CNRS Informatics Laboratory of Paris Nord

Paris, France

CNRS Informatics Laboratory of Paris Nord

Paris, France
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Manzonetto G.,CNRS Informatics Laboratory of Paris Nord | Tranquilli P.,University Claude Bernard Lyon 1
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

We provide a strong normalization result for MLF, a type system generalizing ML with first-class polymorphism as in system F. The proof is achieved by translating MLF into a calculus of coercions, and showing that this calculus is just a decorated version of system F. © 2010 Springer-Verlag.


Belenguer J.-M.,University Of Valncia | Benavent E.,University Of Valncia | Prins C.,University of Technology of Troyes | Prodhon C.,University of Technology of Troyes | Wolfler Calvo R.,CNRS Informatics Laboratory of Paris Nord
Computers and Operations Research | Year: 2011

Recent researches in the design of logistic networks have shown that the overall distribution cost may be excessive if routing decisions are ignored when locating depots. The Location-Routing Problem (LRP) overcomes this drawback by simultaneously tackling location and routing decisions. The aim of this paper is to propose an exact approach based on a Branch-and-Cut algorithm for solving the LRP with capacity constraints on depots and vehicles. The proposed method is based on a zero-one linear model strengthened by new families of valid inequalities. The computational evaluation on three sets of instances (34 instances in total), with 510 potential depots and 2088 customers, shows that 26 instances with five depots are solved to optimality, including all instances with up to 40 customers and three with 50 customers. © 2010 Elsevier Ltd. All rights reserved.


Vourdas A.,University of Bradford | Banderier C.,CNRS Informatics Laboratory of Paris Nord
Journal of Physics A: Mathematical and Theoretical | Year: 2010

Quantum systems where the position and momentum are in the ring (Z d is an odd integer) are considered. Symplectic transformations are studied, and the order of Sp(2,Zd)is calculated. Quantum tomography is also discussed. It is shown that measurements (used in the inverse Radon transform) need to be made on J2(d) lines (where J2(d) is the Jordan totient function). © 2010 IOP Publishing Ltd.


Ngueveu S.U.,University of Technology of Troyes | Prins C.,University of Technology of Troyes | Wolfler Calvo R.,CNRS Informatics Laboratory of Paris Nord
Computers and Operations Research | Year: 2010

The cumulative capacitated vehicle routing problem (CCVRP) is a transportation problem which occurs when the objective is to minimize the sum of arrival times at customers, instead of the classical route length, subject to vehicle capacity constraints. This type of challenges arises whenever priority is given to the satisfaction of the customer need, e.g. vital goods supply or rescue after a natural disaster. The CCVRP generalizes the NP-hard traveling repairman problem (TRP), by adding capacity constraints and a homogeneous vehicle fleet. This paper presents the first upper and lower bounding procedures for this new problem. The lower bounds are derived from CCVRP properties. Upper bounds are given by a memetic algorithm using non-trivial evaluations of cost variations in the local search. Good results are obtained not only on the CCVRP, but also on the special case of the TRP, outperforming the only TRP metaheuristic published. © 2009 Elsevier Ltd. All rights reserved.


Kanawati R.,CNRS Informatics Laboratory of Paris Nord
International Conference on Multimedia Computing and Systems -Proceedings | Year: 2014

In this paper we propose a new approach for efficiently identifying ego-centered communities in complex networks. Most existing approaches are based on applying a greedy optimisation process guided by a given objective function. Different objective functions has been proposed in the scientific literature, each capturing some specific feature of desired communities. In this work, we propose to apply ensemble ranking approaches in order to combine different objective functions. Preliminary Results obtained from experiments on benchmark networks argue for the relevancy of our approach. © 2014 IEEE.


Kanawati R.,CNRS Informatics Laboratory of Paris Nord
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI | Year: 2015

In this paper we propose a new approach for efficiently identifying local communities in complex networks. Most existing approaches are based on applying a greedy optimization process guided by a given objective function. Different objective functions have been proposed in the scientific literature, each capturing some specific feature of desired communities. In this work, we propose exploring a new multi-objective approach that allows combining different objective functions. First results obtained from experiments on benchmark networks argue for the relevancy of our approach. © 2014 IEEE.


Baldacci R.,University of Bologna | Mingozzi A.,University of Bologna | Calvo R.W.,CNRS Informatics Laboratory of Paris Nord
Operations Research | Year: 2011

The capacitated location-routing problem (LRP) consists of opening one or more depots on a given set of a-priori defined depot locations, and designing, for each opened depot, a number of routes in order to supply the demands of a given set of customers. With each depot are associated a fixed cost for opening it and a capacity that limits the quantity that can be delivered to the customers. The objective is to minimize the sum of the fixed costs for opening the depots and the costs of the routes operated from the depots. This paper describes a new exact method for solving the LRP based on a set-partitioning- like formulation of the problem. The lower bounds produced by different bounding procedures, based on dynamic programming and dual ascent methods, are used by an algorithm that decomposes the LRP into a limited set of multicapacitated depot vehicle-routing problems (MCDVRPs). Computational results on benchmark instances from the literature show that the proposed method outperforms the current best-known exact methods, both for the quality of the lower bounds achieved and the number and the dimensions of the instances solved to optimality. Subject classifications: location routing; set partitioning; dual ascent; dynamic programming. Area of review: Transportation. History: Received July 2009; revisions received July 2010, October 2010; accepted January 2011. © 2011 INFORMS.


Pujari M.,CNRS Informatics Laboratory of Paris Nord | Kanawati R.,CNRS Informatics Laboratory of Paris Nord
WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion | Year: 2012

In this paper we propose a new topological approach for link prediction in dynamic complex networks. The proposed approach applies a supervised rank aggregation method. This functions as follows: first we rank the list of unlinked nodes in a network at instant t according to different topological measures (nodes characteristics aggregation, nodes neighborhood based measures, distance based measures, etc). Each measure provides its own rank. Observing the network at instant t + 1 where some new links appear, we weight each topological measure according to its performances in predicting these observed new links. These learned weights are then used in a modified version of classical computational social choice algorithms (such as Borda, Kemeny, etc) in order to have a model for predicting new links. We show the effectiveness of this approach through different experimentations applied to co-authorship networks extracted from the DBLP bibliographical database. Results we obtain, are also compared with the outcome of classical supervised machine learning based link prediction approaches applied to the same datasets. Copyright is held by the International World Wide Web Conference Committee (IW3C2).


Kanawati R.,CNRS Informatics Laboratory of Paris Nord
Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011 | Year: 2011

In this work we propose a new efficient algorithm for communities construction based on the idea that a community is animated by a set of leaders that are followed by a set of nodes. A node can follow different leaders animating different communities. The algorithm is structured into two mains steps: identifying nodes in the network playing the role of leaders, then assigning other nodes to some leaders. We provide a general framework for implementing such an approach. First experimental results obtained by applying the algorithm on different real networks show the effectiveness of the proposed approach. © 2011 IEEE.


Benchettara N.,CNRS Informatics Laboratory of Paris Nord | Kanawati R.,CNRS Informatics Laboratory of Paris Nord | Rouveirol C.,CNRS Informatics Laboratory of Paris Nord
Proceedings - 2010 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2010 | Year: 2010

This work copes with the problem of link prediction in large-scale two-mode social networks. Two variations of the link prediction tasks are studied: predicting links in a bipartite graph and predicting links in a unimodal graph obtained by the projection of a bipartite graph over one of its node sets. For both tasks, we show in an empirical way, that taking into account the bipartite nature of the graph can enhance substantially the performances of prediction models we learn. This is achieved by introducing new variations of topological atttributes to measure the likelihood of two nodes to be connected. Our approach, for both tasks, consists in expressing the link prediction problem as a two class discrimination problem. Classical supervised machine learning approaches can then be applied in order to learn prediction models. Experimental validation of the proposed approach is carried out on two real data sets: a co-authoring network extracted from the DBLP bibliographical database and bipartite graph 8-years history of transactions on an on-line music e-commerce site. © 2010 IEEE.

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