Belief Approach for Social Networks

Nowadays, social networks became essential in information exchange between individuals. Indeed, as users of these networks, we can send messages to other people according to the links connecting us. Moreover, given the large volume of exchanged messages, detecting the true nature of the received message becomes a challenge. For this purpose, it is interesting to consider this new tendency with reasoning under uncertainty by using the theory of belief functions. In this paper, we tried to model a social network as being a network of fusion of information and determine the true nature of the received message in a well-defined node by proposing a new model: the belief social network

Une mesure d'expertise pour le crowdsourcing

International audienceCrowdsourcing, a major economic issue, is the fact that the firm outsources internal task to the crowd. It is a form of digital subcontracting for the general public.The evaluation of the participants work quality is a major issue in crowdsourcing. Indeed, contributions must be controlled to ensure the effectiveness and relevance of the campaign. We are particularly interested in small, fast and not automatable tasks. Several methods have been proposed to solve this problem, but they are applicable when the "golden truth" is not always known. This work has the particularity to propose a method for calculating the degree of expertise in the presence of gold data in crowdsourcing. This method is based on the belief function theory and proposes a structuring of data using graphs. The proposed approach will be assessed and applied to the data.Le crowdsourcing, un enjeu économique majeur, est le fait d'exter-naliser une tâche interne d'une entreprise vers le grand-public, la foule. C'est ainsi une forme de sous-traitance digitale destinée à toute personne susceptible de pouvoir réaliser la tâche demandée généralement rapide et non automatisable. L'évaluation de la qualité du travail des participants est cependant un problème majeur en crowdsourcing. En effet, les contributions doivent être contrôlées pour assurer l'efficacité et la pertinence d'une campagne. Plusieurs méthodes ont été proposées pour évaluer le niveau d'expertise des participants. Ce travail a la par-ticularité de proposer une méthode de calcul de degrés d'expertise en présence de données dont l'ordre de classement est connu. Les degrés d'expertise sont en-suite considérés sur des données sans ordre pré-établi. Cette méthode fondée sur la théorie des fonctions de croyance tient compte des incertitudes des réponses et est évaluée sur des données réelles d'une campagne réalisée en 2016

A Belief Approach for Detecting Spammed Links in Social Networks

International audienceNowadays, we are interconnected with people whether professionally or personally using different social networks. However, we sometimes receive messages or advertisements that are not correlated to the nature of the relation established between the persons. Therefore, it became important to be able to sort out our relationships. Thus, based on the type of links that connect us, we can decide if this last is spammed and should be deleted. Thereby, we propose in this paper a belief approach in order to detect the spammed links. Our method consists on modelling the belief that a link is perceived as spammed by taking into account the prior information of the nodes, the links and the messages that pass through them. To evaluate our method, we first add some noise to the messages, then to both links and messages in order to distinguish the spammed links in the network. Second, we select randomly spammed links of the network and observe if our model is able to detect them. The results of the proposed approach are compared with those of the baseline and to the k-nn algorithm. The experiments indicate the efficiency of the proposed model

Measuring the Expertise of Workers for Crowdsourcing Applications

International audienceCrowdsourcing platforms enable companies to propose tasks to a large crowd of users. The workers receive a compensation for their work according to the serious of the tasks they managed to accomplish. The evaluation of the quality of responses obtained from the crowd remains one of the most important problems in this context. Several methods have been proposed to estimate the expertise level of crowd workers. We propose an innovative measure of expertise assuming that we possess a dataset with an objective comparison of the items concerned. Our method is based on the definition of four factors with the theory of belief functions. We compare our method to the Fagin distance on a dataset from a real experiment, where users have to assess the quality of some audio recordings. Then, we propose to fuse both the Fagin distance and our expertise measure

BSocial: Deciphering Social Behaviors within Mixed Microbial Populations

BSocial Analysis: http://m4m.ugr.es/BSocial.htmlEcosystem functionality depends on interactions among populations, of the same or different taxa, and these are not just the sum of pairwise interactions. Thus, know-how of the social interactions occurring in mixed-populations are of high interest, however they are commonly unknown due to the limitations posed in tagging each population. The limitations include costs/time in tediously fluorescent tagging, and the number of different fluorescent tags. Tag-free strategies exist, such as high-throughput sequencing, but ultimately both strategies require the use of expensive machinery. Our work appoints social behaviors on individual strains in mixed-populations, offering a web-tool (BSocial http://m4m.ugr.es/BSocial.html) for analyzing the community framework. Our quick and cheap approach includes the periodic monitoring of optical density (OD) from a full combinatorial testing of individual strains, where number of generations and growth rate are determined. The BSocial analyses then enable us to determine how the addition/absence of a particular species affects the net productivity of a microbial community and use this to select productive combinations, i.e., designate their social effect on a general community. Positive, neutral, or negative assignations are applied to describe the social behavior within the community by comparing fitness effects of the community against the individual strain. The usefulness of this tool for selection of optimal inoculum in biofilm-based methyl tert-butyl ether (MTBE) bioremediation was demonstrated. The studied model uses seven bacterial strains with diverse MTBE degradation/growth capacities. Full combinatorial testing of seven individual strains (triplicate tests of 127 combinations) were implemented, along with MTBE degradation as the desired function. Sole observation of highest species fitness did not render the best functional outcome, and only when strains with positive and neutral social assignations were mixed (Rhodococcus ruber EE6, Agrobacterium sp. MS2 and Paenibacillus etheri SH7), was this obtained. Furthermore, the use of positive and neutral strains in all its combinations had a significant higher degradation mean (x1.75) than exclusive negative strain combinations. Thus, social microbial processes benefit bioremediation more than negative social microbial combinations. The BSocial webtool is a great contributor to the study of social interactions in bioremediation processes, and may be used in other natural or synthetic habitat studies.JP was funded by Junta de Andalucía through the “Programa Proyectos de Excelencia” (Project reference P10-RNM-6153). The work was funded by CEIBioTic through their “II Convocatoria de Proyectos I+D+I” (project reference CEI2013-MP-31), the Spanish Ministry of Science and Technology (project reference TIN2012-38805), and the Consejeria de Innovacion, Investigacion y Ciencia, Junta de Andalucia (project reference TIC-02788)

MIPAP : modele d'interpretation parallele pour Prolog

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc