• [Conférence intertnationale] Leila Ghorbel, Corinne Amel Zayani, Ikram Amous Ben Amor et Florence Sèdes. Learner's Pro le Hierarchization in an Interoperable Education System. International Conference on Intelligent Systems Design and Applications (ISDA), decembre, 2016, Portugal
    Abstract
    In recent years, several education systems have been developed. Consequently, each learner can have di fferent pro files which each one is related to a system. Each profi le can be completed and enriched by the data coming from the other profi les in order to return results reflecting the learner's need. The profi le enrichment requires the establishment of an interoperable system which i) resolves the problem of learner's profi le heterogeneity based on a matching process and ii) integrates the data in the diff erent profi les based on a data fusion process. The data fusion approaches mainly aim at resolving the conficts occurring in the data values. They are based on non organized pro les which may produce inconsistent results. The profi le organization is done either by using the machine learning techniques or the notion of temperature. In this paper, we propose a new data fusion approach to improve the conflict resolution by organized profi les. Each profi le is organized by respectively merging a clustering algorithm and the temperature and by taking into account the data semantic relationship.
  • [Conférence intertnationale] Ahlem Kalaï, Abdelghani Wafa, Corinne Amel Zayani et Ikram Amous Ben Amor. LoTrust: A Social Trust Level Model based on Time-Aware Social Interactions and Interests Similarity. Fourteenth Annual Conference on Privacy, Security and Trust (PST 2016), decembre, 2016, Nouvelle-Zélande
    Abstract

    With the immense growth of online social applications, trust plays a more and more important role in connecting users to each other, sharing their personal information and attracting him to receive recommendations. Therefore, how to obtain trust relationships through mining online social networks became a critical issue. To calculate the level of trust between two users, many computational trust models are proposed which mainly rely on the social network structure, the explicit trust from user to another, the users’ behaviors, or the users’ similarity, etc. However, the majority of these models ignored the temporal factor. In this paper, we propose a trust relationship detection mechanism from an egocentric social network in order to compute the trust level between an active user and his directed friends. We propose a Level of social Trust model, that we called LoTrust, which is suitable for personalized recommendation purpose. This computational model founded on novel trust metric which is based not only on the users’ interests similarity according to their semantic social profiles (RDF/FOAF), but also takes into account the time factor of the users’ active interactions (e.g comments, share photo, wall posts, messages). We perform experiments on real life dataset extracted from Facebook. The experimental results demonstrated how our LoTrust model produces satisfactory results than other computational models.

  • [Conférence intertnationale] Inès Zribi. An Automatic Process for Tunisian Arabic Orthography Normalization. HrTAL2016, octobre, 2016, Croatie
    Abstract

    Arabic dialects have no standard dialectal spelling systems. Arbitrary transcription of dialect words will display varieties of orthographic forms. This causes problems for natural language processing (NLP). In this paper, we present an automatic process for normalization of spontaneously spelled Tunisian Arabic (TA) into a conventional orthography CODA-TA [1]. We show that rule-based and statistical methods can reduce the transcription errors by 77.73% over this baseline on an unseen test set.

  • [Chapitre] Ahlem Kalaï, Corinne Amel Zayani, Ikram Amous Ben Amor et Florence Sèdes. Expertise and Trust –Aware Social Web Service Recommendation. ICSOC 2016, octobre, 2016, 14th International Conference, ICSOC, Banff, Alberta, Canada
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    Abstract

    With the increasing number of Web services, the personalized recommendation of Web services has become more and more important. Fortunately, the social network popularity nowadays brings a good alternative for social recommendation to avoid the data sparsity problem that is not treated very well in the collaborative filtering approach. Since the social network provides a big data about the users, the trust concept has become necessary to filter this abundance and to foster the successful interactions between the users. In this paper, we firstly propose a trusted friend detection mechanism in a social network. The dynamic of the users’ interactions over time and the similarity of their interests have been considered. Secondly, we propose a Web service social recommendation mechanism which considers the expertise of the trusted friends according to their past invocation histories and the active user’s query. The experiments of each mechanism produced satisfactory results.

  • [Conférence intertnationale] Mohamed Ali Hadj Taieb et Mohamed Ben Aouicha. WSD-TIC: Word Sense Disambiguation using Taxonomic Information Content. 8th International Conference on Computational Collective Intelligence (ICCCI 2016), septembre, 2016, Grèce
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  • [Conférence intertnationale] Inès Zribi, Inès Kammoun, Mariem Ellouze, Lamia Belguith et Philippe Blache. Sentence Boundary Detection for Transcribed Tunisian Arabic. KONVENS2016, septembre, 2016, Bochum, Allemagne
  • [Conférence intertnationale] Hanen Abbes et Faiez Gargouri. Big Data Integration: a MongoDB Database and Modular Ontologies based Approach. 20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, septembre, 2016, Procedia Computer Science, york, Royaume-Uni
    Abstract

    Big Data are collections of data sets so large and complex to process using classical database management tools. Their main characteristics are volume, variety and velocity. Big Data integration is a new research area that faces new challenges due to these characteristics. Ontologies represent knowledge as a formal description of a domain of interest. They are widely used in data integration. This paper illustrates an approach for ontology based Big Data integration taking into account their characteristics. Our approach is based on a NOSQL database namely MongoDB and modular ontologies. It follows three steps: wrapping data sources to MongoDB databases, generating local ontologies, composing the local ontologies to get a global one. A tool implementing the generation of the local ontologies is also detailed.

  • [Conférence intertnationale] Noura Borchani, Tarak Chaari et Rafik Bouaziz. Towards an automatic Intention Recognition from client request. ICCCI, septembre, 2016, Springer, Grèce
  • [Conférence intertnationale] Fatma Ghorbel, Nebrasse Ellouze Bouaziz, Elisabeth Métais, Fayçal Hamdi, Faiez Gargouri et Noura Herradi. MEMO GRAPH: An Ontology Visualization Tool for Everyone. 20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, septembre, 2016, York, Royaume-Uni
    Abstract
    This paper presents a user-friendly tool, called MEMO GRAPH, for visualizing and navigating ontologies. Compared to related work, MEMO GRAPH is designed to be used by everyone, including ontology experts and users not familiar with ontologies. It provides an accessible and understandable user interface that follows the “design-for-all” philosophy. Precisely, it offers an Alzheimer’s patients-friendly interface. The MEMO GRAPH ontology visualization tool is integrated in the CAPTAIN MEMO memory prosthesis and it is applied for visualizing a small-scale ontology (PersonLink) and a large-scale ontology (DBpedia). We discuss the encouraging results derived from the preliminary empirical evaluation, which confirms that MEMO GRAPH is an intuitive and usable ontology visualization tool.
  • [Journal] Elyes Zarrouk et Yassine Ben Ayed. Hybrid SVM/HMM model for the Arab phonemes recognition. , septembre, 2016, The International Arab Journal of Information Technology (IAJIT), Volume 13 issue no 5
    Abstract

    Hidden Markov Models (HMM) are currently widely used in Automatic Speech Recognition (ASR) as being the most effective models. Yet, they sometimes pose some problems of discrimination. The hybridization of Artificial Neural Networks(ANN) in particular Multi Layer Perceptrons (MLP) with HMM is a promising technique to overcome these limitations. In order to ameliorate results of recognition system, we use Support Vector Machines (SVM) witch characterized by a high predictive power and discrimination. The incorporation of SVM with HMM brings into existence of the new system of ASR. So,

    by using 2800 occurrences of Arabic phonemes, this work arises a comparative study of our acknowledgment system of it as the following: The use of especially the HMM standards lead to a recognition rate of 66.98%. Also, with the hybrid system MLP/HMM we succeed in achieving the value of 73.78%. Moreover, our proposed system SVM/HMM realizes the best performances, whereby, we achieve 75.8% as a recognition frequency.
  • Date de création : 19 Décembre 2012
    MIRADOC 2011 : Journées doctorales du laboratoire Miracl. Décembre 2011. Touzeur , Tunisie
  • Date de création : 19 Décembre 2012
    MIRADOC’2010, Rencontre Doctorale de Miracl Date : 19-22 décembre 2010. Lieu: Douz , Tunisie
  • Date de création : 19 Décembre 2012
    Les 2éme Journées sur les Réseaux Bayésiens et leurs Applications Date et lieu : octobre 2011, Sfax, Tunisie. Abstract The Bayesian Networks are graphical models that are easy to interpret and update. These models are useful if the knowledge is uncertain, but they lack some means to express ambiguity. To face this problem, we propose Fuzzy Evidence in Bayesian Networks and combine the Fuzzy Logic and Bayesian Network. This has allowed to benefit from mutual advantages of these two approaches, and to overcome the problem of data and observation ambiguity. This paper proposes an inference algorithm which uses the Bayesian Network and Fuzzy Logic reliability. This solution has been implemented, tested and evaluated in comparison with the existing methods.
  • Faiez Gargouri, Wassim Jaziri  Ontology Theory, Management and Design: Advanced Tools and Models ». IGI Global. ISBN-978-1-61520-   859-3. Mars 2010. Description Ontologies and formal representations of knowledge are extremely powerful tools for modeling and managing large applications in several domains ranging from knowledge engineering, to data mining, to the semantic web. Ontology Theory, Management and Design: Advanced Tools and Models, explores the wide range of applications for ontologies, while providing a complete view of the both the theory behind the design and the problems posed by the practical development and use of ontologies. This reference presents an in-depth and forward looking analysis of current research, illustrating the importance of this field and pointing toward to the future of knowledge engineering, management and information technology.  
  • Titre : Finite State Language Engineering Autheurs : BEN HAMADOU A., MESFAR S., SILBERZTEIN M.  ISBN : 978-9973-37602-2 Livre, Edition CPU, Tunis, 2010
  • Titre :  Les Systèmes décisionnels : Théorie et pratique Autheurs : BEN-ABDALLAH H., BOULMARKOUL A., BOUSSAID O., FEKI J., GARGOURI F. ISBN : 789973990020. Nombre des pages: 130 pages Livre, Nouha Editions, 2010.