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2013, , Moez Hammami
Nouvelle méthode de détection de dérive basée sur la distance entre les erreurs de classification
5e Journées Doctorales Journées Nationales MACS, Strasbourg : France (2013), 2013
Abstract
La classification dynamique s’intéresse au traitement des données non-stationnaires issues des environnements évolutifs dans le temps. Ces données peuvent présenter des dérives, qui affectent la performance du modèle d’apprentissage initialement construit. Aujourd’hui, beaucoup d’intérêts sont portés sur la surveillance, la mise à jour et le diagnostic de ces dérives afin d’améliorer la performance du modèle d’apprentissage. Dans ce contexte, une nouvelle méthode de détection de dérive basée sur la distance entre les erreurs de classification est présentée. Cette méthode, nommée EDIST, surveille la distribution des distances des erreurs de classification entre deux fenêtres de données afin de détecter une différence à travers un test d’hypothèse statistique. EDIST a été testée à travers des bases de données artificielles et réelles. Des résultats encourageants ont été trouvés par rapport à des méthodes similaires. EDIST a pu trouver les meilleurs taux d’erreur de classification dans la plupart des cas et a montré une robustesse envers le bruit et les fausses alarmes.
, Meriem EnnigrouParticle Swarm Optimization combined with Tabu Search in a Multi-Agent model for Flexible Job Shop Problem
ICSI, 2013
Abstract
Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine and has a processing time depending on the machine used. The objective is to minimize the makespan, i.e., the total duration of the schedule. In this article, we propose a multi-agent model based on the hybridization of the tabu search (TS) method and particle swarm optimization (PSO) in order to solve FJSP. Different techniques of diversification have also been explored in order to improve the performance of our model. Our approach has been tested on a set of benchmarks existing in the literature. The results obtained show that the hybridization of TS and PSO led to promising results.
Meriam Jemel, Nadia Ben Azzouna,A novel approach for dynamic authorisation planning in constrained workow systems
In the 6th International Conference on Security of Information and Networks (SIN 2013), July 21-23 2013, Izmir, Turkey, pages 388-391, ACM, 2013., 2013
Abstract
In this paper we present a specification of the most common static and dynamic workflow authorisation constraints. We propose an authorisation model that includes a planning phase, an execution phase and an adjustment phase. In addition, we focus on how the problems of role-task assignment and user-task assignment are respectively translated into CSP (Constraint Satisfaction Problem) and DyCSP (Dynamic constraint Satisfaction Problem) and solved using the explanation concept. In case of an inconsistent assignment problem, we propose to restore problem consistency based upon inconsistency explanation.
Meriam Jemel, Nadia Ben Azzouna,Towards a dynamic authorisation planning satisfying intra-instance and inter-instance constraints
In the 6th International Conference on Security of Information and Networks (SIN 2013), July 21-23 2013, Izmir, Turkey, pages 440-443, ACM, 2013., 2013
Abstract
Role-Based Access Control (RBAC) model has been developed as an alternative to traditional approaches to handle access control in workflow systems. Accordingly, authorisation constraints must be defined to enforce the legal assignment of access privileges to roles and roles to users. The authorisation planning ensures that there is at least one way to complete the workflow instance without breaching any of the authorisation constraints. Authorisation planning with considering intra-instance constraints has been discussed in the research literature. However, the inter-instance constraints also need to be considered to mitigate the security fraud. In this paper, a novel authorisation system that incorporates intra-instance and inter-instance constraints is proposed. It includes the planning phase, the execution phase, and the adjustment phase. It is in charge of generating user/role assignment plans, verifying them and eventually updating them to take into account the dynamic (intra-instance and inter-instance) constraints. Besides, grounded upon agent technology and publish-subscribe communication model, a mechanism for the consideration of dynamic constraints (intra-instance and inter-intance) to generate valid assignment plans is demonstrated.
, ,A Multi-agent Approach for Routing on Vehicular Ad-Hoc Networks,
The 4th International Conference on Ambient Systems, Networks and Technologies, 2013
Abstract
Vehicular Ad-Hoc Network is a special form of mobile ad -hoc networks (MANETs) which is a vehicle to vehicle and vehicle roadside wireless communication network. VANET is a new standard that integrates Wi-Fi, Bluetooth and other mobile connectivity protocols. The essential requirement of VANET is that it should be able to communicate in any environment irrespective of traffic densities and vehicle locations. Vehicular communications are made in fluctuating environment and should work both in urban and rural areas. Considering the large number of nodes participating in these networks and their high mobility, debates still exist about the feasibility of routing protocols. Analyzes of traditional routing protocols for MANETs demonstrated that their performance is poor in VANETs. The main problem with these protocols in VANETs environments is their route instability. Consequently, many packets are dropped and the overhead due to route repairs or failure notifications increases significantly, leading to low delivery ratios and high transmission delays. This paper introduces a multi-agent system approach to solve the problems mentioned above and improve Vehicular ad-hoc network routing.
Kalthoum Rezgui,Theoretical formulas of semantic measure: a survey
-, 2013
Abstract
In recent years, several semantic similarity and relatedness measures have been developed and applied in many domains including linguistics, biomedical informatics, GeoInformatics, and Semantic Web. This paper discusses different semantic measures which compute similarity and relatedness scores between concepts based on a knowledge representation model offered by ontologies and semantic networks. The benchmarks and approaches used for the evaluation of semantic similarity methods are also described. The aim of this paper is to give a comprehensive view of these measures which helps researchers to choose the best semantic similarity or relatedness metric for their needs.
Saoussen Bel Haj Kacem, ,RAMOLI: A generic knowledge-based systems shell for symbolic data
In : 2013 World Congress on Computer and Information Technology (WCCIT). IEEE, 2013. p. 1-6., 2013
Abstract
Non classical logics were introduced to allow handling imperfect concepts in intelligent systems. One of the principal non classical logic is multi-valued logic that has the particularity to support symbolic data. We introduced in a previous work an approximate reasoning in the multi-valued framework based on linguistic modifiers that checks approximate reasoning axiomatics. This paper describes the development of software model for the treatment of imperfection with our approach of approximate reasoning. It is a knowledge-based systems shell for symbolic data called RAMOLI. This shell provides simple and interactive Graphical User Interface to introduce knowledge and to infer with our approximate reasoning.
Wiem Hammami, Lamjed Ben SaidA DISTRIBUTED PRIVACY-PRESERVING MODEL FOR E-SERVICES
the paper propose a model for privacy protection of users in the context of e-services. A system based on our model has to respect a set of properties to preserve the user privacy, 2013
Abstract
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2012Wassim Ayadi, ,
Pattern-driven neighborhood search for biclustering of microarray data
BMC Bioinformatics 13(S-7): S11, 2012
Abstract
Background
Biclustering aims at finding subgroups of genes that show highly correlated behaviors across a subgroup of conditions. Biclustering is a very useful tool for mining microarray data and has various practical applications. From a computational point of view, biclustering is a highly combinatorial search problem and can be solved with optimization methods.
Results
We describe a stochastic pattern-driven neighborhood search algorithm for the biclustering problem. Starting from an initial bicluster, the proposed method improves progressively the quality of the bicluster by adjusting some genes and conditions. The adjustments are based on the quality of each gene and condition with respect to the bicluster and the initial data matrix. The performance of the method was evaluated on two well-known microarray datasets (Yeast cell cycle and Saccharomyces cerevisiae), showing that it is able to obtain statistically and biologically significant biclusters. The proposed method was also compared with six reference methods from the literature.
Conclusions
The proposed method is computationally fast and can be applied to discover significant biclusters. It can also used to effectively improve the quality of existing biclusters provided by other biclustering methods.
Wassim Ayadi, ,BicFinder: a biclustering algorithm for microarray data analysis
Knowl. Inf. Syst. 30(2): 341-358, 2012
Abstract
In the context of microarray data analysis, biclustering allows the simultaneous identification of a maximum group of genes that show highly correlated expression patterns through a maximum group of experimental conditions (samples). This paper introduces a heuristic algorithm called BicFinder (The BicFinder software is available at: http://www.info.univ-angers.fr/pub/hao/BicFinder.html) for extracting biclusters from microarray data. BicFinder relies on a new evaluation function called Average Correspondence Similarity Index (ACSI) to assess the coherence of a given bicluster and utilizes a directed acyclic graph to construct its biclusters. The performance of BicFinder is evaluated on synthetic and three DNA microarray datasets. We test the biological significance using a gene annotation web-tool to show that our proposed algorithm is able to produce biologically relevant biclusters. Experimental results show that BicFinder is able to identify coherent and overlapping biclusters.
