un système multi-agents pour calculer les indicateurs d'activités d'apprentissage humain à partir d'une ingénierie diririgée par les modèles

International audienc

Security solution for semantic SCADA optimized by ECC mixed coordinates

International audienceWeb services have become a significant part of embedded systems as SCADA and internet applications embedded in RTU, because (WS) was XML/SOAP support, independent to platform and very simple to use, these advantages make (WS) vulnerable to many new and old security attacks. Now, it becomes easier to attack (WS) because their semantic data is publicly accessible in UDDI registry and (WS) use http protocol and the 80 TCP port as an open tunneling as a very big vulnerability. In this article we proposes new security solution for SCADA system, a new implementation of security protocols (SSL/TLS, IPSEC, HTTPS) and the WS-Security framework. In our solution implemented WS-Security framework optimized for SCADA in ten (10) steps, we use mixed coordinates ECC cryptography, we propose in this article our (Mixed-Coordinates-ECC) algorithm for optimizing the semantic security solution based on WS-Security framework and to adapt it to embedded complex systems as SCADA RTU using semantic distributed bloc (I/F/AV) and security ontology's

Probabilistic Modeling for Face Detection and Gender Classification

International audienceIn this paper, we contribute to solve the simultaneous problems of face detection and gender classification from any viewpoint. We use an invariant model for accurate face localization based on a combination of appearance and geometry. A probabilistic matching of visual traits allows to classify the gender of face even when pose changes. We deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. This feature contributes to determine the gender of the face. We evaluate our model by testing it in experiments on different databases. The experimental results show that the face model performs well to detect face and gives a good gender recognition rate in the presence of viewpoint changes and facial appearance variability

SBT-IM: Système à base de traces-Indicateurs d'interactions Moodle

National audienceL’utilisation des indicateur dans les EIAH reste difficile car nécessite non seulement la conception d’indicateurs intéressants pour le suivi, l’animation et l’évaluation d’une activité d’apprentissage, mais aussi une mise en œuvre technique (collecte des événements, élaboration des indicateurs, etc.) mobilisant des compétences dépassant souvent celles des concepteurs et utilisateurs de ces indicateurs (concepteurs de cours, enseignants, tuteurs, apprenants). Pour rendre cette tâche plus facile, nous avons proposé un cadre général reposant sur la collecte et la modélisation de traces d’interaction selon un méta-modèle générique (M-Trace) et sur la gestion de transformations explicites des traces collectées pour obtenir les informations nécessaires au calcul, explicite également, des indicateurs

Viewpoint Invariant Gender Recognition

International audienceIn this paper, we address a problem of gender classification of faces taken from arbitrary viewpoints. We use a face model for accurate face localization based on a combination of appearance and geometry. A probabilistic matching of particular traits on face allows to classify the gender of face even in case of important pose changes. We deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. Such feature contributes to determine the gender associated to a given face. We evaluate the model by testing it simultaneously in face localization and gender classification experiments on PIE, FERET and CMU-Profiles databases. The experimental results show that the probabilistic invariant model performs well to detect faces and gives a rate of 92.1% of accurate gender classification in the presence of viewpoint changes and large appearance variability of faces

Probabilistic Modeling for Face Detection and Gender Classification

International audienceIn this paper, we contribute to solve the simultaneous problems of face detection and gender classification from any viewpoint. We use an invariant model for accurate face localization based on a combination of appearance and geometry. A probabilistic matching of visual traits allows to classify the gender of face even when pose changes. We deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. This feature contributes to determine the gender of the face. We evaluate our model by testing it in experiments on different databases. The experimental results show that the face model performs well to detect face and gives a good gender recognition rate in the presence of viewpoint changes and facial appearance variability

Viewpoint Invariant Gender Recognition

International audienceIn this paper, we address a problem of gender classification of faces taken from arbitrary viewpoints. We use a face model for accurate face localization based on a combination of appearance and geometry. A probabilistic matching of particular traits on face allows to classify the gender of face even in case of important pose changes. We deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. Such feature contributes to determine the gender associated to a given face. We evaluate the model by testing it simultaneously in face localization and gender classification experiments on PIE, FERET and CMU-Profiles databases. The experimental results show that the probabilistic invariant model performs well to detect faces and gives a rate of 92.1% of accurate gender classification in the presence of viewpoint changes and large appearance variability of faces

Indicators computation from modeled traces in the context of computer Human Learning environment

International audienceWe present in this paper TBS-IM a Trace Based System to calculate collaborative and individual humanlearning Indicators in Moodle. The system we propose is based on the concept of modeled trace, and allowsusers to build and reuse indicators using transformations trace models without using programming cod