Mahmoud Neji

The Affective Tutoring System

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 paper represents a study about the integration of this new area in the intelligent tutoring system. We argue that socially appropriate affective behaviors would provide a new dimension for collaborative learning systems. The main goal is to analyses learner facial expressions and show how Affective Computing could contribute for this interaction, being part of the complete student tracking (traceability) to monitor student behaviors during learning sessions.

The integration of an emotional system in the intelligent system

Summary form only given. In this article, we present a new architecture of the intelligent tutoring system (ITS) and we suggest an original method, which allows recognizing the expression of the learners face during exercise. It helps evaluate his affective state in an emotional system in order to distinguish his influence on his responses. Accordingly, we should first be able to detect his face, extract his important features translating the state of his expression (characteristical features: eyes and mouth,), then we should analyse their configuration and characteristics in order to recognize the expression, which describe and interpret it. Our architecture is based on the observation of the behaviour of the learner; detect engaging signs so as to detect affective responses, which can be the manifestation of feelings of interest, excitement and confusion. From the observation and the identification of the emotional state of the learner, the tutor can undertake actions which influences the quality of learning and its execution (important remarks may reduce the feeling of failure of the learner or avoid the risk of interrupting his work as soon as he feels bored). A scientific and especially emotional analysis is necessary to evaluate and help the learner during exercise.

A multi-agent based system for affective peer-e-learning

Recent research results indicate that cognitive processes are related to emotions. Since emotions are omnipresent in any kind of interaction it can be advantageous to take them into account, particularly in the context of e-learning. Emotions are now seen as closely related to cognition processes including decision-making, memory, attention, etc. Thus, e-learning environments have begun to take into consideration the emotional state of the learner in order to enhance his performance. This paper presents a tool for man-machine interaction which uses facial input. We believe that facial interaction will have its place among the interaction techniques in the near future. Our work concerns the improvement of computer based learning by mean of a lifelike presence in learning environment. Our approach combines Intelligent Tutoring System with research on human emotion in Cognitive Science, Psychology and Communication. According for relations between emotion, cognition and action in contextual learning, we propose an emotional intelligent tutoring system based on multi-agent architecture to design of adaptive distributed collaborative and peer to peer e-learning environments. An emotionally intelligent tutoring system should be able to provide feedback to students, taking into account relevant aspects of the mental state of the student.

An approach for the selection of evaluation methods for interactive adaptive systems using analytic hierarchy process

The evaluation of interactive adaptive systems is the source of many difficulties for novice evaluators as well as for expert ones. Issues arise in the need to identify the appropriate evaluation methods to be used for a given evaluation context. This paper proposes an approach for the choice of evaluation methods for interactive adaptive systems with regard to their suitability depending on specific evaluation contexts. A multicriteria decision making approach, namely the Analytic Hierarchy Process, is proposed for the selection methodology. The approach is based on a pair-wise comparison of a number of criteria that affect the choice of the appropriate evaluation methods. Four user-centred evaluation methods in conjunction with the layered evaluation for interactive adaptive systems are presented in the cases studies. These methods are: user-as-wizard, heuristic evaluation, focus group, and user test. The proposed approach is flexible in the sense that it can be used in the case of different evaluation methods. The results depend on the evaluation context where the methods will be used. Hence, we illustrate the proposed approach in the case of two evaluation contexts.

Vision Based Hand Gesture Recognition for Mobile Devices: A Review

The desire to interact with a mobile device in an intuitive and natural way is growing. In fact, research in this field aims to develop systems able to model, analyze and recognize user’s hand gestures to control mobiles without having the need to touch the screen. We give in this paper an overview of current research works and an analysis of comparative studies in this field. This paper focuses on the main steps of hand gesture recognition for mobile devices like detection, tracking and recognition. This work also gives an analysis of the existing literature on gesture recognition systems for human-computer interaction by classifying them under various key parameters. At the end we conclude with some reflections on future works.

Hand pose estimation system based on Viola-Jones algorithm for Android devices

This paper focuses on hand pose estimation by proposing a system that solves real-time static hand gesture detection and recognition issues for interacting with smartphones. The first step of our work consists in detecting and tracking the hand in a complex background. The second step consists in recognizing hand gestures using SVM “Support Vector Machine”. Finally, we developed a grammar to generate gesture commands for mobile applications control. This work presents a system based on a real-time hand gesture recognition algorithm for Android devices. The idea here is to allow the user interacting with the mobile device without the need to touch the screen. In this system, the Smartphone is able to perform all necessary steps to recognize gestures without the need to connect to a distant device.

Agent-based collaborative affective e-learning system

In order to promote a more dynamic and flexible communication between the learner and the system, we integrate five kinds of adaptive agents in emotional framework. We focus on human facial characteristics to develop general-purpose agents that can recognize human emotion and create emotional framework with the implications of peer-to-peer technology. Emotions play an important role in cognitive processes and specially in learning tasks. Online learning is no exception. Detecting a learners emotional reaction for a given situation is an essential element for every e-learning system. In this paper a system for identifying facial expressions by using facial features is presented, it can recognizes 6 basic emotional expressions (happiness, sadness, surprise, fear, anger, and disgust).

A classification and comparison of usability evaluation methods for interactive adaptive systems

Usability is a crucial factor for interactive adaptive system success. Several methods have been proposed to support the usability evaluation of interactive adaptive systems. However, the diversity of these methods leaves the evaluators wondering what would be the most appropriate usability evaluation methods for their specific goals. This paper examines the characteristics of usability evaluation methods for interactive adaptive systems and identifies the most relevant features for categorizing them. The comparison and classification of these methods will serve as a reference for researchers in the field of interactive adaptive systems and as a guide for the choice of suitable usability evaluation methods.

A framework for supporting the choice of usability evaluation methods for interactive adaptive systems

The evaluation of interactive adaptive systems is considered as a difficult endeavour due to the complex nature of these systems. Issues arise in the need to identify, for a given evaluation situation the set of suitable usability evaluation methods to be used by the evaluators. This paper describes a framework for supporting the choice of appropriate methods for the usability evaluation of interactive adaptive systems. It is based on the comparison of usability evaluation methods using relevant criteria. This framework is then validated through a case study; it concerns an adaptive hypermedia system in transportation field.

Hand Pose Estimation System Based on a Cascade Approach for Mobile Devices

The rise in the use of mobile devices requires finding new ways to interact with this type of devices. Gestures are an effective way to interact with the mobile device and to place order to it. However, gesture recognition in this context constitute a challenging task due the limited computational capacities of this type of devices. In this work, we present a hand pose estimation system for mobile device. The gesture is recognized by using a boosting algorithm and Haar-like features. The system is designed for Android devices. The method used consists of capturing gestures by a smartphone’s camera to recognize the hand sign. It presents a system based on a real-time hand posture recognition algorithm for mobile devices. The aim of this system is to allow the mobile device interpreting hand signs made by users without the need to touch the screen.