Sustainable Cooperative Coevolution with a Multi-Armed Bandit

This paper proposes a self-adaptation mechanism to manage the resources allocated to the different species comprising a cooperative coevolutionary algorithm. The proposed approach relies on a dynamic extension to the well-known multi-armed bandit framework. At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions. We show experimentally, on a benchmark and a real-world problem, that evolving the different populations at different paces allows not only to identify solutions more rapidly, but also improves the capacity of cooperative coevolution to solve more complex problems.Comment: Accepted at GECCO 201

Mesure et évaluation de la qualité des pratiques de développement des compétences informationnelles au sein du réseau de l'Université du Québec

Comprend des références bibliographiquesUne multitude de pratiques diversifiées ont été mises en oeuvre afin de favoriser le développement des compétences informationnelles (CI) chez les étudiants du réseau de l’Université du Québec. Ces pratiques n’ont jamais fait l’objet d’évaluation de leur pertinence, ni des effets engendrés. Afin de contribuer à solutionner cette problématique, une étude méthodologique en trois phases a été conduite au sein de ce réseau. La première phase a permis de valider 339 critères de qualité des pratiques de développement des compétences informationnelles (PDCI) auprès d’experts. La deuxième phase consistait à expérimenter un processus de mesure de la qualité des formations documentaires et de la collaboration interprofessionnelle. Dans la troisième phase, des cercles de qualité constitués dans six universités ont procédé à l’évaluation des leurs résultats. La mise en commun de ces évaluations a permis d’identifier les forces et les points faibles significatifs en matière de PDCI. Les forces identifiées sont la qualité de la prestation et le niveau d’expertise démontrés par les bibliothécaires ainsi que leur relation avec les professeurs. Les points faibles se réfèrent à la faible collaboration interprofessionnelle et aux stratégies pédagogiques employées dans le cadre des formations documentaires. Une observation pour le moins paradoxale se dégage des résultats. Alors que la relation entre les bibliothécaires et les professeurs constitue la troisième force en importance, la collaboration interprofessionnelle a été identifiée comme le principal point faible. Ce constat a conduit les auteurs à définir un continuum de pratiques collaboratives permettant de préciser les différents niveaux de travail collaboratif

Audiovisual data fusion for successive speakers tracking

International audienceIn this paper, a human speaker tracking method on audio and video data is presented. It is applied to con- versation tracking with a robot. Audiovisual data fusion is performed in a two-steps process. Detection is performed independently on each modality: face detection based on skin color on video data and sound source localization based on the time delay of arrival on audio data. The results of those detection processes are then fused thanks to an adaptation of bayesian filter to detect the speaker. The robot is able to detect the face of the talking person and to detect a new speaker in a conversation

Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition.

International audienceA tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be true or false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize running, jumping, falling and standing-up actions as well as high jump, pole vault, triple jump and {long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided

Multi-layer Dictionary Learning for Image Classification

International audienceThis paper presents a multi-layer dictionary learning method for classification tasks. The goal of the proposed multi-layer framework is to use the supervised dictionary learning approach locally on raw images in order to learn local features. This method starts by building a sparse representation at the patch-level and relies on a hierarchy of learned dictionaries to output a global sparse representation for the whole image. It relies on a succession of sparse coding and pooling steps in order to find an efficient representation of the data for classification. This method has been tested on a classification task with good results

Rejection-based classification for action recognition using a spatio-temporal dictionary

International audienceThis paper presents a method for human action recognition in videos which learns a dictionary whose atoms are spatio-temporal patches. We use these gray-level spatio-temporal patches to learn motion patterns inside the videos. This method also relies on a part-based human detector in order to segment and narrow down several interesting regions inside the videos without a need for bounding boxes annotations. We show that the utilization of these parts improves the classification performance. We introduce a rejection-based classification method which is based on a Support Vector Machine. This method has been tested on UCF sports action dataset with good results

An Evidential Filter for Indoor Navigation of a Mobile Robot in Dynamic Environment

International audienceRobots are destined to live with humans and perform tasks for them. In order to do that, an adapted representation of the world including human detection is required. Evidential grids enable the robot to handle partial information and ignorance, which can be useful in various situations. This paper deals with an audiovisual perception scheme of a robot in indoor environment (apartment, house..). As the robot moves, it must take into account its environment and the humans in presence. This article presents the key-stages of the multimodal fusion: an evidential grid is built from each modality using a modified Dempster combination, and a temporal fusion is made using an evidential filter based on an adapted version of the generalized bayesian theorem. This enables the robot to keep track of the state of its environment. A decision can then be made on the next move of the robot depending on the robot's mission and the extracted information. The system is tested on a simulated environment under realistic conditions

Human Shape-Motion Analysis In Athletics Videos for Coarse To Fine Action/Activity Recognition Using Transferable BeliefModel

We present an automatic human shape-motion analysis method based on a fusion architecture for human action and activity recognition in athletic videos. Robust shape and motion features are extracted from human detection and tracking. The features are combined within the Transferable Belief Model (TBM framework for two levels of recognition. The TBM-based modelling of the fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. First, in a coarse step, actions are roughly recognized. Then, in a fine step, an action sequence recognition method is used to discriminate activities. Belief on actions are made smooth by a Temporal Credal Filter and action sequences, i.e. activities, are recognized using a state machine, called belief scheduler, based on TBM. The belief scheduler is also exploited for feedback information extraction in order to improve tracking results. The system is tested on real videos of athletics meetings to recognize four types of actions (running, jumping, falling and standing) and four types of activities (high jump, pole vault, triple jump and long jump). Results on actions, activities and feedback demonstrate the relevance of the proposed features and as well the efficiency of the proposed recognition approach based on TBM