Determination of human personality using multi-agent paradigm

In the current research work “Development of a Virtual students Community to communicate with an e-learning platform”, we proposed a virtual student community (VSC) that will substitute a group of human students, which will minimize the support and organization cost. In order to make virtual students able to represent as faithfully as possible human students in their diversity, we were confronted with the notion of profile and personality type for a computer agent. This article describes the concept of our proposal for the integration of psychological profiles or personality types in the development of virtual student communit

Automated Model Driven Testing Using AndroMDA and UML2 Testing Profile in Scrum Process

AbstractSoftware testing is an important step in the life cycle of agile development; it represents an efficient way to ensure the good functioning of the product. But as the complexity of a system increases, the effort and expertise to test it also increases. To significantly reduce these efforts, and reduce the cost and time; several studies have been carried out and various tools and test automation techniques have been proposed. In this paper, we present an approach to automatic generation of test cases from UML 2 Models at the Scrum agile process. This approach automates two important steps: the transformation of design models into test models and generating test cases, based on an open source MDA framework

Performance Optimization of WiMAX Mobile Networks with a Predictive Handover Process

Worldwide Interoperability for Microwave Access (WiMAX) is one of the most promising technologies for the next generation networks as it provides high data rates at medium and long range with full support of mobility. The technology is based on IEEE 802.16 standards and amendments specifying the MAC and PHY layers for fixed, nomadic, portable and mobile access. High speed mobility scenarios require low delay handover so as not to degrade end-to-end QoS indicators, such as delay time or data loss, that are significantly more affected. The WiMAX Forum Network Working Group aims to provide methods for controlled transition of the MS between BSs without significant loss of data or decreased QoS. It has been observed that prediction-based methods reduce handover latency and by the way the packets loss rate. In this paper we use the linear regression model to build a predictive hard handover algorithm that predict the Received Signal Strength Indicator (RSSI) value and advertize the MS to trigger the scanning procedure and the handover process operations reducing so the total handover latency and packets loss rate. The numerical analysis and simulation results show that the proposed method significantly reduces the handover latency and the packets loss rate

Local feature extraction based facial emotion recognition: a survey

Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively

Development of Intelligent Multi-agents System for Collaborative e-learning Support

The aim of this paper is the introduction of intelligence in e-learning collaborative system. In such system, the tutor plays an important role to facilitate collaboration between users and boost less active among them to get more involved for good pedagogical action. However, the problem lies in the large number of platform users, and the tutor tasks become difficult if not impossible. Therefore, we used fuzzy logic technics in order to solve this problem by automating tutor tasks and creating an artificial agent. This agent is elaborate in basing on the learners activities, especially the assessment of their collaborative behaviors. After the implementation of intelligent collaborative system by using Moodle platform, we have tested it. The reader will discover our approach and relevant results

Caracterisations optique, electrique et physico-chimique du selenium polycristallin en couches minces et des interfaces metal-selenium (M=Ni, Al, Te)

SIGLEINIST T 74341 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Instrumentation des activités des tuteurs à l’aide d’un système multi-agents d’analyse automatique des interactions

International audienceResearch presented in this article is dedicated to the tutor instrumentation in distance collaborative learning situations. We are particularly interested in the reuse of interaction analysis indicators. In this paper, we present our system SYSAT; a multi-agent system for monitoring the activities of learners. The aim of SYSAT is to reuse indicators (social, cognitive, emotional ...) reported in the literature, in an open and adaptive system. We tested our system on the interaction data from two experiments conducted with two master students of the Ibn Tofail University. The article presents the results and discusses the prospects for Research.Ce travail s'inscrit dans le cadre des recherches sur les Environnements Informatiques pour l'Apprentissage Humain (EIAH), et plus particulièrement dans l’assistance du tuteur dans le suivi des apprenants lors des activités d’apprentissage collaboratives en ligne. Cet article décrit l’architecture du système SYSAT, un système multi-agents d’analyse automatique des interactions. L’objectif de SYSAT est de réutiliser les indicateurs (sociaux, cognitifs, affectifs…) rapportés dans la littérature, au sein d’un système adaptatif et ouvert. Nous avons testé notre système sur les données d’interactions issues de deux expérimentations menées avec les étudiants de deux masters à l’université Ibn Tofail. L’article présente les résultats obtenus et évoque les perspectives de recherche

Determination of Distant Learner’s Sociological Profile Based on Fuzzy Logic and Naïve Bayes Techniques

The present article is elaborated in the context of e-learning softwares that provide assistance and functionalities to learners engaged in distance learning. Our contribution consists of a system that estimates a behavioral (sociological) profile for each student. This estimation is based on automatic analysis of students’ textual asynchronous conversations. In general, the automatic analysis of textual conversations is based on speech acts for classification and categorization of messages. This technique has several disadvantages like the absence of standardization of speech acts for determining social behaviors of learners. To overcome this, we propose a multi-agent system based on fuzzy logic reasoning and supervised learning technique for automatic classification and categorization of textual conversation. The determined profiles are proposed to teachers to provide them assistance during tutoring tasks. The objective of this article is to share our reflections around these issues by presenting our experience in the analysis of asynchronous online discussion forums. In this paper, we specifically propose (i) definitions for the used sociological profiles and (ii) introduce the architecture of the Multi-Agent System (MAS) that determines the profiles. The system was experimented with the students of the Master Program “Software Quality” in the Ibn Tofail University. The results obtained from this experience, presented and discussed in this paper, show that the proposed approach can be of interest

An Assessment of Serious Games Technology: Toward an Architecture for Serious Games Design

The design of an engaging and motivating serious game (SG) requires a strong knowledge of learning domain, pedagogy, and game design components, which are hard to be found and restrained by an individual or one entity. Therefore and in the light of this statement, the collaboration between domain content, pedagogical, and playful experts is required and crucial. Despite the fact that the existing models that support SG design are intended to have a combination of learning and fun, the design of SG remains difficult to achieve. It would then be appreciated to propose means and guidelines that facilitate this design. To do so, this paper proposes a taxonomy, which classifies models that treat SG design, and then presents an opening as a functional architecture for supporting SG conception, which promotes the separation during the design, the collaboration between different involved experts, and the reuse of prior expert productions

A novel adaptable approach for sentiment analysis on big social data

Abstract Gathering public opinion by analyzing big social data has attracted wide attention due to its interactive and real time nature. For this, recent studies have relied on both social media and sentiment analysis in order to accompany big events by tracking people’s behavior. In this paper, we propose an adaptable sentiment analysis approach that analyzes social media posts and extracts user’s opinion in real-time. The proposed approach consists of first constructing a dynamic dictionary of words’ polarity based on a selected set of hashtags related to a given topic, then, classifying the tweets under several classes by introducing new features that strongly fine-tune the polarity degree of a post. To validate our approach, we classified the tweets related to the 2016 US election. The results of prototype tests have performed a good accuracy in detecting positive and negative classes and their sub-classes