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The explosion of social media and microblogging services has gradually increased the microblogging data and particularly tweets data. In microblogging services such as Twitter, the users may become overwhelmed by the rise of data. Although, Twitter allows people to micro-blog about a broad range of topics in real time, it is often hard to understand what these tweets are about. In this work, we study the problem of Tweet Categorization (TC), which aims to automatically classify tweets based on their topic. The accurate TC, however, is a challenging task within the 140-character limit imposed by Twitter. The majority of TC approaches use lexical features such as Bag of Words (BoW) and Bag of Entities (BoE) extracted from a Tweet content. In this paper, we propose a semantic approach of improving the accuracy of TC based on feature expansion from external Knowledge Bases (KBs) and the use of eXtended WordNet Domain as a classifier. In particular, we propose a deep enrichment strategy to extend tweets with additional features by exploiting the concepts present in the semantic graph structures of the KBs. Then, our supervised categorization relies only on the ontological knowledge and classifier training is not required. Empirical results indicate that this enriched representation of text items can substantially improve the TC performance.

IT infrastructure is one of the important pillars of any country development. Emerging wireless technologies are offering new opportunities to develop very quickly IT infrastructures in order to deploy immediately important and useful community services. One big challenge is to provide a possibility to build a network that can grow in term of coverage to offer service access such as internet access for a large number of people with different needs from the network. In this context, we propose to analyse a wireless mesh network extended by an ad hoc network capable to grow in an ad hoc way by using ad hoc routing capabilities. The security challenges are related first to the authentication architecture, and second to the data confidentiality. More precisely, PANA combined with a new EAP-EHash method is proposed and discussed in a multi-hop mesh network, and a security analysis is provided., Les infrastructures TIC sont essentielles au développement d'un pays. Les technologies de réseaux sans fil offrent de nouvelles opportunités de développer très rapidement des infrastructures TIC dans le but d'un déploiement immédiat de services utiles à la communauté. Tout l'enjeu réside dans la construction d'un réseau extensible du point de vue de sa couverture radio et qui apporte l'accès au service comme l'accès à Internet au plus grand nombre, et ce sans négliger les attentes en matière de communication propres à chaque utilisateur. Dans ce contexte, nous proposons d'analyser un réseau mesh sans fil dont la couverture est étendue par un réseau ad hoc doté de capacités de routage ad hoc. Les enjeux de sécurité sont liés à l'architecture d'authentification et à la confidentialité des données. Plus précisément, le couplage de PANA avec la nouvelle méthode EAP-EHash est proposée, étudiée dans un contexte ad hoc muti-sauts et finalement analysée du point de vue de la sécurité

It is well known in information retrieval area that one important issue is the gap between the query and document vocabularies. Concept-based representation of both the document and the query is one of the most effective approaches that lowers the effect of text mismatch and allows the selection of relevant documents that deal with the shared semantics hidden behind both. However, identifying the best representative concepts from texts is still challenging. In this paper, we propose a graph-based method to select the most significant concepts to be integrated into a conceptual indexing system. More specifically, we build the graph whose nodes represented concepts and weighted edges represent semantic distances. The importance of concepts are computed using centrality algorithms that levrage between structural and contextual importance. We experimentally evaluated our method of concept selection using the standard ImageClef2009 medical data set. Results showed that our approach significantly improves the retrieval effectiveness in comparison to state-of-the-art retrieval models.