of NRT imagery for visualization have been added which have expanded the user base. Additionally systems to convert data to information such as the MODIS hotspot/active fire data are also provided through the Fire Information for Resource Management System (FIRMS). LANCE services include: FTP/HTTP file distribution, Rapid Response (RR), Worldview, Global Imagery Browse Services (GIBS) and FIRMS. This paper discusses how NASA has developed services specifically for LANCE and is taking the lessons learned through these activities to develop an Earthdata Web Infrastructure. This infrastructure is being used as a platform to support development of data portals that address specific science issues for much of EOSDIS data.

System Re-engineering Project Executive Summary

DTIC Science & Technology

1991-11-01

Management Information System (STAMIS) application. This project involved reverse engineering, evaluation of structured design and object-oriented design, and re- implementation of the system in Ada. This executive summary presents the approach to re-engineering the system, the lessons learned while going through the process, and issues to be considered in future tasks of this nature.... Computer-Aided Software Engineering (CASE), Distributed Software, Ada, COBOL, Systems Analysis, Systems Design, Life Cycle Development, Functional Decomposition, Object-Oriented

The Collaborative Lecture Annotation System (CLAS): A New TOOL for Distributed Learning

ERIC Educational Resources Information Center

Risko, E. F.; Foulsham, T.; Dawson, S.; Kingstone, A.

2013-01-01

In the context of a lecture, the capacity to readily recognize and synthesize key concepts is crucial for comprehension and overall educational performance. In this paper, we introduce a tool, the Collaborative Lecture Annotation System (CLAS), which has been developed to make the extraction of important information a more collaborative and…

A Hardware Testbed for Distributed Learning, Estimation, and Approximation Theory with Sensor Vehicle Networks

DTIC Science & Technology

2012-04-25

Virginia Tech VAL. Because of the excellent performance of the Trimble-based systems that were tested in the past, the Trimble subsidy Applanix was...initially contacted for available systems. The lowest cost, turnkey Trimble/ Applanix the POS LV 210 far exceeded the performance requirements of the

Activity Systems and Conflict Resolution in an Online Professional Communication Course

ERIC Educational Resources Information Center

Walker, Kristin

2004-01-01

Conflicts often arise in online professional communication class discussions as students discuss sensitive ethical issues relating to the workplace. When conflicts arise in an online class, the activity system of the class has to be kept in balance for the course to continue functioning effectively. Activity theory and distributed learning theory…

Interpreting beyond Syntactics: A Semiotic Learning Model for Computer Programming Languages

ERIC Educational Resources Information Center

May, Jeffrey; Dhillon, Gurpreet

2009-01-01

In the information systems field there are numerous programming languages that can be used in specifying the behavior of concurrent and distributed systems. In the literature it has been argued that a lack of pragmatic and semantic consideration decreases the effectiveness of such specifications. In other words, to simply understand the syntactic…

Cyber Operations Virtual Environment

DTIC Science & Technology

2010-09-01

automated system affects reliance on that system (e.g., Dzindolet, Peterson , Pomranky, Pierce, & Beck, 2003; Lee & Moray, 1994; Lee & See, 2004...described a need for instruction to enable interactive, realistic training ( Hershey , 2008): Network Warfare and Operations Distributed Training...knowledge or needs beyond this shallow level (Beck, Stern, & Haugsjaa, 1996 ). The immediate feedback model employed in behaviorist learning has

Mobile-IT Education (MIT.EDU): M-Learning Applications for Classroom Settings

ERIC Educational Resources Information Center

Sung, M.; Gips, J.; Eagle, N.; Madan, A.; Caneel, R.; DeVaul, R.; Bonsen, J.; Pentland, A.

2005-01-01

In this paper, we describe the Mobile-IT Education (MIT.EDU) system, which demonstrates the potential of using a distributed mobile device architecture for rapid prototyping of wireless mobile multi-user applications for use in classroom settings. MIT.EDU is a stable, accessible system that combines inexpensive, commodity hardware, a flexible…

Scheduling lessons learned from the Autonomous Power System

NASA Technical Reports Server (NTRS)

Ringer, Mark J.

1992-01-01

The Autonomous Power System (APS) project at NASA LeRC is designed to demonstrate the applications of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution systems. The project consists of three elements: the Autonomous Power Expert System (APEX) for Fault Diagnosis, Isolation, and Recovery (FDIR); the Autonomous Intelligent Power Scheduler (AIPS) to efficiently assign activities start times and resources; and power hardware (Brassboard) to emulate a space-based power system. The AIPS scheduler was tested within the APS system. This scheduler is able to efficiently assign available power to the requesting activities and share this information with other software agents within the APS system in order to implement the generated schedule. The AIPS scheduler is also able to cooperatively recover from fault situations by rescheduling the affected loads on the Brassboard in conjunction with the APEX FDIR system. AIPS served as a learning tool and an initial scheduling testbed for the integration of FDIR and automated scheduling systems. Many lessons were learned from the AIPS scheduler and are now being integrated into a new scheduler called SCRAP (Scheduler for Continuous Resource Allocation and Planning). This paper will service three purposes: an overview of the AIPS implementation, lessons learned from the AIPS scheduler, and a brief section on how these lessons are being applied to the new SCRAP scheduler.

Statistical learning of speech sounds is most robust during the period of perceptual attunement.

PubMed

Liu, Liquan; Kager, René

2017-12-01

Although statistical learning has been shown to be a domain-general mechanism, its constraints, such as its interactions with perceptual development, are less well understood and discussed. This study is among the first to investigate the distributional learning of lexical pitch in non-tone-language-learning infants, exploring its interaction with language-specific perceptual attunement during the first 2years after birth. A total of 88 normally developing Dutch infants of 5, 11, and 14months were tested via a distributional learning paradigm and were familiarized on a unimodal or bimodal distribution of high-level versus high-falling tones in Mandarin Chinese. After familiarization, they were tested on a tonal contrast that shared equal distributional information in either modality. At 5months, infants in both conditions discriminated the contrast, whereas 11-month-olds showed discrimination only in the bimodal condition. By 14months, infants failed to discriminate the contrast in either condition. Results indicate interplay between infants' long-term linguistic experience throughout development and short-term distributional learning during the experiment, and they suggest that the influence of tonal distributional learning varies along the perceptual attunement trajectory, such that opportunities for distributional learning effects appear to be constrained in the beginning and at the end of perceptual attunement. The current study contributes to previous research by demonstrating an effect of age on learning from distributional cues. Copyright © 2017 Elsevier Inc. All rights reserved.

A Distributed Multi-Agent System for Collaborative Information Management and Learning

NASA Technical Reports Server (NTRS)

Chen, James R.; Wolfe, Shawn R.; Wragg, Stephen D.; Koga, Dennis (Technical Monitor)

2000-01-01

In this paper, we present DIAMS, a system of distributed, collaborative agents to help users access, manage, share and exchange information. A DIAMS personal agent helps its owner find information most relevant to current needs. It provides tools and utilities for users to manage their information repositories with dynamic organization and virtual views. Flexible hierarchical display is integrated with indexed query search-to support effective information access. Automatic indexing methods are employed to support user queries and communication between agents. Contents of a repository are kept in object-oriented storage to facilitate information sharing. Collaboration between users is aided by easy sharing utilities as well as automated information exchange. Matchmaker agents are designed to establish connections between users with similar interests and expertise. DIAMS agents provide needed services for users to share and learn information from one another on the World Wide Web.

Commentary on "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning"

ERIC Educational Resources Information Center

Hewitt, Jim

2015-01-01

The article, "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning" is an interesting study that operates at the intersection of learning theory and learning analytics. The authors observe that the relationship between learning theory and research in the learning analytics field is constrained by several…

Distributed Control with Collective Intelligence

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Wheeler, Kevin R.; Tumer, Kagan

1998-01-01

We consider systems of interacting reinforcement learning (RL) algorithms that do not work at cross purposes , in that their collective behavior maximizes a global utility function. We call such systems COllective INtelligences (COINs). We present the theory of designing COINs. Then we present experiments validating that theory in the context of two distributed control problems: We show that COINs perform near-optimally in a difficult variant of Arthur's bar problem [Arthur] (and in particular avoid the tragedy of the commons for that problem), and we also illustrate optimal performance in the master-slave problem.

Clinical Named Entity Recognition Using Deep Learning Models.

PubMed

Wu, Yonghui; Jiang, Min; Xu, Jun; Zhi, Degui; Xu, Hua

2017-01-01

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have been increasing efforts to apply deep learning models to improve the performance of current clinical NER systems. This study examined two popular deep learning architectures, the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN), to extract concepts from clinical texts. We compared the two deep neural network architectures with three baseline Conditional Random Fields (CRFs) models and two state-of-the-art clinical NER systems using the i2b2 2010 clinical concept extraction corpus. The evaluation results showed that the RNN model trained with the word embeddings achieved a new state-of-the- art performance (a strict F1 score of 85.94%) for the defined clinical NER task, outperforming the best-reported system that used both manually defined and unsupervised learning features. This study demonstrates the advantage of using deep neural network architectures for clinical concept extraction, including distributed feature representation, automatic feature learning, and long-term dependencies capture. This is one of the first studies to compare the two widely used deep learning models and demonstrate the superior performance of the RNN model for clinical NER.

Clinical Named Entity Recognition Using Deep Learning Models

PubMed Central

Wu, Yonghui; Jiang, Min; Xu, Jun; Zhi, Degui; Xu, Hua

2017-01-01

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have been increasing efforts to apply deep learning models to improve the performance of current clinical NER systems. This study examined two popular deep learning architectures, the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN), to extract concepts from clinical texts. We compared the two deep neural network architectures with three baseline Conditional Random Fields (CRFs) models and two state-of-the-art clinical NER systems using the i2b2 2010 clinical concept extraction corpus. The evaluation results showed that the RNN model trained with the word embeddings achieved a new state-of-the- art performance (a strict F1 score of 85.94%) for the defined clinical NER task, outperforming the best-reported system that used both manually defined and unsupervised learning features. This study demonstrates the advantage of using deep neural network architectures for clinical concept extraction, including distributed feature representation, automatic feature learning, and long-term dependencies capture. This is one of the first studies to compare the two widely used deep learning models and demonstrate the superior performance of the RNN model for clinical NER. PMID:29854252

Temperature based Restricted Boltzmann Machines

NASA Astrophysics Data System (ADS)

Li, Guoqi; Deng, Lei; Xu, Yi; Wen, Changyun; Wang, Wei; Pei, Jing; Shi, Luping

2016-01-01

Restricted Boltzmann machines (RBMs), which apply graphical models to learning probability distribution over a set of inputs, have attracted much attention recently since being proposed as building blocks of multi-layer learning systems called deep belief networks (DBNs). Note that temperature is a key factor of the Boltzmann distribution that RBMs originate from. However, none of existing schemes have considered the impact of temperature in the graphical model of DBNs. In this work, we propose temperature based restricted Boltzmann machines (TRBMs) which reveals that temperature is an essential parameter controlling the selectivity of the firing neurons in the hidden layers. We theoretically prove that the effect of temperature can be adjusted by setting the parameter of the sharpness of the logistic function in the proposed TRBMs. The performance of RBMs can be improved by adjusting the temperature parameter of TRBMs. This work provides a comprehensive insights into the deep belief networks and deep learning architectures from a physical point of view.

Exploring the Role of Distributed Learning in Distance Education at Allama Iqbal Open University: Academic Challenges at Postgraduate Level

ERIC Educational Resources Information Center

Bukhsh, Qadir; Chaudhary, Muhammad Ajmal

2015-01-01

Distributed learning is derived from the concept of distributed resources. Different institutions around the globe connected through network and the learners are diverse, located in the different cultures and communities. Distributed learning provides global standards of quality to all learners through synchronous and asynchronous communications…

Distributed Learning and Constructivist Philosophy (Uzaktan Ögretim Ve Yapilandirmaci Felsefe)

ERIC Educational Resources Information Center

Tekinarslan, Erkan

2003-01-01

Distance education and its new form of distributed learning have been used in many countries to provide education to people who need training. Recent developments in instructional technology enable the institutions to distribute their education to more people in distant places than ever before. The field of distributed learning has a lot of…

Peer-to-Peer JXTA Architecture for Continuing Mobile Medical Education Incorporated in Rural Public Health Centers.

PubMed

Rajasekaran, Rajkumar; Iyengar, Nallani Chackravatula Sriman Narayana

2013-04-01

Mobile technology helps to improve continuing medical education; this includes all aspects of public health care as well as keeping one's knowledge up-to-date. The program of continuing medical and health education is intertwined with mobile health technology, which forms an imperative component of national strategies in health. Continuing mobile medical education (CMME) programs are designed to ensure that all medical and health-care professionals stay up-to-date with the knowledge required through mobile JXTA to appraise modernized strategies so as to achieve national goals of health-care information distribution. In this study, a 20-item questionnaire was distributed to 280 health professionals practicing traditional training learning methodologies (180 nurses, 60 doctors, and 40 health inspectors) in 25 rural hospitals. Among the 83% respondents, 56% are eager to take new learning methodologies as part of their evaluation, which is considered for promotion to higher grades, increments, or as part of their work-related activities. The proposed model was executed in five public health centers in which nurses and health inspectors registered in the JXTA network were referred to the record peer group by administrators. A mobile training program on immunization was conducted through the ADVT, with the lectures delivered on their mobiles. Credits are given after taking the course and completing an evaluation test. The system is faster compared with traditional learning. Medical knowledge management and mobile-streaming application support the CMME system through JXTA. The mobile system includes online lectures and practice quizzes, as well as assignments and interactions with health professionals. Evaluation and assessments are done online and credits certificates are provided based on the score the student obtains. The acceptance of mobile JXTA peer-to-peer learning has created a drastic change in learning methods among rural health professionals. The professionals undergo training and should pass an exam in order to obtain the credits. The system is controlled and monitored by the administrator peer group, which makes it more flexible and structured. Compared with traditional learning system, enhanced study improves cloud-based mobile medical education technology.

Peer-to-Peer JXTA Architecture for Continuing Mobile Medical Education Incorporated in Rural Public Health Centers

PubMed Central

Rajasekaran, Rajkumar; Iyengar, Nallani Chackravatula Sriman Narayana

2013-01-01

Objectives: Mobile technology helps to improve continuing medical education; this includes all aspects of public health care as well as keeping one’s knowledge up-to-date. The program of continuing medical and health education is intertwined with mobile health technology, which forms an imperative component of national strategies in health. Continuing mobile medical education (CMME) programs are designed to ensure that all medical and health-care professionals stay up-to-date with the knowledge required through mobile JXTA to appraise modernized strategies so as to achieve national goals of health-care information distribution. Methods: In this study, a 20-item questionnaire was distributed to 280 health professionals practicing traditional training learning methodologies (180 nurses, 60 doctors, and 40 health inspectors) in 25 rural hospitals. Among the 83% respondents, 56% are eager to take new learning methodologies as part of their evaluation, which is considered for promotion to higher grades, increments, or as part of their work-related activities. Results: The proposed model was executed in five public health centers in which nurses and health inspectors registered in the JXTA network were referred to the record peer group by administrators. A mobile training program on immunization was conducted through the ADVT, with the lectures delivered on their mobiles. Credits are given after taking the course and completing an evaluation test. The system is faster compared with traditional learning. Conclusion: Medical knowledge management and mobile-streaming application support the CMME system through JXTA. The mobile system includes online lectures and practice quizzes, as well as assignments and interactions with health professionals. Evaluation and assessments are done online and credits certificates are provided based on the score the student obtains. The acceptance of mobile JXTA peer-to-peer learning has created a drastic change in learning methods among rural health professionals. The professionals undergo training and should pass an exam in order to obtain the credits. The system is controlled and monitored by the administrator peer group, which makes it more flexible and structured. Compared with traditional learning system, enhanced study improves cloud-based mobile medical education technology. PMID:24159539

Laying the Groundwork: Lessons Learned from the Telecommunications Industry for Distributed Generation; Preprint

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

Wise, A. L.

2008-05-01

The telecommunications industry went through growing pains in the past that hold some interesting lessons for the growing distributed generation (DG) industry. The technology shifts and stakeholders involved with the historic market transformation of the telecommunications sector mirror similar factors involved in distributed generation today. An examination of these factors may inform best practices when approaching the conduits necessary to accelerate the shifting of our nation's energy system to cleaner forms of generation and use. From a technical perspective, the telecom industry in the 1990s saw a shift from highly centralized systems that had no capacity for adaptation to highlymore » adaptive, distributed network systems. From a management perspective, the industry shifted from small, private-company structures to big, capital-intensive corporations. This presentation will explore potential correlation and outline the lessons that we can take away from this comparison.« less

Thinking about Distributed Learning? Issues and Questions To Ponder.

ERIC Educational Resources Information Center

Sorg, Steven

2001-01-01

Introduces other articles in this issue devoted to distributed learning at metropolitan universities. Discusses issues that institutions should address if considering distributed learning: institutional goals and strategic plans, faculty development needs and capabilities, student support services, technical and personnel infrastructure, policies,…

NASA's Earth Science Data Systems - Lessons Learned and Future Directions

NASA Technical Reports Server (NTRS)

Ramapriyan, Hampapuram K.

2010-01-01

In order to meet the increasing demand for Earth Science data, NASA has significantly improved the Earth Science Data Systems over the last two decades. This improvement is reviewed in this slide presentation. Many Earth Science disciplines have been able to access the data that is held in the Earth Observing System (EOS) Data and Information System (EOSDIS) at the Distributed Active Archive Centers (DAACs) that forms the core of the data system.

Pipe and Solids Analysis: What Can I Learn?

EPA Science Inventory

This presentation gives a brief overview of techniques that regulators, utilities and consultants might want to request from laboratories to anticipate or solve water treatment and distribution system water quality problems. Actual examples will be given from EPA collaborations,...

Mapping the distribution of language related genes FoxP1, FoxP2, and CntnaP2 in the brains of vocal learning bat species.

PubMed

Rodenas-Cuadrado, Pedro M; Mengede, Janine; Baas, Laura; Devanna, Paolo; Schmid, Tobias A; Yartsev, Michael; Firzlaff, Uwe; Vernes, Sonja C

2018-06-01

Genes including FOXP2, FOXP1, and CNTNAP2, have been implicated in human speech and language phenotypes, pointing to a role in the development of normal language-related circuitry in the brain. Although speech and language are unique to humans a comparative approach is possible by addressing language-relevant traits in animal systems. One such trait, vocal learning, represents an essential component of human spoken language, and is shared by cetaceans, pinnipeds, elephants, some birds and bats. Given their vocal learning abilities, gregarious nature, and reliance on vocalizations for social communication and navigation, bats represent an intriguing mammalian system in which to explore language-relevant genes. We used immunohistochemistry to detail the distribution of FoxP2, FoxP1, and Cntnap2 proteins, accompanied by detailed cytoarchitectural histology in the brains of two vocal learning bat species; Phyllostomus discolor and Rousettus aegyptiacus. We show widespread expression of these genes, similar to what has been previously observed in other species, including humans. A striking difference was observed in the adult P. discolor bat, which showed low levels of FoxP2 expression in the cortex that contrasted with patterns found in rodents and nonhuman primates. We created an online, open-access database within which all data can be browsed, searched, and high resolution images viewed to single cell resolution. The data presented herein reveal regions of interest in the bat brain and provide new opportunities to address the role of these language-related genes in complex vocal-motor and vocal learning behaviors in a mammalian model system. © 2018 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

Mapping the distribution of language related genes FoxP1, FoxP2, and CntnaP2 in the brains of vocal learning bat species

PubMed Central

Rodenas‐Cuadrado, Pedro M.; Mengede, Janine; Baas, Laura; Devanna, Paolo; Schmid, Tobias A.; Yartsev, Michael; Firzlaff, Uwe

2018-01-01

Abstract Genes including FOXP2, FOXP1, and CNTNAP2, have been implicated in human speech and language phenotypes, pointing to a role in the development of normal language‐related circuitry in the brain. Although speech and language are unique to humans a comparative approach is possible by addressing language‐relevant traits in animal systems. One such trait, vocal learning, represents an essential component of human spoken language, and is shared by cetaceans, pinnipeds, elephants, some birds and bats. Given their vocal learning abilities, gregarious nature, and reliance on vocalizations for social communication and navigation, bats represent an intriguing mammalian system in which to explore language‐relevant genes. We used immunohistochemistry to detail the distribution of FoxP2, FoxP1, and Cntnap2 proteins, accompanied by detailed cytoarchitectural histology in the brains of two vocal learning bat species; Phyllostomus discolor and Rousettus aegyptiacus. We show widespread expression of these genes, similar to what has been previously observed in other species, including humans. A striking difference was observed in the adult P. discolor bat, which showed low levels of FoxP2 expression in the cortex that contrasted with patterns found in rodents and nonhuman primates. We created an online, open‐access database within which all data can be browsed, searched, and high resolution images viewed to single cell resolution. The data presented herein reveal regions of interest in the bat brain and provide new opportunities to address the role of these language‐related genes in complex vocal‐motor and vocal learning behaviors in a mammalian model system. PMID:29297931

Learning overcomplete representations from distributed data: a brief review

NASA Astrophysics Data System (ADS)

Raja, Haroon; Bajwa, Waheed U.

2016-05-01

Most of the research on dictionary learning has focused on developing algorithms under the assumption that data is available at a centralized location. But often the data is not available at a centralized location due to practical constraints like data aggregation costs, privacy concerns, etc. Using centralized dictionary learning algorithms may not be the optimal choice in such settings. This motivates the design of dictionary learning algorithms that consider distributed nature of data as one of the problem variables. Just like centralized settings, distributed dictionary learning problem can be posed in more than one way depending on the problem setup. Most notable distinguishing features are the online versus batch nature of data and the representative versus discriminative nature of the dictionaries. In this paper, several distributed dictionary learning algorithms that are designed to tackle different problem setups are reviewed. One of these algorithms is cloud K-SVD, which solves the dictionary learning problem for batch data in distributed settings. One distinguishing feature of cloud K-SVD is that it has been shown to converge to its centralized counterpart, namely, the K-SVD solution. On the other hand, no such guarantees are provided for other distributed dictionary learning algorithms. Convergence of cloud K-SVD to the centralized K-SVD solution means problems that are solvable by K-SVD in centralized settings can now be solved in distributed settings with similar performance. Finally, cloud K-SVD is used as an example to show the advantages that are attainable by deploying distributed dictionary algorithms for real world distributed datasets.

Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation

PubMed Central

Garrido, Jesús A.; Luque, Niceto R.; D'Angelo, Egidio; Ros, Eduardo

2013-01-01

Adaptable gain regulation is at the core of the forward controller operation performed by the cerebro-cerebellar loops and it allows the intensity of motor acts to be finely tuned in a predictive manner. In order to learn and store information about body-object dynamics and to generate an internal model of movement, the cerebellum is thought to employ long-term synaptic plasticity. LTD at the PF-PC synapse has classically been assumed to subserve this function (Marr, 1969). However, this plasticity alone cannot account for the broad dynamic ranges and time scales of cerebellar adaptation. We therefore tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., 2001; Gao et al., 2012) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum. The incorporation of further plasticity mechanisms and of spiking signal processing will allow this concept to be extended in a more realistic computational scenario. PMID:24130518

Machine learning based Intelligent cognitive network using fog computing

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Exploring the roles of interaction and flow in explaining nurses' e-learning acceptance.

PubMed

Cheng, Yung-Ming

2013-01-01

To provide safe and competent patient care, it is very important that medical institutions should provide nurses with continuing education by using appropriate learning methods. As compared to traditional learning, electronic learning (e-learning) is a more flexible method for nurses' in-service learning. Hence, e-learning is expected to play a pivotal role in providing continuing education for nurses. This study's purpose was to explore the role and relevance of interaction factors, intrinsic motivator (i.e., flow), and extrinsic motivators (i.e., perceived usefulness (PU) and perceived ease of use (PEOU)) in explaining nurses' intention to use the e-learning system. Based on the technology acceptance model (TAM) with the flow theory, this study's research model presents three types of interaction factors, learner-system interaction, instructor-learner interaction, and learner-learner interaction to construct an extended TAM to explore nurses' intention to use the e-learning system. Sample data were gathered from nurses at two regional hospitals in Taiwan. A total of 320 questionnaires were distributed, 254 (79.375%) questionnaires were returned. Consequently, 218 usable questionnaires were analyzed in this study, with a usable response rate of 68.125%. First, confirmatory factor analysis was used to develop the measurement model. Second, to explore the causal relationships among all constructs, the structural model for the research model was tested by using structural equation modeling. First, learner-system interaction, instructor-learner interaction, and learner-learner interaction respectively had significant effects on PU, PEOU, and flow. Next, flow had significant effects on PU and PEOU, and PEOU had a significant effect on PU. Finally, the effects of flow, PU, and PEOU on intention to use were significant. Synthetically speaking, learner-system interaction, instructor-learner interaction, and learner-learner interaction can indirectly make significant impacts on nurses' usage intention of the e-learning system via their extrinsic motivators (i.e., PU and PEOU) and intrinsic motivator (i.e., flow). Copyright © 2012 Elsevier Ltd. All rights reserved.

Cooperative Learning for Distributed In-Network Traffic Classification

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Infrastructure to support learning health systems: are we there yet? Innovative solutions and lessons learned from American Recovery and Reinvestment Act CER investments.

PubMed

Holve, Erin; Segal, Courtney

2014-11-01

The 11 big health data networks participating in the AcademyHealth Electronic Data Methods Forum represent cutting-edge efforts to harness the power of big health data for research and quality improvement. This paper is a comparative case study based on site visits conducted with a subset of these large infrastructure grants funded through the Recovery Act, in which four key issues emerge that can inform the evolution of learning health systems, including the importance of acknowledging the challenges of scaling specialized expertise needed to manage and run CER networks; the delicate balance between privacy protections and the utility of distributed networks; emerging community engagement strategies; and the complexities of developing a robust business model for multi-use networks.

Applying Distributed Learning Theory in Online Business Communication Courses.

ERIC Educational Resources Information Center

Walker, Kristin

2003-01-01

Focuses on the critical use of technology in online formats that entail relatively new teaching media. Argues that distributed learning theory is valuable for teachers of online business communication courses for several reasons. Discusses the application of distributed learning theory to the teaching of business communication online. (SG)

Neuronal avalanches and learning

NASA Astrophysics Data System (ADS)

de Arcangelis, Lucilla

2011-05-01

Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

PubMed

Cui, Yuwei; Ahmad, Subutai; Hawkins, Jeff

2017-01-01

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

A Self-Synthesis Approach to Perceptual Learning for Multisensory Fusion in Robotics

PubMed Central

Axenie, Cristian; Richter, Christoph; Conradt, Jörg

2016-01-01

Biological and technical systems operate in a rich multimodal environment. Due to the diversity of incoming sensory streams a system perceives and the variety of motor capabilities a system exhibits there is no single representation and no singular unambiguous interpretation of such a complex scene. In this work we propose a novel sensory processing architecture, inspired by the distributed macro-architecture of the mammalian cortex. The underlying computation is performed by a network of computational maps, each representing a different sensory quantity. All the different sensory streams enter the system through multiple parallel channels. The system autonomously associates and combines them into a coherent representation, given incoming observations. These processes are adaptive and involve learning. The proposed framework introduces mechanisms for self-creation and learning of the functional relations between the computational maps, encoding sensorimotor streams, directly from the data. Its intrinsic scalability, parallelisation, and automatic adaptation to unforeseen sensory perturbations make our approach a promising candidate for robust multisensory fusion in robotic systems. We demonstrate this by applying our model to a 3D motion estimation on a quadrotor. PMID:27775621

FODEM: Developing Digital Learning Environments in Widely Dispersed Learning Communities

ERIC Educational Resources Information Center

Suhonen, Jarkko; Sutinen, Erkki

2006-01-01

FODEM (FOrmative DEvelopment Method) is a design method for developing digital learning environments for widely dispersed learning communities. These are communities in which the geographical distribution and density of learners is low when compared to the kind of learning communities in which there is a high distribution and density of learners…

Learning methods and strategies of anatomy among medical students in two different Institutions in Riyadh, Saudi Arabia.

PubMed

Al-Mohrej, Omar A; Al-Ayedh, Noura K; Masuadi, Emad M; Al-Kenani, Nader S

2017-04-01

Anatomy instructors adopt individual teaching methods and strategies to convey anatomical information to medical students for learning. Students also exhibit their own individual learning preferences. Instructional methods preferences vary between both instructors and students across different institutions. In attempt to bridge the gap between teaching methods and the students' learning preferences, this study aimed to identify students' learning methods and different strategies of studying anatomy in two different Saudi medical schools in Riyadh. A cross-sectional study, conducted in Saudi Arabia in April 2015, utilized a three-section questionnaire, which was distributed to a consecutive sample of 883 medical students to explore their methods and strategies in learning and teaching anatomy in two separate institutions in Riyadh, Saudi Arabia. Medical students' learning styles and preferences were found to be predominantly affected by different cultural backgrounds, gender, and level of study. Many students found it easier to understand and remember anatomy components using study aids. In addition, almost half of the students felt confident to ask their teachers questions after class. The study also showed that more than half of the students found it easier to study by concentrating on a particular part of the body rather than systems. Students' methods of learning were distributed equally between memorizing facts and learning by hands-on dissection. In addition, the study showed that two thirds of the students felt satisfied with their learning method and believed it was well suited for anatomy. There is no single teaching method which proves beneficial; instructors should be flexible in their teaching in order to optimize students' academic achievements.

Rapid learning of visual ensembles.

PubMed

Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

2017-02-01

We recently demonstrated that observers are capable of encoding not only summary statistics, such as mean and variance of stimulus ensembles, but also the shape of the ensembles. Here, for the first time, we show the learning dynamics of this process, investigate the possible priors for the distribution shape, and demonstrate that observers are able to learn more complex distributions, such as bimodal ones. We used speeding and slowing of response times between trials (intertrial priming) in visual search for an oddly oriented line to assess internal models of distractor distributions. Experiment 1 demonstrates that two repetitions are sufficient for enabling learning of the shape of uniform distractor distributions. In Experiment 2, we compared Gaussian and uniform distractor distributions, finding that following only two repetitions Gaussian distributions are represented differently than uniform ones. Experiment 3 further showed that when distractor distributions are bimodal (with a 30° distance between two uniform intervals), observers initially treat them as uniform, and only with further repetitions do they begin to treat the distributions as bimodal. In sum, observers do not have strong initial priors for distribution shapes and quickly learn simple ones but have the ability to adjust their representations to more complex feature distributions as information accumulates with further repetitions of the same distractor distribution.

The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery

2016-10-01

This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Integrating machine learning to achieve an automatic parameter prediction for practical continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Liu, Weiqi; Huang, Peng; Peng, Jinye; Fan, Jianping; Zeng, Guihua

2018-02-01

For supporting practical quantum key distribution (QKD), it is critical to stabilize the physical parameters of signals, e.g., the intensity, phase, and polarization of the laser signals, so that such QKD systems can achieve better performance and practical security. In this paper, an approach is developed by integrating a support vector regression (SVR) model to optimize the performance and practical security of the QKD system. First, a SVR model is learned to precisely predict the time-along evolutions of the physical parameters of signals. Second, such predicted time-along evolutions are employed as feedback to control the QKD system for achieving the optimal performance and practical security. Finally, our proposed approach is exemplified by using the intensity evolution of laser light and a local oscillator pulse in the Gaussian modulated coherent state QKD system. Our experimental results have demonstrated three significant benefits of our SVR-based approach: (1) it can allow the QKD system to achieve optimal performance and practical security, (2) it does not require any additional resources and any real-time monitoring module to support automatic prediction of the time-along evolutions of the physical parameters of signals, and (3) it is applicable to any measurable physical parameter of signals in the practical QKD system.

Sadie Cox | NREL

Science.gov Websites

, advancing technical solutions for resilient power systems, and assessing development impacts of clean energy applications in developing countries Distributed generation policies and impacts Education M.A. in global Impacts Associated with Low Emission Development Strategies: Lessons Learned from Pilot Efforts in Kenya

Alignment in Teacher Education and Distribution of Leadership: An Example Concerning Learning Study

ERIC Educational Resources Information Center

Nilsson, Ingrid

2008-01-01

The critical aspects distribution of professional leadership, alignment in learning and research close to practices, were lifted forward in order to exemplify a research project with learning study as an approach for alignment between teacher education and practice, and as consequence an instrument for distribution of power. The results showed…

A distributed algorithm for machine learning

NASA Astrophysics Data System (ADS)

Chen, Shihong

2018-04-01

This paper considers a distributed learning problem in which a group of machines in a connected network, each learning its own local dataset, aim to reach a consensus at an optimal model, by exchanging information only with their neighbors but without transmitting data. A distributed algorithm is proposed to solve this problem under appropriate assumptions.

Learning about and from a Distribution of Program Impacts Using Multisite Trials

ERIC Educational Resources Information Center

Raudenbush, Stephen W.; Bloom, Howard S.

2015-01-01

The present article provides a synthesis of the conceptual and statistical issues involved in using multisite randomized trials to learn about and from a distribution of heterogeneous program impacts across individuals and/or program sites. Learning "about" such a distribution involves estimating its mean value, detecting and quantifying…

Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

NASA Astrophysics Data System (ADS)

Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

2017-04-01

In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

Logical optimization for database uniformization

NASA Technical Reports Server (NTRS)

Grant, J.

1984-01-01

Data base uniformization refers to the building of a common user interface facility to support uniform access to any or all of a collection of distributed heterogeneous data bases. Such a system should enable a user, situated anywhere along a set of distributed data bases, to access all of the information in the data bases without having to learn the various data manipulation languages. Furthermore, such a system should leave intact the component data bases, and in particular, their already existing software. A survey of various aspects of the data bases uniformization problem and a proposed solution are presented.

Active Learning Using Arbitrary Binary Valued Queries

DTIC Science & Technology

1990-10-01

active learning in the sense that the learner has complete choice in the information received. Specifically, we allow the learner to ask arbitrary yes...no questions. We consider both active learning under a fixed distribution and distribution-free active learning . In the case of active learning , the...a concept class is actively learnable iff it is finite, so that active learning is in fact less powerful than the usual passive learning model. We

Generation method of synthetic training data for mobile OCR system

NASA Astrophysics Data System (ADS)

Chernyshova, Yulia S.; Gayer, Alexander V.; Sheshkus, Alexander V.

2018-04-01

This paper addresses one of the fundamental problems of machine learning - training data acquiring. Obtaining enough natural training data is rather difficult and expensive. In last years usage of synthetic images has become more beneficial as it allows to save human time and also to provide a huge number of images which otherwise would be difficult to obtain. However, for successful learning on artificial dataset one should try to reduce the gap between natural and synthetic data distributions. In this paper we describe an algorithm which allows to create artificial training datasets for OCR systems using russian passport as a case study.

An Evaluation method for C2 Cyber-Physical Systems Reliability Based on Deep Learning

DTIC Science & Technology

2014-06-01

the reliability testing data of the system, we obtain the prior distribution of the relia- bility is 1 1( ) ( ; , )R LG R r  . By Bayes theo- rem ...criticality cyber-physical sys- tems[C]//Proc of ICDCS. Piscataway, NJ: IEEE, 2010:169-178. [17] Zimmer C, Bhat B, Muller F, et al. Time-based intrusion de

An Oracle-based co-training framework for writer identification in offline handwriting

NASA Astrophysics Data System (ADS)

Porwal, Utkarsh; Rajan, Sreeranga; Govindaraju, Venu

2012-01-01

State-of-the-art techniques for writer identification have been centered primarily on enhancing the performance of the system for writer identification. Machine learning algorithms have been used extensively to improve the accuracy of such system assuming sufficient amount of data is available for training. Little attention has been paid to the prospect of harnessing the information tapped in a large amount of un-annotated data. This paper focuses on co-training based framework that can be used for iterative labeling of the unlabeled data set exploiting the independence between the multiple views (features) of the data. This paradigm relaxes the assumption of sufficiency of the data available and tries to generate labeled data from unlabeled data set along with improving the accuracy of the system. However, performance of co-training based framework is dependent on the effectiveness of the algorithm used for the selection of data points to be added in the labeled set. We propose an Oracle based approach for data selection that learns the patterns in the score distribution of classes for labeled data points and then predicts the labels (writers) of the unlabeled data point. This method for selection statistically learns the class distribution and predicts the most probable class unlike traditional selection algorithms which were based on heuristic approaches. We conducted experiments on publicly available IAM dataset and illustrate the efficacy of the proposed approach.

Distributed interactive virtual environments for collaborative experiential learning and training independent of distance over Internet2.

PubMed

Alverson, Dale C; Saiki, Stanley M; Jacobs, Joshua; Saland, Linda; Keep, Marcus F; Norenberg, Jeffrey; Baker, Rex; Nakatsu, Curtis; Kalishman, Summers; Lindberg, Marlene; Wax, Diane; Mowafi, Moad; Summers, Kenneth L; Holten, James R; Greenfield, John A; Aalseth, Edward; Nickles, David; Sherstyuk, Andrei; Haines, Karen; Caudell, Thomas P

2004-01-01

Medical knowledge and skills essential for tomorrow's healthcare professionals continue to change faster than ever before creating new demands in medical education. Project TOUCH (Telehealth Outreach for Unified Community Health) has been developing methods to enhance learning by coupling innovations in medical education with advanced technology in high performance computing and next generation Internet2 embedded in virtual reality environments (VRE), artificial intelligence and experiential active learning. Simulations have been used in education and training to allow learners to make mistakes safely in lieu of real-life situations, learn from those mistakes and ultimately improve performance by subsequent avoidance of those mistakes. Distributed virtual interactive environments are used over distance to enable learning and participation in dynamic, problem-based, clinical, artificial intelligence rules-based, virtual simulations. The virtual reality patient is programmed to dynamically change over time and respond to the manipulations by the learner. Participants are fully immersed within the VRE platform using a head-mounted display and tracker system. Navigation, locomotion and handling of objects are accomplished using a joy-wand. Distribution is managed via the Internet2 Access Grid using point-to-point or multi-casting connectivity through which the participants can interact. Medical students in Hawaii and New Mexico (NM) participated collaboratively in problem solving and managing of a simulated patient with a closed head injury in VRE; dividing tasks, handing off objects, and functioning as a team. Students stated that opportunities to make mistakes and repeat actions in the VRE were extremely helpful in learning specific principles. VRE created higher performance expectations and some anxiety among VRE users. VRE orientation was adequate but students needed time to adapt and practice in order to improve efficiency. This was also demonstrated successfully between Western Australia and UNM. We successfully demonstrated the ability to fully immerse participants in a distributed virtual environment independent of distance for collaborative team interaction in medical simulation designed for education and training. The ability to make mistakes in a safe environment is well received by students and has a positive impact on their understanding, as well as memory of the principles involved in correcting those mistakes. Bringing people together as virtual teams for interactive experiential learning and collaborative training, independent of distance, provides a platform for distributed "just-in-time" training, performance assessment and credentialing. Further validation is necessary to determine the potential value of the distributed VRE in knowledge transfer, improved future performance and should entail training participants to competence in using these tools.

Secure VM for Monitoring Industrial Process Controllers

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

Dasgupta, Dipankar; Ali, Mohammad Hassan; Abercrombie, Robert K

2011-01-01

In this paper, we examine the biological immune system as an autonomic system for self-protection, which has evolved over millions of years probably through extensive redesigning, testing, tuning and optimization process. The powerful information processing capabilities of the immune system, such as feature extraction, pattern recognition, learning, memory, and its distributive nature provide rich metaphors for its artificial counterpart. Our study focuses on building an autonomic defense system, using some immunological metaphors for information gathering, analyzing, decision making and launching threat and attack responses. In order to detection Stuxnet like malware, we propose to include a secure VM (or dedicatedmore » host) to the SCADA Network to monitor behavior and all software updates. This on-going research effort is not to mimic the nature but to explore and learn valuable lessons useful for self-adaptive cyber defense systems.« less

Information Acquisition, Analysis and Integration

DTIC Science & Technology

2016-08-03

of sensing and processing, theory, applications, signal processing, image and video processing, machine learning , technology transfer. 16. SECURITY... learning . 5. Solved elegantly old problems like image and video debluring, intro- ducing new revolutionary approaches. 1 DISTRIBUTION A: Distribution...Polatkan, G. Sapiro, D. Blei, D. B. Dunson, and L. Carin, “ Deep learning with hierarchical convolution factor analysis,” IEEE 6 DISTRIBUTION A

A self-organizing neural network for job scheduling in distributed systems

NASA Astrophysics Data System (ADS)

Newman, Harvey B.; Legrand, Iosif C.

2001-08-01

The aim of this work is to describe a possible approach for the optimization of the job scheduling in large distributed systems, based on a self-organizing Neural Network. This dynamic scheduling system should be seen as adaptive middle layer software, aware of current available resources and making the scheduling decisions using the "past experience." It aims to optimize job specific parameters as well as the resource utilization. The scheduling system is able to dynamically learn and cluster information in a large dimensional parameter space and at the same time to explore new regions in the parameters space. This self-organizing scheduling system may offer a possible solution to provide an effective use of resources for the off-line data processing jobs for future HEP experiments.

Distributional Learning of Lexical Tones: A Comparison of Attended vs. Unattended Listening.

PubMed

Ong, Jia Hoong; Burnham, Denis; Escudero, Paola

2015-01-01

This study examines whether non-tone language listeners can acquire lexical tone categories distributionally and whether attention in the training phase modulates the effect of distributional learning. Native Australian English listeners were trained on a Thai lexical tone minimal pair and their performance was assessed using a discrimination task before and after training. During Training, participants either heard a Unimodal distribution that would induce a single central category, which should hinder their discrimination of that minimal pair, or a Bimodal distribution that would induce two separate categories that should facilitate their discrimination. The participants either heard the distribution passively (Experiments 1A and 1B) or performed a cover task during training designed to encourage auditory attention to the entire distribution (Experiment 2). In passive listening (Experiments 1A and 1B), results indicated no effect of distributional learning: the Bimodal group did not outperform the Unimodal group in discriminating the Thai tone minimal pairs. Moreover, both Unimodal and Bimodal groups improved above chance on most test aspects from Pretest to Posttest. However, when participants' auditory attention was encouraged using the cover task (Experiment 2), distributional learning was found: the Bimodal group outperformed the Unimodal group on a novel test syllable minimal pair at Posttest relative to at Pretest. Furthermore, the Bimodal group showed above-chance improvement from Pretest to Posttest on three test aspects, while the Unimodal group only showed above-chance improvement on one test aspect. These results suggest that non-tone language listeners are able to learn lexical tones distributionally but only when auditory attention is encouraged in the acquisition phase. This implies that distributional learning of lexical tones is more readily induced when participants attend carefully during training, presumably because they are better able to compute the relevant statistics of the distribution.

Distributional Learning of Lexical Tones: A Comparison of Attended vs. Unattended Listening

PubMed Central

Ong, Jia Hoong; Burnham, Denis; Escudero, Paola

2015-01-01

This study examines whether non-tone language listeners can acquire lexical tone categories distributionally and whether attention in the training phase modulates the effect of distributional learning. Native Australian English listeners were trained on a Thai lexical tone minimal pair and their performance was assessed using a discrimination task before and after training. During Training, participants either heard a Unimodal distribution that would induce a single central category, which should hinder their discrimination of that minimal pair, or a Bimodal distribution that would induce two separate categories that should facilitate their discrimination. The participants either heard the distribution passively (Experiments 1A and 1B) or performed a cover task during training designed to encourage auditory attention to the entire distribution (Experiment 2). In passive listening (Experiments 1A and 1B), results indicated no effect of distributional learning: the Bimodal group did not outperform the Unimodal group in discriminating the Thai tone minimal pairs. Moreover, both Unimodal and Bimodal groups improved above chance on most test aspects from Pretest to Posttest. However, when participants’ auditory attention was encouraged using the cover task (Experiment 2), distributional learning was found: the Bimodal group outperformed the Unimodal group on a novel test syllable minimal pair at Posttest relative to at Pretest. Furthermore, the Bimodal group showed above-chance improvement from Pretest to Posttest on three test aspects, while the Unimodal group only showed above-chance improvement on one test aspect. These results suggest that non-tone language listeners are able to learn lexical tones distributionally but only when auditory attention is encouraged in the acquisition phase. This implies that distributional learning of lexical tones is more readily induced when participants attend carefully during training, presumably because they are better able to compute the relevant statistics of the distribution. PMID:26214002

Dynamic adaptive learning for decision-making supporting systems

NASA Astrophysics Data System (ADS)

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

2008-03-01

This paper proposes a novel adaptive learning method for data mining in support of decision-making systems. Due to the inherent characteristics of information ambiguity/uncertainty, high dimensionality and noisy in many homeland security and defense applications, such as surveillances, monitoring, net-centric battlefield, and others, it is critical to develop autonomous learning methods to efficiently learn useful information from raw data to help the decision making process. The proposed method is based on a dynamic learning principle in the feature spaces. Generally speaking, conventional approaches of learning from high dimensional data sets include various feature extraction (principal component analysis, wavelet transform, and others) and feature selection (embedded approach, wrapper approach, filter approach, and others) methods. However, very limited understandings of adaptive learning from different feature spaces have been achieved. We propose an integrative approach that takes advantages of feature selection and hypothesis ensemble techniques to achieve our goal. Based on the training data distributions, a feature score function is used to provide a measurement of the importance of different features for learning purpose. Then multiple hypotheses are iteratively developed in different feature spaces according to their learning capabilities. Unlike the pre-set iteration steps in many of the existing ensemble learning approaches, such as adaptive boosting (AdaBoost) method, the iterative learning process will automatically stop when the intelligent system can not provide a better understanding than a random guess in that particular subset of feature spaces. Finally, a voting algorithm is used to combine all the decisions from different hypotheses to provide the final prediction results. Simulation analyses of the proposed method on classification of different US military aircraft databases show the effectiveness of this method.

Power System Simulation for Policymaking and Making Policymakers

NASA Astrophysics Data System (ADS)

Cohen, Michael Ari

Power system simulation is a vital tool for anticipating, planning for and ultimately addressing future conditions on the power grid, especially in light of contemporary shifts in power generation, transmission and use that are being driven by a desire to utilize more environmentally responsible energy sources. This dissertation leverages power system simulation and engineering-economic analysis to provide initial answers to one open question about future power systems: how will high penetrations of distributed (rooftop) solar power affect the physical and economic operation of distribution feeders? We find that the overall impacts of distributed solar power (both positive and negative) on the feeders we modeled are minor compared to the overall cost of energy, but that there is on average a small net benefit provided by distributed generation. We then describe an effort to make similar analyses more accessible to a non-engineering (high school) audience by developing an educational video game called "Griddle" that is based on the same power system simulation techniques used in the first study. We describe the design and evaluation of Griddle and find that it demonstrates potential to provide students with insights about key power system learning objectives.

Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks.

PubMed

Shah, Shashi; Kittipiyakul, Somsak; Lim, Yuto; Tan, Yasuo

2018-05-10

The paper presents a game theoretic solution for distributed subchannel allocation problem in small cell networks (SCNs) analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the welfare of the SCNs, defined as the total system capacity. Although the problem can be addressed through best-response (BR) dynamics, the existence of a steady-state solution, i.e., a pure strategy Nash equilibrium (NE), cannot be guaranteed. Potential games (PGs) ensure convergence to a pure strategy NE when players rationally play according to some specified learning rules. However, such a performance guarantee comes at the expense of complete knowledge of the SCNs. To overcome such requirements, properties of PGs are exploited for scalable implementations, where we utilize the concept of marginal contribution (MC) as a tool to design learning rules of players’ utility and propose the marginal contribution-based best-response (MCBR) algorithm of low computational complexity for the distributed subchannel allocation problem. Finally, we validate and evaluate the proposed scheme through simulations for various performance metrics.

Global assessment of soil organic carbon stocks and spatial distribution of histosols: the Machine Learning approach

NASA Astrophysics Data System (ADS)

Hengl, Tomislav

2016-04-01

Preliminary results of predicting distribution of soil organic soils (Histosols) and soil organic carbon stock (in tonnes per ha) using global compilations of soil profiles (about 150,000 points) and covariates at 250 m spatial resolution (about 150 covariates; mainly MODIS seasonal land products, SRTM DEM derivatives, climatic images, lithological and land cover and landform maps) are presented. We focus on using a data-driven approach i.e. Machine Learning techniques that often require no knowledge about the distribution of the target variable or knowledge about the possible relationships. Other advantages of using machine learning are (DOI: 10.1371/journal.pone.0125814): All rules required to produce outputs are formalized. The whole procedure is documented (the statistical model and associated computer script), enabling reproducible research. Predicted surfaces can make use of various information sources and can be optimized relative to all available quantitative point and covariate data. There is more flexibility in terms of the spatial extent, resolution and support of requested maps. Automated mapping is also more cost-effective: once the system is operational, maintenance and production of updates are an order of magnitude faster and cheaper. Consequently, prediction maps can be updated and improved at shorter and shorter time intervals. Some disadvantages of automated soil mapping based on Machine Learning are: Models are data-driven and any serious blunders or artifacts in the input data can propagate to order-of-magnitude larger errors than in the case of expert-based systems. Fitting machine learning models is at the order of magnitude computationally more demanding. Computing effort can be even tens of thousands higher than if e.g. linear geostatistics is used. Many machine learning models are fairly complex often abstract and any interpretation of such models is not trivial and require special multidimensional / multivariable plotting and data mining tools. Results of model fitting using the R packages nnet, randomForest and the h2o software (machine learning functions) show that significant models can be fitted for soil classes, bulk density (R-square 0.76), soil organic carbon (R-square 0.62) and coarse fragments (R-square 0.59). Consequently, we were able to estimate soil organic carbon stock for majority of the land mask (excluding permanent ice) and detect patches of landscape containing mainly organic soils (peat and similar). Our results confirm that hotspots of soil organic carbon in Tropics are peatlands in Indonesia, north of Peru, west Amazon and Congo river basin. Majority of world soil organic carbon stock is likely in the Northern latitudes (tundra and taiga of the north). Distribution of histosols seems to be mainly controlled by climatic conditions (especially temperature regime and water vapor) and hydrologic position in the landscape. Predicted distributions of organic soils (probability of occurrence) and total soil organic carbon stock at resolutions of 1 km and 250 m are available via the SoilGrids.org project homepage.

Strategic Decision-Making Learning from Label Distributions: An Approach for Facial Age Estimation.

PubMed

Zhao, Wei; Wang, Han

2016-06-28

Nowadays, label distribution learning is among the state-of-the-art methodologies in facial age estimation. It takes the age of each facial image instance as a label distribution with a series of age labels rather than the single chronological age label that is commonly used. However, this methodology is deficient in its simple decision-making criterion: the final predicted age is only selected at the one with maximum description degree. In many cases, different age labels may have very similar description degrees. Consequently, blindly deciding the estimated age by virtue of the highest description degree would miss or neglect other valuable age labels that may contribute a lot to the final predicted age. In this paper, we propose a strategic decision-making label distribution learning algorithm (SDM-LDL) with a series of strategies specialized for different types of age label distribution. Experimental results from the most popular aging face database, FG-NET, show the superiority and validity of all the proposed strategic decision-making learning algorithms over the existing label distribution learning and other single-label learning algorithms for facial age estimation. The inner properties of SDM-LDL are further explored with more advantages.

Strategic Decision-Making Learning from Label Distributions: An Approach for Facial Age Estimation

PubMed Central

Zhao, Wei; Wang, Han

2016-01-01

Nowadays, label distribution learning is among the state-of-the-art methodologies in facial age estimation. It takes the age of each facial image instance as a label distribution with a series of age labels rather than the single chronological age label that is commonly used. However, this methodology is deficient in its simple decision-making criterion: the final predicted age is only selected at the one with maximum description degree. In many cases, different age labels may have very similar description degrees. Consequently, blindly deciding the estimated age by virtue of the highest description degree would miss or neglect other valuable age labels that may contribute a lot to the final predicted age. In this paper, we propose a strategic decision-making label distribution learning algorithm (SDM-LDL) with a series of strategies specialized for different types of age label distribution. Experimental results from the most popular aging face database, FG-NET, show the superiority and validity of all the proposed strategic decision-making learning algorithms over the existing label distribution learning and other single-label learning algorithms for facial age estimation. The inner properties of SDM-LDL are further explored with more advantages. PMID:27367691

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Learning Based Bidding Strategy for HVAC Systems in Double Auction Retail Energy Markets

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

Sun, Yannan; Somani, Abhishek; Carroll, Thomas E.

In this paper, a bidding strategy is proposed using reinforcement learning for HVAC systems in a double auction market. The bidding strategy does not require a specific model-based representation of behavior, i.e., a functional form to translate indoor house temperatures into bid prices. The results from reinforcement learning based approach are compared with the HVAC bidding approach used in the AEP gridSMART® smart grid demonstration project and it is shown that the model-free (learning based) approach tracks well the results from the model-based behavior. Successful use of model-free approaches to represent device-level economic behavior may help develop similar approaches tomore » represent behavior of more complex devices or groups of diverse devices, such as in a building. Distributed control requires an understanding of decision making processes of intelligent agents so that appropriate mechanisms may be developed to control and coordinate their responses, and model-free approaches to represent behavior will be extremely useful in that quest.« less

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

NASA Astrophysics Data System (ADS)

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

2010-04-01

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

Students' perception of the learning environment in a distributed medical programme.

PubMed

Veerapen, Kiran; McAleer, Sean

2010-09-24

The learning environment of a medical school has a significant impact on students' achievements and learning outcomes. The importance of equitable learning environments across programme sites is implicit in distributed undergraduate medical programmes being developed and implemented. To study the learning environment and its equity across two classes and three geographically separate sites of a distributed medical programme at the University of British Columbia Medical School that commenced in 2004. The validated Dundee Ready Educational Environment Survey was sent to all students in their 2nd and 3rd year (classes graduating in 2009 and 2008) of the programme. The domains of the learning environment surveyed were: students' perceptions of learning, students' perceptions of teachers, students' academic self-perceptions, students' perceptions of the atmosphere, and students' social self-perceptions. Mean scores, frequency distribution of responses, and inter- and intrasite differences were calculated. The perception of the global learning environment at all sites was more positive than negative. It was characterised by a strongly positive perception of teachers. The work load and emphasis on factual learning were perceived negatively. Intersite differences within domains of the learning environment were more evident in the pioneer class (2008) of the programme. Intersite differences consistent across classes were largely related to on-site support for students. Shared strengths and weaknesses in the learning environment at UBC sites were evident in areas that were managed by the parent institution, such as the attributes of shared faculty and curriculum. A greater divergence in the perception of the learning environment was found in domains dependent on local arrangements and social factors that are less amenable to central regulation. This study underlines the need for ongoing comparative evaluation of the learning environment at the distributed sites and interaction between leaders of these sites.

Interactive Video-Based Industrial Training in Basic Electronics.

ERIC Educational Resources Information Center

Mirkin, Barry

The Wisconsin Foundation for Vocational, Technical, and Adult Education is currently involved in the development, implementation, and distribution of a sophisticated interactive computer and video learning system. Designed to offer trainees an open entry and open exit opportunity to pace themselves through a comprehensive competency-based,…

Innovations in Online Learning: Moving beyond No Significant Difference. The Pew Symposia in Learning and Technology (4th, Phoenix, Arizona, December 8-9, 2000).

ERIC Educational Resources Information Center

Twigg, Carol A.

Symposium participants gathered to discuss how to move online learning beyond being "as good as" traditional education. Participants were asked to analyze their assumptions about distributed learning, identify the strengths of each type of distributed learning discussed, and explore what needs to be done to improve online education. This paper…

Predictive Anomaly Management for Resilient Virtualized Computing Infrastructures

DTIC Science & Technology

2015-05-27

PREC: Practical Root Exploit Containment for Android Devices, ACM Conference on Data and Application Security and Privacy (CODASPY) . 03-MAR-14...05-OCT-11, . : , Hiep Nguyen, Yongmin Tan, Xiaohui Gu. Propagation-aware Anomaly Localization for Cloud Hosted Distributed Applications , ACM...Workshop on Managing Large-Scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques (SLAML) in conjunction with SOSP

The deployment of routing protocols in distributed control plane of SDN.

PubMed

Jingjing, Zhou; Di, Cheng; Weiming, Wang; Rong, Jin; Xiaochun, Wu

2014-01-01

Software defined network (SDN) provides a programmable network through decoupling the data plane, control plane, and application plane from the original closed system, thus revolutionizing the existing network architecture to improve the performance and scalability. In this paper, we learned about the distributed characteristics of Kandoo architecture and, meanwhile, improved and optimized Kandoo's two levels of controllers based on ideological inspiration of RCP (routing control platform). Finally, we analyzed the deployment strategies of BGP and OSPF protocol in a distributed control plane of SDN. The simulation results show that our deployment strategies are superior to the traditional routing strategies.

The Rhode Island Medical Emergency Distribution System (MEDS).

PubMed

Banner, Greg

2004-01-01

The State of Rhode Island conducted an exercise to obtain and dispense a large volume of emergency medical supplies in response to a mass casualty incident. The exercise was conducted in stages that included requesting supplies from the Strategic National Stockpile and distributing the supplies around the state. The lessons learned included how to better structure an exercise, what types of problems were encountered with requesting and distributing supplies, how to better work with members of the private medical community who are not involved in disaster planning, and how to become aware of the needs of special population groups.

Finite-key analysis for measurement-device-independent quantum key distribution.

PubMed

Curty, Marcos; Xu, Feihu; Cui, Wei; Lim, Charles Ci Wen; Tamaki, Kiyoshi; Lo, Hoi-Kwong

2014-04-29

Quantum key distribution promises unconditionally secure communications. However, as practical devices tend to deviate from their specifications, the security of some practical systems is no longer valid. In particular, an adversary can exploit imperfect detectors to learn a large part of the secret key, even though the security proof claims otherwise. Recently, a practical approach--measurement-device-independent quantum key distribution--has been proposed to solve this problem. However, so far its security has only been fully proven under the assumption that the legitimate users of the system have unlimited resources. Here we fill this gap and provide a rigorous security proof against general attacks in the finite-key regime. This is obtained by applying large deviation theory, specifically the Chernoff bound, to perform parameter estimation. For the first time we demonstrate the feasibility of long-distance implementations of measurement-device-independent quantum key distribution within a reasonable time frame of signal transmission.

High-Performance Monitoring Architecture for Large-Scale Distributed Systems Using Event Filtering

NASA Technical Reports Server (NTRS)

Maly, K.

1998-01-01

Monitoring is an essential process to observe and improve the reliability and the performance of large-scale distributed (LSD) systems. In an LSD environment, a large number of events is generated by the system components during its execution or interaction with external objects (e.g. users or processes). Monitoring such events is necessary for observing the run-time behavior of LSD systems and providing status information required for debugging, tuning and managing such applications. However, correlated events are generated concurrently and could be distributed in various locations in the applications environment which complicates the management decisions process and thereby makes monitoring LSD systems an intricate task. We propose a scalable high-performance monitoring architecture for LSD systems to detect and classify interesting local and global events and disseminate the monitoring information to the corresponding end- points management applications such as debugging and reactive control tools to improve the application performance and reliability. A large volume of events may be generated due to the extensive demands of the monitoring applications and the high interaction of LSD systems. The monitoring architecture employs a high-performance event filtering mechanism to efficiently process the large volume of event traffic generated by LSD systems and minimize the intrusiveness of the monitoring process by reducing the event traffic flow in the system and distributing the monitoring computation. Our architecture also supports dynamic and flexible reconfiguration of the monitoring mechanism via its Instrumentation and subscription components. As a case study, we show how our monitoring architecture can be utilized to improve the reliability and the performance of the Interactive Remote Instruction (IRI) system which is a large-scale distributed system for collaborative distance learning. The filtering mechanism represents an Intrinsic component integrated with the monitoring architecture to reduce the volume of event traffic flow in the system, and thereby reduce the intrusiveness of the monitoring process. We are developing an event filtering architecture to efficiently process the large volume of event traffic generated by LSD systems (such as distributed interactive applications). This filtering architecture is used to monitor collaborative distance learning application for obtaining debugging and feedback information. Our architecture supports the dynamic (re)configuration and optimization of event filters in large-scale distributed systems. Our work represents a major contribution by (1) survey and evaluating existing event filtering mechanisms In supporting monitoring LSD systems and (2) devising an integrated scalable high- performance architecture of event filtering that spans several kev application domains, presenting techniques to improve the functionality, performance and scalability. This paper describes the primary characteristics and challenges of developing high-performance event filtering for monitoring LSD systems. We survey existing event filtering mechanisms and explain key characteristics for each technique. In addition, we discuss limitations with existing event filtering mechanisms and outline how our architecture will improve key aspects of event filtering.

Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion.

PubMed

Fierimonte, Roberto; Scardapane, Simone; Uncini, Aurelio; Panella, Massimo

2016-08-26

Distributed learning refers to the problem of inferring a function when the training data are distributed among different nodes. While significant work has been done in the contexts of supervised and unsupervised learning, the intermediate case of Semi-supervised learning in the distributed setting has received less attention. In this paper, we propose an algorithm for this class of problems, by extending the framework of manifold regularization. The main component of the proposed algorithm consists of a fully distributed computation of the adjacency matrix of the training patterns. To this end, we propose a novel algorithm for low-rank distributed matrix completion, based on the framework of diffusion adaptation. Overall, the distributed Semi-supervised algorithm is efficient and scalable, and it can preserve privacy by the inclusion of flexible privacy-preserving mechanisms for similarity computation. The experimental results and comparison on a wide range of standard Semi-supervised benchmarks validate our proposal.

Multi Car Elevator Control by using Learning Automaton

NASA Astrophysics Data System (ADS)

Shiraishi, Kazuaki; Hamagami, Tomoki; Hirata, Hironori

We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons (LAs.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an efficient MCE control is difficult for top-down approaches. For example, “bunching up together" is one of the typical phenomenon in a simple traffic environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple traffic control. First, we assign a stochastic automaton (SA) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation efficiency is evaluated through simulation experiments. Results show the technique enables reducing waiting times efficiently, and we confirm the system can adapt to the dynamic environment.

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Smartphone Application Enabling Global Graph Exploitation and Proactive Dissemination Service (DSPro) Integration (Revised Fiscal Year 2015)

DTIC Science & Technology

2015-09-01

interface. 15. SUBJECT TERMS smartphone, HDPT, global graph, DSPro, ozone widget framework, distributed common ground system, web service 16. SECURITY...Lee M. Lessons learned with a global graph and ozone widget framework (OWF) testbed. Aberdeen Proving Ground (MD): Army Research Laboratory (US); 2013

Tracking Instructional Quality across Secondary Mathematics and English Language Arts Classes

ERIC Educational Resources Information Center

Donaldson, Morgaen L.; LeChasseur, Kimberly; Mayer, Anysia

2017-01-01

Teachers have the largest school-based influence on student learning, yet there is little research on how instructional practice is systematically distributed within tracking systems. We examine whether teaching practice varies significantly across track levels and, if so, which aspects of instructional practice differ systematically. Using…

Exploiting Discrete Structure for Learning On-Line in Distributed Robot Systems

DTIC Science & Technology

2009-10-21

accelerating rate over the next 20 years. Service robotics currently shares some important characteristics with the automobile industry in the early...Authorization Act for Fiscal Year 2001, S. 2549, Sec. 217). The same impact is expected for pilotless air and water vehicles, where drone aircraft for

A Three-Level Analysis of Collaborative Learning in Dual-Interaction Spaces

ERIC Educational Resources Information Center

Lonchamp, Jacques

2009-01-01

CSCL systems which follow the dual-interaction spaces paradigm support the synchronous construction and discussion of shared artifacts by distributed or colocated small groups of learners. The most recent generic dual-interaction space environments, either model based or component based, can be deeply customized by teachers for supporting…

Access, excess, and ethics--towards a sustainable distribution model for antibiotics.

PubMed

Heyman, Gabriel; Cars, Otto; Bejarano, Maria-Teresa; Peterson, Stefan

2014-05-01

The increasing antibiotic resistance is a global threat to health care as we know it. Yet there is no model of distribution ready for a new antibiotic that balances access against excessive or inappropriate use in rural settings in low- and middle-income countries (LMICs) where the burden of communicable diseases is high and access to quality health care is low. Departing from a hypothetical scenario of rising antibiotic resistance among pneumococci, 11 stakeholders in the health systems of various LMICs were interviewed one-on-one to give their view on how a new effective antibiotic should be distributed to balance access against the risk of inappropriate use. Transcripts were subjected to qualitative 'framework' analysis. The analysis resulted in four main themes: Barriers to rational access to antibiotics; balancing access and excess; learning from other communicable diseases; and a system-wide intervention. The tension between access to antibiotics and rational use stems from shortcomings found in the health systems of LMICs. Constructing a sustainable yet accessible model of antibiotic distribution for LMICs is a task of health system-wide proportions, which is why we strongly suggest using systems thinking in future research on this issue.

Access, excess, and ethics—towards a sustainable distribution model for antibiotics

PubMed Central

Heyman, Gabriel; Cars, Otto; Bejarano, Maria-Teresa

2014-01-01

The increasing antibiotic resistance is a global threat to health care as we know it. Yet there is no model of distribution ready for a new antibiotic that balances access against excessive or inappropriate use in rural settings in low- and middle-income countries (LMICs) where the burden of communicable diseases is high and access to quality health care is low. Departing from a hypothetical scenario of rising antibiotic resistance among pneumococci, 11 stakeholders in the health systems of various LMICs were interviewed one-on-one to give their view on how a new effective antibiotic should be distributed to balance access against the risk of inappropriate use. Transcripts were subjected to qualitative ‘framework’ analysis. The analysis resulted in four main themes: Barriers to rational access to antibiotics; balancing access and excess; learning from other communicable diseases; and a system-wide intervention. The tension between access to antibiotics and rational use stems from shortcomings found in the health systems of LMICs. Constructing a sustainable yet accessible model of antibiotic distribution for LMICs is a task of health system-wide proportions, which is why we strongly suggest using systems thinking in future research on this issue. PMID:24735111

Emergent latent symbol systems in recurrent neural networks

NASA Astrophysics Data System (ADS)

Monner, Derek; Reggia, James A.

2012-12-01

Fodor and Pylyshyn [(1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2), 3-71] famously argued that neural networks cannot behave systematically short of implementing a combinatorial symbol system. A recent response from Frank et al. [(2009). Connectionist semantic systematicity. Cognition, 110(3), 358-379] claimed to have trained a neural network to behave systematically without implementing a symbol system and without any in-built predisposition towards combinatorial representations. We believe systems like theirs may in fact implement a symbol system on a deeper and more interesting level: one where the symbols are latent - not visible at the level of network structure. In order to illustrate this possibility, we demonstrate our own recurrent neural network that learns to understand sentence-level language in terms of a scene. We demonstrate our model's learned understanding by testing it on novel sentences and scenes. By paring down our model into an architecturally minimal version, we demonstrate how it supports combinatorial computation over distributed representations by using the associative memory operations of Vector Symbolic Architectures. Knowledge of the model's memory scheme gives us tools to explain its errors and construct superior future models. We show how the model designs and manipulates a latent symbol system in which the combinatorial symbols are patterns of activation distributed across the layers of a neural network, instantiating a hybrid of classical symbolic and connectionist representations that combines advantages of both.

Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure

NASA Astrophysics Data System (ADS)

Chen, Jiaxi; Li, Junmin

2018-02-01

In this paper, we investigate the perfect consensus problem for second-order linearly parameterised multi-agent systems (MAS) with imprecise communication topology structure. Takagi-Sugeno (T-S) fuzzy models are presented to describe the imprecise communication topology structure of leader-following MAS, and a distributed adaptive iterative learning control protocol is proposed with the dynamic of leader unknown to any of the agent. The proposed protocol guarantees that the follower agents can track the leader perfectly on [0,T] for the consensus problem. Under alignment condition, a sufficient condition of the consensus for closed-loop MAS is given based on Lyapunov stability theory. Finally, a numerical example and a multiple pendulum system are given to illustrate the effectiveness of the proposed algorithm.

An expert fitness diagnosis system based on elastic cloud computing.

PubMed

Tseng, Kevin C; Wu, Chia-Chuan

2014-01-01

This paper presents an expert diagnosis system based on cloud computing. It classifies a user's fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user's physiological data, such as age, gender, and body mass index (BMI). In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8%) and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.

Selecting a Laboratory Information Management System for Biorepositories in Low- and Middle-Income Countries: The H3Africa Experience and Lessons Learned

PubMed Central

Musinguzi, Henry; Lwanga, Newton; Kezimbira, Dafala; Kigozi, Edgar; Katabazi, Fred Ashaba; Wayengera, Misaki; Joloba, Moses Lutaakome; Abayomi, Emmanuel Akin; Swanepoel, Carmen; Croxton, Talishiea; Ozumba, Petronilla; Thankgod, Anazodo; van Zyl, Lizelle; Mayne, Elizabeth Sarah; Kader, Mukthar; Swartz, Garth

2017-01-01

Biorepositories in Africa need significant infrastructural support to meet International Society for Biological and Environmental Repositories (ISBER) Best Practices to support population-based genomics research. ISBER recommends a biorepository information management system which can manage workflows from biospecimen receipt to distribution. The H3Africa Initiative set out to develop regional African biorepositories where Uganda, Nigeria, and South Africa were successfully awarded grants to develop the state-of-the-art biorepositories. The biorepositories carried out an elaborate process to evaluate and choose a laboratory information management system (LIMS) with the aim of integrating the three geographically distinct sites. In this article, we review the processes, African experience, lessons learned, and make recommendations for choosing a biorepository LIMS in the African context.

Developing a Hypothetical Learning Trajectory for the Sampling Distribution of the Sample Means

NASA Astrophysics Data System (ADS)

Syafriandi

2018-04-01

Special types of probability distribution are sampling distributions that are important in hypothesis testing. The concept of a sampling distribution may well be the key concept in understanding how inferential procedures work. In this paper, we will design a hypothetical learning trajectory (HLT) for the sampling distribution of the sample mean, and we will discuss how the sampling distribution is used in hypothesis testing.

Fast phonetic learning occurs already in 2-to-3-month old infants: an ERP study

PubMed Central

Wanrooij, Karin; Boersma, Paul; van Zuijen, Titia L.

2014-01-01

An important mechanism for learning speech sounds in the first year of life is “distributional learning,” i.e., learning by simply listening to the frequency distributions of the speech sounds in the environment. In the lab, fast distributional learning has been reported for infants in the second half of the first year; the present study examined whether it can also be demonstrated at a much younger age, long before the onset of language-specific speech perception (which roughly emerges between 6 and 12 months). To investigate this, Dutch infants aged 2 to 3 months were presented with either a unimodal or a bimodal vowel distribution based on the English /æ/~/ε/ contrast, for only 12 minutes. Subsequently, mismatch responses (MMRs) were measured in an oddball paradigm, where one half of the infants in each group heard a representative [æ] as the standard and a representative [ε] as the deviant, and the other half heard the same reversed. The results (from the combined MMRs during wakefulness and active sleep) disclosed a larger MMR, implying better discrimination of [æ] and [ε], for bimodally than unimodally trained infants, thus extending an effect of distributional training found in previous behavioral research to a much younger age when speech perception is still universal rather than language-specific, and to a new method (using event-related potentials). Moreover, the analysis revealed a robust interaction between the distribution (unimodal vs. bimodal) and the identity of the standard stimulus ([æ] vs. [ε]), which provides evidence for an interplay between a perceptual asymmetry and distributional learning. The outcomes show that distributional learning can affect vowel perception already in the first months of life. PMID:24701203

Team Learning in Technology-Mediated Distributed Teams

ERIC Educational Resources Information Center

Andres, Hayward P.; Shipps, Belinda P.

2010-01-01

This study examines technological, educational/learning, and social affordances associated with the facilitation of project-based learning and problem solving in technology-mediated distributed teams. An empirical interpretive research approach using direct observation is used to interpret, evaluate and rate observable manifested behaviors and…

Distributed System Design Checklist

NASA Technical Reports Server (NTRS)

Hall, Brendan; Driscoll, Kevin

2014-01-01

This report describes a design checklist targeted to fault-tolerant distributed electronic systems. Many of the questions and discussions in this checklist may be generally applicable to the development of any safety-critical system. However, the primary focus of this report covers the issues relating to distributed electronic system design. The questions that comprise this design checklist were created with the intent to stimulate system designers' thought processes in a way that hopefully helps them to establish a broader perspective from which they can assess the system's dependability and fault-tolerance mechanisms. While best effort was expended to make this checklist as comprehensive as possible, it is not (and cannot be) complete. Instead, we expect that this list of questions and the associated rationale for the questions will continue to evolve as lessons are learned and further knowledge is established. In this regard, it is our intent to post the questions of this checklist on a suitable public web-forum, such as the NASA DASHLink AFCS repository. From there, we hope that it can be updated, extended, and maintained after our initial research has been completed.

A Database for Decision-Making in Training and Distributed Learning Technology

DTIC Science & Technology

1998-04-01

developer must answer these questions: ♦ Who will develop the courseware? Should we outsource ? ♦ What media should we use? How much will it cost? ♦ What...to develop , the database can be useful for answering staffing questions and planning transitions to technology- assisted courses. The database...of distributed learning curricula in com- parison to traditional methods. To develop a military-wide distributed learning plan, the existing course

Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT.

PubMed

Lavassani, Mehrzad; Forsström, Stefan; Jennehag, Ulf; Zhang, Tingting

2018-05-12

Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications.

Small molecule analysis and imaging of fatty acids in the zebra finch song system using time-of-flight-secondary ion mass spectrometry.

PubMed

Amaya, Kensey R; Sweedler, Jonathan V; Clayton, David F

2011-08-01

Fatty acids are central to brain metabolism and signaling, but their distributions within complex brain circuits have been difficult to study. Here we applied an emerging technique, time-of-flight secondary ion mass spectrometry (ToF-SIMS), to image specific fatty acids in a favorable model system for chemical analyses of brain circuits, the zebra finch (Taeniopygia guttata). The zebra finch, a songbird, produces complex learned vocalizations under the control of an interconnected set of discrete, dedicated brain nuclei 'song nuclei'. Using ToF-SIMS, the major song nuclei were visualized by virtue of differences in their content of essential and non-essential fatty acids. Essential fatty acids (arachidonic acid and docosahexaenoic acid) showed distinctive distributions across the song nuclei, and the 18-carbon fatty acids stearate and oleate discriminated the different core and shell subregions of the lateral magnocellular nucleus of the anterior nidopallium. Principal component analysis of the spectral data set provided further evidence of chemical distinctions between the song nuclei. By analyzing the robust nucleus of the arcopallium at three different ages during juvenile song learning, we obtain the first direct evidence of changes in lipid content that correlate with progression of song learning. The results demonstrate the value of ToF-SIMS to study lipids in a favorable model system for probing the function of lipids in brain organization, development and function. © 2011 The Authors. Journal of Neurochemistry © 2011 International Society for Neurochemistry.

Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT

PubMed Central

Lavassani, Mehrzad; Jennehag, Ulf; Zhang, Tingting

2018-01-01

Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications. PMID:29757227

A Supervised Learning Process to Validate Online Disease Reports for Use in Predictive Models.

PubMed

Patching, Helena M M; Hudson, Laurence M; Cooke, Warrick; Garcia, Andres J; Hay, Simon I; Roberts, Mark; Moyes, Catherine L

2015-12-01

Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%-79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is being used to validate the data that our system uses to produce updated predictive disease maps on a weekly basis.

The implementation of aerial object recognition algorithm based on contour descriptor in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Babayan, Pavel; Smirnov, Sergey; Strotov, Valery

2017-10-01

This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Toward cognitive robotics

NASA Astrophysics Data System (ADS)

Laird, John E.

2009-05-01

Our long-term goal is to develop autonomous robotic systems that have the cognitive abilities of humans, including communication, coordination, adapting to novel situations, and learning through experience. Our approach rests on the recent integration of the Soar cognitive architecture with both virtual and physical robotic systems. Soar has been used to develop a wide variety of knowledge-rich agents for complex virtual environments, including distributed training environments and interactive computer games. For development and testing in robotic virtual environments, Soar interfaces to a variety of robotic simulators and a simple mobile robot. We have recently made significant extensions to Soar that add new memories and new non-symbolic reasoning to Soar's original symbolic processing, which should significantly improve Soar abilities for control of robots. These extensions include episodic memory, semantic memory, reinforcement learning, and mental imagery. Episodic memory and semantic memory support the learning and recalling of prior events and situations as well as facts about the world. Reinforcement learning provides the ability of the system to tune its procedural knowledge - knowledge about how to do things. Mental imagery supports the use of diagrammatic and visual representations that are critical to support spatial reasoning. We speculate on the future of unmanned systems and the need for cognitive robotics to support dynamic instruction and taskability.

Large-scale machine learning and evaluation platform for real-time traffic surveillance

NASA Astrophysics Data System (ADS)

Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

2016-09-01

In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks.

PubMed

Kajita, Seiji; Ohba, Nobuko; Jinnouchi, Ryosuke; Asahi, Ryoji

2017-12-05

Material informatics (MI) is a promising approach to liberate us from the time-consuming Edisonian (trial and error) process for material discoveries, driven by machine-learning algorithms. Several descriptors, which are encoded material features to feed computers, were proposed in the last few decades. Especially to solid systems, however, their insufficient representations of three dimensionality of field quantities such as electron distributions and local potentials have critically hindered broad and practical successes of the solid-state MI. We develop a simple, generic 3D voxel descriptor that compacts any field quantities, in such a suitable way to implement convolutional neural networks (CNNs). We examine the 3D voxel descriptor encoded from the electron distribution by a regression test with 680 oxides data. The present scheme outperforms other existing descriptors in the prediction of Hartree energies that are significantly relevant to the long-wavelength distribution of the valence electrons. The results indicate that this scheme can forecast any functionals of field quantities just by learning sufficient amount of data, if there is an explicit correlation between the target properties and field quantities. This 3D descriptor opens a way to import prominent CNNs-based algorithms of supervised, semi-supervised and reinforcement learnings into the solid-state MI.

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

PubMed

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

2016-12-01

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

Distributional learning aids linguistic category formation in school-age children.

PubMed

Hall, Jessica; Owen VAN Horne, Amanda; Farmer, Thomas

2018-05-01

The goal of this study was to determine if typically developing children could form grammatical categories from distributional information alone. Twenty-seven children aged six to nine listened to an artificial grammar which contained strategic gaps in its distribution. At test, we compared how children rated novel sentences that fit the grammar to sentences that were ungrammatical. Sentences could be distinguished only through the formation of categories of words with shared distributional properties. Children's ratings revealed that they could discriminate grammatical and ungrammatical sentences. These data lend support to the hypothesis that distributional learning is a potential mechanism for learning grammatical categories in a first language.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

Analyzing Distributional Learning of Phonemic Categories in Unsupervised Deep Neural Networks

PubMed Central

Räsänen, Okko; Nagamine, Tasha; Mesgarani, Nima

2017-01-01

Infants’ speech perception adapts to the phonemic categories of their native language, a process assumed to be driven by the distributional properties of speech. This study investigates whether deep neural networks (DNNs), the current state-of-the-art in distributional feature learning, are capable of learning phoneme-like representations of speech in an unsupervised manner. We trained DNNs with unlabeled and labeled speech and analyzed the activations of each layer with respect to the phones in the input segments. The analyses reveal that the emergence of phonemic invariance in DNNs is dependent on the availability of phonemic labeling of the input during the training. No increased phonemic selectivity of the hidden layers was observed in the purely unsupervised networks despite successful learning of low-dimensional representations for speech. This suggests that additional learning constraints or more sophisticated models are needed to account for the emergence of phone-like categories in distributional learning operating on natural speech. PMID:29359204

The evolution of phenotypic correlations and ‘developmental memory’

PubMed Central

Watson, Richard A.; Wagner, Günter P.; Pavlicev, Mihaela; Weinreich, Daniel M.; Mills, Rob

2014-01-01

Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent non-linear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can ‘store’ and ‘recall’ multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and ‘generalise’ (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviours follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well-understood in the context of neural networks. This helps to explain how development facilitates the evolution of high-fitness phenotypes and how this ability changes over evolutionary time. PMID:24351058

A Policy Representation Using Weighted Multiple Normal Distribution

NASA Astrophysics Data System (ADS)

Kimura, Hajime; Aramaki, Takeshi; Kobayashi, Shigenobu

In this paper, we challenge to solve a reinforcement learning problem for a 5-linked ring robot within a real-time so that the real-robot can stand up to the trial and error. On this robot, incomplete perception problems are caused from noisy sensors and cheap position-control motor systems. This incomplete perception also causes varying optimum actions with the progress of the learning. To cope with this problem, we adopt an actor-critic method, and we propose a new hierarchical policy representation scheme, that consists of discrete action selection on the top level and continuous action selection on the low level of the hierarchy. The proposed hierarchical scheme accelerates learning on continuous action space, and it can pursue the optimum actions varying with the progress of learning on our robotics problem. This paper compares and discusses several learning algorithms through simulations, and demonstrates the proposed method showing application for the real robot.

Supervised learning of probability distributions by neural networks

NASA Technical Reports Server (NTRS)

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.

Privacy-preserving backpropagation neural network learning.

PubMed

Chen, Tingting; Zhong, Sheng

2009-10-01

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

Physics textbooks from the viewpoint of network structures

NASA Astrophysics Data System (ADS)

Králiková, Petra; Teleki, Aba

2017-01-01

We can observe self-organized networks all around us. These networks are, in general, scale invariant networks described by the Bianconi-Barabasi model. The self-organized networks (networks formed naturally when feedback acts on the system) show certain universality. These networks, in simplified models, have scale invariant distribution (Pareto distribution type I) and parameter α has value between 2 and 5. The textbooks are extremely important in the learning process and from this reason we studied physics textbook at the level of sentences and physics terms (bipartite network). The nodes represent physics terms, sentences, and pictures, tables, connected by links (by physics terms and transitional words and transitional phrases). We suppose that learning process are more robust and goes faster and easier if the physics textbook has a structure similar to structures of self-organized networks.

Advancements in Distributed Learning (ADL) Environment in Support of Transformation

DTIC Science & Technology

2017-01-01

REPORT TR-HFM-212 Advancements in Distributed Learning (ADL) Environment in Support of Transformation (Progrès en apprentissage distribué (ADL) à...l’appui de la transformation ) This report documents the findings of Task Group 212. The primary objective of this Task Group was to explore an agile...STO TECHNICAL REPORT TR-HFM-212 Advancements in Distributed Learning (ADL) Environment in Support of Transformation (Progrès en apprentissage

Fostering Distributed Science Learning through Collaborative Technologies

ERIC Educational Resources Information Center

Vazquez-Abad, Jesus; Brousseau, Nancy; Guillermina, Waldegg C.; Vezina, Mylene; Martinez, Alicia D.; de Verjovsky, Janet Paul

2004-01-01

TACTICS (French and Spanish acronym standing for Collaborative Work and Learning in Science with Information and Communications Technologies) is an ongoing project aimed at investigating a distributed community of learning and practice in which information and communications technologies (ICT) take the role of collaborative tools to support social…

Fostering Self-Regulation in Distributed Learning

ERIC Educational Resources Information Center

Terry, Krista P.; Doolittle, Peter

2006-01-01

Although much has been written about fostering self-regulated learning in traditional classroom settings, there has been little that addresses how to facilitate self-regulated learning skills in distributed and online environments. This article will examine some such strategies by specifically focusing on time management. Specific principles for…

Towards a Better Distributed Framework for Learning Big Data

DTIC Science & Technology

2017-06-14

UNLIMITED: PB Public Release 13. SUPPLEMENTARY NOTES 14. ABSTRACT This work aimed at solving issues in distributed machine learning. The PI’s team proposed...communication load. Finally, the team proposed the parallel least-squares policy iteration (parallel LSPI) to parallelize a reinforcement policy learning. 15

Synchrony detection and amplification by silicon neurons with STDP synapses.

PubMed

Bofill-i-petit, Adria; Murray, Alan F

2004-09-01

Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.

Students' perception of the learning environment in a distributed medical programme

PubMed Central

Veerapen, Kiran; McAleer, Sean

2010-01-01

Background The learning environment of a medical school has a significant impact on students' achievements and learning outcomes. The importance of equitable learning environments across programme sites is implicit in distributed undergraduate medical programmes being developed and implemented. Purpose To study the learning environment and its equity across two classes and three geographically separate sites of a distributed medical programme at the University of British Columbia Medical School that commenced in 2004. Method The validated Dundee Ready Educational Environment Survey was sent to all students in their 2nd and 3rd year (classes graduating in 2009 and 2008) of the programme. The domains of the learning environment surveyed were: students' perceptions of learning, students' perceptions of teachers, students' academic self-perceptions, students' perceptions of the atmosphere, and students' social self-perceptions. Mean scores, frequency distribution of responses, and inter- and intrasite differences were calculated. Results The perception of the global learning environment at all sites was more positive than negative. It was characterised by a strongly positive perception of teachers. The work load and emphasis on factual learning were perceived negatively. Intersite differences within domains of the learning environment were more evident in the pioneer class (2008) of the programme. Intersite differences consistent across classes were largely related to on-site support for students. Conclusions Shared strengths and weaknesses in the learning environment at UBC sites were evident in areas that were managed by the parent institution, such as the attributes of shared faculty and curriculum. A greater divergence in the perception of the learning environment was found in domains dependent on local arrangements and social factors that are less amenable to central regulation. This study underlines the need for ongoing comparative evaluation of the learning environment at the distributed sites and interaction between leaders of these sites. PMID:20922033

Teaching of the Society for Fetal Urology grading system for pediatric hydronephrosis is improved by e-Learning using Computer Enhanced Visual Learning (CEVL): A multi-institutional trial.

PubMed

Liu, Dennis B; Palmer, Blake; Herndon, C D Anthony; Maizels, Max

2015-08-01

It is unclear how clinicians learn to grade pediatric hydronephrosis (HN) and how effective their training has been. We sought to: 1. Assess how clinicians learn to grade HN and their confidence in their training and abilities and 2. To assess Computer Enhanced Visual Learning (CEVL) e-Learning to learn the Society for Fetal Urology (SFU) grading system for pediatric HN. A multi-institutional online survey was distributed to pediatric urologists, nephrologists, and radiologists. Respondents used a 6-point Likert scale (0 = not confident to 5 = very confident) to assess their confidence in knowledge of the criteria, indications, and ability to grade HN, and how they learned to grade. Participants assigned SFU grades to 15 neonatal ultrasounds (US). A CEVL module on the SFU grading system was accessed and a post-CEVL survey completed. Changes in confidence and accuracy of grading were compared before and after CEVL e-Learning. The most common method of learning was "casually during training" (44.5%). Significant increases in confidence in knowledge of criteria, indications, and ability to grade, as well as the accuracy of grading were seen following CEVL e-Learning (Figure A and B). Although the SFU grading system is considered the predominant grading system for HN, its application in clinical practice has been inconsistent. While this may be due to the grading system itself, it is possible that deficient training and confidence are the root causes. Our data supports this by demonstrating that most clinicians receive only casual training and accordingly, report low confidence in their knowledge and ability to grade HN. Therefore, we conclude that there exists a strong need to improve the teaching of the SFU grading system. e-Learning has been shown to be effective in teaching difficult topics and skills. We demonstrate that e-Learning with CEVL is effective in increasing both the confidence and accuracy of SFU grading of pediatric HN. Limitations of our study include a small sample size, low response rate, and discrepant participation. Furthermore, we did not assess the extent to which the CEVL module was used or include a control group learning through traditional means. Therefore, we were unable to evaluate the efficiency of learning or be certain that the improvements seen were derived exclusively from CEVL. Current training in SFU grading of HN is mostly unstructured and inaccurate grading is common. Learners who use CEVL show improvements in their confidence and ability to SFU grade HN. Copyright © 2015 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

A Model-Based Expert System for Space Power Distribution Diagnostics

NASA Technical Reports Server (NTRS)

Quinn, Todd M.; Schlegelmilch, Richard F.

1994-01-01

When engineers diagnose system failures, they often use models to confirm system operation. This concept has produced a class of advanced expert systems that perform model-based diagnosis. A model-based diagnostic expert system for the Space Station Freedom electrical power distribution test bed is currently being developed at the NASA Lewis Research Center. The objective of this expert system is to autonomously detect and isolate electrical fault conditions. Marple, a software package developed at TRW, provides a model-based environment utilizing constraint suspension. Originally, constraint suspension techniques were developed for digital systems. However, Marple provides the mechanisms for applying this approach to analog systems such as the test bed, as well. The expert system was developed using Marple and Lucid Common Lisp running on a Sun Sparc-2 workstation. The Marple modeling environment has proved to be a useful tool for investigating the various aspects of model-based diagnostics. This report describes work completed to date and lessons learned while employing model-based diagnostics using constraint suspension within an analog system.

Frequency Control Using On line Learning Method for Island Smart Grid with EVs and PVs

DTIC Science & Technology

2014-07-06

deviation from PVs are modeled as the power disturbance for the system . A. Case 1: active power disturbance without EVs constraints In this case, there are...IEEE Transactions on, vol. 3, no. 1, pp. 565–577, 2012. [7] M. Datta and T. Senjyu, “Fuzzy control of distributed pv inverters /energy storage systems ...this linearity assumption. In island smart grid with photovoltaics ( PVs ) and EVs, system state parameters and operating conditions are changing

The Deployment of Routing Protocols in Distributed Control Plane of SDN

PubMed Central

Jingjing, Zhou; Di, Cheng; Weiming, Wang; Rong, Jin; Xiaochun, Wu

2014-01-01

Software defined network (SDN) provides a programmable network through decoupling the data plane, control plane, and application plane from the original closed system, thus revolutionizing the existing network architecture to improve the performance and scalability. In this paper, we learned about the distributed characteristics of Kandoo architecture and, meanwhile, improved and optimized Kandoo's two levels of controllers based on ideological inspiration of RCP (routing control platform). Finally, we analyzed the deployment strategies of BGP and OSPF protocol in a distributed control plane of SDN. The simulation results show that our deployment strategies are superior to the traditional routing strategies. PMID:25250395

Distributed Emotions in the Design of Learning Technologies

ERIC Educational Resources Information Center

Kim, Beaumie; Kim, Mi Song

2010-01-01

Learning is a social activity, which requires interactions with the environment, tools, people, and also ourselves (e.g., our previous experiences). Each interaction provides different meanings to learners, and the associated emotion affects their learning and performance. With the premise that emotion and cognition are distributed, the authors…

Social Networks and Performance in Distributed Learning Communities

ERIC Educational Resources Information Center

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

2012-01-01

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

An Integrated Evaluation Method for E-Learning: A Case Study

ERIC Educational Resources Information Center

Rentroia-Bonito, M. A.; Figueiredo, F.; Martins, A.; Jorge, J. A.; Ghaoui, C.

2006-01-01

Technological improvements in broadband and distributed computing are making it possible to distribute live media content cost-effectively. Because of this, organizations are looking into cost-effective approaches to implement e-Learning initiatives. Indeed, computing resources are not enough by themselves to promote better e-Learning experiences.…

A Critical Analysis of Job-Embedded Professional Learning within a Distributed Leadership Framework

ERIC Educational Resources Information Center

Campoli, Ashley Jimerson

2011-01-01

Leadership style and professional learning have been linked to student achievement. Studies have linked leadership styles such as distributed leadership to job-embedded professional learning. However, research is mixed when these two constructs are related to student achievement. This study evaluated the relationship between distributed…

Distributed Scaffolding in a Service-Learning Course

ERIC Educational Resources Information Center

Smagorinsky, Peter; Clayton, Christopher M.; Johnson, Lindy L.

2015-01-01

This article argues that the instructional scaffolding metaphor may be reconceived as distributed scaffolding when multiple means of influence are provided in a service-learning setting. In the service-learning course described here, the professor's role is largely as designer of activity settings for preservice teacher candidates, through…

Spontaneous neuronal activity as a self-organized critical phenomenon

NASA Astrophysics Data System (ADS)

de Arcangelis, L.; Herrmann, H. J.

2013-01-01

Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour. Avalanche activity can be modelled within the self-organized criticality framework, including threshold firing, refractory period and activity-dependent synaptic plasticity. The size and duration distributions confirm that the system acts in a critical state, whose scaling behaviour is very robust. Next, we discuss the temporal organization of neuronal avalanches. This is given by the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Finally, we verify if a system with no characteristic response can ever learn in a controlled and reproducible way. Learning in the model occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. Learning is a truly collective process and the learning dynamics exhibits universal features. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.

Smart Grid Educational Series | Energy Systems Integration Facility | NREL

Science.gov Websites

generation through transmission, all the way to the distribution infrastructure. Download presentation | Text on key takeaways from breakout group discussions. Learn more about the workshop. Text Version Text presentation PDF | Text Version Using MultiSpeak Data Model Standard & Essence Anomaly Detection for ICS

Promoting Probabilistic Programming System (PPS) Development in Probabilistic Programming for Advancing Machine Learning (PPAML)

DTIC Science & Technology

2018-03-01

MARCH 2018 FINAL TECHNICAL REPORT APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED STINFO COPY AIR FORCE RESEARCH LABORATORY INFORMATION...SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) Air Force Research Laboratory/RITA DARPA 525 Brooks Road 675 North Randolph Street Rome...1 3.0 METHODS , ASSUMPTIONS, AND PROCEDURES

Lifelong Learning, Equality and Social Cohesion

ERIC Educational Resources Information Center

Green, Andy

2011-01-01

This article compares the evidence from the 2009 PISA survey on the distribution of skills amongst 15-year-olds in different regions and country groups and explores how education systems in these regions contribute to different levels of inequality. In the second part, it presents evidence from surveys on adult skills and attitudes on how skills…

Podcast Pilots for Distance Planning, Programming, and Development

ERIC Educational Resources Information Center

Cordes, Sean

2005-01-01

This paper examines podcasting as a library support for distance learning and information systems and services. The manuscript provides perspective on the knowledge base in the growing area of podcasting in libraries and academia. A walkthrough of the podcast creation and distribution process using basic computing skills and open source tools is…

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature

PubMed Central

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems. PMID:29099838

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.

PubMed

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.

Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps.

PubMed

Moya, José M; Araujo, Alvaro; Banković, Zorana; de Goyeneche, Juan-Mariano; Vallejo, Juan Carlos; Malagón, Pedro; Villanueva, Daniel; Fraga, David; Romero, Elena; Blesa, Javier

2009-01-01

The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.

Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps

PubMed Central

Moya, José M.; Araujo, Álvaro; Banković, Zorana; de Goyeneche, Juan-Mariano; Vallejo, Juan Carlos; Malagón, Pedro; Villanueva, Daniel; Fraga, David; Romero, Elena; Blesa, Javier

2009-01-01

The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals. PMID:22291569

Proceedings of the Ship Control Systems Symposium (9th) Held in Bethesda, Maryland on 10-14 September 1990. Theme: Automation in Surface Ship Control Systems, Today’s Applications and Future Trends. Volume 3

DTIC Science & Technology

1990-09-14

expectations tend to follow a set pattern as he sets out an the learning curve for the aplication of distributed processor systems, 3.149 those "new" systems...do the job more efficiently. Another example is the use of aplication software to replace the perfectly adequate Wilt-in logic involved with...combat system survivability. The probability of kill, Pk, of the combat system is shown decreasing from current firepower kill levels to that of mobility

Distributed Leadership and Digital Collaborative Learning: A Synergistic Relationship?

ERIC Educational Resources Information Center

Harris, Alma; Jones, Michelle; Baba, Suria

2013-01-01

This paper explores the synergy between distributed leadership and digital collaborative learning. It argues that distributed leadership offers an important theoretical lens for understanding and explaining how digital collaboration is best supported and led. Drawing upon evidence from two online educational platforms, the paper explores the…

Application of the actor model to large scale NDE data analysis

NASA Astrophysics Data System (ADS)

Coughlin, Chris

2018-03-01

The Actor model of concurrent computation discretizes a problem into a series of independent units or actors that interact only through the exchange of messages. Without direct coupling between individual components, an Actor-based system is inherently concurrent and fault-tolerant. These traits lend themselves to so-called "Big Data" applications in which the volume of data to analyze requires a distributed multi-system design. For a practical demonstration of the Actor computational model, a system was developed to assist with the automated analysis of Nondestructive Evaluation (NDE) datasets using the open source Myriad Data Reduction Framework. A machine learning model trained to detect damage in two-dimensional slices of C-Scan data was deployed in a streaming data processing pipeline. To demonstrate the flexibility of the Actor model, the pipeline was deployed on a local system and re-deployed as a distributed system without recompiling, reconfiguring, or restarting the running application.

Investigating Actuation Force Fight with Asynchronous and Synchronous Redundancy Management Techniques

NASA Technical Reports Server (NTRS)

Hall, Brendan; Driscoll, Kevin; Schweiker, Kevin; Dutertre, Bruno

2013-01-01

Within distributed fault-tolerant systems the term force-fight is colloquially used to describe the level of command disagreement present at redundant actuation interfaces. This report details an investigation of force-fight using three distributed system case-study architectures. Each case study architecture is abstracted and formally modeled using the Symbolic Analysis Laboratory (SAL) tool chain from the Stanford Research Institute (SRI). We use the formal SAL models to produce k-induction based proofs of a bounded actuation agreement property. We also present a mathematically derived bound of redundant actuation agreement for sine-wave stimulus. The report documents our experiences and lessons learned developing the formal models and the associated proofs.

Decoupling Coupled Constraints Through Utility Design

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

Li, N; Marden, JR

2014-08-01

Several multiagent systems exemplify the need for establishing distributed control laws that ensure the resulting agents' collective behavior satisfies a given coupled constraint. This technical note focuses on the design of such control laws through a game-theoretic framework. In particular, this technical note provides two systematic methodologies for the design of local agent objective functions that guarantee all resulting Nash equilibria optimize the system level objective while also satisfying a given coupled constraint. Furthermore, the designed local agent objective functions fit into the framework of state based potential games. Consequently, one can appeal to existing results in game-theoretic learning tomore » derive a distributed process that guarantees the agents will reach such an equilibrium.« less

Learning and feedback from the Danish patient safety incident reporting system can be improved.

PubMed

Moeller, Anders Damgaard; Rasmussen, Kurt; Nielsen, Kent Jacob

2016-06-01

The perceived usefulness of incident reporting systems is an important motivational factor for reporting. The usefulness may be facilitated by well-established feedback mechanisms and by learning processes. The aim of this study was to investigate how feedback mechanisms and learning processes were implemented at four Danish hospital units all located in one of the five Danish regions. Based on the concepts of feedback and learning from incident processes, a questionnaire was developed and distributed to 335 patient safety representatives from 200 departments at four Danish hospital units in one of the five Danish regions. The study showed that external reporters were rarely contacted for dialogue, grouped front-line staff were sparsely involved in the learning process, few evaluated the effectiveness of implemented interventions and personal factors were frequently perceived as a primary contributory factor to these incidents. In contrast, the patient safety representatives perceived their competencies as sufficient for the job, internal reporters were often contacted for dialogue, evaluation was widely used and management supported the work with incident reports. The results of the study identified several shortcomings in the implementation of learning processes and feedback mechanisms. The apparent existence of a person-focused approach stands out as an element of notice. The insufficient implementation we observed indicates that there is room for improvement in the efforts made to maximise learning from incidents in the investigated population. not relevant. not relevant.

Classification framework for partially observed dynamical systems

NASA Astrophysics Data System (ADS)

Shen, Yuan; Tino, Peter; Tsaneva-Atanasova, Krasimira

2017-04-01

We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space. In contrast to the existing approaches using point estimates of model parameters to represent individual data items, we employ posterior distributions over model parameters, thus taking into account in a principled manner the uncertainty due to both the generative (observational and/or dynamic noise) and observation (sampling in time) processes. We evaluate the framework on two test beds: a biological pathway model and a stochastic double-well system. Crucially, we show that the classification performance is not impaired when the model structure used for inferring posterior distributions is much more simple than the observation-generating model structure, provided the reduced-complexity inferential model structure captures the essential characteristics needed for the given classification task.

Precise Positioning Method for Logistics Tracking Systems Using Personal Handy-Phone System Based on Mahalanobis Distance

NASA Astrophysics Data System (ADS)

Yokoi, Naoaki; Kawahara, Yasuhiro; Hosaka, Hiroshi; Sakata, Kenji

Focusing on the Personal Handy-phone System (PHS) positioning service used in physical distribution logistics, a positioning error offset method for improving positioning accuracy is invented. A disadvantage of PHS positioning is that measurement errors caused by the fluctuation of radio waves due to buildings around the terminal are large, ranging from several tens to several hundreds of meters. In this study, an error offset method is developed, which learns patterns of positioning results (latitude and longitude) containing errors and the highest signal strength at major logistic points in advance, and matches them with new data measured in actual distribution processes according to the Mahalanobis distance. Then the matching resolution is improved to 1/40 that of the conventional error offset method.

Distributed deep learning networks among institutions for medical imaging.

PubMed

Chang, Ken; Balachandar, Niranjan; Lam, Carson; Yi, Darvin; Brown, James; Beers, Andrew; Rosen, Bruce; Rubin, Daniel L; Kalpathy-Cramer, Jayashree

2018-03-29

Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data. We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer. We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

Selected Lessons Learned in Space Shuttle Orbiter Propulsion and Power Subsystems

NASA Technical Reports Server (NTRS)

Hernandez, Francisco J.; Martinez, Hugo; Ryan, Abigail; Westover, Shayne; Davies, Frank

2011-01-01

Over its 30 years of space flight history, plus the nearly 10 years of design, development test and evaluation, the Space Shuttle Orbiter is full of lessons learned in all of its numerous and complex subsystems. In the current paper, only selected lessons learned in the areas of the Orbiter propulsion and power subsystems will be described. The particular Orbiter subsystems include: Auxiliary Power Unit (APU), Hydraulics and Water Spray Boiler (WSB), Mechanical Flight Controls, Main Propulsion System (MPS), Fuel Cells and Power Reactant and Storage Devices (PRSD), Orbital Maneuvering System (OMS), Reaction Control System (RCS), Electrical Power Distribution (EPDC), electrical wiring and pyrotechnics. Given the complexity and extensive history of each of these subsystems, and the limited scope of this paper, it is impossible to include most of the lessons learned; instead the attempt will be to present a selected few or key lessons, in the judgment of the authors. Each subsystem is presented separate, beginning with an overview of the hardware and their function, a short description of a few historical problems and their lessons, followed by a more comprehensive table listing of the major subsystem problems and lessons. These tables serve as a quick reference for lessons learned in each subsystem. In addition, this paper will establish common lessons across subsystems as well as concentrate on those lessons which are deemed to have the highest applicability to future space flight programs.

Integrating Experiential and Distributional Data to Learn Semantic Representations

ERIC Educational Resources Information Center

Andrews, Mark; Vigliocco, Gabriella; Vinson, David

2009-01-01

The authors identify 2 major types of statistical data from which semantic representations can be learned. These are denoted as "experiential data" and "distributional data". Experiential data are derived by way of experience with the physical world and comprise the sensory-motor data obtained through sense receptors. Distributional data, by…

Assessing Distributed Leadership for Learning and Teaching Quality: A Multi-Institutional Study

ERIC Educational Resources Information Center

Carbone, Angela; Evans, Julia; Ross, Bella; Drew, Steve; Phelan, Liam; Lindsay, Katherine; Cottman, Caroline; Stoney, Susan; Ye, Jing

2017-01-01

Distributed leadership has been explored internationally as a leadership model that will promote and advance excellence in learning and teaching in higher education. This paper presents an assessment of how effectively distributed leadership was enabled at five Australian institutions implementing a collaborative teaching quality development…

Instructional Design Issues in a Distributed Collaborative Engineering Design (CED) Instructional Environment

ERIC Educational Resources Information Center

Koszalka, Tiffany A.; Wu, Yiyan

2010-01-01

Changes in engineering practices have spawned changes in engineering education and prompted the use of distributed learning environments. A distributed collaborative engineering design (CED) course was designed to engage engineering students in learning about and solving engineering design problems. The CED incorporated an advanced interactive…

An Evaluation of Short-Term Distributed Online Learning Events

ERIC Educational Resources Information Center

Barker, Bradley; Brooks, David

2005-01-01

The purpose of this study was to evaluate the effectiveness of short-term distributed online training events using an adapted version of the compressed evaluation form developed by Wisher and Curnow (1998). Evaluating online distributed training events provides insight into course effectiveness, the contribution of prior knowledge to learning, and…

Instructional Designers' Media Selection Practices for Distributed Problem-Based Learning Environments

ERIC Educational Resources Information Center

Fells, Stephanie

2012-01-01

The design of online or distributed problem-based learning (dPBL) is a nascent, complex design problem. Instructional designers are challenged to effectively unite the constructivist principles of problem-based learning (PBL) with appropriate media in order to create quality dPBL environments. While computer-mediated communication (CMC) tools and…

The Pathway Program: How a Collaborative, Distributed Learning Program Showed Us the Future of Social Work Education

ERIC Educational Resources Information Center

Morris, Teresa; Mathias, Christine; Swartz, Ronnie; Jones, Celeste A; Klungtvet-Morano, Meka

2013-01-01

This paper describes a three-campus collaborative, distributed learning program that delivers social work education to remote rural and desert communities in California via distance learning modalities. This "Pathway Program" provides accredited social work education for a career ladder beginning with advising and developing an academic…

Learning through Telepresence with iPads: Placing Schools in Local/Global Communities

ERIC Educational Resources Information Center

Meyer, Bente

2015-01-01

Distributed learning is a growing issue in education following the mainstreaming of technologies such as videoconferencing. However, though distance and distributed learning have been common in adult education and business since the 1990s little is still known about the use of videoconferencing in elementary education. This paper reports from…

Distributed Learning Enhances Relational Memory Consolidation

ERIC Educational Resources Information Center

Litman, Leib; Davachi, Lila

2008-01-01

It has long been known that distributed learning (DL) provides a mnemonic advantage over massed learning (ML). However, the underlying mechanisms that drive this robust mnemonic effect remain largely unknown. In two experiments, we show that DL across a 24 hr interval does not enhance immediate memory performance but instead slows the rate of…

Model of Distributed Learning Objects Repository for a Heterogenic Internet Environment

ERIC Educational Resources Information Center

Kaczmarek, Jerzy; Landowska, Agnieszka

2006-01-01

In this article, an extension of the existing structure of learning objects is described. The solution addresses the problem of the access and discovery of educational resources in the distributed Internet environment. An overview of e-learning standards, reference models, and problems with educational resources delivery is presented. The paper…

Enabling Functional Neural Circuit Simulations with Distributed Computing of Neuromodulated Plasticity

PubMed Central

Potjans, Wiebke; Morrison, Abigail; Diesmann, Markus

2010-01-01

A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e., on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator, or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity. PMID:21151370

Architecture and evolution of Goddard Space Flight Center Distributed Active Archive Center

NASA Technical Reports Server (NTRS)

Bedet, Jean-Jacques; Bodden, Lee; Rosen, Wayne; Sherman, Mark; Pease, Phil

1994-01-01

The Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) has been developed to enhance Earth Science research by improved access to remote sensor earth science data. Building and operating an archive, even one of a moderate size (a few Terabytes), is a challenging task. One of the critical components of this system is Unitree, the Hierarchical File Storage Management System. Unitree, selected two years ago as the best available solution, requires constant system administrative support. It is not always suitable as an archive and distribution data center, and has moderate performance. The Data Archive and Distribution System (DADS) software developed to monitor, manage, and automate the ingestion, archive, and distribution functions turned out to be more challenging than anticipated. Having the software and tools is not sufficient to succeed. Human interaction within the system must be fully understood to improve efficiency to improve efficiency and ensure that the right tools are developed. One of the lessons learned is that the operability, reliability, and performance aspects should be thoroughly addressed in the initial design. However, the GSFC DAAC has demonstrated that it is capable of distributing over 40 GB per day. A backup system to archive a second copy of all data ingested is under development. This backup system will be used not only for disaster recovery but will also replace the main archive when it is unavailable during maintenance or hardware replacement. The GSFC DAAC has put a strong emphasis on quality at all level of its organization. A Quality team has also been formed to identify quality issues and to propose improvements. The DAAC has conducted numerous tests to benchmark the performance of the system. These tests proved to be extremely useful in identifying bottlenecks and deficiencies in operational procedures.

Broadening conceptions of learning in medical education: the message from teamworking.

PubMed

Bleakley, Alan

2006-02-01

There is a mismatch between the broad range of learning theories offered in the wider education literature and a relatively narrow range of theories privileged in the medical education literature. The latter are usually described under the heading of 'adult learning theory'. This paper critically addresses the limitations of the current dominant learning theories informing medical education. An argument is made that such theories, which address how an individual learns, fail to explain how learning occurs in dynamic, complex and unstable systems such as fluid clinical teams. Models of learning that take into account distributed knowing, learning through time as well as space, and the complexity of a learning environment including relationships between persons and artefacts, are more powerful in explaining and predicting how learning occurs in clinical teams. Learning theories may be privileged for ideological reasons, such as medicine's concern with autonomy. Where an increasing amount of medical education occurs in workplace contexts, sociocultural learning theories offer a best-fit exploration and explanation of such learning. We need to continue to develop testable models of learning that inform safe work practice. One type of learning theory will not inform all practice contexts and we need to think about a range of fit-for-purpose theories that are testable in practice. Exciting current developments include dynamicist models of learning drawing on complexity theory.

Fast and Accurate Learning When Making Discrete Numerical Estimates.

PubMed

Sanborn, Adam N; Beierholm, Ulrik R

2016-04-01

Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates.

Fast and Accurate Learning When Making Discrete Numerical Estimates

PubMed Central

Sanborn, Adam N.; Beierholm, Ulrik R.

2016-01-01

Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates. PMID:27070155

Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

2015-01-01

The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.

The influence of statistical properties of Fourier coefficients on random Gaussian surfaces.

PubMed

de Castro, C P; Luković, M; Andrade, R F S; Herrmann, H J

2017-05-16

Many examples of natural systems can be described by random Gaussian surfaces. Much can be learned by analyzing the Fourier expansion of the surfaces, from which it is possible to determine the corresponding Hurst exponent and consequently establish the presence of scale invariance. We show that this symmetry is not affected by the distribution of the modulus of the Fourier coefficients. Furthermore, we investigate the role of the Fourier phases of random surfaces. In particular, we show how the surface is affected by a non-uniform distribution of phases.

Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems.

PubMed

Choi, Hyungsuk; Choi, Woohyuk; Quan, Tran Minh; Hildebrand, David G C; Pfister, Hanspeter; Jeong, Won-Ki

2014-12-01

As the size of image data from microscopes and telescopes increases, the need for high-throughput processing and visualization of large volumetric data has become more pressing. At the same time, many-core processors and GPU accelerators are commonplace, making high-performance distributed heterogeneous computing systems affordable. However, effectively utilizing GPU clusters is difficult for novice programmers, and even experienced programmers often fail to fully leverage the computing power of new parallel architectures due to their steep learning curve and programming complexity. In this paper, we propose Vivaldi, a new domain-specific language for volume processing and visualization on distributed heterogeneous computing systems. Vivaldi's Python-like grammar and parallel processing abstractions provide flexible programming tools for non-experts to easily write high-performance parallel computing code. Vivaldi provides commonly used functions and numerical operators for customized visualization and high-throughput image processing applications. We demonstrate the performance and usability of Vivaldi on several examples ranging from volume rendering to image segmentation.

A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.

PubMed

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.

Automatically Finding the Control Variables for Complex System Behavior

NASA Technical Reports Server (NTRS)

Gay, Gregory; Menzies, Tim; Davies, Misty; Gundy-Burlet, Karen

2010-01-01

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the factors most likely to cause a mission-critical failure. The goal of this research is to comparatively assess treatment learning against state-of-the-art numerical optimization techniques. To achieve this, this paper benchmarks the TAR3 and TAR4.1 treatment learners against optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. The results clearly show that treatment learning is both faster and more accurate than traditional optimization methods.

Cognitive Bias for Learning Speech Sounds From a Continuous Signal Space Seems Nonlinguistic.

PubMed

van der Ham, Sabine; de Boer, Bart

2015-10-01

When learning language, humans have a tendency to produce more extreme distributions of speech sounds than those observed most frequently: In rapid, casual speech, vowel sounds are centralized, yet cross-linguistically, peripheral vowels occur almost universally. We investigate whether adults' generalization behavior reveals selective pressure for communication when they learn skewed distributions of speech-like sounds from a continuous signal space. The domain-specific hypothesis predicts that the emergence of sound categories is driven by a cognitive bias to make these categories maximally distinct, resulting in more skewed distributions in participants' reproductions. However, our participants showed more centered distributions, which goes against this hypothesis, indicating that there are no strong innate linguistic biases that affect learning these speech-like sounds. The centralization behavior can be explained by a lack of communicative pressure to maintain categories.

Cognitive Bias for Learning Speech Sounds From a Continuous Signal Space Seems Nonlinguistic

PubMed Central

de Boer, Bart

2015-01-01

When learning language, humans have a tendency to produce more extreme distributions of speech sounds than those observed most frequently: In rapid, casual speech, vowel sounds are centralized, yet cross-linguistically, peripheral vowels occur almost universally. We investigate whether adults’ generalization behavior reveals selective pressure for communication when they learn skewed distributions of speech-like sounds from a continuous signal space. The domain-specific hypothesis predicts that the emergence of sound categories is driven by a cognitive bias to make these categories maximally distinct, resulting in more skewed distributions in participants’ reproductions. However, our participants showed more centered distributions, which goes against this hypothesis, indicating that there are no strong innate linguistic biases that affect learning these speech-like sounds. The centralization behavior can be explained by a lack of communicative pressure to maintain categories. PMID:27648212

Problem-Based Learning and Problem-Solving Tools: Synthesis and Direction for Distributed Education Environments.

ERIC Educational Resources Information Center

Friedman, Robert S.; Deek, Fadi P.

2002-01-01

Discusses how the design and implementation of problem-solving tools used in programming instruction are complementary with both the theories of problem-based learning (PBL), including constructivism, and the practices of distributed education environments. Examines how combining PBL, Web-based distributed education, and a problem-solving…

Redistributed Leadership for Sustainable Professional Learning Communities

ERIC Educational Resources Information Center

Hargreaves, Andy; Fink, Dean

2006-01-01

Distributed leadership in schools is not exclusive to professional learning communities; it is distributed in all schools, for good purposes and for bad, by design and by emergence. In this article, we describe a normative view of distributed leadership that tends to be a leadership of advocacy, and we offer a descriptive perspective that argues…

Technology, Learning and Instruction: Distributed Cognition in the Secondary English Classroom

ERIC Educational Resources Information Center

Gomez, Mary Louise; Schieble, Melissa; Curwood, Jen Scott; Hassett, Dawnene

2010-01-01

In this paper, we analyse interactions between secondary students and pre-service teachers in an online environment in order to understand how their meaning-making processes embody distributed cognition. We begin by providing a theoretical review of the ways in which literacy learning is distributed across learners, objects, tools, symbols,…

Some Technical Implications of Distributed Cognition on the Design on Interactive Learning Environments.

ERIC Educational Resources Information Center

Dillenbourg, Pierre

1996-01-01

Maintains that diagnosis, explanation, and tutoring, the functions of an interactive learning environment, are collaborative processes. Examines how human-computer interaction can be improved using a distributed cognition framework. Discusses situational and distributed knowledge theories and provides a model on how they can be used to redesign…

Representing Color Ensembles.

PubMed

Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

2017-10-01

Colors are rarely uniform, yet little is known about how people represent color distributions. We introduce a new method for studying color ensembles based on intertrial learning in visual search. Participants looked for an oddly colored diamond among diamonds with colors taken from either uniform or Gaussian color distributions. On test trials, the targets had various distances in feature space from the mean of the preceding distractor color distribution. Targets on test trials therefore served as probes into probabilistic representations of distractor colors. Test-trial response times revealed a striking similarity between the physical distribution of colors and their internal representations. The results demonstrate that the visual system represents color ensembles in a more detailed way than previously thought, coding not only mean and variance but, most surprisingly, the actual shape (uniform or Gaussian) of the distribution of colors in the environment.

Handwritten document age classification based on handwriting styles

NASA Astrophysics Data System (ADS)

Ramaiah, Chetan; Kumar, Gaurav; Govindaraju, Venu

2012-01-01

Handwriting styles are constantly changing over time. We approach the novel problem of estimating the approximate age of Historical Handwritten Documents using Handwriting styles. This system will have many applications in handwritten document processing engines where specialized processing techniques can be applied based on the estimated age of the document. We propose to learn a distribution over styles across centuries using Topic Models and to apply a classifier over weights learned in order to estimate the approximate age of the documents. We present a comparison of different distance metrics such as Euclidean Distance and Hellinger Distance within this application.

The Effects of Frequency, Distribution, Mode of Presentation, and First Language on Learning an Artificial Language

ERIC Educational Resources Information Center

Miyata, Munehiko

2011-01-01

This dissertation presents results from a series of experiments investigating adult learning of an artificial language and the effects that input frequency (high vs. low token frequency), frequency distribution (skewed vs. balanced), presentation mode (structured vs. scrambled), and first language (English vs. Japanese) have on such learning.…

Building an Efficient and Effective Test Management System in an ODL Institution

ERIC Educational Resources Information Center

Yusof, Safiah Md; Lim, Tick Meng; Png, Leo; Khatab, Zainuriyah Abd; Singh, Harvinder Kaur Dharam

2017-01-01

Open University Malaysia (OUM) is progressively moving towards implementing assessment on demand and online assessment. This move is deemed necessary for OUM to continue to be the leading provider of flexible learning. OUM serves a very large number of students each semester and these students are vastly distributed throughout the country. As the…

Using Emotions and Personal Memory Associations to Acquire Vocabulary

ERIC Educational Resources Information Center

Randolph, Patrick T.

2018-01-01

Of all the possible tools available to help out English language Learners (ELLs) acquire vocabulary, the use of emotions is one of the most powerful because "we are learning that emotions are the result of multiple brain and body systems that are distributed over the whole person". If we go one step further and connect emotions to…

Towards Situation Driven Mobile Tutoring System for Learning Languages and Communication Skills: Application to Users with Specific Needs

ERIC Educational Resources Information Center

Khemaja, Maha; Taamallah, Aroua

2016-01-01

Current advances in portable devices and wireless technologies had drastically impacted mobile and pervasive computing development and use. Nowadays, mobile and or pervasive applications, are increasingly being used to support users' everyday activities. These apps either distributed or standalone are characterized by the variability of the…

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

Fischer, M. L.; Sweeney, C.

The vertical distributions of CO 2, CH 4, and other gases provide important constraints when determining terrestrial and ocean sources and sinks of carbon and other biogeochemical processes in the Earth system. The U.S. Department of Energy's (DOE) Office of Biological and Environmental Research and the National Oceanic and Atmospheric Administration's Earth System Research Laboratory to quantify the vertically resolved distribution of atmospheric carbon-cycle gases(CO 2, CH 4 ) within approximately 99% of the atmospheric column at the DOE ’s Atmospheric Radiation Measurement Southern Great Plains (SGP) site in Oklahoma . During the 2012 to 2014 campaign period, 12 successfulmore » Air C ore flights were conducted from the SGP site . In addition to providing critical data for evaluating remote sensing and earth system models, valuable lessons were learned that motivate improvements to the sampling and recovery systems and campaign logistics . With the launch of the Orbiting Carbon Observatory - 2 (OCO - 2) and Greenhouse gases Observing Satellite ( GOSAT ) satellites, we look forward to proposing additional sampling and analysis efforts at the SGP site and at other sites to characterize the vertical distribution of CO 2, CH 4 over time and space.« less

A Self-Organizing Incremental Neural Network based on local distribution learning.

PubMed

Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

2016-12-01

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

An evaluation of semi-automated methods for collecting ecosystem-level data in temperate marine systems.

PubMed

Griffin, Kingsley J; Hedge, Luke H; González-Rivero, Manuel; Hoegh-Guldberg, Ove I; Johnston, Emma L

2017-07-01

Historically, marine ecologists have lacked efficient tools that are capable of capturing detailed species distribution data over large areas. Emerging technologies such as high-resolution imaging and associated machine-learning image-scoring software are providing new tools to map species over large areas in the ocean. Here, we combine a novel diver propulsion vehicle (DPV) imaging system with free-to-use machine-learning software to semi-automatically generate dense and widespread abundance records of a habitat-forming algae over ~5,000 m 2 of temperate reef. We employ replicable spatial techniques to test the effectiveness of traditional diver-based sampling, and better understand the distribution and spatial arrangement of one key algal species. We found that the effectiveness of a traditional survey depended on the level of spatial structuring, and generally 10-20 transects (50 × 1 m) were required to obtain reliable results. This represents 2-20 times greater replication than have been collected in previous studies. Furthermore, we demonstrate the usefulness of fine-resolution distribution modeling for understanding patterns in canopy algae cover at multiple spatial scales, and discuss applications to other marine habitats. Our analyses demonstrate that semi-automated methods of data gathering and processing provide more accurate results than traditional methods for describing habitat structure at seascape scales, and therefore represent vastly improved techniques for understanding and managing marine seascapes.

Optimizing area under the ROC curve using semi-supervised learning

PubMed Central

Wang, Shijun; Li, Diana; Petrick, Nicholas; Sahiner, Berkman; Linguraru, Marius George; Summers, Ronald M.

2014-01-01

Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.1 PMID:25395692

Optimizing area under the ROC curve using semi-supervised learning.

PubMed

Wang, Shijun; Li, Diana; Petrick, Nicholas; Sahiner, Berkman; Linguraru, Marius George; Summers, Ronald M

2015-01-01

Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.

Managing Learning for Performance.

ERIC Educational Resources Information Center

Kuchinke, K. Peter

1995-01-01

Presents findings of organizational learning literature that could substantiate claims of learning organization proponents. Examines four learning processes and their contribution to performance-based learning management: knowledge acquisition, information distribution, information interpretation, and organizational memory. (SK)

Machine learning of network metrics in ATLAS Distributed Data Management

NASA Astrophysics Data System (ADS)

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.

Intelligent E-Learning Systems: Automatic Construction of Ontologies

NASA Astrophysics Data System (ADS)

Peso, Jesús del; de Arriaga, Fernando

2008-05-01

During the last years a new generation of Intelligent E-Learning Systems (ILS) has emerged with enhanced functionality due, mainly, to influences from Distributed Artificial Intelligence, to the use of cognitive modelling, to the extensive use of the Internet, and to new educational ideas such as the student-centered education and Knowledge Management. The automatic construction of ontologies provides means of automatically updating the knowledge bases of their respective ILS, and of increasing their interoperability and communication among them, sharing the same ontology. The paper presents a new approach, able to produce ontologies from a small number of documents such as those obtained from the Internet, without the assistance of large corpora, by using simple syntactic rules and some semantic information. The method is independent of the natural language used. The use of a multi-agent system increases the flexibility and capability of the method. Although the method can be easily improved, the results so far obtained, are promising.

Cox process representation and inference for stochastic reaction-diffusion processes

NASA Astrophysics Data System (ADS)

Schnoerr, David; Grima, Ramon; Sanguinetti, Guido

2016-05-01

Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reaction-diffusion process from data. Our solution relies on a non-trivial connection between stochastic reaction-diffusion processes and spatio-temporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling.

From telepathology to virtual pathology institution: the new world of digital pathology.

PubMed

Kayser, K; Kayser, G; Radziszowski, D; Oehmann, A

Telepathology has left its childhood. Its technical development is mature, and its use for primary (frozen section) and secondary (expert consultation) diagnosis has been expanded to a great amount. This is in contrast to a virtual pathology laboratory, which is still under technical constraints. Similar to telepathology, which can also be used for e-learning and e-training in pathology, as exemplarily is demonstrated on Digital Lung Pathology (Klaus.Kayser@charite.de) at least two kinds of virtual pathology laboratories will be implemented in the near future: a) those with distributed pathologists and distributed (> or = 1) laboratories associated to individual biopsy stations/surgical theatres, and b) distributed pathologists (usually situated in one institution) and a centralized laboratory, which digitizes complete histological slides. Both scenarios are under intensive technical investigations. The features of virtual pathology comprise a virtual pathology institution (mode a) that accepts a complete case with the patient's history, clinical findings, and (pre-selected) images for first diagnosis. The diagnostic responsibility is that of a conventional institution. The Internet serves as platform for information transfer, and an open server such as the iPATH (http://telepath.patho.unibas.ch) for coordination and performance of the diagnostic procedure. The size and number of transferred images have to be limited, and usual different magnifications have to be used. The sender needs to possess experiences in image sampling techniques, which present with the most significant information. A group of pathologists is "on duty", or selects one member for a predefined duty period. The diagnostic statement of the pathologist(s) on duty is retransmitted to the sender with full responsibility. The first experiences of a virtual pathology institution group working with the iPATH server working with a small hospital of the Salomon islands are promising. A centralized virtual pathology institution (mode b) depends upon the digitalization of a complete slide, and the transfer of large sized images to different pathologists working in one institution. The technical performance of complete slide digitalization is still under development. Virtual pathology can be combined with e-learning and e-training, that will serve for a powerful daily-work-integrated pathology system. At present, e-learning systems are "stand-alone" solutions distributed on CD or via Internet. A characteristic example is the Digital Lung Pathology CD, which includes about 60 different rare and common lung diseases with some features of electronic communication. These features include access to scientific library systems (PubMed), distant measurement servers (EuroQuant), automated immunohisto-chemistry measurements, or electronic journals (Elec J Pathol Histol, www.pathology-online.org). It combines e-learning and e-training with some acoustic support. A new and complete database based upon this CD will combine e-learning and e-teaching with the actual workflow in a virtual pathology institution (mode a). The technological problems are solved and do not depend upon technical constraints such as slide scanning systems. At present, telepathology serves as promoter for a complete new landscape in diagnostic pathology, the so-called virtual pathology institution. Industrial and scientific efforts will probably allow an implementation of this technique within the next two years with exciting diagnostic and scientific perspectives.

Building a Trustworthy Environmental Science Data Repository: Lessons Learned from the ORNL DAAC

NASA Astrophysics Data System (ADS)

Wei, Y.; Santhana Vannan, S. K.; Boyer, A.; Beaty, T.; Deb, D.; Hook, L.

2017-12-01

The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, https://daac.ornl.gov) for biogeochemical dynamics is one of NASA's Earth Observing System Data and Information System (EOSDIS) data centers. The mission of the ORNL DAAC is to assemble, distribute, and provide data services for a comprehensive archive of terrestrial biogeochemistry and ecological dynamics observations and models to facilitate research, education, and decision-making in support of NASA's Earth Science. Since its establishment in 1994, ORNL DAAC has been continuously building itself into a trustworthy environmental science data repository by not only ensuring the quality and usability of its data holdings, but also optimizing its data publication and management process. This paper describes the lessons learned from ORNL DAAC's effort toward this goal. ORNL DAAC has been proactively implementing international community standards throughout its data management life cycle, including data publication, preservation, discovery, visualization, and distribution. Data files in standard formats, detailed documentation, and metadata following standard models are prepared to improve the usability and longevity of data products. Assignment of a Digital Object Identifier (DOI) ensures the identifiability and accessibility of every data product, including the different versions and revisions of its life cycle. ORNL DAAC's data citation policy assures data producers receive appropriate recognition of use of their products. Web service standards, such as OpenSearch and Open Geospatial Consortium (OGC), promotes the discovery, visualization, distribution, and integration of ORNL DAAC's data holdings. Recently, ORNL DAAC began efforts to optimize and standardize its data archival and data publication workflows, to improve the efficiency and transparency of its data archival and management processes.

Effectiveness of an e-Learning Platform for Image Interpretation Education of Medical Staff and Students.

PubMed

Ogura, Akio; Hayashi, Norio; Negishi, Tohru; Watanabe, Haruyuki

2018-05-09

Medical staff must be able to perform accurate initial interpretations of radiography to prevent diagnostic errors. Education in medical image interpretation is an ongoing need that is addressed by text-based and e-learning platforms. The effectiveness of these methods has been previously reported. Here, we describe the effectiveness of an e-learning platform used for medical image interpretation education. Ten third-year medical students without previous experience in chest radiography interpretation were provided with e-learning instructions. Accuracy of diagnosis using chest radiography was provided before and after e-learning education. We measured detection accuracy for two image groups: nodular shadow and ground-glass shadow. We also distributed the e-learning system to the two groups and analyzed the effectiveness of education for both types of image shadow. The mean correct answer rate after the 2-week e-learning period increased from 34.5 to 72.7%. Diagnosis of the ground glass shadow improved significantly more than that of the mass shadow. Education using the e-leaning platform is effective for interpretation of chest radiography results. E-learning is particularly effective for the interpretation of chest radiography images containing ground glass shadow.

Distribution of Practice and Metacognition in Learning and Long-Term Retention of a Discrete Motor Task

ERIC Educational Resources Information Center

Dail, Teresa K.; Christina, Robert W.

2004-01-01

This study examined judgments of learning and the long-term retention of a discrete motor task (golf putting) as a function of practice distribution. The results indicated that participants in the distributed practice group performed more proficiently than those in the massed practice group during both acquisition and retention phases. No…

Learning Grammatical Categories from Distributional Cues: Flexible Frames for Language Acquisition

ERIC Educational Resources Information Center

St. Clair, Michelle C.; Monaghan, Padraic; Christiansen, Morten H.

2010-01-01

Numerous distributional cues in the child's environment may potentially assist in language learning, but what cues are useful to the child and when are these cues utilised? We propose that the most useful source of distributional cue is a flexible frame surrounding the word, where the language learner integrates information from the preceding and…

Interactive Learning During Solar Maximum

NASA Technical Reports Server (NTRS)

Ashour-Abdalla, Maha; Curtis, Steven (Technical Monitor)

2001-01-01

The goal of this project is to develop and distribute e-educational material for space science during times of solar activity that emphasizes underlying basic science principles of solar disturbances and their effects on Earth. This includes materials such as simulations, animations, group projects and other on-line materials to be used by students either in high school or at the introductory college level. The on-line delivery tool originally intended to be used is known as Interactive Multimedia Education at a Distance (IMED), which is a web-based software system used at UCLA for interactive distance learning. IMED is a password controlled system that allows students to access text, images, bulletin boards, chat rooms, animation, simulations and individual student web sites to study science and to collaborate on group projects.

Quality Assurance in Distance Education: The Challenges to Be Addressed

ERIC Educational Resources Information Center

Stella, Antony; Gnanam, A.

2004-01-01

Integration of technology in all forms of education has narrowed down the gap between the on- and off-campus students and has resulted in the use of the more broad-based term "distributed learning". Consequently, distance learning is seen as a subset of distributed learning, focusing on students who may be separated in time and space from their…

Second Language Vocabulary Learning through Extensive Reading with Audio Support: How Do Frequency and Distribution of Occurrence Affect Learning?

ERIC Educational Resources Information Center

Webb, Stuart; Chang, Anna C-S.

2015-01-01

This study investigated (1) the extent of vocabulary learning through reading and listening to 10 graded readers, and (2) the relationship between vocabulary gain and the frequency and distribution of occurrence of 100 target words in the graded readers. The experimental design expanded on earlier studies that have typically examined incidental…

Cross-domain active learning for video concept detection

NASA Astrophysics Data System (ADS)

Li, Huan; Li, Chao; Shi, Yuan; Xiong, Zhang; Hauptmann, Alexander G.

2011-08-01

As video data from a variety of different domains (e.g., news, documentaries, entertainment) have distinctive data distributions, cross-domain video concept detection becomes an important task, in which one can reuse the labeled data of one domain to benefit the learning task in another domain with insufficient labeled data. In this paper, we approach this problem by proposing a cross-domain active learning method which iteratively queries labels of the most informative samples in the target domain. Traditional active learning assumes that the training (source domain) and test data (target domain) are from the same distribution. However, it may fail when the two domains have different distributions because querying informative samples according to a base learner that initially learned from source domain may no longer be helpful for the target domain. In our paper, we use the Gaussian random field model as the base learner which has the advantage of exploring the distributions in both domains, and adopt uncertainty sampling as the query strategy. Additionally, we present an instance weighting trick to accelerate the adaptability of the base learner, and develop an efficient model updating method which can significantly speed up the active learning process. Experimental results on TRECVID collections highlight the effectiveness.

What does fault tolerant Deep Learning need from MPI?

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

Amatya, Vinay C.; Vishnu, Abhinav; Siegel, Charles M.

Deep Learning (DL) algorithms have become the {\\em de facto} Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive -- even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults -- requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: {\\em What is needed from MPI for designing fault tolerant DL implementations?} In this paper, we address this problem for permanent faults. We motivate the need for amore » fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by extending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet neural network topology demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM.« less

Distributional Vowel Training Is Less Effective for Adults than for Infants. A Study Using the Mismatch Response

PubMed Central

Wanrooij, Karin; Boersma, Paul; van Zuijen, Titia L.

2014-01-01

Distributional learning of speech sounds (i.e., learning from simple exposure to frequency distributions of speech sounds in the environment) has been observed in the lab repeatedly in both infants and adults. The current study is the first attempt to examine whether the capacity for using the mechanism is different in adults than in infants. To this end, a previous event-related potential study that had shown distributional learning of the English vowel contrast /æ/∼/ε/ in 2-to-3-month old Dutch infants was repeated with Dutch adults. Specifically, the adults were exposed to either a bimodal distribution that suggested the existence of the two vowels (as appropriate in English), or to a unimodal distribution that did not (as appropriate in Dutch). After exposure the participants were tested on their discrimination of a representative [æ] and a representative [ε], in an oddball paradigm for measuring mismatch responses (MMRs). Bimodally trained adults did not have a significantly larger MMR amplitude, and hence did not show significantly better neural discrimination of the test vowels, than unimodally trained adults. A direct comparison between the normalized MMR amplitudes of the adults with those of the previously tested infants showed that within a reasonable range of normalization parameters, the bimodal advantage is reliably smaller in adults than in infants, indicating that distributional learning is a weaker mechanism for learning speech sounds in adults (if it exists in that group at all) than in infants. PMID:25289935

Affordable non-traditional source data mining for context assessment to improve distributed fusion system robustness

NASA Astrophysics Data System (ADS)

Bowman, Christopher; Haith, Gary; Steinberg, Alan; Morefield, Charles; Morefield, Michael

2013-05-01

This paper describes methods to affordably improve the robustness of distributed fusion systems by opportunistically leveraging non-traditional data sources. Adaptive methods help find relevant data, create models, and characterize the model quality. These methods also can measure the conformity of this non-traditional data with fusion system products including situation modeling and mission impact prediction. Non-traditional data can improve the quantity, quality, availability, timeliness, and diversity of the baseline fusion system sources and therefore can improve prediction and estimation accuracy and robustness at all levels of fusion. Techniques are described that automatically learn to characterize and search non-traditional contextual data to enable operators integrate the data with the high-level fusion systems and ontologies. These techniques apply the extension of the Data Fusion & Resource Management Dual Node Network (DNN) technical architecture at Level 4. The DNN architecture supports effectively assessment and management of the expanded portfolio of data sources, entities of interest, models, and algorithms including data pattern discovery and context conformity. Affordable model-driven and data-driven data mining methods to discover unknown models from non-traditional and `big data' sources are used to automatically learn entity behaviors and correlations with fusion products, [14 and 15]. This paper describes our context assessment software development, and the demonstration of context assessment of non-traditional data to compare to an intelligence surveillance and reconnaissance fusion product based upon an IED POIs workflow.

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

PubMed

S K, Somasundaram; P, Alli

2017-11-09

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection of DR screening system using Bagging Ensemble Classifier (BEC) is investigated. With the help of voting the process in ML-BEC, bagging minimizes the error due to variance of the base classifier. With the publicly available retinal image databases, our classifier is trained with 25% of RI. Results show that the ensemble classifier can achieve better classification accuracy (CA) than single classification models. Empirical experiments suggest that the machine learning-based ensemble classifier is efficient for further reducing DR classification time (CT).

Interactions between statistical and semantic information in infant language development

PubMed Central

Lany, Jill; Saffran, Jenny R.

2013-01-01

Infants can use statistical regularities to form rudimentary word categories (e.g. noun, verb), and to learn the meanings common to words from those categories. Using an artificial language methodology, we probed the mechanisms by which two types of statistical cues (distributional and phonological regularities) affect word learning. Because linking distributional cues vs. phonological information to semantics make different computational demands on learners, we also tested whether their use is related to language proficiency. We found that 22-month-old infants with smaller vocabularies generalized using phonological cues; however, infants with larger vocabularies showed the opposite pattern of results, generalizing based on distributional cues. These findings suggest that both phonological and distributional cues marking word categories promote early word learning. Moreover, while correlations between these cues are important to forming word categories, we found infants’ weighting of these cues in subsequent word-learning tasks changes over the course of early language development. PMID:21884336

Sample Complexity Bounds for Differentially Private Learning

PubMed Central

Chaudhuri, Kamalika; Hsu, Daniel

2013-01-01

This work studies the problem of privacy-preserving classification – namely, learning a classifier from sensitive data while preserving the privacy of individuals in the training set. In particular, the learning algorithm is required in this problem to guarantee differential privacy, a very strong notion of privacy that has gained significant attention in recent years. A natural question to ask is: what is the sample requirement of a learning algorithm that guarantees a certain level of privacy and accuracy? We address this question in the context of learning with infinite hypothesis classes when the data is drawn from a continuous distribution. We first show that even for very simple hypothesis classes, any algorithm that uses a finite number of examples and guarantees differential privacy must fail to return an accurate classifier for at least some unlabeled data distributions. This result is unlike the case with either finite hypothesis classes or discrete data domains, in which distribution-free private learning is possible, as previously shown by Kasiviswanathan et al. (2008). We then consider two approaches to differentially private learning that get around this lower bound. The first approach is to use prior knowledge about the unlabeled data distribution in the form of a reference distribution chosen independently of the sensitive data. Given such a reference , we provide an upper bound on the sample requirement that depends (among other things) on a measure of closeness between and the unlabeled data distribution. Our upper bound applies to the non-realizable as well as the realizable case. The second approach is to relax the privacy requirement, by requiring only label-privacy – namely, that the only labels (and not the unlabeled parts of the examples) be considered sensitive information. An upper bound on the sample requirement of learning with label privacy was shown by Chaudhuri et al. (2006); in this work, we show a lower bound. PMID:25285183

Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain

NASA Astrophysics Data System (ADS)

Virkar, Yogesh S.; Shew, Woodrow L.; Restrepo, Juan G.; Ott, Edward

2016-10-01

Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model composed of two interacting networks: a model neural network interconnected by synapses that undergo spike-timing-dependent plasticity; and a model glial network interconnected by gap junctions that diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.

Criticality in the brain

NASA Astrophysics Data System (ADS)

de Arcangelis, L.; Lombardi, F.; Herrmann, H. J.

2014-03-01

Spontaneous brain activity has been recently characterized by avalanche dynamics with critical features for systems in vitro and in vivo. In this contribution we present a review of experimental results on neuronal avalanches in cortex slices, together with numerical results from a neuronal model implementing several physiological properties of living neurons. Numerical data reproduce experimental results for avalanche statistics. The temporal organization of avalanches can be characterized by the distribution of waiting times between successive avalanches. Experimental measurements exhibit a non-monotonic behaviour, not usually found in other natural processes. Numerical simulations provide evidence that this behaviour is a consequence of the alternation between states of high and low activity, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homoeostatic mechanisms. Interestingly, the same homoeostatic balance is detected for neuronal activity at the scale of the whole brain. We finally review the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules and the learning dynamics exhibits universal features as a function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

Adaptive, Distributed Control of Constrained Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Bieniawski, Stefan; Wolpert, David H.

2004-01-01

Product Distribution (PO) theory was recently developed as a broad framework for analyzing and optimizing distributed systems. Here we demonstrate its use for adaptive distributed control of Multi-Agent Systems (MASS), i.e., for distributed stochastic optimization using MAS s. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (Probability dist&&on on the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. One common way to find that equilibrium is to have each agent run a Reinforcement Learning (E) algorithm. PD theory reveals this to be a particular type of search algorithm for minimizing the Lagrangian. Typically that algorithm i s quite inefficient. A more principled alternative is to use a variant of Newton's method to minimize the Lagrangian. Here we compare this alternative to RL-based search in three sets of computer experiments. These are the N Queen s problem and bin-packing problem from the optimization literature, and the Bar problem from the distributed RL literature. Our results confirm that the PD-theory-based approach outperforms the RL-based scheme in all three domains.

Product Distribution Theory for Control of Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Lee, Chia Fan; Wolpert, David H.

2004-01-01

Product Distribution (PD) theory is a new framework for controlling Multi-Agent Systems (MAS's). First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint stare of the agents. Accordingly we can consider a team game in which the shared utility is a performance measure of the behavior of the MAS. For such a scenario the game is at equilibrium - the Lagrangian is optimized - when the joint distribution of the agents optimizes the system's expected performance. One common way to find that equilibrium is to have each agent run a reinforcement learning algorithm. Here we investigate the alternative of exploiting PD theory to run gradient descent on the Lagrangian. We present computer experiments validating some of the predictions of PD theory for how best to do that gradient descent. We also demonstrate how PD theory can improve performance even when we are not allowed to rerun the MAS from different initial conditions, a requirement implicit in some previous work.

Using Multiple Big Datasets and Machine Learning to Produce a New Global Particulate Dataset: A Technology Challenge Case Study

NASA Astrophysics Data System (ADS)

Lary, D. J.

2013-12-01

A BigData case study is described where multiple datasets from several satellites, high-resolution global meteorological data, social media and in-situ observations are combined using machine learning on a distributed cluster using an automated workflow. The global particulate dataset is relevant to global public health studies and would not be possible to produce without the use of the multiple big datasets, in-situ data and machine learning.To greatly reduce the development time and enhance the functionality a high level language capable of parallel processing has been used (Matlab). A key consideration for the system is high speed access due to the large data volume, persistence of the large data volumes and a precise process time scheduling capability.

Distributed Learning and Institutional Restructuring.

ERIC Educational Resources Information Center

Hawkins, Brian L.

1999-01-01

Discusses the following challenges institutions must consider as they enter the new marketplace of distributed learning: library access, faculty workload, faculty incentives, faculty-support structures, intellectual property, articulation agreements, financial aid, pricing, cross-subsidization of programs, institutional loyalty and philanthropy,…

Bose-Einstein condensates form in heuristics learned by ciliates deciding to signal 'social' commitments.

PubMed

Clark, Kevin B

2010-03-01

Fringe quantum biology theories often adopt the concept of Bose-Einstein condensation when explaining how consciousness, emotion, perception, learning, and reasoning emerge from operations of intact animal nervous systems and other computational media. However, controversial empirical evidence and mathematical formalism concerning decoherence rates of bioprocesses keep these frameworks from satisfactorily accounting for the physical nature of cognitive-like events. This study, inspired by the discovery that preferential attachment rules computed by complex technological networks obey Bose-Einstein statistics, is the first rigorous attempt to examine whether analogues of Bose-Einstein condensation precipitate learned decision making in live biological systems as bioenergetics optimization predicts. By exploiting the ciliate Spirostomum ambiguum's capacity to learn and store behavioral strategies advertising mating availability into heuristics of topologically invariant computational networks, three distinct phases of strategy use were found to map onto statistical distributions described by Bose-Einstein, Fermi-Dirac, and classical Maxwell-Boltzmann behavior. Ciliates that sensitized or habituated signaling patterns to emit brief periods of either deceptive 'harder-to-get' or altruistic 'easier-to-get' serial escape reactions began testing condensed on initially perceived fittest 'courting' solutions. When these ciliates switched from their first strategy choices, Bose-Einstein condensation of strategy use abruptly dissipated into a Maxwell-Boltzmann computational phase no longer dominated by a single fittest strategy. Recursive trial-and-error strategy searches annealed strategy use back into a condensed phase consistent with performance optimization. 'Social' decisions performed by ciliates showing no nonassociative learning were largely governed by Fermi-Dirac statistics, resulting in degenerate distributions of strategy choices. These findings corroborate previous work demonstrating ciliates with improving expertise search grouped 'courting' assurances at quantum efficiencies and verify efficient processing by primitive 'social' intelligences involves network forms of Bose-Einstein condensation coupled to preceding thermodynamic-sensitive computational phases. 2009 Elsevier Ireland Ltd. All rights reserved.

The evolution of automated launch processing

NASA Technical Reports Server (NTRS)

Tomayko, James E.

1988-01-01

The NASA Launch Processing System (LPS) to which attention is presently given has arrived at satisfactory solutions for the distributed-computing, good user interface and dissimilar-hardware interface, and automation-related problems that emerge in the specific arena of spacecraft launch preparations. An aggressive effort was made to apply the lessons learned in the 1960s, during the first attempts at automatic launch vehicle checkout, to the LPS. As the Space Shuttle System continues to evolve, the primary contributor to safety and reliability will be the LPS.

A Collaborative Framework for Distributed Privacy-Preserving Support Vector Machine Learning

PubMed Central

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates “privacy-insensitive” intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner. PMID:23304414

The Optimal Distribution of Practice for the Acquisition of L2 Morphology: A Conceptual Replication and Extension

ERIC Educational Resources Information Center

Suzuki, Yuichi

2017-01-01

This study examined optimal learning schedules for second language (L2) acquisition of a morphological structure. Sixty participants studied the simple and complex morphological rules of a novel miniature language system so as to use them for oral production. They engaged in four training sessions in either shorter spaced (3.3-day interval) or…

Chase: Control of Heterogeneous Autonomous Sensors for Situational Awareness

DTIC Science & Technology

2016-08-03

remained the discovery and analysis of new foundational methodology for information collection and fusion that exercises rigorous feedback control over...simultaneously achieve quantified information and physical objectives. New foundational methodology for information collection and fusion that exercises...11.2.1. In the general area of novel stochastic systems analysis it seems appropriate to mention the pioneering work on non -Bayesian distributed learning

The Differences across Distributed Leadership Practices by School Position According to the Comprehensive Assessment of Leadership for Learning (CALL)

ERIC Educational Resources Information Center

Blitz, Mark H.; Modeste, Marsha

2015-01-01

The Comprehensive Assessment of Leadership for Learning (CALL) is a multi-source assessment of distributed instructional leadership. As part of the validation of CALL, researchers examined differences between teacher and leader ratings in assessing distributed leadership practices. The authors utilized a t-test for equality of means for the…

Parallel Distributed Processing at 25: further explorations in the microstructure of cognition.

PubMed

Rogers, Timothy T; McClelland, James L

2014-08-01

This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called "deep networks"-systems that build on key ideas presented in the PDP volumes. Copyright © 2014 Cognitive Science Society, Inc.

1393 Ring Bus at JPL: Description and Status

NASA Technical Reports Server (NTRS)

Wysocky, Terry R.

2007-01-01

Completed Ring Bus IC V&V Phase - Ring Bus Test Plan Completed for SIM Project - Applicable to Other Projects Implemented a Avionics Bus Based upon the IEEE 1393 Standard - Excellent Starting Point for a General Purpose High-Speed Spacecraft Bus - Designed to Meet SIM Requirements for - Real-time deterministic, distributed systems. - Control system requirements - Fault detection and recovery Other JPL Projects Considering Implementation F'light Software Ring Bus Driver Module Began in 2006, Continues Participating in Standard Revision. Search for Earth-like planets orbiting nearby stars and measure the masses and orbits of the planets it finds. Survey 2000 nearby stars for planetary systems to learn whether our Solar System is unusual, or typical. Make a new catalog of star position 100 times more accurate than current measurements. Learn how our galaxy formed and will evolve by studying the dynamics of its stars. Critically test models of exactly how stars shine, including exotic objects like black holes, neutron stars and white dwarfs.

The Emergent Capabilities of Distributed Satellites and Methods for Selecting Distributed Satellite Science Missions

NASA Astrophysics Data System (ADS)

Corbin, B. A.; Seager, S.; Ross, A.; Hoffman, J.

2017-12-01

Distributed satellite systems (DSS) have emerged as an effective and cheap way to conduct space science, thanks to advances in the small satellite industry. However, relatively few space science missions have utilized multiple assets to achieve their primary scientific goals. Previous research on methods for evaluating mission concepts designs have shown that distributed systems are rarely competitive with monolithic systems, partially because it is difficult to quantify the added value of DSSs over monolithic systems. Comparatively little research has focused on how DSSs can be used to achieve new, fundamental space science goals that cannot be achieved with monolithic systems or how to choose a design from a larger possible tradespace of options. There are seven emergent capabilities of distributed satellites: shared sampling, simultaneous sampling, self-sampling, census sampling, stacked sampling, staged sampling, and sacrifice sampling. These capabilities are either fundamentally, analytically, or operationally unique in their application to distributed science missions, and they can be leveraged to achieve science goals that are either impossible or difficult and costly to achieve with monolithic systems. The Responsive Systems Comparison (RSC) method combines Multi-Attribute Tradespace Exploration with Epoch-Era Analysis to examine benefits, costs, and flexible options in complex systems over the mission lifecycle. Modifications to the RSC method as it exists in previously published literature were made in order to more accurately characterize how value is derived from space science missions. New metrics help rank designs by the value derived over their entire mission lifecycle and show more accurate cumulative value distributions. The RSC method was applied to four case study science missions that leveraged the emergent capabilities of distributed satellites to achieve their primary science goals. In all four case studies, RSC showed how scientific value was gained that would be impossible or unsatisfactory with monolithic systems and how changes in design and context variables affected the overall mission value. Each study serves as a blueprint for how to conduct a Pre-Phase A study using these methods to learn more about the tradespace of a particular mission.

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

ERIC Educational Resources Information Center

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

2016-01-01

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

Optimal Reward Functions in Distributed Reinforcement Learning

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Tumer, Kagan

2000-01-01

We consider the design of multi-agent systems so as to optimize an overall world utility function when (1) those systems lack centralized communication and control, and (2) each agents runs a distinct Reinforcement Learning (RL) algorithm. A crucial issue in such design problems is to initialize/update each agent's private utility function, so as to induce best possible world utility. Traditional 'team game' solutions to this problem sidestep this issue and simply assign to each agent the world utility as its private utility function. In previous work we used the 'Collective Intelligence' framework to derive a better choice of private utility functions, one that results in world utility performance up to orders of magnitude superior to that ensuing from use of the team game utility. In this paper we extend these results. We derive the general class of private utility functions that both are easy for the individual agents to learn and that, if learned well, result in high world utility. We demonstrate experimentally that using these new utility functions can result in significantly improved performance over that of our previously proposed utility, over and above that previous utility's superiority to the conventional team game utility.

Active learning of cortical connectivity from two-photon imaging data.

PubMed

Bertrán, Martín A; Martínez, Natalia L; Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this "active learning" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

How learning might strengthen existing visual object representations in human object-selective cortex.

PubMed

Brants, Marijke; Bulthé, Jessica; Daniels, Nicky; Wagemans, Johan; Op de Beeck, Hans P

2016-02-15

Visual object perception is an important function in primates which can be fine-tuned by experience, even in adults. Which factors determine the regions and the neurons that are modified by learning is still unclear. Recently, it was proposed that the exact cortical focus and distribution of learning effects might depend upon the pre-learning mapping of relevant functional properties and how this mapping determines the informativeness of neural units for the stimuli and the task to be learned. From this hypothesis we would expect that visual experience would strengthen the pre-learning distributed functional map of the relevant distinctive object properties. Here we present a first test of this prediction in twelve human subjects who were trained in object categorization and differentiation, preceded and followed by a functional magnetic resonance imaging session. Specifically, training increased the distributed multi-voxel pattern information for trained object distinctions in object-selective cortex, resulting in a generalization from pre-training multi-voxel activity patterns to after-training activity patterns. Simulations show that the increased selectivity combined with the inter-session generalization is consistent with a training-induced strengthening of a pre-existing selectivity map. No training-related neural changes were detected in other regions. In sum, training to categorize or individuate objects strengthened pre-existing representations in human object-selective cortex, providing a first indication that the neuroanatomical distribution of learning effects depends upon the pre-learning mapping of visual object properties. Copyright © 2015 Elsevier Inc. All rights reserved.

Imbalanced learning for pattern recognition: an empirical study

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Man, Hong; Desai, Sachi; Quoraishee, Shafik

2010-10-01

The imbalanced learning problem (learning from imbalanced data) presents a significant new challenge to the pattern recognition and machine learning society because in most instances real-world data is imbalanced. When considering military applications, the imbalanced learning problem becomes much more critical because such skewed distributions normally carry the most interesting and critical information. This critical information is necessary to support the decision-making process in battlefield scenarios, such as anomaly or intrusion detection. The fundamental issue with imbalanced learning is the ability of imbalanced data to compromise the performance of standard learning algorithms, which assume balanced class distributions or equal misclassification penalty costs. Therefore, when presented with complex imbalanced data sets these algorithms may not be able to properly represent the distributive characteristics of the data. In this paper we present an empirical study of several popular imbalanced learning algorithms on an army relevant data set. Specifically we will conduct various experiments with SMOTE (Synthetic Minority Over-Sampling Technique), ADASYN (Adaptive Synthetic Sampling), SMOTEBoost (Synthetic Minority Over-Sampling in Boosting), and AdaCost (Misclassification Cost-Sensitive Boosting method) schemes. Detailed experimental settings and simulation results are presented in this work, and a brief discussion of future research opportunities/challenges is also presented.

Unifying practice schedules in the timescales of motor learning and performance.

PubMed

Verhoeven, F Martijn; Newell, Karl M

2018-06-01

In this article, we elaborate from a multiple time scales model of motor learning to examine the independent and integrated effects of massed and distributed practice schedules within- and between-sessions on the persistent (learning) and transient (warm-up, fatigue) processes of performance change. The timescales framework reveals the influence of practice distribution on four learning-related processes: the persistent processes of learning and forgetting, and the transient processes of warm-up decrement and fatigue. The superposition of the different processes of practice leads to a unified set of effects for massed and distributed practice within- and between-sessions in learning motor tasks. This analysis of the interaction between the duration of the interval of practice trials or sessions and parameters of the introduced time scale model captures the unified influence of the between trial and session scheduling of practice on learning and performance. It provides a starting point for new theoretically based hypotheses, and the scheduling of practice that minimizes the negative effects of warm-up decrement, fatigue and forgetting while exploiting the positive effects of learning and retention. Copyright © 2018 Elsevier B.V. All rights reserved.

Semantic Coherence Facilitates Distributional Learning.

PubMed

Ouyang, Long; Boroditsky, Lera; Frank, Michael C

2017-04-01

Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of association with other words (e.g., they both tend to occur with words like "deliver," "truck," "package"). In contrast to these computational results, artificial language learning experiments suggest that distributional statistics alone do not facilitate learning of linguistic categories. However, experiments in this paradigm expose participants to entirely novel words, whereas real language learners encounter input that contains some known words that are semantically organized. In three experiments, we show that (a) the presence of familiar semantic reference points facilitates distributional learning and (b) this effect crucially depends both on the presence of known words and the adherence of these known words to some semantic organization. Copyright © 2016 Cognitive Science Society, Inc.

Distributed synaptic weights in a LIF neural network and learning rules

NASA Astrophysics Data System (ADS)

Perthame, Benoît; Salort, Delphine; Wainrib, Gilles

2017-09-01

Leaky integrate-and-fire (LIF) models are mean-field limits, with a large number of neurons, used to describe neural networks. We consider inhomogeneous networks structured by a connectivity parameter (strengths of the synaptic weights) with the effect of processing the input current with different intensities. We first study the properties of the network activity depending on the distribution of synaptic weights and in particular its discrimination capacity. Then, we consider simple learning rules and determine the synaptic weight distribution it generates. We outline the role of noise as a selection principle and the capacity to memorize a learned signal.

Derivatives of logarithmic stationary distributions for policy gradient reinforcement learning.

PubMed

Morimura, Tetsuro; Uchibe, Eiji; Yoshimoto, Junichiro; Peters, Jan; Doya, Kenji

2010-02-01

Most conventional policy gradient reinforcement learning (PGRL) algorithms neglect (or do not explicitly make use of) a term in the average reward gradient with respect to the policy parameter. That term involves the derivative of the stationary state distribution that corresponds to the sensitivity of its distribution to changes in the policy parameter. Although the bias introduced by this omission can be reduced by setting the forgetting rate gamma for the value functions close to 1, these algorithms do not permit gamma to be set exactly at gamma = 1. In this article, we propose a method for estimating the log stationary state distribution derivative (LSD) as a useful form of the derivative of the stationary state distribution through backward Markov chain formulation and a temporal difference learning framework. A new policy gradient (PG) framework with an LSD is also proposed, in which the average reward gradient can be estimated by setting gamma = 0, so it becomes unnecessary to learn the value functions. We also test the performance of the proposed algorithms using simple benchmark tasks and show that these can improve the performances of existing PG methods.

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Gray, A.

2014-04-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors. This is likely of particular interest to the radio astronomy community given, for example, that survey projects contain groups dedicated to this topic. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Astronomy Data Centre, Canadian

2014-01-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors, and the local outlier factor. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

Using transfer learning to detect galaxy mergers

NASA Astrophysics Data System (ADS)

Ackermann, Sandro; Schawinksi, Kevin; Zhang, Ce; Weigel, Anna K.; Turp, M. Dennis

2018-05-01

We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers. Moreover, we investigate the use of transfer learning in conjunction with CNNs, by retraining networks first trained on pictures of everyday objects. We test the hypothesis that transfer learning is useful for improving classification performance for small training sets. This would make transfer learning useful for finding rare objects in astronomical imaging datasets. We find that these deep learning methods perform significantly better than current state-of-the-art merger detection methods based on nonparametric systems like CAS and GM20. Our method is end-to-end and robust to image noise and distortions; it can be applied directly without image preprocessing. We also find that transfer learning can act as a regulariser in some cases, leading to better overall classification accuracy (p = 0.02). Transfer learning on our full training set leads to a lowered error rate from 0.0381 down to 0.0321, a relative improvement of 15%. Finally, we perform a basic sanity-check by creating a merger sample with our method, and comparing with an already existing, manually created merger catalogue in terms of colour-mass distribution and stellar mass function.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Construction of a Digital Learning Environment Based on Cloud Computing

ERIC Educational Resources Information Center

Ding, Jihong; Xiong, Caiping; Liu, Huazhong

2015-01-01

Constructing the digital learning environment for ubiquitous learning and asynchronous distributed learning has opened up immense amounts of concrete research. However, current digital learning environments do not fully fulfill the expectations on supporting interactive group learning, shared understanding and social construction of knowledge.…

BCR CDR3 length distributions differ between blood and spleen and between old and young patients, and TCR distributions can be used to detect myelodysplastic syndrome

NASA Astrophysics Data System (ADS)

Pickman, Yishai; Dunn-Walters, Deborah; Mehr, Ramit

2013-10-01

Complementarity-determining region 3 (CDR3) is the most hyper-variable region in B cell receptor (BCR) and T cell receptor (TCR) genes, and the most critical structure in antigen recognition and thereby in determining the fates of developing and responding lymphocytes. There are millions of different TCR Vβ chain or BCR heavy chain CDR3 sequences in human blood. Even now, when high-throughput sequencing becomes widely used, CDR3 length distributions (also called spectratypes) are still a much quicker and cheaper method of assessing repertoire diversity. However, distribution complexity and the large amount of information per sample (e.g. 32 distributions of the TCRα chain, and 24 of TCRβ) calls for the use of machine learning tools for full exploration. We have examined the ability of supervised machine learning, which uses computational models to find hidden patterns in predefined biological groups, to analyze CDR3 length distributions from various sources, and distinguish between experimental groups. We found that (a) splenic BCR CDR3 length distributions are characterized by low standard deviations and few local maxima, compared to peripheral blood distributions; (b) healthy elderly people's BCR CDR3 length distributions can be distinguished from those of the young; and (c) a machine learning model based on TCR CDR3 distribution features can detect myelodysplastic syndrome with approximately 93% accuracy. Overall, we demonstrate that using supervised machine learning methods can contribute to our understanding of lymphocyte repertoire diversity.

A New Monte Carlo Filtering Method for the Diagnosis of Mission-Critical Failures

NASA Technical Reports Server (NTRS)

Gay, Gregory; Menzies, Tim; Davies, Misty; Gundy-Burlet, Karen

2009-01-01

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the settings most likely to cause a mission-critical failure. This research benchmarks two treatment learning methods against standard optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. It is shown that these treatment learners are both faster than traditional methods and show demonstrably better results.

Uncertainty Quantification of Medium-Term Heat Storage From Short-Term Geophysical Experiments Using Bayesian Evidential Learning

NASA Astrophysics Data System (ADS)

Hermans, Thomas; Nguyen, Frédéric; Klepikova, Maria; Dassargues, Alain; Caers, Jef

2018-04-01

In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy efficiency is often lower than expected from simulations due to spatial heterogeneity of hydraulic properties or non-favorable hydrogeological conditions. A proper design of ATES systems should therefore consider the uncertainty of the prediction related to those parameters. We use a novel framework called Bayesian Evidential Learning (BEL) to estimate the heat storage capacity of an alluvial aquifer using a heat tracing experiment. BEL is based on two main stages: pre- and postfield data acquisition. Before data acquisition, Monte Carlo simulations and global sensitivity analysis are used to assess the information content of the data to reduce the uncertainty of the prediction. After data acquisition, prior falsification and machine learning based on the same Monte Carlo are used to directly assess uncertainty on key prediction variables from observations. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data, without any explicit full model inversion. We demonstrate the methodology in field conditions and validate the framework using independent measurements.

Distributing vs. Blocking Learning Questions in a Web-Based Learning Environment

ERIC Educational Resources Information Center

Kapp, Felix; Proske, Antje; Narciss, Susanne; Körndle, Hermann

2015-01-01

Effective studying in web-based learning environments (web-LEs) requires cognitive engagement and demands learners to regulate their learning activities. One way to support learners in web-LEs is to provide interactive learning questions within the learning environment. Even though research on learning questions has a long tradition, there are…

Leading toward value: the role of strategic human resource management in health system adaptability.

PubMed

Garman, Andrew N; Polavarapu, Nandakishor; Grady, Jane C; Canar, W Jeffrey

2013-01-01

Personnel costs typically account for 60% or more of total operating expenses in health systems, and as such become a necessary focus in most if not all substantive health reform adaptations. This study sought to assess whether strategic alignment of the human resource (HR) and learning functions was associated with greater adaptive capacity in U.S. health systems. Data were gathered using a survey that was distributed electronically to chief human resource officers from two U.S.-based associations. The survey included questions about organizational structure, strategic human resource management, strategic learning, and organizational response to health reform. Significant correlations were found between strategic alignment of HR and HR's involvement in responses related to cost control (r = 0.46, p < 0.01); quality improvement (r = 0.45, p < 0.01), and patient access (r = 0.39, p < 0.01). However, no significant relationships were found between strategic alignment of organizational learning and HR involvement with these responses. Results suggest that HR structure may affect an organization's capacity for adaptive response. Top-management teams in health systems should consider positioning HR as part of the core leadership team, with a reporting relationship that allows HR to maximally participate in formulating and implementing organizational adaptation.

New developments in digital pathology: from telepathology to virtual pathology laboratory.

PubMed

Kayser, Klaus; Kayser, Gian; Radziszowski, Dominik; Oehmann, Alexander

2004-01-01

To analyse the present status and future development of computerized diagnostic pathology in terms of work-flow integrative telepathology and virtual laboratory. Telepathology has left its childhood. The technical development of telepathology is mature, in contrast to that of virtual pathology. Two kinds of virtual pathology laboratories are emerging: a) those with distributed pathologists and distributed (>=1) laboratories associated to individual biopsy stations/surgical theatres, and b) distributed pathologists working in a centralized laboratory. Both are under technical development. Telepathology can be used for e-learning and e-training in pathology, as exemplarily demonstrated on Digital Lung Pathology Pathology (www.pathology-online.org). A virtual pathology institution (mode a) accepts a complete case with the patient's history, clinical findings, and (pre-selected) images for first diagnosis. The diagnostic responsibility is that of a conventional institution. The internet serves as platform for information transfer, and an open server such as the iPATH (http://telepath.patho.unibas.ch) for coordination and performance of the diagnostic procedure. The size of images has to be limited, and usual different magnifications have to be used. A group of pathologists is "on duty", or selects one member for a predefined duty period. The diagnostic statement of the pathologist(s) on duty is retransmitted to the sender with full responsibility. First experiences of a virtual pathology institution group working with the iPATH server (Dr. L. Banach, Dr. G. Haroske, Dr. I. Hurwitz, Dr. K. Kayser, Dr. K.D. Kunze, Dr. M. Oberholzer,) working with a small hospital of the Salomon islands are promising. A centralized virtual pathology institution (mode b) depends upon the digitalisation of a complete slide, and the transfer of large sized images to different pathologists working in one institution. The technical performance of complete slide digitalisation is still under development and does not completely fulfil the requirements of a conventional pathology institution at present. VIRTUAL PATHOLOGY AND E-LEARNING: At present, e-learning systems are "stand-alone" solutions distributed on CD or via internet. A characteristic example is the Digital Lung Pathology CD (www.pathology-online.org), which includes about 60 different rare and common lung diseases and internet access to scientific library systems (PubMed), distant measurement servers (EuroQuant), or electronic journals (Elec J Pathol Histol). A new and complete data base based upon this CD will combine e-learning and e-teaching with the actual workflow in a virtual pathology institution (mode a). The technological problems are solved and do not depend upon technical constraints such as slide scanning systems. Telepathology serves as promotor for a new landscape in diagnostic pathology, the so-called virtual pathology institution. Industrial and scientific efforts will probably allow an implementation of this technique within the next two years.

Learning stochastic reward distributions in a speeded pointing task.

PubMed

Seydell, Anna; McCann, Brian C; Trommershäuser, Julia; Knill, David C

2008-04-23

Recent studies have shown that humans effectively take into account task variance caused by intrinsic motor noise when planning fast hand movements. However, previous evidence suggests that humans have greater difficulty accounting for arbitrary forms of stochasticity in their environment, both in economic decision making and sensorimotor tasks. We hypothesized that humans can learn to optimize movement strategies when environmental randomness can be experienced and thus implicitly learned over several trials, especially if it mimics the kinds of randomness for which subjects might have generative models. We tested the hypothesis using a task in which subjects had to rapidly point at a target region partly covered by three stochastic penalty regions introduced as "defenders." At movement completion, each defender jumped to a new position drawn randomly from fixed probability distributions. Subjects earned points when they hit the target, unblocked by a defender, and lost points otherwise. Results indicate that after approximately 600 trials, subjects approached optimal behavior. We further tested whether subjects simply learned a set of stimulus-contingent motor plans or the statistics of defenders' movements by training subjects with one penalty distribution and then testing them on a new penalty distribution. Subjects immediately changed their strategy to achieve the same average reward as subjects who had trained with the second penalty distribution. These results indicate that subjects learned the parameters of the defenders' jump distributions and used this knowledge to optimally plan their hand movements under conditions involving stochastic rewards and penalties.

An Attack-Resilient Middleware Architecture for Grid Integration of Distributed Energy Resources

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

Wu, Yifu; Mendis, Gihan J.; He, Youbiao

In recent years, the increasing penetration of Distributed Energy Resources (DERs) has made an impact on the operation of the electric power systems. In the grid integration of DERs, data acquisition systems and communications infrastructure are crucial technologies to maintain system economic efficiency and reliability. Since most of these generators are relatively small, dedicated communications investments for every generator are capital cost prohibitive. Combining real-time attack-resilient communications middleware with Internet of Things (IoTs) technologies allows for the use of existing infrastructure. In our paper, we propose an intelligent communication middleware that utilizes the Quality of Experience (QoE) metrics to complementmore » the conventional Quality of Service (QoS) evaluation. Furthermore, our middleware employs deep learning techniques to detect and defend against congestion attacks. The simulation results illustrate the efficiency of our proposed communications middleware architecture.« less

Innovation in Open & Distance Learning: Successful Development of Online and Web-Based Learning.

ERIC Educational Resources Information Center

Lockwood, Fred, Ed.; Gooley, Anne, Ed.

This book contains 19 papers examining innovation in open and distance learning through development of online and World Wide Web-based learning. The following papers are included: "Innovation in Distributed Learning: Creating the Environment" (Fred Lockwood); "Innovation in Open and Distance Learning: Some Lessons from Experience…

New Definitions for New Higher Education Institutions

ERIC Educational Resources Information Center

Meyer, Katrina A.

2009-01-01

New terms were exploding early in the development of distance learning and virtual universities. Distance learning, online learning, e-learning, and distributed learning were applied to the various new forms of learning using online or Web-based materials and processes. However, largely thanks to the immediate popularity of the Western Governors'…

Visualizing Big Data Outliers through Distributed Aggregation.

PubMed

Wilkinson, Leland

2017-08-29

Visualizing outliers in massive datasets requires statistical pre-processing in order to reduce the scale of the problem to a size amenable to rendering systems like D3, Plotly or analytic systems like R or SAS. This paper presents a new algorithm, called hdoutliers, for detecting multidimensional outliers. It is unique for a) dealing with a mixture of categorical and continuous variables, b) dealing with big-p (many columns of data), c) dealing with big-n (many rows of data), d) dealing with outliers that mask other outliers, and e) dealing consistently with unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, hdoutliers is based on a distributional model that allows outliers to be tagged with a probability. This critical feature reduces the likelihood of false discoveries.

Self-directed learning readiness of Asian students: students perspective on a hybrid problem based learning curriculum.

PubMed

Leatemia, Lukas D; Susilo, Astrid P; van Berkel, Henk

2016-12-03

To identify the student's readiness to perform self-directed learning and the underlying factors influencing it on the hybrid problem based learning curriculum. A combination of quantitative and qualitative studies was conducted in five medical schools in Indonesia. In the quantitative study, the Self Directed Learning Readiness Scale was distributed to all students in all batches, who had experience with the hybrid problem based curriculum. They were categorized into low- and high -level based on the score of the questionnaire. Three focus group discussions (low-, high-, and mixed level) were conducted in the qualitative study with six to twelve students chosen randomly from each group to find the factors influencing their self-directed learning readiness. Two researchers analysed the qualitative data as a measure of triangulation. The quantitative study showed only half of the students had a high-level of self-directed learning readiness, and a similar trend also occurred in each batch. The proportion of students with a high level of self-directed learning readiness was lower in the senior students compared to more junior students. The qualitative study showed that problem based learning processes, assessments, learning environment, students' life styles, students' perceptions of the topics, and mood, were factors influencing their self-directed learning. A hybrid problem based curriculum may not fully affect the students' self-directed learning. The curriculum system, teacher's experiences, student's background and cultural factors might contribute to the difficulties for the student's in conducting self-directed learning.

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

ERIC Educational Resources Information Center

Oblinger, Diana G.; Maruyama, Mark K.

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

Transforming Distance Education Curricula through Distributive Leadership

ERIC Educational Resources Information Center

Keppell, Mike; O'Dwyer, Carolyn; Lyon, Betsy; Childs, Merilyn

2011-01-01

This paper examines a core leadership strategy for transforming learning and teaching in distance education through flexible and blended learning. It focuses on a project centred on distributive leadership that involves collaboration, shared purpose, responsibility and recognition of leadership irrespective of role or position within an…

Transforming Distance Education Curricula through Distributive Leadership

ERIC Educational Resources Information Center

Keppell, Mike; O'Dwyer, Carolyn; Lyon, Betsy; Childs, Merilyn

2010-01-01

This paper examines a core leadership strategy for transforming learning and teaching in distance education through flexible and blended learning. It focuses on a project centred on distributive leadership that involves collaboration, shared purpose, responsibility and recognition of leadership irrespective of role or position within an…

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning.

PubMed

Zhao, Chao; Jiang, Jingchi; Guan, Yi; Guo, Xitong; He, Bin

2018-05-01

Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient. We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance. As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level. Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions. Copyright © 2018 Elsevier B.V. All rights reserved.

Lessons Learned from the Puerto Rico Battery Energy Storage System

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

BOYES, JOHN D.; DE ANA, MINDI FARBER; TORRES, WENCESLANO

1999-09-01

The Puerto Rico Electric Power Authority (PREPA) installed a distributed battery energy storage system in 1994 at a substation near San Juan, Puerto Rico. It was patterned after two other large energy storage systems operated by electric utilities in California and Germany. The U.S. Department of Energy (DOE) Energy Storage Systems Program at Sandia National Laboratories has followed the progress of all stages of the project since its inception. It directly supported the critical battery room cooling system design by conducting laboratory thermal testing of a scale model of the battery under simulated operating conditions. The Puerto Rico facility ismore » at present the largest operating battery storage system in the world and is successfully providing frequency control, voltage regulation, and spinning reserve to the Caribbean island. The system further proved its usefulness to the PREPA network in the fall of 1998 in the aftermath of Hurricane Georges. The owner-operator, PREPA, and the architect/engineer, vendors, and contractors learned many valuable lessons during all phases of project development and operation. In documenting these lessons, this report will help PREPA and other utilities in planning to build large energy storage systems.« less

Lessons learned from hybrid wind/PV village power system installations in Mexico

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

Bergey, M.

1995-09-01

In the last three years eight decentralized village power systems utilizing small wind turbines as the primary energy source have been installed in rural Mexico. Hybrid wind/PV systems have been installed in five States and by three vendors. Seven out of the eight systems, which range i size from 9.3--71.2kW in combined wind and PV capacity, utilize one or more 10 kW wind turbines. All of these installations have battery banks and use static inverters to provide AC power for distribution to homes, businesses, and community facilities. On all but one of the systems a diesel generator is used tomore » provide back-up power. This paper attempts to summarize the range of costs and economics, performance, and operational experiences for all eight installations. Several of the systems are monitored for performance, including one that is extensively monitored under a cooperative program between the Instituto de Investigaciones Electricas and Sandia National Laboratory. Lessons learned from these systems provide insights that may allow future village power systems of this architecture to be installed at lower costs, to be operated more effectively and efficiently, and to be better able to satisfy customer requirements.« less

Photovoltaics as a terrestrial energy source. Volume 1: An introduction

NASA Technical Reports Server (NTRS)

Smith, J. L.

1980-01-01

Photovoltaic (PV) systems were examined their potential for terrestrial application and future development. Photovoltaic technology, existing and potential photovoltaic applications, and the National Photovoltaics Program are reviewed. The competitive environment for this electrical source, affected by the presence or absence of utility supplied power is evaluated in term of systems prices. The roles of technological breakthroughs, directed research and technology development, learning curves, and commercial demonstrations in the National Program are discussed. The potential for photovoltaics to displace oil consumption is examined, as are the potential benefits of employing PV in either central-station or non-utility owned, small, distributed systems.

Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

PubMed

Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

2017-10-01

Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that distributed learning is the future of sharing data in health care. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid and Building-to-Grid Interface

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

Stewart, Emma M.; Hendrix, Val; Chertkov, Michael

This white paper introduces the application of advanced data analytics to the modernized grid. In particular, we consider the field of machine learning and where it is both useful, and not useful, for the particular field of the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper wemore » consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid and the building-to-grid interface. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. Machine learning is a subfield of computer science that studies and constructs algorithms that can learn from data and make predictions and improve forecasts. Incorporation of machine learning in grid monitoring and analysis tools may have the potential to solve data and operational challenges that result from increasing penetration of distributed and behind-the-meter energy resources. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors – such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals – such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop.« less

Distributed operations as applied in a large multi-instrument space mission: lessons learned from the Cassini-Huygens Program

NASA Technical Reports Server (NTRS)

Cheng, L. Y.; Larsen, B.

2004-01-01

Launched in 1997, the Cassini-Huygens Mission sent the largest interplanetary spacecraft ever built in the service of science. Carrying a suite of 12 scientific instruments and an atmospheric entry probe, this complex spacecraft to explore the Saturn system may not have gotten off the ground without undergoing significant design changes and cost reductions.

Judgement of Countability and Plural Marking in English by Native and Non-Native English Speakers

ERIC Educational Resources Information Center

Tsang, Art

2017-01-01

Learning whether English nouns are countable or not is a source of great difficulty for many ESL/EFL learners. In the present study, a grammaticality judgement task comprised of a range of nouns representative of the different facets of the countability system in English was distributed to 82 native speakers of English (NSs) and 98 non-native…

Learning from Experience? Evidence on the Impact and Distribution of Teacher Experience and the Implications for Teacher Policy

ERIC Educational Resources Information Center

Rice, Jennifer King

2013-01-01

Teacher experience has long been a central pillar of teacher workforce policies in U.S. school systems. The underlying assumption behind many of these policies is that experience promotes effectiveness, but is this really the case? What does existing evidence tell us about how, why, and for whom teacher experience matters? This policy brief…

SimCenter Hawaii: Virtual Reality Applications for Health Care Education and Training

DTIC Science & Technology

2008-12-01

systems can provide realistic, procedural skills training,(12) the scenarios developed for triage would primarily develop and assess cognitive skill...Education and Training Conclusions Simulator-based training has been shown to improve outcomes for both cognitive as well as motor-skills...training.(7) Cognitive modules can be distributed through advanced learning networks.(4) This has significant implications, because enterprise wide

A Message Exchange Protocol in Command and Control Systems Integration, using the JC3IEDM

DTIC Science & Technology

2014-06-01

19TH International Command and Control Research and Technology Symposium C2 Agility: Lessons Learned from Research and Operations. A Message...distribution unlimited 13. SUPPLEMENTARY NOTES Presented at the 18th International Command & Control Research & Technology Symposium (ICCRTS) held 16...presents approaches of integration, compares their technologies , points out their advantages, proposes requirements, and provides the design of a protocol

Safety leadership and systems thinking: application and evaluation of a Risk Management Framework in the mining industry.

PubMed

Donovan, Sarah-Louise; Salmon, Paul M; Lenné, Michael G; Horberry, Tim

2017-10-01

Safety leadership is an important factor in supporting safety in high-risk industries. This article contends that applying systems-thinking methods to examine safety leadership can support improved learning from incidents. A case study analysis was undertaken of a large-scale mining landslide incident in which no injuries or fatalities were incurred. A multi-method approach was adopted, in which the Critical Decision Method, Rasmussen's Risk Management Framework and Accimap method were applied to examine the safety leadership decisions and actions which enabled the safe outcome. The approach enabled Rasmussen's predictions regarding safety and performance to be examined in the safety leadership context, with findings demonstrating the distribution of safety leadership across leader and system levels, and the presence of vertical integration as key to supporting the successful safety outcome. In doing so, the findings also demonstrate the usefulness of applying systems-thinking methods to examine and learn from incidents in terms of what 'went right'. The implications, including future research directions, are discussed. Practitioner Summary: This paper presents a case study analysis, in which systems-thinking methods are applied to the examination of safety leadership decisions and actions during a large-scale mining landslide incident. The findings establish safety leadership as a systems phenomenon, and furthermore, demonstrate the usefulness of applying systems-thinking methods to learn from incidents in terms of what 'went right'. Implications, including future research directions, are discussed.

Habitat Demonstration Unit (HDU) Pressurized Excursion Module (PEM) Systems Integration Strategy

NASA Technical Reports Server (NTRS)

Gill, Tracy; Merbitz, Jerad; Kennedy, Kriss; Tri, Terry; Toups, Larry; Howe, A. Scott

2011-01-01

The Habitat Demonstration Unit (HDU) project team constructed an analog prototype lunar surface laboratory called the Pressurized Excursion Module (PEM). The prototype unit subsystems were integrated in a short amount of time, utilizing a rapid prototyping approach that brought together over 20 habitation-related technologies from a variety of NASA centers. This paper describes the system integration strategies and lessons learned, that allowed the PEM to be brought from paper design to working field prototype using a multi-center team. The system integration process was based on a rapid prototyping approach. Tailored design review and test and integration processes facilitated that approach. The use of collaboration tools including electronic tools as well as documentation enabled a geographically distributed team take a paper concept to an operational prototype in approximately one year. One of the major tools used in the integration strategy was a coordinated effort to accurately model all the subsystems using computer aided design (CAD), so conflicts were identified before physical components came together. A deliberate effort was made following the deployment of the HDU PEM for field operations to collect lessons learned to facilitate process improvement and inform the design of future flight or analog versions of habitat systems. Significant items within those lessons learned were limitations with the CAD integration approach and the impact of shell design on flexibility of placing systems within the HDU shell.

Learning to read aloud: A neural network approach using sparse distributed memory

NASA Technical Reports Server (NTRS)

Joglekar, Umesh Dwarkanath

1989-01-01

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

A Large-scale Distributed Indexed Learning Framework for Data that Cannot Fit into Memory

DTIC Science & Technology

2015-03-27

learn a classifier. Integrating three learning techniques (online, semi-supervised and active learning ) together with a selective sampling with minimum communication between the server and the clients solved this problem.

Innovative Socio-Technical Environments in Support of Distributed Intelligence and Lifelong Learning

ERIC Educational Resources Information Center

Fischer, G; Konomi, S.

2007-01-01

Individual, unaided human abilities are constrained. Media have helped us to transcend boundaries in thinking, working, learning and collaborating by supporting "distributed intelligence". Wireless and mobile technologies provide new opportunities for creating novel socio-technical environments and thereby empowering humans, but not without…

Trans-species learning of cellular signaling systems with bimodal deep belief networks.

PubMed

Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

2015-09-15

Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These 'deep learning' models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. xinghua@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

How much to trust the senses: Likelihood learning

PubMed Central

Sato, Yoshiyuki; Kording, Konrad P.

2014-01-01

Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of prior-likelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood. PMID:25398975

Emergent Learning and Interactive Media Artworks: Parameters of Interaction for Novice Groups

ERIC Educational Resources Information Center

Kawka, Marta; Larkin, Kevin; Danaher, P. A.

2011-01-01

Emergent learning describes learning that occurs when participants interact and distribute knowledge, where learning is self-directed, and where the learning destination of the participants is largely unpredictable (Williams, Karousou, & Mackness, 2011). These notions of learning arise from the topologies of social networks and can be applied to…

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning.

PubMed

Hsu, Anne; Griffiths, Thomas L

2016-01-01

A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning.

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning

PubMed Central

2016-01-01

A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning. PMID:27310576

CMS distributed data analysis with CRAB3

NASA Astrophysics Data System (ADS)

Mascheroni, M.; Balcas, J.; Belforte, S.; Bockelman, B. P.; Hernandez, J. M.; Ciangottini, D.; Konstantinov, P. B.; Silva, J. M. D.; Ali, M. A. B. M.; Melo, A. M.; Riahi, H.; Tanasijczuk, A. J.; Yusli, M. N. B.; Wolf, M.; Woodard, A. E.; Vaandering, E.

2015-12-01

The CMS Remote Analysis Builder (CRAB) is a distributed workflow management tool which facilitates analysis tasks by isolating users from the technical details of the Grid infrastructure. Throughout LHC Run 1, CRAB has been successfully employed by an average of 350 distinct users each week executing about 200,000 jobs per day. CRAB has been significantly upgraded in order to face the new challenges posed by LHC Run 2. Components of the new system include 1) a lightweight client, 2) a central primary server which communicates with the clients through a REST interface, 3) secondary servers which manage user analysis tasks and submit jobs to the CMS resource provisioning system, and 4) a central service to asynchronously move user data from temporary storage in the execution site to the desired storage location. The new system improves the robustness, scalability and sustainability of the service. Here we provide an overview of the new system, operation, and user support, report on its current status, and identify lessons learned from the commissioning phase and production roll-out.

Learning comunication strategies for distributed artificial intelligence

NASA Astrophysics Data System (ADS)

Kinney, Michael; Tsatsoulis, Costas

1992-08-01

We present a methodology that allows collections of intelligent system to automatically learn communication strategies, so that they can exchange information and coordinate their problem solving activity. In our methodology communication between agents is determined by the agents themselves, which consider the progress of their individual problem solving activities compared to the communication needs of their surrounding agents. Through learning, communication lines between agents might be established or disconnected, communication frequencies modified, and the system can also react to dynamic changes in the environment that might force agents to cease to exist or to be added. We have established dynamic, quantitative measures of the usefulness of a fact, the cost of a fact, the work load of an agent, and the selfishness of an agent (a measure indicating an agent's preference between transmitting information versus performing individual problem solving), and use these values to adapt the communication between intelligent agents. In this paper we present the theoretical foundations of our work together with experimental results and performance statistics of networks of agents involved in cooperative problem solving activities.

A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning.

PubMed

Arguello Casteleiro, Mercedes; Maseda Fernandez, Diego; Demetriou, George; Read, Warren; Fernandez Prieto, Maria Jesus; Des Diz, Julio; Nenadic, Goran; Keane, John; Stevens, Robert

2017-01-01

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning. The evaluation conducted concentrates on sepsis, a major life-threatening condition, and shows that Deep Learning models outperform LSA and LDA with much higher precision.

Integrated material state awareness system with self-learning symbiotic diagnostic algorithms and models

NASA Astrophysics Data System (ADS)

Banerjee, Sourav; Liu, Lie; Liu, S. T.; Yuan, Fuh-Gwo; Beard, Shawn

2011-04-01

Materials State Awareness (MSA) goes beyond traditional NDE and SHM in its challenge to characterize the current state of material damage before the onset of macro-damage such as cracks. A highly reliable, minimally invasive system for MSA of Aerospace Structures, Naval structures as well as next generation space systems is critically needed. Development of such a system will require a reliable SHM system that can detect the onset of damage well before the flaw grows to a critical size. Therefore, it is important to develop an integrated SHM system that not only detects macroscale damages in the structures but also provides an early indication of flaw precursors and microdamages. The early warning for flaw precursors and their evolution provided by an SHM system can then be used to define remedial strategies before the structural damage leads to failure, and significantly improve the safety and reliability of the structures. Thus, in this article a preliminary concept of developing the Hybrid Distributed Sensor Network Integrated with Self-learning Symbiotic Diagnostic Algorithms and Models to accurately and reliably detect the precursors to damages that occur to the structure are discussed. Experiments conducted in a laboratory environment shows potential of the proposed technique.

Nicephor[e]: a web-based solution for teaching forensic and scientific photography.

PubMed

Voisard, R; Champod, C; Furrer, J; Curchod, J; Vautier, A; Massonnet, G; Buzzini, P

2007-04-11

Nicephor[e] is a project funded by "Swiss Virtual Campus" and aims at creating a distant or mixed web-based learning system in forensic and scientific photography and microscopy. The practical goal is to organize series of on-line modular courses corresponding to the educational requirements of undergraduate academic programs. Additionally, this program could be used in the context of continuing educational programs. The architecture of the project is designed to guarantee a high level of knowledge in forensic and scientific photographic techniques, and to have an easy content production and the ability to create a number of different courses sharing the same content. The e-learning system Nicephor[e] consists of three different parts. The first one is a repository of learning objects that gathers all theoretical subject matter of the project such as texts, animations, images, and films. This repository is a web content management system (Typo3) that permits creating, publishing, and administrating dynamic content via a web browser as well as storing it into a database. The flexibility of the system's architecture allows for an easy updating of the content to follow the development of photographic technology. The instructor of a course can decide which modular contents need to be included in the course, and in which order they will be accessed by students. All the modular courses are developed in a learning management system (WebCT or Moodle) that can deal with complex learning scenarios, content distribution, students, tests, and interaction with instructor. Each course has its own learning scenario based on the goals of the course and the student's profile. The content of each course is taken from the content management system. It is then structured in the learning management system according to the pedagogical goals defined by the instructor. The modular courses are created in a highly interactive setting and offer autoevaluating tests to the students. The last part of the system is a digital assets management system (Extensis Portfolio). The practical portion of each course is to produce images of different marks or objects. The collection of all this material produced, indexed by the students and corrected by the instructor is essential to the development of a knowledge base of photographic techniques applied to a specific forensic subject. It represents also an extensible collection of different marks from known sources obtained under various conditions. It allows to reuse these images for creating image-based case files.

A Developmental Approach to Machine Learning?

PubMed Central

Smith, Linda B.; Slone, Lauren K.

2017-01-01

Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems. Rather than equal experiences with all kinds of things, toddlers experience extremely skewed distributions with many repeated occurrences of a very few things. And though highly variable when considered as a whole, individual views of things are experienced in a specific order – with slow, smooth visual changes moment-to-moment, and developmentally ordered transitions in scene content. We propose that the skewed, ordered, biased visual experiences of infants and toddlers are the training data that allow human learners to develop a way to recognize everything, both the pervasively present entities and the rarely encountered ones. The joint consideration of real-world statistics for learning by researchers of human and machine learning seems likely to bring advances in both disciplines. PMID:29259573

Statistical learning and the challenge of syntax: Beyond finite state automata

NASA Astrophysics Data System (ADS)

Elman, Jeff

2003-10-01

Over the past decade, it has been clear that even very young infants are sensitive to the statistical structure of language input presented to them, and use the distributional regularities to induce simple grammars. But can such statistically-driven learning also explain the acquisition of more complex grammar, particularly when the grammar includes recursion? Recent claims (e.g., Hauser, Chomsky, and Fitch, 2002) have suggested that the answer is no, and that at least recursion must be an innate capacity of the human language acquisition device. In this talk evidence will be presented that indicates that, in fact, statistically-driven learning (embodied in recurrent neural networks) can indeed enable the learning of complex grammatical patterns, including those that involve recursion. When the results are generalized to idealized machines, it is found that the networks are at least equivalent to Push Down Automata. Perhaps more interestingly, with limited and finite resources (such as are presumed to exist in the human brain) these systems demonstrate patterns of performance that resemble those in humans.

Validity and Reliability Testing of an e-learning Questionnaire for Chemistry Instruction

NASA Astrophysics Data System (ADS)

Guspatni, G.; Kurniawati, Y.

2018-04-01

The aim of this paper is to examine validity and reliability of a questionnaire used to evaluate e-learning implementation in chemistry instruction. 48 questionnaires were filled in by students who had studied chemistry through e-learning system. The questionnaire consisted of 20 indicators evaluating students’ perception on using e-learning. Parametric testing was done as data were assumed to follow normal distribution. Item validity of the questionnaire was examined through item-total correlation using Pearson’s formula while its reliability was assessed with Cronbach’s alpha formula. Moreover, convergent validity was assessed to see whether indicators building a factor had theoretically the same underlying construct. The result of validity testing revealed 19 valid indicators while the result of reliability testing revealed Cronbach’s alpha value of .886. The result of factor analysis showed that questionnaire consisted of five factors, and each of them had indicators building the same construct. This article shows the importance of factor analysis to get a construct valid questionnaire before it is used as research instrument.

Linking Infants' Distributional Learning Abilities to Natural Language Acquisition

ERIC Educational Resources Information Center

van Heugten, Marieke; Johnson, Elizabeth K.

2010-01-01

This study examines the link between distributional patterns in the input and infants' acquisition of non-adjacent dependencies. In two Headturn Preference experiments, Dutch-learning 24-month-olds (but not 17-month-olds) were found to track the remote dependency between the definite article "het" and the diminutive suffix…

Distributed Revisiting: An Analytic for Retention of Coherent Science Learning

ERIC Educational Resources Information Center

Svihla, Vanessa; Wester, Michael J.; Linn, Marcia C.

2015-01-01

Designing learning experiences that support the development of coherent understanding of complex scientific phenomena is challenging. We sought to identify analytics that can also guide such designs to support retention of coherent understanding. Based on prior research that distributing study of material over time supports retention, we explored…

Implications of the Advanced Distributed Learning Initiative for Education. Urban Diversity Series.

ERIC Educational Resources Information Center

Fletcher, J. D.; Tobias, Sigmund

This monograph in the Urban Diversity Series describes the The Advanced Distributed Learning (ADL)initiative, relates it to research dealing with instruction generally and computer-mediated instruction specifically, and discusses its implications for education. ADL was undertaken to make instructional material universally accessible primarily, but…

Professional Learning for Distributed Leadership: Primary Headteachers' Perspectives

ERIC Educational Resources Information Center

Torrance, Deirdre

2015-01-01

This article draws from a small-scale study of headteachers motivated to positively impact on the quality of pupil experience by involving all staff in a distributed perspective on leadership. Each headteacher perceived leadership as involving learned processes requiring support and experience, expending considerable effort in providing a fertile…

Distance-Based and Distributed Learning: A Decision Tool for Education Leaders.

ERIC Educational Resources Information Center

McGraw, Tammy M.; Ross, John D.

This decision tool presents a progression of data collection and decision-making strategies that can increase the effectiveness of distance-based or distributed learning instruction. A narrative and flow chart cover the following steps: (1) basic assumptions, including purpose of instruction, market scan, and financial resources; (2) needs…

Distributing Leadership to Establish Developing and Learning School Organisations in the Swedish Context

ERIC Educational Resources Information Center

Liljenberg, Mette

2015-01-01

Leadership is considered to be significant for creating a developing and learning school organisation. In Sweden, distributed leadership and teacher teams are an "institutionalised practice"; despite this, sustainable school improvement is difficult to achieve. This article presents findings from a case study of three schools that…

Distance Education and Distributed Learning. Current Perspectives on Applied Information Technologies.

ERIC Educational Resources Information Center

Vrasidas, Charalambos, Ed.; Glass, Gene V., Ed.

This book describes the current state of developments in distance education and distributed learning. The volume brings together some of the leading contemporary contributors in the areas of educational technology and distance education. Topics covered include research and evaluation in distance education, online communities, faculty productivity,…

Distributional Learning in College Students with Developmental Language Disorder

ERIC Educational Resources Information Center

Hall, Jessica; Van Horne, Amanda Owen; McGregor, Karla K.; Farmer, Thomas

2017-01-01

Purpose: This study examined whether college students with developmental language disorder (DLD) could use distributional information in an artificial language to learn about grammatical category membership in a way similar to their typically developing (TD) peers. Method: Seventeen college students with DLD and 17 TD college students participated…

An Invitation to Imitation

DTIC Science & Technology

2015-03-15

Super Mario Bros, 2010b. URL http://www.youtube.com/watch?v=anOI0xZ3kGM. S. Ross and J. A. Bagnell. Efficient reductions for imitation learning. In...learning to drive a car in a video game by performing a direct mapping from screen shots to steering angles. Figure 4 illustrates the classic super - an...same distribution and thus the super - vised learning assumption of independent and identically distributed (i.i.d.) data is badly violated. A natural

An integrated logit model for contamination event detection in water distribution systems.

PubMed

Housh, Mashor; Ostfeld, Avi

2015-05-15

The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Critical neural networks with short- and long-term plasticity.

PubMed

Michiels van Kessenich, L; Luković, M; de Arcangelis, L; Herrmann, H J

2018-03-01

In recent years self organized critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behavior of neuronal systems. It has been shown that dynamical synapses, as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as Hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms, acting on two different time scales. The measured avalanche statistics are compatible with experimental results for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons. The time series of neuronal activity exhibits temporal bursts leading to 1/f decay in the power spectrum. The presence of long-term plasticity gives the system the ability to learn binary rules such as xor, providing the foundation of future research on more complicated tasks such as pattern recognition.

Critical neural networks with short- and long-term plasticity

NASA Astrophysics Data System (ADS)

Michiels van Kessenich, L.; Luković, M.; de Arcangelis, L.; Herrmann, H. J.

2018-03-01

In recent years self organized critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behavior of neuronal systems. It has been shown that dynamical synapses, as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as Hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms, acting on two different time scales. The measured avalanche statistics are compatible with experimental results for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons. The time series of neuronal activity exhibits temporal bursts leading to 1 /f decay in the power spectrum. The presence of long-term plasticity gives the system the ability to learn binary rules such as xor, providing the foundation of future research on more complicated tasks such as pattern recognition.

Towards Standardized Patient Data Exchange: Integrating a FHIR Based API for the Open Medical Record System.

PubMed

Kasthurirathne, Suranga N; Mamlin, Burke; Grieve, Grahame; Biondich, Paul

2015-01-01

Interoperability is essential to address limitations caused by the ad hoc implementation of clinical information systems and the distributed nature of modern medical care. The HL7 V2 and V3 standards have played a significant role in ensuring interoperability for healthcare. FHIR is a next generation standard created to address fundamental limitations in HL7 V2 and V3. FHIR is particularly relevant to OpenMRS, an Open Source Medical Record System widely used across emerging economies. FHIR has the potential to allow OpenMRS to move away from a bespoke, application specific API to a standards based API. We describe efforts to design and implement a FHIR based API for the OpenMRS platform. Lessons learned from this effort were used to define long term plans to transition from the legacy OpenMRS API to a FHIR based API that greatly reduces the learning curve for developers and helps enhance adhernce to standards.

Open Online Spaces of Professional Learning: Context, Personalisation and Facilitation

ERIC Educational Resources Information Center

Evans, Peter

2015-01-01

This article explores professional learning through online discussion events as sites of communities of learning. The rise of distributed work places and networked labour coincides with a privileging of individualised professional learning. Alongside this focus on the individual has been a growth in informal online learning communities and…

E-Learning QUICK Checklist

ERIC Educational Resources Information Center

Khan, Badrul

2005-01-01

"E-Learning QUICK Checklist" walks readers through the various factors important to developing, evaluating and implementing an open, flexible and distributed learning environment. This book is designed as a quick checklist for e-learning. It contains many practical items that the reader can use as review criteria to check if e-learning modules,…

Supervised and Unsupervised Learning of Multidimensional Acoustic Categories

ERIC Educational Resources Information Center

Goudbeek, Martijn; Swingley, Daniel; Smits, Roel

2009-01-01

Learning to recognize the contrasts of a language-specific phonemic repertoire can be viewed as forming categories in a multidimensional psychophysical space. Research on the learning of distributionally defined visual categories has shown that categories defined over 1 dimension are easy to learn and that learning multidimensional categories is…

Lessons Learned while Exploring Cloud-Native Architectures for NASA EOSDIS Applications and Systems

NASA Astrophysics Data System (ADS)

Pilone, D.

2016-12-01

As new, high data rate missions begin collecting data, the NASA's Earth Observing System Data and Information System (EOSDIS) archive is projected to grow roughly 20x to over 300PBs by 2025. To prepare for the dramatic increase in data and enable broad scientific inquiry into larger time series and datasets, NASA has been exploring the impact of applying cloud technologies throughout EOSDIS. In this talk we will provide an overview of NASA's prototyping and lessons learned in applying cloud architectures to: Highly scalable and extensible ingest and archive of EOSDIS data Going "all-in" on cloud based application architectures including "serverless" data processing pipelines and evaluating approaches to vendor-lock in Rethinking data distribution and approaches to analysis in a cloud environment Incorporating and enforcing security controls while minimizing the barrier for research efforts to deploy to NASA compliant, operational environments. NASA's Earth Observing System (EOS) is a coordinated series of satellites for long term global observations. NASA's Earth Observing System Data and Information System (EOSDIS) is a multi-petabyte-scale archive of environmental data that supports global climate change research by providing end-to-end services from EOS instrument data collection to science data processing to full access to EOS and other earth science data. On a daily basis, the EOSDIS ingests, processes, archives and distributes over 3 terabytes of data from NASA's Earth Science missions representing over 6000 data products ranging from various types of science disciplines. EOSDIS has continually evolved to improve the discoverability, accessibility, and usability of high-impact NASA data spanning the multi-petabyte-scale archive of Earth science data products.

Astrobiology Extends Biology into Deep Time and Space

NASA Technical Reports Server (NTRS)

Desmarais, David

2003-01-01

To understand our own origins and to search for biospheres beyond Earth, we need a more robust concept of life itself. We must learn how to discriminate between attributes that are fundamental to all living systems versus those that represent principally local outcomes of long-term survival on Earth. We should identify the most basic environmental needs of life, chart the distribution of other habitable worlds, and understand the factors that created their distribution. Studies of microbial communities and the geologic record will be summarized that offer clues about the early evolution of our own biosphere as well as the signatures of life that we might find in the heavens.

Remembering forward: Neural correlates of memory and prediction in human motor adaptation

PubMed Central

Scheidt, Robert A; Zimbelman, Janice L; Salowitz, Nicole M G; Suminski, Aaron J; Mosier, Kristine M; Houk, James; Simo, Lucia

2011-01-01

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions - including prefrontal, parietal and hippocampal cortices - exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancellation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures. PMID:21840405

Are Learning Style Preferences of Health Science Students Predictive of Their Attitudes towards E-Learning?

ERIC Educational Resources Information Center

Brown, Ted; Zoghi, Maryam; Williams, Brett; Jaberzadeh, Shapour; Roller, Louis; Palermo, Claire; McKenna, Lisa; Wright, Caroline; Baird, Marilyn; Schneider-Kolsky, Michal; Hewitt, Lesley; Sim, Jenny; Holt, Tangerine-Ann

2009-01-01

The objective for this study was to determine whether learning style preferences of health science students could predict their attitudes to e-learning. A survey comprising the "Index of Learning Styles" (ILS) and the "Online Learning Environment Survey" (OLES) was distributed to 2885 students enrolled in 10 different health…

Parallel Distributed Processing Theory in the Age of Deep Networks.

PubMed

Bowers, Jeffrey S

2017-12-01

Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory. Copyright © 2017. Published by Elsevier Ltd.