A Distance-Based Decision in the Credal Level

Belief function theory provides a flexible way to combine information provided by different sources. This combination is usually followed by a decision making which can be handled by a range of decision rules. Some rules help to choose the most likely hypothesis. Others allow that a decision is made on a set of hypotheses. In [6], we proposed a decision rule based on a distance measure. First, in this paper, we aim to demonstrate that our proposed decision rule is a particular case of the rule proposed in [4]. Second, we give experiments showing that our rule is able to decide on a set of hypotheses. Some experiments are handled on a set of mass functions generated randomly, others on real databases

A distance-based decision in the credal level

Abstract. Belief function theory provides a flexible way to combine information provided by different sources. This combination is usually followed by a decision making which can be handled by a range of decision rules. Some rules help to choose the most likely hypothesis. Others allow that a decision is made on a set of hypotheses. In [6], we proposed a decision rule based on a distance measure. First, in this paper, we aim to demonstrate that our proposed decision rule is a particular case of the rule proposed i

Security and Privacy Dimensions in Next Generation DDDAS/Infosymbiotic Systems: A Position Paper

AbstractThe omnipresent pervasiveness of personal devices will expand the applicability of the Dynamic Data Driven Application Systems (DDDAS) paradigm in innumerable ways. While every single smartphone or wearable device is potentially a sensor with powerful computing and data capabilities, privacy and security in the context of human participants must be addressed to leverage the infinite possibilities of dynamic data driven application systems. We propose a security and privacy preserving framework for next generation systems that harness the full power of the DDDAS paradigm while (1) ensuring provable privacy guarantees for sensitive data; (2) enabling field-level, intermediate, and central hierarchical feedback-driven analysis for both data volume mitigation and security; and (3) intrinsically addressing uncertainty caused either by measurement error or security-driven data perturbation. These thrusts will form the foundation for secure and private deployments of large scale hybrid participant-sensor DDDAS systems of the future

3,4-Phenylenedioxythiophene (PheDOT): a novel platform for the synthesis of planar substituted pi-donor conjugated systems

3,4-Phenylenedioxythiophene (PheDOT), a benzenic analogue of 3,4-ethylenedioxythiophene (EDOT), has been synthesized using two different routes namely etherification of 2,5-dicarboethoxy-3,4-dihydroxythiophene with halo-aromatics and transetherification of 3,4-dimethoxythiophene with catechols. Quantum calculations and electrochemical measurements show that replacement of the ethylene bridge of EDOT by a phenyl group leads to an increase of the HOMO level and to a stabilization of the cation radical, making electropolymerization of PheDOT more difficult than that of EDOT. The synthesis of several PheDOT derivatives is described together with preliminary results on their electrochemical polymerization and on the properties of the resulting polymers and copolymers

Conceptual Modeling of Prosopographic Databases Integrating Quality Dimensions

International audienceProsopographic databases, which allow the study of social groups through their bibliography, are used today by a significant number of historians. Computerization has allowed intensive and large-scale exploitation of these databases. The modeling of these proposopographic databases has given rise to several data models. An important problem is to ensure a level of quality of the stored information. In this article , we propose a generic data model allowing to describe most of the existing prosopographic databases and to enrich them by integrating several quality concepts such as uncertainty, reliability, accuracy or completeness

Modélisation de l’imprécision et de l’incertitude de données dans les plateformes de crowdsourcing

Le crowdsourcing consiste à l’externalisation de tâches à une foule de contributeurs rémunérés pour les effectuer. Il permet aux entreprises d’obtenir rapidement des résultats à bas coût. La foule, généralement très diversifiée, peut inclure des contributeurs non-qualifiés pour la tâche et/ou non-sérieux. Une bonne modélisation des réponses et de l’expertise des contributeurs est nécessaire pour exploiter au mieux les données issues des plateformes de crowdsourcing. Différentes méthodes existent à l’heure actuelle pour modéliser les données et/ou le comportement du contributeur : utilisation d’un corpus de référence, apprentissage automatique, méthode par vote majoritaire ou probabilistes. Néanmoins, ces méthodes ont leurs limites, nous nous intéressons dans ce rapport à l’utilisation des fonctions de croyances plus pertinente à notre sens. Nous présentons ici une nouvelle méthode de modélisation des réponses et de l’expertise du contributeur dans les plateformes de crowdsourcing se fondant sur la théorie des fonctions de croyance

A Perspective on Applied Human Factors in Support to the Maritime Big Data challenge

none3noneFrancesca de Rosa, Anne-Laure Jousselme, Alessandro De Gloriade ROSA, Francesca; Jousselme, Anne-Laure; DE GLORIA, Alessandr