scholarly journals Building Learning System for Content Knowledge and Social Knowledge

2018 ◽  
Vol 13 (01) ◽  
pp. 4
Author(s):  
S.M.F.D Syed Mustapha
Keyword(s):  
Content Knowledge ◽  
Intelligent Tutoring System ◽  
Individual Learning ◽  
Learning Systems ◽  
Learning System ◽  
Social Knowledge ◽  
Computer Based Training ◽  
Web 3.0 ◽  
Knowledge Models ◽  
Computer Aided

In the late 50’s or early 60’s, there were huge interests towards building learning systems for individual learning and they are called with various names such as Intelligent Tutoring System, Microworld, Computer Based Training, Computer Aided System, Intelligent Computer Aided Instruction and others. They are made to be different with regard to the technological approaches and the learning pedagogies, knowledge models and student models. Over the years, the interest of building learning systems has migrated from individual learning on content knowledge to community learning as the result of the recent Web 2.0 and Web 3.0 sociotechnological wave. The paper describes the work that was done to develop the learning system in both situations – content knowledge and social knowledge where the experiences mainly in capturing the knowledge and representing them are different

scholarly journals Automatic Multi-task Learning System for Abnormal Network Traffic Detection

2018 ◽  
Vol 13 (04) ◽  
pp. 4 ◽  
Author(s):  
He Huang ◽  
Haojiang Deng ◽  
Jun Chen ◽  
Luchao Han ◽  
Wei Wang
Keyword(s):  
Deep Learning ◽  
Network Traffic ◽  
Learning Systems ◽  
Learning System ◽  
Traffic Classification ◽  
Learning Methods ◽  
Web 3.0 ◽  
Task Learning ◽  
Knowledge Models ◽  
End To End

Since the last decade of the 20th century, the Internet had become flourishing, which drew great interest in the detection of abnormal network traffic. Particular-ly, it’s impossible to manually detect the abnormal patterns from enormous traffic flow in real time. Therefore, multiple machine learning methods are adopted to solve this learning problem. Those methods differ in mathematical models, knowledge models, application scenarios and target flows. In recent years, as a consequence of the technological breakthrough of Web 3.0, the traditional types of traffic classifiers are getting outdated and people start to focus on deep learning methods. Deep learning provides the potential for end-to-end learning systems to automatically learn the abnormal patterns without massive feature engineering, saving plenty of detecting time. In this study, to further save both memory and times of learning systems, we propose a novel multi-task learning system based on convolutional neural network, which can simultaneously solve the tasks of malware detection, VPN-capsulation recognition and Trojan classification. To the best of our knowledge, it’s the first time to apply an end-to-end multi-task learn-ing system in traffic classification. In order to validate this method, we establish experiments on public malware dataset CTU-13 and VPN traffic dataset ISCX. Our system found a synergy among all these tasks and managed to achieve the state-of-the-art output for most of the experiments.

scholarly journals Neural arbitration between social and individual learning systems

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Andreea Oliviana Diaconescu ◽  
Madeline Stecy ◽  
Lars Kasper ◽  
Christopher J Burke ◽  
Zoltan Nagy ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Functional Neuroimaging ◽  
Learning Task ◽  
Brain Regions ◽  
Individual Learning ◽  
Learning Systems ◽  
Learning System ◽  
Relative Precision ◽  
Decision Confidence ◽  
Dorsolateral Prefrontal

Decision making requires integrating knowledge gathered from personal experiences with advice from others. The neural underpinnings of the process of arbitrating between information sources has not been fully elucidated. In this study, we formalized arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modeling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our definition of arbitration as a ratio of precisions. Functional neuroimaging demonstrated that arbitration signals were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favor of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favor of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioral and neural levels.

The Role of Affect in an Agent-Based Collaborative E-Learning System Used for Engineering Education

2009 ◽  
pp. 510-528
Author(s):  
Mohamed Ben Ammar ◽  
Mahmoud Neji ◽  
Adel M. Alimi
Keyword(s):  
Facial Expressions ◽  
Affective Computing ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning Systems ◽  
Learning System ◽  
Agent Based ◽  
Affective Behaviors ◽  
E Learning

Affective computing is a new artificial intelligence area that deals with the possibility of making computers able to recognize human emotions in different ways. This chapter represents an implemented framework, which integrates this new area with an intelligent tutoring system. The authors argue that tutor agents providing socially appropriate affective behaviors would provide a new dimension for collaborative learning systems. The main goal is to analyse learner facial expressions and show how affective computing could contribute to learning interactions, both by recognizing learner emotions during learning sessions and by responding appropriately.

scholarly journals Neural Arbitration between Social and Individual Learning Systems

2019 ◽  
Author(s):  
Andreea O. Diaconescu ◽  
Madeline Stecy ◽  
Lars Kasper ◽  
Christopher J. Burke ◽  
Zoltan Nagy ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Functional Neuroimaging ◽  
Learning Task ◽  
Brain Regions ◽  
Individual Learning ◽  
Learning Systems ◽  
Learning System ◽  
The Other ◽  
Relative Precision ◽  
Temporoparietal Junction

AbstractDecision making often requires integrating self-gathered information with information acquired from observing others. Depending on the situation, it may be beneficial to rely more on one than the other source, taking into account that either information may be imprecise or deceiving. The process by which one source is selected over the other based on perceived reliability, here defined as arbitration, has not been fully elucidated. In this study, we formalised arbitration as the relative reliability (precision) of predictions afforded by each learning system using hierarchical Bayesian models. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and self-sampled outcomes. The number of points participants wagered on their predictions reflected arbitration: The higher the relative precision of one learning system over the other and the lower the intention volatility, the more points participants wagered on a given trial. Functional neuroimaging demonstrated that the arbitration signal was independent of decision confidence and involved modalityspecific brain regions. Arbitrating in favour of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain whereas arbitrating in favour of social information engaged ventromedial prefrontal cortex and the temporoparietal junction. These findings are in line with domain specificity and indicate that relative precision captures arbitration between social and individual learning systems at both the behavioural and neural level.

Factors Associated with students’ Intention to use E-Learning System: A Study from Public Universities of Lahore

2021 ◽  
Vol 9 (2) ◽  
pp. 167-173
Author(s):  
Shagufta Shaheen ◽  
Mubasher Muhammad Kamran ◽  
Saira Naeem ◽  
Tahir Mahmood
Keyword(s):  
Undergraduate Students ◽  
Public Universities ◽  
Positive Impact ◽  
Cross Sectional Study ◽  
Learning Systems ◽  
Learning System ◽  
Policy Implications ◽  
Intention To Use ◽  
Cross Sectional ◽  
E Learning

The study's primary purpose is to explore the factors affecting the students' intention to use e-learning systems in the COVID pandemic. The model of the “Unified theory of acceptance and use of technology” (UTAUT) was used as a theoretical underpinning. The Independent variables include “performance expectancy, effort expectancy, social influence, facilitating condition,” and the dependent variable is the intention to use e-learning systems. The quantitative data were collected from the postgraduate and undergraduate students of the public universities of Lahore. A total of n=411 students were approached, out of which the responses of only 399 were considered valid and were used for Multiple linear regression through SPSS 25. It was a cross-sectional study. It was found that almost all constructs of the model have a significant positive impact on intention to use e-learning systems.  The study's main contribution is exposing the factors that affect the acceptance and use of e-learning systems. This study has several policy implications for policy experts of higher education”.

scholarly journals Trust between Humans and Learning Machines: Developing the Gray Box

2017 ◽  
Vol 139 (06) ◽  
pp. S9-S13 ◽  
Author(s):  
James C. Christensen ◽  
Joseph B. Lyons
Keyword(s):  
Situation Awareness ◽  
Task Allocation ◽  
Intelligent Agent ◽  
Semantic Space ◽  
Learning Systems ◽  
Learning System ◽  
Sufficient Information ◽  
Learning Technology ◽  
Learning Machines ◽  
Potential Impact

This article explores the notion of the ‘Gray Box’ to symbolize the idea of providing sufficient information about the learning technology to establish trust. The term system is used throughout this article to represent an intelligent agent, robot, or other form of automation that possesses both decision initiative and authority to act. The article also discusses a proposed and tested Situation Awareness-based Agent Transparency (SAT) model, which posits that users need to understand the system’s perception, comprehension, and projection of a situation. One of the key challenges is that a learning system may adopt behavior that is difficult to understand and challenging to condense to traditional if-then statements. Without a shared semantic space, the system will have little basis for communicating with the human. One of the key recommendations of this article is that there is a need to provide learning systems with transparency as to the state of the human operator, including their momentary capabilities and potential impact of changes in task allocation and teaming approach.

scholarly journals POSSIBILITIES OF DEVELOPING INTERACTIVE E-LEARNING SYSTEM FOR BORDER GUARDS TRAINING IN THE STATE BORDER GUARD COLLEGE OF THE REPUBLIC OF LATVIA

2020 ◽  
Vol 3 (8) ◽  
pp. 45-53
Author(s):  
Mārtiņš Spridzāns ◽  
Jans Pavlovičs ◽  
Diāna Soboļeva
Keyword(s):  
Scientific Literature ◽  
Online Communication ◽  
The State ◽  
Learning Systems ◽  
Learning System ◽  
Digital Learning ◽  
E Learning ◽  
Research Findings ◽  
The Republic ◽  
Learning Development

Efficient use of educational technology and digital learning possibilities has always been the strategic area of high importance in border guards training at the State Border Guard College of Latvia. Recently, issues related to training during the Covid-19, have spurred and revived the discussion, topicality and practical need to use the potential of e-learning opportunities which brought up unexpected, additional, previously unsolved, unexplored, challenges and tasks to border guards training. New opportunities and challenges for trainers, learners and administration of training process both in online communication and learning administration contexts. In order to find out and define further e-learning development possibilities at the State Border Guard College the authors of this research explore the scientific literature on the current research findings, methodologies, approaches on developing interactive e-learning systems in educational contexts, particularly within the sphere of law enforcement. Based on scientific literature research findings authors put forward suggestions on improving the e-learning systems for border guards training.

scholarly journals Incremental and developmental perspectives for general-purpose learning systems

2017 ◽  
Vol 20 (60) ◽  
pp. 24
Author(s):  
Fernando Martínez-Plumed
Keyword(s):  
Knowledge Acquisition ◽  
Recommender Systems ◽  
Cognitive Abilities ◽  
Incremental Learning ◽  
General Purpose ◽  
Learning Systems ◽  
Learning System ◽  
Self Driving Cars ◽  
Evaluation Techniques ◽  
Standing Problem

The stupefying success of Articial Intelligence (AI) for specic problems, from recommender systems to self-driving cars, has not yet been matched with a similar progress in general AI systems, coping with a variety of (dierent) problems. This dissertation deals with the long-standing problem of creating more general AI systems, through the analysis of their development and the evaluation of their cognitive abilities. It presents a declarative general-purpose learning system and a developmental and lifelong approach for knowledge acquisition, consolidation and forgetting. It also analyses the use of the use of more ability-oriented evaluation techniques for AI evaluation and provides further insight for the understanding of the concepts of development and incremental learning in AI systems.

scholarly journals INCA: A cognitive multi-agents architecture for designing intelligent & adaptive learning systems

2005 ◽  
Vol 2 (2) ◽  
pp. 99-114 ◽  
Author(s):  
Thierry Nabeth ◽  
Liana Razmerita ◽  
Albert Angehrn ◽  
Claudia Roda
Keyword(s):  
Knowledge Sharing ◽  
Adaptive Learning ◽  
Management Practices ◽  
Learning Systems ◽  
Learning System ◽  
Cognitive Agents ◽  
E Learning ◽  
Personal Agent ◽  
Adaptive Learning Systems ◽  
Multi Agents

This paper presents a cognitive multi-agents architecture called Intelligent Cognitive Agents (InCA) that was elaborated for the design of Intelligent Adaptive Learning Systems. The InCA architecture relies on a personal agent that is aware of the user's characteristics, and that coordinates the intervention of a set of expert cognitive agents (such as story telling agents, assessment agents, stimulation agents or help agents). This InCA architecture has been applied for the design of K"InCA, an e-learning system aimed at helping people to learn and adopt knowledge-sharing management practices.

scholarly journals Adaptive Learning Systems and Interference in Causal Inference

2021 ◽  
Author(s):  
Alexander Olof Savi ◽  
Nick ten Broeke ◽  
Abe Dirk Hofman
Keyword(s):  
Causal Inference ◽  
Adaptive Learning ◽  
External Validity ◽  
Learning Systems ◽  
Learning System ◽  
Adaptive Learning System ◽  
Adaptive Learning Systems

Adaptive learning systems can be susceptible to between-subject cross-condition interference by design. This interference has important implications for the implementation and evaluation of A/B tests in such systems, as it obstructs causal inference and hurts external validity. We illustrate the problem in an Elo based adaptive learning system, discuss sources and degrees of interference, and provide solutions, using an example in the study of dropout.

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