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Haut Commissariat au Plan -------------------------------------------------------------------------------------- Institut National de Statistique et d’Economie Appliquée (INSEA) -----------------------------------------------------------------------------------

Centre d’Etudes Doctorales - Sciences, Ingénierie et Développement Durable (CEDOC-SIDD)

Laboratoire de recherche Systèmes d’Information, Systèmes Intelligents et Modélisation

Mathématique (SI2M)

Les sujets de thèse prévus pour l’année 2017-2018

Filière Systèmes d’Information et Systèmes Intelligents

Octobre 2017

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Optimisation 1: Méthodes de résolution pour les problèmes MPECs (Pr. A. Kadrani)

La résolution de problèmes dégénérés est souvent difficile, mais les algorithmes modernes laissent entrevoir de l'espoir ; en particulier, je progresse dans le développement d'algorithmes de résolution pour des programmes mathématiques avec des contraintes d’équilibre (MPECs). Un MPEC est un problème d'optimisation dans lequel les contraintes comprennent des restrictions de complémentarité ou des inégalités variationnelles paramétriques. Ce programme mathématique est au centre de nombreux problèmes tels que la conception d'ingénierie, la théorie des jeux ou l'équilibre économique et les taxes en économie. Les MPECs peuvent être considérés comme des extensions de programmes mathématiques à deux niveaux et sont également appelés programmes mathématiques avec contraintes de complémentarité (MPCCs) dont le concept de complémentarité est synonyme de la notion d'équilibre du système.

Ces problèmes ont fait l'objet de beaucoup de recherches récentes en raison de leurs applications théoriques et pratiques. Du point de vue de la programmation non linéaire (PNL), les MPCCs sont classés parmi les problèmes les plus dégénérés, en conséquence, la plupart des théories bien développées de la programmation non linéaire ne réussit pas souvent à résoudre ces problèmes. Une autre difficulté dans le traitement des MPCCs est leur nature combinatoire due aux contraintes de complémentarité. Par conséquent, le développement d'un algorithme efficace pour résoudre ces problèmes est d'un grand intérêt.

Nous essayons de développer des nouvelles approches d’approximation ou de régularisation permettant de surmonter le manque de régularité de ces problèmes avec moins d’hypothèses sur les contraintes et appliquer par la suite des différents algorithmes de la programmation non linéaire classique.

References

1. D. Ralph and S. Wright, Some properties of regularization and penalization schemes for MPEC. Optimization Methods and Software, 19 (2004), pp. 527–556.

2. A. Kadrani, J-P. Dussault, and A. Benchakroun. A globally convergent algorithm for MPCC. Euro Journal of computational optimization 3(3) : 263-296 (2015).

3. C. Kanzow and A. Schwartz. A New Regularization Method for Mathematical Programs with Complementarity Constraints with Strong Convergence Properties. SIAM Journal on Optimization, 23(2):770-798, apr 2013.

4. C. Kanzow and A. Schwartz. The Price of Inexactness: Convergence Properties of Relaxation Methods for Mathematical Programs with Complementarity Constraints Revisited. Mathematics of Operations Research, 40(2):253-275, may 18/21 2015.

5. T. Migot, J-P. Dussault, M. Haddou and A. Kadrani. How to compute a local minimum of the MPCC. (2017).

6. T. Migot, M. Haddou and J-P. Dussault. The new butterfly relaxation methods for Mathematical Programs with Complementarity Constraints (MPCC). (2017).

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Optimisation 2 : Développement d’un modèle de Credit Scoring basée sur la méthode Reject inference utilisant des techniques de machine Learning (Pr. Mohammed OUZINEB)

Le sujet de thèse proposé porte sur le développement d’un modèle de credit scoring basé sur la méthode Rejet inference. Pour ce faire, on propose d’utiliser des méthodes de machine learning. Les modèles de classifications liés aux crédits sont généralement construits sur une base de données des clients dont le prêt a été accepté. Cette base de données englobe également des informations liées aux remboursements ainsi qu’aux comportements des clients. Ceci dit, il est important de souligner que dans la pratique, ces modèles sont régulièrement appliqués aux clients dont le prêt n’a pas été encore accepté.

Le rejet d’inférence (reject inference) est une technique permettant d'inférer les résultats pour les clients dont la demande de prêt a été rejetée. En outre, cette technique permet d’intégrer les résultats dans le système de notation afin d’améliorer la précision de décision. L’objectif de cette thèse portera sur deux volets. Le premier volet, porte sur la proposition de nouvelles méthodes d’apprentissage automatique. Le second volet de cette thèse, porte sur l’application du modèle HMM comme technique d’apprentissage au problème de classification dans l’objectif d’améliorer la performance des modèles de credit scoring liés aux crédits.

OPTIMISATION 3 : Problème d'optimisation conjointe de tournées des véhicules et de planification des opérations de maintenance (Pr. A. Kadrani/ Pr. R. BENMANSOUR) Nous considérons un ensemble de techniciens effectuant des opérations de maintenance sur des installations situées dans différents sites clients. Les installations sont sujettes à des défaillances aléatoires et nécessitent de ce fait une stratégie de maintenance adaptée à leurs utilisations. Dans cette perspective, le travail demandé consiste :

1) à proposer des stratégies de maintenance adaptées à chaque installation et à planifier leurs exécutions

2) à planifier le routage pour permettre à chaque installation d'être réparée (ou remplacée) à temps,

De façon conjointe tout en minimisant les coûts indus sur la période considérée. Plusieurs contraintes sont à prendre en compte dans le cadre de cette étude (notamment l'expertise des techniciens, le nombre de dépôt, la sous-traitance, etc). Profil recherché :

- des compétences en recherche opérationnelle, optimisation combinatoire et/ou

optimisationsousincertitude.

- desconnaissancesdansledomainedesmétaheuristiques.

- Maitrised'unlogicield'optimisation:CPLEX,Gurobi

SYSTEMES INTELLIGENTS 1 : SYNERGY comme langage de modélisation et de simulation des Réseaux Complexes (Pr. KABBAJ Adil) L'étude des réseaux complexes fait partie de la Science des Systèmes Complexes. Le langage SYNERGY a été développé par Pr. A. KABBAJ comme langage de modélisation et de simulation basé Graphe Conceptuel (version unifiée des Réseaux Sémantiques). Le sujet vise à explorer l'utilisation de SYNERGY comme

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langage de modélisation et de simulation des Réseaux Complexes. SYSTEMES INTELLIGENTS 2 : Communication entre Agents Intelligents en utilisant un "Arabe Contrôlé" (Pr. KABBAJ Adil) Le sujet consiste à explorer la possibilité de développement un analyseur syntaxico-sémantique et un générateur pour un sous-ensemble de l'Arabe ("Arabe contrôlé") et d'utiliser ce sous ensemble comme langage de communication entre Agents Intelligents. SYSTEMES INTELLIGENTS 3 : Génération automatique d'histoires pour enfants (Pr. KABBAJ Adil) Le sujet consiste à étudier le domaine de la génération automatique d'histoires, et en particulier des histoires pour enfants, en se basant sur les Systèmes Multi-Agents, le Raisonnement basé Expérience, la Créativité, les travaux sur les émotions, la morale, les valeurs, la culture, etc. SYSTEMES INTELLIGENTS 4 : Deep learning on contraint programming Networks (Pr. BENELALLAM Imade) Constraint Programming (CP) techniques have been proved to be a very elegant paradigm to model and solve various problems in different fields. The human models the problem and the machine solves it. Solver developers on Artificial Intelligence (AI) and end users often face several issues and questions : - Which configuration settings should I use to optimize my solvers' empirical performance? - How does solver's performance depend on the settings of a certain parameter on constraint networks? - What characteristics distinguish easy from hard problem instances? - Which of two (or more) available solvers will perform best on a given new instance? In this research project, we will describe fully formalized domain-independent methods to answer these questions based on deep learning and constraints optimization techniques. References :

• Tarek Menouer, Nitin Sukhija, and Le Cun Bertrand. 2017. A learning Portfolio solver for optimizing the performance of constraint programming problems on multi-core computing systems. Concurrency and Computation: Practice and Experience 29, 4 (2017).

• Deep Learning for Algorithm Portfolios, Andrea Lorreggia, Horst Samulowitz, Yuri Malitsky, Vijay Saraswat (2016), 30th AAAI Conference on Artificial Intelligence, 2016. http://www.cs.toronto.edu/~horst/cogrobo/papers/DLforAP.pdf

• Model-based Genetic Algorithms for Algorithm Configuration [ pdf ] Carlos Ansotegui, Yuri Malitsky, Horst Samulowitz, Meinolf Sellmann, Kevin Tierney(2015), IJCAI, 2015.

• Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering [ pdf] Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, and Meinolf Sellmann (2013) 23rd International Joint Conference on Artificial Intelligence, Beijing, China, IJCAI

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• Snappy: A Simple Algorithm Portfolio (Tool-Paper) [ pdf ] Horst Samulowitz, Chandra Reddy, Ashish Sabharwal, Meinolf Sellmann (2013) To be published in 16th International Conference on Theory and Applications of Satisfiability Testing, SAT

• Frank Hutter, Lin Xu, Holger Hoos, Kevin Leyton-Brown (2014). Algorithm runtime prediction: Methods and evaluation. Artificial Intelligence Journal (AIJ) 206:79–111.

• Frank Hutter, Holger Hoos, Kevin Leyton-Brown (2011). Sequential Model-Based Optimization for General Algorithm Configuration. Proceedings of the 5th Learning and Intelligent Optimization Conference (LION 2011).

• Chris Thornton, Frank Hutter, Holger Hoos, Kevin Leyton-Brown (2013). Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms. Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2013).

SYSTEMES INTELLIGENTS 5 : Constraint programming solvers for Big Data problems (Pr. BENELALLAM Imade) With the evolution of devices and the produced data, some problems that can be represented as CSP, may contain a big number of variables, namely BigCSP, where one machine may be unable to solve the problem or it will take a long time to find a solution. Nowadays, in Big Data technologies, there are many intelligent solutions to split missions and involve many nodes to find collectively a solution. In this research project, we will demonstrate how to represent any BigCSP as a Big data problem, and propose a new algorithm to solve the BigCSP problem. References :

• Arnaud Malapert, Jean-Charles Régin, and Mohamed Rezgui. 2016. Embarrassingly Parallel Search in Constraint Programming. Journal of Artificial Intelligence Research 57 (2016), 421–464.

• Lucas Bordeaux, Youssef Hamadi, and Horst Samulowitz. 2009. Experiments with Massively Parallel Constraint Solving. In IJCAI, Vol. 2009. 443–448.

• Carl Christian Rolf and Krzysztof Kuchcinski. 2010. Combining parallel search and parallel consistency in constraint programming. In TRICS workshop at the international conference on principles and practice of constraint programming. 38–52.

SYSTEMES INTELLIGENTS 6 : Distributed Constraint Reasoning based Blockchains technologies (Pr. BENELALLAM Imade) Recently, there has been a huge explosion of interest in blockchain-based distributed ledger systems such as Bitcoin and many others. On Distributed constraint Programming many questions raised, such as privacy, ethics, security, and so on, are all issues familiar to Constraint Programming community. In this research project, we will demonstrate how to represent distributed constraint network

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using Bockchains in order to guarantee privacy, ethics and security of exchanged data. References :

• Maurice Herlihy. 2017. Blockchains and the Future of Distributed Computing. In Proceedings of the ACM Symposium on Principles of Distributed Computing (PODC '17). ACM, New York, NY, USA, 155-155. DOI : https://doi.org/10.1145/3087801.3087873

• Blockchain Technology: Principles and Applications Research Handbook on Digital Transformations, edited by F. Xavier Olleros and Majlinda Zhegu. Edward Elgar, 2016

• Blockchain technology: Beyond bitcoin, M Crosby, P Pattanayak, S Verma, 2016 • Distributed Constraint Programming, Boi Faltings, https://doi.org/10.1016/S1574-

6526(06)80024-6 SYSTEMES INTELLIGENTS 7 : Deep Learning pour l’analyse d’image médicale (Pr. SAIDI Nabil) Au cours des dernières décennies, nous avons assisté à l'importance de l'imagerie médicale, tel que la résonance magnétique (MR), la mammographie, l'échographie, etc., pour la détection, le diagnostic et le traitement des maladies. L’objectif de cette thèse est le développement et la validation d’un système d’aide au diagnostic fondé sur les méthodes de machine Learning à partir de données d’IRM, pour pré identifier les sites de lésions et leur classification. On s’intéressera plus particulièrement à l’étude de tumeurs du cerveau et son diagnostic, vu que les tumeurs cérébrales sont sérieuses et difficile à guérir, car le cerveau a une structure très complexe. A titre d’exemple, à l’issue d’un examen d’IRM, on peut déterminer si un patient est diagnostiqué avec une tumeur cérébrale d'une manière non invasive. Une tumeur et ses régions doivent être détectées à l'aide d'une segmentation pour aider son traitement, mais ca reste une procédure couteuse en terme de temps et variable entre les experts pour un même cas ,si cette elle est effectuée manuellement. Afin de pallier à ces problèmes, un certain nombre de méthodes automatiques et semi-automatiques ont été proposées au cours des années pour aider les médecins dans le processus décisionnel. Les méthodes basées sur machine Learning ont suscité un fort intérêt dans le domaine de la segmentation des tumeurs cérébrales. Avec l'avènement de méthodes d'apprentissage approfondies et leur succès dans de nombreuses applications de vision par ordinateur telles que la classification d'image, ces méthodes ont également commencé à gagner en popularité dans l'analyse d'image médicale. Les méthodes de Deep Learning traitent de l'apprentissage de la représentation en apprenant automatiquement une hiérarchie (architecture) de fonctionnalités complexes directement à partir des données, au lieu de développer des caractéristiques conçue à la main, qui peuvent nécessiter des connaissances spécialisées. Mots clés : Machine Learning - deep learning - Imagerie médicale, segmentation de tumeur cérébrale, classification, système d’aide au diagnostic Bibliographie :

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[1] K Usman, K Rajpoot . «Brain tumor classification from multi-modality MRI using wavelets and machine learning ». Pattern Analysis and Applications, 20(3): 871–881, 2017 . [2] Dinggang Shen, Guorong Wu, and Heung-Il Suk . «Deep Learning in Medical Image Analysis». Annual Review of Biomedical Engineering, Vol.19:221-248,2017. [3] S. Bauer, R. Wiest, L. Nolte, and M. Reyes. «A survey of mri-based medical image analysis for brain tumor studies». Physics in medicine and biology, 58(13): 97–129, 2013. [4] LeCun Y, Bengio Y, Hinton G Nature. « Deep Learning » May 28; 521(7553):436-44. 2015. SYSTEMES INTELLIGENTS 8 : Mise en place d'un système opérationnel de traitement de données satellites multi dates et multi échelles pour suivre la qualité des eaux marines en relation avec les ressources halieutiques du Maroc. ( Pr. Mohamed Nabil SAIDI/Dr. A. ATILLAH et Pr. M.F SMIEJ) Le candidat doit établir des indicateurs pertinents issus des images satellites de différents capteurs (optiques, thermiques et radar) et des données de mesures in situ ainsi que les produits issus de la modélisation, pour décrire la qualité des eaux de la mer le long des côtes marocaines et déterminer les variations spatio temporelles de cette qualité. Ces indicateurs doivent servir par la suite à identifier les zones potentiellement favorables au développement des ressources marines (ressources halieutiques et aquaculture). Après la définition et le choix des indicateurs, le candidat devra concevoir et mettre en place un système de traitement automatique des toutes les données disponibles (longues séries temporelles) pour extraire aussi bien les indicateurs et leur combinaison pour identifier en temps quasi réel les zones géographiques potentiellement favorables à la concentration des ressources halieutiques (implémentation méthodes statistiques et des différents algorithmes de traitement d'images, automatisation de la chaine de traitement....). Mots clés : Images de Télédétection, apprentissage automatique, extraction de caractéristiques, Extraction de connaissance à partir de donnée. SYSTEMES INTELLIGENTS 9 : OPINION MINING & SENTIMENT ANALYSIS: Lexical and ML approaches (Pr. CHERIF Walid/Pr. BENELALLAM Imade) Every day, Internet users generate a huge volume of opinions related to different aspects of their lives. These opinions are expressed in various forms such as articles, reviews, tweets, pictures, etc. In general, opinion mining refers to the task of automatically extracting users’ opinions on certain issues, news, services, products, while Sentiment Analysis extracts subjectivity and polarity. In this research project, we will focus on two fields:

- Opinion mining from textual data such as Tweets: text processing, decision rules, optimization of weights, ML for classification.

- Facial expression recognition: image processing with some expert rules and some elementary characteristics such as the color, the shape, and the texture, and ML for classification.

References:

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• Zhao, S., Gao, Y., Jiang, X., Yao, H., Chua, T. S., & Sun, X. (2014, November). Exploring principles-of-art features for image emotion recognition. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 47-56). ACM.

• Tian, Y., Kanade, T., & Cohn, J. F. (2011). Facial expression recognition. In Handbook of face recognition (pp. 487-519). Springer London.

• Cherif, W., Madani, A., & Kissi, M. (2016). A hybrid optimal weighting scheme and machine learning for rendering sentiments in tweets. International Journal of Intelligent Engineering Informatics, 4(3-4), 322-339.

SYSTEMES INTELLIGENTS 10 : MACHINE LEARNING: Deep Learning and Hybrid Fuzzy-ML for supervised classification and regression (Pr. CHERIF Walid/Pr. SAIDI Nabil) Data mining has, over recent years, seen big advances because of the tremendous volume of available data, and also the immense advances in technologies which facilitate the analysis of these data. These algorithms are applied in many fields, and the need to construct more reliable approaches is becoming more and more indispensable. In this research project, we will overcome main limits of current approaches of classification and regression. We will then optimize their algorithms by fuzzy logic, by a combination of learning models, and by metaheuristic. References:

• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

• Cherif, W. (2018). Hybrid Reliability-Similarity based Approach for Supervised Machine Learning, 20th International Conference on Data Mining and Applications, Miami, USA. March 12 – 13 https://www.waset.org/conference/2018/03/miami/ICDMA/program

• Cherif, W., Madani, A., & Kissi, M. (2015). Towards an efficient opinion measurement in Arabic comments. Procedia Computer Science, 73, 122-129.

• Huang, C. L., & Dun, J. F. (2008). A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Applied soft computing, 8(4), 1381-1391.

SYSTEMES INTELLIGENTS 11 : Développement de modèles efficaces pour la classification des données complexe et de grande taille (Pr. OUZINEB Mohamed/Pr. SAIDI Nabil) Chaque jour, 2500 pétaoctets (2.5*1018) de donnée est crée, nous sommes dans l’ère du Big data. Les données sont devenues de plus en plus nombreuses et plus complexes, sont récupérées de sources diverses, des données structurées et non structurées. Face aux montagnes de données collectées, les grands groupes doivent analyser ces données pour développer des modèles et prendre des décisions à temps.

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Dans notre thèse on va s’intéresser au développement d’algorithmes de classification et à les appliqués aux systèmes de recommandation et l’intelligence artificielle. La classification est un problème central du Machine Learning. L’algorithme développé doit permettre à l’ordinateur l’apprentissage automatique pour affecter un élément à un groupe à partir d’autres exemples. Les modèles de classification que nous comptant développé doivent faire fasse à l’augmentation des volumes de données et à la complexité de ces derniers. De plus, il doit être programmé avec un langage de programmation efficace et évolutif et doit être validé et testé sur plusieurs types de données et prouver son efficacité en face des données volumineuses. Pour les entreprises, la classification est un enjeu important qui est devenu un facteur compétitivité et d’innovation. De plus, la classification est applicable dans tous les domaines d’activité à savoir la reconnaissance de formes, l’analyse d’image, la recherche d'information, dans la bio-informatique pour regrouper les séquences homologues dans les familles de gènes dans la biologie évolutive, dans la médecine pour classer les antibiotiques en fonction de leur activité antibactérienne et ainsi développée des médicaments plus efficaces, dans le marketing pour partitionner la population en saignements de marché et mieux positionné les produits, dans les banques pour le Credit Scoring, dans les réseaux sociaux et les moteurs de recherche pour offrir des résultats de recherche plus pertinents et identifier les groupes de personnes, dans la climatologie, la sécurité informatique, la compression de données et beaucoup plus encore.

THÉORIE DES JEUX 1 : Mechanism design and characterization of multi-winner decision rules. (Pr. DOGMI Ahmed) In Mechanism Design theory, the ̀ `planner" or the mechanism designer (representative of state, a local collectivity, a region, manager of a firm, etc…) has a goal in mind---to maximize an objective function called social choice correspondence (or rule}). This correspondence represents the social objectives that the society or its representatives want to achieve. A difficulty arises in the asymmetry of information between planner and agents. This asymmetric information results because the planner does not know the exact preferences of agents. If, for example, the ``options" or outcomes are public goods, the agents will state false preferences in order to participate with lower costs, and once the public property is constructed, they can use it like everyone. In order for the agents to reveal their true preferences, the planner will implement a mechanism (non-cooperative game). It is a way of collecting messages and making a decision; it represents the communication and decision aspects of the organization. This mechanism is a list of messages or ``strategy" spaces, and an outcome function mapping messages into allocations, where the strategies of players depend essentially on the preference profiles and the options (or alternatives) set. A social choice correspondence (SCC) is implementable in a given solution concept if the payment with this solution of the game corresponds to the socially desired alternative and vice versa. The planner, thus, hopes to, via this game, get the agents to reveal their sincere preferences.

In the first project, the SCC is a multi-winner voting that we want to characterize in a non-cooperative game. In the second project we want to study alternative mechanism to solve the problem of fair division in private good economies with different types of preferences. The third project concerns matching markets. These markets constitute a branch of public decision problems that have been extensively analyzed in the literature. They are two principle classes: one-side matching markets and two-side matching markets. The first class studies the assignment of a set of objects with finite capacities to some agents. As examples, we cite the matching of office spaces to faculty members, the matching of student to dormitories etc. The

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second class, that we are focusing on in this paper, is the one of the most ``popular" classes in matching theory; it explores two sides where each side is assumed to have strict preferences over the opposite side. Gale and Shapley (1962) might be the first to study these classes of matching theory. In these environments, stability is considered as a key property to ensure a such assignment. It is checked if the two properties of individual rationality and no-blocking are held. Individual rationality requires that each agent finds her or his match acceptable. The property of no-blocking means that no agent can improve by breaking up with their current match in order to match up with each other instead. Gale and Shapley (1962) proved the existence of stable matchings by providing an algorithm, called the Deferred Acceptance Algorithm, that has been a central role to design matching programs across several institutions and markets. We distinguish among three categories: one-to-one, many-to-one, and many-to-many matching problems. The first category studies the assignment of each agent on one side on the market to at most one agent on the other side. The best known example for this class is the problem of marriages where there is a certain community with a set of men and a set of women, where men only desires women and women only desires men that we would to match. Each man ranks a set of women in accordance with his preferences for a mariage and vis versa. For this class of matching, Gale and Shapley (1962) showed that there always is a stable set of marriages. The second category establishes different examples of matching problems in real world applications: on one side, colleges admit many student, firms hire many workers, hospitals employ many interns, and on the other side, student attend one college, workers work for one firm, and interns work for one hospital. The very known example is the college admission problem that is introduced by Gale and Shapley (1962). Each student ranks a set of college in accordance with his preferences for a matching and each college ranks a set of students with a quota. They proved that a stable college admissions matching exists. The third category analyses many situations where the very known example is given by Roth and Sotomayor (1990) who examine the assignment problem in the market for medical interns in the U.K. .

Some References :

Black D (1958) The theory of committees and elections. Cambridge University Press, Cambridge

Bock H, DayW,McMorris F (1998) Consensus rules for committee elections.Math Soc Sci 35(3):219–232

Caragiannis I, Kalaitzis D, Markakis E (2010) Approximation algorithms and mechanism design for minimax approval voting. In: Proceedings of the 24th AAAI conference on artificial intelligence, pp 737–742 Diss M, Doghmi A (2016) Multi-winner scoring election methods: Condorcet consistency and paradoxes. Public Choice, Volume 169, Issue 1–2, pp 97–116 Doghmi, A., and A. Ziad. 2015. “Nash Implementation in Private Good Economies with Single-Plateaued Preferences and in Matching Problems.” Mathematical Social Sciences 73:32–9. Elkind1 E, Piotr Faliszewski2 P, Skowron1 P, ) and Slinko A, (2017) Properties of multiwinner voting rules. Soc Choice Welf. Gale, D., Shapley, L., 1962. College admission and the stability of marriage. Amer. Math. Monthly 69, 9–15

M. Sotomayor. Three remarks on the many-to-many stable matching problem.

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Mathematical Social Siences, 38:55{70, 1999.

Thomson, W. 2010. “Implementation to Solutions to the Problem of Fair Allocations When Preferences Are Single-Peaked.” Review of Economic Design 14:1–15 THÉORIE DES JEUX 2 : Essays on allocation problems from a mechanism design perspective. (Pr. DOGMI Ahmed) In Mechanism Design theory, the ̀ `planner" or the mechanism designer (representative of state, a local collectivity, a region, manager of a firm, etc…) has a goal in mind---to maximize an objective function called social choice correspondence (or rule}). This correspondence represents the social objectives that the society or its representatives want to achieve. A difficulty arises in the asymmetry of information between planner and agents. This asymmetric information results because the planner does not know the exact preferences of agents. If, for example, the ``options" or outcomes are public goods, the agents will state false preferences in order to participate with lower costs, and once the public property is constructed, they can use it like everyone. In order for the agents to reveal their true preferences, the planner will implement a mechanism (non-cooperative game). It is a way of collecting messages and making a decision; it represents the communication and decision aspects of the organization. This mechanism is a list of messages or ``strategy" spaces, and an outcome function mapping messages into allocations, where the strategies of players depend essentially on the preference profiles and the options (or alternatives) set. A social choice correspondence (SCC) is implementable in a given solution concept if the payment with this solution of the game corresponds to the socially desired alternative and vice versa. The planner, thus, hopes to, via this game, get the agents to reveal their sincere preferences.

In the first project, the SCC is a multi-winner voting that we want to characterize in a non-cooperative game. In the second project we want to study alternative mechanism to solve the problem of fair division in private good economies with different types of preferences. The third project concerns matching markets. These markets constitute a branch of public decision problems that have been extensively analyzed in the literature. They are two principle classes: one-side matching markets and two-side matching markets. The first class studies the assignment of a set of objects with finite capacities to some agents. As examples, we cite the matching of office spaces to faculty members, the matching of student to dormitories etc. The second class, that we are focusing on in this paper, is the one of the most ``popular" classes in matching theory; it explores two sides where each side is assumed to have strict preferences over the opposite side. Gale and Shapley (1962) might be the first to study these classes of matching theory. In these environments, stability is considered as a key property to ensure a such assignment. It is checked if the two properties of individual rationality and no-blocking are held. Individual rationality requires that each agent finds her or his match acceptable. The property of no-blocking means that no agent can improve by breaking up with their current match in order to match up with each other instead. Gale and Shapley (1962) proved the existence of stable matchings by providing an algorithm, called the Deferred Acceptance Algorithm, that has been a central role to design matching programs across several institutions and markets. We distinguish among three categories: one-to-one, many-to-one, and many-to-many matching problems. The first category studies the assignment of each agent on one side on the market to at most one agent on the other side. The best known example for this class is the problem of marriages where there is a certain community with a set of men and a set of

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women, where men only desires women and women only desires men that we would to match. Each man ranks a set of women in accordance with his preferences for a mariage and vis versa. For this class of matching, Gale and Shapley (1962) showed that there always is a stable set of marriages. The second category establishes different examples of matching problems in real world applications: on one side, colleges admit many student, firms hire many workers, hospitals employ many interns, and on the other side, student attend one college, workers work for one firm, and interns work for one hospital. The very known example is the college admission problem that is introduced by Gale and Shapley (1962). Each student ranks a set of college in accordance with his preferences for a matching and each college ranks a set of students with a quota. They proved that a stable college admissions matching exists. The third category analyses many situations where the very known example is given by Roth and Sotomayor (1990) who examine the assignment problem in the market for medical interns in the U.K. .

Some References :

Black D (1958) The theory of committees and elections. Cambridge University Press, Cambridge

Bock H, DayW,McMorris F (1998) Consensus rules for committee elections.Math Soc Sci 35(3):219–232

Caragiannis I, Kalaitzis D, Markakis E (2010) Approximation algorithms and mechanism design for minimax approval voting. In: Proceedings of the 24th AAAI conference on artificial intelligence, pp 737–742 Diss M, Doghmi A (2016) Multi-winner scoring election methods: Condorcet consistency and paradoxes. Public Choice, Volume 169, Issue 1–2, pp 97–116 Doghmi, A., and A. Ziad. 2015. “Nash Implementation in Private Good Economies with Single-Plateaued Preferences and in Matching Problems.” Mathematical Social Sciences 73:32–9. Elkind1 E, Piotr Faliszewski2 P, Skowron1 P, ) and Slinko A, (2017) Properties of multiwinner voting rules. Soc Choice Welf. Gale, D., Shapley, L., 1962. College admission and the stability of marriage. Amer. Math. Monthly 69, 9–15 M. Sotomayor. Three remarks on the many-to-many stable matching problem.

Mathematical Social Siences, 38:55{70, 1999. Thomson, W. 2010. “Implementation to Solutions to the Problem of Fair Allocations When Preferences Are Single-Peaked.” Review of Economic Design 14:1–15 THÉORIE DES JEUX 3 : Essay on the behavioral aspects for matching markets. (Pr. DOGMI Ahmed) In Mechanism Design theory, the ̀ `planner" or the mechanism designer (representative of state, a local collectivity, a region, manager of a firm, etc…) has a goal in mind---to maximize an objective function called social choice correspondence (or rule}). This correspondence represents the social objectives that the society or its representatives want to achieve. A difficulty arises in the asymmetry of information between planner and agents. This asymmetric

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information results because the planner does not know the exact preferences of agents. If, for example, the ``options" or outcomes are public goods, the agents will state false preferences in order to participate with lower costs, and once the public property is constructed, they can use it like everyone. In order for the agents to reveal their true preferences, the planner will implement a mechanism (non-cooperative game). It is a way of collecting messages and making a decision; it represents the communication and decision aspects of the organization. This mechanism is a list of messages or ``strategy" spaces, and an outcome function mapping messages into allocations, where the strategies of players depend essentially on the preference profiles and the options (or alternatives) set. A social choice correspondence (SCC) is implementable in a given solution concept if the payment with this solution of the game corresponds to the socially desired alternative and vice versa. The planner, thus, hopes to, via this game, get the agents to reveal their sincere preferences.

In the first project, the SCC is a multi-winner voting that we want to characterize in a non-cooperative game. In the second project we want to study alternative mechanism to solve the problem of fair division in private good economies with different types of preferences. The third project concerns matching markets. These markets constitute a branch of public decision problems that have been extensively analyzed in the literature. They are two principle classes: one-side matching markets and two-side matching markets. The first class studies the assignment of a set of objects with finite capacities to some agents. As examples, we cite the matching of office spaces to faculty members, the matching of student to dormitories etc. The second class, that we are focusing on in this paper, is the one of the most ``popular" classes in matching theory; it explores two sides where each side is assumed to have strict preferences over the opposite side. Gale and Shapley (1962) might be the first to study these classes of matching theory. In these environments, stability is considered as a key property to ensure a such assignment. It is checked if the two properties of individual rationality and no-blocking are held. Individual rationality requires that each agent finds her or his match acceptable. The property of no-blocking means that no agent can improve by breaking up with their current match in order to match up with each other instead. Gale and Shapley (1962) proved the existence of stable matchings by providing an algorithm, called the Deferred Acceptance Algorithm, that has been a central role to design matching programs across several institutions and markets. We distinguish among three categories: one-to-one, many-to-one, and many-to-many matching problems. The first category studies the assignment of each agent on one side on the market to at most one agent on the other side. The best known example for this class is the problem of marriages where there is a certain community with a set of men and a set of women, where men only desires women and women only desires men that we would to match. Each man ranks a set of women in accordance with his preferences for a mariage and vis versa. For this class of matching, Gale and Shapley (1962) showed that there always is a stable set of marriages. The second category establishes different examples of matching problems in real world applications: on one side, colleges admit many student, firms hire many workers, hospitals employ many interns, and on the other side, student attend one college, workers work for one firm, and interns work for one hospital. The very known example is the college admission problem that is introduced by Gale and Shapley (1962). Each student ranks a set of college in accordance with his preferences for a matching and each college ranks a set of students with a quota. They proved that a stable college admissions matching exists. The third category analyses many situations where the very known example is given by Roth and Sotomayor (1990) who examine the assignment problem in the market for medical interns in the U.K. .

Some References :

14

Black D (1958) The theory of committees and elections. Cambridge University Press, Cambridge

Bock H, DayW,McMorris F (1998) Consensus rules for committee elections.Math Soc Sci 35(3):219–232

Caragiannis I, Kalaitzis D, Markakis E (2010) Approximation algorithms and mechanism design for minimax approval voting. In: Proceedings of the 24th AAAI conference on artificial intelligence, pp 737–742 Diss M, Doghmi A (2016) Multi-winner scoring election methods: Condorcet consistency and paradoxes. Public Choice, Volume 169, Issue 1–2, pp 97–116 Doghmi, A., and A. Ziad. 2015. “Nash Implementation in Private Good Economies with Single-Plateaued Preferences and in Matching Problems.” Mathematical Social Sciences 73:32–9. Elkind1 E, Piotr Faliszewski2 P, Skowron1 P, ) and Slinko A, (2017) Properties of multiwinner voting rules. Soc Choice Welf. Gale, D., Shapley, L., 1962. College admission and the stability of marriage. Amer. Math. Monthly 69, 9–15 M. Sotomayor. Three remarks on the many-to-many stable matching problem.

Mathematical Social Siences, 38:55{70, 1999. Thomson, W. 2010. “Implementation to Solutions to the Problem of Fair Allocations When Preferences Are Single-Peaked.” Review of Economic Design 14:1–15. SYSTEMES D’INFORMATION 1 : Approche Dirigée par les modèles pour la mise en œuvre des processus métiers ubiquitaires (Pr. SAIDI Rajaa/Alaeeddine Yousfi , Hasso Plattner Institute, Potsdam, Allemagne) Un SI a été souvent considéré comme un ensemble de sous-SI centrés sur un Processus Métier (PM) (Business Process). Il s’agit d’une structure modulaire spécifique à un SI donné. C’est un processus au cœur du système, représentatif d’une partie du métier de l’entreprise et qui constitue une solution particulière à une classe de problèmes définie pour un domaine métier donné.

BPMN (Business Process Modeling Notation) [1] de l'OMG est une notation graphique standardisée portant sur la modélisation des processus métiers. Elle vise à fournir une notation facilement compréhensible par les utilisateurs métiers y compris les analystes métiers, les développeurs et ceux qui devront gérer et surveiller les processus en cours d’exécution.

De nos jours, le développement et l’utilisation des SI centrés processus métiers fait face à trois défis : l’évolution rapide des règles métiers, l’évolution technologique des dispositifs d’interaction qui intègrent parfois des terminaux mobiles et l’évolution des usages. En effet, une adaptation facile et rapide, d’un processus métier aux changements internes et externes, est ainsi un facteur clé améliorant son utilisabilité et sa performance. Par ailleurs, introduite par Weiser [2], l’informatique ubiquitaire (Pervasive ou Omniprésente) [3] est considérée comme un paradigme de l’informatique personnelle ayant pour objectif de permettre aux utilisateurs de consulter des données n’importe où et n’importe quand, à travers leurs dispositifs d’accès. Ces Dispositifs Mobiles (DM), étant propres à l’informatique

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ubiquitaire, soulignent certaines caractéristiques propres à leurs modes d’usages parmi elles, on mentionne : Stockage limité, Puissance de traitement limitée, Bande passante limitée, Hétérogénéité des terminaux.

En effet, un processus exécuté sur un dispositif mobile doit donc respecter les points cités ci-dessus afin de permettre à un utilisateur nomade d’interagir avec lui d’une manière confortable. En d’autres termes, l’informatique ubiquitaire vise à concevoir des processus clientes légères utilisant des logiciels dont l’exécution, d’une part, mobilise peu de ressources de traitement ainsi que de mémoire et, d’autre part, minimise la latence de transmission des données tout en respectant une IHM compatible avec les terminaux.

Objectifs

Les objectifs de cette thèse se résument comme suit :

- Etudier les concepts de l’informatique ubiquitaire - Etudier les approches de spécification de processus métiers ubiquitaires en utilisant le

langage BPMN [4] - Proposition d’une approche dirigée par les modèles pour spécifier un processus métier

ubiquitaire - Validation des propositions par une plateforme et des études de cas

Références

1. http://www.bpmn.org

2. M. Weiser. The computer for 21st Century. Sientific American, USA, 1991. 3. « Fundamentals of ubiquitous computing »,

http://sociotech.pbworks.com/f/UbiquitousComputFundamen.pdf 4. E-CARe:uneméthoded’ingénieriedesSystèmesd’Informationubiquitaires

5. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&u

act=8&ved=0ahUKEwjh0raLn6PRAhUCPRQKHRC1DYwQFggZMAA&url=http%3A%2F%

2Fwww.theses.fr%2F2012GRENM020.pdf&usg=AFQjCNGvCQzGnvwSFdXCYri_dfoR2_

xGXw&sig2=sIkWa1R_u8hY-UeG-9xHGw&bvm=bv.142059868,d.d24

6. http://www.pros.upv.es/sites/bp-meet-iot2017/

7. http://ceur-ws.org/Vol-247/FORUM_02.pdf

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SYSTEMES D’INFORMATION 2 : Social network analysis and Business process management (Pr. RADGUI Maryam/Pr. SAIDI Rajaa) Business Process Management seeks to manage the processes companies typically undertake on a day to day basis. In line with many management techniques, improvements can made through analysing at varying granularity how processes are actually undertaken compared to how management may consider they are being accomplished and vice versa. One innovative way Business Process Management may be improved is through the use of Social Network Analysis to observe actual working relationships among users. This latter technique permits the workflow manager specifically to consider how well matched users are to their workflow and as a result of this, we have a means of either reconstructing workflows or alternatively users practices.

In fact, social network is fundamentally affecting the way organizations are interacting, both internally and externally. However, when organizations adopt social media initiatives, they will not achieve its full potential unless the changes are integrated with both its operational processes and its approach to continuous improvement.

In this sense, Social business process management (BPM) is an approach to improve business processes that seeks to break down silos by encouraging a more collaborative, transparent approach to process improvement. A major benefit of social BPM is that it helps eliminate the barrier between BPM decision makers and the users affected by their decisions. Social BPM takes advantage of social media tools such as subscription feeds, real-time collaboration, communities, tagging to improve communication.

This research will focus on how Socially Oriented Process Modeling can be used effectively to enable business model renewal, service improvements, customer performance focus.

References:

• Ademir Macedo Nascimento, Using Social Media for Business Process Improvement: A Systematic Review, New Advances in Information Systems and Technologies pp 47-58, 2016

• Peter Busch, Business process management, social network analysis and knowledge management: A triangulation of sorts?, January 2010

• Henrik von Scheel, Mona von Rosing and Zakaria Maamar, Social Media and Business Process Management, the complete business process handbook

SYSTEMES D’INFORMATION 3 : Improving user's services in smart environment using process mining techniques (Pr. RADGUI Maryam/Pr. BENELALLAM Imade) One of the main goals of a smart environment is to recognize the user’s situation and to automatically adapt its behavior accordingly. Indeed, understanding what the user is doing in a Smart Environment is important not only for adapting the environment behavior, for example, by providing the most appropriate combination of services for the recognized situation, but also for identifying situations that could be problematic for the user.

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In fact, prediction, proactivity and decision-making capabilities are important in helping users to achieve their goals in a smart environment. This assistance becomes fundamental when the smart home is inhabited by elderly people or by persons with special needs. To this aim there is a huge amount of work in automatically learning models of the user’s routines especially in the home environment. Most proposed approaches model routines of daily activities as process workflows. Manually building models of the user processes is a complex, costly and error-prone engineering task. Hence, the interest in automatically learning them from examples of actual procedures emerges. The ability to express and learn complex conditions on the involved tasks, are also desirable. In this sense, process mining techniques could be an interesting way to express and learn user’s behaviors and so predict his needs. Indeed, Process mining is a relatively young research discipline that sits between computational intelligence and data mining on the one hand, and process modeling and analysis on the other hand. Process mining techniques aim to extract knowledge from event logs in order to discover, monitor and improve processes. The main objective of this research will be applying process mining techniques to learn the user’s daily routines, and so predicting his needs in smart environment. References:

• Stefano Ferilli, Berardina De Carolis, Domenico Redavid,Logic-Based Incremental Process Mining in Smart Environments, International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp 392-401, 2013

• Wil van der Aalst1 and all, Process Mining Manifesto, BPM 2011 Workshops, Part I, LNBIP 99, pp. 169–194, 2012

• Vikramaditya R. Jakkula, Aaron S. Crandall, Diane J. Cook, Knowledge Discovery in Entity Based Smart Environment Resident Data Using Temporal Relation Based Data Mining, Data Mining Workshops, 2007

• Wil van der Aalst, Process Mining: Discovery, Conformance and Enhancement of Business Processes, Spriger, 2011.

• Jans, M., van der Werf, J.M., Lybaert, N., Vanhoof, K. (2011) A business process mining application for internal transaction fraud mitigation, Expert Systems with Applications, 38 (10), 13351–13359

• http://www.processmining.org/ SYSTEMES D’INFORMATION 4 : Evaluation de l’alignement stratégique des SI (Pr. ELHARI Kaoutar/Pr. KABBAJ ADIL) L'alignement stratégique des systèmes d’information joue un rôle fondamental dans la performance des organisations et dans l’acquisition de la flexibilité nécessaire afin de réagir aux changements de la stratégie. Son évaluation est de plus en plus utilisée par les organisations comme outil de gouvernance incontournable. D’autre part, l’Architecture d’Entreprise d’un organisme consiste en une description simple et homogène des systèmes d’information devenant de plus en complexes dans le but de les rendre plus agiles face aux différents changements qu’ils subissent de jour en jour. Il est à noter que de nombreuses méthodologies, ont été développées pour aider à la construction d’Architecture d’Entreprise.

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Le sujet proposé consiste en l’élaboration d’une méthodologie basée sur le concept d’Architecture d’Entreprise pour l’évaluation de l’alignement stratégique du système d’information d’une organisation. Plusieurs voies de recherche et de développement sont possibles : i) optimisation des méthodes existantes, ii) identification de plus de critères d’évaluation, de métriques tout en les quantifiant et qui s’adaptent avec les nouveaux systèmes d’information, iii) la contribution à l’élaboration d’un modèle de maturité d’alignement stratégique, etc. Méthodologie

• Etat de l’art des études qui concernent l’évaluation de l’alignement stratégique et l’architecture d’entreprise.

• Synthèse des différentes études l’état de l’art et définition de la problématique. • Proposition des améliorations de la méthodologie d’évaluation des approches existantes

Proposition d’une nouvelle approche, le développement de son outil et son application sur un système d’information spécifique. Planning Description du planning du travail et de la durée des différentes phases de la recherche: 1ère année:

• Etat de l’art de l’évaluation de l’alignement stratégique et de l’architecture d’entreprise.

• Synthèse relevant le manque ou les critiques des méthodes d’évaluation de l’état de l’art.

2ème année: • Proposition de plus de critères d’évaluation, amélioration de des méthodologies

proposées, amélioration de modèle de maturité existant. 3ème année:

• Proposition d’une nouvelle approche, et son application sur un système d’information spécifique.

Rédaction