A Predictive Approach for the Efficient Distribution of Agent-Based Systems on a Hybrid-Cloud

International audienceHybrid clouds are increasingly used to outsource non-critical applications to public clouds. However, the main challenge within such environments, is to ensure a cost-efficient distribution of the systems between the resources that are on/off premises. For Multi Agent Systems (MAS), this challenge is deepened due to irregular workload progress and intensive communication between the agents, which may result in high computing and data transfer costs. Thus, in this paper we propose a generic framework for adaptive cost-efficient deployment of MAS with a special focus on hybrid clouds. The framework is based mainly on the use of a performance evaluation process that consists of simulating various partitioning options to estimate and optimize the overall deployment costs. Further, to cope with the irregular workload changes within a MAS and dynamically adapt its initial deployment, we propose an extended version of the Fiduccia-Mattheyses algorithm (E-FM). The experimental results highlight the efficiency of E-FM and show that an efficient MAS deployment to hybrid clouds depends on various factors such as the cloud providers and their different cost-models, the network state, the used partitioning algorithm, and the initial deployment

Coupling agent based simulation with dynamic networks analysis to study the emergence of mutual knowledge as a percolation phenomenon

International audienceThe emergence of mutual knowledge is a major cognitive mechanism for the robustness of complex socio technical systems. It has been extensively studied from an ethnomethodological point of view and empirically reproduced by multi agent simulations. Whilst such simulations have been used to design real work settings the underlying theoretical grounding for the process is vague. The aim of this paper is to investigate whether the emergence of mutual knowledge (MK) in a group of co-located individuals can be explained as a percolation phenomenon. The followed methodology consists in coupling agent-based simulation with dynamic networks analysis to study information propagation phenomena: after using an agent-based simulation we generated and then analysed its traces as networks where agents met and exchanged knowledge. Deep analysis of the resulting networks clearly shows that the emergence of MK is comparable to a percolation process. We specifically focus on how changes at the microscopic level in our agent based simulator affect percolation and robustness. These results therefore provides theoretical basis for the analysis of social organizations

SoS paradigm benefits SaaS integration: novel approach and first results

International audienceTo fulfill its complex business demands, an organization may require the integration of many SaaS applications. A main challenge in this regard is to select the suitable SaaS offerings that satisfy all the business requirements. Due to the diversity of QoS needs and the large number of SaaS providers, selecting the suitable set of SaaS applications to be integrated may result in QoS degradation and higher costs. In this paper, we propose to use a System-of-Systems (SoS)-based approach for the selection and integration of SaaS. We believe that the use of SoS and its dynamic properties could improve the service selection at SaaS level. To do so, we propose three Mixed Integer Programming models that optimize the global SaaS behavior by minimizing the overall delivery costs, and maximizing the global functional and QoS utilities. The implementation of the approach and the experimental results highlight the efficiency of our proposed solution

Coupling Case Based Reasoning and Process Mining for a Web Based Crisis Management Decision Support System.

International audienceThis paper presents a research in progress that aims to design and develop a web-based shared environment for stakeholders involved in disaster management. The goal of this environment is two-fold. Firstly it will provide a reliable disaster information source to facilitate the exchange and the analysis of previous crisis information. Secondly, it will assimilate best practices and provide recommendations based on experiences from previous disasters. One of the first steps towards such an environment is to elaborate a common and generic disaster model. This model is also a reference to define a template for the case base of previous disasters. In order for our system to provide recommendations based on previous practices, we combine case based reasoning with process mining. This article presents the first step towards a disaster management decision support system, specifically providing guidance on how to integrate process mining in the case based reasoning cycle

A Comparative Study between Possibilistic and Probabilistic Approaches for Monolingual Word Sense Disambiguation

International audienceThis paper proposes and assesses a new possibilistic approach for automatic monolingual word sense disambiguation (WSD). In fact, in spite of their advantages, the traditional dictionaries suffer from the lack of accurate information useful for WSD. Moreover, there exists a lack of high-coverage semantically labeled corpora on which methods of learning could be trained. For these multiple reasons, it became important to use a semantic dictionary of contexts (SDC) ensuring the machine learning in a semantic platform of WSD. Our approach combines traditional dictionaries and labeled corpora to build a SDC and identify the sense of a word by using a possibilistic matching model. Besides, we present and evaluate a second new probabilistic approach for automatic monolingual WSD. This approach uses and extends an existing probabilistic semantic distance to compute similarities between words by exploiting a semantic graph of a traditional dictionary and the SDC. To assess and compare these two approaches, we performed experiments on the standard ROMANSEVAL test collection and we compared our results to some existing French monolingual WSD systems. Experiments showed an encouraging improvement in terms of disambiguation rates of French words. These results reveal the contribution of possibility theory as a mean to treat imprecision in information systems

Evaluation d'une approche possibiliste pour la désambiguïsation des textes arabes (TALN'2014 – Traitement Automatique des Langues Naturelles, Marseille France, 01/07/14-04/07/14)

Papier long de TALN 2014International audienceMorphological disambiguation of Arabic words consists in identifying their appropriate morphological analysis. In this paper, we present three models of morphological disambiguation of non-vocalized Arabic texts based on possibilistic classification. This approach deals with imprecise training and testing datasets, as we learn from untagged texts. We experiment our approach on two corpora i.e. the Hadith corpus and the Arabic Treebank. These corpora contain data of different types: traditional and modern. We compare our models to probabilistic and statistical classifiers. To do this, we transform the structure of the training and the test sets to deal with imprecise data.La désambiguïsation morphologique d'un mot arabe consiste à identifier l'analyse morphologique appropriée correspondante à ce mot. Dans cet article, nous présentons trois modèles de désambiguïsation morphologique de textes arabes non voyellés basés sur la classification possibiliste. Cette approche traite les données imprécises dans les phases d'apprentissage et de test, étant donné que notre modèle apprend à partir de données non étiquetés. Nous testons notre approche sur deux corpus, à savoir le corpus du Hadith et le Treebank Arabe. Ces corpus contiennent des données de types différents classiques et modernes. Nous comparons nos modèles avec des classifieurs probabilistes et statistiques. Pour ce faire, nous transformons la structure des ensembles d'apprentissage et de test pour remédier au problème d'imperfection des données

Towards a New Standard Arabic Test Collection for Mono- and Cross-Language Information Retrieval (poster)

International audienceWe propose in this paper a new standard Arabic test collection for mono- and cross-language Information Retrieval (CLIR). To do this, we exploit the “Hadith” texts and we provide a portal for sampling and evaluation of Had-iths' results listed in both Arabic and English versions. The new called “Kunuz” standard Arabic test collection will promote and restart the development of Ar-abic mono retrieval and CLIR systems blocked since the earlier TREC-2001 and TREC-2002 editions

Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

Networks used in biological applications at different scales (molecule, cell and population) are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system) as well as in their discrete Boolean versions (e.g., non-linear Hopfield system); in both cases, the notion of interaction graph G(J) associated to its Jacobian matrix J, and also the concepts of frustrated nodes, positive or negative circuits of G(J), kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i) attractor entropy, (ii) isochronal entropy and (iii) entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment