Framework for robot skill learning using reinforcement learning
Wei, Yingzi; Zhao, Mingyang
2003-09-01
Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is an on-line actor critic method for a robot to develop its skill. The reinforcement function has become the critical component for its effect of evaluating the action and guiding the learning process. We present an augmented reward function that provides a new way for RL controller to incorporate prior knowledge and experience into the RL controller. Also, the difference form of augmented reward function is considered carefully. The additional reward beyond conventional reward will provide more heuristic information for RL. In this paper, we present a strategy for the task of complex skill learning. Automatic robot shaping policy is to dissolve the complex skill into a hierarchical learning process. The new form of value function is introduced to attain smooth motion switching swiftly. We present a formal, but practical, framework for robot skill learning and also illustrate with an example the utility of method for learning skilled robot control on line.
A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses
Directory of Open Access Journals (Sweden)
Karim El-Laithy
2011-01-01
Full Text Available An integration of both the Hebbian-based and reinforcement learning (RL rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.
Experiments with Online Reinforcement Learning in Real-Time Strategy Games
DEFF Research Database (Denmark)
Toftgaard Andersen, Kresten; Zeng, Yifeng; Dahl Christensen, Dennis
2009-01-01
Real-time strategy (RTS) games provide a challenging platform to implement online reinforcement learning (RL) techniques in a real application. Computer, as one game player, monitors opponents' (human or other computers) strategies and then updates its own policy using RL methods. In this article......, we first examine the suitability of applying the online RL in various computer games. Reinforcement learning application depends on both RL complexity and the game features. We then propose a multi-layer framework for implementing online RL in an RTS game. The framework significantly reduces RL...... the effectiveness of our proposed framework and shed light on relevant issues in using online RL in RTS games....
Tank War Using Online Reinforcement Learning
DEFF Research Database (Denmark)
Toftgaard Andersen, Kresten; Zeng, Yifeng; Dahl Christensen, Dennis
2009-01-01
Real-Time Strategy(RTS) games provide a challenging platform to implement online reinforcement learning(RL) techniques in a real application. Computer as one player monitors opponents'(human or other computers) strategies and then updates its own policy using RL methods. In this paper, we propose...... a multi-layer framework for implementing the online RL in a RTS game. The framework significantly reduces the RL computational complexity by decomposing the state space in a hierarchical manner. We implement the RTS game - Tank General, and perform a thorough test on the proposed framework. The results...... show the effectiveness of our proposed framework and shed light on relevant issues on using the RL in RTS games....
Genetic regulation of IL1RL1 methylation and IL1RL1-a protein levels in asthma.
Dijk, F Nicole; Xu, Chengjian; Melén, Erik; Carsin, Anne-Elie; Kumar, Asish; Nolte, Ilja M; Gruzieva, Olena; Pershagen, Goran; Grotenboer, Neomi S; Savenije, Olga E M; Antó, Josep Maria; Lavi, Iris; Dobaño, Carlota; Bousquet, Jean; van der Vlies, Pieter; van der Valk, Ralf J P; de Jongste, Johan C; Nawijn, Martijn C; Guerra, Stefano; Postma, Dirkje S; Koppelman, Gerard H
2018-03-01
Interleukin-1 receptor-like 1 ( IL1RL1 ) is an important asthma gene. (Epi)genetic regulation of IL1RL1 protein expression has not been established. We assessed the association between IL1RL1 single nucleotide polymorphisms (SNPs), IL1RL1 methylation and serum IL1RL1-a protein levels, and aimed to identify causal pathways in asthma.Associations of IL1RL1 SNPs with asthma were determined in the Dutch Asthma Genome-wide Association Study cohort and three European birth cohorts, BAMSE (Children/Barn, Allergy, Milieu, Stockholm, an Epidemiological survey), INMA (Infancia y Medio Ambiente) and PIAMA (Prevention and Incidence of Asthma and Mite Allergy), participating in the Mechanisms of the Development of Allergy study. We performed blood DNA IL1RL1 methylation quantitative trait locus (QTL) analysis (n=496) and (epi)genome-wide protein QTL analysis on serum IL1RL1-a levels (n=1462). We investigated the association of IL1RL1 CpG methylation with asthma (n=632) and IL1RL1-a levels (n=548), with subsequent causal inference testing. Finally, we determined the association of IL1RL1-a levels with asthma and its clinical characteristics (n=1101). IL1RL1 asthma-risk SNPs strongly associated with IL1RL1 methylation (rs1420101; p=3.7×10 -16 ) and serum IL1RL1-a levels (p=2.8×10 -56 ). IL1RL1 methylation was not associated with asthma or IL1RL1-a levels. IL1RL1-a levels negatively correlated with blood eosinophil counts, whereas there was no association between IL1RL1-a levels and asthma.In conclusion, asthma-associated IL1RL1 SNPs strongly regulate IL1RL1 methylation and serum IL1RL1-a levels, yet neither these IL1RL1- methylation CpG sites nor IL1RL1-a levels are associated with asthma. Copyright ©ERS 2018.
Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf
2016-02-01
Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).
RL5SORT/RL5PLOT. A graphite package for the JRC-Ispra IBM version of RELAP5/MOD1
International Nuclear Information System (INIS)
Kolar, W.; Brewka, W.
1984-01-01
The present report describes the programs RL5SORT and RL5PLOT, their implementation and ''how to use''. Both programs base on the IBM version of RELAP5 as developed at JRC-ISPRA. RL5SORT creates from the output file (restart plot file) of a RELAP5 calculation data files, which serve as input data base for the program RL5PLOT. RL5PLOT retrieves the previous stored data records (minor edit quantities of RELAP5), allows arithmetic operations with the retrieved data and enables a print or graphic output on the terminal screen of a TEKTRONIX graphic terminal. A set of commands, incorporated in the program RL5PLOT, facilitates the user's work. Program RL5SORT has been developed as a batch program, while RL5PLOT has been conceived for interactive working mode
A Bayesian foundation for individual learning under uncertainty
Directory of Open Access Journals (Sweden)
Christoph eMathys
2011-05-01
Full Text Available Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g., environmental volatility and perceptual uncertainty. The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next higher level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i are analytical and extremely efficient, enabling real-time learning, (ii have a natural interpretation in terms of RL, and (iii contain parameters representing processes which play a key role in current theories of learning, e.g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i.e., situations with and without perceptual uncertainty. These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability
A bayesian foundation for individual learning under uncertainty.
Mathys, Christoph; Daunizeau, Jean; Friston, Karl J; Stephan, Klaas E
2011-01-01
Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g., environmental volatility and perceptual uncertainty). The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next highest level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean-field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i) are analytical and extremely efficient, enabling real-time learning, (ii) have a natural interpretation in terms of RL, and (iii) contain parameters representing processes which play a key role in current theories of learning, e.g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i.e., situations with and without perceptual uncertainty). These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability theory.
DOE-RL Integrated Safety Management System Description
International Nuclear Information System (INIS)
SHOOP, D.S.
2000-01-01
The purpose of this Integrated Safety Management System Description (ISMSD) is to describe the U.S. Department of Energy (DOE), Richland Operations Office (RL) ISMS as implemented through the RL Integrated Management System (RIMS). This ISMSD does not impose additional requirements but rather provides an overview describing how various parts of the ISMS fit together. Specific requirements for each of the core functions and guiding principles are established in other implementing processes, procedures, and program descriptions that comprise RIMS. RL is organized to conduct work through operating contracts; therefore, it is extremely difficult to provide an adequate ISMS description that only addresses RL functions. Of necessity, this ISMSD contains some information on contractor processes and procedures which then require RL approval or oversight. This ISMSD does not purport to contain a full description of the contractors' ISM System Descriptions
DOE-RL Integrated Safety Management System Description
Shoop, D S
2000-01-01
The purpose of this Integrated Safety Management System Description (ISMSD) is to describe the U.S. Department of Energy (DOE), Richland Operations Office (RL) ISMS as implemented through the RL Integrated Management System (RIMS). This ISMSD does not impose additional requirements but rather provides an overview describing how various parts of the ISMS fit together. Specific requirements for each of the core functions and guiding principles are established in other implementing processes, procedures, and program descriptions that comprise RIMS. RL is organized to conduct work through operating contracts; therefore, it is extremely difficult to provide an adequate ISMS description that only addresses RL functions. Of necessity, this ISMSD contains some information on contractor processes and procedures which then require RL approval or oversight. This ISMSD does not purport to contain a full description of the contractors' ISM System Descriptions.
CAD2RL: Real Single-Image Flight without a Single Real Image
Sadeghi, Fereshteh; Levine, Sergey
2016-01-01
Deep reinforcement learning has emerged as a promising and powerful technique for automatically acquiring control policies that can process raw sensory inputs, such as images, and perform complex behaviors. However, extending deep RL to real-world robotic tasks has proven challenging, particularly in safety-critical domains such as autonomous flight, where a trial-and-error learning process is often impractical. In this paper, we explore the following question: can we train vision-based navig...
Directory of Open Access Journals (Sweden)
Tina E Faber
Full Text Available Targets for intervention are required for respiratory syncytial virus (RSV bronchiolitis, a common disease during infancy for which no effective treatment exists. Clinical and genetic studies indicate that IL1RL1 plays an important role in the development and exacerbations of asthma. Human IL1RL1 encodes three isoforms, including soluble IL1RL1-a, that can influence IL33 signalling by modifying inflammatory responses to epithelial damage. We hypothesized that IL1RL1 gene variants and soluble IL1RL1-a are associated with severe RSV bronchiolitis.We studied the association between RSV and 3 selected IL1RL1 single-nucleotide polymorphisms rs1921622, rs11685480 or rs1420101 in 81 ventilated and 384 non-ventilated children under 1 year of age hospitalized with primary RSV bronchiolitis in comparison to 930 healthy controls. Severe RSV infection was defined by need for mechanical ventilation. Furthermore, we examined soluble IL1RL1-a concentration in nasopharyngeal aspirates from children hospitalized with primary RSV bronchiolitis. An association between SNP rs1921622 and disease severity was found at the allele and genotype level (p = 0.011 and p = 0.040, respectively. In hospitalized non-ventilated patients, RSV bronchiolitis was not associated with IL1RL1 genotypes. Median concentrations of soluble IL1RL1-a in nasopharyngeal aspirates were >20-fold higher in ventilated infants when compared to non-ventilated infants with RSV (median [and quartiles] 9,357 [936-15,528] pg/ml vs. 405 [112-1,193] pg/ml respectively; p<0.001.We found a genetic link between rs1921622 IL1RL1 polymorphism and disease severity in RSV bronchiolitis. The potential biological role of IL1RL1 in the pathogenesis of severe RSV bronchiolitis was further supported by high local concentrations of IL1RL1 in children with most severe disease. We speculate that IL1RL1a modifies epithelial damage mediated inflammatory responses during RSV bronchiolitis and thus may serve as a
Framework for pedagogical learning analytics
Heilala, Ville
2018-01-01
Learning analytics is an emergent technological practice and a multidisciplinary scientific discipline, which goal is to facilitate effective learning and knowledge of learning. In this design science research, I combine knowledge discovery process, a concept of pedagogical knowledge, ethics of learning analytics and microservice architecture. The result is a framework for pedagogical learning analytics. The framework is applied and evaluated in the context of agency analytics. The framework ...
Fractional-order RC and RL circuits
Radwan, Ahmed Gomaa
2012-05-30
This paper is a step forward to generalize the fundamentals of the conventional RC and RL circuits in fractional-order sense. The effect of fractional orders is the key factor for extra freedom, more flexibility, and novelty. The conditions for RC and RL circuits to act as pure imaginary impedances are derived, which are unrealizable in the conventional case. In addition, the sensitivity analyses of the magnitude and phase response with respect to all parameters showing the locations of these critical values are discussed. A qualitative revision for the fractional RC and RL circuits in the frequency domain is provided. Numerical and PSpice simulations are included to validate this study. © Springer Science+Business Media, LLC 2012.
BlueSky Cloud Framework: An E-Learning Framework Embracing Cloud Computing
Dong, Bo; Zheng, Qinghua; Qiao, Mu; Shu, Jian; Yang, Jie
Currently, E-Learning has grown into a widely accepted way of learning. With the huge growth of users, services, education contents and resources, E-Learning systems are facing challenges of optimizing resource allocations, dealing with dynamic concurrency demands, handling rapid storage growth requirements and cost controlling. In this paper, an E-Learning framework based on cloud computing is presented, namely BlueSky cloud framework. Particularly, the architecture and core components of BlueSky cloud framework are introduced. In BlueSky cloud framework, physical machines are virtualized, and allocated on demand for E-Learning systems. Moreover, BlueSky cloud framework combines with traditional middleware functions (such as load balancing and data caching) to serve for E-Learning systems as a general architecture. It delivers reliable, scalable and cost-efficient services to E-Learning systems, and E-Learning organizations can establish systems through these services in a simple way. BlueSky cloud framework solves the challenges faced by E-Learning, and improves the performance, availability and scalability of E-Learning systems.
An e-Learning Theoretical Framework
Aparicio, Manuela; Bacao, Fernando; Oliveira, Tiago
2016-01-01
E-learning systems have witnessed a usage and research increase in the past decade. This article presents the e-learning concepts ecosystem. It summarizes the various scopes on e-learning studies. Here we propose an e-learning theoretical framework. This theory framework is based upon three principal dimensions: users, technology, and services…
Deep Reinforcement Learning: An Overview
Li, Yuxi
2017-01-01
We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsuperv...
RL-1: a certified uranium reference ore
International Nuclear Information System (INIS)
Steger, H.F.; Bowman, W.S.
1985-06-01
A 145-kg sample of a uranium ore from Rabbit Lake, Saskatchewan, has been prepared as a compositional reference material. RL-1 was ground to minus 74 μm and mixed in one lot. Approximately one half of this ore was bottled in 100-g units, the remainder being stored in bulk. The homogeneity of RL-1 with respect to uranium and nickel was confirmed by neutron activation and X-ray fluorescence analytical techniques. In a 'free choice' analytical program, 13 laboratories contributed results for one or more of uranium, nickel and arsenic in one bottle of RL-1. Based on a statistical analysis of the data, the following recommended values were assigned: U, 0.201%; Ni, 185 μg/g; and As, 19.6 μg/g
Genetic regulation ofmethylation and IL1RL1-a protein levels in asthma
Dijk, F Nicole; Xu, Chengjian; Melén, Erik; Carsin, Anne-Elie; Kumar, Asish; Nolte, Ilja M; Gruzieva, Olena; Pershagen, Goran; Grotenboer, Neomi S; Savenije, Olga E M; Antó, Josep Maria; Lavi, Iris; Dobaño, Carlota; Bousquet, Jean; van der Vlies, Pieter; van der Valk, Ralf J P; de Jongste, Johan C; Nawijn, Martijn C; Guerra, Stefano; Postma, Dirkje S; Koppelman, Gerard H
2018-01-01
Interleukin-1 receptor-like 1 (IL1RL1) is an important asthma gene. (Epi)genetic regulation ofIL1RL1protein expression has not been established. We assessed the association betweenIL1RL1single nucleotide polymorphisms (SNPs),IL1RL1methylation and serum IL1RL1-a protein levels, and aimed to identify
Learning frameworks as an alternative to repositories
DEFF Research Database (Denmark)
Dalsgaard, Christian
2005-01-01
This paper presents the concept of ‘learning frameworks’. The purpose of the paper is to discuss and question collections of digital learning objects in large repositories and to argue for large learning frameworks which organise a number of thematically related digital learning materials. Whereas...... a learning object repository contains all kinds of materials, a learning framework consists of an organisation of materials related to a common theme. Further, a repository consists of single, self-contained objects, whereas a learning framework is an open-ended environment which presents a number...
A Framework for Mobile Learning for Enhancing Learning in Higher Education
Barreh, Kadar Abdillahi; Abas, Zoraini Wati
2015-01-01
As mobile learning becomes increasingly pervasive, many higher education institutions have initiated a number of mobile learning initiatives to support their traditional learning modes. This study proposes a framework for mobile learning for enhancing learning in higher education. This framework for mobile learning is based on research conducted…
A Learning Activity Design Framework for Supporting Mobile Learning
Directory of Open Access Journals (Sweden)
Jalal Nouri
2016-01-01
Full Text Available This article introduces the Learning Activity Design (LEAD framework for the development and implementation of mobile learning activities in primary schools. The LEAD framework draws on methodological perspectives suggested by design-based research and interaction design in the specific field of technology-enhanced learning (TEL. The LEAD framework is grounded in four design projects conducted over a period of six years. It contributes a new understanding of the intricacies and multifaceted aspects of the design-process characterizing the development and implementation of mobile devices (i.e. smart phones and tablets in curricular activities conducted in Swedish primary schools. This framework is intended to provide both designers and researchers with methodological tools that take account of the pedagogical foundations of technologically-based educational interventions, usability issues related to the interaction with the mobile application developed, multiple data streams generated during the design project, multiple stakeholders involved in the design process and sustainability aspects of the mobile learning activities implemented in the school classroom.
Campos-Sanchez, Antonio; Martin-Piedra, Miguel-Angel; Carriel, Victor; Gonzalez-Andrades, Miguel; Garzon, Ingrid; Sanchez-Quevedo, Maria-Carmen; Alaminos, Miguel
2012-01-01
Two questionnaires were used to investigate students' perceptions of their motivation to opt for reception learning (RL) or self-discovery learning (SDL) in histology and their choices of complementary learning strategies (CLS). The results demonstrated that the motivation to attend RL sessions was higher than the motivation to attend SDL to gain…
Learning to learn in the European Reference Framework for lifelong learning
Pirrie, Anne; Thoutenhoofd, Ernst D.
2013-01-01
This article explores the construction of learning to learn that is implicit in the document Key Competences for Lifelong LearningEuropean Reference Framework and related education policy from the European Commission. The authors argue that the hallmark of learning to learn is the development of a
Optimal and Autonomous Control Using Reinforcement Learning: A Survey.
Kiumarsi, Bahare; Vamvoudakis, Kyriakos G; Modares, Hamidreza; Lewis, Frank L
2018-06-01
This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal and control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.
An Improved Reinforcement Learning System Using Affective Factors
Directory of Open Access Journals (Sweden)
Takashi Kuremoto
2013-07-01
Full Text Available As a powerful and intelligent machine learning method, reinforcement learning (RL has been widely used in many fields such as game theory, adaptive control, multi-agent system, nonlinear forecasting, and so on. The main contribution of this technique is its exploration and exploitation approaches to find the optimal solution or semi-optimal solution of goal-directed problems. However, when RL is applied to multi-agent systems (MASs, problems such as “curse of dimension”, “perceptual aliasing problem”, and uncertainty of the environment constitute high hurdles to RL. Meanwhile, although RL is inspired by behavioral psychology and reward/punishment from the environment is used, higher mental factors such as affects, emotions, and motivations are rarely adopted in the learning procedure of RL. In this paper, to challenge agents learning in MASs, we propose a computational motivation function, which adopts two principle affective factors “Arousal” and “Pleasure” of Russell’s circumplex model of affects, to improve the learning performance of a conventional RL algorithm named Q-learning (QL. Compared with the conventional QL, computer simulations of pursuit problems with static and dynamic preys were carried out, and the results showed that the proposed method results in agents having a faster and more stable learning performance.
Off-Policy Reinforcement Learning for Synchronization in Multiagent Graphical Games.
Li, Jinna; Modares, Hamidreza; Chai, Tianyou; Lewis, Frank L; Xie, Lihua
2017-10-01
This paper develops an off-policy reinforcement learning (RL) algorithm to solve optimal synchronization of multiagent systems. This is accomplished by using the framework of graphical games. In contrast to traditional control protocols, which require complete knowledge of agent dynamics, the proposed off-policy RL algorithm is a model-free approach, in that it solves the optimal synchronization problem without knowing any knowledge of the agent dynamics. A prescribed control policy, called behavior policy, is applied to each agent to generate and collect data for learning. An off-policy Bellman equation is derived for each agent to learn the value function for the policy under evaluation, called target policy, and find an improved policy, simultaneously. Actor and critic neural networks along with least-square approach are employed to approximate target control policies and value functions using the data generated by applying prescribed behavior policies. Finally, an off-policy RL algorithm is presented that is implemented in real time and gives the approximate optimal control policy for each agent using only measured data. It is shown that the optimal distributed policies found by the proposed algorithm satisfy the global Nash equilibrium and synchronize all agents to the leader. Simulation results illustrate the effectiveness of the proposed method.
Effect of ferrocene-substituted porphyrin RL-91 on Candida albicans biofilm formation.
Lippert, Rainer; Vojnovic, Sandra; Mitrovic, Aleksandra; Jux, Norbert; Ivanović-Burmazović, Ivana; Vasiljevic, Branka; Stankovic, Nada
2014-08-01
Ferrocene-substituted porphyrin RL-91 exhibits antifungal activity against opportune human pathogen Candida albicans. RL-91 efficiently inhibits growth of both planktonic C. albicans cells and cells within biofilms without photoactivation. The minimal inhibitory concentration for plankton form (PMIC) was established to be 100 μg/mL and the same concentration killed 80% of sessile cells in the mature biofilm (SMIC80). Furthermore PMIC of RL-91 efficiently prevents C. albicans biofilm formation. RL-91 is cytotoxic for human fibroblasts in vitro in concentration of 10 μg/mL, however it does not cause hemolysis in concentrations of up to 50 μg/mL. These findings open possibility for application of RL-91 as an antifungal agent for external antibiofilm treatment of medical devices as well as a scaffold for further development of porphyrin based systemic antifungals. Copyright © 2014 Elsevier Ltd. All rights reserved.
A Framework for Narration and Learning in Educational Multimedia
DEFF Research Database (Denmark)
Mosegaard, Jesper; Bennedsen, Jens
2003-01-01
In this article we describe a multimedia adventure game framework for a learning environment to support the teaching and learning of introductory programming. In the framework we have conceptualized two important aspects of such an environment: narration and learning topics. We describe...... the interplay between these aspects and how the framework utilizes this to adapt the learning process to the individual student. The motivation for the separation is to help the teacher balance the two main driving forces of an edutainment product: entertainment and learning. It is the responsibility...... of the teacher to define the range of stories and topics using the framework. The framework provides a complete learning environment where the teacher merely needs to define the content....
Analyzing Learning in Professional Learning Communities: A Conceptual Framework
Van Lare, Michelle D.; Brazer, S. David
2013-01-01
The purpose of this article is to build a conceptual framework that informs current understanding of how professional learning communities (PLCs) function in conjunction with organizational learning. The combination of sociocultural learning theories and organizational learning theories presents a more complete picture of PLC processes that has…
E-learning process maturity level: a conceptual framework
Rahmah, A.; Santoso, H. B.; Hasibuan, Z. A.
2018-03-01
ICT advancement is a sure thing with the impact influencing many domains, including learning in both formal and informal situations. It leads to a new mindset that we should not only utilize the given ICT to support the learning process, but also improve it gradually involving a lot of factors. These phenomenon is called e-learning process evolution. Accordingly, this study attempts to explore maturity level concept to provide the improvement direction gradually and progression monitoring for the individual e-learning process. Extensive literature review, observation, and forming constructs are conducted to develop a conceptual framework for e-learning process maturity level. The conceptual framework consists of learner, e-learning process, continuous improvement, evolution of e-learning process, technology, and learning objectives. Whilst, evolution of e-learning process depicted as current versus expected conditions of e-learning process maturity level. The study concludes that from the e-learning process maturity level conceptual framework, it may guide the evolution roadmap for e-learning process, accelerate the evolution, and decrease the negative impact of ICT. The conceptual framework will be verified and tested in the future study.
Amygdala and ventral striatum make distinct contributions to reinforcement learning
Costa, Vincent D.; Monte, Olga Dal; Lucas, Daniel R.; Murray, Elisabeth A.; Averbeck, Bruno B.
2016-01-01
Summary Reinforcement learning (RL) theories posit that dopaminergic signals are integrated within the striatum to associate choices with outcomes. Often overlooked is that the amygdala also receives dopaminergic input and is involved in Pavlovian processes that influence choice behavior. To determine the relative contributions of the ventral striatum (VS) and amygdala to appetitive RL we tested rhesus macaques with VS or amygdala lesions on deterministic and stochastic versions of a two-arm bandit reversal learning task. When learning was characterized with a RL model relative to controls, amygdala lesions caused general decreases in learning from positive feedback and choice consistency. By comparison, VS lesions only affected learning in the stochastic task. Moreover, the VS lesions hastened the monkeys’ choice reaction times, which emphasized a speed-accuracy tradeoff that accounted for errors in deterministic learning. These results update standard accounts of RL by emphasizing distinct contributions of the amygdala and VS to RL. PMID:27720488
Explicit and implicit reinforcement learning across the psychosis spectrum.
Barch, Deanna M; Carter, Cameron S; Gold, James M; Johnson, Sheri L; Kring, Ann M; MacDonald, Angus W; Pizzagalli, Diego A; Ragland, J Daniel; Silverstein, Steven M; Strauss, Milton E
2017-07-01
Motivational and hedonic impairments are core features of a variety of types of psychopathology. An important aspect of motivational function is reinforcement learning (RL), including implicit (i.e., outside of conscious awareness) and explicit (i.e., including explicit representations about potential reward associations) learning, as well as both positive reinforcement (learning about actions that lead to reward) and punishment (learning to avoid actions that lead to loss). Here we present data from paradigms designed to assess both positive and negative components of both implicit and explicit RL, examine performance on each of these tasks among individuals with schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis, and examine their relative relationships to specific symptom domains transdiagnostically. None of the diagnostic groups differed significantly from controls on the implicit RL tasks in either bias toward a rewarded response or bias away from a punished response. However, on the explicit RL task, both the individuals with schizophrenia and schizoaffective disorder performed significantly worse than controls, but the individuals with bipolar did not. Worse performance on the explicit RL task, but not the implicit RL task, was related to worse motivation and pleasure symptoms across all diagnostic categories. Performance on explicit RL, but not implicit RL, was related to working memory, which accounted for some of the diagnostic group differences. However, working memory did not account for the relationship of explicit RL to motivation and pleasure symptoms. These findings suggest transdiagnostic relationships across the spectrum of psychotic disorders between motivation and pleasure impairments and explicit RL. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Applications of Deep Learning and Reinforcement Learning to Biological Data.
Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano
2018-06-01
Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.
Reinforcement Learning Based Novel Adaptive Learning Framework for Smart Grid Prediction
Directory of Open Access Journals (Sweden)
Tian Li
2017-01-01
Full Text Available Smart grid is a potential infrastructure to supply electricity demand for end users in a safe and reliable manner. With the rapid increase of the share of renewable energy and controllable loads in smart grid, the operation uncertainty of smart grid has increased briskly during recent years. The forecast is responsible for the safety and economic operation of the smart grid. However, most existing forecast methods cannot account for the smart grid due to the disabilities to adapt to the varying operational conditions. In this paper, reinforcement learning is firstly exploited to develop an online learning framework for the smart grid. With the capability of multitime scale resolution, wavelet neural network has been adopted in the online learning framework to yield reinforcement learning and wavelet neural network (RLWNN based adaptive learning scheme. The simulations on two typical prediction problems in smart grid, including wind power prediction and load forecast, validate the effectiveness and the scalability of the proposed RLWNN based learning framework and algorithm.
Status of CSR RL06 GRACE reprocessing and preliminary results
Save, H.
2017-12-01
The GRACE project plans to re-processes the GRACE mission data in order to be consistent with the first gravity products released by the GRACE-FO project. The RL06 reprocessing will harmonize the GRACE time-series with the first release of GRACE-FO. This paper catalogues the changes in the upcoming RL06 release and discusses the quality improvements as compared to the current RL05 release. The processing and parameterization changes as compared to the current release are also discussed. This paper discusses the evolution of the quality of the GRACE solutions and characterize the errors over the past few years. The possible challenges associated with connecting the GRACE time series with that from GRACE-FO are also discussed.
Pengembangan Framework untuk Mengukur Tingkat Keberhasilan Implementasi Reverse Logistics
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Farida Pulansari
2015-12-01
Full Text Available Electronic Waste (E-waste is growing attention at the end of this decade. E-waste must be managed properly. E-waste has a negative impact on the environment. E-waste contain B3 (Bahan Berbahaya dan Beracun. Reverse Logistics (RL is one approach that able to solve these problems. The use of secondary materials through the process of remanufacturing, recycling, refurbishing, recondition are strategy to minimizing the number of e-waste. Many companies claim successfully implementing of RL. However, there are no clear indicators or parameters to assess the success of RL implementation. In this research we propose the design of reverse logistics maturity framework. Grounded Theory (GT is chosen to design this framework. GT is a research methodology involving the construction of theory based on phenomena. This framework will be divided into five levels of maturity: Level Conventional, Managed, Developed, Innovative and Optimized. Results of research from three consumer electronics companies were in the Level Managed.
Research on machine learning framework based on random forest algorithm
Ren, Qiong; Cheng, Hui; Han, Hai
2017-03-01
With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.
Online reinforcement learning control for aerospace systems
Zhou, Y.
2018-01-01
Reinforcement Learning (RL) methods are relatively new in the field of aerospace guidance, navigation, and control. This dissertation aims to exploit RL methods to improve the autonomy and online learning of aerospace systems with respect to the a priori unknown system and environment, dynamical
Evaluation of Learning Materials: A Holistic Framework
Bundsgaard, Jeppe; Hansen, Thomas Illum
2011-01-01
This paper presents a holistic framework for evaluating learning materials and designs for learning. A holistic evaluation comprises investigations of the potential learning potential, the actualised learning potential, and the actual learning. Each aspect is explained and exemplified through theoretical models and definitions. (Contains 3 figures…
The ICCE Framework: Framing Learning Experiences Afforded by Games
Foster, Aroutis; Shah, Mamta
2015-01-01
There is a need for game-based learning frameworks that provide a lens for understanding learning experiences afforded in digital games. These frameworks should aim to facilitate game analyses, identification of learning opportunities, and support for learner experiences. This article uses the inquiry, communication, construction, and expression…
Systems control with generalized probabilistic fuzzy-reinforcement learning
Hinojosa, J.; Nefti, S.; Kaymak, U.
2011-01-01
Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be
One lens missing? Clarifying the clinical microsystem framework with learning theories.
Norman, Ann-Charlott; Fritzen, Lena; Fridh, Marianne Lindblad
2013-01-01
The clinical microsystem (CMS) approach is widely used and is perceived as helpful in practice but, we ask the question: "Is its learning potential sufficiently utilized?" To scrutinize aspects of learning within the CMS framework and to clarify the learning aspects the framework includes and thereby support the framework with the enhanced learning perspective that becomes visible. Literature on the CMS framework was systematically searched and selected using inclusion criteria. An analytical tool was constructed in the form of a theoretical lens that was used to clarify learning aspects that are associated with the framework. The analysis revealed 3 learning aspects: (1) The CMS framework describes individual and social learning but not how to adapt learning strategies for purposes of change. (2) The metaphorical language of how to reach a holistic health care system for each patient has developed over time but can still be improved by naming social interactions to transcend organizational boundaries. (3) Power structures are recognized but not as a characteristic that restricts learning due to asymmetric communication. The "lens" perspective reveals new meanings to learning that enhance our understanding of health care as a social system and provides new practical learning strategies.
Stochastic abstract policies: generalizing knowledge to improve reinforcement learning.
Koga, Marcelo L; Freire, Valdinei; Costa, Anna H R
2015-01-01
Reinforcement learning (RL) enables an agent to learn behavior by acquiring experience through trial-and-error interactions with a dynamic environment. However, knowledge is usually built from scratch and learning to behave may take a long time. Here, we improve the learning performance by leveraging prior knowledge; that is, the learner shows proper behavior from the beginning of a target task, using the knowledge from a set of known, previously solved, source tasks. In this paper, we argue that building stochastic abstract policies that generalize over past experiences is an effective way to provide such improvement and this generalization outperforms the current practice of using a library of policies. We achieve that contributing with a new algorithm, AbsProb-PI-multiple and a framework for transferring knowledge represented as a stochastic abstract policy in new RL tasks. Stochastic abstract policies offer an effective way to encode knowledge because the abstraction they provide not only generalizes solutions but also facilitates extracting the similarities among tasks. We perform experiments in a robotic navigation environment and analyze the agent's behavior throughout the learning process and also assess the transfer ratio for different amounts of source tasks. We compare our method with the transfer of a library of policies, and experiments show that the use of a generalized policy produces better results by more effectively guiding the agent when learning a target task.
Progress towards CSR RL06 GRACE gravity solutions
Save, Himanshu
2017-04-01
The GRACE project plans to re-processes the GRACE mission data in order to be consistent with the first gravity products released by the GRACE-FO project. The next generation Release-06 (RL06) gravity products from GRACE will include the improvements in GRACE Level-1 data products, background gravity models and the processing methodology. This paper will outline the planned improvements for CSR - RL06 and discuss the preliminary results. This paper will discuss the evolution of the quality of the GRACE solutions, especially over the past few years. We will also discuss the possible challenges we may face in connecting/extending the measurements of mass fluxes from the GRACE era to the GRACE-FO era due quality of the GRACE solutions from recent years.
High-resolution CSR GRACE RL05 mascons
Save, Himanshu; Bettadpur, Srinivas; Tapley, Byron D.
2016-10-01
The determination of the gravity model for the Gravity Recovery and Climate Experiment (GRACE) is susceptible to modeling errors, measurement noise, and observability issues. The ill-posed GRACE estimation problem causes the unconstrained GRACE RL05 solutions to have north-south stripes. We discuss the development of global equal area mascon solutions to improve the GRACE gravity information for the study of Earth surface processes. These regularized mascon solutions are developed with a 1° resolution using Tikhonov regularization in a geodesic grid domain. These solutions are derived from GRACE information only, and no external model or data is used to inform the constraints. The regularization matrix is time variable and will not bias or attenuate future regional signals to some past statistics from GRACE or other models. The resulting Center for Space Research (CSR) mascon solutions have no stripe errors and capture all the signals observed by GRACE within the measurement noise level. The solutions are not tailored for specific applications and are global in nature. This study discusses the solution approach and compares the resulting solutions with postprocessed results from the RL05 spherical harmonic solutions and other global mascon solutions for studies of Arctic ice sheet processes, ocean bottom pressure variation, and land surface total water storage change. This suite of comparisons leads to the conclusion that the mascon solutions presented here are an enhanced representation of the RL05 GRACE solutions and provide accurate surface-based gridded information that can be used without further processing.
Reinforcement Learning for Online Control of Evolutionary Algorithms
Eiben, A.; Horvath, Mark; Kowalczyk, Wojtek; Schut, Martijn
2007-01-01
The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We
Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks
Directory of Open Access Journals (Sweden)
Hasan A. A. Al-Rawi
2014-01-01
Full Text Available Cognitive radio (CR enables unlicensed users (or secondary users, SUs to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs. Reinforcement learning (RL is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs’ network performance without significantly jeopardizing PUs’ network performance, specifically SUs’ interference to PUs.
Vicarious reinforcement learning signals when instructing others.
Apps, Matthew A J; Lesage, Elise; Ramnani, Narender
2015-02-18
Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors. Copyright © 2015 Apps et al.
Intercultural Historical Learning: A Conceptual Framework
Nordgren, Kenneth; Johansson, Maria
2015-01-01
This paper outlines a conceptual framework in order to systematically discuss the meaning of intercultural learning in history education and how it could be advanced. We do so by bringing together theories of historical consciousness, intercultural competence and postcolonial thinking. By combining these theories into one framework, we identify…
A Framework for Mobile Learning for the enhancement of Learning in Higher Education
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Kadar Abdillahi Barreh
2015-07-01
Full Text Available As mobile learning becomes increasingly pervasive, many higher education institutions have embarked on a number of mobile learning initiatives to support their traditional learning modes. This study proposes a framework for mobile learning for the enhancement of learning in higher education. This framework for mobile learning is based on the research conducted on the second year course entitled “Internet Technology,” taught to second year students in the Department of Mathematics and Computer Science at the University of Djibouti. While the entire gamut of mobile technologies and academic applications needs to be considered, special emphasis and focus is provided to Short Message Services (SMS and popular social network sites such as Facebook, which is widely used for recreation. This paper highlights how mobile learning using SMS and Facebook can be designed to enhance student learning in order to help achieve learning outcomes.
Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters
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Chao Lu
2016-08-01
Full Text Available Reinforcement Learning (RL has been proposed to deal with ramp control problems under dynamic traffic conditions; however, there is a lack of sufficient research on the behaviour and impacts of different learning parameters. This paper describes a ramp control agent based on the RL mechanism and thoroughly analyzed the influence of three learning parameters; namely, learning rate, discount rate and action selection parameter on the algorithm performance. Two indices for the learning speed and convergence stability were used to measure the algorithm performance, based on which a series of simulation-based experiments were designed and conducted by using a macroscopic traffic flow model. Simulation results showed that, compared with the discount rate, the learning rate and action selection parameter made more remarkable impacts on the algorithm performance. Based on the analysis, some suggestionsabout how to select suitable parameter values that can achieve a superior performance were provided.
Driver. D530.2 – Tools for the Lessons Learned Framework
Schaik, M.G. van; et al
2016-01-01
In this deliverable D530.2 “Tools for the Lessons Learned Framework” the overall lessons learned framework will be clarified based on the delivery D53.1 “Lessons Learned Framework Concept” and aligned with the deliverable D52.1 “Harmonized competence framework”. The Tools for the Lessons Learned
Application of Reinforcement Learning in Cognitive Radio Networks: Models and Algorithms
Directory of Open Access Journals (Sweden)
Kok-Lim Alvin Yau
2014-01-01
Full Text Available Cognitive radio (CR enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL, which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR.
A Design Framework for Personal Learning Environments
Rahimi, E.
2015-01-01
The purpose of our research was to develop a PLE (personal learning environment) design framework for workplace settings. By doing such, the research has answered this research question, how should a technology-based personal learning environment be designed, aiming at supporting learners to gain
Indiana Department of Education, 2015
2015-01-01
The "Foundations" (English/language arts, mathematics, social emotional skills, approaches to play and learning, science, social studies, creative arts, and physical health and growth) are Indiana's early learning development framework and are aligned to the 2014 Indiana Academic Standards. This framework provides core elements that…
A Framework for the Flexible Content Packaging of Learning Objects and Learning Designs
Lukasiak, Jason; Agostinho, Shirley; Burnett, Ian; Drury, Gerrard; Goodes, Jason; Bennett, Sue; Lockyer, Lori; Harper, Barry
2004-01-01
This paper presents a platform-independent method for packaging learning objects and learning designs. The method, entitled a Smart Learning Design Framework, is based on the MPEG-21 standard, and uses IEEE Learning Object Metadata (LOM) to provide bibliographic, technical, and pedagogical descriptors for the retrieval and description of learning…
Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming
2012-01-31
Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.
Reinforcement learning: Solving two case studies
Duarte, Ana Filipa; Silva, Pedro; dos Santos, Cristina Peixoto
2012-09-01
Reinforcement Learning algorithms offer interesting features for the control of autonomous systems, such as the ability to learn from direct interaction with the environment, and the use of a simple reward signalas opposed to the input-outputs pairsused in classic supervised learning. The reward signal indicates the success of failure of the actions executed by the agent in the environment. In this work, are described RL algorithmsapplied to two case studies: the Crawler robot and the widely known inverted pendulum. We explore RL capabilities to autonomously learn a basic locomotion pattern in the Crawler, andapproach the balancing problem of biped locomotion using the inverted pendulum.
A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks
De Jong, Tim; Fuertes, Alba; Schmeits, Tally; Specht, Marcus; Koper, Rob
2008-01-01
De Jong, T., Fuertes, A., Schmeits, T., Specht, M., & Koper, R. (2009). A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks. In D. Goh (Ed.), Multiplatform E-Learning Systems and Technologies: Mobile Devices for Ubiquitous ICT-Based Education (pp.
Proposing a Framework for Mobile Applications in Disaster Health Learning.
Liu, Alexander G; Altman, Brian A; Schor, Kenneth; Strauss-Riggs, Kandra; Thomas, Tracy N; Sager, Catherine; Leander-Griffith, Michelle; Harp, Victoria
2017-08-01
Mobile applications, or apps, have gained widespread use with the advent of modern smartphone technologies. Previous research has been conducted in the use of mobile devices for learning. However, there is decidedly less research into the use of mobile apps for health learning (eg, patient self-monitoring, medical student learning). This deficiency in research on using apps in a learning context is especially severe in the disaster health field. The objectives of this article were to provide an overview of the current state of disaster health apps being used for learning, to situate the use of apps in a health learning context, and to adapt a learning framework for the use of mobile apps in the disaster health field. A systematic literature review was conducted by using the PRISMA checklist, and peer-reviewed articles found through the PubMed and CINAHL databases were examined. This resulted in 107 nonduplicative articles, which underwent a 3-phase review, culminating in a final selection of 17 articles. While several learning models were identified, none were sufficient as an app learning framework for the field. Therefore, we propose a learning framework to inform the use of mobile apps in disaster health learning. (Disaster Med Public Health Preparedness. 2017;11:487-495).
Design Framework for an Adaptive MOOC Enhanced by Blended Learning
DEFF Research Database (Denmark)
Gynther, Karsten
2016-01-01
The research project has developed a design framework for an adaptive MOOC that complements the MOOC format with blended learning. The design framework consists of a design model and a series of learning design principles which can be used to design in-service courses for teacher professional...
A Framework for Creating Semantically Adaptive Collaborative E-learning Environments
Directory of Open Access Journals (Sweden)
Marija Cubric
2009-09-01
Full Text Available In this paper we present a framework that can be used to generate web-based, semantically adaptive, e-learning and computer-assisted assessment (CAA tools for any given knowledge domain, based upon dynamic ontological modeling. We accomplish this by generating “learning ontologies” for a given knowledge domain. The generated learning ontologies are built upon our previous work on a domain “Glossary” ontology and augmented with additional conceptual relations from the WordNet 3.0 lexical database, using Text2Onto, an open source ontology extraction tool. The main novelty of this work is in “on the fly” generation of computer assisted assessments based on the underlying ontology and pre-defined question templates that are founded on the Bloom’s taxonomy of educational objectives. The main deployment scenario for the framework is a web-service providing collaborative e- learning and knowledge management capabilities to various learning communities. The framework can be extended to provide collection and exploitation of the users’ learning behaviour metrics, in order to further adapt the generated e-learning environment to the learners’ needs.
Collaborative Learning Framework in Business Management Systems
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Vladimir GRIGORE
2008-01-01
Full Text Available This paper presents a solution based on collaboration with experts and practitioner from university and ERP companies involved in process learning by training and learning by working. The solution uses CPI test to establish proper team for framework modules: Real-Time Chat Room, Discussion Forum, E-mail Support and Learning through Training. We define novice, practitioner and expert competence level based on CORONET train methodology. ERP companies have own roles for mentoring services to knowledge workers and evaluate the performance of learning process with teachers’ cooperation in learning by teaching and learning by working module.
Simulation-Based E-Learning Framework for Entrepreneurship Education and Training
Directory of Open Access Journals (Sweden)
Constanţa-Nicoleta Bodea
2015-02-01
Full Text Available The paper proposes an e-Learning framework in entrepreneurship. The framework has three main components, for identification the business opportunities, for developing business scenarios and for risk analysis. A common database assures the components integration. The main components of this framework are already available; the main challenging for those interested in using them is to design an integrated flow of activities, adapted with their curricula and other educational settings. The originality of the approach is that the framework is domain independent and uses advanced IT technologies, such as recommendation algorithms, agent-based simulations and extended graphical support. Using this e-learning framework, the students can learn how to choose relevant characteristics/aspects for a type of business and how important is each of them according specific criteria; how to set realistic values for different characteristics/aspects of the business, how a business scenario can be changed in order to fit better to the business context and how to assess/evaluate business scenarios.
Kim, Su Kyoung; Kirchner, Elsa Andrea; Stefes, Arne; Kirchner, Frank
2017-12-14
Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback during robot RL is advantageous, since an explicit reward function can be easily adapted. However, it is very demanding and tiresome for a human to continuously and explicitly generate feedback. Therefore, the development of implicit approaches is of high relevance. In this paper, we used an error-related potential (ErrP), an event-related activity in the human electroencephalogram (EEG), as an intrinsically generated implicit feedback (rewards) for RL. Initially we validated our approach with seven subjects in a simulated robot learning scenario. ErrPs were detected online in single trial with a balanced accuracy (bACC) of 91%, which was sufficient to learn to recognize gestures and the correct mapping between human gestures and robot actions in parallel. Finally, we validated our approach in a real robot scenario, in which seven subjects freely chose gestures and the real robot correctly learned the mapping between gestures and actions (ErrP detection (90% bACC)). In this paper, we demonstrated that intrinsically generated EEG-based human feedback in RL can successfully be used to implicitly improve gesture-based robot control during human-robot interaction. We call our approach intrinsic interactive RL.
Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities
Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.
2018-02-01
Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.
Modeling Geomagnetic Variations using a Machine Learning Framework
Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.
2017-12-01
We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.
ENGAGE: A Game Based Learning and Problem Solving Framework
2012-07-13
Gamification Summit 2012 Mensa Colloquium 2012.2: Social and Video Games Seattle Science Festival TED Salon Vancouver : http...From - To) 6/1/2012 – 6/30/2012 4. TITLE AND SUBTITLE ENGAGE: A Game Based Learning and Problem Solving Framework 5a. CONTRACT NUMBER N/A 5b...Popović ENGAGE: A Game Based Learning and Problem Solving Framework (Task 1 Month 4) Progress, Status and Management Report Monthly Progress
An Integrated Framework Of Web 2.0 Technology And A Collaborative Learning
Directory of Open Access Journals (Sweden)
Mohamed Madar
2015-05-01
Full Text Available Abstract This paper contributes to the suitability of web 2.0 technology in implementing collaborative learning and proposes an integrated framework of Web 2.0 tools and collaborative learning activities. This paper is also identifying the mismatch between adopting web 2.0 technologies and the delivery of the curriculum on the cloud or via the Internet. It is found that Web 2.0 and a collaborative learning are two platforms to be easily synchronized due to their common attributes that enable their complementariness. This paper argues that integrated framework of Web 2.0 and CL allow users exploit teachinglearning materials maximally and at the same upsurges learners understanding in the subject knowledge. Suitable of Web 2.0 in implementing curriculum was also encouraged since the proposed framework consists of both components of Web 2.0 functions and activities of collaborative learning environment. Pedagogically there has been a mismatch between E-learning technologies and mode of delivery for instance E-learning platforms are widely used to increase content accessibility only while now this framework introduces that Web 2.0 technology of E-learning can also be used to create share knowledge among users. The proposed framework if efficiently exploited will also allow users at all levels create personalized learning environment which suits perspective teachinglearning styles of the users. Apart from academic achievement or enhancements of the teaching and learning processes the proposed framework also would help learners develop generic skills which are very important in the workplaces. As a result of this fast and independent learning technically depend on technology based pedagogy and in this case this proposed model has two dimensions which are very crucial to the enrichment of students learning activities.
Calibration of piezoelectric RL shunts with explicit residual mode correction
DEFF Research Database (Denmark)
Høgsberg, Jan Becker; Krenk, Steen
2017-01-01
Piezoelectric RL (resistive-inductive) shunts are passive resonant devices used for damping of dominant vibration modes of a flexible structure and their efficiency relies on the precise calibration of the shunt components. In the present paper improved calibration accuracy is attained by an exte......Piezoelectric RL (resistive-inductive) shunts are passive resonant devices used for damping of dominant vibration modes of a flexible structure and their efficiency relies on the precise calibration of the shunt components. In the present paper improved calibration accuracy is attained...... by an extension of the local piezoelectric transducer displacement by two additional terms, representing the flexibility and inertia contributions from the residual vibration modes not directly addressed by the shunt damping. This results in an augmented dynamic model for the targeted resonant vibration mode...
TEXPLORE temporal difference reinforcement learning for robots and time-constrained domains
Hester, Todd
2013-01-01
This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuou...
Integrating distributed Bayesian inference and reinforcement learning for sensor management
Grappiolo, C.; Whiteson, S.; Pavlin, G.; Bakker, B.
2009-01-01
This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically
A framework for studying teacher learning by design
Voogt, Joke; McKenney, Susan; Janssen, Fred; Berry, Amanda; Kicken, Wendy; Coenders, Fer
2012-01-01
Voogt, J., McKenney, S., Janssen, F., Berry, A., Kicken, W., & Coenders, F. (2012, 2-6 July). A framework for studying teacher learning by design. Paper presentation at the Teachers as Designers of Technology Enhanced Learning pre-conference workshop in conjunction with the ISLS annual meeting,
Fernández-Peña, Rosario; Fuentes-Pumarola, Concepció; Malagón-Aguilera, M Carme; Bonmatí-Tomàs, Anna; Bosch-Farré, Cristina; Ballester-Ferrando, David
2016-09-01
Adapting university programmes to European Higher Education Area criteria has required substantial changes in curricula and teaching methodologies. Reflective learning (RL) has attracted growing interest and occupies an important place in the scientific literature on theoretical and methodological aspects of university instruction. However, fewer studies have focused on evaluating the RL methodology from the point of view of nursing students. To assess nursing students' perceptions of the usefulness and challenges of RL methodology. Mixed method design, using a cross-sectional questionnaire and focus group discussion. The research was conducted via self-reported reflective learning questionnaire complemented by focus group discussion. Students provided a positive overall evaluation of RL, highlighting the method's capacity to help them better understand themselves, engage in self-reflection about the learning process, optimize their strengths and discover additional training needs, along with searching for continuous improvement. Nonetheless, RL does not help them as much to plan their learning or identify areas of weakness or needed improvement in knowledge, skills and attitudes. Among the difficulties or challenges, students reported low motivation and lack of familiarity with this type of learning, along with concerns about the privacy of their reflective journals and about the grading criteria. In general, students evaluated RL positively. The results suggest areas of needed improvement related to unfamiliarity with the methodology, ethical aspects of developing a reflective journal and the need for clear evaluation criteria. Copyright © 2016 Elsevier Ltd. All rights reserved.
Martey, Orleans; Nimick, Mhairi; Taurin, Sebastien; Sundararajan, Vignesh; Greish, Khaled; Rosengren, Rhonda J
2017-01-01
Patients with triple negative breast cancer have a poor prognosis due in part to the lack of targeted therapies. In the search for novel drugs, our laboratory has developed a second-generation curcumin derivative, 3,5-bis(3,4,5-trimethoxybenzylidene)-1-methylpiperidine-4-one (RL71), that exhibits potent in vitro cytotoxicity. To improve the clinical potential of this drug, we have encapsulated it in styrene maleic acid (SMA) micelles. SMA-RL71 showed improved biodistribution, and drug accumulation in the tumor increased 16-fold compared to control. SMA-RL71 (10 mg/kg, intravenously, two times a week for 2 weeks) also significantly suppressed tumor growth compared to control in a xenograft model of triple negative breast cancer. Free RL71 was unable to alter tumor growth. Tumors from SMA-RL71-treated mice showed a decrease in angiogenesis and an increase in apoptosis. The drug treatment also modulated various cell signaling proteins including the epidermal growth factor receptor, with the mechanisms for tumor suppression consistent with previous work with RL71 in vitro. The nanoformulation was also nontoxic as shown by normal levels of plasma markers for liver and kidney injury following weekly administration of SMA-RL71 (10 mg/kg) for 90 days. Thus, we report clinical potential following encapsulation of a novel curcumin derivative, RL71, in SMA micelles.
Framework of Strategic Learning: The PDCA Cycle
Directory of Open Access Journals (Sweden)
Michał Pietrzak
2015-06-01
Full Text Available Nowadays, strategic planning has to be permanent process and organizational learning should support it. Researchers in theories of organizational learning attempt to understand processes, which lead to changes in organizational knowledge, as well as the effects of learning on organizational performance. In traditional approach, the strategy is viewed as one shot event. However, in contemporary turbulent environment this could not be still valid. There is a need of elastic strategic management, which employs organizational learning process. The crucial element of such process is information acquisition, which allows refining the initial version of strategic plan. In this article authors discuss the PDCA cycle as a framework of strategic learning process, including both single-loop and double loop learning. Authors proposed the ideas for further research in area of organizational learning and strategic management.
Bakic, Jasmina; Pourtois, Gilles; Jepma, Marieke; Duprat, Romain; De Raedt, Rudi; Baeken, Chris
2017-01-01
Major depressive disorder (MDD) creates debilitating effects on a wide range of cognitive functions, including reinforcement learning (RL). In this study, we sought to assess whether reward processing as such, or alternatively the complex interplay between motivation and reward might potentially account for the abnormal reward-based learning in MDD. A total of 35 treatment resistant MDD patients and 44 age matched healthy controls (HCs) performed a standard probabilistic learning task. RL was titrated using behavioral, computational modeling and event-related brain potentials (ERPs) data. MDD patients showed comparable learning rate compared to HCs. However, they showed decreased lose-shift responses as well as blunted subjective evaluations of the reinforcers used during the task, relative to HCs. Moreover, MDD patients showed normal internal (at the level of error-related negativity, ERN) but abnormal external (at the level of feedback-related negativity, FRN) reward prediction error (RPE) signals during RL, selectively when additional efforts had to be made to establish learning. Collectively, these results lend support to the assumption that MDD does not impair reward processing per se during RL. Instead, it seems to alter the processing of the emotional value of (external) reinforcers during RL, when additional intrinsic motivational processes have to be engaged. © 2016 Wiley Periodicals, Inc.
The 4C framework for making reasonable adjustments for people with learning disabilities.
Marsden, Daniel; Giles, Rachel
2017-01-18
Background People with learning disabilities experience significant inequalities in accessing healthcare. Legal frameworks, such as the Equality Act 2010, are intended to reduce such disparities in care, and require organisations to make 'reasonable adjustments' for people with disabilities, including learning disabilities. However, reasonable adjustments are often not clearly defined or adequately implemented in clinical practice. Aim To examine and synthesise the challenges in caring for people with learning disabilities to develop a framework for making reasonable adjustments for people with learning disabilities in hospital. This framework would assist ward staff in identifying and managing the challenges of delivering person-centred, safe and effective healthcare to people with learning disabilities in this setting. Method Fourth-generation evaluation, collaborative thematic analysis, reflection and a secondary analysis were used to develop a framework for making reasonable adjustments in the hospital setting. The authors attended ward manager and matron group meetings to collect their claims, concerns and issues, then conducted a collaborative thematic analysis with the group members to identify the main themes. Findings Four main themes were identified from the ward manager and matron group meetings: communication, choice-making, collaboration and coordination. These were used to develop the 4C framework for making reasonable adjustments for people with learning disabilities in hospital. Discussion The 4C framework has provided a basis for delivering person-centred care for people with learning disabilities. It has been used to inform training needs analyses, develop audit tools to review delivery of care that is adjusted appropriately to the individual patient; and to develop competencies for learning disability champions. The most significant benefit of the 4C framework has been in helping to evaluate and resolve practice-based scenarios. Conclusion Use of
PeRL: A circum-Arctic Permafrost Region Pond and Lake database
Muster, Sina; Roth, Kurt; Langer, Moritz; Lange, Stephan; Cresto Aleina, Fabio; Bartsch, Annett; Morgenstern, Anne; Grosse, Guido; Jones, Benjamin; Sannel, A.B.K.; Sjoberg, Ylva; Gunther, Frank; Andresen, Christian; Veremeeva, Alexandra; Lindgren, Prajna R.; Bouchard, Frédéric; Lara, Mark J.; Fortier, Daniel; Charbonneau, Simon; Virtanen, Tarmo A.; Hugelius, Gustaf; Palmtag, J.; Siewert, Matthias B.; Riley, William J.; Koven, Charles; Boike, Julia
2017-01-01
Ponds and lakes are abundant in Arctic permafrost lowlands. They play an important role in Arctic wetland ecosystems by regulating carbon, water, and energy fluxes and providing freshwater habitats. However, ponds, i.e., waterbodies with surface areas smaller than 1. 0 × 104 m2, have not been inventoried on global and regional scales. The Permafrost Region Pond and Lake (PeRL) database presents the results of a circum-Arctic effort to map ponds and lakes from modern (2002–2013) high-resolution aerial and satellite imagery with a resolution of 5 m or better. The database also includes historical imagery from 1948 to 1965 with a resolution of 6 m or better. PeRL includes 69 maps covering a wide range of environmental conditions from tundra to boreal regions and from continuous to discontinuous permafrost zones. Waterbody maps are linked to regional permafrost landscape maps which provide information on permafrost extent, ground ice volume, geology, and lithology. This paper describes waterbody classification and accuracy, and presents statistics of waterbody distribution for each site. Maps of permafrost landscapes in Alaska, Canada, and Russia are used to extrapolate waterbody statistics from the site level to regional landscape units. PeRL presents pond and lake estimates for a total area of 1. 4 × 106 km2 across the Arctic, about 17 % of the Arctic lowland ( s.l.) land surface area. PeRL waterbodies with sizes of 1. 0 × 106 m2 down to 1. 0 × 102 m2 contributed up to 21 % to the total water fraction. Waterbody density ranged from 1. 0 × 10 to 9. 4 × 101 km−2. Ponds are the dominant waterbody type by number in all landscapes representing 45–99 % of the total waterbody number. The implementation of PeRL size distributions in land surface models will greatly improve the investigation and projection of surface inundation and carbon fluxes in permafrost lowlands. Waterbody maps, study area
Emotion in reinforcement learning agents and robots: A survey
Moerland, T.M.; Broekens, D.J.; Jonker, C.M.
2018-01-01
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for ...
Optimal Control via Reinforcement Learning with Symbolic Policy Approximation
Kubalìk, Jiřì; Alibekov, Eduard; Babuska, R.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri
2017-01-01
Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper
An Analytic Framework to Support E.Learning Strategy Development
Marshall, Stephen J.
2012-01-01
Purpose: The purpose of this paper is to discuss and demonstrate the relevance of a new conceptual framework for leading and managing the development of learning and teaching to e.learning strategy development. Design/methodology/approach: After reviewing and discussing the research literature on e.learning in higher education institutions from…
Insights into the Earth System mass variability from CSR-RL05 GRACE gravity fields
Bettadpur, S.
2012-04-01
The next-generation Release-05 GRACE gravity field data products are the result of extensive effort applied to the improvements to the GRACE Level-1 (tracking) data products, and to improvements in the background gravity models and processing methodology. As a result, the squared-error upper-bound in RL05 fields is half or less than the squared-error upper-bound in RL04 fields. The CSR-RL05 field release consists of unconstrained gravity fields as well as a regularized gravity field time-series that can be used for several applications without any post-processing error reduction. This paper will describe the background and the nature of these improvements in the data products, and provide an error characterization. We will describe the insights these new series offer in measuring the mass flux due to diverse Hydrologic, Oceanographic and Cryospheric processes.
Directory of Open Access Journals (Sweden)
Samira Sadat Sajadi
2014-09-01
Full Text Available This paper presents an investigation on the theory of constructivism applicable for learners with learning difficulties, specifically learners with Attention Deficit Hyperactivity Disorder (ADHD. The primary objective of this paper is to determine whether a constructivist technology enhanced learning pedagogy could be used to help ADHD learners cope with their educational needs within a social-media learning environment. Preliminary work is stated here, in which we are seeking evidence to determine the viability of a constructivist approach for learners with ADHD. The novelty of this research lies in the proposals to support ADHD learners to overcome their weaknesses with appropriate pedagogically sound interventions. As a result, a framework has been designed to illuminate areas in which constructivist pedagogies require to address the limitations of ADHD learners. An analytical framework addressing the suitability of a constructivist learning for ADHD is developed from a combination of literature and expert advice from those involved in the education of learners with ADHD. This analytical framework is married to a new model of pedagogy, which the authors have derived from literature analysis. Future work will expand this model to develop a constructivist social network-based learning and eventually test it in specialist schools with ADHD learners.
Koh, Jansen
2016-01-01
Lifelong learning is an essential trait that is expected of every physician. The CanMeds 2005 Physician Competency Framework emphasizes lifelong learning as a key competency that physicians must achieve in becoming better physicians. However, many physicians are not competent at engaging in lifelong learning. The current medical education system is deficient in preparing medical students to develop and carry out their own lifelong learning curriculum upon graduation. Despite understanding how physicians learn at work, medical students are not trained to learn while working. Similarly, although barriers to lifelong learning are known, medical students are not adequately skilled in overcoming these barriers. Learning to learn is just as important, if not more, as acquiring the skills and knowledge required of a physician. The medical undergraduate curriculum lacks a specific learning strategy to prepare medical students in becoming an adept lifelong learner. In this article, we propose a learning strategy for lifelong learning at the undergraduate level. In developing this novel strategy, we paid particular attention to two parameters. First, this strategy should be grounded on literature describing a physician’s lifelong learning process. Second, the framework for implementing this strategy must be based on existing undergraduate learning strategies to obviate the need for additional resources, learner burden, and faculty time. In this paper, we propose a Problem, Analysis, Independent Research Reporting, Experimentation Debriefing (PAIRED) framework that follows the learning process of a physician and serves to synergize the components of problem-based learning and simulation-based learning in specifically targeting the barriers to lifelong learning. PMID:27446767
Ahmadibasir, Mohammad
In this study an interpretive learning framework that aims to measure learning on the classroom level is introduced. In order to develop and evaluate the value of the framework, a theoretical/empirical study is designed. The researcher attempted to illustrate how the proposed framework provides insights on the problem of classroom-level learning. The framework is developed by construction of connections between the current literature on science learning and Wittgenstein's language-game theory. In this framework learning is defined as change of classroom language-game or discourse. In the proposed framework, learning is measured by analysis of classroom discourse. The empirical explanation power of the framework is evaluated by applying the framework in the analysis of learning in a fifth-grade science classroom. The researcher attempted to analyze how students' colloquial discourse changed to a discourse that bears more resemblance to science discourse. The results of the empirical part of the investigation are presented in three parts: first, the gap between what students did and what they were supposed to do was reported. The gap showed that students during the classroom inquiry wanted to do simple comparisons by direct observation, while they were supposed to do tool-assisted observation and procedural manipulation for a complete comparison. Second, it was illustrated that the first attempt to connect the colloquial to science discourse was done by what was immediately intelligible for students and then the teacher negotiated with students in order to help them to connect the old to the new language-game more purposefully. The researcher suggested that these two events in the science classroom are critical in discourse change. Third, it was illustrated that through the academic year, the way that students did the act of comparison was improved and by the end of the year more accurate causal inferences were observable in classroom communication. At the end of the
Sugiyanta, Lipur; Sukardjo, Moch.
2018-04-01
The 2013 curriculum requires teachers to be more productive, creative, and innovative in encouraging students to be more independent by strengthening attitudes, skills and knowledge. Teachers are given the options to create lesson plan according to the environment and conditions of their students. At the junior level, Core Competence (KI) and Basic Competence (KD) have been completely designed. In addition, there had already guidebooks, both for teacher manuals (Master’s Books) and for learners (Student Books). The lesson plan and guidebooks which already exist are intended only for learning in the classroom/in-school. Many alternative classrooms and alternatives learning models opened up using educational technology. The advance of educational technology opened opportunity for combination of class interaction using mobile learning applications. Mobile learning has rapidly evolved in education for the last ten years and many initiatives have been conducted worldwide. However, few of these efforts have produced any lasting outcomes. It is evident that mobile education applications are complex and hence, will not become sustainable. Long-term sustainability remains a risk. Long-term sustainability usually was resulted from continuous adaptation to changing conditions [4]. Frameworks are therefore required to avoid sustainability pitfalls. The implementation should start from simple environment then gradually become complex through adaptation steps. Therefore, our paper developed the framework of mobile learning (m-learning) adaptation for grade 7th (junior high school). The environment setup was blended mobile learning (not full mobile learning) and emphasize on Algebra. The research is done by R&D method (research and development). Results of the framework includes requirements and adaptation steps. The adjusted m-learning framework is designed to be a guidance for teachers to adopt m-learning to support blended learning environments. During mock-up prototype, the
Is "Learning without Limits" a Framework of Values?
Booth, Tony
2015-01-01
In this article the author connects his own work with Brian Simon's writing on IQ (intelligence quotient) testing and selection and with the Learning without Limits project. He discusses the significance he gives to a values framework in the development of education and asks whether "Learning without Limits," in part, stands for a…
Expanding the frontiers of national qualifications frameworks through lifelong learning
Owusu-Agyeman, Yaw
2017-10-01
The adoption of a national qualifications framework (NQF) by some governments in all world regions has shown some success in the area of formal learning. However, while NQFs continue to enhance formal learning in many countries, the same cannot be said for the recognition, validation and accreditation (RVA) of non-formal and informal learning. Focusing on competency-based technical and vocational education and training (TVET) within its NQF, Ghana introduced the National Technical and Vocational Education and Training Qualifications Framework (NTVETQF) as a sub-framework in 2012. In the wake of the NTVETQF's limited success, the author of this article reasons that a lifelong learning approach could enhance its effectiveness considerably. Comparing national and international policies, he argues that the NTVETQF should be able to properly address the issues of progression from informal and non-formal to formal modes of lifelong learning within the country's broad context of education. In addition, the study conceptualises the integration of lifelong learning within a broad NQF in four key domains: (1) individual; (2) institutional; (3) industry; and (4) state. The author concludes that, for the NTVETQF to achieve its goal of facilitating access to further education and training while also promoting lifelong learning for all (including workers in the informal economy), effective integration of all modes of lifelong learning is required. Although this entails some challenges, such as recognition of prior learning and validation of all modes of learning, it will help to widen access to education as well as providing individuals with a pathway for achieving their educational aspirations.
Directory of Open Access Journals (Sweden)
Chi-Chang Chang
2014-12-01
Full Text Available Endometrial cancer is a common malignancy of the female genital tract. This study demonstrates that Siegesbeckia orientalis ethanol extract (SOE significantly inhibited the proliferation of RL95-2 human endometrial cancer cells. Treating RL95-2 cells with SOE caused cell arrest in the G2/M phase and induced apoptosis of RL95-2 cells by up-regulating Bad, Bak and Bax protein expression and down-regulation of Bcl-2 and Bcl-xL protein expression. Treatment with SOE increased protein expression of caspase-3, -8 and -9 dose-dependently, indicating that apoptosis was through the intrinsic and extrinsic apoptotic pathways. Moreover, SOE was also effective against A549 (lung cancer, Hep G2 (hepatoma, FaDu (pharynx squamous cancer, MDA-MB-231 (breast cancer, and especially on LNCaP (prostate cancer cell lines. In total, 10 constituents of SOE were identified by Gas chromatography-mass analysis. Caryophyllene oxide and caryophyllene are largely responsible for most cytotoxic activity of SOE against RL95-2 cells. Overall, this study suggests that SOE is a promising anticancer agent for treating endometrial cancer.
Visual Hybrid Development Learning System (VHDLS) framework for children with autism.
Banire, Bilikis; Jomhari, Nazean; Ahmad, Rodina
2015-10-01
The effect of education on children with autism serves as a relative cure for their deficits. As a result of this, they require special techniques to gain their attention and interest in learning as compared to typical children. Several studies have shown that these children are visual learners. In this study, we proposed a Visual Hybrid Development Learning System (VHDLS) framework that is based on an instructional design model, multimedia cognitive learning theory, and learning style in order to guide software developers in developing learning systems for children with autism. The results from this study showed that the attention of children with autism increased more with the proposed VHDLS framework.
A Teaching - Learning Framework for MEMS Education
International Nuclear Information System (INIS)
Sheeparamatti, B G; Angadi, S A; Sheeparamatti, R B; Kadadevaramath, J S
2006-01-01
Micro-Electro-Mechanical Systems (MEMS) technology has been identified as one of the most promising technologies in the 21st century. MEMS technology has opened up a wide array of unforeseen applications. Hence it is necessary to train the technocrats of tomorrow in this emerging field to meet the industrial/societal demands. The drive behind fostering of MEMS technology is the reduction in the cost, size, weight, and power consumption of the sensors, actuators, and associated electronics. MEMS is a multidisciplinary engineering and basic science area which includes electrical engineering, mechanical engineering, material science and biomedical engineering. Hence MEMS education needs a special approach to prepare the technocrats for a career in MEMS. The modern education methodology using computer based training systems (CBTS) with embedded modeling and simulation tools will help in this direction. The availability of computer based learning resources such as MATLAB, ANSYS/Multiphysics and rapid prototyping tools have contributed to proposition of an efficient teaching-learning framework for MEMS education presented in this paper. This paper proposes a conceptual framework for teaching/learning MEMS in the current technical education scenario
A Framework for Building an Interactive Satellite TV Based M-Learning Environment
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Ghassan Issa
2010-07-01
Full Text Available This paper presents a description of an interactive satellite TV based mobile learning (STV-ML framework, in which a satellite TV station is used as an integral part of a comprehensive interactive mobile learning (M-Learning environment. The proposed framework assists in building a reliable, efficient, and cost-effective environment to meet the growing demands of M-Learning all over the world, especially in developing countries. It utilizes recent advances in satellite reception, broadcasting technologies, and interactive TV to facilitate the delivery of gigantic learning materials. This paper also proposed a simple and flexible three-phase implementation methodology which includes construction of earth station, expansion of broadcasting channels, and developing true user interactivity. The proposed framework and implementation methodology ensure the construction of a true, reliable, and cost effective M-Learning system that can be used efficiently and effectively by a wide range of users and educational institutions to deliver ubiquitous learning.
Directory of Open Access Journals (Sweden)
Umut Osmanlı
2017-06-01
Full Text Available Abstract This study examines the concept of crafting which means becoming an expert in an activity. Specializing in a field, in order to facilitate the life, backs as early as the first human settlements in human history. Crafting is a phenomenon in which the person makes the craft in the best possible way for its own goodness. However, it has been gone through some transformations since its formation. The history of crafting includes some milestones such as institution of crafting, its unification and its transformation after the Industrial Revolution. The most essential one of those transformations has been experienced after the Industrial Revolution and it has become difficult to define the crafting followingly. At the present time, crafting has been diminished to the status of a simple worker. In this work, which the historical transformation of crafting is held, It will be examined respectively: the historical roots of crafting, the social status of crafting, professional organizations that hold crafting altogether, the effects on crafting brought by new economic order of industrial revolution and the ideas of Richard Sennett on crafting in order to evaluate crafting in our present society. Hereby, I will try to reach the definitions of crafting that are able to address our today’s society by taking references from the roots of archaic crafting phenomenon. Öz Bu çalışmada, bilinçli olarak yapılan bir eylemde ustalaşma/uzmanlaşma anlamına gelen zanaatkârlık kavramı incelenmiştir. Hayatı kolaylaştırmak adına herhangi bir alanda çalışma ve bu alanda beceri kazanarak ustalaşmak, insanların bir arada yaşamaya başlaması kadar eskidir. Zanaatkârlık, kişinin kendi iyiliği için uğraş verdiği şeyi mümkün olan en iyi şekilde yapmasıdır. Fakat bu durum ortaya çıktığı günden günümüze gelene kadar çeşitli dönüşümlere uğramıştır. Zanaatkârlığın tarihsel serüveni, zanaatkârl
Wani, Parvaze Ahmad; Khan, Mohammad Saghir
2013-07-01
Pollution of the biosphere by heavy metals is a global threat that has accelerated dramatically since the beginning of industrial revolution. The aim of the study is to check the resistance of RL9 towards the metals and to observe the effect of Rhizobium species on growth, pigment content, protein and nickel uptake by lentil in the presence and absence of nickel. The multi metal tolerant and plant growth promoting Rhizobium strain RL9 was isolated from the nodules of lentil. The strain not only tolerated nickel but was also tolerant o cadmium, chromium, nickel, lead, zinc and copper. The strain tolerated nickel 500 μg/mL, cadmium 300 μg/mL, chromium 400 μg/mL, lead 1,400 μg/mL, zinc 1,000 μg/mL and copper 300 μg/mL, produced good amount of indole acetic acid and was also positive for siderophore, hydrogen cyanide and ammonia. The strain RL9 was further assessed with increasing concentrations of nickel when lentil was used as a test crop. The strain RL9 significantly increased growth, nodulation, chlorophyll, leghaemoglobin, nitrogen content, seed protein and seed yield compared to plants grown in the absence of bioinoculant but amended with nickel The strain RL9 decreased uptake of nickel in lentil compared to plants grown in the absence of bio-inoculant. Due to these intrinsic abilities strain RL9 could be utilized for growth promotion as well as for the remediation of nickel in nickel contaminated soil.
The extraction and integration framework: a two-process account of statistical learning.
Thiessen, Erik D; Kronstein, Alexandra T; Hufnagle, Daniel G
2013-07-01
The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other. 2013 APA, all rights reserved
Zhou, Jin; Yu, Lu; Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Markon, Sandor
Elevator Group Supervisory Control System (EGSCS) is a very large scale stochastic dynamic optimization problem. Due to its vast state space, significant uncertainty and numerous resource constraints such as finite car capacities and registered hall/car calls, it is hard to manage EGSCS using conventional control methods. Recently, many solutions for EGSCS using Artificial Intelligence (AI) technologies have been reported. Genetic Network Programming (GNP), which is proposed as a new evolutionary computation method several years ago, is also proved to be efficient when applied to EGSCS problem. In this paper, we propose an extended algorithm for EGSCS by introducing Reinforcement Learning (RL) into GNP framework, and an improvement of the EGSCS' performances is expected since the efficiency of GNP with RL has been clarified in some other studies like tile-world problem. Simulation tests using traffic flows in a typical office building have been made, and the results show an actual improvement of the EGSCS' performances comparing to the algorithms using original GNP and conventional control methods. Furthermore, as a further study, an importance weight optimization algorithm is employed based on GNP with RL and its efficiency is also verified with the better performances.
A Framework for Collaborative Networked Learning in Higher Education: Design & Analysis
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Ghassan F. Issa
2014-06-01
Full Text Available This paper presents a comprehensive framework for building collaborative learning networks within higher educational institutions. This framework focuses on systems design and implementation issues in addition to a complete set of evaluation, and analysis tools. The objective of this project is to improve the standards of higher education in Jordan through the implementation of transparent, collaborative, innovative, and modern quality educational programs. The framework highlights the major steps required to plan, design, and implement collaborative learning systems. Several issues are discussed such as unification of courses and program of studies, using appropriate learning management system, software design development using Agile methodology, infrastructure design, access issues, proprietary data storage, and social network analysis (SNA techniques.
Kino repertuāra pārlūks Android ierīcēm
Zvirbulis, Jānis
2013-01-01
Kvalifikācijas darbā “Kino repertuāra pārlūks Android ierīcēm” tiek aprakstīta Android lietojumprogrammas “Kino repertuāra pārlūks” izstrāde un funkcionalitāte. Lietotne paredzēta kinoteātra repertuāra aplūkošanai izmantojot planšetdatorus un mobilos tālruņus, kas darbojas ar Android operētājsistēmu. Tā ir domāta kā parocīgāka alternatīva filmu apraksta un seansa laiku uzzināšanai caur kinoteātra mājaslapu, skrejlapām vai afišām. Atslēgvārdi: Android, filmas, pārlūks....
A Conceptual Framework for Evolving, Recommender Online Learning Systems
Peiris, K. Dharini Amitha; Gallupe, R. Brent
2012-01-01
A comprehensive conceptual framework is developed and described for evolving recommender-driven online learning systems (ROLS). This framework describes how such systems can support students, course authors, course instructors, systems administrators, and policy makers in developing and using these ROLS. The design science information systems…
Directory of Open Access Journals (Sweden)
Yasutaka Kishima
2013-01-01
Full Text Available Many studies have been conducted on the application of reinforcement learning (RL to robots. A robot which is made for general purpose has redundant sensors or actuators because it is difficult to assume an environment that the robot will face and a task that the robot must execute. In this case, -space on RL contains redundancy so that the robot must take much time to learn a given task. In this study, we focus on the importance of sensors with regard to a robot’s performance of a particular task. The sensors that are applicable to a task differ according to the task. By using the importance of the sensors, we try to adjust the state number of the sensors and to reduce the size of -space. In this paper, we define the measure of importance of a sensor for a task with the correlation between the value of each sensor and reward. A robot calculates the importance of the sensors and makes the size of -space smaller. We propose the method which reduces learning space and construct the learning system by putting it in RL. In this paper, we confirm the effectiveness of our proposed system with an experimental robot.
The permeability of EUDRAGIT RL and HEMA-MMA microcapsules to glucose and inulin.
Douglas, J A; Sefton, M V
1990-10-05
Measurement of the rate of glucose diffusion from EUDGRAGIT RL and HEMA-MMA microcapsules coupled with a Thiele modulus/Biot number analysis of the glucose utilization rate suggests that pancreatic islets and CHO (Chinese hamster ovary) cells (at moderate to high cell densities) should not be adversely affected by the diffusion restrictions associated with these capsule membranes. The mass transfer coefficients for glucose at 20 degrees C were of the same order of magnitude for both capsules, based on release measurements: approximately 5 x 10(-6) cm/s for EUDRAGIT RL and approximately 2 x 10(-6) for HEMA-MMA. Inulin release from EUDRAGIT RL was slower than for glucose (mass transfer coefficient 14 +/- 4 x 10(-8) cm/s). The Thiele moduli were much less than 1, either for a single islet at the center of a capsule or CHO cells uniformly distributed throughout a capsule at 10(-6) cells/ mL, so that diffusion restrictions within the cells in EUDRAGIT RL or 800 microm HEMA-MMA capsules should be negligible. The ratio of external to internal diffusion resistance (Biot number) was less than 1, so that at most, only a small diffusion effect on glucose utilization should be expected (i.e., the overall effectiveness factors were greater than 0.8). These calculations were consistent with experimental observation of encapsulated islet behavior but not fully with CHO cell behavior. Permeability restricted cell viability and growth is potentially a major limitation of encapsulated cells; further analysis is warranted.
Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning.
Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien
2015-01-01
Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.
Hossain, Mohammad B; Li, Huiqi; Hedmer, Maria; Tinnerberg, Håkan; Albin, Maria; Broberg, Karin
2015-12-01
Welders are at risk for cardiovascular disease. Recent studies linked tobacco smoke exposure to hypomethylation of the F2RL3 (coagulation factor II (thrombin) receptor-like 3) gene, a marker for cardiovascular disease prognosis and mortality. However, whether welding fumes cause hypomethylation of F2RL3 remains unknown. We investigated 101 welders (median span of working as a welder: 7 years) and 127 unexposed controls (non-welders with no obvious exposure to respirable dust at work), age range 23-60 years, all currently non-smoking, in Sweden. The participants were interviewed about their work history, lifestyle factors and diseases. Personal sampling of respirable dust was performed for the welders. DNA methylation of F2RL3 in blood was assessed by pyrosequencing of four CpG sites, CpG_2 (corresponds to cg03636183) to CpG_5, in F2RL3. Multivariable linear regression analysis was used to assess the association between exposure to welding fumes and F2RL3 methylation. Welders had 2.6% lower methylation of CpG_5 than controls (pWelding fumes exposure and previous smoking were associated with F2RL3 hypomethylation. This finding links low-to-moderate exposure to welding fumes to adverse effects on the cardiovascular system, and suggests a potential mechanistic pathway for this link, via epigenetic effects on F2RL3 expression. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Active-constructive-interactive: a conceptual framework for differentiating learning activities.
Chi, Michelene T H
2009-01-01
Active, constructive, and interactive are terms that are commonly used in the cognitive and learning sciences. They describe activities that can be undertaken by learners. However, the literature is actually not explicit about how these terms can be defined; whether they are distinct; and whether they refer to overt manifestations, learning processes, or learning outcomes. Thus, a framework is provided here that offers a way to differentiate active, constructive, and interactive in terms of observable overt activities and underlying learning processes. The framework generates a testable hypothesis for learning: that interactive activities are most likely to be better than constructive activities, which in turn might be better than active activities, which are better than being passive. Studies from the literature are cited to provide evidence in support of this hypothesis. Moreover, postulating underlying learning processes allows us to interpret evidence in the literature more accurately. Specifying distinct overt activities for active, constructive, and interactive also offers suggestions for how learning activities can be coded and how each kind of activity might be elicited. Copyright © 2009 Cognitive Science Society, Inc.
PeRL: a circum-Arctic Permafrost Region Pond and Lake database
Muster, Sina; Roth, Kurt; Langer, Moritz; Lange, Stephan; Cresto Aleina, Fabio; Bartsch, Annett; Morgenstern, Anne; Grosse, Guido; Jones, Benjamin; Sannel, A. Britta K.; Sjöberg, Ylva; Günther, Frank; Andresen, Christian; Veremeeva, Alexandra; Lindgren, Prajna R.; Bouchard, Frédéric; Lara, Mark J.; Fortier, Daniel; Charbonneau, Simon; Virtanen, Tarmo A.; Hugelius, Gustaf; Palmtag, Juri; Siewert, Matthias B.; Riley, William J.; Koven, Charles D.; Boike, Julia
2017-06-01
Ponds and lakes are abundant in Arctic permafrost lowlands. They play an important role in Arctic wetland ecosystems by regulating carbon, water, and energy fluxes and providing freshwater habitats. However, ponds, i.e., waterbodies with surface areas smaller than 1. 0 × 104 m2, have not been inventoried on global and regional scales. The Permafrost Region Pond and Lake (PeRL) database presents the results of a circum-Arctic effort to map ponds and lakes from modern (2002-2013) high-resolution aerial and satellite imagery with a resolution of 5 m or better. The database also includes historical imagery from 1948 to 1965 with a resolution of 6 m or better. PeRL includes 69 maps covering a wide range of environmental conditions from tundra to boreal regions and from continuous to discontinuous permafrost zones. Waterbody maps are linked to regional permafrost landscape maps which provide information on permafrost extent, ground ice volume, geology, and lithology. This paper describes waterbody classification and accuracy, and presents statistics of waterbody distribution for each site. Maps of permafrost landscapes in Alaska, Canada, and Russia are used to extrapolate waterbody statistics from the site level to regional landscape units. PeRL presents pond and lake estimates for a total area of 1. 4 × 106 km2 across the Arctic, about 17 % of the Arctic lowland ( pangaea.de/10.1594/PANGAEA.868349" target="_blank">https://doi.pangaea.de/10.1594/PANGAEA.868349.
Grounding theories of W(e)Learn: a framework for online interprofessional education.
Casimiro, Lynn; MacDonald, Colla J; Thompson, Terrie Lynn; Stodel, Emma J
2009-07-01
Interprofessional care (IPC) is a prerequisite for enhanced communication between healthcare team members, improved quality of care, and better outcomes for patients. A move to an IPC model requires changing the learning experiences of healthcare providers during and after their qualification program. With the rapid growth of online and blended approaches to learning, an educational framework that explains how to construct quality learning events to provide IPC is pressing. Such a framework would offer a quality standard to help educators design, develop, deliver, and evaluate online interprofessional education (IPE) programs. IPE is an extremely delicate process due to issues related to knowledge, status, power, accountability, personality traits, and culture that surround IPC. In this paper, a review of the pertinent literature that would inform the development of such a framework is presented. The review covers IPC, IPE, learning theories, and eLearning in healthcare.
Overcoming complexities: Damage detection using dictionary learning framework
Alguri, K. Supreet; Melville, Joseph; Deemer, Chris; Harley, Joel B.
2018-04-01
For in situ damage detection, guided wave structural health monitoring systems have been widely researched due to their ability to evaluate large areas and their ability detect many types of damage. These systems often evaluate structural health by recording initial baseline measurements from a pristine (i.e., undamaged) test structure and then comparing later measurements with that baseline. Yet, it is not always feasible to have a pristine baseline. As an alternative, substituting the baseline with data from a surrogate (nearly identical and pristine) structure is a logical option. While effective in some circumstance, surrogate data is often still a poor substitute for pristine baseline measurements due to minor differences between the structures. To overcome this challenge, we present a dictionary learning framework to adapt surrogate baseline data to better represent an undamaged test structure. We compare the performance of our framework with two other surrogate-based damage detection strategies: (1) using raw surrogate data for comparison and (2) using sparse wavenumber analysis, a precursor to our framework for improving the surrogate data. We apply our framework to guided wave data from two 108 mm by 108 mm aluminum plates. With 20 measurements, we show that our dictionary learning framework achieves a 98% accuracy, raw surrogate data achieves a 92% accuracy, and sparse wavenumber analysis achieves a 57% accuracy.
A Conceptual Framework for Educational Design at Modular Level to Promote Transfer of Learning
Botma, Yvonne; Van Rensburg, G. H.; Coetzee, I. M.; Heyns, T.
2015-01-01
Students bridge the theory-practice gap when they apply in practice what they have learned in class. A conceptual framework was developed that can serve as foundation to design for learning transfer at modular level. The framework is based on an adopted and adapted systemic model of transfer of learning, existing learning theories, constructive…
The CABES (Clare Adult Basic Education Service) Framework as a Tool for Teaching and Learning
Greene, Moira
2015-01-01
This article describes a Framework that can be used to help bridge the gap between theory and practice in adult learning. The Framework promotes practice informed by three strands important to adult literacy work: social theories of literacy, social-constructivist learning theory and principles of adult learning. The Framework shows how five key…
Reinforcement Learning in Distributed Domains: Beyond Team Games
Wolpert, David H.; Sill, Joseph; Turner, Kagan
2000-01-01
Distributed search algorithms are crucial in dealing with large optimization problems, particularly when a centralized approach is not only impractical but infeasible. Many machine learning concepts have been applied to search algorithms in order to improve their effectiveness. In this article we present an algorithm that blends Reinforcement Learning (RL) and hill climbing directly, by using the RL signal to guide the exploration step of a hill climbing algorithm. We apply this algorithm to the domain of a constellations of communication satellites where the goal is to minimize the loss of importance weighted data. We introduce the concept of 'ghost' traffic, where correctly setting this traffic induces the satellites to act to optimize the world utility. Our results indicated that the bi-utility search introduced in this paper outperforms both traditional hill climbing algorithms and distributed RL approaches such as team games.
Tumor Cells Express FcγRl Which Contributes to Tumor Cell Growth and a Metastatic Phenotype
Directory of Open Access Journals (Sweden)
M. Bud Nelson
2001-01-01
Full Text Available High levels of circulating immune complexes containing tumor-associated antigens are associated with a poor prognosis for individuals with cancer. The ability of B cells, previously exposed to tumor-associated antigens, to promote both in vitro and in vivo tumor growth formed the rationale to evaluate the mechanism by which immune complexes may promote tumor growth. In elucidating this mechanism, FcγRl expression by tumor cells was characterized by flow cytometry, polymerase chain reaction, and sequence analysis. Immune complexes containing shed tumor antigen and anti-shed tumor antigen Ab cross-linked FcγRl-expressing tumor cells, which resulted in an induction of tumor cell proliferation and of shed tumor antigen production. Use of selective tyrosine kinase inhibitors demonstrated that tumor cell proliferation induced by immune complex cross-linking of FcγRl is dependent on the tyrosine kinase signal transduction pathway. A selective inhibitor of phosphatidylinositol-3 kinase also inhibited this induction of tumor cell proliferation. These findings support a role for immune complexes and FcγRl expression by tumor cells in augmentation of tumor growth and a metastatic phenotype.
Directory of Open Access Journals (Sweden)
Martey O
2017-10-01
Full Text Available Orleans Martey,1 Mhairi Nimick,1 Sebastien Taurin,1 Vignesh Sundararajan,1 Khaled Greish,2 Rhonda J Rosengren1 1Department of Pharmacology and Toxicology, University of Otago, Dunedin, New Zealand; 2Department of Molecular Medicine, College of Medicine and Medical Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain Abstract: Patients with triple negative breast cancer have a poor prognosis due in part to the lack of targeted therapies. In the search for novel drugs, our laboratory has developed a second-generation curcumin derivative, 3,5-bis(3,4,5-trimethoxybenzylidene-1-methylpiperidine-4-one (RL71, that exhibits potent in vitro cytotoxicity. To improve the clinical potential of this drug, we have encapsulated it in styrene maleic acid (SMA micelles. SMA-RL71 showed improved biodistribution, and drug accumulation in the tumor increased 16-fold compared to control. SMA-RL71 (10 mg/kg, intravenously, two times a week for 2 weeks also significantly suppressed tumor growth compared to control in a xenograft model of triple negative breast cancer. Free RL71 was unable to alter tumor growth. Tumors from SMA-RL71-treated mice showed a decrease in angiogenesis and an increase in apoptosis. The drug treatment also modulated various cell signaling proteins including the epidermal growth factor receptor, with the mechanisms for tumor suppression consistent with previous work with RL71 in vitro. The nanoformulation was also nontoxic as shown by normal levels of plasma markers for liver and kidney injury following weekly administration of SMA-RL71 (10 mg/kg for 90 days. Thus, we report clinical potential following encapsulation of a novel curcumin derivative, RL71, in SMA micelles. Keywords: curcumin derivatives, nanomedicine, EGFR, biodistribution
Biotreatment of anthraquinone dye Drimarene Blue K 2 RL | Siddiqui ...
African Journals Online (AJOL)
Drimarene Blue (Db) K2RL is a reactive anthraquinone dye, used extensively in textile industry, due to poor adsorbability to textile fiber; it has a higher exhaustion rate in wastewater. The dye is toxic, carcinogenic, mutagenic and resistant to degradation. Decolorization of this dye was studied in two different systems.
A rule-learning program in high energy physics event classification
International Nuclear Information System (INIS)
Clearwater, S.H.; Stern, E.G.
1991-01-01
We have applied a rule-learning program to the problem of event classification in high energy physics. The program searches for event classifications, i.e. rules, and effectively allows an exploration of many more possible classifications than is practical by a physicist. The program, RL4, is particularly useful because it can easily explore multi-dimensional rules as well as rules that may seem non-intuitive at first to the physicist. RL4 is also contrasted with other learning programs. (orig.)
Directory of Open Access Journals (Sweden)
Benjamin L. Wiggins
2017-05-01
Full Text Available STEM classrooms (science, technology, engineering, and mathematics in postsecondary education are rapidly improved by the proper use of active learning techniques. These techniques occupy a descriptive spectrum that transcends passive teaching toward active, constructive, and, finally, interactive methods. While aspects of this framework have been examined, no large-scale or actual classroom-based data exist to inform postsecondary education STEM instructors about possible learning gains. We describe the results of a quasi-experimental study to test the apex of the ICAP framework (interactive, constructive, active, and passive in this ecological classroom environment. Students in interactive classrooms demonstrate significantly improved learning outcomes relative to students in constructive classrooms. This improvement in learning is relatively subtle; similar experimental designs without repeated measures would be unlikely to have the power to observe this significance. We discuss the importance of seemingly small learning gains that might propagate throughout a course or departmental curriculum, as well as improvements with the necessity for faculty to develop and implement similar activities.
Koedinger, Kenneth R; Corbett, Albert T; Perfetti, Charles
2012-07-01
Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices. Copyright © 2012 Cognitive Science Society, Inc.
Orchestration Framework for Learning Activities in Augmented Reality Environments
Ibáñez, María Blanca; Delgado Kloos, Carlos; Di Serio, Angela
2011-01-01
Proceedings of: Across Spaces11 Workshop in conjunction with the EC-TEL2011, Palermo, Italy, September 21, 2011 In this paper we show how Augmented Reality (AR) technology restricted to the use of mobiles or PCs, can be used to develop learning activities with the minimun level of orchestation required by meaningful learning sequences. We use Popcode as programming language to deploy orchestrated learning activities specified with an AR framework. Publicado
A Conceptual Framework for Ambient Learning Displays
Börner, Dirk; Kalz, Marco; Specht, Marcus
2011-01-01
Börner, D., Kalz, M., & Specht, M. (2010, 29 November-3 December). A Conceptual Framework for Ambient Learning Displays. Poster presented at the Work-in-Progress Poster and Invited Young Researcher Symposium of the 18th International Conference on Computers in Education, Putrajaya, Malaysia:
Lappas, Pantelis Z.; Kritikos, Manolis N.
2018-01-01
The main objective of this paper is to propose a didactic framework for teaching Applied Mathematics in higher education. After describing the structure of the framework, several applications of inquiry-based learning in teaching numerical analysis and optimization are provided to illustrate the potential of the proposed framework. The framework…
Games and Simulations in Online Learning: Research and Development Frameworks
Gibson, David; Aldrich, Clark; Prensky, Marc
2007-01-01
Games and Simulations in Online Learning: Research and Development Frameworks examines the potential of games and simulations in online learning, and how the future could look as developers learn to use the emerging capabilities of the Semantic Web. It presents a general understanding of how the Semantic Web will impact education and how games and…
A Machine Learning Framework for Plan Payment Risk Adjustment.
Rose, Sherri
2016-12-01
To introduce cross-validation and a nonparametric machine learning framework for plan payment risk adjustment and then assess whether they have the potential to improve risk adjustment. 2011-2012 Truven MarketScan database. We compare the performance of multiple statistical approaches within a broad machine learning framework for estimation of risk adjustment formulas. Total annual expenditure was predicted using age, sex, geography, inpatient diagnoses, and hierarchical condition category variables. The methods included regression, penalized regression, decision trees, neural networks, and an ensemble super learner, all in concert with screening algorithms that reduce the set of variables considered. The performance of these methods was compared based on cross-validated R 2 . Our results indicate that a simplified risk adjustment formula selected via this nonparametric framework maintains much of the efficiency of a traditional larger formula. The ensemble approach also outperformed classical regression and all other algorithms studied. The implementation of cross-validated machine learning techniques provides novel insight into risk adjustment estimation, possibly allowing for a simplified formula, thereby reducing incentives for increased coding intensity as well as the ability of insurers to "game" the system with aggressive diagnostic upcoding. © Health Research and Educational Trust.
An Organizational Learning Framework for Patient Safety.
Edwards, Marc T
Despite concerted effort to improve quality and safety, high reliability remains a distant goal. Although this likely reflects the challenge of organizational change, persistent controversy over basic issues suggests that weaknesses in conceptual models may contribute. The essence of operational improvement is organizational learning. This article presents a framework for identifying leverage points for improvement based on organizational learning theory and applies it to an analysis of current practice and controversy. Organizations learn from others, from defects, from measurement, and from mindfulness. These learning modes correspond with contemporary themes of collaboration, no blame for human error, accountability for performance, and managing the unexpected. The collaborative model has dominated improvement efforts. Greater attention to the underdeveloped modes of organizational learning may foster more rapid progress in patient safety by increasing organizational capabilities, strengthening a culture of safety, and fixing more of the process problems that contribute to patient harm.
A Conceptual Framework over Contextual Analysis of Concept Learning within Human-Machine Interplays
DEFF Research Database (Denmark)
Badie, Farshad
2016-01-01
This research provides a contextual description concerning existential and structural analysis of ‘Relations’ between human beings and machines. Subsequently, it will focus on conceptual and epistemological analysis of (i) my own semantics-based framework [for human meaning construction] and of (ii......) a well-structured machine concept learning framework. Accordingly, I will, semantically and epistemologically, focus on linking those two frameworks for logical analysis of concept learning in the context of human-machine interrelationships. It will be demonstrated that the proposed framework provides...
A Framework for Narration and Learning in Educational Multimedia
DEFF Research Database (Denmark)
Mosegaard, Jesper; Bennedsen, Jens
2006-01-01
In this article we describe a multimedia adventure game framework for a learningenvironment to support the teaching and learning of introductory programming. In theframework we have conceptualized two important aspects of such an environment: narrationand learning topics. We describe the interplay...... between these aspects and how the frameworkutilizes this to adapt the learning process to the individual student. The motivation for theseparation is to help the teacher balance the two main driving forces of an edutainmentproduct: entertainment and learning. It is the responsibility of the teacher...
A Conceptual Framework for Ambient Learning Displays
Börner, Dirk; Kalz, Marco; Specht, Marcus
2010-01-01
Börner, D., Kalz, M., & Specht, M. (2010). A Conceptual Framework for Ambient Learning Displays. In B. Chang, T. Hirashima, & H. Ogata (Eds.), Joint Proceedings of the Work-in-Progress Poster and Invited Young Researcher Symposium for the 18th International Conference on Computers in Education (pp.
A framework for designing and improving learning environments fostering creativity
Directory of Open Access Journals (Sweden)
Norio Ishii
Full Text Available This paper proposes a framework for designing and improving learning environment for creativity in engineering. The framework consists of the following three components: instructional design based on knowledge from psychology, development of systems for supporting creative activities, and objective evaluation of learning results related to creativity. Based on that framework, we design and practice course based in the programation of a robot at a Japan University in the 2004 academic year. As a result, we confirm the following two advantages of our framework: learners' idea generation skills were improved and their meta-cognitive activities were also activated. In the 2005 academic year, we improve the course based on 2004 results. As a result, we confirm that the number of uploads of activity data from students have increased in the 2005 course, students' reflection sheets have become more detailed, and their volume of information have also increased.
Fast Conflict Resolution Based on Reinforcement Learning in Multi-agent System
Institute of Scientific and Technical Information of China (English)
PIAOSonghao; HONGBingrong; CHUHaitao
2004-01-01
In multi-agent system where each agen thas a different goal (even the team of agents has the same goal), agents must be able to resolve conflicts arising in the process of achieving their goal. Many researchers presented methods for conflict resolution, e.g., Reinforcement learning (RL), but the conventional RL requires a large computation cost because every agent must learn, at the same time the overlap of actions selected by each agent results in local conflict. Therefore in this paper, we propose a novel method to solve these problems. In order to deal with the conflict within the multi-agent system, the concept of potential field function based Action selection priority level (ASPL) is brought forward. In this method, all kinds of environment factor that may have influence on the priority are effectively computed with the potential field function. So the priority to access the local resource can be decided rapidly. By avoiding the complex coordination mechanism used in general multi-agent system, the conflict in multi-agent system is settled more efficiently. Our system consists of RL with ASPL module and generalized rules module. Using ASPL, RL module chooses a proper cooperative behavior, and generalized rule module can accelerate the learning process. By applying the proposed method to Robot Soccer, the learning process can be accelerated. The results of simulation and real experiments indicate the effectiveness of the method.
3D Laser Processing : The Renault Rl5
Rolland, Olivier C.; Meyer, Bernard D.
1986-11-01
The RL5, a five-axis robot, is designed to steer a powerful laser beam on 3 dimensional (3D) trajectories with a great accuracy. Cutting and welding with a CO2 laser beam, drilling with a YAG laser beam are some applications of this machine which can be integrated in a production line. Easy management and modifications of trajectories, obtained either in a teaching mode or by a CAD-CAM system, give the laser tool its main interest : flexibility.
Social innovation education: towards a framework for learning design
Alden Rivers, Bethany; Armellini, Alejandro; Maxwell, Rachel; Allen, Sue; Durkin, Chris
2015-01-01
Purpose—This paper proposes a theoretical framework to support the embedding of social innovation education in existing academic programmes.\\ud Design/methodology/approach—By adopting Conole et al.’s (2004) methodological approach to reviewing, mapping and modelling learning theory, this study addresses four research questions: 1) How can social innovation education be defined? 2) Which learning theories best support social innovation education? 3) How do such learning theories relate to exis...
An analytical quality framework for learning cities and regions
Preisinger-Kleine, Randolph
2013-09-01
There is broad agreement that innovation, knowledge and learning have become the main source of wealth, employment and economic development of cities, regions and nations. Over the past two decades, the number of European cities and regions which label themselves as "learning city" or "learning region" has constantly grown. However, there are also pitfalls and constraints which not only hinder them in unlocking their full potential, but also significantly narrow their effects and their wider impact on society. Most prominently, learning cities and regions manifest serious difficulties in rendering transparent the surplus value they generate, which is vital for attracting investment into lifelong learning. While evaluation and quality management are still perceived as being a bureaucratic necessity rather than a lesson one could learn from or an investment in the future, it is also true that without evaluation and quality assurance local networks do not have the means to examine their strengths and weaknesses. In order to design strategies to maximise the strengths and effectively address the weaknesses it is necessary to understand the factors that contribute to success and those that pose challenges. This article proposes an analytical quality framework which is generic and can be used to promote a culture of quality in learning cities and regions. The proposed framework builds on the findings and results of the R3L+ project, part-funded by the European Commission under the Grundtvig (adult education) strand of the Lifelong Learning programme 2007-2013.
Collins, Anne G E; Frank, Michael J
2018-03-06
Learning from rewards and punishments is essential to survival and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electro-encephalography to reveal single-trial computations beyond that afforded by behavior alone. Neural dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Within- and cross-trial dynamics revealed a cooperative interplay between systems for learning, in which WM contributes expectations to guide RL, despite competition between systems during choice. Together, these results provide a deeper understanding of how multiple neural systems interact for learning and decision-making and facilitate analysis of their disruption in clinical populations.
Dafalla, Tarig Dafalla Mohamed; Kushniruk, Andre W; Borycki, Elizabeth M
2015-01-01
A pragmatic evaluation framework for evaluating the usability and usefulness of an e-learning intervention for a patient clinical information scheduling system is presented in this paper. The framework was conceptualized based on two different but related concepts (usability and usefulness) and selection of appropriate and valid methods of data collection and analysis that included: (1) Low-Cost Rapid Usability Engineering (LCRUE), (2) Cognitive Task Analysis (CTA), (3) Heuristic Evaluation (HE) criteria for web-based learning, and (4) Software Usability Measurement Inventory (SUMI). The results of the analysis showed some areas where usability that were related to General Interface Usability (GIU), instructional design and content was problematic; some of which might account for the poorly rated aspects of usability when subjectively measured. This paper shows that using a pragmatic framework can be a useful way, not only for measuring the usability and usefulness, but also for providing a practical objective evidences for learning and continuous quality improvement of e-learning systems. The findings should be of interest to educators, developers, designers, researchers, and usability practitioners involved in the development of e-learning systems in healthcare. This framework could be an appropriate method for assessing the usability, usefulness and safety of health information systems both in the laboratory and in the clinical context.
Emergent frameworks of research teaching and learning in a cohort ...
African Journals Online (AJOL)
... frameworks for doctoral pedagogies –“democratic teaching/learning participation”, “structured scaffolding”, “Ubuntu” and “serendipity”– as useful explanatory shaping influences which underpin and frame the model promoting a contextually relevant and appropriate doctoral research teaching and learning pedagogy.
A Conceptual Framework for Mentoring in a Learning Organization
Klinge, Carolyn M.
2015-01-01
The purpose of this article is to provide a conceptual framework for mentoring as an added component of a learning organization in the context of adult learning and development theories. Mentoring is traditionally a process in which an experienced person (the mentor) guides another person (the mentee or protégé) in the development of her or his…
International Nuclear Information System (INIS)
Alseddiqi, M; Mishra, R; Pislaru, C
2012-01-01
The paper presents the results from a quality framework to measure the effectiveness of a new engineering course entitled 'school-based learning (SBL) to work-based learning (WBL) transition module' in the Technical and Vocational Education (TVE) system in Bahrain. The framework is an extended version of existing information quality frameworks with respect to pedagogical and technological contexts. It incorporates specific pedagogical and technological dimensions as per the Bahrain modern industry requirements. Users' views questionnaire on the effectiveness of the new transition module was distributed to various stakeholders including TVE teachers and students. The aim was to receive critical information in diagnosing, monitoring and evaluating different views and perceptions about the effectiveness of the new module. The analysis categorised the quality dimensions by their relative importance. This was carried out using the principal component analysis available in SPSS. The analysis clearly identified the most important quality dimensions integrated in the new module for SBL-to-WBL transition. It was also apparent that the new module contains workplace proficiencies, prepares TVE students for work placement, provides effective teaching and learning methodologies, integrates innovative technology in the process of learning, meets modern industrial needs, and presents a cooperative learning environment for TVE students. From the principal component analysis finding, to calculate the percentage of relative importance of each factor and its quality dimensions, was significant. The percentage comparison would justify the most important factor as well as the most important quality dimensions. Also, the new, re-arranged quality dimensions from the finding with an extended number of factors tended to improve the extended version of the quality information framework to a revised quality framework.
Theoretical frameworks for the learning of geometrical reasoning
Jones, Keith
1998-01-01
With the growth in interest in geometrical ideas it is important to be clear about the nature of geometrical reasoning and how it develops. This paper provides an overview of three theoretical frameworks for the learning of geometrical reasoning: the van Hiele model of thinking in geometry, Fischbein’s theory of figural concepts, and Duval’s cognitive model of geometrical reasoning. Each of these frameworks provides theoretical resources to support research into the development of geometrical...
A Data Protection Framework for Learning Analytics
Cormack, Andrew
2016-01-01
Most studies on the use of digital student data adopt an ethical framework derived from human-subject research, based on the informed consent of the experimental subject. However, consent gives universities little guidance on using learning analytics as a routine part of educational provision: which purposes are legitimate and which analyses…
Frustration-Instigated Behavior and Learned Helplessness.
Winefield, Anthony H.
1979-01-01
Compares M. E. P. Seligman's recent work on learned helplessness with N. R. F. Maier's 30-year-old work on frustration behavior. Notes striking similarities between the two approaches. Concludes that the learned helplessness model might explain the "abnormal fixations" that Maier reported. (Author/RL)
Tunnel Ventilation Control Using Reinforcement Learning Methodology
Chu, Baeksuk; Kim, Dongnam; Hong, Daehie; Park, Jooyoung; Chung, Jin Taek; Kim, Tae-Hyung
The main purpose of tunnel ventilation system is to maintain CO pollutant concentration and VI (visibility index) under an adequate level to provide drivers with comfortable and safe driving environment. Moreover, it is necessary to minimize power consumption used to operate ventilation system. To achieve the objectives, the control algorithm used in this research is reinforcement learning (RL) method. RL is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment. The goal of RL is to maximize a reward which is an evaluative feedback from the environment. In the process of constructing the reward of the tunnel ventilation system, two objectives listed above are included, that is, maintaining an adequate level of pollutants and minimizing power consumption. RL algorithm based on actor-critic architecture and gradient-following algorithm is adopted to the tunnel ventilation system. The simulations results performed with real data collected from existing tunnel ventilation system and real experimental verification are provided in this paper. It is confirmed that with the suggested controller, the pollutant level inside the tunnel was well maintained under allowable limit and the performance of energy consumption was improved compared to conventional control scheme.
Validation of an e-Learning 3.0 Critical Success Factors Framework: A Qualitative Research
Directory of Open Access Journals (Sweden)
Paula Miranda
2017-09-01
Full Text Available Aim/Purpose: As e-Learning 3.0 evolves from a theoretical construct into an actual solution for online learning, it becomes crucial to accompany this progress by scrutinising the elements that are at the origin of its success. Background: This paper outlines a framework of e-Learning 3.0’s critical success factors and its empirical validation. Methodology: The framework is the result of an extensive literature review and its empirical substantiation derives from semi-structured interviews with e-Learning experts. Contribution: The viewpoints of the experts enable the confirmation and the refinement of the original framework and serve as a foundation for the prospective implementation of e-Learning 3.0. Findings: The analysis of the interviews demonstrates that e-Learning 3.0 remains in its early stages with a reticent dissemination. Nonetheless, the interviewees invoked factors related to technology, content and stakeholders as being critical for the success of this new phase of e-Learning. Recommendations for Practitioners: Practitioners can use the framework as a guide for promoting and implementing effective e-Learning 3.0 initiatives. Recommendation for Researchers: As a new phenomenon with uncharted potential, e-Learning 3.0 should be placed at the centre of educational research. Impact on Society: The understanding of what drives the success of e-Learning 3.0 is fundamental for its implementation and for the progress of online education in this new stage of its evolution. Future Research: Future research ventures can include the design of quantitative and self-administered data collection instruments that can provide further insight into the elements of the framework.
A DBR Framework for Designing Mobile Virtual Reality Learning Environments
Cochrane, Thomas Donald; Cook, Stuart; Aiello, Stephen; Christie, Duncan; Sinfield, David; Steagall, Marcus; Aguayo, Claudio
2017-01-01
This paper proposes a design based research (DBR) framework for designing mobile virtual reality learning environments. The application of the framework is illustrated by two design-based research projects that aim to develop more authentic educational experiences and learner-centred pedagogies in higher education. The projects highlight the first…
A new 2DS·2RL Robertsonian translocation transfers stem rust resistance gene Sr59 into wheat.
Rahmatov, Mahbubjon; Rouse, Matthew N; Nirmala, Jayaveeramuthu; Danilova, Tatiana; Friebe, Bernd; Steffenson, Brian J; Johansson, Eva
2016-07-01
A new stem rust resistance gene Sr59 from Secale cereale was introgressed into wheat as a 2DS·2RL Robertsonian translocation. Emerging new races of the wheat stem rust pathogen (Puccinia graminis f. sp. tritici), from Africa threaten global wheat (Triticum aestivum L.) production. To broaden the resistance spectrum of wheat to these widely virulent African races, additional resistance genes must be identified from all possible gene pools. From the screening of a collection of wheat-rye (Secale cereale L.) chromosome substitution lines developed at the Swedish University of Agricultural Sciences, we described the line 'SLU238' 2R (2D) as possessing resistance to many races of P. graminis f. sp. tritici, including the widely virulent race TTKSK (isolate synonym Ug99) from Africa. The breakage-fusion mechanism of univalent chromosomes was used to produce a new Robertsonian translocation: T2DS·2RL. Molecular marker analysis and stem rust seedling assays at multiple generations confirmed that the stem rust resistance from 'SLU238' is present on the rye chromosome arm 2RL. Line TA5094 (#101) was derived from 'SLU238' and was found to be homozygous for the T2DS·2RL translocation. The stem rust resistance gene on chromosome 2RL arm was designated as Sr59. Although introgressions of rye chromosome arms into wheat have most often been facilitated by irradiation, this study highlights the utility of the breakage-fusion mechanism for rye chromatin introgression. Sr59 provides an additional asset for wheat improvement to mitigate yield losses caused by stem rust.
Orchestration in Learning Technology Research: Evaluation of a Conceptual Framework
Prieto, Luis P.; Dimitriadis, Yannis; Asensio-Pérez, Juan I.; Looi, Chee-Kit
2015-01-01
The term "orchestrating learning" is being used increasingly often, referring to the coordination activities performed while applying learning technologies to authentic settings. However, there is little consensus about how this notion should be conceptualised, and what aspects it entails. In this paper, a conceptual framework for…
Jia, Junshuang; Lin, Xiaolin; Lin, Xia; Lin, Taoyan; Chen, Bangzhu; Hao, Weichao; Cheng, Yushuang; Liu, Yu; Dian, Meijuan; Yao, Kaitai; Xiao, Dong; Gu, Weiwang
2016-10-01
The Cre/loxP system has become an important tool for the conditional gene knockout and conditional gene expression in genetically engineered mice. The applications of this system depend on transgenic reporter mouse lines that provide Cre recombinase activity with a defined cell type-, tissue-, or developmental stage-specificity. To develop a sensitive assay for monitoring Cre-mediated DNA excisions in mice, we generated Cre-mediated excision reporter mice, designated R/L mice (R/L: mRFP(monomeric red fluorescent protein)/luciferase), express mRFP throughout embryonic development and adult stages, while Cre-mediated excision deletes a loxP-flanked mRFP reporter gene and STOP sequence, thereby activating the expression of the second reporter gene luciferase, as assayed by in vivo and ex vivo bioluminescence imaging. After germ line deletion of the floxed mRFP and STOP sequence in R/L mice by EIIa-Cre mice, the resulting luciferase transgenic mice in which the loxP-mRFP-STOP-loxP cassette is excised from all cells express luciferase in all tissues and organs examined. The expression of luciferase transgene was activated in liver of RL/Alb-Cre double transgenic mice and in brain of RL/Nestin-Cre double transgenic mice when R/L reporter mice were mated with Alb-Cre mice and Nestin-Cre mice, respectively. Our findings reveal that the double reporter R/L mouse line is able to indicate the occurrence of Cre-mediated excision from early embryonic to adult lineages. Taken together, these findings demonstrate that the R/L mice serve as a sensitive reporter for Cre-mediated DNA excision both in living animals and in organs, tissues, and cells following necropsy.
Mangir, Safaie; Othman, Zakirah; Udin, Zulkifli Mohamed
2016-01-01
The objective of this paper is to develop a framework on e-learning acceptance among agricultural extension agents in Malaysian agricultural sector. E-learning is viewed as a solution in response to the increasing need for learning and training. This paper will review past literatures for the relevant factors that influence behavioral intention for e-learning acceptance as well as the relevant behavioral theories that provide the foundation for developing research framework to illustrate the ...
A Framework of Metacognitive Scaffolding in Learning Authoring System through Facebook
Jumaat, Nurul Farhana; Tasir, Zaidatun
2016-01-01
Scaffolding refers to a guidance that helps students during their learning sessions whereby it makes learning easier for them. This study aims to develop a framework of metacognitive scaffolding (MS) to guide students in learning Authoring System through Facebook. Thirty-seven master degree students who were enrolled in Authoring System course…
Zhang, Chengwei; Li, Xiaohong; Li, Shuxin; Feng, Zhiyong
2017-09-20
Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis. Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.
An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation
Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.
2015-01-01
Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.
Directory of Open Access Journals (Sweden)
G.M. Steyn
2008-07-01
Full Text Available To transform education in this country, South African teachers need to be appropriately equipped to meet the evolving challenges and needs of the country. The national policy framework for teacher education and development is an attempt to address the need for suitably qualified teachers in South Africa. Its aim is to improve the quality of education by focusing on the professional development of teachers. This article attempts to address the following research problem: Does continuing professional development for teachers (CPDT, as stipulated by the national policy framework, have the potential to contribute to the development of teachers as proposed by social learning systems? The answer to this question has the potential to inform and influence the policy and its implementation. The answer also describes how conceptual frameworks for learning in Wenger’s social learning systems conflict with effective professional development (PD programmes and CPDT.
A Plant Control Technology Using Reinforcement Learning Method with Automatic Reward Adjustment
Eguchi, Toru; Sekiai, Takaaki; Yamada, Akihiro; Shimizu, Satoru; Fukai, Masayuki
A control technology using Reinforcement Learning (RL) and Radial Basis Function (RBF) Network has been developed to reduce environmental load substances exhausted from power and industrial plants. This technology consists of the statistic model using RBF Network, which estimates characteristics of plants with respect to environmental load substances, and RL agent, which learns the control logic for the plants using the statistic model. In this technology, it is necessary to design an appropriate reward function given to the agent immediately according to operation conditions and control goals to control plants flexibly. Therefore, we propose an automatic reward adjusting method of RL for plant control. This method adjusts the reward function automatically using information of the statistic model obtained in its learning process. In the simulations, it is confirmed that the proposed method can adjust the reward function adaptively for several test functions, and executes robust control toward the thermal power plant considering the change of operation conditions and control goals.
A framework for technological learning in the supply chain: A case study on CdTe photovoltaics
International Nuclear Information System (INIS)
Bergesen, Joseph D.; Suh, Sangwon
2016-01-01
Highlights: • A framework for technological learning in the supply chain is proposed. • This framework separates learning effects on value added and intermediate inputs. • Supply-chain learning can project both changing environmental impacts and costs. • Learning upstream in the supply chain can influence observed learning rates. • An example for CdTe photovoltaics illustrates how this framework can be implemented. - Abstract: Accounting for technological changes and innovation is important when assessing the implications of rapidly-developing greenhouse gas (GHG) mitigation technologies. Technological learning curves have been commonly used as a tool to understand technological change as a function of cumulative production. Traditional learning curve approaches, however, do not distinguish the direct and upstream, supply chain technological changes by which cost reductions are achieved. While recent advances in learning curves have focused on distinguishing the different physical and economic drivers of learning, forecasted technological changes have not been applied to estimate the potential changes in the environmental performance of a technology. This article illustrates how distinguishing the different effects of technological learning throughout the supply chain can help assess the changing costs, environmental impacts and natural resource implications of technologies as they develop. We propose a mathematical framework to distinguish the effects of learning on the direct inputs to a technology from the effects of learning on value added, and we incorporate those effects throughout the supply chain of a technology using a life cycle assessment (LCA) framework. An example for cadmium telluride (CdTe) photovoltaics (PV) illustrates how the proposed framework can be implemented. Results show that that life cycle GHG emissions can decrease at least 40% and costs can decrease at least 50% as cumulative production of CdTe reaches 100 GW. Technological
Jacobson, Michael J.; Kapur, Manu; Reimann, Peter
2016-01-01
This article proposes a conceptual framework of learning based on perspectives and methodologies being employed in the study of complex physical and social systems to inform educational research. We argue that the contexts in which learning occurs are complex systems with elements or agents at different levels--including neuronal, cognitive,…
Accurate calibration of RL shunts for piezoelectric vibration damping of flexible structures
DEFF Research Database (Denmark)
Høgsberg, Jan Becker; Krenk, Steen
2016-01-01
Piezoelectric RL (resistive-inductive) shunts are passive resonant devices used for damping of dominantvibration modes of a flexible structure and their efficiency relies on precise calibration of the shuntcomponents. In the present paper improved calibration accuracy is attained by an extension...
Learning to Rank for Information Retrieval from User Interactions
Hofmann, K.; Whiteson, S.; Schuth, A.; de Rijke, M.
2014-01-01
In this article we give an overview of our recent work on online learning to rank for information retrieval (IR). This work addresses IR from a reinforcement learning (RL) point of view, with the aim to enable systems that can learn directly from interactions with their users. Learning directly from
A Multimodal Interaction Framework for Blended Learning
DEFF Research Database (Denmark)
Vidakis, Nikolaos; Kalafatis, Konstantinos; Triantafyllidis, Georgios
2016-01-01
Humans interact with each other by utilizing the five basic senses as input modalities, whereas sounds, gestures, facial expressions etc. are utilized as output modalities. Multimodal interaction is also used between humans and their surrounding environment, although enhanced with further senses ...... framework enabling deployment of a vast variety of modalities, tailored appropriately for use in blended learning environment....
Designing for Learning and Play - The Smiley Model as Framework
DEFF Research Database (Denmark)
Weitze, Charlotte Lærke
2016-01-01
digital games. The Smiley Model inspired and provided a scaffold or a heuristic for the overall gamified learning design –- as well as for the students’ learning game design processes when creating small games turning the learning situation into an engaging experience. The audience for the experiments......This paper presents a framework for designing engaging learning experiences in games – the Smiley Model. In this Design-Based Research project, student-game-designers were learning inside a gamified learning design - while designing and implementing learning goals from curriculum into the small...... was adult upper secondary general students as well as 7th grade primary school students. The intention with this article is to inspire future learning designers that would like to experiment with integrating learning and play....
Directory of Open Access Journals (Sweden)
Carol Russell
2009-12-01
Full Text Available There are hopes that new learning technologies will help to transform university learning and teaching into a more engaging experience for twenty-first-century students. But since 2000 the changes in campus university teaching have been more limited than expected. I have drawn on ideas from organisational change management research to investigate why this is happening in one particular campus university context. My study examines the strategies of individual lecturers for adopting e-learning within their disciplinary, departmental and university work environments to develop a conceptual framework for analysing university learning and teaching as a complex adaptive system. This conceptual framework links the processes through which university teaching changes, the resulting forms of learning activity and the learning technologies used – all within the organisational context of the university. The framework suggests that systemic transformation of a university's learning and teaching requires coordinated change across activities that have traditionally been managed separately in campus universities. Without such coordination, established ways of organising learning and teaching will reassert themselves, as support staff and lecturers seek to optimise their own work locally. The conceptual framework could inform strategies for realising the full benefits of new learning technologies in other campus universities.
Expanding the Frontiers of National Qualifications Frameworks through Lifelong Learning
Owusu-Agyeman, Yaw
2017-01-01
The adoption of a national qualifications framework (NQF) by some governments in all world regions has shown some success in the area of formal learning. However, while NQFs continue to enhance "formal" learning in many countries, the same cannot be said for the recognition, validation and accreditation (RVA) of "non-formal"…
Directory of Open Access Journals (Sweden)
Lisa C Crossman
2008-07-01
Full Text Available This work centres on the genomic comparisons of two closely-related nitrogen-fixing symbiotic bacteria, Rhizobium leguminosarum biovar viciae 3841 and Rhizobium etli CFN42. These strains maintain a stable genomic core that is also common to other rhizobia species plus a very variable and significant accessory component. The chromosomes are highly syntenic, whereas plasmids are related by fewer syntenic blocks and have mosaic structures. The pairs of plasmids p42f-pRL12, p42e-pRL11 and p42b-pRL9 as well large parts of p42c with pRL10 are shown to be similar, whereas the symbiotic plasmids (p42d and pRL10 are structurally unrelated and seem to follow distinct evolutionary paths. Even though purifying selection is acting on the whole genome, the accessory component is evolving more rapidly. This component is constituted largely for proteins for transport of diverse metabolites and elements of external origin. The present analysis allows us to conclude that a heterogeneous and quickly diversifying group of plasmids co-exists in a common genomic framework.
Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning
Directory of Open Access Journals (Sweden)
Maria Drakaki
2017-02-01
Full Text Available Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs and reinforcement learning (RL. CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties.
SQL Collaborative Learning Framework Based on SOA
Armiati, S.; Awangga, RM
2018-04-01
The research is focused on designing collaborative learning-oriented framework fulfilment service in teaching SQL Oracle 10g. Framework built a foundation of academic fulfilment service performed by a layer of the working unit in collaboration with Program Studi Manajemen Informatika. In the design phase defined what form of collaboration models and information technology proposed for Program Studi Manajemen Informatika by using a framework of collaboration inspired by the stages of modelling a Service Oriented Architecture (SOA). Stages begin with analyzing subsystems, this activity is used to determine subsystem involved and reliance as well as workflow between the subsystems. After the service can be identified, the second phase is designing the component specifications, which details the components that are implemented in the service to include the data, rules, services, profiles can be configured, and variations. The third stage is to allocate service, set the service to the subsystems that have been identified, and its components. Implementation framework contributes to the teaching guides and application architecture that can be used as a landing realize an increase in service by applying information technology.
Environmental Management Performance Report to DOE-RL June 2002
International Nuclear Information System (INIS)
EDER, D.M.
2002-01-01
The purpose of this report is to provide the Department of Energy Richland Operations Office (RL) a monthly summary of the Central Plateau Contractor's Environmental Management (EM) performance by Fluor Hanford (FH) and its subcontractors. Only current FH workscope responsibilities are described and other contractor/RL managed work is excluded. Please refer to other sections (BHI, PNNL) for other contractor information. Section A, Executive Summary, provides an executive level summary of the cost, schedule, and technical performance described in this report. It summarizes performance for the period covered, highlights areas worthy of management attention, and provides a forward look to some of the upcoming key performance activities as extracted from the contractor baseline. The remaining sections provide detailed performance data relative to each individual subproject (e.g., Plutonium Finishing Plant, Spent Nuclear Fuels, etc.), in support of Section A of the report. All information is updated as of the end of June 2002 unless otherwise noted. ''Stoplight'' boxes are used to indicate at a glance the condition of a particular safety area. Green boxes denote either (1) the data are stable at a level representing acceptable performance, or (2) an improving trend exists. Yellows denote the data are stable at a level from which improvement is needed. Red denotes a trend exists in a non-improving direction
Applying reinforcement learning to the weapon assignment problem in air defence
CSIR Research Space (South Africa)
Mouton, H
2011-12-01
Full Text Available . The techniques investigated in this article were two methods from the machine-learning subfield of reinforcement learning (RL), namely a Monte Carlo (MC) control algorithm with exploring starts (MCES), and an off-policy temporal-difference (TD) learning...
Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho
2015-05-01
This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.
Switching Reinforcement Learning for Continuous Action Space
Nagayoshi, Masato; Murao, Hajime; Tamaki, Hisashi
Reinforcement Learning (RL) attracts much attention as a technique of realizing computational intelligence such as adaptive and autonomous decentralized systems. In general, however, it is not easy to put RL into practical use. This difficulty includes a problem of designing a suitable action space of an agent, i.e., satisfying two requirements in trade-off: (i) to keep the characteristics (or structure) of an original search space as much as possible in order to seek strategies that lie close to the optimal, and (ii) to reduce the search space as much as possible in order to expedite the learning process. In order to design a suitable action space adaptively, we propose switching RL model to mimic a process of an infant's motor development in which gross motor skills develop before fine motor skills. Then, a method for switching controllers is constructed by introducing and referring to the “entropy”. Further, through computational experiments by using robot navigation problems with one and two-dimensional continuous action space, the validity of the proposed method has been confirmed.
A leadership framework to support the use of e-learning resources.
McCutcheon, Karen
2014-06-01
Recognition needs to be given to emerging postgraduate nursing students' status of 'consumer', and the challenge for nurse education is to remain relevant and competitive in a consumer-led market. An e-learning model has been suggested as a competitive and contemporary way forward for student consumers, but successful introduction of this requires leadership and strong organisational management systems. This article applies the NHS leadership framework to nurse education in relation to implementation of e-learning and describes and interprets each element for application in higher education settings. By applying a leadership framework that acknowledges the skills and abilities of staff and encourages the formation of collaborative partnerships in the wider university community, educators can begin to develop skills and confidence in teaching using e-learning resources.
Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes
Daskalaki, Elena; Diem, Peter; Mougiakakou, Stavroula G.
2016-01-01
Although reinforcement learning (RL) is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the feasibility of RL in the framework of artificial pancreas development for type 1 diabetes (T1D). In this approach, an Actor-Critic (AC) learning algorithm is designed and developed for the optimisation of insulin infusion for personalised glucose regulation. AC optimises the daily basal insulin rate and insulin:carbohydrate ratio for each patient, on the basis of his/her measured glucose profile. Automatic, personalised tuning of AC is based on the estimation of information transfer (IT) from insulin to glucose signals. Insulin-to-glucose IT is linked to patient-specific characteristics related to total daily insulin needs and insulin sensitivity (SI). The AC algorithm is evaluated using an FDA-accepted T1D simulator on a large patient database under a complex meal protocol, meal uncertainty and diurnal SI variation. The results showed that 95.66% of time was spent in normoglycaemia in the presence of meal uncertainty and 93.02% when meal uncertainty and SI variation were simultaneously considered. The time spent in hypoglycaemia was 0.27% in both cases. The novel tuning method reduced the risk of severe hypoglycaemia, especially in patients with low SI. PMID:27441367
PeRL: a circum-Arctic Permafrost Region Pond and Lake database
Directory of Open Access Journals (Sweden)
S. Muster
2017-06-01
Full Text Available Ponds and lakes are abundant in Arctic permafrost lowlands. They play an important role in Arctic wetland ecosystems by regulating carbon, water, and energy fluxes and providing freshwater habitats. However, ponds, i.e., waterbodies with surface areas smaller than 1. 0 × 104 m2, have not been inventoried on global and regional scales. The Permafrost Region Pond and Lake (PeRL database presents the results of a circum-Arctic effort to map ponds and lakes from modern (2002–2013 high-resolution aerial and satellite imagery with a resolution of 5 m or better. The database also includes historical imagery from 1948 to 1965 with a resolution of 6 m or better. PeRL includes 69 maps covering a wide range of environmental conditions from tundra to boreal regions and from continuous to discontinuous permafrost zones. Waterbody maps are linked to regional permafrost landscape maps which provide information on permafrost extent, ground ice volume, geology, and lithology. This paper describes waterbody classification and accuracy, and presents statistics of waterbody distribution for each site. Maps of permafrost landscapes in Alaska, Canada, and Russia are used to extrapolate waterbody statistics from the site level to regional landscape units. PeRL presents pond and lake estimates for a total area of 1. 4 × 106 km2 across the Arctic, about 17 % of the Arctic lowland ( < 300 m a.s.l. land surface area. PeRL waterbodies with sizes of 1. 0 × 106 m2 down to 1. 0 × 102 m2 contributed up to 21 % to the total water fraction. Waterbody density ranged from 1. 0 × 10 to 9. 4 × 101 km−2. Ponds are the dominant waterbody type by number in all landscapes representing 45–99 % of the total waterbody number. The implementation of PeRL size distributions in land surface models will greatly improve the investigation and projection of surface inundation and carbon fluxes in permafrost lowlands
Computational modeling of epiphany learning.
Chen, Wei James; Krajbich, Ian
2017-05-02
Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems to happen all at once. Limited prior research on these "epiphanies" has shown evidence of sudden changes in behavior, but it remains unclear how such epiphanies occur. We propose a sequential-sampling model of epiphany learning (EL) and test it using an eye-tracking experiment. In the experiment, subjects repeatedly play a strategic game that has an optimal strategy. Subjects can learn over time from feedback but are also allowed to commit to a strategy at any time, eliminating all other options and opportunities to learn. We find that the EL model is consistent with the choices, eye movements, and pupillary responses of subjects who commit to the optimal strategy (correct epiphany) but not always of those who commit to a suboptimal strategy or who do not commit at all. Our findings suggest that EL is driven by a latent evidence accumulation process that can be revealed with eye-tracking data.
Wu, Zujian; Pang, Wei; Coghill, George M
2015-01-01
Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology. In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems. In the proposed framework, interactions between reactants in the candidate models for a target biochemical system are evolved and eventually identified by the application of a qualitative model learning approach with an evolution strategy. Kinetic rates of the models generated from qualitative model learning are then further optimised by employing a quantitative approach with simulated annealing. Experimental results indicate that our proposed integrative framework is feasible to learn the relationships between biochemical reactants qualitatively and to make the model replicate the behaviours of the target system by optimising the kinetic rates quantitatively. Moreover, potential reactants of a target biochemical system can be discovered by hypothesising complex reactants in the synthetic models. Based on the biochemical models learned from the proposed framework, biologists can further perform experimental study in wet laboratory. In this way, natural biochemical systems can be better understood.
Knowledge-Based Reinforcement Learning for Data Mining
Kudenko, Daniel; Grzes, Marek
Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent’s activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human
Evaluating QR Code Case Studies Using a Mobile Learning Framework
Rikala, Jenni
2014-01-01
The aim of this study was to evaluate the feasibility of Quick Response (QR) codes and mobile devices in the context of Finnish basic education. The feasibility was analyzed through a mobile learning framework, which includes the core characteristics of mobile learning. The study is part of a larger research where the aim is to develop a…
Fisher, Tony; Denning, Tim; Higgins, Chris; Loveless, Avril
2012-01-01
This article describes a project to apply and validate a conceptual framework of clusters of purposeful learning activity involving ICT tools. The framework, which is based in a socio-cultural perspective, is described as "DECK", and comprises the following major categories of the use of digital technologies to support learning:…
DEFF Research Database (Denmark)
Damkjær, Sidsel Marie Skov; Andersen, Claus Erik
2010-01-01
This review describes 40 years of experience gained at Risø The radioluminescence (RL) signal from fiber coupled Al2O3:C can be used for real-time in vivo dosimetry during radiotherapy. RL generally provides measurements with a reproducibility of 2% (one standard deviation). However, we have...
A Conceptual Framework for Web-Based Learning Design
Alomyan, Hesham
2017-01-01
The purpose of this paper is to provide a coherent framework to present the relationship between individual differences and web-based learning. Two individual difference factors have been identified for investigation within the present paper: Cognitive style and prior knowledge. The importance of individual differences is reviewed and previous…
Zhang, Chen; Sun, Chao; Gao, Liqiang; Zheng, Nenggan; Chen, Weidong; Zheng, Xiaoxiang
2013-01-01
Bio-robots based on brain computer interface (BCI) suffer from the lack of considering the characteristic of the animals in navigation. This paper proposed a new method for bio-robots' automatic navigation combining the reward generating algorithm base on Reinforcement Learning (RL) with the learning intelligence of animals together. Given the graded electrical reward, the animal e.g. the rat, intends to seek the maximum reward while exploring an unknown environment. Since the rat has excellent spatial recognition, the rat-robot and the RL algorithm can convergent to an optimal route by co-learning. This work has significant inspiration for the practical development of bio-robots' navigation with hybrid intelligence.
Directory of Open Access Journals (Sweden)
Pragathi Priyadharsini Balasubramani
2014-04-01
Full Text Available Although empirical and neural studies show that serotonin (5HT plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL-framework. The model depicts the roles of dopamine (DA and serotonin (5HT in Basal Ganglia (BG. In this model, the DA signal is represented by the temporal difference error (δ, while the 5HT signal is represented by a parameter (α that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: 1 Risk-sensitive decision making, where 5HT controls risk assessment, 2 Temporal reward prediction, where 5HT controls time-scale of reward prediction, and 3 Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG.
Validation of an e-Learning 3.0 Critical Success Factors Framework: A Qualitative Research
Paula Miranda; Pedro Isaias; Carlos J Costa; Sara Pifano
2017-01-01
Aim/Purpose: As e-Learning 3.0 evolves from a theoretical construct into an actual solution for online learning, it becomes crucial to accompany this progress by scrutinising the elements that are at the origin of its success. Background: This paper outlines a framework of e-Learning 3.0’s critical success factors and its empirical validation. Methodology: The framework is the result of an extensive literature review and its empirical substantiation derives from semi-structured inte...
A Framework for Developing Self-Directed Technology Use for Language Learning
Lai, Chun
2013-01-01
Critical to maximizing the potential of technology for learning is enhancing language learners' self-directed use of technology for learning purposes. This study aimed to enhance our understanding of the determinants of self-directed technology use through the construction of a structural equation modelling (SEM) framework of factors and…
Social Support System in Learning Network for lifelong learners: A Conceptual framework
Nadeem, Danish; Stoyanov, Slavi; Koper, Rob
2009-01-01
Nadeem, D., Stoyanov, S., & Koper, R. (2009). Social support system in learning network for lifelong learners: A Conceptual framework [Special issue]. International Journal of Continuing Engineering Education and Life-Long Learning, 19(4/5/6), 337-351.
Energy Technology Data Exchange (ETDEWEB)
Aziz, H. M. Abdul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Zhu, Feng [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering; Ukkusuri, Satish V. [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering
2017-10-04
Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO2, NOx, VOC, PM10) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.
‘Living' theory: a pedagogical framework for process support in networked learning
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Philipa Levy
2006-12-01
Full Text Available This paper focuses on the broad outcome of an action research project in which practical theory was developed in the field of networked learning through case-study analysis of learners' experiences and critical evaluation of educational practice. It begins by briefly discussing the pedagogical approach adopted for the case-study course and the action research methodology. It then identifies key dimensions of four interconnected developmental processes–orientation, communication, socialisation and organisation–that were associated with ‘learning to learn' in the course's networked environment, and offers a flavour of participants' experiences in relation to these processes. A number of key evaluation issues that arose are highlighted. Finally, the paper presents the broad conceptual framework for the design and facilitation of process support in networked learning that was derived from this research. The framework proposes a strong, explicit focus on support for process as well as domain learning, and progression from tighter to looser design and facilitation structures for process-focused (as well as domain-focused learning tasks.
Children Learning to Use Technologies through Play: A Digital Play Framework
Bird, Jo; Edwards, Susan
2015-01-01
Digital technologies are increasingly acknowledged as an important aspect of early childhood education. A significant problem for early childhood education has been how to understand the pedagogical use of technologies in a sector that values play-based learning. This paper presents a new framework to understand how children learn to use…
Implementation of a Framework for Collaborative Social Networks in E-Learning
Maglajlic, Seid
2016-01-01
This paper describes the implementation of a framework for the construction and utilization of social networks in ELearning. These social networks aim to enhance collaboration between all E-Learning participants (i.e. both traineeto-trainee and trainee-to-tutor communication are targeted). E-Learning systems that include a so-called "social…
Evaluation Framework EFI for Measuring the Impact of Learning, Education and Training
Stracke, Christian M.
2016-01-01
This article introduces the Evaluation Framework EFI for the Impact Measurement of learning, education and training: The Evaluation Framework for Impact Measurement was developed for specifying the evaluation phase and its objectives and tasks within the IDEAL Reference Model for the introduction
High-frequency signal and noise estimates of CSR GRACE RL04
Bonin, Jennifer A.; Bettadpur, Srinivas; Tapley, Byron D.
2012-12-01
A sliding window technique is used to create daily-sampled Gravity Recovery and Climate Experiment (GRACE) solutions with the same background processing as the official CSR RL04 monthly series. By estimating over shorter time spans, more frequent solutions are made using uncorrelated data, allowing for higher frequency resolution in addition to daily sampling. Using these data sets, high-frequency GRACE errors are computed using two different techniques: assuming the GRACE high-frequency signal in a quiet area of the ocean is the true error, and computing the variance of differences between multiple high-frequency GRACE series from different centers. While the signal-to-noise ratios prove to be sufficiently high for confidence at annual and lower frequencies, at frequencies above 3 cycles/year the signal-to-noise ratios in the large hydrological basins looked at here are near 1.0. Comparisons with the GLDAS hydrological model and high frequency GRACE series developed at other centers confirm CSR GRACE RL04's poor ability to accurately and reliably measure hydrological signal above 3-9 cycles/year, due to the low power of the large-scale hydrological signal typical at those frequencies compared to the GRACE errors.
Reinforcement learning solution for HJB equation arising in constrained optimal control problem.
Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong
2015-11-01
The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.
Gaussian Processes for Data-Efficient Learning in Robotics and Control.
Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward
2015-02-01
Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.
A Framework for Hierarchical Perception-Action Learning Utilizing Fuzzy Reasoning.
Windridge, David; Felsberg, Michael; Shaukat, Affan
2013-02-01
Perception-action (P-A) learning is an approach to cognitive system building that seeks to reduce the complexity associated with conventional environment-representation/action-planning approaches. Instead, actions are directly mapped onto the perceptual transitions that they bring about, eliminating the need for intermediate representation and significantly reducing training requirements. We here set out a very general learning framework for cognitive systems in which online learning of the P-A mapping may be conducted within a symbolic processing context, so that complex contextual reasoning can influence the P-A mapping. In utilizing a variational calculus approach to define a suitable objective function, the P-A mapping can be treated as an online learning problem via gradient descent using partial derivatives. Our central theoretical result is to demonstrate top-down modulation of low-level perceptual confidences via the Jacobian of the higher levels of a subsumptive P-A hierarchy. Thus, the separation of the Jacobian as a multiplying factor between levels within the objective function naturally enables the integration of abstract symbolic manipulation in the form of fuzzy deductive logic into the P-A mapping learning. We experimentally demonstrate that the resulting framework achieves significantly better accuracy than using P-A learning without top-down modulation. We also demonstrate that it permits novel forms of context-dependent multilevel P-A mapping, applying the mechanism in the context of an intelligent driver assistance system.
Yata, Chikahiko; Hamamoto, Kengo; Oguri, Takenori
2014-01-01
This study analyzed the learning activities in a textbook on technology education for teachers, in order to examine the learning processes and learning scenes detailed therein. Results of analyzing learning process, primary learning activity found each contents framework. Other learning activities designated to be related to complementary in learning process. Results of analyzing learning scene, 14 learning scenes, among them "Scene to recognize the impact on social life and progress of techn...
Abro, Kashif Ali; Memon, Anwar Ahmed; Uqaili, Muhammad Aslam
2018-03-01
This research article is analyzed for the comparative study of RL and RC electrical circuits by employing newly presented Atangana-Baleanu and Caputo-Fabrizio fractional derivatives. The governing ordinary differential equations of RL and RC electrical circuits have been fractionalized in terms of fractional operators in the range of 0 ≤ ξ ≤ 1 and 0 ≤ η ≤ 1. The analytic solutions of fractional differential equations for RL and RC electrical circuits have been solved by using the Laplace transform with its inversions. General solutions have been investigated for periodic and exponential sources by implementing the Atangana-Baleanu and Caputo-Fabrizio fractional operators separately. The investigated solutions have been expressed in terms of simple elementary functions with convolution product. On the basis of newly fractional derivatives with and without singular kernel, the voltage and current have interesting behavior with several similarities and differences for the periodic and exponential sources.
Nkhata, Bimo Abraham; Breen, Charles
2010-02-01
This article discusses how the concept of integrated learning systems provides a useful means of exploring the functional linkages between the governance and management of public protected areas. It presents a conceptual framework of an integrated learning system that explicitly incorporates learning processes in governance and management subsystems. The framework is premised on the assumption that an understanding of an integrated learning system is essential if we are to successfully promote learning across multiple scales as a fundamental component of adaptability in the governance and management of protected areas. The framework is used to illustrate real-world situations that reflect the nature and substance of the linkages between governance and management. Drawing on lessons from North America and Africa, the article demonstrates that the establishment and maintenance of an integrated learning system take place in a complex context which links elements of governance learning and management learning subsystems. The degree to which the two subsystems are coupled influences the performance of an integrated learning system and ultimately adaptability. Such performance is largely determined by how integrated learning processes allow for the systematic testing of societal assumptions (beliefs, values, and public interest) to enable society and protected area agencies to adapt and learn in the face of social and ecological change. It is argued that an integrated perspective provides a potentially useful framework for explaining and improving shared understanding around which the concept of adaptability is structured and implemented.
A Framework for Re-thinking Learning in Science from Recent Cognitive Science Perspectives
Tytler, Russell; Prain, Vaughan
2010-10-01
Recent accounts by cognitive scientists of factors affecting cognition imply the need to reconsider current dominant conceptual theories about science learning. These new accounts emphasize the role of context, embodied practices, and narrative-based representation rather than learners' cognitive constructs. In this paper we analyse data from a longitudinal study of primary school children's learning to outline a framework based on these contemporary accounts and to delineate key points of difference from conceptual change perspectives. The findings suggest this framework provides strong theoretical and practical insights into how children learn and the key role of representational negotiation in this learning. We argue that the nature and process of conceptual change can be re-interpreted in terms of the development of students' representational resources.
Collaborative learning framework for online stakeholder engagement.
Khodyakov, Dmitry; Savitsky, Terrance D; Dalal, Siddhartha
2016-08-01
Public and stakeholder engagement can improve the quality of both research and policy decision making. However, such engagement poses significant methodological challenges in terms of collecting and analysing input from large, diverse groups. To explain how online approaches can facilitate iterative stakeholder engagement, to describe how input from large and diverse stakeholder groups can be analysed and to propose a collaborative learning framework (CLF) to interpret stakeholder engagement results. We use 'A National Conversation on Reducing the Burden of Suicide in the United States' as a case study of online stakeholder engagement and employ a Bayesian data modelling approach to develop a CLF. Our data modelling results identified six distinct stakeholder clusters that varied in the degree of individual articulation and group agreement and exhibited one of the three learning styles: learning towards consensus, learning by contrast and groupthink. Learning by contrast was the most common, or dominant, learning style in this study. Study results were used to develop a CLF, which helps explore multitude of stakeholder perspectives; identifies clusters of participants with similar shifts in beliefs; offers an empirically derived indicator of engagement quality; and helps determine the dominant learning style. The ability to detect learning by contrast helps illustrate differences in stakeholder perspectives, which may help policymakers, including Patient-Centered Outcomes Research Institute, make better decisions by soliciting and incorporating input from patients, caregivers, health-care providers and researchers. Study results have important implications for soliciting and incorporating input from stakeholders with different interests and perspectives. © 2015 The Authors. Health Expectations Published by John Wiley & Sons Ltd.
DEFF Research Database (Denmark)
May, Michael; Neutszky-Wulff, Chresteria; Rosthøj, Susanne
2016-01-01
for teachers at the University of Copenhagen a new and simpler pedagogical design pattern framework was developed for interfaculty sharing of experiences and enhancing communities of practice in relation to online and blended learning across the university. The framework of pedagogical design patterns were...... applied to describe the learning design in four online and blended learning courses within different academic disciplines: Classical Greek, Biostatistics, Environmental Management in Europe, and Climate Change Impacts, Adaptation and Mitigation. Future perspectives for using the framework for developing...... new E-learning patterns for online and blended learning courses are discussed....
Verginin Ağırlıklandırılmış Fiyat Elastikiyetinin Hesaplanması: Türkiye (1998-2013
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Engin YILMAZ
2015-04-01
Full Text Available Bu çalışma içerisinde, enflasyonun vergi gelirleri üzerindeki etkilerini inceleyen ilk çalışmalarda ortaya konulan, “gelişmekte olan ülkelerde verginin ağırlıklandırılmış fiyat elastikiyetinin birim olduğu” varsayımı Türkiye için 1998 -2013 periyodu için yeniden değerlendirilecektir. Ağırlıklandırılmış fiyat elastikiyetinin hesaplanmasında Türk vergi gelirleri ve fiyat endeksleri verileri kullanılmıştır. Dinamik En Küçük Kareler (DOLS yöntemiyle, vergi sisteminin uzun dönem ağırlıklandırılmış fiyat elastikiyeti tahmin edilmiştir. Çalışmanın önemi Türkiye için verginin ağırlıklandırılmış fiyat elastikiyetini hesaplamaya yönelik ilk çalışma olmasıdır. Bu anlamda “gelişmekte olan ülkelerde verginin ağırlıklandırılmış fiyat elastikiyetinin birim olduğu” varsayımının yeniden gözden geçirilmesi için yol gösterici bir çalışma olacaktır.
DEFF Research Database (Denmark)
Weitze, Charlotte Lærke; Ørngreen, Rikke
2012-01-01
Based on a preliminary action research study investigating the design of digital music games and years of experiences from interaction design processes of learning resources, this extended abstract presents a framework that mixes designs for learning principles and game design with a process view...... using a simple interaction design lifecycle. Though the first outset was to design engaging music games, the resulting framework has a more generic character....
New wheat-rye 5DS-4RS·4RL and 4RS-5DS·5DL translocation lines with powdery mildew resistance.
Fu, Shulan; Ren, Zhenglong; Chen, Xiaoming; Yan, Benju; Tan, Feiquan; Fu, Tihua; Tang, Zongxiang
2014-11-01
Powdery mildew is one of the serious diseases of wheat (Triticum aestivum L., 2 n = 6 × = 42, genomes AABBDD). Rye (Secale cereale L., 2 n = 2 × = 14, genome RR) offers a rich reservoir of powdery mildew resistant genes for wheat breeding program. However, extensive use of these resistant genes may render them susceptible to new pathogen races because of co-evolution of host and pathogen. Therefore, the continuous exploration of new powdery mildew resistant genes is important to wheat breeding program. In the present study, we identified several wheat-rye addition lines from the progeny of T. aestivum L. Mianyang11 × S. cereale L. Kustro, i.e., monosomic addition lines of the rye chromosomes 4R and 6R; a disomic addition line of 6R; and monotelosomic or ditelosomic addition lines of the long arms of rye chromosomes 4R (4 RL) and 6R (6 RL). All these lines displayed immunity to powdery mildew. Thus, we concluded that both the 4 RL and 6 RL arms of Kustro contain powdery mildew resistant genes. It is the first time to discover that 4 RL arm carries powdery mildew resistant gene. Additionally, wheat lines containing new wheat-rye translocation chromosomes were also obtained: these lines retained a short arm of wheat chromosome 5D (5 DS) on which rye chromosome 4R was fused through the short arm 4 RS (designated 5 DS-4 RS · 4 RL; 4 RL stands for the long arm of rye chromosome 4R); or they had an extra short arm of rye chromosome 4R (4 RS) that was attached to the short arm of wheat chromosome 5D (5 DS) (designated 4 RS-5 DS · 5 DL; 5 DL stands for the long arm of wheat chromosome 5D). These two translocation chromosomes could be transmitted to next generation stably, and the wheat lines containing 5 DS-4 RS · 4 RL chromosome also displayed immunity to powdery mildew. The materials obtained in this study can be used for wheat powdery mildew resistant breeding program.
Formal Learning Sequences and Progression in the Studio: A Framework for Digital Design Education
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Pontus Wärnestål
2016-02-01
Full Text Available This paper examines how to leverage the design studio learning environment throughout long-term Digital Design education in order to support students to progress from tactical, well-defined, device-centric routine design, to confidently design sustainable solutions for strategic, complex, problems for a wide range of devices and platforms in the digital space. We present a framework derived from literature on design, creativity, and theories on learning that: (a implements a theory of formal learning sequences as a user-centered design process in the studio; and (b describes design challenge progressions in the design studio environment modeled in seven dimensions. The framework can be used as a tool for designing, evaluating, and communicating course progressions within – and between series of – design studio courses. This approach is evaluated by implementing a formal learning sequence framework in a series of design studio courses that progress in an undergraduate design-oriented Informatics program. Reflections from students, teachers, and external clients indicate high student motivation and learning goal achievement, high teacher satisfaction and skill development, and high satisfaction among external clients.
Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters.
Khamassi, Mehdi; Enel, Pierre; Dominey, Peter Ford; Procyk, Emmanuel
2013-01-01
Converging evidence suggest that the medial prefrontal cortex (MPFC) is involved in feedback categorization, performance monitoring, and task monitoring, and may contribute to the online regulation of reinforcement learning (RL) parameters that would affect decision-making processes in the lateral prefrontal cortex (LPFC). Previous neurophysiological experiments have shown MPFC activities encoding error likelihood, uncertainty, reward volatility, as well as neural responses categorizing different types of feedback, for instance, distinguishing between choice errors and execution errors. Rushworth and colleagues have proposed that the involvement of MPFC in tracking the volatility of the task could contribute to the regulation of one of RL parameters called the learning rate. We extend this hypothesis by proposing that MPFC could contribute to the regulation of other RL parameters such as the exploration rate and default action values in case of task shifts. Here, we analyze the sensitivity to RL parameters of behavioral performance in two monkey decision-making tasks, one with a deterministic reward schedule and the other with a stochastic one. We show that there exist optimal parameter values specific to each of these tasks, that need to be found for optimal performance and that are usually hand-tuned in computational models. In contrast, automatic online regulation of these parameters using some heuristics can help producing a good, although non-optimal, behavioral performance in each task. We finally describe our computational model of MPFC-LPFC interaction used for online regulation of the exploration rate and its application to a human-robot interaction scenario. There, unexpected uncertainties are produced by the human introducing cued task changes or by cheating. The model enables the robot to autonomously learn to reset exploration in response to such uncertain cues and events. The combined results provide concrete evidence specifying how prefrontal
Lee, Eunbae; Hannafin, Michael J.
2016-01-01
Student-centered learning (SCL) identifies students as the owners of their learning. While SCL is increasingly discussed in K-12 and higher education, researchers and practitioners lack current and comprehensive framework to design, develop, and implement SCL. We examine the implications of theory and research-based evidence to inform those who…
Supporting Collective Inquiry: A Technology Framework for Distributed Learning
Tissenbaum, Michael
This design-based study describes the implementation and evaluation of a technology framework to support smart classrooms and Distributed Technology Enhanced Learning (DTEL) called SAIL Smart Space (S3). S3 is an open-source technology framework designed to support students engaged in inquiry investigations as a knowledge community. To evaluate the effectiveness of S3 as a generalizable technology framework, a curriculum named PLACE (Physics Learning Across Contexts and Environments) was developed to support two grade-11 physics classes (n = 22; n = 23) engaged in a multi-context inquiry curriculum based on the Knowledge Community and Inquiry (KCI) pedagogical model. This dissertation outlines three initial design studies that established a set of design principles for DTEL curricula, and related technology infrastructures. These principles guided the development of PLACE, a twelve-week inquiry curriculum in which students drew upon their community-generated knowledge base as a source of evidence for solving ill-structured physics problems based on the physics of Hollywood movies. During the culminating smart classroom activity, the S3 framework played a central role in orchestrating student activities, including managing the flow of materials and students using real-time data mining and intelligent agents that responded to emergent class patterns. S3 supported students' construction of knowledge through the use individual, collective and collaborative scripts and technologies, including tablets and interactive large-format displays. Aggregate and real-time ambient visualizations helped the teacher act as a wondering facilitator, supporting students in their inquiry where needed. A teacher orchestration tablet gave the teacher some control over the flow of the scripted activities, and alerted him to critical moments for intervention. Analysis focuses on S3's effectiveness in supporting students' inquiry across multiple learning contexts and scales of time, and in
A blended learning framework for curriculum design and professional development
Directory of Open Access Journals (Sweden)
Negin Mirriahi
2015-10-01
Full Text Available The need for flexibility in learning and the affordances of technology provided the impetus for the rise of blended learning (BL globally across higher education institutions. However, the adoption of BL practices continues at a low pace due to academics’ low digital fluency, various views and BL definitions, and limited standards-based tools to guide academic practice. To address these issues, this paper introduces a BL framework, based on one definition and with criteria and standards of practice to support the evaluation and advancement of BL in higher education. The framework is theoretically underpinned by the extant literature and supported by focus group discussions. The evidence supporting the criteria and standards are discussed with suggestions for how they can be used to guide course design, academic practice, and professional development.
Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.
Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline; Sanchez, Justin C
2012-01-01
Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.
LATENCIA DEL HERPESVIRUS BOVINO-1: EL PAPEL DE LOS TRANSCRITOS RELACIONADOS CON LATENCIA (RL
Directory of Open Access Journals (Sweden)
JULIÁN, RUIZ
2008-01-01
Full Text Available El herpesvirus bovino-1 es un virus de distribución mundial causante de graves pérdidas económicas debidas principalmente a la disminución de la eficiencia y en los indicadores de salud y productividad de cualquier hato ganadero infectado. Luego de la infección inicial del tracto respiratorio de los animales, el virus establece un estado de latencia viral en las neuronas sensoriales del ganglio trigémino y en los centros germinales de las tonsilas faríngeas. Periódicamente, el virus es reactivado y excretado en secreciones a través de las cuales puede infectar a otros animales susceptibles. Durante dicho estado de latencia hay disminución dramática de la expresión de genes virales, llevando solo a la expresión de dos transcritos: El RNA codificado por el gen relacionado con latencia (RL y el ORF-E viral. Múltiples estudios demuestran como el RL y el ORF-E están involucrados en la regulación del complejo ciclo de latencia y reactivación de la infección. La presente revisión de literatura se enfocará en describir y analizar los distintos estudios que han llevado a dilucidar el papel jugado por el gen RL y el ORF-E, sus transcritos y sus productos proteicos en el establecimiento, mantenimiento y reactivación de la latencia del HVB-1.
CULTURE, CULTURE LEARNING AND NEW TECHNOLOGIES: TOWARDS A PEDAGOGICAL FRAMEWORK
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Mike Levy
2007-02-01
Full Text Available This paper seeks to improve approaches to the learning and teaching of culture using new technologies by relating the key qualities and dimensions of the culture concept to elements within a pedagogical framework. In Part One, five facets of the culture concept are developed: culture as elemental; culture as relative; culture as group membership; culture as contested; and culture as individual (variable and multiple. Each perspective aims to provide a focus for thinking about culture, and thereby to provide a valid and useful point of departure for thinking about the practice of culture learning and teaching with new technologies. The referenced literature draws from a broad range of disciplines and definitions of culture. In Part Two, five projects are chosen to represent relevant technologies currently in use for culture learning: e-mail, chat, a discussion forum and a Web-based project. Each project is used to illustrate facets of the culture concept discussed in Part One with a view to identifying key elements within a pedagogical framework that can help us respond effectively to the challenge of culture learning and teaching utilising new technologies. Thus the goal is to align fundamental qualities of the culture concept with specific pedagogical designs, tasks and technologies.
Video copy protection and detection framework (VPD) for e-learning systems
ZandI, Babak; Doustarmoghaddam, Danial; Pour, Mahsa R.
2013-03-01
This Article reviews and compares the copyright issues related to the digital video files, which can be categorized as contended based and Digital watermarking copy Detection. Then we describe how to protect a digital video by using a special Video data hiding method and algorithm. We also discuss how to detect the copy right of the file, Based on expounding Direction of the technology of the video copy detection, and Combining with the own research results, brings forward a new video protection and copy detection approach in terms of plagiarism and e-learning systems using the video data hiding technology. Finally we introduce a framework for Video protection and detection in e-learning systems (VPD Framework).
Yang, Myung Sung; Montplaisir, Jacques; Desautels, Alex; Winkelman, John W; Cramer Bornemann, Michel A; Earley, Christopher J; Allen, Richard P
2014-01-01
Individuals with restless legs syndrome (RLS) (Willis-Ekbom disease [WED]) usually have periodic leg movements (PLMs). The suggested immobilization test (SIT) measures sensory and motor features of WED during wakefulness. Surface electromyogram (EMG) recordings of the anterior tibialis (AT) are used as the standard for counting PLMs. However, due to several limitations, leg activity meters such as the PAM-RL were advanced as a potential substitute. In our study, we assessed the validity of the measurements of PLM during wakefulness (PLMW) in the SIT for PAM-RL using both default and custom detection threshold parameters compared to AT EMG. Data were obtained from 39 participants who were diagnosed with primary WED and who were on stable medication as part of another study using the SIT to repeatedly evaluate WED symptoms over 6-12 months. EMG recordings and PAM-RL, when available, were used to detect PLMW for each SIT. Complete PAM-RL and polysomnography (PSG) EMG data were available for 253 SITs from that study. The default PAM-RL (dPAM-RL) detected leg movements based on manufacturer's noise (resting) and signal (movement) amplitude criteria developed to accurately detect PLM during sleep (PLMS). The custom PAM-RL (cPAM-RL) similarly detected leg movements except the noise and movement detection parameters were adjusted to match the PAM-RL data for each SIT. The distributions of the differences between either dPAM-RL or cPAM-RL and EMG PLMW were strongly leptokurtic (Kurtosis >2) with many small differences and a few unusually large differences. These distributions are better described by median and quartile ranges than mean and standard deviation. Despite an adequate correlation (r=0.66) between the dPAM-RL and EMG recordings, the dPAM-RL on average significantly underscored the number of PLMW (median: quartiles=-13: -51.2, 0.0) and on Bland-Altman plots had a significant magnitude bias with greater underscoring for larger average PLMW/h. There also was an
Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes.
Directory of Open Access Journals (Sweden)
Elena Daskalaki
Full Text Available Although reinforcement learning (RL is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the feasibility of RL in the framework of artificial pancreas development for type 1 diabetes (T1D. In this approach, an Actor-Critic (AC learning algorithm is designed and developed for the optimisation of insulin infusion for personalised glucose regulation. AC optimises the daily basal insulin rate and insulin:carbohydrate ratio for each patient, on the basis of his/her measured glucose profile. Automatic, personalised tuning of AC is based on the estimation of information transfer (IT from insulin to glucose signals. Insulin-to-glucose IT is linked to patient-specific characteristics related to total daily insulin needs and insulin sensitivity (SI. The AC algorithm is evaluated using an FDA-accepted T1D simulator on a large patient database under a complex meal protocol, meal uncertainty and diurnal SI variation. The results showed that 95.66% of time was spent in normoglycaemia in the presence of meal uncertainty and 93.02% when meal uncertainty and SI variation were simultaneously considered. The time spent in hypoglycaemia was 0.27% in both cases. The novel tuning method reduced the risk of severe hypoglycaemia, especially in patients with low SI.
A reference model and technical framework for mobile social software for learning
De Jong, Tim; Specht, Marcus; Koper, Rob
2008-01-01
De Jong,T., Specht, M., & Koper, R. (2008). A reference model and technical framework for mobile social software for learning. In I. A. Sánchez & P. Isaías (Eds.), Proceedings of the IADIS Mobile Learning Conference 2008 (pp. 206-210). April, 11-13, 2008, Carvoeiro, Portugal.
Directory of Open Access Journals (Sweden)
Yeonjeong Park
2011-02-01
Full Text Available Instructional designers and educators recognize the potential of mobile technologies as a learning tool for students and have incorporated them into the distance learning environment. However, little research has been done to categorize the numerous examples of mobile learning in the context of distance education, and few instructional design guidelines based on a solid theoretical framework for mobile learning exist. In this paper I compare mobile learning (m-learning with electronic learning (e-learning and ubiquitous learning (u-learning and describe the technological attributes and pedagogical affordances of mobile learning presented in previous studies. I modify transactional distance (TD theory and adopt it as a relevant theoretical framework for mobile learning in distance education. Furthermore, I attempt to position previous studies into four types of mobile learning: 1 high transactional distance socialized m-learning, 2 high transactional distance individualized m-learning, 3 low transactional distance socialized m-learning, and 4 low transactional distance individualized m-learning. As a result, this paper can be used by instructional designers of open and distance learning to learn about the concepts of mobile learning and how mobile technologies can be incorporated into their teaching and learning more effectively.
Framework for the Development of OER-Based Learning Materials in ODL Environment
Teng, Khor Ean; Hung, Chung Sheng
2013-01-01
This paper describes the framework for the development of OER-based learning materials "TCC121/05 Programming Fundamentals with Java" for ODL learners in Wawasan Open University (WOU) using three main development phases mainly: creation, evaluation and production phases. The proposed framework has further been tested on ODL learners to…
Directory of Open Access Journals (Sweden)
Chresteria Neutszky-Wulff
2016-12-01
Full Text Available ”Design patterns” were originally proposed in architecture and later in software engineering as a methodology to sketch and share solutions to recurring design problems. In recent years ”pedagogical design patterns” have been introduced as a way to sketch and share good practices in teaching and learning; specifically in the context of technology-enhanced learning (e-learning. Several attempts have been made to establish a framework for describing and sharing such e-learning patterns, but so far they have had limited success. At a series of workshops in a competence-development project for teachers at the University of Copenhagen a new and simpler pedagogical design pattern framework was developed for interfaculty sharing of experiences and enhancing communities of practice in relation to online and blended learning across the university. In this study, the new pedagogical design pattern framework is applied to describe the learning design in four online and blended learning courses within different academic disciplines: Classical Greek, Biostatistics, Environmental Management in Europe, and Climate Change Impacts, Adaptation and Mitigation. Future perspectives for using the framework for developing new E-learning patterns for online and blended learning courses are discussed.
Learning in Physics by Doing Laboratory Work: Towards a New Conceptual Framework
Danielsson, Anna Teresia; Linder, Cedric
2009-01-01
Drawing on a study that explores university students' experiences of doing laboratory work in physics, this article outlines a proposed conceptual framework for extending the exploration of the gendered experience of learning. In this framework situated cognition and post-structural gender theory are merged together. By drawing on data that aim at…
Adult Learning, 2012
2012-01-01
This article presents the Belem Framework for Action. This framework focuses on harnessing the power and potential of adult learning and education for a viable future. This framework begins with a preamble on adult education and towards lifelong learning.
Developing an Evaluation Framework of Quality Indicators for Learning Analytics
Scheffel, Maren; Drachsler, Hendrik; Specht, Marcus
2017-01-01
This paper presents results from the continuous process of developing an evaluation framework of quality indicators for learning analytics (LA). Building on a previous study, a group concept mapping approach that uses multidimensional scaling and hierarchical clustering, the study presented here
Psychological theory and pedagogical effectiveness: the learning promotion potential framework.
Tomlinson, Peter
2008-12-01
After a century of educational psychology, eminent commentators are still lamenting problems besetting the appropriate relating of psychological insights to teaching design, a situation not helped by the persistence of crude assumptions concerning the nature of pedagogical effectiveness. To propose an analytical or meta-theoretical framework based on the concept of learning promotion potential (LPP) as a basis for understanding the basic relationship between psychological insights and teaching strategies, and to draw out implications for psychology-based pedagogical design, development and research. This is a theoretical and meta-theoretical paper relying mainly on conceptual analysis, though also calling on psychological theory and research. Since teaching consists essentially in activity designed to promote learning, it follows that a teaching strategy has the potential in principle to achieve particular kinds of learning gains (LPP) to the extent that it embodies or stimulates the relevant learning processes on the part of learners and enables the teacher's functions of on-line monitoring and assistance for such learning processes. Whether a teaching strategy actually does realize its LPP by way of achieving its intended learning goals depends also on the quality of its implementation, in conjunction with other factors in the situated interaction that teaching always involves. The core role of psychology is to provide well-grounded indication of the nature of such learning processes and the teaching functions that support them, rather than to directly generate particular ways of teaching. A critically eclectic stance towards potential sources of psychological insight is argued for. Applying this framework, the paper proposes five kinds of issue to be attended to in the design and evaluation of psychology-based pedagogy. Other work proposing comparable ideas is briefly reviewed, with particular attention to similarities and a key difference with the ideas of Oser
Mapping of Supply Chain Learning: A Framework for SMEs
Thakkar, Jitesh; Kanda, Arun; Deshmukh, S. G.
2011-01-01
Purpose: The aim of this paper is to propose a mapping framework for evaluating supply chain learning potential for the context of small- to medium-sized enterprises (SMEs). Design/methodology/approach: The extracts of recently completed case based research for ten manufacturing SME units and facts reported in the previous research are utilized…
Caniglia, Guido; John, Beatrice; Kohler, Martin; Bellina, Leonie; Wiek, Arnim; Rojas, Christopher; Laubichler, Manfred D.; Lang, Daniel
2016-01-01
Purpose: This paper aims to present an experience-based learning framework that provides a bottom-up, student-centered entrance point for the development of systems thinking, normative and collaborative competencies in sustainability. Design/methodology/approach: The framework combines mental mapping with exploratory walking. It interweaves…
Architectural Design and the Learning Environment: A Framework for School Design Research
Gislason, Neil
2010-01-01
This article develops a theoretical framework for studying how instructional space, teaching and learning are related in practice. It is argued that a school's physical design can contribute to the quality of the learning environment, but several non-architectural factors also determine how well a given facility serves as a setting for teaching…
PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
Liu, Feng; Li, Hao; Ren, Chao; Bo, Xiaochen; Shu, Wenjie
2016-06-22
Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from many issues. We developed a deep learning-based algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and demonstrated that PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to iteratively learn from 22 training cell types/tissues. Our results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) of sensitivity and specificity across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues.
Directory of Open Access Journals (Sweden)
Yannick eBoddez
2014-11-01
Full Text Available Blocking is the most important phenomenon in the history of associative learning theory: For over 40 years, blocking has inspired a whole generation of learning models. Blocking is part of a family of effects that are typically termed cue competition effects. Common amongst all cue competition effects is that a cue-outcome relation is poorly learned or poorly expressed because the cue is trained in the presence of an alternative predictor or cause of the outcome. We provide an overview of the cognitive processes involved in cue competition effects in humans and propose a stage framework that brings these processes together. The framework contends that the behavioral display of cue competition is cognitively construed following three stages that include (1 an encoding stage, (2 a retention stage, and (3 a performance stage. We argue that the stage framework supports a comprehensive understanding of cue competition effects.
Framework for e-learning assessment in dental education: a global model for the future.
Arevalo, Carolina R; Bayne, Stephen C; Beeley, Josie A; Brayshaw, Christine J; Cox, Margaret J; Donaldson, Nora H; Elson, Bruce S; Grayden, Sharon K; Hatzipanagos, Stylianos; Johnson, Lynn A; Reynolds, Patricia A; Schönwetter, Dieter J
2013-05-01
The framework presented in this article demonstrates strategies for a global approach to e-curricula in dental education by considering a collection of outcome assessment tools. By combining the outcomes for overall assessment, a global model for a pilot project that applies e-assessment tools to virtual learning environments (VLE), including haptics, is presented. Assessment strategies from two projects, HapTEL (Haptics in Technology Enhanced Learning) and UDENTE (Universal Dental E-learning), act as case-user studies that have helped develop the proposed global framework. They incorporate additional assessment tools and include evaluations from questionnaires and stakeholders' focus groups. These measure each of the factors affecting the classical teaching/learning theory framework as defined by Entwistle in a standardized manner. A mathematical combinatorial approach is proposed to join these results together as a global assessment. With the use of haptic-based simulation learning, exercises for tooth preparation assessing enamel and dentine were compared to plastic teeth in manikins. Equivalence for student performance for haptic versus traditional preparation methods was established, thus establishing the validity of the haptic solution for performing these exercises. Further data collected from HapTEL are still being analyzed, and pilots are being conducted to validate the proposed test measures. Initial results have been encouraging, but clearly the need persists to develop additional e-assessment methods for new learning domains.
A machine learning-based framework to identify type 2 diabetes through electronic health records.
Zheng, Tao; Xie, Wei; Xu, Liling; He, Xiaoying; Zhang, Ya; You, Mingrong; Yang, Gong; Chen, You
2017-01-01
To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. We apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (∼0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature
Developing a Framework for Social Technologies in Learning via Design-Based Research
Parmaxi, Antigoni; Zaphiris, Panayiotis
2015-01-01
This paper reports on the use of design-based research (DBR) for the development of a framework that grounds the use of social technologies in learning. The paper focuses on three studies which step on the learning theory of constructionism. Constructionism assumes that knowledge is better gained when students find this knowledge for themselves…
Conceptual Framework: Development of Interactive Reading Malay Language Learning System (I-ReaMaLLS
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Ismail Nurulisma
2018-01-01
Full Text Available Reading is very important to access knowledge. Reading skills starts during preschool level no matter of the types of languages. At present, there are many preschool children who are still unable to recognize letters or even words. This leads to the difficulties in reading. Therefore, there is a need of intervention in reading to overcome such problems. Thus, technologies were adapted in enhancing learning skills, especially in learning to read among the preschool children. Phonological is one of the factors to be considered to ensure a smooth of transition into reading. Phonological concept enables the first learner to easily learn reading such to learn reading Malay language. The medium of learning to read Malay language can be assisted via the supportive of multimedia technology to enhance the preschool children learning. Thus, an interactive system is proposed via a development of interactive reading Malay language learning system, which is called as I-ReaMaLLS. As a part of the development of I-ReaMaLLS, this paper focus on the development of conceptual framework in developing interactive reading Malay language learning system (I-ReaMaLLS. I-ReaMaLLS is voice based system that facilitates the preschool learner in learning reading Malay language. The conceptual framework of developing I-ReaMaLLS is conceptualized based on the initial study conducted via methods of literature review and observation with the preschool children, aged 5 – 6 years. As the result of the initial study, research objectives have been affirmed that finally contributes to the design of conceptual framework for the development of I-ReaMaLLS.
Towards a Framework for Learning in the OSMA Serious Game Engine
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Tanguy Coenen
2010-11-01
Full Text Available Online multiplayer serious games offer a way to support learning in a gaming paradigm that is familiar to many players and has proven its effectiveness in providing sustainably enjoyable gameplay. We aim to decrease development cost for these games by providing a modular game design framework and a component-based technical architecture. The technical architecture and the game design framework will be implemented and iteratively refined through two proofs of concept.
Curiosity driven reinforcement learning for motion planning on humanoids
Frank, Mikhail; Leitner, Jürgen; Stollenga, Marijn; Förster, Alexander; Schmidhuber, Jürgen
2014-01-01
Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment. PMID:24432001
Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images
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Sriram Ganapathi Subramanian
2018-04-01
Full Text Available Machine learning algorithms have increased tremendously in power in recent years but have yet to be fully utilized in many ecology and sustainable resource management domains such as wildlife reserve design, forest fire management, and invasive species spread. One thing these domains have in common is that they contain dynamics that can be characterized as a spatially spreading process (SSP, which requires many parameters to be set precisely to model the dynamics, spread rates, and directional biases of the elements which are spreading. We present related work in artificial intelligence and machine learning for SSP sustainability domains including forest wildfire prediction. We then introduce a novel approach for learning in SSP domains using reinforcement learning (RL where fire is the agent at any cell in the landscape and the set of actions the fire can take from a location at any point in time includes spreading north, south, east, or west or not spreading. This approach inverts the usual RL setup since the dynamics of the corresponding Markov Decision Process (MDP is a known function for immediate wildfire spread. Meanwhile, we learn an agent policy for a predictive model of the dynamics of a complex spatial process. Rewards are provided for correctly classifying which cells are on fire or not compared with satellite and other related data. We examine the behavior of five RL algorithms on this problem: value iteration, policy iteration, Q-learning, Monte Carlo Tree Search, and Asynchronous Advantage Actor-Critic (A3C. We compare to a Gaussian process-based supervised learning approach and also discuss the relation of our approach to manually constructed, state-of-the-art methods from forest wildfire modeling. We validate our approach with satellite image data of two massive wildfire events in Northern Alberta, Canada; the Fort McMurray fire of 2016 and the Richardson fire of 2011. The results show that we can learn predictive, agent
Mondi, Makingu; Woods, Peter; Rafi, Ahmad
2007-01-01
This paper presents the systematic development of a "Uses and Gratification Expectancy" (UGE) conceptual framework which is able to predict students' "Perceived e-Learning Experience." It is argued that students' UGE as regards e-learning resources cannot be implicitly or explicitly explored without first examining underlying communication…
DEFF Research Database (Denmark)
Nissen, Jakob Dahl; Diness, Jonas Goldin; Diness, Thomas Goldin
2009-01-01
of this study was to evaluate potential antiarrhythmic effects of compound induced IKs activation using the benzodiazepine L-364,373 (R-L3). Ventricular myocytes from guinea pigs were isolated and whole-cell current clamping was performed at 35 degrees C. It was found that 1 microM R-L3 significantly reduced...
A web-based e-learning framework for public perception and acceptance on nuclear energy
International Nuclear Information System (INIS)
Zhou Yangping; Yoshikawa, Hidekazu; Liu Jingquan; Ouyang, Jun; Lu Daogang
2005-01-01
Now, public acceptance plays a central role in the nuclear energy. Public concerns on safety and sustainability of nuclear energy, ground nuclear power in many countries and territories to a stop or even a downfall. In this study, an e-learning framework by using Internet, is proposed for public education in order to boost public perception on nuclear energy, which will certainly affect public acceptance toward it. This study aims at investigating public perception and acceptance on nuclear energy in a continuous and accurate manner. In addition, this e-learning framework can promote public perception on nuclear energy by using teaching material with a graphical hierarchy about knowledge of nuclear energy. This web-based e-learning framework mainly consists of two components: (1) an e-learning support module which continuously investigates public perception and acceptance toward nuclear energy and teaches public knowledge about nuclear energy; (2) an updating module which may improve the education materials by analyzing the effect of education or proving the materials submitted by the visitors through Wiki pages. Advantages and future work of this study are also generally described. (author)
When Playing Meets Learning: Methodological Framework for Designing Educational Games
Linek, Stephanie B.; Schwarz, Daniel; Bopp, Matthias; Albert, Dietrich
Game-based learning builds upon the idea of using the motivational potential of video games in the educational context. Thus, the design of educational games has to address optimizing enjoyment as well as optimizing learning. Within the EC-project ELEKTRA a methodological framework for the conceptual design of educational games was developed. Thereby state-of-the-art psycho-pedagogical approaches were combined with insights of media-psychology as well as with best-practice game design. This science-based interdisciplinary approach was enriched by enclosed empirical research to answer open questions on educational game-design. Additionally, several evaluation-cycles were implemented to achieve further improvements. The psycho-pedagogical core of the methodology can be summarized by the ELEKTRA's 4Ms: Macroadaptivity, Microadaptivity, Metacognition, and Motivation. The conceptual framework is structured in eight phases which have several interconnections and feedback-cycles that enable a close interdisciplinary collaboration between game design, pedagogy, cognitive science and media psychology.
A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles.
Wang, Ning; Sun, Jing-Chao; Er, Meng Joo; Liu, Yan-Cheng
2016-05-01
In this paper, an extreme learning control (ELC) framework using the single-hidden-layer feedforward network (SLFN) with random hidden nodes for tracking an unmanned surface vehicle suffering from unknown dynamics and external disturbances is proposed. By combining tracking errors with derivatives, an error surface and transformed states are defined to encapsulate unknown dynamics and disturbances into a lumped vector field of transformed states. The lumped nonlinearity is further identified accurately by an extreme-learning-machine-based SLFN approximator which does not require a priori system knowledge nor tuning input weights. Only output weights of the SLFN need to be updated by adaptive projection-based laws derived from the Lyapunov approach. Moreover, an error compensator is incorporated to suppress approximation residuals, and thereby contributing to the robustness and global asymptotic stability of the closed-loop ELC system. Simulation studies and comprehensive comparisons demonstrate that the ELC framework achieves high accuracy in both tracking and approximation.
Crocco, Laura; Madill, Catherine J; McCabe, Patricia
2017-01-01
The study systematically reviews evidence-based frameworks for teaching and learning of classical singing training. This is a systematic review. A systematic literature search of 15 electronic databases following the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines was conducted. Eligibility criteria included type of publication, participant characteristics, intervention, and report of outcomes. Quality rating scales were applied to support assessment of the included literature. Data analysis was conducted using meta-aggregation. Nine papers met the inclusion criteria. No complete evidence-based teaching and learning framework was found. Thematic content analysis showed that studies either (1) identified teaching practices in one-to-one lessons, (2) identified student learning strategies in one-to-one lessons or personal practice sessions, and (3) implemented a tool to enhance one specific area of teaching and learning in lessons. The included studies showed that research in music education is not always specific to musical genre or instrumental group, with four of the nine studies including participant teachers and students of classical voice training only. The overall methodological quality ratings were low. Research in classical singing training has not yet developed an evidence-based framework for classical singing training. This review has found that introductory information on teaching and learning practices has been provided, and tools have been suggested for use in the evaluation of the teaching-learning process. High-quality methodological research designs are needed. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
Bodily, Robert; Nyland, Rob; Wiley, David
2017-01-01
The RISE (Resource Inspection, Selection, and Enhancement) Framework is a framework supporting the continuous improvement of open educational resources (OER). The framework is an automated process that identifies learning resources that should be evaluated and either eliminated or improved. This is particularly useful in OER contexts where the…
Formal Framework to improve the reliability of concurrent and collaborative learning games
Directory of Open Access Journals (Sweden)
Mounier
2014-05-01
Full Text Available Multi-player learning games are complex software applications resulting from a costly and complex engineering process, and involving multiple stakeholders (domain experts, teachers, game designers, programmers, testers, etc.. Moreover, they are dynamic systems that evolve over time and implement complex interactions between objects and players. Usually, once a learning game is developed, testing activities are conducted by humans who explore the possible executions of the game’s scenario to detect bugs. The complexity and the dynamic nature of multiplayer learning games enforces the complexity of testing activities. Indeed, it is impracticable to explore manually all possible executions due to their huge number. Moreover, the test cannot verify some properties on multi-player and collaborative scenarios, such as paths leading to deadlock between learners or prevent learners to meet all objectives and win the game. This type of properties should be verified at the design stage. We propose a framework enabling a formal modeling of game scenarios and an associated automatic verification of learning game’s scenario at the design stage of the development process.We use Symmetric Petri nets as a modeling language and choose to verify properties by means of model checkers. This paper discusses the possibilities offered by this framework to verify learning game’s properties before the programming stage.
A Framework for Culturally Relevant Online Learning: Lessons from Alaska's Tribal Health Workers.
Cueva, Katie; Cueva, Melany; Revels, Laura; Lanier, Anne P; Dignan, Mark; Viswanath, K; Fung, Teresa T; Geller, Alan C
2018-03-22
Culturally relevant health promotion is an opportunity to reduce health inequities in diseases with modifiable risks, such as cancer. Alaska Native people bear a disproportionate cancer burden, and Alaska's rural tribal health workers consequently requested cancer education accessible online. In response, the Alaska Native Tribal Health Consortium cancer education team sought to create a framework for culturally relevant online learning to inform the creation of distance-delivered cancer education. Guided by the principles of community-based participatory action research and grounded in empowerment theory, the project team conducted a focus group with 10 Alaska Native education experts, 12 culturally diverse key informant interviews, a key stakeholder survey of 62 Alaska Native tribal health workers and their instructors/supervisors, and a literature review on distance-delivered education with Alaska Native or American Indian people. Qualitative findings were analyzed in Atlas.ti, with common themes presented in this article as a framework for culturally relevant online education. This proposed framework includes four principles: collaborative development, interactive content delivery, contextualizing learning, and creating connection. As an Alaskan tribal health worker shared "we're all in this together. All about conversations, relationships. Always learn from you/with you, together what we know and understand from the center of our experience, our ways of knowing, being, caring." The proposed framework has been applied to support cancer education and promote cancer control with Alaska Native people and has motivated health behavior change to reduce cancer risk. This framework may be adaptable to other populations to guide effective and culturally relevant online interventions.
An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
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Guanqiu Qi
2017-10-01
Full Text Available Image fusion is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Medical image fusion, as an important image fusion application, can extract the details of multiple images from different imaging modalities and combine them into an image that contains complete and non-redundant information for increasing the accuracy of medical diagnosis and assessment. The quality of the fused image directly affects medical diagnosis and assessment. However, existing solutions have some drawbacks in contrast, sharpness, brightness, blur and details. This paper proposes an integrated dictionary-learning and entropy-based medical image-fusion framework that consists of three steps. First, the input image information is decomposed into low-frequency and high-frequency components by using a Gaussian filter. Second, low-frequency components are fused by weighted average algorithm and high-frequency components are fused by the dictionary-learning based algorithm. In the dictionary-learning process of high-frequency components, an entropy-based algorithm is used for informative blocks selection. Third, the fused low-frequency and high-frequency components are combined to obtain the final fusion results. The results and analyses of comparative experiments demonstrate that the proposed medical image fusion framework has better performance than existing solutions.
Validation of an e-Learning 3.0 Critical Success Factors Framework: A Qualitative Research
Miranda, Paula; Isaias, Pedro; Costa, Carlos J.; Pifano, Sara
2017-01-01
Aim/Purpose: As e-Learning 3.0 evolves from a theoretical construct into an actual solution for online learning, it becomes crucial to accompany this progress by scrutinising the elements that are at the origin of its success. Background: This paper outlines a framework of e-Learning 3.0's critical success factors and its empirical validation.…
Hutchings, Maggie; Scammell, Janet; Quinney, Anne
2013-09-01
While there is growing evidence of theoretical perspectives adopted in interprofessional education, learning theories tend to foreground the individual, focusing on psycho-social aspects of individual differences and professional identity to the detriment of considering social-structural factors at work in social practices. Conversely socially situated practice is criticised for being context-specific, making it difficult to draw generalisable conclusions for improving interprofessional education. This article builds on a theoretical framework derived from earlier research, drawing on the dynamics of Dewey's experiential learning theory and Archer's critical realist social theory, to make a case for a meta-theoretical framework enabling social-constructivist and situated learning theories to be interlinked and integrated through praxis and reflexivity. Our current analysis is grounded in an interprofessional curriculum initiative mediated by a virtual community peopled by health and social care users. Student perceptions, captured through quantitative and qualitative data, suggest three major disruptive themes, creating opportunities for congruence and disjuncture and generating a model of zones of interlinked praxis associated with professional differences and identity, pedagogic strategies and technology-mediated approaches. This model contributes to a framework for understanding the complexity of interprofessional learning and offers bridges between individual and structural factors for engaging with the enablements and constraints at work in communities of practice and networks for interprofessional education.
Toward a common theory for learning from reward, affect, and motivation: the SIMON framework.
Madan, Christopher R
2013-10-07
While the effects of reward, affect, and motivation on learning have each developed into their own fields of research, they largely have been investigated in isolation. As all three of these constructs are highly related, and use similar experimental procedures, an important advance in research would be to consider the interplay between these constructs. Here we first define each of the three constructs, and then discuss how they may influence each other within a common framework. Finally, we delineate several sources of evidence supporting the framework. By considering the constructs of reward, affect, and motivation within a single framework, we can develop a better understanding of the processes involved in learning and how they interplay, and work toward a comprehensive theory that encompasses reward, affect, and motivation.
Younghak Shin; Balasingham, Ilangko
2017-07-01
Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.
Directory of Open Access Journals (Sweden)
Eric Chalmers
2016-12-01
Full Text Available The mammalian brain is thought to use a version of Model-based Reinforcement Learning (MBRL to guide goal-directed behavior, wherein animals consider goals and make plans to acquire desired outcomes. However, conventional MBRL algorithms do not fully explain animals’ ability to rapidly adapt to environmental changes, or learn multiple complex tasks. They also require extensive computation, suggesting that goal-directed behavior is cognitively expensive. We propose here that key features of processing in the hippocampus support a flexible MBRL mechanism for spatial navigation that is computationally efficient and can adapt quickly to change. We investigate this idea by implementing a computational MBRL framework that incorporates features inspired by computational properties of the hippocampus: a hierarchical representation of space, forward sweeps through future spatial trajectories, and context-driven remapping of place cells. We find that a hierarchical abstraction of space greatly reduces the computational load (mental effort required for adaptation to changing environmental conditions, and allows efficient scaling to large problems. It also allows abstract knowledge gained at high levels to guide adaptation to new obstacles. Moreover, a context-driven remapping mechanism allows learning and memory of multiple tasks. Simulating dorsal or ventral hippocampal lesions in our computational framework qualitatively reproduces behavioral deficits observed in rodents with analogous lesions. The framework may thus embody key features of how the brain organizes model-based RL to efficiently solve navigation and other difficult tasks.
A framework to develop a clinical learning culture in health facilities: ideas from the literature.
Henderson, A; Briggs, J; Schoonbeek, S; Paterson, K
2011-06-01
Internationally, there is an increase in demand to educate nurses within the clinical practice environment. Clinical practice settings that encourage teaching and learning during episodes of care delivery can be powerful in educating both the existing nursing workforce and nursing students. This paper presents a framework, informed by the literature, that identifies the key factors that are needed to encourage the interactions fundamental to learning in clinical practice. Learning occurs when nurses demonstrate good practice, share their knowledge through conversations and discussions, and also provide feedback to learners, such as students and novices. These types of interactions occur when positive leadership practices encourage trust and openness between staff; when the management team provides sessions for staff to learn how to interact with learners, and also when partnerships provide support and guidance around learning in the workplace. APPLICATION OF CONCEPTS: This framework presents how the concepts of leadership, management and partnership interact to create and sustain learning environments. The feedback from proposed measurement tools can provide valuable information about the positive and negative aspects of these concepts in the clinical learning environment. Analysis of the subscales can assist in identifying appropriate recommended strategies outlined in the framework to guide nurses in improving the recognized deficits in the relationship between the concepts. Leadership, management and partnerships are pivotal for the creation and maintenance of positive learning environments. Diagnostic measurement tools can provide specific information about weaknesses across these areas. This knowledge can guide future initiatives. © 2011 The Authors. International Nursing Review © 2011 International Council of Nurses.
Humanoids Learning to Walk: A Natural CPG-Actor-Critic Architecture.
Li, Cai; Lowe, Robert; Ziemke, Tom
2013-01-01
The identification of learning mechanisms for locomotion has been the subject of much research for some time but many challenges remain. Dynamic systems theory (DST) offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL) has offered a promising method to adaptively link the dynamic system to the environment it interacts with via a reward-based value system. In this paper, we propose a model that integrates the above perspectives and applies it to the case of a humanoid (NAO) robot learning to walk the ability of which emerges from its value-based interaction with the environment. In the model, a simplified central pattern generator (CPG) architecture inspired by neuroscientific research and DST is integrated with an actor-critic approach to RL (cpg-actor-critic). In the cpg-actor-critic architecture, least-square-temporal-difference based learning converges to the optimal solution quickly by using natural gradient learning and balancing exploration and exploitation. Futhermore, rather than using a traditional (designer-specified) reward it uses a dynamic value function as a stability indicator that adapts to the environment. The results obtained are analyzed using a novel DST-based embodied cognition approach. Learning to walk, from this perspective, is a process of integrating levels of sensorimotor activity and value.
A Framework for Research on E-Learning Assimilation in SMEs: A Strategic Perspective
Raymond, Louis; Uwizeyemungu, Sylvestre; Bergeron, Francois; Gauvin, Stephane
2012-01-01
Purpose: This study aims to propose an integrative conceptual framework of e-learning adoption and assimilation that is adapted to the specific context of small to medium-sized enterprises (SMEs). Design/methodology/approach: The literature on the state of e-learning usage in SMEs and on the IT adoption and assimilation factors that can be…
Understanding Human Hand Gestures for Learning Robot Pick-and-Place Tasks
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Hsien-I Lin
2015-05-01
Full Text Available Programming robots by human demonstration is an intuitive approach, especially by gestures. Because robot pick-and-place tasks are widely used in industrial factories, this paper proposes a framework to learn robot pick-and-place tasks by understanding human hand gestures. The proposed framework is composed of the module of gesture recognition and the module of robot behaviour control. For the module of gesture recognition, transport empty (TE, transport loaded (TL, grasp (G, and release (RL from Gilbreth's therbligs are the hand gestures to be recognized. A convolution neural network (CNN is adopted to recognize these gestures from a camera image. To achieve the robust performance, the skin model by a Gaussian mixture model (GMM is used to filter out non-skin colours of an image, and the calibration of position and orientation is applied to obtain the neutral hand pose before the training and testing of the CNN. For the module of robot behaviour control, the corresponding robot motion primitives to TE, TL, G, and RL, respectively, are implemented in the robot. To manage the primitives in the robot system, a behaviour-based programming platform based on the Extensible Agent Behavior Specification Language (XABSL is adopted. Because the XABSL provides the flexibility and re-usability of the robot primitives, the hand motion sequence from the module of gesture recognition can be easily used in the XABSL programming platform to implement the robot pick-and-place tasks. The experimental evaluation of seven subjects performing seven hand gestures showed that the average recognition rate was 95.96%. Moreover, by the XABSL programming platform, the experiment showed the cube-stacking task was easily programmed by human demonstration.
Towards a common theory for learning from reward, affect, and motivation: The SIMON framework
Directory of Open Access Journals (Sweden)
Christopher R Madan
2013-10-01
Full Text Available While the effects of reward, affect, and motivation on learning have each developed into their own fields of research, they largely have been investigated in isolation. As all three of these constructs are highly related, and use similar experimental procedures, an important advance in research would be to consider the interplay between these constructs. Here we first define each of the three constructs, and then discuss how they may influence each other within a common framework. Finally, we delineate several sources of evidence supporting the framework. By considering the constructs of reward, affect, and motivation within a single framework, we can develop a better understanding of the processes involved in learning and how they interplay, and work towards a comprehensive theory that encompasses reward, affect, and motivation.
Directory of Open Access Journals (Sweden)
Xiaoyan Ren
2016-03-01
Full Text Available Background/Aims: Preeclampsia (PE is a systemic inflammatory response syndrome involving varieties of cytokines, and previous studies have shown that IL-33 and its receptor IL-1RL1 play pivotal roles in the development of it. As a polygenetic hereditary disease, it is necessary to study the gene analysis for PE. Therefore, the present study was to determine whether IL-33 rs3939286 and IL-1RL1 rs13015714 associated with susceptibility to PE in Chinese Han women. Methods: 1,031 PE patients and 1,298 controls were enrolled and the genotyping for rs3939286 in IL-33 and rs13015714 in IL-1RL1 was performed by TaqMan allelic discrimination real-time PCR. Hardy-Weinberg equilibrium (HWE was examined to ensure the group representativeness and Pearson's chi-square test was used to compare the differences in genetic distributions between the two groups. Results: No significant differences in genotypic and allelic frequencies of the two polymorphisms loci were observed between cases and controls. There were also no significant differences in genetic distributions between mild/severe and early/late-onset PE and control groups. Conclusion: Although our data suggested that the polymorphisms of IL-33 rs3939286 and IL-1RL1 rs13015714 might not be critical risk factors for PE in Chinese Han women, the results need to be validated in different nations.
The Social Outcomes of Older Adult Learning in Taiwan: Evaluation Framework and Indicators
Lin, Li-Hui
2015-01-01
The purpose of this study is to explore the social outcomes of older adult learning in Taiwan. In light of our society's aging population structure, the task of establishing evaluation framework and indicators for the social outcomes of learning (SOL) as applied to older adults is urgent. In order to construct evaluation indicators for older adult…
Quantitative Reasoning Learning Progressions for Environmental Science: Developing a Framework
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Robert L. Mayes
2013-01-01
Full Text Available Quantitative reasoning is a complex concept with many definitions and a diverse account in the literature. The purpose of this article is to establish a working definition of quantitative reasoning within the context of science, construct a quantitative reasoning framework, and summarize research on key components in that framework. Context underlies all quantitative reasoning; for this review, environmental science serves as the context.In the framework, we identify four components of quantitative reasoning: the quantification act, quantitative literacy, quantitative interpretation of a model, and quantitative modeling. Within each of these components, the framework provides elements that comprise the four components. The quantification act includes the elements of variable identification, communication, context, and variation. Quantitative literacy includes the elements of numeracy, measurement, proportional reasoning, and basic probability/statistics. Quantitative interpretation includes the elements of representations, science diagrams, statistics and probability, and logarithmic scales. Quantitative modeling includes the elements of logic, problem solving, modeling, and inference. A brief comparison of the quantitative reasoning framework with the AAC&U Quantitative Literacy VALUE rubric is presented, demonstrating a mapping of the components and illustrating differences in structure. The framework serves as a precursor for a quantitative reasoning learning progression which is currently under development.
Colas, Jaron T; Pauli, Wolfgang M; Larsen, Tobias; Tyszka, J Michael; O'Doherty, John P
2017-10-01
Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models-namely, "actor/critic" models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning.
Vanderlinde, Elizabeth M.; Magnus, Samantha A.; Tambalo, Dinah D.; Koval, Susan F.; Yost, Christopher K.
2011-01-01
The bacterial cell envelope is of critical importance to the function and survival of the cell; it acts as a barrier against harmful toxins while allowing the flow of nutrients into the cell. It also serves as a point of physical contact between a bacterial cell and its host. Hence, the cell envelope of Rhizobium leguminosarum is critical to cell survival under both free-living and symbiotic conditions. Transposon mutagenesis of R. leguminosarum strain 3841 followed by a screen to isolate mutants with defective cell envelopes led to the identification of a novel conserved operon (RL3499-RL3502) consisting of a putative moxR-like AAA+ ATPase, a hypothetical protein with a domain of unknown function (designated domain of unknown function 58), and two hypothetical transmembrane proteins. Mutation of genes within this operon resulted in increased sensitivity to membrane-disruptive agents such as detergents, hydrophobic antibiotics, and alkaline pH. On minimal media, the mutants retain their rod shape but are roughly 3 times larger than the wild type. On media containing glycine or peptides such as yeast extract, the mutants form large, distorted spheres and are incapable of sustained growth under these culture conditions. Expression of the operon is maximal during the stationary phase of growth and is reduced in a chvG mutant, indicating a role for this sensor kinase in regulation of the operon. Our findings provide the first functional insight into these genes of unknown function, suggesting a possible role in cell envelope development in Rhizobium leguminosarum. Given the broad conservation of these genes among the Alphaproteobacteria, the results of this study may also provide insight into the physiological role of these genes in other Alphaproteobacteria, including the animal pathogen Brucella. PMID:21357485
Real World Learning: toward a differentiated framework for outdoor learning for sustainability
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Lewis Winks
2015-12-01
Full Text Available The Real World Learning network (RWLn set out in 2011 to explore elements which contribute to a ‘deep and meaningful’ outdoor education experience. Following three years of work, the RWLn developed the ‘Hand Model’, a learning model designed to support educators in the development of Outdoor Learning for Sustainability (OLfS. Since its launch in early 2014, the model has been used for planning, delivering and reflecting upon OLfS experiences. Making use of the comments made in Činčera’s (2015 Real World Learning: a critical analysis which highlights inconsistencies existent within the model’s internal logic, this paper considers the perceived contradiction between emancipatory and instrumental approaches to learning. Beginning with a comprehensive introduction to the Hand model, this paper goes on to discuss the theoretical divide which the model spans between a goal-led, knowledge based approach promoted by the model’s focus upon understanding and values, and a pluralistic and exploratory approach typified by aspects of educational empowerment and experience. In response to this and augmented by examples, a differentiated conceptual framework is presented to facilitate a pragmatic application of the model from a practice perspective, making use of what has been termed a ‘blended approach’, whilst acknowledging degrees of inconsistency and dissonance from a theoretical perspective. Additionally, the model is viewed from a context perspective where questions are asked regarding the appropriateness of particular approaches depending upon the setting in which learning takes place. It is hoped that by moving beyond theoretically entrenched positions a mediated middle ground for the model’s application may be established.
Leite, Maici Duarte; Marczal, Diego; Pimentel, Andrey Ricardo; Direne, Alexandre Ibrahim
2014-01-01
This paper presents the application of some concepts of Intelligent Tutoring Systems (ITS) to elaborate a conceptual framework that uses the remediation of errors with Multiple External Representations (MERs) in Learning Objects (LO). To this is demonstrated a development of LO for teaching the Pythagorean Theorem through this framework. This…
Quantum machine learning with glow for episodic tasks and decision games
Clausen, Jens; Briegel, Hans J.
2018-02-01
We consider a general class of models, where a reinforcement learning (RL) agent learns from cyclic interactions with an external environment via classical signals. Perceptual inputs are encoded as quantum states, which are subsequently transformed by a quantum channel representing the agent's memory, while the outcomes of measurements performed at the channel's output determine the agent's actions. The learning takes place via stepwise modifications of the channel properties. They are described by an update rule that is inspired by the projective simulation (PS) model and equipped with a glow mechanism that allows for a backpropagation of policy changes, analogous to the eligibility traces in RL and edge glow in PS. In this way, the model combines features of PS with the ability for generalization, offered by its physical embodiment as a quantum system. We apply the agent to various setups of an invasion game and a grid world, which serve as elementary model tasks allowing a direct comparison with a basic classical PS agent.
Humanoids Learning to Walk: a Natural CPG-Actor-Critic Architecture
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CAI eLI
2013-04-01
Full Text Available The identification of learning mechanisms for locomotion has been the subject of much researchfor some time but many challenges remain. Dynamic systems theory (DST offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL has offered a promising method to adaptively link the dynamic system to the environment it interacts with via a reward-based value system.In this paper, we propose a model that integrates the above perspectives and applies it to the case of a humanoid (NAO robot learning to walk the ability of which emerges from its value-based interaction with the environment. In the model,a simplified central pattern generator (CPG architecture inspired by neuroscientific research and DST is integrated with an actor-critic approach to RL (cpg-actor-critic. In the cpg-actor-critic architecture, least-square-temporal-difference (LSTD based learning converges to the optimal solution quickly by using natural gradient and balancing exploration and exploitation. Futhermore, rather than using a traditional (designer-specified reward it uses a dynamic value function as a stability indicator (SI that adapts to the environment.The results obtained are analyzed and explained by using a novel DST embodied cognition approach. Learning to walk, from this perspective, is a process of integrating sensorimotor levels and value.
Shah, Mamta; Foster, Aroutis
2014-01-01
There is a paucity of research frameworks that focus on aiding game selection and use, analyzing the game as a holistic system, and studying learner experiences in games. There is a need for frameworks that provide a lens for understanding learning experiences afforded in digital games and facilitating knowledge construction and motivation to…
Space Objects Maneuvering Detection and Prediction via Inverse Reinforcement Learning
Linares, R.; Furfaro, R.
This paper determines the behavior of Space Objects (SOs) using inverse Reinforcement Learning (RL) to estimate the reward function that each SO is using for control. The approach discussed in this work can be used to analyze maneuvering of SOs from observational data. The inverse RL problem is solved using the Feature Matching approach. This approach determines the optimal reward function that a SO is using while maneuvering by assuming that the observed trajectories are optimal with respect to the SO's own reward function. This paper uses estimated orbital elements data to determine the behavior of SOs in a data-driven fashion.
Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework
Varun Mithal; Guruprasad Nayak; Ankush Khandelwal; Vipin Kumar; Ramakrishna Nemani; Nikunj C. Oza
2018-01-01
This paper presents an application of a novel machine-learning framework on MODIS (moderate-resolution imaging spectroradiometer) data to map burned areas over tropical forests of South America and South-east Asia. The RAPT (RAre Class Prediction in the absence of True labels) framework is able to build data adaptive classification models using noisy training labels. It is particularly suitable when expert annotated training samples are difficult to obtain as in the case of wild fires in the ...
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Fathia LAHWAL
2016-10-01
Full Text Available This study about interactive multimedia e-learning aims to improve our understanding about the dynamics of e-learning. The objective is to critical evaluate and better understand the interrelationships in the proposed framework between internal, external and the pedagogy dimensions in adoption of interactive multimedia and e-learning. It develops a tool to measure creative user adoption of interactive multimedia and e-learning services by using Partial Least Squares algorithm as the method of estimation and the major analytical tool in this study. Finding of a small scale data sampling of students in United Kingdom indicate that the proposed measurement framework is an acceptable fit with the data. Overall, the findings supply a precise tool for measuring creative user adoption of interactive multimedia and e-learning services, providing further insights for researchers and may provide to guide research and practice in interactive multimedia and e-learning by using communication media.
Humanoids Learning to Walk: A Natural CPG-Actor-Critic Architecture
Li, Cai; Lowe, Robert; Ziemke, Tom
2013-01-01
The identification of learning mechanisms for locomotion has been the subject of much research for some time but many challenges remain. Dynamic systems theory (DST) offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL) has offered a promising method to adaptively link the dynamic system to the environment it interacts with via a reward-based value system. In this paper, we propose a model that integrates the above perspectives and applies...
Emotion in reinforcement learning agents and robots : A survey
Moerland, T.M.; Broekens, D.J.; Jonker, C.M.
2018-01-01
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action
Optimizing microstimulation using a reinforcement learning framework.
Brockmeier, Austin J; Choi, John S; Distasio, Marcello M; Francis, Joseph T; Príncipe, José C
2011-01-01
The ability to provide sensory feedback is desired to enhance the functionality of neuroprosthetics. Somatosensory feedback provides closed-loop control to the motor system, which is lacking in feedforward neuroprosthetics. In the case of existing somatosensory function, a template of the natural response can be used as a template of desired response elicited by electrical microstimulation. In the case of no initial training data, microstimulation parameters that produce responses close to the template must be selected in an online manner. We propose using reinforcement learning as a framework to balance the exploration of the parameter space and the continued selection of promising parameters for further stimulation. This approach avoids an explicit model of the neural response from stimulation. We explore a preliminary architecture--treating the task as a k-armed bandit--using offline data recorded for natural touch and thalamic microstimulation, and we examine the methods efficiency in exploring the parameter space while concentrating on promising parameter forms. The best matching stimulation parameters, from k = 68 different forms, are selected by the reinforcement learning algorithm consistently after 334 realizations.
Leveraging Competency Framework to Improve Teaching and Learning: A Methodological Approach
Shankararaman, Venky; Ducrot, Joelle
2016-01-01
A number of engineering education programs have defined learning outcomes and course-level competencies, and conducted assessments at the program level to determine areas for continuous improvement. However, many of these programs have not implemented a comprehensive competency framework to support the actual delivery and assessment of an…
Learning state representation for deep actor-critic control
Munk, J.; Kober, J.; Babuska, R.; Bullo, Francesco; Prieur, Christophe; Giua, Alessandro
2016-01-01
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). One advantage of DNNs is that they can cope with large input dimensions. Instead of relying on feature engineering to lower the input dimension, DNNs can extract the features from raw observations. The
A framework for adaptive e-learning for continuum mechanics and structural analysis
Mosquera Feijoo, Juan Carlos; Plaza Beltrán, Luis Francisco; González Rodrigo, Beatriz
2015-01-01
This paper presents a project for providing the students of Structural Engineering with the flexibility to learn outside classroom schedules. The goal is a framework for adaptive E-learning based on a repository of open educational courseware with a set of basic Structural Engineering concepts and fundamentals. These are paramount for students to expand their technical knowledge and skills in structural analysis and design of tall buildings, arch-type structures as well as bridges. Thus, conc...
Barnhardt, Bradford; Ginns, Paul
2014-01-01
This article orients a recently proposed alienation-based framework for student learning theory (SLT) to the empirical basis of the approaches to learning perspective. The proposed framework makes new macro-level interpretations of an established micro-level theory, across three levels of interpretation: (1) a context-free psychological state…
Shea, Peter; Gozza-Cohen, Mary; Uzuner, Sedef; Mehta, Ruchi; Valtcheva, Anna Valentinova; Hayes, Suzanne; Vickers, Jason
2011-01-01
This paper presents both a conceptual and empirical investigation of teaching and learning in online courses. Employing both the Community of Inquiry framework (CoI) and the Structure of Observed Learning Outcomes (SOLO) taxonomy, two complete online courses were examined for the quality of both collaborative learning processes and learning…
Lahwal, Fathia; Al-Ajlan, Ajlan S.; Amain, Mohamad
2016-01-01
This study focuses on interactive multimedia e-learning aims to improve our understanding about the dynamics of e-learning. The objective is to critical evaluate and better understand the interrelationships in the proposed framework between internal, external and the pedagogy dimensions in adoption of interactive multimedia and e-learning. It…
Paull, Megan; Whitsed, Craig; Girardi, Antonia
2016-01-01
Global perspectives and interpersonal and intercultural communication competencies are viewed as a priority within higher education. For management educators, globalisation, student mobility and widening pathways present numerous challenges, but afford opportunities for curriculum innovation. The "Interaction for Learning Framework"…
Reinforcement learning using a continuous time actor-critic framework with spiking neurons.
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Nicolas Frémaux
2013-04-01
Full Text Available Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD learning of Doya (2000 to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.
Learner Analysis Framework for Globalized E-Learning: A Case Study
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Mamta Saxena
2011-06-01
Full Text Available The shift to technology-mediated modes of instructional delivery and increased global connectivity has led to a rise in globalized e-learning programs. Educational institutions face multiple challenges as they seek to design effective, engaging, and culturally competent instruction for an increasingly diverse learner population. The purpose of this study was to explore strategies for expanding learner analysis within the instructional design process to better address cultural influences on learning. A case study approach leveraged the experience of practicing instructional designers to build a framework for culturally competent learner analysis.The study discussed the related challenges and recommended strategies to improve the effectiveness of cross-cultural learner analysis. Based on the findings, a framework for conducting cross-cultural learner analysis to guide the cultural analysis of diverse learners was proposed. The study identified the most critical factors in improving cross-cultural learner analysis as the judicious use of existing research on cross-cultural theories and joint deliberation on the part of all the participants from the management to the learners. Several strategies for guiding and improving the cultural inquiry process were summarized. Barriers and solutions for the requirements are also discussed.
Doyle, Louise; Kelliher, Felicity; Harrington, Denis
2016-01-01
The aim of this paper is to review the relevant literature on organisational learning and offer a preliminary conceptual framework as a basis to explore how the multi-levels of individual learning and team learning interact in a public healthcare organisation. The organisational learning literature highlights a need for further understanding of…
Threat driven modeling framework using petri nets for e-learning system.
Khamparia, Aditya; Pandey, Babita
2016-01-01
Vulnerabilities at various levels are main cause of security risks in e-learning system. This paper presents a modified threat driven modeling framework, to identify the threats after risk assessment which requires mitigation and how to mitigate those threats. To model those threat mitigations aspects oriented stochastic petri nets are used. This paper included security metrics based on vulnerabilities present in e-learning system. The Common Vulnerability Scoring System designed to provide a normalized method for rating vulnerabilities which will be used as basis in metric definitions and calculations. A case study has been also proposed which shows the need and feasibility of using aspect oriented stochastic petri net models for threat modeling which improves reliability, consistency and robustness of the e-learning system.
Using apps for learning across the curriculum a literacy-based framework and guide
Beach, Richard
2014-01-01
How can apps be used to foster learning with literacy across the curriculum? This book offers both a theoretical framework for considering app affordances and practical ways to use apps to build students' disciplinary literacies and to foster a wide range of literacy practices.Using Apps for Learning Across the Curriculumpresents a wide range of different apps and also assesses their value features methods for and apps related to planning instruction and assessing student learning identifies favorite apps whose affordances are most likely to foster certain disciplinary literacies includes reso
Proverbs as Theoretical Frameworks for Lifelong Learning in Indigenous African Education
Avoseh, Mejai B. M.
2013-01-01
Every aspect of a community's life and values in indigenous Africa provide the theoretical framework for education. The holistic worldview of the traditional system places a strong emphasis on the centrality of the human element and orature in the symmetrical relationship between life and learning. This article focuses on proverbs and the words…
A basic framework for integrating social and collaborative applications into learning environments
Moghnieh, Ayman; Blat, Josep
2009-01-01
Moghnieh, A., & Blat, J. (2009). A basic framework for integrating social and collaborative applications into learning environments. Proceedings of the first conference on Research, Reflection, and Innovations in Integrating ICT in Education: Vol. 2 (pp. 1057-1061). April, 22-24, 2009, Lisbon,
Sawyer, Taylor; White, Marjorie; Zaveri, Pavan; Chang, Todd; Ades, Anne; French, Heather; Anderson, JoDee; Auerbach, Marc; Johnston, Lindsay; Kessler, David
2015-08-01
Acquisition of competency in procedural skills is a fundamental goal of medical training. In this Perspective, the authors propose an evidence-based pedagogical framework for procedural skill training. The framework was developed based on a review of the literature using a critical synthesis approach and builds on earlier models of procedural skill training in medicine. The authors begin by describing the fundamentals of procedural skill development. Then, a six-step pedagogical framework for procedural skills training is presented: Learn, See, Practice, Prove, Do, and Maintain. In this framework, procedural skill training begins with the learner acquiring requisite cognitive knowledge through didactic education (Learn) and observation of the procedure (See). The learner then progresses to the stage of psychomotor skill acquisition and is allowed to deliberately practice the procedure on a simulator (Practice). Simulation-based mastery learning is employed to allow the trainee to prove competency prior to performing the procedure on a patient (Prove). Once competency is demonstrated on a simulator, the trainee is allowed to perform the procedure on patients with direct supervision, until he or she can be entrusted to perform the procedure independently (Do). Maintenance of the skill is ensured through continued clinical practice, supplemented by simulation-based training as needed (Maintain). Evidence in support of each component of the framework is presented. Implementation of the proposed framework presents a paradigm shift in procedural skill training. However, the authors believe that adoption of the framework will improve procedural skill training and patient safety.
Avatars, Media Usage, and the Linkages to E-learning Effectiveness
2011-03-01
theory, and emotional interest theory were used to predict media usage and learning engagement. Media Richness Theory Daft and Lengel described...2010, from http://www.cogtech.usc.edu/publications/aera_ onlinelearningresearch_clark4_09.pdf Daft , R.L., Lengel, R.H. (1986). Organizational
Tai, Joanna Hong Meng; Canny, Benedict J; Haines, Terry P; Molloy, Elizabeth K
2017-01-01
Phenomenon: Peer learning has many benefits and can assist students in gaining the educational skills required in future years when they become teachers themselves. Peer learning may be particularly useful in clinical learning environments, where students report feeling marginalized, overwhelmed, and unsupported. Educational interventions often fail in the workplace environment, as they are often conceived in the "ideal" rather than the complex, messy real world. This work sought to explore barriers and facilitators to implementing peer learning activities in a clinical curriculum. Previous peer learning research results and a matrix of empirically derived peer learning activities were presented to local clinical education experts to generate discussion around the realities of implementing such activities. Potential barriers and limitations of and strategies for implementing peer learning in clinical education were the focus of the individual interviews. Thematic analysis of the data identified three key considerations for real-world implementation of peer learning: culture, epistemic authority, and the primacy of patient-centered care. Strategies for peer learning implementation were also developed from themes within the data, focusing on developing a culture of safety in which peer learning could be undertaken, engaging both educators and students, and establishing expectations for the use of peer learning. Insights: This study identified considerations and strategies for the implementation of peer learning activities, which took into account both educator and student roles. Reported challenges were reflective of those identified within the literature. The resultant framework may aid others in anticipating implementation challenges. Further work is required to test the framework's application in other contexts and its effect on learner outcomes.
Directory of Open Access Journals (Sweden)
H. Shayeghi
2017-12-01
Full Text Available This paper presents an online two-stage Q-learning based multi-agent (MA controller for load frequency control (LFC in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs. The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO algorithm and are fixed. The second one is a reinforcement learning (RL based supplementary controller that has a flexible structure and improves the output of the first stage adaptively based on the system dynamical behavior. Due to the use of RL paradigm integrated with PID controller in this strategy, it is called RL-PID controller. The primary motivation for the integration of RL technique with PID controller is to make the existing local controllers in the industry compatible to reduce the control efforts and system costs. This novel control strategy combines the advantages of the PID controller with adaptive behavior of MA to achieve the desired level of robust performance under different kind of uncertainties caused by stochastically power generation of DERs, plant operational condition changes, and physical nonlinearities of the system. The suggested decentralized controller is composed of the autonomous intelligent agents, who learn the optimal control policy from interaction with the system. These agents update their knowledge about the system dynamics continuously to achieve a good frequency oscillation damping under various severe disturbances without any knowledge of them. It leads to an adaptive control structure to solve LFC problem in the multi-source power system with stochastic DERs. The results of RL-PID controller in comparison to the traditional PID and fuzzy-PID controllers is verified in a multi-area power system integrated with DERs through some performance indices.
A decision analysis framework for stakeholder involvement and learning in groundwater management
Karjalainen, T. P.; Rossi, P. M.; Ala-aho, P.; Eskelinen, R.; Reinikainen, K.; Kløve, B.; Pulido-Velazquez, M.; Yang, H.
2013-12-01
Multi-criteria decision analysis (MCDA) methods are increasingly used to facilitate both rigorous analysis and stakeholder involvement in natural and water resource planning. Decision-making in that context is often complex and multi-faceted with numerous trade-offs between social, environmental and economic impacts. However, practical applications of decision-support methods are often too technically oriented and hard to use, understand or interpret for all participants. The learning of participants in these processes is seldom examined, even though successful deliberation depends on learning. This paper analyzes the potential of an interactive MCDA framework, the decision analysis interview (DAI) approach, for facilitating stakeholder involvement and learning in groundwater management. It evaluates the results of the MCDA process in assessing land-use management alternatives in a Finnish esker aquifer area where conflicting land uses affect the groundwater body and dependent ecosystems. In the assessment process, emphasis was placed on the interactive role of the MCDA tool in facilitating stakeholder participation and learning. The results confirmed that the structured decision analysis framework can foster learning and collaboration in a process where disputes and diverse interests are represented. Computer-aided interviews helped the participants to see how their preferences affected the desirability and ranking of alternatives. During the process, the participants' knowledge and preferences evolved as they assessed their initial knowledge with the help of fresh scientific information. The decision analysis process led to the opening of a dialogue, showing the overall picture of the problem context and the critical issues for the further process.
The POL Model: Using a Social Constructivist Framework to Develop Blended and Online Learning
DEFF Research Database (Denmark)
Dalsgaard, Christian; Godsk, Mikkel
2007-01-01
The paper presents a model for developing blended and online learning based on a given curriculum and typical learning objectives for university courses. The model consists of a three-step-process in which the instructor formulates product-oriented tasks, develops and structures the learning...... materials and tools, outlines a schedule, and supports the students' learning activity in developing a product. The model is based on our experiences with transforming traditional lecture-based lessons into problem-based blended and online learning using a social constructivist approach and a standard...... virtual learning environment (VLE). Our initial experiments indicate that our model is useful to develop blended and online modules and, furthermore, it seems fruitful to use a social constructivist framework and orienting learning activities towards the development of products....
A Symbiotic Framework for coupling Machine Learning and Geosciences in Prediction and Predictability
Ravela, S.
2017-12-01
In this presentation we review the two directions of a symbiotic relationship between machine learning and the geosciences in relation to prediction and predictability. In the first direction, we develop ensemble, information theoretic and manifold learning framework to adaptively improve state and parameter estimates in nonlinear high-dimensional non-Gaussian problems, showing in particular that tractable variational approaches can be produced. We demonstrate these applications in the context of autonomous mapping of environmental coherent structures and other idealized problems. In the reverse direction, we show that data assimilation, particularly probabilistic approaches for filtering and smoothing offer a novel and useful way to train neural networks, and serve as a better basis than gradient based approaches when we must quantify uncertainty in association with nonlinear, chaotic processes. In many inference problems in geosciences we seek to build reduced models to characterize local sensitivies, adjoints or other mechanisms that propagate innovations and errors. Here, the particular use of neural approaches for such propagation trained using ensemble data assimilation provides a novel framework. Through these two examples of inference problems in the earth sciences, we show that not only is learning useful to broaden existing methodology, but in reverse, geophysical methodology can be used to influence paradigms in learning.
Cooper, Katelyn M; Ashley, Michael; Brownell, Sara E
2017-01-01
There has been a national movement to transition college science courses from passive lectures to active learning environments. Active learning has been shown to be a more effective way for students to learn, yet there is concern that some students are resistant to active learning approaches. Although there is much discussion about student resistance to active learning, few studies have explored this topic. Furthermore, a limited number of studies have applied theoretical frameworks to student engagement in active learning. We propose using a theoretical lens of expectancy value theory to understand student resistance to active learning. In this study, we examined student perceptions of active learning after participating in 40 hours of active learning. We used the principal components of expectancy value theory to probe student experience in active learning: student perceived self-efficacy in active learning, value of active learning, and potential cost of participating in active learning. We found that students showed positive changes in the components of expectancy value theory and reported high levels of engagement in active learning, which provide proof of concept that expectancy value theory can be used to boost student perceptions of active learning and their engagement in active learning classrooms. From these findings, we have built a theoretical framework of expectancy value theory applied to active learning.
A framework for understanding outcomes of mutual learning situations in IT projects
DEFF Research Database (Denmark)
Hansen, Magnus Rotvit Perlt
2012-01-01
How do we analyse and understand design decisions derived from mutual learning (ML) situations and how may practitioners take advantage of these in IT projects? In the following we present a framework of design decisions inferred from ML situations that occurred between end-users and stakeholders...
Directory of Open Access Journals (Sweden)
KimMarie McGoldrick
2011-10-01
Full Text Available The recent financial crisis has motivated economic educators to rethink what economics should be taught, acknowledging disconnects between classroom content and real world events. We introduce a learning theory approach that is broader, one that goes beyond such context specific discussions of foundational knowledge and application (i.e., teaching about this specific crisis and provide a framework to address the broader issue of how teaching practices can, by their very nature, minimize such disconnects and provide more effective processes for teaching about current economic conditions. The theory of significant learning (Fink 2003 is presented as a model of how experiences can be used to develop a deep approach to learning, learning that lasts. Experiential learning pedagogies are timeless in that they can be readily modified to promote deeper understanding over a wide range of economic environments. Focusing on one category of significant learning, the human dimension, and one component of the financial crisis, unemployment, examples which modify existing experiential learning practices are described to demonstrate how such pedagogic practices can be readily adapted to teaching and learning about current economic conditions. In short, we demonstrate that incorporating student experiences into pedagogic practice provides a natural alignment of teaching content and real world events, regardless of how those change over time.
A framework for learning and planning against switching strategies in repeated games
Hernandez-Leal, Pablo; Munoz de Cote, Enrique; Sucar, L. Enrique
2014-04-01
Intelligent agents, human or artificial, often change their behaviour as they interact with other agents. For an agent to optimise its performance when interacting with such agents, it must be capable of detecting and adapting according to such changes. This work presents an approach on how to effectively deal with non-stationary switching opponents in a repeated game context. Our main contribution is a framework for online learning and planning against opponents that switch strategies. We present how two opponent modelling techniques work within the framework and prove the usefulness of the approach experimentally in the iterated prisoner's dilemma, when the opponent is modelled as an agent that switches between different strategies (e.g. TFT, Pavlov and Bully). The results of both models were compared against each other and against a state-of-the-art non-stationary reinforcement learning technique. Results reflect that our approach obtains competitive results without needing an offline training phase, as opposed to the state-of-the-art techniques.
Lim, Sung-joo; Holt, Lori L
2011-01-01
Although speech categories are defined by multiple acoustic dimensions, some are perceptually weighted more than others and there are residual effects of native-language weightings in non-native speech perception. Recent research on nonlinguistic sound category learning suggests that the distribution characteristics of experienced sounds influence perceptual cue weights: Increasing variability across a dimension leads listeners to rely upon it less in subsequent category learning (Holt & Lotto, 2006). The present experiment investigated the implications of this among native Japanese learning English /r/-/l/ categories. Training was accomplished using a videogame paradigm that emphasizes associations among sound categories, visual information, and players' responses to videogame characters rather than overt categorization or explicit feedback. Subjects who played the game for 2.5h across 5 days exhibited improvements in /r/-/l/ perception on par with 2-4 weeks of explicit categorization training in previous research and exhibited a shift toward more native-like perceptual cue weights. Copyright © 2011 Cognitive Science Society, Inc.
Place preference and vocal learning rely on distinct reinforcers in songbirds.
Murdoch, Don; Chen, Ruidong; Goldberg, Jesse H
2018-04-30
In reinforcement learning (RL) agents are typically tasked with maximizing a single objective function such as reward. But it remains poorly understood how agents might pursue distinct objectives at once. In machines, multiobjective RL can be achieved by dividing a single agent into multiple sub-agents, each of which is shaped by agent-specific reinforcement, but it remains unknown if animals adopt this strategy. Here we use songbirds to test if navigation and singing, two behaviors with distinct objectives, can be differentially reinforced. We demonstrate that strobe flashes aversively condition place preference but not song syllables. Brief noise bursts aversively condition song syllables but positively reinforce place preference. Thus distinct behavior-generating systems, or agencies, within a single animal can be shaped by correspondingly distinct reinforcement signals. Our findings suggest that spatially segregated vocal circuits can solve a credit assignment problem associated with multiobjective learning.
Huh, Yeol; Reigeluth, Charles M.
2017-01-01
A modified conceptual framework called the Continuous-Change Framework for self-regulated learning (SRL) is presented. Common elements and limitations among the past frameworks are discussed in relation to the modified conceptual framework. The iterative nature of the goal setting process and overarching presence of self-efficacy and motivational…
Jevsikova, Tatjana; Berniukevicius, Andrius; Kurilovas, Eugenijus
2017-01-01
The paper is aimed to present a methodology of learning personalisation based on applying Resource Description Framework (RDF) standard model. Research results are two-fold: first, the results of systematic literature review on Linked Data, RDF "subject-predicate-object" triples, and Web Ontology Language (OWL) application in education…
Joy, Distress, Hope, and Fear in Reinforcement Learning (Extended Abstract)
Jacobs, E.J.; Broekens, J.; Jonker, C.M.
2014-01-01
In this paper we present a mapping between joy, distress, hope and fear, and Reinforcement Learning primitives. Joy / distress is a signal that is derived from the RL update signal, while hope/fear is derived from the utility of the current state. Agent-based simulation experiments replicate
Defining a risk-informed framework for whole-of-government lessons learned: A Canadian perspective.
Friesen, Shaye K; Kelsey, Shelley; Legere, J A Jim
Lessons learned play an important role in emergency management (EM) and organizational agility. Virtually all aspects of EM can derive benefit from a lessons learned program. From major security events to exercises, exploiting and applying lessons learned and "best practices" is critical to organizational resilience and adaptiveness. A robust lessons learned process and methodology provides an evidence base with which to inform decisions, guide plans, strengthen mitigation strategies, and assist in developing tools for operations. The Canadian Safety and Security Program recently supported a project to define a comprehensive framework that would allow public safety and security partners to regularly share event response best practices, and prioritize recommendations originating from after action reviews. This framework consists of several inter-locking elements: a comprehensive literature review/environmental scan of international programs; a survey to collect data from end users and management; the development of a taxonomy for organizing and structuring information; a risk-informed methodology for selecting, prioritizing, and following through on recommendations; and standardized templates and tools for tracking recommendations and ensuring implementation. This article discusses the efforts of the project team, which provided "best practice" advice and analytical support to ensure that a systematic approach to lessons learned was taken by the federal community to improve prevention, preparedness, and response activities. It posits an approach by which one might design a systematic process for information sharing and event response coordination-an approach that will assist federal departments to institutionalize a cross-government lessons learned program.
216-A-29 Ditch supplemental information to the Hanford Facility Contingency Plan (DOE/RL-93-75)
International Nuclear Information System (INIS)
Ingle, S.J.
1996-05-01
This document is a unit-specific contingency plan for the 216-A-29 Ditch and is intended to be used as a supplement to DOE/RL-93-75, Hanford Facility Contingency Plan (DOE-RL 1993). This unit-specific plan is to be used to demonstrate compliance with the contingency plan requirements of the Washington Administrative Code, Chapter 173- 303 for certain Resource Conservation and Recovery Act of 1976 waste management units. The 216-A-29 Ditch is a surface impoundment that received nonregulated process and cooling water and other dangerous wastes primarily from operations of the Plutonium/Uranium Extraction Plant. Active between 1955 and 1991, the ditch has been physically isolated and will be closed. Because it is no longer receiving discharges, waste management activities are no longer required at the unit. The ditch does not present a significant hazard to adjacent units, personnel, or the environment. It is unlikely that any incidents presenting hazards to public health or the environment would occur at the 216-A-29 Ditch
216-U-12 Crib supplemental information to the Hanford Facility Contingency Plan (DOE/RL-93-75)
International Nuclear Information System (INIS)
Ingle, S.J.
1996-05-01
This document is a unit-specific contingency plan for the 216-U-12 Crib and is intended to be used as a supplement to DOE/RL-93-75, Hanford Facility Contingency Plan (DOE-RL 1993). This unit-specific plan is to be used to demonstrate compliance with the contingency plan requirements of the Washington Administrative Code, Chapter 173- 303 for certain Resource Conservation and Recovery Act of 1976 waste management units. The 216-U-12 Crib is a landfill that received waste from the 291-U-1 Stack, 244-WR Vault, 244-U via tank C-5, and the UO 3 Plant. The crib pipeline was cut and permanently capped in 1988, and the crib has been backfilled. The unit will be closed under final facility standards. Waste management activities are no longer required at the unit. The crib does not present a significant hazard to adjacent units, personnel, or the environment. It is unlikely that any incidents presenting hazards to public health or the environment would occur at the 216-U-12 Crib
Ligozat, Florence; Almqvist, Jonas
2018-01-01
This special issue of the "European Educational Research Journal" presents a series of research papers reflecting the trends and evolutions in conceptual frameworks that took place within the EERA 27 "Didactics--Learning and Teaching" network during its first ten years of existence. Most conceptual tools used in this field were…
A Framework for Evaluating and Enhancing Alignment in Self-Regulated Learning Research
Dent, Amy L.; Hoyle, Rick H.
2015-01-01
We discuss the articles of this special issue with reference to an important yet previously only implicit dimension of study quality: alignment across the theoretical and methodological decisions that collectively define an approach to self-regulated learning. Integrating and extending work by leaders in the field, we propose a framework for…
Directory of Open Access Journals (Sweden)
Ahmad Karim
Full Text Available Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS, disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.
Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram
2016-01-01
Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.
Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram
2016-01-01
Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks’ back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps’ detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies. PMID:26978523
Roelle, Julian; Müller, Claudia; Roelle, Detlev; Berthold, Kirsten
2015-01-01
Although instructional explanations are commonly provided when learners are introduced to new content, they often fail because they are not integrated into effective learning activities. The recently introduced active-constructive-interactive framework posits an effectiveness hierarchy in which interactive learning activities are at the top; these are then followed by constructive and active learning activities, respectively. Against this background, we combined instructional explanations with different types of prompts that were designed to elicit these learning activities and tested the central predictions of the active-constructive-interactive framework. In Experiment 1, N = 83 students were randomly assigned to one of four combinations of instructional explanations and prompts. To test the active learning hypothesis, the learners received either (1) complete explanations and engaging prompts designed to elicit active activities or (2) explanations that were reduced by inferences and inference prompts designed to engage learners in constructing the withheld information. Furthermore, in order to explore how interactive learning activities can be elicited, we gave the learners who had difficulties in constructing the prompted inferences adapted remedial explanations with either (3) unspecific engaging prompts or (4) revision prompts. In support of the active learning hypothesis, we found that the learners who received reduced explanations and inference prompts outperformed the learners who received complete explanations and engaging prompts. Moreover, revision prompts were more effective in eliciting interactive learning activities than engaging prompts. In Experiment 2, N = 40 students were randomly assigned to either (1) a reduced explanations and inference prompts or (2) a reduced explanations and inference prompts plus adapted remedial explanations and revision prompts condition. In support of the constructive learning hypothesis, the learners who received
Active Learning Framework for Non-Intrusive Load Monitoring: Preprint
Energy Technology Data Exchange (ETDEWEB)
Jin, Xin
2016-05-16
Non-Intrusive Load Monitoring (NILM) is a set of techniques that estimate the electricity usage of individual appliances from power measurements taken at a limited number of locations in a building. One of the key challenges in NILM is having too much data without class labels yet being unable to label the data manually for cost or time constraints. This paper presents an active learning framework that helps existing NILM techniques to overcome this challenge. Active learning is an advanced machine learning method that interactively queries a user for the class label information. Unlike most existing NILM systems that heuristically request user inputs, the proposed method only needs minimally sufficient information from a user to build a compact and yet highly representative load signature library. Initial results indicate the proposed method can reduce the user inputs by up to 90% while still achieving similar disaggregation performance compared to a heuristic method. Thus, the proposed method can substantially reduce the burden on the user, improve the performance of a NILM system with limited user inputs, and overcome the key market barriers to the wide adoption of NILM technologies.
Asiri, Mohammed J. Sherbib; Mahmud, Rosnaini bt; Bakar, Kamariah Abu; Ayub, Ahmad Fauzi bin Mohd
2012-01-01
The purpose of this paper is to present the theoretical framework underlying a research on factors that influence utilization of the Jusur Learning Management System (Jusur LMS) in Saudi Arabian public universities. Development of the theoretical framework was done based on library research approach. Initially, the existing literature relevant to…
UAS Conflict-Avoidance Using Multiagent RL with Abstract Strategy Type Communication
Rebhuhn, Carrie; Knudson, Matt; Tumer, Kagan
2014-01-01
The use of unmanned aerial systems (UAS) in the national airspace is of growing interest to the research community. Safety and scalability of control algorithms are key to the successful integration of autonomous system into a human-populated airspace. In order to ensure safety while still maintaining efficient paths of travel, these algorithms must also accommodate heterogeneity of path strategies of its neighbors. We show that, using multiagent RL, we can improve the speed with which conflicts are resolved in cases with up to 80 aircraft within a section of the airspace. In addition, we show that the introduction of abstract agent strategy types to partition the state space is helpful in resolving conflicts, particularly in high congestion.
A Machine LearningFramework to Forecast Wave Conditions
Zhang, Y.; James, S. C.; O'Donncha, F.
2017-12-01
Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in
Statistical Learning Framework with Adaptive Retraining for Condition-Based Maintenance
International Nuclear Information System (INIS)
An, Sang Ha; Chang, Soon Heung; Heo, Gyun Young; Seo, Ho Joon; Kim, Su Young
2009-01-01
As systems become more complex and more critical in our daily lives, the need for the maintenance based on the reliable monitoring and diagnosis has become more apparent. However, in reality, the general opinion has been that 'maintenance is a necessary evil' or 'nothing can be done to improve maintenance costs'. Perhaps these were true statements twenty years ago when many of the diagnostic technologies were not fully developed. The developments of microprocessor or computer based instrumentation that can be used to monitor the operating condition of plant equipment, machinery and systems have provided the means to manage the maintenance operation. They have provided the means to reduce or eliminate unnecessary repairs, prevent catastrophic machine failures and reduce the negative impact of the maintenance operation on the profitability of manufacturing and production plants. Condition-based maintenance (CBM) techniques help determine the condition of in-service equipment in order to predict when maintenance should be performed. Most of the statistical learning techniques are only valid as long as the physics of a system does not change. If any significant change such as the replacement of a component or equipment occurs in the system, the statistical learning model should be re-trained or re-developed to adapt the new system. In this research, authors will propose a statistical learning framework which can be applicable for various CBMs, and the concept of the adaptive retraining technique will be described to support the execution of the framework so that the monitoring system does not need to be re-developed or re-trained even though there are any significant changes in the system or component
A pedagogical framework for facilitating parents' learning in nurse-parent partnership.
Hopwood, Nick; Clerke, Teena; Nguyen, Anne
2018-04-01
Nursing work increasingly demands forms of expertise that complement specialist knowledge. In child and family nursing, this need arises when nurses work in partnership with parents of young children at risk. Partnership means working with parents in respectful, negotiated and empowering ways. Existing partnership literature emphasises communicative and relational skills, but this paper focuses on nurses' capacities to facilitate parents' learning. Referring to data from home visiting, day-stay and specialist toddler clinic services in Sydney, a pedagogical framework is presented. Analysis shows how nurses notice aspects of children, parents and parent-child interactions as a catalyst for building on parents' strengths, enhancing guided chance or challenging unhelpful constructs. Prior research shows the latter can be a sticking point in partnership, but this paper reveals diverse ways in which challenges are folded into learning process that position parents as agents of positive change. Noticing is dependent on embodied and communicative expertise, conceptualised in terms of sensory and reported channels. The framework offers a new view of partnership as mind-expanding for the parent and specifies the nurse's role in facilitating this process. © 2017 John Wiley & Sons Ltd.
Park, Ji Yong; Nuntrakune, Tippawan
2013-01-01
The Thailand education reform adopted cooperative learning to improve the quality of education. However, it has been reported that the introduction and maintenance of cooperative learning has been difficult and uncertain because of the cultural differences. The study proposed a conceptual framework developed based on making a connection between…
Kaendler, Celia; Wiedmann, Michael; Rummel, Nikol; Spada, Hans
2015-01-01
This article describes teacher competencies for implementing collaborative learning in the classroom. Research has shown that the effectiveness of collaborative learning largely depends on the quality of student interaction. We therefore focus on what a "teacher" can do to foster student interaction. First, we present a framework that…
Energy Technology Data Exchange (ETDEWEB)
Kim, Ji Young [Department of Anatomy and Cell Biology, College of Medicine, Dong-A University, Busan 602-714 (Korea, Republic of); Medical Research Science Center, Dong-A University, Busan 602-714 (Korea, Republic of); Lee, Seung Gee [Department of Anatomy and Cell Biology, College of Medicine, Dong-A University, Busan 602-714 (Korea, Republic of); Mitochondria Hub Regulation Center, Dong-A University, Busan 602-714 (Korea, Republic of); Chung, Jin-Yong [Department of Anatomy and Cell Biology, College of Medicine, Dong-A University, Busan 602-714 (Korea, Republic of); Medical Research Science Center, Dong-A University, Busan 602-714 (Korea, Republic of); Kim, Yoon-Jae [Department of Anatomy and Cell Biology, College of Medicine, Dong-A University, Busan 602-714 (Korea, Republic of); Mitochondria Hub Regulation Center, Dong-A University, Busan 602-714 (Korea, Republic of); Park, Ji-Eun [Department of Anatomy and Cell Biology, College of Medicine, Dong-A University, Busan 602-714 (Korea, Republic of); Medical Research Science Center, Dong-A University, Busan 602-714 (Korea, Republic of); Oh, Seunghoon [Department of Physiology, College of Medicine, Dankook University, Cheonan 330-714 (Korea, Republic of); Lee, Se Yong [Department of Obstetrics and Gynecology, Busan Medical Center, Busan 611-072 (Korea, Republic of); Choi, Hong Jo [Department of General Surgery, College of Medicine, Dong-A University, Busan 602-714 (Korea, Republic of); Yoo, Young Hyun, E-mail: yhyoo@dau.ac.kr [Department of Anatomy and Cell Biology, College of Medicine, Dong-A University, Busan 602-714 (Korea, Republic of); Mitochondria Hub Regulation Center, Dong-A University, Busan 602-714 (Korea, Republic of); Medical Research Science Center, Dong-A University, Busan 602-714 (Korea, Republic of); and others
2012-04-15
7,12-Dimethylbenzanthracene (DMBA), a polycyclic aromatic hydrocarbon, exhibits mutagenic, carcinogenic, immunosuppressive, and apoptogenic properties in various cell types. To achieve these functions effectively, DMBA is modified to its active form by cytochrome P450 1 (CYP1). Exposure to DMBA causes cytotoxicity-mediated apoptosis in bone marrow B cells and ovarian cells. Although uterine endometrium constitutively expresses CYP1A1 and CYP1B1, their apoptotic role after exposure to DMBA remains to be elucidated. Therefore, we chose RL95-2 endometrial cancer cells as a model system for studying DMBA-induced cytotoxicity and cell death and hypothesized that exposure to DMBA causes apoptosis in this cell type following CYP1A1 and/or CYP1B1 activation. We showed that DMBA-induced apoptosis in RL95-2 cells is associated with activation of caspases. In addition, mitochondrial changes, including decrease in mitochondrial potential and release of mitochondrial cytochrome c into the cytosol, support the hypothesis that a mitochondrial pathway is involved in DMBA-induced apoptosis. Exposure to DMBA upregulated the expression of AhR, Arnt, CYP1A1, and CYP1B1 significantly; this may be necessary for the conversion of DMBA to DMBA-3,4-diol-1,2-epoxide (DMBA-DE). Although both CYP1A1 and CYP1B1 were significantly upregulated by DMBA, only CYP1B1 exhibited activity. Moreover, knockdown of CYP1B1 abolished DMBA-induced apoptosis in RL95-2 cells. Our data show that RL95-2 cells are susceptible to apoptosis by exposure to DMBA and that CYP1B1 plays a pivotal role in DMBA-induced apoptosis in this system. -- Highlights: ► Cytotoxicity-mediated apoptogenic action of DMBA in human endometrial cancer cells. ► Mitochondrial pathway in DMBA-induced apoptosis of RL95-2 endometrial cancer cells. ► Requirement of ligand-selective activation of CYP1B1 in DMBA-induced apoptosis.
Liu, Jing; Zhao, Songzheng; Wang, Gang
2018-01-01
With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., drug indication and beneficial effect) could hold between the drug and adverse event mentions, making ADE relation extraction - distinguishing ADE relationship from other relation types - necessary. However, conducting ADE relation extraction in social media environment is not a trivial task because of the expertise-dependent, time-consuming and costly annotation process, and the feature space's high-dimensionality attributed to intrinsic characteristics of social media data. This study aims to develop a framework for ADE relation extraction using patient-generated content in social media with better performance than that delivered by previous efforts. To achieve the objective, a general semi-supervised ensemble learning framework, SSEL-ADE, was developed. The framework exploited various lexical, semantic, and syntactic features, and integrated ensemble learning and semi-supervised learning. A series of experiments were conducted to verify the effectiveness of the proposed framework. Empirical results demonstrate the effectiveness of each component of SSEL-ADE and reveal that our proposed framework outperforms most of existing ADE relation extraction methods The SSEL-ADE can facilitate enhanced ADE relation extraction performance, thereby providing more reliable support for pharmacovigilance. Moreover, the proposed semi-supervised ensemble methods have the potential of being applied to effectively deal with other social media-based problems. Copyright © 2017 Elsevier B.V. All rights reserved.
Zimmerman, Heather Toomey; McClain, Lucy Richardson
2014-01-01
Using a sociocultural framework to approach intergenerational learning, this inquiry examines learning processes used by families during visits to one nature center. Data were collected from videotaped observations of families participating in an environmental education program and a follow-up task to draw the habitat of raptors. Based on a…
DEFF Research Database (Denmark)
Pedersen, Rikke; Jørgensen, Mette; Harrison, Roger
This paper presents a framework for the development of research within the emerging areas of internationalisation and technology that connect to build potential learning spaces within intercultural and global settings.......This paper presents a framework for the development of research within the emerging areas of internationalisation and technology that connect to build potential learning spaces within intercultural and global settings....
A deep learning and novelty detection framework for rapid phenotyping in high-content screening
Sommer, Christoph; Hoefler, Rudolf; Samwer, Matthias; Gerlich, Daniel W.
2017-01-01
Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening. PMID:28954863
Off-policy reinforcement learning for H∞ control design.
Luo, Biao; Wu, Huai-Ning; Huang, Tingwen
2015-01-01
The H∞ control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear H∞ control problem can be transformed into solving the so-called Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, model-based approaches cannot be used for approximately solving HJI equation, when the accurate system model is unavailable or costly to obtain in practice. To overcome these difficulties, an off-policy reinforcement leaning (RL) method is introduced to learn the solution of HJI equation from real system data instead of mathematical system model, and its convergence is proved. In the off-policy RL method, the system data can be generated with arbitrary policies rather than the evaluating policy, which is extremely important and promising for practical systems. For implementation purpose, a neural network (NN)-based actor-critic structure is employed and a least-square NN weight update algorithm is derived based on the method of weighted residuals. Finally, the developed NN-based off-policy RL method is tested on a linear F16 aircraft plant, and further applied to a rotational/translational actuator system.
Reduction of characteristic RL time for fast, efficient magnetic levitation
Directory of Open Access Journals (Sweden)
Yuqing Li
2017-09-01
Full Text Available We demonstrate the reduction of characteristic time in resistor-inductor (RL circuit for fast, efficient magnetic levitation according to Kirchhoff’s circuit laws. The loading time is reduced by a factor of ∼4 when a high-power resistor is added in series with the coils. By using the controllable output voltage of power supply and voltage of feedback circuit, the loading time is further reduced by ∼ 3 times. The overshoot loading in advance of the scheduled magnetic field gradient is equivalent to continuously adding a resistor without heating. The magnetic field gradient with the reduced loading time is used to form the upward magnetic force against to the gravity of the cooled Cs atoms, and we obtain an effectively levitated loading of the Cs atoms to a crossed optical dipole trap.
Cooper, Katelyn M.; Ashley, Michael; Brownell, Sara E.
2017-01-01
There has been a national movement to transition college science courses from passive lectures to active learning environments. Active learning has been shown to be a more effective way for students to learn, yet there is concern that some students are resistant to active learning approaches. Although there is much discussion about student resistance to active learning, few studies have explored this topic. Furthermore, a limited number of studies have applied theoretical frameworks to studen...
Nguyen, Minh Q.; Allebach, Jan P.
2015-01-01
In our previous work1 , we presented a block-based technique to analyze printed page uniformity both visually and metrically. The features learned from the models were then employed in a Support Vector Machine (SVM) framework to classify the pages into one of the two categories of acceptable and unacceptable quality. In this paper, we introduce a set of tools for machine learning in the assessment of printed page uniformity. This work is primarily targeted to the printing industry, specifically the ubiquitous laser, electrophotographic printer. We use features that are well-correlated with the rankings of expert observers to develop a novel machine learning framework that allows one to achieve the minimum "false alarm" rate, subject to a chosen "miss" rate. Surprisingly, most of the research that has been conducted on machine learning does not consider this framework. During the process of developing a new product, test engineers will print hundreds of test pages, which can be scanned and then analyzed by an autonomous algorithm. Among these pages, most may be of acceptable quality. The objective is to find the ones that are not. These will provide critically important information to systems designers, regarding issues that need to be addressed in improving the printer design. A "miss" is defined to be a page that is not of acceptable quality to an expert observer that the prediction algorithm declares to be a "pass". Misses are a serious problem, since they represent problems that will not be seen by the systems designers. On the other hand, "false alarms" correspond to pages that an expert observer would declare to be of acceptable quality, but which are flagged by the prediction algorithm as "fails". In a typical printer testing and development scenario, such pages would be examined by an expert, and found to be of acceptable quality after all. "False alarm" pages result in extra pages to be examined by expert observers, which increases labor cost. But "false
216-A-36B Crib supplemental information to the Hanford Facility Contingency Plan (DOE/RL-93-75)
International Nuclear Information System (INIS)
Ingle, S.J.
1996-05-01
This document is a unit-specific contingency plan for the 216-A-36B Crib and is intended to be used as a supplement to DOE/RL-93-75, Hanford Facility Contingency Plan (DOE-RL 1993). This unit-specific plan is to be used to demonstrate compliance with the contingency plan requirements of the Washington Administrative Code, Chapter 173- 303 for certain Resource Conservation and Recovery Act of 1976 waste management units. The 216-A-36B Crib is a landfill that received ammonia scrubber waste from the 202-A Building (Plutonium/Uranium Extraction Plant) between 1966 and 1972. In 1982, the unit was reactivated to receive additional waste from Plutonium/Uranium Extraction operations. Discharges ceased in 1987, and the crib will be closed under final facility standards. Because the crib is not receiving discharges, waste management activities are no longer required. The crib does not present a significant hazard to adjacent units, personnel, or the environment. There is little likelihood that any incidents presenting hazards to public health or the environment would occur at the 216-A-36B Crib
DEFF Research Database (Denmark)
Høgsberg, Jan Becker; Krenk, Steen
2015-01-01
Resonant RL shunt circuits constitute a robust approach to piezoelectric damping, where the performance with respect to damping of flexible structures requires a precise calibration of the corresponding circuit components. The balanced calibration procedure of the present paper is based on equal ...... that the procedure leads to equal modal damping and effective response reduction, even for rather indirect placement of the transducer, provided that the correction for background flexibility is included in the calibration procedure....
Rockinson-Szapkiw, Amanda J.; Wendt, Jillian; Wighting, Mervyn; Nisbet, Deanna
2016-01-01
The Community of Inquiry framework has been widely supported by research to provide a model of online learning that informs the design and implementation of distance learning courses. However, the relationship between elements of the CoI framework and perceived learning warrants further examination as a predictive model for online graduate student…
Bao, Wei; Yue, Jun; Rao, Yulei
2017-01-01
The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.
Oyao, Sheila G.; Holbrook, Jack; Rannikmäe, Miia; Pagunsan, Marmon M.
2015-01-01
This article proposes a competence-based learning framework for science teaching, applied to the study of "big ideas", in this case to the study of natural hazards and disaster risk reduction (NH&DRR). The framework focuses on new visions of competence, placing emphasis on nurturing connectedness and behavioral actions toward…
Kagawa, Rina; Kawazoe, Yoshimasa; Ida, Yusuke; Shinohara, Emiko; Tanaka, Katsuya; Imai, Takeshi; Ohe, Kazuhiko
2017-07-01
Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects. We propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects. We employ expert knowledge as rules to exclude obvious control patients and machine learning to increase accuracy for complicated patients. We developed phenotyping algorithms on the basis of our framework and performed binary classification to determine whether a patient has T2DM. To facilitate development of practical phenotyping algorithms, this study introduces new evaluation metrics: area under the precision-sensitivity curve (AUPS) with a high sensitivity and AUPS with a high positive predictive value. The proposed phenotyping algorithms based on our framework show higher performance than baseline algorithms. Our proposed framework can be used to develop 2 types of phenotyping algorithms depending on the tuning approach: one for screening, the other for identifying research subjects. We develop a novel phenotyping framework that can be easily implemented on the basis of proper evaluation metrics, which are in accordance with users' objectives. The phenotyping algorithms based on our framework are useful for extraction of T2DM patients in retrospective studies.
Kobayashi, Mina; Namba, Kazuyoshi; Ito, Eiki; Nishida, Shingo; Nakamura, Masaki; Ueki, Yoichiro; Furudate, Naomichi; Kagimura, Tatsuo; Usui, Akira; Inoue, Yuichi
2014-01-01
The status of night-to-night variability for periodic limb movements in sleep (PLMS) has not been clarified. With this in mind, we investigated the validity of PLMS measurement by actigraphy with the PAM-RL device in Japanese patients with suspected restless legs syndrome (RLS) or periodic limb movement disorder (PLMD) and the night-to-night variability of PLMS among the subjects. Forty-one subjects (mean age, 52.1±16.1 years) underwent polysomnography (PSG) and PAM-RL measurement simultaneously. Thereafter, subjects used the PAM-RL at home on four more consecutive nights. The correlation between PLMS index on PSG (PLMSI-PSG) and PLM index on PAM-RL (PLMI-PAM) was 0.781 (PPAM-RL. PAM-RL is thought to be valuable for assessing PLMS even in Japanese subjects. Recording of PAM-RL for three or more consecutive nights may be required to ensure the screening reliability of a patient with suspected pathologically frequent PLMS. Copyright © 2013 Elsevier B.V. All rights reserved.
Comparison Between CCCM and CloudSat Radar-Lidar (RL) Cloud and Radiation Products
Ham, Seung-Hee; Kato, Seiji; Rose, Fred G.; Sun-Mack, Sunny
2015-01-01
To enhance cloud properties, LaRC and CIRA developed each combination algorithm for obtained properties from passive, active and imager in A-satellite constellation. When comparing global cloud fraction each other, LaRC-produced CERES-CALIPSO-CloudSat-MODIS (CCCM) products larger low-level cloud fraction over tropic ocean, while CIRA-produced Radar-Lidar (RL) shows larger mid-level cloud fraction for high latitude region. The reason for different low-level cloud fraction is due to different filtering method of lidar-detected cloud layers. Meanwhile difference in mid-level clouds is occurred due to different priority of cloud boundaries from lidar and radar.
Kamp, Annelies
2003-01-01
Against a backtground of 'second-wave' lifelong learning in Aotearoa New Zealand a new framework for post-compulsory national qualfications was introduced. The restulting competency-based system was argued to present a number of benefits for mature women including flexibility in curriculum and delivery and portability across educational sectors. Competency-based education was to include provision for recognition of prior skills and knowledge gained in formal learning environments and the work...
An Instructional Design Framework to Improve Student Learning in a First-Year Engineering Class
Yelamarthi, Kumar; Drake, Eron; Prewett, Matthew
2016-01-01
Increasingly, numerous universities have identified benefits of flipped learning environments and have been encouraging instructors to adapt such methodologies in their respective classrooms, at a time when departments are facing significant budget constraints. This article proposes an instructional design framework utilized to strategically…
An Instructional Design Framework to Improve Student Learning in a First-Year Engineering Class
Directory of Open Access Journals (Sweden)
Kumar Yelamarthi
2016-12-01
Full Text Available Increasingly, numerous universities have identified benefits of flipped learning environments and have been encouraging instructors to adapt such methodologies in their respective classrooms, at a time when departments are facing significant budget constraints. This article proposes an instructional design framework utilized to strategically enhance traditional flipped methodologies in a first-year engineering course, by using low-cost technology aids and proven pedagogical techniques to enhance student learning. Implemented in a first-year engineering course, this modified flipped model demonstrated an improved student awareness of essential engineering concepts and improved academic performance through collaborative and active learning activities, including flipped learning methodologies, without the need for expensive, formal active learning spaces. These findings have been validated through two studies and have shown similar results confirming that student learning is improved by the implementation of multi-pedagogical strategies in-formed by the use of an instructional design in a traditional classroom setting.
Bao, Wei; Rao, Yulei
2017-01-01
The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance. PMID:28708865
Directory of Open Access Journals (Sweden)
Wei Bao
Full Text Available The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT, stacked autoencoders (SAEs and long-short term memory (LSTM are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.
Yield curve and Recession Forecasting in a Machine Learning Framework
Theophilos Papadimitriou; Periklis Gogas; Maria Matthaiou; Efthymia Chrysanthidou
2014-01-01
In this paper, we investigate the forecasting ability of the yield curve in terms of the U.S. real GDP cycle. More specifically, within a Machine Learning (ML) framework, we use data from a variety of short (treasury bills) and long term interest rates (bonds) for the period from 1976:Q3 to 2011:Q4 in conjunction with the real GDP for the same period, to create a model that can successfully forecast output fluctuations (inflation and output gaps) around its long-run trend. We focus our attent...
Liu, Derong; Yang, Xiong; Wang, Ding; Wei, Qinglai
2015-07-01
The design of stabilizing controller for uncertain nonlinear systems with control constraints is a challenging problem. The constrained-input coupled with the inability to identify accurately the uncertainties motivates the design of stabilizing controller based on reinforcement-learning (RL) methods. In this paper, a novel RL-based robust adaptive control algorithm is developed for a class of continuous-time uncertain nonlinear systems subject to input constraints. The robust control problem is converted to the constrained optimal control problem with appropriately selecting value functions for the nominal system. Distinct from typical action-critic dual networks employed in RL, only one critic neural network (NN) is constructed to derive the approximate optimal control. Meanwhile, unlike initial stabilizing control often indispensable in RL, there is no special requirement imposed on the initial control. By utilizing Lyapunov's direct method, the closed-loop optimal control system and the estimated weights of the critic NN are proved to be uniformly ultimately bounded. In addition, the derived approximate optimal control is verified to guarantee the uncertain nonlinear system to be stable in the sense of uniform ultimate boundedness. Two simulation examples are provided to illustrate the effectiveness and applicability of the present approach.
Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework.
El-Assady, Mennatallah; Sevastjanova, Rita; Sperrle, Fabian; Keim, Daniel; Collins, Christopher
2018-01-01
Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.
Systemic assessment framework of a learning organization's competitive positioning
Directory of Open Access Journals (Sweden)
Wissam EL Hachem
2014-09-01
Full Text Available Purpose: The purpose of this paper is to devise an innovative feasible, replicable and comprehensive assessment framework of a learning organization's competitive positioning. Design/methodology/approach: The three characteristics listed above are approached as follows. Feasible refers to being easy and not in need of much resources (time, personnel,.... This is done through early elimination of non-important variables. Replicable is having a well structured methodology based on scientific proven methods. Following this methodology would result in good results that can be explained if needed and replicated if deemed necessary. Comprehensive translates into a holistic set of indices that measure performance as well as organizational learning. Findings and Originality/value: The three attributes (feasible, replicable and comprehensive have become crucial for ensuring any kind of added value for such a methodology that hopes to tackle the modern dynamic business environment and gaining a sustainable competitive advantage. Research limitations/implications: Such a methodology would require several full contextual applications to be able to set its final design. It entails thorough internal revision of a company's structure. Therefore a great deal of transparency and self-transcendence from the individual involved is a pre-requisite for any chance of success. Originality/value: It offers a systematic way to assess a company's performance/competitive positioning while accounting for the crucial attribute of organizational learning in its makeup.
Sumsion, Jennifer; Wong, Sandie
2011-01-01
In this article, the authors interrogate the use of "belonging" in "Belonging, Being and Becoming: the Early Years Learning Framework for Australia" (EYLF), Australia's first national curriculum for early childhood education and care settings and, from the authors' interrogation, possibilities are offered for thinking about and…
Younger but not older adults benefit from salient feedback during learning
Directory of Open Access Journals (Sweden)
Michael eHerbert
2011-08-01
Full Text Available Older adults are impaired in reinforcement learning (RL when feedback is partially ambiguous (e.g., Eppinger and Kray, 2011. In this study we examined whether older adults benefit from salient feedback information during learning. We used an electrophysiological approach and investigated 15 younger and 15 older adults with a RL task in which they had to learn stimulus-response associations under two learning conditions. In the positive learning conditions, participants could gain 50 Cents for a correct response but did not gain or lose money (*00 Cent for an incorrect response. In negative learning conditions, they could lose 50 Cents for an incorrect response but did not gain or lose money (*00 Cent for a correct response. As the identical outcome 00 Cent is either better or worse than the alternative outcome depending on the learning condition, this feedback type is ambiguous. To examine the influence of feedback salience we compared this condition with a condition in which positive and negative outcomes were color-coded and thereby clearly separable. The behavioral results indicated that younger adults reached higher accuracy levels under salient feedback conditions. Moreover, the error-related negativity (ERN and the feedback-related negativity (FRN for losses were larger if the good-bad dimension of feedback was salient. Hence, in younger adults salient feedback facilitates the rapid evaluation of outcomes on a good-bad dimension and by this supports learning. In contrast, for older adults we obtained neither behavioral nor electrophysiological effects of feedback salience. The older adults’ performance monitoring system therefore appears less flexible in integrating additional information in this evaluation process.
Directory of Open Access Journals (Sweden)
Ryan Henderson
2017-09-01
Full Text Available Picasso is a free open-source (Eclipse Public License web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks. Picasso ships with occlusion maps and saliency maps, two visualizations which help reveal issues that evaluation metrics like loss and accuracy might hide: for example, learning a proxy classification task. Picasso works with the Tensorflow deep learning framework, and Keras (when the model can be loaded into the Tensorflow backend. Picasso can be used with minimal configuration by deep learning researchers and engineers alike across various neural network architectures. Adding new visualizations is simple: the user can specify their visualization code and HTML template separately from the application code.
Yang, Liu; Jin, Rong; Mummert, Lily; Sukthankar, Rahul; Goode, Adam; Zheng, Bin; Hoi, Steven C H; Satyanarayanan, Mahadev
2010-01-01
Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming
Directory of Open Access Journals (Sweden)
Alia Asheralieva
2016-01-01
Full Text Available We propose a dynamic resource allocation algorithm for device-to-device (D2D communication underlying a Long Term Evolution Advanced (LTE-A network with reinforcement learning (RL applied for unlicensed channel allocation. In a considered system, the inband and outband resources are assigned by the LTE evolved NodeB (eNB to different device pairs to maximize the network utility subject to the target signal-to-interference-and-noise ratio (SINR constraints. Because of the absence of an established control link between the unlicensed and cellular radio interfaces, the eNB cannot acquire any information about the quality and availability of unlicensed channels. As a result, a considered problem becomes a stochastic optimization problem that can be dealt with by deploying a learning theory (to estimate the random unlicensed channel environment. Consequently, we formulate the outband D2D access as a dynamic single-player game in which the player (eNB estimates its possible strategy and expected utility for all of its actions based only on its own local observations using a joint utility and strategy estimation based reinforcement learning (JUSTE-RL with regret algorithm. A proposed approach for resource allocation demonstrates near-optimal performance after a small number of RL iterations and surpasses the other comparable methods in terms of energy efficiency and throughput maximization.
Lie, Désirée A.; Forest, Christopher P.; Walsh, Anne; Banzali, Yvonne; Lohenry, Kevin
2016-01-01
Background The student-run clinic (SRC) has the potential to address interprofessional learning among health professions students. Purpose To derive a framework for understanding student learning during team-based care provided in an interprofessional SRC serving underserved patients. Methods The authors recruited students for a focus group study by purposive sampling and snowballing. They constructed two sets of semi-structured questions for uniprofessional and multiprofessional groups. Sessions were audiotaped, and transcripts were independently coded and adjudicated. Major themes about learning content and processes were extracted. Grounded theory was followed after data synthesis and interpretation to establish a framework for interprofessional learning. Results Thirty-six students from four professions (medicine, physician assistant, occupational therapy, and pharmacy) participated in eight uniprofessional groups; 14 students participated in three multiprofessional groups (N = 50). Theme saturation was achieved. Six common themes about learning content from uniprofessional groups were role recognition, team-based care appreciation, patient experience, advocacy-/systems-based models, personal skills, and career choices. Occupational therapy students expressed self-advocacy, and medical students expressed humility and self-discovery. Synthesis of themes from all groups suggests a learning continuum that begins with the team huddle and continues with shared patient care and social interactions. Opportunity to observe and interact with other professions in action is key to the learning process. Discussion Interprofessional SRC participation promotes learning ‘with, from, and about’ each other. Participation challenges misconceptions and sensitizes students to patient experiences, health systems, advocacy, and social responsibility. Learning involves interprofessional interactions in the patient encounter, reinforced by formal and informal communications
Directory of Open Access Journals (Sweden)
Kim SR
2015-08-01
Full Text Available Sung Rae Kim,1 Myoung Jin Ho,2 Eugene Lee,3 Joon Woo Lee,3 Young Wook Choi,1 Myung Joo Kang21College of Pharmacy, Chung-Ang University, Dongjak-gu, Seoul, 2College of Pharmacy, Dankook University, Dongnam-gu, Cheonan, Chungnam, 3Department of Radiology, Seoul National University Bundang Hospital, Bundang-gu, Seongnam, Gyeonggi-do, South KoreaAbstract: Positively surface-charged poly(lactide-co-glycolide (PLGA/Eudragit RL nanoparticles (NPs were designed to increase retention time and sustain release profile in joints after intra-articular injection, by forming micrometer-sized electrostatic aggregates with hyaluronic acid, an endogenous anionic polysaccharide found in high amounts in synovial fluid. The cationic NPs consisting of PLGA, Eudragit RL, and polyvinyl alcohol were fabricated by solvent evaporation technique. The NPs were 170.1 nm in size, with a zeta potential of 21.3 mV in phosphate-buffered saline. Hyperspectral imaging (CytoViva® revealed the formation of the micrometer-sized filamentous aggregates upon admixing, due to electrostatic interaction between NPs and the polysaccharides. NPs loaded with a fluorescent probe (1,1'-dioctadecyl-3,3,3',3' tetramethylindotricarbocyanine iodide, DiR displayed a significantly improved retention time in the knee joint, with over 50% preservation of the fluorescent signal 28 days after injection. When DiR solution was injected intra-articularly, the fluorescence levels rapidly decreased to 30% of the initial concentration within 3 days in mice. From these findings, we suggest that PLGA-based cationic NPs could be a promising tool for prolonged delivery of therapeutic agents in joints selectively.Keywords: PLGA, Eudragit RL, hyaluronic acid, cationic nanoparticles, intra-articular injection, electrostatic interaction
Torregrosa García, Blas
2015-01-01
The present study aims at designing and developing new approaches to detect malicious applications in Android-based devices. More precisely, MaLDroide (Machine Learning-based Detector for Android malware), a framework for detection of Android malware based on machine learning techniques, is introduced here. It is devised to identify malicious applications. Este trabajo tiene como objetivo el diseño y el desarrollo de nuevas formas de detección de aplicaciones maliciosas en los dispositivos...
Wang, Qing; Li, Huiping; Pang, Weiguo; Liang, Shuo; Su, Yiliang
2016-01-05
Medical schools have been making efforts to develop their own problem-based learning (PBL) approaches based on their educational conditions, human resources and existing curriculum structures. This study aimed to explore a new framework by integrating the essential features of PBL and coaching psychology applicable to the undergraduate medical education context. A participatory research design was employed. Four educational psychology researchers, eight undergraduate medical school students and two accredited PBL tutors participated in a four-month research programme. Data were collected through participatory observation, focus groups, semi-structured interviews, workshop documents and feedback surveys and then subjected to thematic content analysis. The triangulation of sources and member checking were used to ensure the credibility and trustworthiness of the research process. Five themes emerged from the analysis: current experience of PBL curriculum; the roles of and relationships between tutors and students; student group dynamics; development of self-directed learning; and coaching in PBL facilitation. On the basis of this empirical data, a systematic model of PBL and coaching psychology was developed. The findings highlighted that coaching psychology could be incorporated into the facilitation system in PBL. The integrated framework of PBL and coaching psychology in undergraduate medical education has the potential to promote the development of the learning goals of cultivating clinical reasoning ability, lifelong learning capacities and medical humanity. Challenges, benefits and future directions for implementing the framework are discussed in this paper.
Smith, Sedef Uzuner; Hayes, Suzanne; Shea, Peter
2017-01-01
After presenting a brief overview of the key elements that underpin Etienne Wenger's communities of practice (CoP) theoretical framework, one of the most widely cited and influential conceptions of social learning, this paper reviews extant empirical work grounded in this framework to investigate online/blended learning in higher education and in…
Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement
DEFF Research Database (Denmark)
Manoonpong, Poramate; Kolodziejski, Christoph; Wörgötter, Florentin
2013-01-01
Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about...... associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor–critic reinforcement...... learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control...
DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters
Kim, Hanjoo; Park, Jaehong; Jang, Jaehee; Yoon, Sungroh
2016-01-01
The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operation...
A Personalized e-Learning Framework
Alhawiti, Mohammed M.; Abdelhamid, Yasser
2017-01-01
With the advent of web based learning and content management tools, e-learning has become a matured learning paradigm, and changed the trend of instructional design from instructor centric learning paradigm to learner centric approach, and evolved from "one instructional design for many learners" to "one design for one learner"…
Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M
2017-02-01
Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.
Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks
Karpatne, A.; Kumar, V.
2017-12-01
Generative adversarial networks (GANs), that have been highly successful in a number of applications involving large volumes of labeled and unlabeled data such as computer vision, offer huge potential for modeling the dynamics of physical processes that have been traditionally studied using simulations of physics-based models. While conventional physics-based models use labeled samples of input/output variables for model calibration (estimating the right parametric forms of relationships between variables) or data assimilation (identifying the most likely sequence of system states in dynamical systems), there is a greater opportunity to explore the full power of machine learning (ML) methods (e.g, GANs) for studying physical processes currently suffering from large knowledge gaps, e.g. ground-water flow. However, success in this endeavor requires a principled way of combining the strengths of ML methods with physics-based numerical models that are founded on a wealth of scientific knowledge. This is especially important in scientific domains like hydrology where the number of data samples is small (relative to Internet-scale applications such as image recognition where machine learning methods has found great success), and the physical relationships are complex (high-dimensional) and non-stationary. We will present a series of methods for guiding the learning of GANs using physics-based models, e.g., by using the outputs of physics-based models as input data to the generator-learner framework, and by using physics-based models as generators trained using validation data in the adversarial learning framework. These methods are being developed under the broad paradigm of theory-guided data science that we are developing to integrate scientific knowledge with data science methods for accelerating scientific discovery.
Architectural frameworks: defining the structures for implementing learning health systems.
Lessard, Lysanne; Michalowski, Wojtek; Fung-Kee-Fung, Michael; Jones, Lori; Grudniewicz, Agnes
2017-06-23
The vision of transforming health systems into learning health systems (LHSs) that rapidly and continuously transform knowledge into improved health outcomes at lower cost is generating increased interest in government agencies, health organizations, and health research communities. While existing initiatives demonstrate that different approaches can succeed in making the LHS vision a reality, they are too varied in their goals, focus, and scale to be reproduced without undue effort. Indeed, the structures necessary to effectively design and implement LHSs on a larger scale are lacking. In this paper, we propose the use of architectural frameworks to develop LHSs that adhere to a recognized vision while being adapted to their specific organizational context. Architectural frameworks are high-level descriptions of an organization as a system; they capture the structure of its main components at varied levels, the interrelationships among these components, and the principles that guide their evolution. Because these frameworks support the analysis of LHSs and allow their outcomes to be simulated, they act as pre-implementation decision-support tools that identify potential barriers and enablers of system development. They thus increase the chances of successful LHS deployment. We present an architectural framework for LHSs that incorporates five dimensions-goals, scientific, social, technical, and ethical-commonly found in the LHS literature. The proposed architectural framework is comprised of six decision layers that model these dimensions. The performance layer models goals, the scientific layer models the scientific dimension, the organizational layer models the social dimension, the data layer and information technology layer model the technical dimension, and the ethics and security layer models the ethical dimension. We describe the types of decisions that must be made within each layer and identify methods to support decision-making. In this paper, we outline
Edwards, Ann R.; Beattie, Rachel L.
2016-01-01
This paper focuses on two research-based frameworks that inform the design of instruction and promote student success in accelerated, developmental mathematics pathways. These are Learning Opportunities--productive struggle on challenging and relevant tasks, deliberate practice, and explicit connections, and Productive Persistence--promoting…
Rienties, Bart; Boroowa, Avinash; Cross, Simon; Kubiak, Chris; Mayles, Kevin; Murphy, Sam
2016-01-01
There is an urgent need to develop an evidence-based framework for learning analytics whereby stakeholders can manage, evaluate, and make decisions about which types of interventions work well and under which conditions. In this article, we will work towards developing a foundation of an Analytics4Action Evaluation Framework (A4AEF) that is…
Directory of Open Access Journals (Sweden)
Marco A Huertas
2016-12-01
Full Text Available The ability to maximize reward and avoid punishment is essential for animal survival. Reinforcement learning (RL refers to the algorithms used by biological or artificial systems to learn how to maximize reward or avoid negative outcomes based on past experiences. While RL is also important in machine learning, the types of mechanistic constraints encountered by biological machinery might be different than those for artificial systems. Two major problems encountered by RL are how to relate a stimulus with a reinforcing signal that is delayed in time (temporal credit assignment, and how to stop learning once the target behaviors are attained (stopping rule. To address the first problem, synaptic eligibility traces were introduced, bridging the temporal gap between a stimulus and its reward. Although these were mere theoretical constructs, recent experiements have provided evidence of their existence. These experiments also reveal that the presence of specific neuromodulators converts the traces into changes in synaptic efficacy. A mechanistic implementation of the stopping rule usually assumes the inhibition of the reward nucleus; however, recent experimental results have shown that learning terminates at the appropriate network state even in setups where the reward cannot be inhibited. In an effort to describe a learning rule that solves the temporal credit assignment problem and implements a biologically plausible stopping rule, we proposed a model based on two separate synaptic eligibility traces, one for long-term potentiation (LTP and one for long-term depression (LTD, each obeying different dynamics and having different effective magnitudes. The model has been shown to successfully generate stable learning in recurrent networks. Although the model assumes the presence of a single neuromodulator, evidence indicates that there are different neuromodulators for expressing the different traces. What could be the role of different
Huertas, Marco A; Schwettmann, Sarah E; Shouval, Harel Z
2016-01-01
The ability to maximize reward and avoid punishment is essential for animal survival. Reinforcement learning (RL) refers to the algorithms used by biological or artificial systems to learn how to maximize reward or avoid negative outcomes based on past experiences. While RL is also important in machine learning, the types of mechanistic constraints encountered by biological machinery might be different than those for artificial systems. Two major problems encountered by RL are how to relate a stimulus with a reinforcing signal that is delayed in time (temporal credit assignment), and how to stop learning once the target behaviors are attained (stopping rule). To address the first problem synaptic eligibility traces were introduced, bridging the temporal gap between a stimulus and its reward. Although, these were mere theoretical constructs, recent experiments have provided evidence of their existence. These experiments also reveal that the presence of specific neuromodulators converts the traces into changes in synaptic efficacy. A mechanistic implementation of the stopping rule usually assumes the inhibition of the reward nucleus; however, recent experimental results have shown that learning terminates at the appropriate network state even in setups where the reward nucleus cannot be inhibited. In an effort to describe a learning rule that solves the temporal credit assignment problem and implements a biologically plausible stopping rule, we proposed a model based on two separate synaptic eligibility traces, one for long-term potentiation (LTP) and one for long-term depression (LTD), each obeying different dynamics and having different effective magnitudes. The model has been shown to successfully generate stable learning in recurrent networks. Although, the model assumes the presence of a single neuromodulator, evidence indicates that there are different neuromodulators for expressing the different traces. What could be the role of different neuromodulators for
Directory of Open Access Journals (Sweden)
Yi Sun
2017-12-01
Full Text Available Bayesian network classifiers (BNCs have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the label-driven learning framework, which incorporates instance-based learning and ensemble learning. For each testing instance, high-confidence labels are first selected by a generalist classifier, e.g., the tree-augmented naive Bayes (TAN classifier. Then, by focusing on these labels, conditional mutual information is redefined to more precisely measure mutual dependence between attributes, thus leading to a refined generalist with a more reasonable network structure. To enable finer discrimination, an expert classifier is tailored for each high-confidence label. Finally, the predictions of the refined generalist and the experts are aggregated. We extend TAN to LTAN (Label-driven TAN by applying the proposed framework. Extensive experimental results demonstrate that LTAN delivers superior classification accuracy to not only several state-of-the-art single-structure BNCs but also some established ensemble BNCs at the expense of reasonable computation overhead.
Directory of Open Access Journals (Sweden)
Judith Seipold
2011-04-01
Full Text Available Against the conceptual and theoretical background of a socio-culturally orientated approach to mobile learning (Pachler, Bachmair and Cook, 2010, this paper examines the evaluation of user-generated contexts by referring to an example from the use of mobile phones in schools. We discuss how mobile device-related, user- generated contexts around structures, agency and cultural practices might be brought into a fruitful relationship with institution-based learning. And, we provide categories for evaluating the use of mobile devices to generate meaning from and with fragmented and discontinuous media and modes at the interface of learning in formal, institutionalised and informal, self-directed settings. The evaluation criteria build on the framework of a socio-cultural ecology of mobile learning developed by the London Mobile Learning Group.
A semi-supervised learning framework for biomedical event extraction based on hidden topics.
Zhou, Deyu; Zhong, Dayou
2015-05-01
Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely
Directory of Open Access Journals (Sweden)
Eileen N. Ariza
2003-10-01
Full Text Available Moore and Kearsley (1996 maintain distance educators should provide for three types of interaction: a learner-content; b learner-instructor; and c learner-learner. According to interactionist second language acquisition (SLA theories that reflect Krashen’s theory (1994 that comprehensible input is critical for second language acquisition, interaction can enhance second language acquisition and fluency. Effective output is necessary as well. We reviewed the research on distance learning for second language learners and concluded that SLA theories can, and should, be the framework that drives the development of courses for students seeking to learn languages by distance technology. This article delineates issues to consider in support of combining SLA theories and research literature as a guide in creating distance language learning courses.
A conceptual framework to identify spatial implications of new ways of learning in higher education
Beckers, R; van der Voordt, Theo; Dewulf, G
2015-01-01
Purpose - The purpose of this paper is to explore the spatial implications of new learning theories and the use of Information and Communication Technologies (ICT) in higher education.
Design/methodology/approach - Based on a review of literature, a theoretical framework has been developed that
A Learning Framework for Control-Oriented Modeling of Buildings
Energy Technology Data Exchange (ETDEWEB)
Rubio-Herrero, Javier; Chandan, Vikas; Siegel, Charles M.; Vishnu, Abhinav; Vrabie, Draguna L.
2018-01-18
Buildings consume a significant amount of energy worldwide. Several building optimization and control use cases require models of energy consumption which are control oriented, have high predictive capability, imposes minimal data pre-processing requirements, and have the ability to be adapted continuously to account for changing conditions as new data becomes available. Data driven modeling techniques, that have been investigated so far, while promising in the context of buildings, have been unable to simultaneously satisfy all the requirements mentioned above. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and big data opportunities. In this paper, we propose a deep learning based methodology for the development of control oriented models for building energy management and test in on data from a real building. Results show that the proposed methodology outperforms other data driven modeling techniques significantly. We perform a detailed analysis of the proposed methodology along dimensions such as topology, sensitivity, and downsampling. Lastly, we conclude by envisioning a building analytics suite empowered by the proposed deep framework, that can drive several use cases related to building energy management.
Environmental Management Performance Report to DOE-RL September 2001
International Nuclear Information System (INIS)
EDER, D.M.
2001-01-01
The purpose of this report is to provide the Department of Energy Richland Operations Office (RL) a monthly summary of the Central Plateau Contractor's Environmental Management (EM) performance by Fluor Hanford (FH) and its subcontractors. Section A, Executive Summary, provides an executive level summary of the cost, schedule, and technical performance described in this report. It summarizes performance for the period covered, highlights areas worthy of management attention, and provides a forward look to some of the upcoming key performance activities as extracted from the contractor baseline. The remaining sections provide detailed performance data relative to each individual Project (e.g., Waste Management, Spent Nuclear Fuels, etc.), in support of Section A of the report. Unless otherwise noted, the Safety, Conduct of Operations, and Cost/Schedule data contained herein is as of July 31, 2001. All other information is updated as of August 22, 2001 unless otherwise noted. ''Stoplight'' boxes are used to indicate at a glance the condition of a particular area. Green boxes denote on schedule. Yellows denote behind schedule but recoverable. Red is either missed or unrecoverable, without agreement by the regulating party
Announced Strategy Types in Multiagent RL for Conflict-Avoidance in the National Airspace
Rebhuhn, Carrie; Knudson, Matthew D.; Tumer, Kagan
2014-01-01
The use of unmanned aerial systems (UAS) in the national airspace is of growing interest to the research community. Safety and scalability of control algorithms are key to the successful integration of autonomous system into a human-populated airspace. In order to ensure safety while still maintaining efficient paths of travel, these algorithms must also accommodate heterogeneity of path strategies of its neighbors. We show that, using multiagent RL, we can improve the speed with which conflicts are resolved in cases with up to 80 aircraft within a section of the airspace. In addition, we show that the introduction of abstract agent strategy types to partition the state space is helpful in resolving conflicts, particularly in high congestion.
Goldman, Susan R.; Britt, M. Anne; Brown, Willard; Cribb, Gayle; George, MariAnne; Greenleaf, Cynthia; Lee, Carol D.; Shanahan, Cynthia
2016-01-01
This article presents a framework and methodology for designing learning goals targeted at what students need to know and be able to do in order to attain high levels of literacy and achievement in three disciplinary areas--literature, science, and history. For each discipline, a team of researchers, teachers, and specialists in that discipline…
A conceptual framework to identify spatial implications of new ways of learning in higher education
Geert Dewulf; Theo van der Voordt; Ronald Beckers
2015-01-01
Purpose – The purpose of this paper is to explore the spatial implications of new learning theories and the use of information and communication technologies (ICT) in higher education. Design/methodology/approach – Based on a review of the literature, a theoretical framework has been developed
A conceptual framework to identify spatial implications of new ways of learning in higher education
Beckers, Ronald; van der Voordt, Theo; Dewulf, Geert P.M.R.
2015-01-01
Purpose – The purpose of this paper is to explore the spatial implications of new learning theories and the use of information and communication technologies (ICT) in higher education. Design/methodology/approach – Based on a review of the literature, a theoretical framework has been developed that
A unified framework of image latent feature learning on Sina microblog
Wei, Jinjin; Jin, Zhigang; Zhou, Yuan; Zhang, Rui
2015-10-01
Large-scale user-contributed images with texts are rapidly increasing on the social media websites, such as Sina microblog. However, the noise and incomplete correspondence between the images and the texts give rise to the difficulty in precise image retrieval and ranking. In this paper, a hypergraph-based learning framework is proposed for image ranking, which simultaneously utilizes visual feature, textual content and social link information to estimate the relevance between images. Representing each image as a vertex in the hypergraph, complex relationship between images can be reflected exactly. Then updating the weight of hyperedges throughout the hypergraph learning process, the effect of different edges can be adaptively modulated in the constructed hypergraph. Furthermore, the popularity degree of the image is employed to re-rank the retrieval results. Comparative experiments on a large-scale Sina microblog data-set demonstrate the effectiveness of the proposed approach.
Ficuciello, Fanny; Siciliano, Bruno
2016-07-01
learning into control naturally leads to relaxing the above requirements through the adoption of coordinated motion patterns and sensory-motor synergies as useful tools leading to a problem of reduced dimension. To this purpose, model-based control strategies relying on synergistic models of manipulation activities learned from human experience can be integrated with real-time learning from actions strategies [5]. In [6] a classification of learning strategies for robotics is provided, while the difference between imitation learning and reinforcement learning (RL) is highlighted in [7]. From recent research in the field [8,9], it seems that RL represents the future toward autonomous and intelligent robots since it provides learning capabilities as those of humans, i.e. based on exploration and trial-and-error policies. In this context, suitable policy search methods to be implemented in a synergy-based framework are to be sought in order to reduce the search space dimension while guaranteeing the convergence and efficiency of the learning algorithm.
Reinforcement Learning for Predictive Analytics in Smart Cities
Directory of Open Access Journals (Sweden)
Kostas Kolomvatsos
2017-06-01
Full Text Available The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller ( Q C that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML. We adopt two learning schemes, i.e., Reinforcement Learning (RL and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a
A multidimensional framework of conceptual change for developing chemical equilibrium learning
Chanyoo, Wassana; Suwannoi, Paisan; Treagust, David F.
2018-01-01
The purposes of this research is to investigate the existing chemical equilibrium lessons in Thailand based on the multidimensional framework of conceptual change, to determine how the existing lessons could enhance students' conceptual change. This research was conducted based on qualitative perspective. Document, observations and interviews were used to collect data. To comprehend all students conceptions, diagnostic tests were applied comprised of The Chemical Equilibrium Diagnostic Test (the CEDT) and The Chemical Equilibrium Test for Reveal Conceptual Change (the CETforRCC). In addition, to study students' motivations, the Motivated Strategies for Learning Questionnaire (the MSLQ) and students' task engagement were applied. Following each perspective of conceptual change - ontological, epistemological, and social/affective - the result showed that the existing chemical equilibrium unit did not enhance students' conceptual change, and some issues were found. The problems obstructed students conceptual change should be remedy under the multidimensional framework of conceptual change. Finally, some suggestions were provided to enhance students' conceptual change in chemical equilibrium effectively
Directory of Open Access Journals (Sweden)
Carolina Mejía Corredor
2015-12-01
Full Text Available Rev.esc.adm.neg Dyslexia is a common learning disability in Spanish-speaking university students, and requires special attention from higher educational institutions in order to support affected individuals during their learning process. In previous studies, a framework to detect, assess and assist university students with reading difficulties related to dyslexia was developed. In this paper, the integration of this framework with a Learning Management System (LMS is presented. Two case studies were performed to test the functionality and the usability of this integration. The first case study was carried out with 20 students, while the second one with four teachers. The results show that both students and teachers were satisfied with the integration performed in Moodle.ce, among others.
ELPSA as A Lesson Design Framework
Directory of Open Access Journals (Sweden)
Tom Lowrie
2015-07-01
Full Text Available This paper offers a framework for mathematics lesson design that is consistent with the way we learn about, and discover, most things in life. In addition, the framework provides a structure for identifying how mathematical concepts and understanding are acquired and developed. This framework is called ELPSA and represents five learning components, namely: Experience, Language, Pictorial, Symbolic and Applications. This framework has been used in developing lessons and teacher professional programs in Indonesia since 2012 in cooperation with the World Bank. This paper describes the theory that underlines the framework in general and in relation to each inter-connected component. Two explicit learning sequences for classroom practice are described, associated with Pythagoras theorem and probability. This paper then concludes with recommendations for using ELPSA in various institutional contexts.
ELPSA AS A LESSON DESIGN FRAMEWORK
Directory of Open Access Journals (Sweden)
Tom Lowrie
2015-07-01
Full Text Available This paper offers a framework for mathematics lesson design that is consistent with the way we learn about, and discover, most things in life. In addition, the framework provides a structure for identifying how mathematical concepts and understanding are acquired and developed. This framework is called ELPSA and represents five learning components, namely: Experience, Language, Pictorial, Symbolic and Applications. This framework has been used in developing lessons and teacher professional programs in Indonesia since 2012 in cooperation with the World Bank. This paper describes the theory that underlines the framework in general and in relation to each inter-connected component. Two explicit learning sequences for classroom practice are described, associated with Pythagoras theorem and probability. This paper then concludes with recommendations for using ELPSA in various institutional contexts.Keywords: ELPSA, lesson design framework, Pythagoras theorem, probability DOI: dx.doi.org/10.22342/jme.62.77
Directory of Open Access Journals (Sweden)
Gang Zhang
2015-01-01
Full Text Available Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT and support vector machine (SVM are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2% ± 2.8% measured by zero-one loss for the first evaluation session and 79.6% ± 3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low.
From a literature review to a multi-perspective framework for reverse logistics barriers and drivers
DEFF Research Database (Denmark)
Govindan, Kannan; Bouzon, Marina
2018-01-01
The emergence of stricter environmental regulations and the growing environmental consciousness of customers have forced industries to start thinking about environmental operations management with the help of reverse logistics application. In this process, influential factors such as drivers...... and barriers have to be examined, and stakeholders’ different perspectives on RL implementation and development should also be considered. This paper presents a multi-perspective framework for reverse logistics implementation using the lens of stakeholder theory. The multiple stakeholders’ perspective...... framework was developed based upon a structured literature review process. Fifty-four papers concerning these topical areas were thoroughly assessed and classified according to their structural dimensions and analytical categories. Two extensive lists of 37 drivers and 36 barriers, categorized and analyzed...
Directory of Open Access Journals (Sweden)
Crumpacker Clyde S
2011-01-01
Full Text Available Abstract Background The major hurdle in the treatment of Human Immunodeficiency virus type 1 (HIV-1 includes the development of drug resistance-associated mutations in the target regions of the virus. Since reverse transcriptase (RT is essential for HIV-1 replication, several nucleoside analogues have been developed to target RT of the virus. Clinical studies have shown that mutations at RT codon 65 and 74 which are located in β3-β4 linkage group of finger sub-domain of RT are selected during treatment with several RT inhibitors, including didanosine, deoxycytidine, abacavir and tenofovir. Interestingly, the co-selection of K65R and L74V is rare in clinical settings. We have previously shown that K65R and L74V are incompatible and a R→K reversion occurs at codon 65 during replication of the virus. Analysis of the HIV resistance database has revealed that similar to K65R+L74V, the double mutant K65R+L74I is also rare. We sought to compare the impact of L→V versus L→I change at codon 74 in the background of K65R mutation, on the replication of doubly mutant viruses. Methods Proviral clones containing K65R, L74V, L74I, K65R+L74V and K65R+L74I RT mutations were created in pNL4-3 backbone and viruses were produced in 293T cells. Replication efficiencies of all the viruses were compared in peripheral blood mononuclear (PBM cells in the absence of selection pressure. Replication capacity (RC of mutant viruses in relation to wild type was calculated on the basis of antigen p24 production and RT activity, and paired analysis by student t-test was performed among RCs of doubly mutant viruses. Reversion at RT codons 65 and 74 was monitored during replication in PBM cells. In vitro processivity of mutant RTs was measured to analyze the impact of amino acid changes at RT codon 74. Results Replication kinetics plot showed that all of the mutant viruses were attenuated as compared to wild type (WT virus. Although attenuated in comparison to WT virus
The National School Safety Framework: A framework for preventing ...
African Journals Online (AJOL)
The National School Safety Framework (NSSF) – approved by the Minister of Education in April 2015 - is located within a range of international and national laws and policies that recognise the safety of learners and educators as a prerequisite for quality learning and teaching at school. The framework affirms the ...
AUDIT SISTEM INFORMASI MENGGUNAKAN FRAMEWORK COBIT 4.1 PADA E-LEARNING UNISNU JEPARA
Directory of Open Access Journals (Sweden)
Noor Azizah
2017-04-01
Full Text Available Perkembangan teknologi saat ini tak bisa dibendung lagi. Kemajuan disetiap bidang tak lepas dari teknologi sebagai penunjangnya, terutama teknologi informasi. Akan tetapi hal tersebut harus dimbangi dengan adanya sebuah evaluasi atau audit terhadap sistem informasi sehingga ancaman atau kerugian dapat dihindari ataupun dicegah. Penelitian ini bertujuan mengetahui sejauh mana kinerja sistem informasi pembelajaran yaitu e-learning sebagai layanan publik yang telah diterapkan pada UNISNU Jepara dan memberikan rekomendasi tata kelola perbaikan setelah mengetahui kesenjangan antara tatakelola saat ini dengan tatakelola yang diharapkan sesuai dengan framework yang digunakan. Framework yang digunakan dalam penelitian ini adalah COBIT versi 4.1 khusus pada domain Deliver and Support (DS. Teknik pengumpulan datanya dilakukan dengan wawancara dan kuisioner dengan narasumber yang telah ditentukan sesuai dengan domain dan Control Objective yang digunakan. Metode analisis data dilakukan beberapa tahap, yaitu penentuan domain, penentuan proses kontrol, penentuan indikator dan pemetaan tingkatkematangan. Hasil dari penelitian ini adalah untuk mengetahui tingkat kematangan (maturity level pada implementasi e-learning UNISNU Jepara khusus pada Domain DS, yaitu berada pada level 4 yang berarti sudah terukur dan terintegrasi antar proses yang berlangsung. Dan analisa GAP antara kondisi yang diharapkan dengan kondisi saat ini rata-rata sebesar 0,6.
A Driver Behavior Learning Framework for Enhancing Traffic Simulation
Directory of Open Access Journals (Sweden)
Ramona Maria Paven
2014-06-01
Full Text Available Traffic simulation provides an essential support for developing intelligent transportation systems. It allows affordable validation of such systems using a large variety of scenarios that involves massive data input. However, realistic traffic models are hard to be implemented especially for microscopic traffic simulation. One of the hardest problems in this context is to model the behavior of drivers, due the complexity of human nature. The work presented in this paper proposes a framework for learning driver behavior based on a Hidden Markov Model technique. Moreover, we propose also a practical method to inject this behavior in a traffic model used by the SUMO traffic simulator. To demonstrate the effectiveness of this method we present a case study involving real traffic collected from Timisoara city area.
Press Play for Learning: A Framework to Guide Serious Computer Game Use in the Classroom
Southgate, Erica; Budd, Janene; Smith, Shamus
2017-01-01
Computer gaming is a global phenomenon and there has been rapid growth in "serious" games for learning. An emergent body of evidence demonstrates how serious games can be used in primary and secondary school classrooms. Despite the popularity of serious games and their pedagogical potential, there are few specialised frameworks to guide…
Elken, Mari
2015-01-01
The European Qualifications Framework (EQF) for lifelong learning has been characterized as a policy instrument with a number of contested ideas, raising questions about the process through which such instruments are developed at European level. The introduction of the EQF is in this article examined through variations of neo-institutional theory:…
Environmental Management Performance Report to DOE-RL December 2001
International Nuclear Information System (INIS)
EDER, D.M.
2001-01-01
The purpose of this report is to provide the Department of Energy Richland Operations Office (RL) a monthly summary of the Central Plateau Contractor's Environmental Management (EM) performance by Fluor Hanford (FH) and its subcontractors. Only current FH workscope responsibilities are described. Please refer to other sections (BHI, PNNL) for other contractor information. Section A, Executive Summary, provides an executive level summary of the cost, schedule, and technical performance described in this report. It summarizes performance for the period covered, highlights areas worthy of management attention, and provides a forward look to some of the upcoming key performance activities as extracted from the contractor baseline. The remaining sections provide detailed performance data relative to each individual subproject (e.g., Plutonium Finishing Plant, Spent Nuclear Fuels, etc.), in support of Section A of the report. All information is updated as of October 31, 2001 unless otherwise noted. ''Stoplight'' boxes are used to indicate at a glance the condition of a particular safety area. Green boxes denote either (1) the data are stable at a level representing ''acceptable'' performance, or (2) an improving trend exists. Yellows denote the data are stable at a level from which improvement is needed. Red denotes a trend exists in a non-improving direction
DEFF Research Database (Denmark)
Andersen, Claus Erik; Morgenthaler Edmund, Jens; Damkjær, Sidsel Marie Skov
2010-01-01
Carbon-doped aluminum oxide (Al2O3:C) crystals attached to 15 m optical fiber cables can be used for online in vivo dosimetry during, for example, remotely afterloaded brachytherapy. Radioluminescence (RL) is generated spontaneously in Al2O3:C during irradiation, and this scintillator-like signal...
Orbitofrontal cortex as a cognitive map of task space.
Wilson, Robert C; Takahashi, Yuji K; Schoenbaum, G; Niv, Yael
2014-01-22
Orbitofrontal cortex (OFC) has long been known to play an important role in decision making. However, the exact nature of that role has remained elusive. Here, we propose a unifying theory of OFC function. We hypothesize that OFC provides an abstraction of currently available information in the form of a labeling of the current task state, which is used for reinforcement learning (RL) elsewhere in the brain. This function is especially critical when task states include unobservable information, for instance, from working memory. We use this framework to explain classic findings in reversal learning, delayed alternation, extinction, and devaluation as well as more recent findings showing the effect of OFC lesions on the firing of dopaminergic neurons in ventral tegmental area (VTA) in rodents performing an RL task. In addition, we generate a number of testable experimental predictions that can distinguish our theory from other accounts of OFC function. Copyright © 2014 Elsevier Inc. All rights reserved.
Lie, Désirée A.; Forest, Christopher P.; Walsh, Anne; Banzali, Yvonne; Lohenry, Kevin
2016-01-01
Background: The student-run clinic (SRC) has the potential to address interprofessional learning among health professions students.Purpose: To derive a framework for understanding student learning during team-based care provided in an interprofessional SRC serving underserved patients.Methods: The authors recruited students for a focus group study by purposive sampling and snowballing. They constructed two sets of semi-structured questions for uniprofessional and multiprofessional groups. Ses...
Kong, Zehui; Zou, Yuan; Liu, Teng
2017-01-01
To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.
Directory of Open Access Journals (Sweden)
Zehui Kong
Full Text Available To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM of power-request is derived. The reinforcement learning (RL is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.
Lerman, Julia
2010-01-01
Get a thorough introduction to ADO.NET Entity Framework 4 -- Microsoft's core framework for modeling and interacting with data in .NET applications. The second edition of this acclaimed guide provides a hands-on tour of the framework latest version in Visual Studio 2010 and .NET Framework 4. Not only will you learn how to use EF4 in a variety of applications, you'll also gain a deep understanding of its architecture and APIs. Written by Julia Lerman, the leading independent authority on the framework, Programming Entity Framework covers it all -- from the Entity Data Model and Object Service
A Framework for Learning Analytics Using Commodity Wearable Devices.
Lu, Yu; Zhang, Sen; Zhang, Zhiqiang; Xiao, Wendong; Yu, Shengquan
2017-06-14
We advocate for and introduce LEARNSense, a framework for learning analytics using commodity wearable devices to capture learner's physical actions and accordingly infer learner context (e.g., student activities and engagement status in class). Our work is motivated by the observations that: (a) the fine-grained individual-specific learner actions are crucial to understand learners and their context information; (b) sensor data available on the latest wearable devices (e.g., wrist-worn and eye wear devices) can effectively recognize learner actions and help to infer learner context information; (c) the commodity wearable devices that are widely available on the market can provide a hassle-free and non-intrusive solution. Following the above observations and under the proposed framework, we design and implement a sensor-based learner context collector running on the wearable devices. The latest data mining and sensor data processing techniques are employed to detect different types of learner actions and context information. Furthermore, we detail all of the above efforts by offering a novel and exemplary use case: it successfully provides the accurate detection of student actions and infers the student engagement states in class. The specifically designed learner context collector has been implemented on the commodity wrist-worn device. Based on the collected and inferred learner information, the novel intervention and incentivizing feedback are introduced into the system service. Finally, a comprehensive evaluation with the real-world experiments, surveys and interviews demonstrates the effectiveness and impact of the proposed framework and this use case. The F1 score for the student action classification tasks achieve 0.9, and the system can effectively differentiate the defined three learner states. Finally, the survey results show that the learners are satisfied with the use of our system (mean score of 3.7 with a standard deviation of 0.55).
Coverdale, John H; McCullough, Laurence B
2014-01-01
Many medical schools now offer students a distinctive clinical and learning opportunity, the student-run clinic (SRC), in which generalist physicians often play the major role. Although SRCs have become popular, they pose as-yet unexplored ethical challenges for the learning experiences of students. In SRCs students not only take on a significant administrative role especially in coordinating care, but also provide direct patient care for a clinically challenging, biopsychosocially vulnerable, medically indigent population of patients. SRCs provide an exemplar of the ethical challenges of care for such patients. The ethical framework proposed in this article emphasizes that these valued learning opportunities for students should occur in the context of professional formation, with explicit attention to developing the professional virtues, with faculty as role models for these virtues. The valued learning opportunities for students in SRCs should occur in the context of professional formation, with explicit attention to developing the professional virtues of integrity, compassion, self-effacement, self-sacrifice, and courage, which are required for the appropriate care of the vulnerable populations served by SRCs.
Cary, Tawnya; Branchaw, Janet
2017-01-01
The Vision and Change in Undergraduate Biology Education: Call to Action report has inspired and supported a nationwide movement to restructure undergraduate biology curricula to address overarching disciplinary concepts and competencies. The report outlines the concepts and competencies generally but does not provide a detailed framework to guide the development of the learning outcomes, instructional materials, and assessment instruments needed to create a reformed biology curriculum. In this essay, we present a detailed Vision and Change core concept framework that articulates key components that transcend subdisciplines and scales for each overarching biological concept, the Conceptual Elements (CE) Framework. The CE Framework was developed using a grassroots approach of iterative revision and incorporates feedback from more than 60 biologists and undergraduate biology educators from across the United States. The final validation step resulted in strong national consensus, with greater than 92% of responders agreeing that each core concept list was ready for use by the biological sciences community, as determined by scientific accuracy and completeness. In addition, we describe in detail how educators and departments can use the CE Framework to guide and document reformation of individual courses as well as entire curricula. PMID:28450444
Integration of Culturally Relevant Pedagogy Into the Science Learning Progression Framework
Bernardo, Cyntra
This study integrated elements of culturally relevant pedagogy into a science learning progression framework, with the goal of enhancing teachers' cultural knowledge and thereby creating better teaching practices in an urban public high school science classroom. The study was conducted using teachers, an administrator, a science coach, and students involved in science courses in public high school. Through a qualitative intrinsic case study, data were collected and analyzed using traditional methods. Data from primary participants (educators) were analyzed through identification of big ideas, open coding, and themes. Through this process, patterns and emergent ideas were reported. Outcomes of this study demonstrated that educators lack knowledge about research-based academic frameworks and multicultural education strategies, but benefit through institutionally-based professional development. Students from diverse cultures responded positively to culturally-based instruction. Their progress was further manifested in better communication and discourse with their teacher and peers, and increased academic outcomes. This study has postulated and provided an exemplar for science teachers to expand and improve multicultural knowledge, ultimately transferring these skills to their pedagogical practice.
DEFF Research Database (Denmark)
Strobel, Bjarne W.
2014-01-01
The following framework for online teaching is a guidance to inspire you on how to to use e-learning in your teaching. Maybe you want to make a whole online course (distance learning) or maybe you want to use e-learning as a part of a course (blended learning). If you want to go further or have...
Repurposing learning object components
Verbert, K.; Jovanovic, J.; Gasevic, D.; Duval, E.; Meersman, R.
2005-01-01
This paper presents an ontology-based framework for repurposing learning object components. Unlike the usual practice where learning object components are assembled manually, the proposed framework enables on-the-fly access and repurposing of learning object components. The framework supports two
Radin Umar, Radin Zaid; Sommerich, Carolyn M; Lavender, Steve A; Sanders, Elizabeth; Evans, Kevin D
2018-05-14
Sound workplace ergonomics and safety-related interventions may be resisted by employees, and this may be detrimental to multiple stakeholders. Understanding fundamental aspects of decision making, behavioral change, and learning cycles may provide insights into pathways influencing employees' acceptance of interventions. This manuscript reviews published literature on thinking processes and other topics relevant to decision making and incorporates the findings into two new conceptual frameworks of the workplace change adoption process. Such frameworks are useful for thinking about adoption in different ways and testing changes to traditional intervention implementation processes. Moving forward, it is recommended that future research focuses on systematic exploration of implementation process activities that integrate principles from the research literature on sensemaking, decision making, and learning processes. Such exploration may provide the groundwork for development of specific implementation strategies that are theoretically grounded and provide a revised understanding of how successful intervention adoption processes work.
Learning Theory Foundations of Simulation-Based Mastery Learning.
McGaghie, William C; Harris, Ilene B
2018-06-01
Simulation-based mastery learning (SBML), like all education interventions, has learning theory foundations. Recognition and comprehension of SBML learning theory foundations are essential for thoughtful education program development, research, and scholarship. We begin with a description of SBML followed by a section on the importance of learning theory foundations to shape and direct SBML education and research. We then discuss three principal learning theory conceptual frameworks that are associated with SBML-behavioral, constructivist, social cognitive-and their contributions to SBML thought and practice. We then discuss how the three learning theory frameworks converge in the course of planning, conducting, and evaluating SBML education programs in the health professions. Convergence of these learning theory frameworks is illustrated by a description of an SBML education and research program in advanced cardiac life support. We conclude with a brief coda.
Häkkinen, Päivi; Järvelä, Sanna; Mäkitalo-Siegl, Kati; Ahonen, Arto; Näykki, Piia; Valtonen, Teemu
2017-01-01
With regard to the growing interest in developing teacher education to match the twenty-first-century skills, while many assumptions have been made, there has been less theoretical elaboration and empirical research on this topic. The aim of this article is to present our pedagogical framework for the twenty-first-century learning practices in…
Moriera, M. A.
1979-01-01
David Ausubel's learning theory was used as a framework for the content organization of an experimental Personalized System of Instruction (PSI) course in physics. Evaluation suggests that the combination of PSI as a method of instruction and Ausubel's theory for organization might result in better learning outcomes. (Author/JMD)
Anayasa Mahkemesi ve Avrupa İnsan Hakları Mahkemesi Kararlarına Göre İfade Özgürlüğünün Sınırlanması
ÇAMAK, Sultan
2016-01-01
Sağlıklı bir toplum ve devlet yapısının oluşturulması için ifade özgürlüğünün sağlanması esastır. Toplumsal yaşamın sonucu olarak bireyler arasında yaşanan menfaat çatışmalarında dengenin sağlanması amacıyla ifade özgürlüğünün dahi sınırlanabileceği genel olarak hukuk sistemlerinde kabul görmektedir. Temel hak ve özgürlüklerin uluslararası alanda korunması anlamında en önemli belge sayılabilecek Avrupa İnsan Hakları Sözleşmesi’nde de sınırlamanın meşru sayıldığı haller düzenlenmiştir. Bu sebe...
Effect of Cognitive Style on Learning and Retrieval of Navigational Environments
Directory of Open Access Journals (Sweden)
Maddalena Boccia
2017-07-01
Full Text Available Field independence (FI has been found to correlate with a wide range of cognitive processes requiring cognitive restructuring. Cognitive restructuring, that is going beyond the information given by the setting, is pivotal in creating stable mental representations of the environment, the so-called “cognitive maps,” and it affects visuo-spatial abilities underpinning environmental navigation. Here we evaluated whether FI, by fostering cognitive restructuring of environmental cues on the basis of an internal frame of reference, affects the learning and retrieval of a novel environment. Fifty-four participants were submitted to the Embedded Figure Test (EFT for assessing their Cognitive Style (CS and to the Perspective Taking/Spatial Orientation Test (PTSOT and the Santa Barbara Sense of Direction Scale (SBSOD for assessing their spatial perspective taking and orientation skills. They were also required to learn a path in a novel, real environment (route learning, RL, to recognize landmarks of this path among distracters (landmark recognition, LR, to order them (landmark ordering, LO and to draw the learned path on a map (map drawing, MD. Retrieval tasks were performed both immediately after learning (immediate-retrieval and the day after (24 h-retrieval. Performances on EFT significantly correlated with the time needed to learn the path, with MD (both in the immediate- and in the 24 h- retrievals, results on LR (in 24-retrieval and performances on PTSOT. Interestingly, we found that gender interacted with CS on RL (time of learning and MD. Females performed significantly worse than males only if they were classified as FD, but did not differ from males if they were classified as FI. These results suggest that CS affects learning and retrieval of navigational environment, especially when a map-like representation is required. We propose that CS may be pivotal in forming the cognitive map of the environment, likely due to the higher ability of FI
Tippett, Christine Diane
Scientific knowledge is constructed and communicated through a range of forms in addition to verbal language. Maps, graphs, charts, diagrams, formulae, models, and drawings are just some of the ways in which science concepts can be represented. Representational competence---an aspect of visual literacy that focuses on the ability to interpret, transform, and produce visual representations---is a key component of science literacy and an essential part of science reading and writing. To date, however, most research has examined learning from representations rather than learning with representations. This dissertation consisted of three distinct projects that were related by a common focus on learning from visual representations as an important aspect of scientific literacy. The first project was the development of an exploratory framework that is proposed for use in investigations of students constructing and interpreting multimedia texts. The exploratory framework, which integrates cognition, metacognition, semiotics, and systemic functional linguistics, could eventually result in a model that might be used to guide classroom practice, leading to improved visual literacy, better comprehension of science concepts, and enhanced science literacy because it emphasizes distinct aspects of learning with representations that can be addressed though explicit instruction. The second project was a metasynthesis of the research that was previously conducted as part of the Explicit Literacy Instruction Embedded in Middle School Science project (Pacific CRYSTAL, http://www.educ.uvic.ca/pacificcrystal). Five overarching themes emerged from this case-to-case synthesis: the engaging and effective nature of multimedia genres, opportunities for differentiated instruction using multimodal strategies, opportunities for assessment, an emphasis on visual representations, and the robustness of some multimodal literacy strategies across content areas. The third project was a mixed
Li, Jinna; Kiumarsi, Bahare; Chai, Tianyou; Lewis, Frank L; Fan, Jialu
2017-12-01
Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method.
An evaluation framework for participatory modelling
Krueger, T.; Inman, A.; Chilvers, J.
2012-04-01
Strong arguments for participatory modelling in hydrology can be made on substantive, instrumental and normative grounds. These arguments have led to increasingly diverse groups of stakeholders (here anyone affecting or affected by an issue) getting involved in hydrological research and the management of water resources. In fact, participation has become a requirement of many research grants, programs, plans and policies. However, evidence of beneficial outcomes of participation as suggested by the arguments is difficult to generate and therefore rare. This is because outcomes are diverse, distributed, often tacit, and take time to emerge. In this paper we develop an evaluation framework for participatory modelling focussed on learning outcomes. Learning encompasses many of the potential benefits of participation, such as better models through diversity of knowledge and scrutiny, stakeholder empowerment, greater trust in models and ownership of subsequent decisions, individual moral development, reflexivity, relationships, social capital, institutional change, resilience and sustainability. Based on the theories of experiential, transformative and social learning, complemented by practitioner experience our framework examines if, when and how learning has occurred. Special emphasis is placed on the role of models as learning catalysts. We map the distribution of learning between stakeholders, scientists (as a subgroup of stakeholders) and models. And we analyse what type of learning has occurred: instrumental learning (broadly cognitive enhancement) and/or communicative learning (change in interpreting meanings, intentions and values associated with actions and activities; group dynamics). We demonstrate how our framework can be translated into a questionnaire-based survey conducted with stakeholders and scientists at key stages of the participatory process, and show preliminary insights from applying the framework within a rural pollution management situation in
Guo, Le-Hang; Wang, Dan; Qian, Yi-Yi; Zheng, Xiao; Zhao, Chong-Ke; Li, Xiao-Long; Bo, Xiao-Wan; Yue, Wen-Wen; Zhang, Qi; Shi, Jun; Xu, Hui-Xiong
2018-04-04
With the fast development of artificial intelligence techniques, we proposed a novel two-stage multi-view learning framework for the contrast-enhanced ultrasound (CEUS) based computer-aided diagnosis for liver tumors, which adopted only three typical CEUS images selected from the arterial phase, portal venous phase and late phase. In the first stage, the deep canonical correlation analysis (DCCA) was performed on three image pairs between the arterial and portal venous phases, arterial and delayed phases, and portal venous and delayed phases respectively, which then generated total six-view features. While in the second stage, these multi-view features were then fed to a multiple kernel learning (MKL) based classifier to further promote the diagnosis result. Two MKL classification algorithms were evaluated in this MKL-based classification framework. We evaluated proposed DCCA-MKL framework on 93 lesions (47 malignant cancers vs. 46 benign tumors). The proposed DCCA-MKL framework achieved the mean classification accuracy, sensitivity, specificity, Youden index, false positive rate, and false negative rate of 90.41 ± 5.80%, 93.56 ± 5.90%, 86.89 ± 9.38%, 79.44 ± 11.83%, 13.11 ± 9.38% and 6.44 ± 5.90%, respectively, by soft margin MKL classifier. The experimental results indicate that the proposed DCCA-MKL framework achieves best performance for discriminating benign liver tumors from malignant liver cancers. Moreover, it is also proved that the three-phase CEUS image based CAD is feasible for liver tumors with the proposed DCCA-MKL framework.
Wall-Bassett, Elizabeth DeVane; Hegde, Archana Vasudeva; Craft, Katelyn; Oberlin, Amber Louise
2018-01-01
The purpose of this study was to investigate an interdisciplinary international service learning program and its impact on student sense of cultural awareness and competence using the Campinha-Bacote's (2002) framework of cultural competency model. Seven undergraduate and one graduate student from Human Development and Nutrition Science…
Drawing-to-Learn: A Framework for Using Drawings to Promote Model-Based Reasoning in Biology
Quillin, Kim; Thomas, Stephen
2015-01-01
The drawing of visual representations is important for learners and scientists alike, such as the drawing of models to enable visual model-based reasoning. Yet few biology instructors recognize drawing as a teachable science process skill, as reflected by its absence in the Vision and Change report’s Modeling and Simulation core competency. Further, the diffuse research on drawing can be difficult to access, synthesize, and apply to classroom practice. We have created a framework of drawing-to-learn that defines drawing, categorizes the reasons for using drawing in the biology classroom, and outlines a number of interventions that can help instructors create an environment conducive to student drawing in general and visual model-based reasoning in particular. The suggested interventions are organized to address elements of affect, visual literacy, and visual model-based reasoning, with specific examples cited for each. Further, a Blooming tool for drawing exercises is provided, as are suggestions to help instructors address possible barriers to implementing and assessing drawing-to-learn in the classroom. Overall, the goal of the framework is to increase the visibility of drawing as a skill in biology and to promote the research and implementation of best practices. PMID:25713094
Nolan, Bernard T.; Fienen, Michael N.; Lorenz, David L.
2015-01-01
We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R2 and a version with R2 within one standard error of the maximum (the 1SE model). The former yielded CV training R2 values of 0.94–1.0. Cross-validation testing R2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.
Wasser, L. A.; Gold, A. U.
2017-12-01
There is a deluge of earth systems data available to address cutting edge science problems yet specific skills are required to work with these data. The Earth analytics education program, a core component of Earth Lab at the University of Colorado - Boulder - is building a data intensive program that provides training in realms including 1) interdisciplinary communication and collaboration 2) earth science domain knowledge including geospatial science and remote sensing and 3) reproducible, open science workflows ("earth analytics"). The earth analytics program includes an undergraduate internship, undergraduate and graduate level courses and a professional certificate / degree program. All programs share the goals of preparing a STEM workforce for successful earth analytics driven careers. We are developing an program-wide evaluation framework that assesses the effectiveness of data intensive instruction combined with domain science learning to better understand and improve data-intensive teaching approaches using blends of online, in situ, asynchronous and synchronous learning. We are using targeted online search engine optimization (SEO) to increase visibility and in turn program reach. Finally our design targets longitudinal program impacts on participant career tracts over time.. Here we present results from evaluation of both an interdisciplinary undergrad / graduate level earth analytics course and and undergraduate internship. Early results suggest that a blended approach to learning and teaching that includes both synchronous in-person teaching and active classroom hands-on learning combined with asynchronous learning in the form of online materials lead to student success. Further we will present our model for longitudinal tracking of participant's career focus overtime to better understand long-term program impacts. We also demonstrate the impact of SEO optimization on online content reach and program visibility.
Huber, Elaine
2017-01-01
Scholarly evaluation practices in learning and teaching projects are under-reported in the literature. In order for robust evaluative measures to be implemented, a project requires a well-designed evaluation plan. This research study describes the development of a practical evaluation planning framework through an action research approach, using…
Directory of Open Access Journals (Sweden)
Juan Carlos Davila
2017-06-01
Full Text Available The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.
Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek
2017-06-07
The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.
International Nuclear Information System (INIS)
Oluyemi, I.O.D.
2008-01-01
Full text: Radiation protection is the science of protecting people and the environment from the harmful effects of ionizing radiation, which includes both particle radiation and high energy electromagnetic radiation. It includes occupational radiation protection, which is the protection of workers; medical radiation protection, which is the protection of patients; and public radiation protection, which is about protection of individual members of the public, and of the population as a whole. ICT has made possible the development of e-learning and several Virtual Learning Environments (VLEs) which can support a wide range of capacity building requirements, ranging from under-graduate and post-graduate programmes, continuing professional development courses, right through to short subject specific and research courses, thereby eliminating the problems of conventional forms of training / learning, some of which are: limited access, cost effectiveness and language / cultural barriers. This paper focuses on the utilization of these ICT-based training / learning for capacity building in radiation protection framework and concludes with suggestions on implementation strategies. (author)
Directory of Open Access Journals (Sweden)
Giuliani do Prado
2007-08-01
Full Text Available Valores determinados em laboratório, de vazão, raio de alcance e do perfil radial de aplicação de água do aspersor canhão PLONA-RL400, foram utilizados em simulações digitais da uniformidade de aplicação de água desse aspersor operando, na ausência de ventos, com diferentes ângulos de giro e espaçamentos entre carreadores, em sistemas autopropelidos de irrigação. Os valores simulados de uniformidade de aplicação de água foram apresentados em três grupos distintos, cada um dos quais representando condições operacionais (bocal e pressão que determinam a ocorrência da mesma forma geométrica (I, II ou III do perfil radial adimensional de aplicação de água do aspersor avaliado. Para os perfis do tipo I, II e III, observou-se que espaçamentos de carreadores menores que 50% do diâmetro molhado ou situados entre 80 e 90% do diâmetro molhado proporcionaram os maiores valores de uniformidade. Em todas as formas geométricas do perfil, os melhores valores de uniformidade de aplicação de água foram obtidos com ângulos de giro do aspersor entre 180 e 210º.Values of flow rate, radius of throw and radial precipitation profile obtained in laboratory with the PLONA-RL400 gun sprinkler are presented. These values were used on digital simulations of water application uniformity, under no wind conditions, provided by traveling gun machines operating with this sprinkler under different combinations of wetted angle and towpath spacing. Simulated uniformity values were presented arranged under three different clusters, each one corresponding to a different set of sprinkler operational conditions (nozzle versus service pressure that results on the same geometrical shape (I, II or III of PLONA-RL400 radial precipitation profile. For the three profile shapes, it was observed that towpath spacings shorter than 50% of the sprinkler wetted diameter and on the range between 80 and 90% of the sprinkler wetted diameter provide higher
Toward a common theory for learning from reward, affect, and motivation: the SIMON framework
Madan, Christopher R.
2013-01-01
While the effects of reward, affect, and motivation on learning have each developed into their own fields of research, they largely have been investigated in isolation. As all three of these constructs are highly related, and use similar experimental procedures, an important advance in research would be to consider the interplay between these constructs. Here we first define each of the three constructs, and then discuss how they may influence each other within a common framework. Finally, we...
KAHRAMAN, Züleyha
1991-01-01
Bu araştırmada, Akkeçi oğlaklarında doğum ve sütten kesim ağırlıkları üzerine ana yaşı, cinsiyet, doğum şekli, ananın vücut ağırlığı ve bunlara ek ola rak oğlakların doğumdaki ağırlıklarının sütten kesim ağırlığına etkileri incelenmiştir. Araştırmanın ma teryalini Ankara Üniversitesi Ziraat Fakültesi Zootekni Bölümü' nde yetiştirilen çeşitli yaştaki Akkeçiler ve bunlardan elde edilen oğlaklar oluşturmuştur. Yapılan önem kontrolleri sonucunda; oğlakların doğum ağ...
ADAPTIVE SELECTION OF AUXILIARY OBJECTIVES IN MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS
Directory of Open Access Journals (Sweden)
I. A. Petrova
2016-05-01
Full Text Available Subject of Research.We propose to modify the EA+RL method, which increases efficiency of evolutionary algorithms by means of auxiliary objectives. The proposed modification is compared to the existing objective selection methods on the example of travelling salesman problem. Method. In the EA+RL method a reinforcement learning algorithm is used to select an objective – the target objective or one of the auxiliary objectives – at each iteration of the single-objective evolutionary algorithm.The proposed modification of the EA+RL method adopts this approach for the usage with a multiobjective evolutionary algorithm. As opposed to theEA+RL method, in this modification one of the auxiliary objectives is selected by reinforcement learning and optimized together with the target objective at each step of the multiobjective evolutionary algorithm. Main Results.The proposed modification of the EA+RL method was compared to the existing objective selection methods on the example of travelling salesman problem. In the EA+RL method and its proposed modification reinforcement learning algorithms for stationary and non-stationary environment were used. The proposed modification of the EA+RL method applied with reinforcement learning for non-stationary environment outperformed the considered objective selection algorithms on the most problem instances. Practical Significance. The proposed approach increases efficiency of evolutionary algorithms, which may be used for solving discrete NP-hard optimization problems. They are, in particular, combinatorial path search problems and scheduling problems.
Readiness of Adults to Learn Using E-Learning, M-Learning and T-Learning Technologies
Vilkonis, Rytis; Bakanoviene, Tatjana; Turskiene, Sigita
2013-01-01
The article presents results of the empirical research revealing readiness of adults to participate in the lifelong learning process using e-learning, m-learning and t-learning technologies. The research has been carried out in the framework of the international project eBig3 aiming at development a new distance learning platform blending virtual…
Cario, Clinton L; Witte, John S
2018-03-15
As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. JWitte@ucsf.edu. Supplementary data are available at Bioinformatics online.
Comprehensive Framework for Evaluating e-Learning Systems: Using BSC Framework
Momeni, Mansor; Jamporazmey, Mona; Mehrafrouz, Mohsen; Bahadori, Fatemeh
2013-01-01
The development of information and communication technology (ICT) is changing the way in which people work, communicate and learn. Recently developing and implementing e-learning solutions have increased dramatically. According to heavily investing in this area, it is essential to evaluate its different aspects and understand measures, which…
'Learning Organizations': a clinician's primer.
O'Connor, Nick; Kotze, Beth
2008-06-01
Most clinicians are poorly informed in relation to the key concepts of organizational learning. Yet the paradigm may offer clinicians a powerful method for using their knowledge and skills to respond to the demands of a changing environment through experimentation and learning. The concept is critically examined. Organizational learning principles are presented, including a conceptual framework for assessing health services as Learning Organizations. Barriers to organizational learning and strategies to overcome these are discussed. The seminal works of Argyris and Senge are reviewed and a framework for assessing organizational learning in health services is proposed. Current area health service actions are evaluated against the 'diagnostic' framework for a Learning Organization. Although critical examination reveals a poor empirical basis for the concept, the metaphor of the Learning Organization provides a useful conceptual framework and tools for individuals and organizations to apply in developing knowledge and effecting change. The Clinical Practice Improvement and Root Cause Analysis programs being conducted across NSW area health services meet the criteria for effective organizational learning. Key concepts from organizational learning theory provide a diagnostic framework for evaluating area health services as Learning Organizations and support two current strategies for overcoming barriers to organizational learning.
Guasch, Teresa; Alvarez, Ibis; Espasa, Anna
This chapter is aimed at presenting an integrated framework of the educational information and communications technology (ICT) competencies that university teachers should have to teach in an online learning environment. Teaching through ICT in higher education involves performing three main roles - pedagogical, socialist, and design/planning - and also two cross-cutting domains that arise from the online environment: technological and managerial. This framework as well as the competencies for university teachers associated with it were validated at a European level by a dual process of net-based focus groups of teachers and teacher trainers in each of the participating countries in a European Project (Elene-TLC) and an online Delphi method involving 78 experts from 14 universities of ten European countries. The competency framework and the examples provided in the chapter are the basis for designing innovative professional development activities in online university environments.
International Nuclear Information System (INIS)
Edens, V.G.
1998-05-01
This document is a unit-specific contingency plan for the 105-DR Large Sodium Fire Facility and is intended to be used as a supplement to DOE/RL-93-75, Hanford Facility Contingency Plan (DOE-RL 1993). This unit-specific plan is to be used to demonstrate compliance with the contingency plan requirements of Washington Administrative Code (WAC) 173-303 for certain Resource Conservation and Recovery Act of 1976 (RCRA) waste management units.The LSFF occupied the former ventilation supply fan room and was established to provide a means of investigating fire and safety aspects associated with large sodium or other metal alkali fires. The unit was used to conduct experiments for studying the behavior of molten alkali metals and alkali metal fires. This unit had also been used for the storage and treatment of alkali metal dangerous waste. Additionally, the Fusion Safety Support Studies programs sponsored intermediate-size safety reaction tests in the LSFF with lithium and lithium-lead compounds. The LSFF, which is a RCRA site, was partially clean closed in 1995 and is documented in 'Transfer of the 105-DR Large Sodium Fire Facility to Bechtel Hanford, Inc.' (BHI 1998). In summary, the 105-DR supply fan room (1720-DR) has been demolished, and a majority of the surrounding soils were clean-closed. The 117-DR Filter Building, 116-DR Exhaust Stack, 119- DR Sampling Building, and associated ducting/tunnels were not covered under this closure
Efficient collective swimming by harnessing vortices through deep reinforcement learning.
Verma, Siddhartha; Novati, Guido; Koumoutsakos, Petros
2018-06-05
Fish in schooling formations navigate complex flow fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behavior has been associated with evolutionary advantages including energy savings, yet the underlying physical mechanisms remain unknown. We show that fish can improve their sustained propulsive efficiency by placing themselves in appropriate locations in the wake of other swimmers and intercepting judiciously their shed vortices. This swimming strategy leads to collective energy savings and is revealed through a combination of high-fidelity flow simulations with a deep reinforcement learning (RL) algorithm. The RL algorithm relies on a policy defined by deep, recurrent neural nets, with long-short-term memory cells, that are essential for capturing the unsteadiness of the two-way interactions between the fish and the vortical flow field. Surprisingly, we find that swimming in-line with a leader is not associated with energetic benefits for the follower. Instead, "smart swimmer(s)" place themselves at off-center positions, with respect to the axis of the leader(s) and deform their body to synchronize with the momentum of the oncoming vortices, thus enhancing their swimming efficiency at no cost to the leader(s). The results confirm that fish may harvest energy deposited in vortices and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep RL can produce navigation algorithms for complex unsteady and vortical flow fields, with promising implications for energy savings in autonomous robotic swarms.
Grove, Nathaniel P.; Bretz, Stacey Lowery
2010-01-01
We have investigated student difficulties with the learning of organic chemistry. Using Perry's Model of Intellectual Development as a framework revealed that organic chemistry students who function as dualistic thinkers struggle with the complexity of the subject matter. Understanding substitution/elimination reactions and multi-step syntheses is…
Directory of Open Access Journals (Sweden)
Liz eFranz
2012-12-01
Full Text Available The present paper builds on the idea that attention is largely in service of our actions. A framework and model which captures the allocation of attention for learning of goal-directed actions is proposed and developed. This framework highlights an evolutionary model based on the notion that rudimentary brain functions have become embedded into increasingly higher levels of networks which all contribute to adaptive learning. Background literature is presented alongside key evidence based on experimental studies in the so-called ‘split-brain’ (surgically divided cerebral hemispheres with a key focus on bimanual actions. The proposed multilevel cognitive-neural system of attention is built upon key processes of a highly-adaptive basal-ganglia-thalamic-cortical system. Although overlap with other existing findings and models is acknowledged where appropriate, the proposed framework is an original synthesis of cognitive experimental findings with supporting evidence of a neural system and a carefully formulated model of attention. It is the hope that this new synthesis will be informative in fields of cognition and other fields of brain sciences and will lead to new avenues for experimentation across domains.
Directory of Open Access Journals (Sweden)
Ilona Buchem
2015-05-01
Full Text Available Physical activity is one of the key factors of ageing healthy and at the same time one of the key motivational challenges for the elderly. Supporting healthy ageing through physical fitness requires interventions that promote healthy levels of physical activity as part of the daily routine. Although wearable devices, such as activity trackers or smart wristbands, have been used by younger adopters to optimize physical fitness, little is known so far about how such emerging technologies may be used to improve well-being and overall health of senior users. In this paper we present the conceptual framework and the architecture of wearable-technology enhanced learning for healthy ageing as part of an R&D project called “Fitness MOOC - interaction of seniors with wearable fitness trackers in the fitness MOOC (fMOOC”, founded by the German Federal Ministry of Education and Research (BMBF. The fMOOC project is a cooperation between Beuth University of Applied Sciences Berlin and the Geriatrics Research Group, Charité - one of the largest medical universities in Europe. The project aims at developing a wearable-technology enhanced learning solution combining the MOOC (Massive Open Online Course approach with embodied and creative learning experience with support of activity trackers. fMOOC integrates an LMS backend with wearable fitness trackers, mobile user interface, gamification and analytics to promote healthy ageing through learning and interacting with senior users.
Code-first development with Entity Framework
Barskiy, Sergey
2015-01-01
This book is intended for software developers with some prior experience with the Microsoft .NET framework who want to learn how to use Entity Framework. This book will get you up and running quickly, providing many examples that illustrate all the key concepts of Entity Framework.
Cognitive Radio Transceivers: RF, Spectrum Sensing, and Learning Algorithms Review
Directory of Open Access Journals (Sweden)
Lise Safatly
2014-01-01
reconfigurable radio frequency (RF parts, enhanced spectrum sensing algorithms, and sophisticated machine learning techniques. In this paper, we present a review of the recent advances in CR transceivers hardware design and algorithms. For the RF part, three types of antennas are presented: UWB antennas, frequency-reconfigurable/tunable antennas, and UWB antennas with reconfigurable band notches. The main challenges faced by the design of the other RF blocks are also discussed. Sophisticated spectrum sensing algorithms that overcome main sensing challenges such as model uncertainty, hardware impairments, and wideband sensing are highlighted. The cognitive engine features are discussed. Moreover, we study unsupervised classification algorithms and a reinforcement learning (RL algorithm that has been proposed to perform decision-making in CR networks.
Working Memory Load Strengthens Reward Prediction Errors.
Collins, Anne G E; Ciullo, Brittany; Frank, Michael J; Badre, David
2017-04-19
Reinforcement learning (RL) in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors (RPEs) are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we investigated how working memory (WM) and incremental RL processes interact to guide human learning. WM load was manipulated by varying the number of stimuli to be learned across blocks. Behavioral results and computational modeling confirmed that learning was best explained as a mixture of two mechanisms: a fast, capacity-limited, and delay-sensitive WM process together with slower RL. Model-based analysis of fMRI data showed that striatum and lateral prefrontal cortex were sensitive to RPE, as shown previously, but, critically, these signals were reduced when the learning problem was within capacity of WM. The degree of this neural interaction related to individual differences in the use of WM to guide behavioral learning. These results indicate that the two systems do not process information independently, but rather interact during learning. SIGNIFICANCE STATEMENT Reinforcement learning (RL) theory has been remarkably productive at improving our understanding of instrumental learning as well as dopaminergic and striatal network function across many mammalian species. However, this neural network is only one contributor to human learning and other mechanisms such as prefrontal cortex working memory also play a key role. Our results also show that these other players interact with the dopaminergic RL system, interfering with its key computation of reward prediction errors. Copyright © 2017 the authors 0270-6474/17/374332-11$15.00/0.
Myers, Catherine E.; Smith, Ian M.; Servatius, Richard J.; Beck, Kevin D.
2014-01-01
Avoidance behaviors, in which a learned response causes omission of an upcoming punisher, are a core feature of many psychiatric disorders. While reinforcement learning (RL) models have been widely used to study the development of appetitive behaviors, less attention has been paid to avoidance. Here, we present a RL model of lever-press avoidance learning in Sprague-Dawley (SD) rats and in the inbred Wistar Kyoto (WKY) rat, which has been proposed as a model of anxiety vulnerability. We focus on “warm-up,” transiently decreased avoidance responding at the start of a testing session, which is shown by SD but not WKY rats. We first show that a RL model can correctly simulate key aspects of acquisition, extinction, and warm-up in SD rats; we then show that WKY behavior can be simulated by altering three model parameters, which respectively govern the tendency to explore new behaviors vs. exploit previously reinforced ones, the tendency to repeat previous behaviors regardless of reinforcement, and the learning rate for predicting future outcomes. This suggests that several, dissociable mechanisms may contribute independently to strain differences in behavior. The model predicts that, if the “standard” inter-session interval is shortened from 48 to 24 h, SD rats (but not WKY) will continue to show warm-up; we confirm this prediction in an empirical study with SD and WKY rats. The model further predicts that SD rats will continue to show warm-up with inter-session intervals as short as a few minutes, while WKY rats will not show warm-up, even with inter-session intervals as long as a month. Together, the modeling and empirical data indicate that strain differences in warm-up are qualitative rather than just the result of differential sensitivity to task variables. Understanding the mechanisms that govern expression of warm-up behavior in avoidance may lead to better understanding of pathological avoidance, and potential pathways to modify these processes. PMID
A reward optimization method based on action subrewards in hierarchical reinforcement learning.
Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming
2014-01-01
Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.
Design of Mobile Augmented Reality in Health Care Education: A Theory-Driven Framework.
Zhu, Egui; Lilienthal, Anneliese; Shluzas, Lauren Aquino; Masiello, Italo; Zary, Nabil
2015-09-18
Augmented reality (AR) is increasingly used across a range of subject areas in health care education as health care settings partner to bridge the gap between knowledge and practice. As the first contact with patients, general practitioners (GPs) are important in the battle against a global health threat, the spread of antibiotic resistance. AR has potential as a practical tool for GPs to combine learning and practice in the rational use of antibiotics. This paper was driven by learning theory to develop a mobile augmented reality education (MARE) design framework. The primary goal of the framework is to guide the development of AR educational apps. This study focuses on (1) identifying suitable learning theories for guiding the design of AR education apps, (2) integrating learning outcomes and learning theories to support health care education through AR, and (3) applying the design framework in the context of improving GPs' rational use of antibiotics. The design framework was first constructed with the conceptual framework analysis method. Data were collected from multidisciplinary publications and reference materials and were analyzed with directed content analysis to identify key concepts and their relationships. Then the design framework was applied to a health care educational challenge. The proposed MARE framework consists of three hierarchical layers: the foundation, function, and outcome layers. Three learning theories-situated, experiential, and transformative learning-provide foundational support based on differing views of the relationships among learning, practice, and the environment. The function layer depends upon the learners' personal paradigms and indicates how health care learning could be achieved with MARE. The outcome layer analyzes different learning abilities, from knowledge to the practice level, to clarify learning objectives and expectations and to avoid teaching pitched at the wrong level. Suggestions for learning activities and the
A pedagogical design pattern framework
DEFF Research Database (Denmark)
May, Michael; Neutzsky-Wulff, Anne Chresteria; Rosthøj, Susanne
2016-01-01
”Design patterns” were originally proposed in architecture and later in software engineering as a methodology to sketch and share solutions to recurring design problems. In recent years ”pedagogical design patterns” have been introduced as a way to sketch and share good practices in teaching...... framework is applied to describe the learning design in four online and blended learning courses within different academic disciplines: Classical Greek, Biostatistics, Environmental Management in Europe, and Climate Change Impacts, Adaptation and Mitigation. Future perspectives for using the framework...
Dynamic Sensor Tasking for Space Situational Awareness via Reinforcement Learning
Linares, R.; Furfaro, R.
2016-09-01
This paper studies the Sensor Management (SM) problem for optical Space Object (SO) tracking. The tasking problem is formulated as a Markov Decision Process (MDP) and solved using Reinforcement Learning (RL). The RL problem is solved using the actor-critic policy gradient approach. The actor provides a policy which is random over actions and given by a parametric probability density function (pdf). The critic evaluates the policy by calculating the estimated total reward or the value function for the problem. The parameters of the policy action pdf are optimized using gradients with respect to the reward function. Both the critic and the actor are modeled using deep neural networks (multi-layer neural networks). The policy neural network takes the current state as input and outputs probabilities for each possible action. This policy is random, and can be evaluated by sampling random actions using the probabilities determined by the policy neural network's outputs. The critic approximates the total reward using a neural network. The estimated total reward is used to approximate the gradient of the policy network with respect to the network parameters. This approach is used to find the non-myopic optimal policy for tasking optical sensors to estimate SO orbits. The reward function is based on reducing the uncertainty for the overall catalog to below a user specified uncertainty threshold. This work uses a 30 km total position error for the uncertainty threshold. This work provides the RL method with a negative reward as long as any SO has a total position error above the uncertainty threshold. This penalizes policies that take longer to achieve the desired accuracy. A positive reward is provided when all SOs are below the catalog uncertainty threshold. An optimal policy is sought that takes actions to achieve the desired catalog uncertainty in minimum time. This work trains the policy in simulation by letting it task a single sensor to "learn" from its performance
Directory of Open Access Journals (Sweden)
Ali İlhan
2015-12-01
Full Text Available Bu çalışmada Türkiye‟nin 7 akarsu havzasından toplanmış olan Horozbina Balığı (Salaria fluviatilis‟na ait boy-ağırlık ilişkisinin ortaya çıkarılması amaçlanmıştır. Marmara, Küçük Menderes, Batı Karadeniz, Antalya, Doğu Akdeniz, Seyhan ve Ceyhan havzalarına ait akarsulardan toplanmış olan 652 birey incelenmiştir. Tüm bireyler dikkate alındığında türün Türkiye içsularındaki total boy dağılımı 2.0-12.9 cm, total ağırlık dağılımı 0.10-33.82 g, boy-ağırlık ilişkisi parametreleri a= 0.0135, b= 3.004, r2= 0.986 olarak hesaplanmıştır. Ayrıca, büyüme tipi havzaların 5‟inde izometrik, 1 havzada pozitif allometrik ve 1 havzada da negatif allometrik olarak belirlenmiştir
Shirley, S; Stampfl, R
1997-12-01
The purpose of this explanatory and prescriptive article is to identify interdisciplinary theories used by hospital development to direct its practice. The article explores, explains, and applies theories and principles from behavioral, social, and managerial disciplines. Learning, motivational, organizational, marketing, and attitudinal theories are incorporated and transformed into the fundamental components of a conceptual framework that provides an overview of the practice of hospital development. How this discipline incorporates these theories to design, explain, and prescribe the focus of its own practice is demonstrated. This interdisciplinary approach results in a framework for practice that is adaptable to changing social, cultural, economic, political, and technological environments.
Learning Resources Organization Using Ontological Framework
Gavrilova, Tatiana; Gorovoy, Vladimir; Petrashen, Elena
The paper describes the ontological approach to the knowledge structuring for the e-learning portal design as it turns out to be efficient and relevant to current domain conditions. It is primarily based on the visual ontology-based description of the content of the learning materials and this helps to provide productive and personalized access to these materials. The experience of ontology developing for Knowledge Engineering coursetersburg State University is discussed and “OntolingeWiki” tool for creating ontology-based e-learning portals is described.
Design of Mobile Augmented Reality in Health Care Education: A Theory-Driven Framework
Lilienthal, Anneliese; Shluzas, Lauren Aquino; Masiello, Italo; Zary, Nabil
2015-01-01
Background Augmented reality (AR) is increasingly used across a range of subject areas in health care education as health care settings partner to bridge the gap between knowledge and practice. As the first contact with patients, general practitioners (GPs) are important in the battle against a global health threat, the spread of antibiotic resistance. AR has potential as a practical tool for GPs to combine learning and practice in the rational use of antibiotics. Objective This paper was driven by learning theory to develop a mobile augmented reality education (MARE) design framework. The primary goal of the framework is to guide the development of AR educational apps. This study focuses on (1) identifying suitable learning theories for guiding the design of AR education apps, (2) integrating learning outcomes and learning theories to support health care education through AR, and (3) applying the design framework in the context of improving GPs’ rational use of antibiotics. Methods The design framework was first constructed with the conceptual framework analysis method. Data were collected from multidisciplinary publications and reference materials and were analyzed with directed content analysis to identify key concepts and their relationships. Then the design framework was applied to a health care educational challenge. Results The proposed MARE framework consists of three hierarchical layers: the foundation, function, and outcome layers. Three learning theories—situated, experiential, and transformative learning—provide foundational support based on differing views of the relationships among learning, practice, and the environment. The function layer depends upon the learners’ personal paradigms and indicates how health care learning could be achieved with MARE. The outcome layer analyzes different learning abilities, from knowledge to the practice level, to clarify learning objectives and expectations and to avoid teaching pitched at the wrong level
Kim, Dongho; Lim, Cheolil
2018-01-01
Despite the emergence of collaborative project-based learning in higher education settings, how it can be supported has received little attention. We noted the positive impact of socially shared metacognitive regulation on students' collaboration processes. The purpose of this study was to present a framework for the design and implementation of…
Remote Laboratories Framework : Focus on Reusability and Security in m-Learning Situations
Directory of Open Access Journals (Sweden)
Jeremy Lardon
2009-08-01
Full Text Available Remote laboratories is a spreading concept which allows the remote use of devices through Internet connexion. The paper deals with the providing of a framework which is reusable for many devices, from different end-user media such as phone, computer or TV and acceptable in industry, therefore taking into account multi information systems securities. The problem is addressed through the point of view of m-learning situations which involves the lack of rich user interactions and the fact that the user belongs to external information systems when he interacts with the remote device. The modelisation of the remote device with ontologies, the use of a central application server, message oriented middleware and standard web services (database, authentication are the keys allowing the independence of the framework to the device. The adaptation of the GUI to the end-user device is made through a proxy which refactor the requests and responses according to the capabilities of the end-user device (size of screen, interactions tools. The use of a user-centric model of identities federation allows us to provide an efficient way to reach the goal of transparency to security constraints.
Hautz, Stefanie C; Hautz, Wolf E; Keller, Niklas; Feufel, Markus A; Spies, Claudia
2015-01-01
In Germany, a national competence based catalogue of learning objectives in medicine (NKLM) was developed by the Society for Medical Education and the Council of Medical Faculties. As many of its international counterparts the NKLM describes the qualifications of medical school graduates. The definition of such outcome frameworks indents to make medical education transparent to students, teachers and society. The NKLM aims to amend existing lists of medical topics for assessment with learnable competencies. All outcome frameworks are structured into chapters, domains or physician roles. The definition of the scholar-role poses a number of questions such as: What distinguishes necessary qualifications of a scientifically qualified physician from those of a medical scientist? 13 outcome frameworks were identified through a systematic three-step literature review and their content compared to the scholar role in the NKLM by means of a qualitative text analysis. The three steps consist of (1) search for outcome frameworks, (2) in- and exclusion, and (3) data extraction, categorization, and validation. The results were afterwards matched with the scholar role of the NKLM. Extracted contents of all frameworks may be summarized into the components Common Basics, Clinical Application, Research, Teaching and Education, and Lifelong Learning. Compared to the included frameworks the NKLM emphasises competencies necessary for research and teaching while clinical application is less prominently mentioned. The scholar role of the NKLM differs from other international outcome frameworks. Discussing these results shall increase propagation and understanding of the NKLM and thus contribute to the qualification of future medical graduates in Germany.
Entity Framework 4.0 Recipes A Problem-solution Approach
Tenny, L
2010-01-01
Entity Framework 4.0 Recipes provides an exhaustive collection of ready-to-use code solutions for Microsoft's Entity Framework, Microsoft's vision for the future of data access. Entity Framework is a model-centric data access platform with an ocean of new concepts and patterns for developers to learn. With this book, you will learn the core concepts of Entity Framework through a broad range of clear and concise solutions to everyday data access tasks. Armed with this experience, you will be ready to dive deep into Entity Framework, experiment with new approaches, and develop ways to solve even
Jedaman, Pornchai; Buaraphan, Khajornsak; Pimdee, Paitoon; Yuenyong, Chokchai; Sukkamart, Aukkapong; Suksup, Charoen
2018-01-01
This article aims to study and analyze the 21st Century of sustainable leadership under the education THAILAND 4.0 Framework, and factor analysis of sustainable leadership for science learning. The study employed both quantitative and qualitative approaches in collecting data including a questionnaire survey, a documentary review and a Participatory Action Learning (PAL). The sample were sampling purposively. There were 225 administrators of Primary and Secondary Education Area Offices throughout Thailand. Out of 225, 183 (83.33%) and 42 (16.67%) respondents were the administrators of Primary and Secondary Education Offices, respectively. The quantitative data was analyzed by descriptive statistical analysis including mean, standard deviation. Also, the Confirmatory Factor Analysis (CFA) was conducted to analyze the factors associated with sustainable leadership under the education THAILAND 4.0 Framework. The qualitative data was analyzed by using three main stages, i.e., data reduction, data organization, data interpretation to conclusion. The study revealed that sustainable leadership under the education THAILAND 4.0 Framework needs to focus on development, awareness of duty and responsibility, equality, moral and knowledge. All aspects should be integrated together in order to achieve the organizational goals, good governance culture and identity. Importantly, there were six "key" elements of sustainable leadership under the education THAILAND 4.0 framework: i) Professional Leadership Role, ii) Leadership Under Change, iii) Leadership Skills 4.0 in the 21st Century, iv) Development in the Pace With Change, v) Creativity and Creative Tension, and vi) Hold True Assessments. The CFA showed that the six key elements of sustainable leadership under the education THAILAND 4.0 framework by weight of each elements were significant at the .01 significance level.
DEFF Research Database (Denmark)
Agarwal, Vernika; Govindan, Kannan; Darbari, Jyoti Dhingra
2016-01-01
Enforced Legislations, social image, corporate citizenship and market competence are forcing manufacturing enterprises (MEs) to incorporate reverse logistics (RL) into their supply chains. RL can be used as a strategic tool to gain customer loyalty and reduce operational costs by maximizing...
International Nuclear Information System (INIS)
Istomin, Yu.P.; Furmanchuk, A.V.
1988-01-01
In the experiments on the C57B1 mice the authors studied the effect of artificial hyperglycemia (AH), amputation of the extremities with tumors as well as combinations of these effects on the intensity of metastatic spreading of carcinoma RL-67 to the lungs. AH did not prove to intensify the process of metastatic spreading if it was conducted on the 1, 3, 5, 7, 9 and 11th days. The average number of metastases did not differ from that in the control group. AH which was conducted one day before amputation of the extremety with the tumor caused a more significant inhibition of metastatic spreading than a surgical intervention
Reinforcement learning for dpm of embedded visual sensor nodes
International Nuclear Information System (INIS)
Khani, U.; Sadhayo, I. H.
2014-01-01
This paper proposes a RL (Reinforcement Learning) based DPM (Dynamic Power Management) technique to learn time out policies during a visual sensor node's operation which has multiple power/performance states. As opposed to the widely used static time out policies, our proposed DPM policy which is also referred to as OLTP (Online Learning of Time out Policies), learns to dynamically change the time out decisions in the different node states including the non-operational states. The selection of time out values in different power/performance states of a visual sensing platform is based on the workload estimates derived from a ML-ANN (Multi-Layer Artificial Neural Network) and an objective function given by weighted performance and power parameters. The DPM approach is also able to dynamically adjust the power-performance weights online to satisfy a given constraint of either power consumption or performance. Results show that the proposed learning algorithm explores the power-performance tradeoff with non-stationary workload and outperforms other DPM policies. It also performs the online adjustment of the tradeoff parameters in order to meet a user-specified constraint. (author)
Directory of Open Access Journals (Sweden)
Alex Smajgl
2015-06-01
Full Text Available Policy and investment decisions in highly connected, developing regions can have implications that extend beyond their initial objectives of national development and poverty reduction. Local level decisions that aim to promote trajectories toward desirable futures are often transformative, unexpectedly altering factors that are determined at higher regional levels. The converse also applies. The ability to realize desirable local futures diminishes if decision-making processes are not coordinated with other influential governance and decision levels. Providing effective support across multiple levels of decision making in a connected, transformative environment requires (a identification and articulation of desired outcomes at the relevant levels of decision making, (b improved understanding of complex cross-scale interactions that link to potentially transforming decisions, and (c learning among decision makers and decision influencers. Research implemented through multiple participatory modalities can facilitate such relevant system learning to contribute to sustainable adaptation pathways. We test application of a systematic policy engagement framework, the Challenge and Reconstruct Learning or ChaRL framework, on a set of interdependent development decisions in the Mekong region. The analysis presented here is focused on the implementations of the ChaRL process in the Nam Ngum River Basin, Lao People's Democratic Republic and the Tonle Sap Lake and environs, Cambodia to exemplify what cross-scale and cross-sectoral insights were generated to inform decision-making processes in the wider Mekong region. The participatory process described aligns the facilitated development of scenarios articulating shared future visions at local and regional levels with agent-based simulations and facilitates learning by contrasting desired outcomes with likely, potentially maladaptive outcomes.
Tamura, Wataru; Ebitani, Takeshi; Yano, Masahiro; Sato, Tadashi; Yamaya, Tomoyuki
2010-01-01
Root system development is an important target for improving yield in cereal crops. Active root systems that can take up nutrients more efficiently are essential for enhancing grain yield. In this study, we attempted to identify quantitative trait loci (QTL) involved in root system development by measuring root length of rice seedlings grown in hydroponic culture. Reliable growth conditions for estimating the root length were first established to renew nutrient solutions daily and supply NH4+ as a single nitrogen source. Thirty-eight chromosome segment substitution lines derived from a cross between ‘Koshihikari’, a japonica variety, and ‘Kasalath’, an indica variety, were used to detect QTL for seminal root length of seedlings grown in 5 or 500 μM NH4+. Eight chromosomal regions were found to be involved in root elongation. Among them, the most effective QTL was detected on a ‘Kasalath’ segment of SL-218, which was localized to the long-arm of chromosome 6. The ‘Kasalath’ allele at this QTL, qRL6.1, greatly promoted root elongation under all NH4+ concentrations tested. The genetic effect of this QTL was confirmed by analysis of the near-isogenic line (NIL) qRL6.1. The seminal root length of the NIL was 13.5–21.1% longer than that of ‘Koshihikari’ under different NH4+ concentrations. Toward our goal of applying qRL6.1 in a molecular breeding program to enhance rice yield, a candidate genomic region of qRL6.1 was delimited within a 337 kb region in the ‘Nipponbare’ genome by means of progeny testing of F2 plants/F3 lines derived from a cross between SL-218 and ‘Koshihikari’. Electronic supplementary material The online version of this article (doi:10.1007/s00122-010-1328-3) contains supplementary material, which is available to authorized users. PMID:20390245
E-Learning dengan Menggunakan COI Framework
Directory of Open Access Journals (Sweden)
Lydiawati Kosasih
2013-12-01
Full Text Available This study discusses some considerations in education to achieve a good quality of learning by utilizing technological advances such as E-Learning. This study uses a model of Community of Inquiry (COI as a comparative study to improve the quality of E-Learning program. Implementation of COI model in discussionforum on BiNusMaya through E-Learning is able to improve the quality of a discussion as improvement of knowledge management. This study aims to provide a proposal to the Department of Information Systems Bina Nusantara University in enhancing the effectiveness of the use of discussion forums on BiNusMaya (ELearning. By presenting the survey results related to the Binusmaya current condition,s such constraints and development expectations of both the lecturers and students for Binusmaya can be described. In addition, theapplication of CoI model is presented in a learning process especially when meeting outside of class (without face-to-face. The results of this study is expected to be the basis for developing a COI model design and implementation plan in Management Information Systems course, that may improve the quality of the use of discussion forums as part of the knowledge management process in future study.
Schuller, Tom
2008-01-01
The main goal of the Inquiry into the Future for Lifelong Learning is to provide a "strategic framework" for the future. In this article, the author considers the key components that will make up the framework. These are: (1) a statement of vision and values; (2) a stock-take of the current position; (3) an "investment…
Ma, Jianzhu; Wang, Sheng
2015-01-01
The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields) model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.
Multi-Agent Framework for Virtual Learning Spaces.
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…
Directory of Open Access Journals (Sweden)
Tel'noy Viktor Ivanovich
2012-10-01
Full Text Available Development of computer-assisted computer technologies and their integration into the academic activity with a view to the control of the academic performance within the framework of distance learning programmes represent the subject matter of the article. The article is a brief overview of the software programme designated for the monitoring of the academic performance of students enrolled in distance learning programmes. The software is developed on Delphi 7.0 for Windows operating system. The strength of the proposed software consists in the availability of the two modes of its operation that differ in the principle of the problem selection and timing parameters. Interim academic performance assessment is to be performed through the employment of computerized testing procedures that contemplate the use of a data base of testing assignments implemented in the eLearning Server media. Identification of students is to be performed through the installation of video cameras at workplaces of students.
Lowe, Rob; Norman, Paul
2017-03-01
The common-sense model (Leventhal, Meyer, & Nerenz, 1980) outlines how illness representations are important for understanding adjustment to health threats. However, psychological processes giving rise to these representations are little understood. To address this, an associative-learning framework was used to model low-level process mechanics of illness representation and coping-related decision making. Associative learning was modeled within a connectionist network simulation. Two types of information were paired: Illness identities (indigestion, heart attack, cancer) were paired with illness-belief profiles (cause, timeline, consequences, control/cure), and specific illness beliefs were paired with coping procedures (family doctor, emergency services, self-treatment). To emulate past experience, the network was trained with these pairings. As an analogue of a current illness event, the trained network was exposed to partial information (illness identity or select representation beliefs) and its response recorded. The network (a) produced the appropriate representation profile (beliefs) for a given illness identity, (b) prioritized expected coping procedures, and (c) highlighted circumstances in which activated representation profiles could include self-generated or counterfactual beliefs. Encoding and activation of illness beliefs can occur spontaneously and automatically; conventional questionnaire measurement may be insensitive to these automatic representations. Furthermore, illness representations may comprise a coherent set of nonindependent beliefs (a schema) rather than a collective of independent beliefs. Incoming information may generate a "tipping point," dramatically changing the active schema as a new illness-knowledge set is invoked. Finally, automatic activation of well-learned information can lead to the erroneous interpretation of illness events, with implications for [inappropriate] coping efforts. (PsycINFO Database Record (c) 2017 APA, all
Directory of Open Access Journals (Sweden)
Qing Ye
2015-01-01
Full Text Available This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.
Chmiel, Aviva S; Shaha, Maya; Schneider, Daniel K
2017-01-01
The aim of this research is to develop a comprehensive evaluation framework involving all actors in a higher education blended learning (BL) program. BL evaluation usually either focuses on students, faculty, technological or institutional aspects. Currently, no validated comprehensive monitoring tool exists that can support introduction and further implementation of BL in a higher education context. Starting from established evaluation principles and standards, concepts that were to be evaluated were firstly identified and grouped. In a second step, related BL evaluation tools referring to students, faculty and institutional level were selected. This allowed setting up and implementing an evaluation framework to monitor the introduction of BL during two succeeding recurrences of the program. The results of the evaluation allowed documenting strengths and weaknesses of the BL format in a comprehensive way, involving all actors. It has led to improvements at program, faculty and course level. The evaluation process and the reporting of the results proved to be demanding in time and personal resources. The evaluation framework allows measuring the most significant dimensions influencing the success of a BL implementation at program level. However, this comprehensive evaluation is resource intensive. Further steps will be to refine the framework towards a sustainable and transferable BL monitoring tool that finds a balance between comprehensiveness and efficiency. Copyright © 2016 Elsevier Ltd. All rights reserved.
Evaluation Framework for Dependable Mobile Learning Scenarios
Bensassi, Manel; Laroussi, Mona
2014-01-01
The goal of the dependability analysis is to predict inconsistencies and to reveal ambiguities and incompleteness in the designed learning scenario. Evaluation, in traditional learning design, is generally planned after the execution of the scenario. In mobile learning, this stage becomes too difficult and expensive to apply due to the complexity…
Framework for Designing Context-Aware Learning Systems
Tortorella, Richard A. W.; Kinshuk; Chen, Nian-Shing
2018-01-01
Today people learn in many diverse locations and contexts, beyond the confines of classical brick and mortar classrooms. This trend is ever increasing, progressing hand-in-hand with the progress of technology. Context-aware learning systems are systems which adapt to the learner's context, providing tailored learning for a particular learning…
Zendehrouh, Sareh
2015-11-01
Recent work on decision-making field offers an account of dual-system theory for decision-making process. This theory holds that this process is conducted by two main controllers: a goal-directed system and a habitual system. In the reinforcement learning (RL) domain, the habitual behaviors are connected with model-free methods, in which appropriate actions are learned through trial-and-error experiences. However, goal-directed behaviors are associated with model-based methods of RL, in which actions are selected using a model of the environment. Studies on cognitive control also suggest that during processes like decision-making, some cortical and subcortical structures work in concert to monitor the consequences of decisions and to adjust control according to current task demands. Here a computational model is presented based on dual system theory and cognitive control perspective of decision-making. The proposed model is used to simulate human performance on a variant of probabilistic learning task. The basic proposal is that the brain implements a dual controller, while an accompanying monitoring system detects some kinds of conflict including a hypothetical cost-conflict one. The simulation results address existing theories about two event-related potentials, namely error related negativity (ERN) and feedback related negativity (FRN), and explore the best account of them. Based on the results, some testable predictions are also presented. Copyright © 2015 Elsevier Ltd. All rights reserved.
Tree exploration for Bayesian RL exploration
Dimitrakakis, C.; Mohammadian, M.
2008-01-01
Research in reinforcement learning has produced algo-rithms for optimal decision making under uncertainty thatfall within two main types. The first employs a Bayesianframework, where optimality improves with increased com-putational time. This is because the resulting planning tasktakes the form of
Miller, Heather; Haller, Philipp; Odersky, Martin
2011-01-01
Implementing machine learning algorithms for large data, such as the Web graph and social networks, is challenging. Even though much research has focused on making sequential algorithms more scalable, their running times continue to be prohibitively long. Meanwhile, parallelization remains a formidable challenge for this class of problems, despite frameworks like MapReduce which hide much of the associated complexity. We present three ongoing efforts within our team, previously presented at v...
Common Mobile Learning Characteristics--An Analysis of Mobile Learning Models and Frameworks
Imtinan, Umera; Chang, Vanessa; Issa, Tomayess
2013-01-01
Mobile learning offers learning opportunities to learners without the limitations of time and space. Mobile learning has introduced a number of flexible options to the learners across disciplines and at different educational levels. However, designing mobile learning content is an equally challenging task for the instructional designers.…
Facilitation of social learning in teacher education: the ‘Dimensions of Social Learning Framework’
de Laat, M.M.; Vrieling, E.; van den Beemt, A.A.J.; McDonald, J.; Cater-Steel, A.
2017-01-01
To understand the organization of social learning by groups in practice, this chapter elaborates on the use of a framework of dimensions and indicators to explore social learning within (prospective) teacher groups. The applied framework that we call the ‘Dimensions of Social Learning (DSL)
Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak
2018-02-08
Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.
Path-finding in real and simulated rats
DEFF Research Database (Denmark)
Tamosiunaite, Minija; Ainge, James; Kulvicius, Tomas
2008-01-01
without affecting the path characteristic two additional mechanisms are implemented: a gradual drop of the learned weights (weight decay) and path length limitation, which prevents learning if the reward is not found after some expected time. Both mechanisms limit the memory of the system and thereby......A large body of experimental evidence suggests that the hippocampal place field system is involved in reward based navigation learning in rodents. Reinforcement learning (RL) mechanisms have been used to model this, associating the state space in an RL-algorithm to the place-field map in a rat...... convergence of RL-algorithms is also influenced by the state space characteristics, different PF-sizes and densities, leading to a different degree of overlap, were also investigated. The model rat learns finding a reward opposite to its starting point. We observed that the combination of biased straight...
Directory of Open Access Journals (Sweden)
K. Shahverdi
2016-02-01
Full Text Available Introduction: Nowadays considering water shortage and weak management in agricultural water sector and for optimal uses of water, irrigation networks performance need to be improveed. Recently, intelligent management of water conveyance and delivery, and better control technologies have been considered for improving the performance of irrigation networks and their operation. For this affair, providing of mathematical model of automatic control system and related structures, which connected with hydrodynamic models, is necessary. The main objective of this research, is development of mathematical model of RL upstream control algorithm inside ICSS hydrodynamic model as a subroutine. Materials and Methods: In the learning systems, a set of state-action rules called classifiers compete to control the system based on the system's receipt from the environment. One could be identified five main elements of the RL: an agent, an environment, a policy, a reward function, and a simulator. The learner (decision-maker is called the agent. The thing it interacts with, comprising everything outside the agent, is called the environment. The agent selects an action based on existing state in the environment. When the agent takes an action and performs on environment, the environment goes new state and reward is assigned based on it. The agent and the environment continually interact to maximize the reward. The policy is a set of state-action pair, which have higher rewards. It defines the agent's behavior and says which action must be taken in which state. The reward function defines the goal in a RL problem. The reward function defines what the good and bad events are for the agent. The higher the reward, the better the action. The simulator provides environment information. In irrigation canals, the agent is the check structures. The action and state are the check structures adjustment and the water depth, respectively. The environment comprises the hydraulic
Social Media and Seamless Learning: Lessons Learned
Panke, Stefanie; Kohls, Christian; Gaiser, Birgit
2017-01-01
The paper discusses best practice approaches and metrics for evaluation that support seamless learning with social media. We draw upon the theoretical frameworks of social learning theory, transfer learning (bricolage), and educational design patterns to elaborate upon different ideas for ways in which social media can support seamless learning.…
Translating Learning into Numbers: A Generic Framework for Learning Analytics
Greller, Wolfgang; Drachsler, Hendrik
2012-01-01
With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning…
Directory of Open Access Journals (Sweden)
Xinen Lv
2018-02-01
Full Text Available It is of great clinical significance to establish an accurate intelligent model to diagnose the somatization disorder of community correctional personnel. In this study, a novel machine learning framework is proposed to predict the severity of somatization disorder in community correction personnel. The core of this framework is to adopt the improved bacterial foraging optimization (IBFO to optimize two key parameters (penalty coefficient and the kernel width of a kernel extreme learning machine (KELM and build an IBFO-based KELM (IBFO-KELM for the diagnosis of somatization disorder patients. The main innovation point of the IBFO-KELM model is the introduction of opposition-based learning strategies in traditional bacteria foraging optimization, which increases the diversity of bacterial species, keeps a uniform distribution of individuals of initial population, and improves the convergence rate of the BFO optimization process as well as the probability of escaping from the local optimal solution. In order to verify the effectiveness of the method proposed in this study, a 10-fold cross-validation method based on data from a symptom self-assessment scale (SCL-90 is used to make comparison among IBFO-KELM, BFO-KELM (model based on the original bacterial foraging optimization model, GA-KELM (model based on genetic algorithm, PSO-KELM (model based on particle swarm optimization algorithm and Grid-KELM (model based on grid search method. The experimental results show that the proposed IBFO-KELM prediction model has better performance than other methods in terms of classification accuracy, Matthews correlation coefficient (MCC, sensitivity and specificity. It can distinguish very well between severe somatization disorder and mild somatization and assist the psychological doctor with clinical diagnosis.
Use of learning programs for SSC trigger strategy studies
International Nuclear Information System (INIS)
Clearwater, S.H.; Cleland, W.E.; Stern, E.G.
1990-01-01
In a novel application of the learning program RL, we are studying ways to develop the trigger for experiments at the SSC. Our initial study, which is still in progress, is to understand how to select top events from background, combining both cuts at the trigger level and in the off-line analysis. Our plan is to carry out these studies for a variety of reactions and thereby build up a comprehensive view of the trigger requirements for a calorimeter-based experiment at the SSC. Our initial results have shown that the learning program can find correlations and cuts that would be quite difficult to find using traditional methods. The program is expected to obtain cuts that are at least as good, if not better, than the the cuts found by traditional methods
QUALIFICATIONS FRAMEWORKS FOR LIFELONG LEARNING CONQUERING THE WORLD?
Directory of Open Access Journals (Sweden)
Arjen Deij
2014-01-01
Full Text Available The paper reveals the international prospects of developing and spreading the qualifications frameworks across the globe. It introduces the key terms and concepts related to the given issue, and examines both the benefits and challenges of qualifications frameworks implementation. The author looks into the origins and causes of the worldwide interest in qualifications framework application, and gives the overview of related recent publications and their conclusions to reinforce the provided argumentation.
A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT
Directory of Open Access Journals (Sweden)
Yu Liu
2013-01-01
Full Text Available Many applications based on Internet of Things (IoT technology have recently founded in industry monitoring area. Thousands of sensors with different types work together in an industry monitoring system. Sensors at different locations can generate streaming data, which can be analyzed in the data center. In this paper, we propose a framework for online sensor fault detection. We motivate our technique in the context of the problem of the data value fault detection and event detection. We use the Statistics Sliding Windows (SSW to contain the recent sensor data and regress each window by Gaussian distribution. The regression result can be used to detect the data value fault. Devices on a production line may work in different workloads and the associate sensors will have different status. We divide the sensors into several status groups according to different part of production flow chat. In this way, the status of a sensor is associated with others in the same group. We fit the values in the Status Transform Window (STW to get the slope and generate a group trend vector. By comparing the current trend vector with history ones, we can detect a rational or irrational event. In order to determine parameters for each status group we build a self-learning worker thread in our framework which can edit the corresponding parameter according to the user feedback. Group-based fault detection (GbFD algorithm is proposed in this paper. We test the framework with a simulation dataset extracted from real data of an oil field. Test result shows that GbFD detects 95% sensor fault successfully.
Kaplan, Neşe; Kaplan, Ali Barış
2011-01-01
Bu çalışmada amacımız, Ken Loach Sinemasının genel özelliklerini ortaya koymak ve spesifik olarak “Ülke ve özgürlük” filmini psikanalitik yöntemle analiz etmektir. Filmlerinde, “Sınıf mücadelesi” ve “bireysel özgürlük” sorununu tartışan Ken Loach, “Ülke ve özgürlük” filmi ile esasen globalleşme süreci içindeki Modern toplumu eleştirmektedir. Yakın geçmişin hikayesini anlatan film, nostaljik değildir; bugüne de mesajı olan dinamik bir anlatı sunar....
Hosseini, Seyede Mehrnoush
2011-01-01
The research aims to define SECI model of knowledge creation (socialization, externalization, combination, and internalization) as a framework of Virtual class management which can lead to better online teaching-learning mechanisms as well as knowledge creation. It has used qualitative research methodology including researcher's close observation…
Directory of Open Access Journals (Sweden)
M'hammed Abdous
2008-10-01
Full Text Available In this paper, we propose a conceptual and operational framework for process reengineering (PR in higher education (HE institutions. Using a case study aimed at streamlining exam scheduling and distribution in a distance learning (DL unit, we outline a sequential and non-linear four-step framework designed to reengineer processes. The first two steps of this framework – initiating and analyzing – are used to initiate, document, and flowchart the process targeted for reengineering, and the last two steps – reengineering/ implementing and evaluating – are intended to prototype, implement, and evaluate the reengineered process. Our early involvement of all stakeholders, and our in-depth analysis and documentation of the existing process, allowed us to avoid the traditional pitfalls associated with business process reengineering (BPR. Consequently, the outcome of our case study indicates a streamlined and efficient process with a higher faculty satisfaction at substantial cost reduction.
Directory of Open Access Journals (Sweden)
Jianzhu Ma
2015-01-01
Full Text Available Motivation. The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. Method. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Results. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.
Developing a holistic policy and intervention framework for global mental health.
Khenti, Akwatu; Fréel, Stéfanie; Trainor, Ruth; Mohamoud, Sirad; Diaz, Pablo; Suh, Erica; Bobbili, Sireesha J; Sapag, Jaime C
2016-02-01
There are significant gaps in the accessibility and quality of mental health services around the globe. A wide range of institutions are addressing the challenges, but there is limited reflection and evaluation on the various approaches, how they compare with each other, and conclusions regarding the most effective approach for particular settings. This article presents a framework for global mental health capacity building that could potentially serve as a promising or best practice in the field. The framework is the outcome of a decade of collaborative global health work at the Centre for Addiction and Mental Health (CAMH) (Ontario, Canada). The framework is grounded in scientific evidence, relevant learning and behavioural theories and the underlying principles of health equity and human rights. Grounded in CAMH's research, programme evaluation and practical experience in developing and implementing mental health capacity building interventions, this article presents the iterative learning process and impetus that formed the basis of the framework. A developmental evaluation (Patton M.2010. Developmental Evaluation: Applying Complexity Concepts to Enhance Innovation and Use. New York: Guilford Press.) approach was used to build the framework, as global mental health collaboration occurs in complex or uncertain environments and evolving learning systems. A multilevel framework consists of five central components: (1) holistic health, (2) cultural and socioeconomic relevance, (3) partnerships, (4) collaborative action-based education and learning and (5) sustainability. The framework's practical application is illustrated through the presentation of three international case studies and four policy implications. Lessons learned, limitations and future opportunities are also discussed. The holistic policy and intervention framework for global mental health reflects an iterative learning process that can be applied and scaled up across different settings through
Innovative design with learning reflexiveness for developing the Hamiltonian circuit learning games
Directory of Open Access Journals (Sweden)
Meng-Chien Yang
2018-02-01
Full Text Available In this study, we use a new proposed framework to develop the Hamiltonian circuit learning games for college students. The framework is for enhancing learners’ activities with learning reflexiveness. The design of these games is based on this framework to achieve the targeted learning outcomes. In recent years, the game-based learning is a very popular research topic. The Hamiltonian circuit is an important concepts for learning many computer science and electric engineering topics, such as IC design routing algorithm. The developed games use guiding rules to enable students to learn the Hamiltonian circuit in complicate graph problem. After the game, the learners are given a reviewing test which using the animation film for explaining the knowledge. This design concept is different from the previous studies. Through this new design, the outcome gets the better learning results under the effect of reflection. The students will have a deeper impression on the subject, and through self-learning and active thinking, in the game will have a deeper experience.
Energy Technology Data Exchange (ETDEWEB)
Wurtz, R. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kaplan, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2015-10-28
Pulse shape discrimination (PSD) is a variety of statistical classifier. Fully-realized statistical classifiers rely on a comprehensive set of tools for designing, building, and implementing. PSD advances rely on improvements to the implemented algorithm. PSD advances can be improved by using conventional statistical classifier or machine learning methods. This paper provides the reader with a glossary of classifier-building elements and their functions in a fully-designed and operational classifier framework that can be used to discover opportunities for improving PSD classifier projects. This paper recommends reporting the PSD classifier’s receiver operating characteristic (ROC) curve and its behavior at a gamma rejection rate (GRR) relevant for realistic applications.
Emergent Learning and Learning Ecologies in Web 2.0
Directory of Open Access Journals (Sweden)
Roy Williams
2011-03-01
Full Text Available This paper describes emergent learning and situates it within learning networks and systems and the broader learning ecology of Web 2.0. It describes the nature of emergence and emergent learning and the conditions that enable emergent, self-organised learning to occur and to flourish. Specifically, it explores whether emergent learning can be validated and self-correcting and whether it is possible to link or integrate emergent and prescribed learning. It draws on complexity theory, communities of practice, and the notion of connectivism to develop some of the foundations for an analytic framework, for enabling and managing emergent learning and networks in which agents and systems co-evolve. It then examines specific cases of learning to test and further develop the analytic framework.The paper argues that although social networking media increase the potential range and scope for emergent learning exponentially, considerable effort is required to ensure an effective balance between openness and constraint. It is possible to manage the relationship between prescriptive and emergent learning, both of which need to be part of an integrated learning ecology.
Data-based decision making : conclusions and a data use framework
Schildkamp, Kim; Lai, M.K.; Schildkamp, K.; Lai, M.K.; Earl, L.
2013-01-01
In this chapter, the results of all the studies presented in this book are summarized. What are the lessons learned? Based on the lessons learned, we developed a data use framework. In this framework, data use is influenced by several enablers and barriers (e.g., the school organization context,
International Nuclear Information System (INIS)
Stigson, Peter; Dotzauer, Erik; Yan Jinyue
2009-01-01
Climate change poses an unprecedented challenge for policy makers. This paper analyzes how industry sector policy expertise can contribute to improved policy making processes. Previous research has identified that policy making benefit by including non-governmental policy analysts in learning processes. Recent climate and energy policy developments, including amendments and the introduction of new initiatives, have rendered current policy regimes as novel to both governments and the industry. This increases business investment risk perceptions and may thus reduce the effectiveness and efficiency of the policy framework. In order to explore how government-industry policy learning can improve policy making in this context, this article studied the Swedish case. A literature survey analyzed how policy learning had been previously addressed, identifying that the current situation regarding novel policies had been overlooked. Interviews provided how industrial actors view Swedish policy implementation processes and participatory aspects thereof. The authors conclude that an increased involvement of the industry sector in policy design and management processes can be an important measure to improve the effectiveness and efficiency of climate and energy policies
Roy, Jan; Sykes, Diane
2017-01-01
The primary purpose of the article was to build a framework for an innovative approach to online internships after examining best practices in hospitality internships. Learning the ins and outs of an industry virtually, using contemporary internship methods strengthens the student's expertise and better prepares them for future workplace…
Directory of Open Access Journals (Sweden)
Juan-Francisco Martínez-Cerdá
2018-03-01
Full Text Available Human beings must develop many skills to cope with the large amount of challenges that currently exist in the world: media empowerment for an active and democratic citizenship, knowledge acquisition and conversion for lifelong and life-wide learning, 21st century skills for matching demand and supply in labor markets, and dispositional employability for unpredictable future career success. One of the tools for achieving these is online education, in which students have the chance to manage their own time, content, and goals. Thus, this paper analyzes these issues from the perspective of skills gained through e-learning and validates the Socio-Technical E-learning Employability System of Measurement (STELEM framework. The research was carried out with former students of the Universitat Oberta de Catalunya. Exploratory and confirmatory factorial analyses validate several consistent and reliable scales in two areas: (i employability, based on educational social capital, media empowerment, knowledge acquisition, knowledge conversion, literacy, digitalness, collaboration, resilience, proactivity, identity, openness, motivation, organizational culture, and employment security; and (ii socio-technical systems existing in this open online university, based on its information and communications technology (ICT, learning tasks, as well as student-centered and organizational approaches. The research provides two new psychometrical scales that are useful for the evaluation, monitoring, and assessment of relationships and influences between socio-technical e-learning organizations and employability skills development, and proposes a set of indicators related to human and social capital, valid in employability contexts.
Decision support frameworks and tools for conservation
Schwartz, Mark W.; Cook, Carly N.; Pressey, Robert L.; Pullin, Andrew S.; Runge, Michael C.; Salafsky, Nick; Sutherland, William J.; Williamson, Matthew A.
2018-01-01
The practice of conservation occurs within complex socioecological systems fraught with challenges that require transparent, defensible, and often socially engaged project planning and management. Planning and decision support frameworks are designed to help conservation practitioners increase planning rigor, project accountability, stakeholder participation, transparency in decisions, and learning. We describe and contrast five common frameworks within the context of six fundamental questions (why, who, what, where, when, how) at each of three planning stages of adaptive management (project scoping, operational planning, learning). We demonstrate that decision support frameworks provide varied and extensive tools for conservation planning and management. However, using any framework in isolation risks diminishing potential benefits since no one framework covers the full spectrum of potential conservation planning and decision challenges. We describe two case studies that have effectively deployed tools from across conservation frameworks to improve conservation actions and outcomes. Attention to the critical questions for conservation project planning should allow practitioners to operate within any framework and adapt tools to suit their specific management context. We call on conservation researchers and practitioners to regularly use decision support tools as standard practice for framing both practice and research.
Pedagogical quality in e-learning
DEFF Research Database (Denmark)
Dalsgaard, Christian
2005-01-01
The article is concerned with design and use of e-learning technology to develop education qualitatively. The purpose is to develop a framework for a pedagogical evaluation of e-learning technology. The approach is that evaluation and design must be grounded in a learning theoretical approach....... Finally, on the basis of the frameworks, the article discusses e-learning technology and, more specifically, design of virtual learning environments and learning objects. It is argued that e-learning technology is not pedagogically neutral, and that it is therefore necessary to focus on design...
Active Learning Using Hint Information.
Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hsuan-Tien
2015-08-01
The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.
New Applications of Learning Machines
DEFF Research Database (Denmark)
Larsen, Jan
* Machine learning framework for sound search * Genre classification * Music separation * MIMO channel estimation and symbol detection......* Machine learning framework for sound search * Genre classification * Music separation * MIMO channel estimation and symbol detection...
Learning from errors in super-resolution.
Tang, Yi; Yuan, Yuan
2014-11-01
A novel framework of learning-based super-resolution is proposed by employing the process of learning from the estimation errors. The estimation errors generated by different learning-based super-resolution algorithms are statistically shown to be sparse and uncertain. The sparsity of the estimation errors means most of estimation errors are small enough. The uncertainty of the estimation errors means the location of the pixel with larger estimation error is random. Noticing the prior information about the estimation errors, a nonlinear boosting process of learning from these estimation errors is introduced into the general framework of the learning-based super-resolution. Within the novel framework of super-resolution, a low-rank decomposition technique is used to share the information of different super-resolution estimations and to remove the sparse estimation errors from different learning algorithms or training samples. The experimental results show the effectiveness and the efficiency of the proposed framework in enhancing the performance of different learning-based algorithms.
Lévano, Marcos; Albornoz, Andrea
2016-01-01
This paper aims to propose a framework to improve the quality in teaching and learning in order to develop good practices to train professionals in the career of computer engineering science. To demonstrate the progress and achievements, our work is based on two principles for the formation of professionals, one based on the model of learning…
Supervised Learning for Dynamical System Learning.
Hefny, Ahmed; Downey, Carlton; Gordon, Geoffrey J
2015-01-01
Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as sparsity or structure. To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L 1 regularization. Many existing spectral methods are special cases of this new framework, using linear regression as the supervised learner. We demonstrate the effectiveness of our framework by showing examples where nonlinear regression or lasso let us learn better state representations than plain linear regression does; the correctness of these instances follows directly from our general analysis.
D.3.3 PLOT Persuasive Learning Design Framework
DEFF Research Database (Denmark)
Gram-Hansen, Sandra Burri
2012-01-01
In this third and final deliverable of WP3: Persuasive Learning Designs, the theoretical cross field between persuasion and learning and the practical analysis of the technological learning tools and products which are currently related to the PLOT project, namely the GLOMaker and the 3ET tool......, are linked together as persuasive learning designs are defined and exemplified through the four e-PLOT cases. Based on the literary study of D.3.1 as well as the subsequent discussions and reflections regarding the theoretical foundation and practical application of persuasive learning technologies......-PLOT work cases. In conclusion, the report presents a number of suggestions regarding the improvement of the two learning tools, which from a theoretical perspective will enhance the persuasive potential, and which can be taken into consideration in WP4 and 5....
National Consumer and Financial Literacy Framework
Ministerial Council for Education, Early Childhood Development and Youth Affairs (NJ1), 2011
2011-01-01
This document is a revised version of the National Consumer and Financial Literacy Framework (the Framework) originally developed in 2005. It articulates a rationale for consumer and financial education in Australian schools; describes essential consumer and financial capabilities that will support lifelong learning; and provides guidance on how…
Framework for Conducting Empirical Observations of Learning Processes.
Fischer, Hans Ernst; von Aufschnaiter, Stephan
1993-01-01
Reviews four hypotheses about learning: Comenius's transmission-reception theory, information processing theory, Gestalt theory, and Piagetian theory. Uses the categories preunderstanding, conceptual change, and learning processes to classify and assess investigations on learning processes. (PR)