Laboratoire d'Informatique de Paris Nord

UMR 7030, Université Paris 13, 99 avenue Jean-Baptiste Clément, 93430 Villetaneuse

up13 cnrs

LIPN : Publications


Voici la liste des articles du LIPN. [Vous pouvez aussi les parcourir à travers une série d'options.]

Vous pouvez également consulter le rapport d'activités du laboratoire 2000-2003, le rapport 2004-2007[.pdf.gz ], ou le rapport 2008-2012[.pdf.gz ]. 


In the course of update...

Chapitres de livres

[1] Etude de la pénétration des anglicismes de type N ou ADJ(-)Ving \`a partir d'un corpus contemporain journalistique : les exemples de bashing et shaming en fran\c cais contemporain
Cartier, Emmanuel and Julie, Viaux

[2] Parallel Model Checking Algorithms for Linear-time Temporal Logic
J. Barnat and V. Bloemen and A. Duret-Lutz and A. Laarman and L. Petrucci and J. van de Pol and E. Renault
54 pages, Springer, Handbook of Parallel Constraint Reasoning, 13, L. Sais and Y. Hamadi, 2017

Articles dans des revues internationales avec comité de lecture

[3] Distributed Fair Allocation of Indivisible Goods
Yann Chevaleyre and Ulle Endriss and Nicolas Maudet
1--22, 242, 10.1016/j.artint.2016.09.005, Artificial Intelligence, 2017

[4] Sch\"utzenberger's factorization on the (completed) Hopf algebra of $q$-stuffle product
Van Chiên BUI and Gérard H. E. DUCHAMP and HOANG NGOC MINH
2, In order to extend the Sch\"utzenberger’s factorization, the combinatorial Hopf algebra of the q-stuffles product is developed systematically in a parallel way with that of the shuffle product and and in emphasizing the Lie elements as studied by Ree. In particular, we will give here an effective construction of pair of bases in duality., 191-215, 30, 0972-5555, JP Journal of Algebra, Number Theory and Applications, Décembre 2017

[5] Prefix-projection global constraint and top-k approach for sequential pattern mining
Kemmar, Amina and Lebbah, Yahia and Loudni, Samir and Boizumault, Patrice and Charnois, Thierry
2, Sequential pattern mining (SPM) is an important data mining problem with broad applications. SPM is a hard problem due to the huge number of intermediate subsequences to be considered. State of the art approaches for SPM (e.g., PrefixSpan Pei et al. 2001) are largely based on the pattern-growth approach, where for each frequent prefix subsequence, only its related suffix subsequences need to be considered, and the database is recursively projected into smaller ones. Many authors have promoted the use of constraints to focus on the most promising patterns according to the interests of the end user. The top-k SPM problem is also used to cope with the difficulty of thresholding and to control the number of solutions. State of the art methods developed for SPM and top-k SPM, though efficient, are locked into a rather rigid search strategy, and suffer from the lack of declarativity and flexibility. Indeed, adding new constraints usually amounts to changing the data-structures used in the core of the algorithm, and combining these new constraints often require new developments. Recent works (e.g. Kemmar et al. 2014; Négrevergne and Guns 2015) have investigated the use of Constraint Programming (CP) for SPM. However, despite their nice declarative aspects, all these modelings have scaling problems, due to the huge size of their constraint networks. To address this issue, we propose the Prefix-Projection global constraint, which encapsulates both the subsequence relation as well as the frequency constraint. Its filtering algorithm relies on the principle of projected databases which allows to keep in the variables domain, only values leading to a frequent pattern in the database. Prefix-Projection filtering algorithm enforces domain consistency on the variable succeeding the current frequent prefix in polynomial time. This global constraint also allows for a straightforward implementation of additional constraints such as size, item membership, regular expressions and any combination of them. Experimental results show that our approach clearly outperforms existing CP approaches and competes well with the state-of-the-art methods on large datasets for mining frequent sequential patterns, sequential patterns under various constraints, and top-k sequential patterns. Unlike existing CP methods, our approach achieves a better scalability., 265--306 , 22, 10.1007/s10601-016-9252-z, Constraints, Avril 2017

Tutoriaux dans des conférences internationales

[6] Parametric Verification
André, É. and Lime, D. and Penczek, W. and Petrucci, L.
1-day tutorial at ICATPN'17, Zaragossa, Spain, Juin 2017

Communications dans des conférences internationales avec comité de lecture

[7] Néoveille, a Web Platform for Neologism Tracking
Cartier, Emmanuel and Gabor, Kata and Lejeune, Gaël and Charnois, Thierry
13, Supplementary Proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING 2017), Avril 2017

[8] Timed ATL: Forget Memory, Just Count
André, É. and Jamroga, W. and Knapik, M. and Penczek, W. and Petrucci, L.
Proc. of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2017), Sao Paulo, Brazil, Mai 2017

[9] Parametric model checking timed automata under non-Zenoness assumption
André, É. and Nguyen, H.G. and Petrucci, L. and Sun, J.
Proc. of the 9th NASA Formal Methods Symposium (NFM2017), Moffett Field, CA, USA, Mai 2017

[10] A Proposal for Classifying the Content of the Web of Data Based on FCA and Pattern Structures.
Justine Reynaud, Mehwish Alam, Yannick Toussaint, Amedeo Napoli.