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Research results
Ph.D de

Group : Heterogeneous Modeling


Starts on 01/03/2014
[QUERCINI Gianluca]

Funding : CIFRE
Affiliation : Centrale Supélec
Laboratory :

Defended on 19/12/2017, committee :
Jérôme Azé – Université de Montpellier, LIRMM

Nicolas LABROCHE – Université François-Rabelais de Tours, LI

Nacéra SEGHOUANI BENNACER – LRI, CentraleSupélec

Gianluca QUERCINI – LRI, CentraleSupélec

Chantal REYNAUD – Université Paris-Sud, LRI

Haïfa ZARGAYOUNA – Université Paris 13, LIPN

Uriel BERDUGO – Wepingo

Research activities :

Abstract :
The social Web grows more and more and gives through the web, access to a wide variety of resources, like sharing sites such as, exchange messages as Twitter, or social networks with the professional purpose such as LinkedIn, or more generally for social purposes, such as Facebook and LiveJournal. Thus, the same individual can be registered and active on different social networks (potentially having different purposes), in which it publishes various information, which are constantly growing, such as its name, locality, communities, messages, various activities, etc. This information is important especially for applications seeking to know their users in order to better understand their needs, activities and interests. The objective of our research is to exploit essentially the textual resources extracted from the different social networks of the same individual in order to construct his characterizing profile, which can be exploited in particular by applications seeking to understand their users, such as recommendation systems. Given its international dimension, the content of the Web is inherently multilingual and intrinsically ambiguous, since individuals from different origin publish it in natural language in a free vocabulary and therefore the exploited textual resources are also multilingual and ambiguous. Nevertheless, we propose automatic, multilingual, and unsupervised approaches using Wikipedia to build an expanded profile for each user by aggregating information from its various social networks. In addition, we analyzed the correlation between user’s personality traits and his / her discovered interests, with a view to further characterizing it.

Ph.D. dissertations & Faculty habilitations
In this thesis, we propose a formal energy model which allows an analytical study of energy consumption, for the first time in the context of population protocols. Population protocols model one special kind of sensor networks where anonymous and uniformly bounded memory sensors move unpredictably and communicate in pairs. To illustrate the power and the usefulness of the proposed energy model, we present formal analyses on time and energy, for the worst and the average cases, for accomplishing the fundamental task of data collection. Two power-aware population protocols, (deterministic) EB-TTFM and (randomized) lazy-TTF, are proposed and studied for two different fairness conditions, respectively. Moreover, to obtain the best parameters in lazy-TTF, we adopt optimization techniques and evaluate the resulting performance by experiments. Then, we continue the study on optimization for the poweraware data collection problem in wireless body area networks. A minmax multi-commodity netow formulation is proposed to optimally route data packets by minimizing the worst power consumption. Then, a variable neighborhood search approach is developed and the numerical results show its efficiency. At last, a stochastic optimization model, namely the chance constrained semidefinite programs, is considered for the realistic decision making problems with random parameters. A novel simulation-based algorithm is proposed with experiments on a real control theory problem. We show that our method allows a less conservative solution, than other approaches, within reasonable time.