Modeling complex systems, including discrete event systems, remains a challenge. The complexity and the size of such systems prevent understanding their models. This article proposes an approach for... Show More

Modeling complex systems, including discrete event systems, remains a challenge. The complexity and the size of such systems prevent understanding their models. This article proposes an approach for reducing queuing networks large models into smaller ones. The objective is to reduce the analysis as well as the simulation times in addition to the better understanding of the system under study. The basic idea is to divide the model into a set of smaller, hierarchically organized and more manageable sub-models, which are analyzed in isolation. The key contributions of this work are the substitution of each sub-model by a single M/G/8 station and the automation of the decomposition process using model transformation techniques. The main conclusion is that the reduction approach provides exact results for the global mean number of clients and mean residence time at the whole network.

Since the fall of 2011, the Massive Open Online Course (MOOC) phenomenon is still being qualified as the most attractive and discussed subject by educational communities and public. In the... Show More

Since the fall of 2011, the Massive Open Online Course (MOOC) phenomenon is still being qualified as the most attractive and discussed subject by educational communities and public. In the literature, there are many researches about this recent e-learning generation that vary as the goals vary from raising pedagogical issues to economics ones. Several case studies state that MOOCs are challenging the use of technologies to enhance learning; others think that MOOCs can induce to disruptive in education and educational institutions. In this paper, we propose an instructional design for a kind of MOOC platforms where mainly the use of disciplines specifications and automated evaluation of MOOC learners are possible to settle the source of these problems. Our proposition is based on ODALA (Ontology-Driven Auto-evaluation Learning Approach) principles and on the disciplines' knowledge capitalization using a meta-model represented as domain ontology for disciplines modeling inspired by this approach.

In this paper, we propose asynchronous virtual meeting (AVM) as a tool that can be integrated to virtual campuses, university Web sites or e-learning platforms. It is a way for participants from... Show More

In this paper, we propose asynchronous virtual meeting (AVM) as a tool that can be integrated to virtual campuses, university Web sites or e-learning platforms. It is a way for participants from academics and students to discuss for a defined period, one or more topics. The AVM participants have to give, respectively, objective and punctual interventions as opinions, responses chosen from a proposed list and/or suggestions. In real time, specific processes for data analysis give a progressive synthesis of the discussions visible for all participants. The proposed tool can avoid different problems and constraints that the higher education meetings organizers are faced to. We developed a prototype for the tool that was tested with a group of participants from our university. The results were interesting and showed contribution to enhance communication and decision making in higher education.

The density classification task (DCT for short) is one of the most studied benchmark problems for analyzing emergent computations performed by cellular automata. Starting from the observation that... Show More

The density classification task (DCT for short) is one of the most studied benchmark problems for analyzing emergent computations performed by cellular automata. Starting from the observation that the performance of the best known solutions is not stable towards initial configurations size; we propose in this paper, some new evolutionary mechanisms for designing new solutions with similar conceptual properties to the best known ones. The approach is based on varying the size of initial configurations which allows making comparisons and analysis between the different solutions. We show then through a set of numerical results that the proposed mechanism allows collecting solutions for the DCT more efficiently and with reduced efforts. Also, we show that the collected solutions are affected by configurations size variations, where only few of them are scalable.