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Your search keyword '"Pôle en ingénierie mathématique (INMA)"' showing total 4 results
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4 results on '"Pôle en ingénierie mathématique (INMA)"'

1. Modelling Influence and Opinion Evolution in Online Collective Behaviour

Author

Julien M. Hendrickx, Peter J. Rentfrow, Vincent D. Blondel, Corentin Vande Kerckhove, Pascal Gend, Samuel Martin, Pôle en ingénierie mathématique (INMA), Université Catholique de Louvain = Catholic University of Louvain (UCL), Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Department of Psychology, University of Cambridge [UK] (CAM), Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), PEPS MADRES, ACI CRAN 2014, ACI CRAN 2016, ANR-13-BS03-0004,COMPACS,Computation Aware Control Systems(2013), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Rentfrow, Peter [0000-0002-9068-2118], Apollo - University of Cambridge Repository, and UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique

Subjects
FOS: Computer and information sciences, Male, [SPI.OTHER]Engineering Sciences [physics]/Other, 0209 industrial biotechnology, Computer science, Social Sciences, 02 engineering and technology, Public opinion, 020901 industrial engineering & automation, Cognition, Opinion dynamics, Sociology, Psychology, Limit (mathematics), Social influence, Multidisciplinary, [SHS.SOCIO]Humanities and Social Sciences/Sociology, 05 social sciences, Computer Science - Social and Information Networks, Social Networks, Social system, [SCCO.PSYC]Cognitive science/Psychology, Social Systems, Medicine, Female, opinion dynamics, Games, Network Analysis, Cognitive psychology, Research Article, Personality, Adult, Computer and Information Sciences, Social Psychology, Process (engineering), computational social sciences, Science, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, Control (management), Decision Making, Models, Psychological, 050105 experimental psychology, Judgment, influenceability, Humans, 0501 psychology and cognitive sciences, Social Behavior, Social and Information Networks (cs.SI), Internet, Behavior, business.industry, Social Influence, Biology and Life Sciences, Group Processes, Social Control, Formal, Games, Experimental, Collective Human Behavior, consensus, Public Opinion, Predictive power, Recreation, Cognitive Science, business, Neuroscience
Abstract

Opinion evolution and judgment revision are mediated through social influence. Based on a large crowdsourced in vitro experiment (n=861), it is shown how a consensus model can be used to predict opinion evolution in online collective behaviour. It is the first time the predictive power of a quantitative model of opinion dynamics is tested against a real dataset. Unlike previous research on the topic, the model was validated on data which did not serve to calibrate it. This avoids to favor more complex models over more simple ones and prevents overfitting. The model is parametrized by the influenceability of each individual, a factor representing to what extent individuals incorporate external judgments. The prediction accuracy depends on prior knowledge on the participants' past behaviour. Several situations reflecting data availability are compared. When the data is scarce, the data from previous participants is used to predict how a new participant will behave. Judgment revision includes unpredictable variations which limit the potential for prediction. A first measure of unpredictability is proposed. The measure is based on a specific control experiment. More than two thirds of the prediction errors are found to occur due to unpredictability of the human judgment revision process rather than to model imperfection., Accepted for publication in PLOS ONE (2016)

Published
2016
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2. Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference

Author

F. Morlot, Arnaud Browet, Francesco Calabrese, Vincent A. Traag, Pôle en ingénierie mathématique (INMA), Université Catholique de Louvain = Catholic University of Louvain (UCL), IBM Almaden Research Center [San Jose], IBM, Theory of networks and communications (TREC), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria), Orange Labs [Issy les Moulineaux], France Télécom, Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria Paris-Rocquencourt

Subjects
Focus (computing), Event (computing), Computer science, business.industry, Bayesian probability, 0211 other engineering and technologies, Probabilistic logic, Mobile computing, Inference, 021107 urban & regional planning, 02 engineering and technology, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Machine learning, computer.software_genre, Data science, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], Mobile phone, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Mobile telephony, Artificial intelligence, business, computer
Abstract

The unprecedented amount of data from mobile phones creates new possibilities to analyze various aspects of human behavior. Over the last few years, much effort has been devoted to studying the mobility patterns of humans. In this paper we will focus on unusually large gatherings of people, i.e. unusual social events. We introduce the methodology of detecting such social events in massive mobile phone data, based on a Bayesian location inference framework. More specifically, we also develop a framework for deciding who is attending an event. We demonstrate the method on a few examples. Finally, we discuss some possible future approaches for event detection, and some possible analyses of the detected social events.

Published
2011

3. Automatic discovery of similar words

Author

Pierre Senellart, Vincent D. Blondel, Integration of data and knowledge distributed over the web (GEMO), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Pôle en ingénierie mathématique (INMA), Université Catholique de Louvain = Catholic University of Louvain (UCL), and Micahel W. Berry, Malu Castellanos

Subjects
Similarity (geometry), Information retrieval, Synonym, business.industry, Computer science, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, 02 engineering and technology, computer.software_genre, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval, ComputingMethodologies_PATTERNRECOGNITION, 020204 information systems, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, tf–idf, computer, Natural language processing
Abstract

We deal with the issue of automatic discovery of similar words (synonyms and near-synonyms) from different kinds of sources: from large corpora of documents, from the Web, and from monolingual dictionaries. We present in detail three algorithms that extract similar words from a large corpus of documents and consider the specific case of the World Wide Web. We then describe a recent method of automatic synonym extraction in a monolingual dictionary. The method is based on an algorithm that computes similarity measures between vertices in graphs. We use the 1913 Webster’s Dictionary and apply the method on four synonym queries. The results obtained are analyzed and compared with those obtained by two other methods.

Published
2008

4. Bilinear functions and trees over the (max,+) semiring

Author

Vincent D. Blondel, Jean Mairesse, Sabrina Mantaci, Mairesse, Jean, Springer-Verlag, Laboratoire d'informatique Algorithmique : Fondements et Applications (LIAFA), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Pôle en ingénierie mathématique (INMA), and Université Catholique de Louvain = Catholic University of Louvain (UCL)

Subjects
0209 industrial biotechnology, Bilinear interpolation, 0102 computer and information sciences, 02 engineering and technology, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], 01 natural sciences, Semiring, Combinatorics, 020901 industrial engineering & automation, Linear form, ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION, Noncommutative signal-flow graph, Mathematics, Discrete mathematics, Formal power series, spectral theory, Tree (graph theory), Bilinear function, [INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM], 010201 computation theory & mathematics, Iterated function, Bilinear map, Computer Science::Formal Languages and Automata Theory, MathematicsofComputing_DISCRETEMATHEMATICS, max-plus semiring
Abstract

We consider the iterates of bilinear functions over the semiring (max, +). Equivalently, our object of study can be viewed as recognizable tree series over the semiring (max, +). In this semiring, a fundamental result associates the asymptotic behaviour of the iterates of a linear function with the maximal average weight of the circuits in a graph naturally associated with the function. Here we provide an analog for the 'iterates' of bilinear functions. We also give a triple recognizing the formal power series of the worst case behaviour. Remark. Due to space limitations, the proofs have been omitted. A full version can be obtained from the authors on request.

Published
2000

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