Éditeur(s) : HAL CCSD
Résumé : International audience
“Affective Computing is computing that relates to, arises from, or deliberately influences emotions” (R.W. Picard, MIT Press, 1997). Specifically, psychosocial equilibrium is now considered as a major issue for both individual and public health. Predicting human emotions via non-intrusive methods is a great challenge triggered by the rise of intelligent assisting systems. In order to determine the emotional state of a subject having a potential psychosocial disorder, it is required to process both neutral and expressive emotions. A computer-engineered method aiming to provide emotional information from videos is proposed. An original approach for affect classification and its interpretation is presented.The database is part of the HUMAINE database, and consists of a large volume of video clips, each displaying individuals, depressed or not, talking with a conversational agent. Both the high-dimensionality of data and its temporal aspect require a pre-training that consists of cleaning, sorting and feature selection, in order to make relevant information usable for further processing by MachineLearning methods. Next step consists in classifying the affective state of subjects according to their positivity or negativity (Russell's model, 1980). By means of six independent classifiers, each running a Support Vector Machine algorithm, separate performances are obtained for each class of emotions. The results reveal an interesting “neutral area phenomenon”, where emotions tend to be harder to be detected.Though more frequent, neutral emotions seem to carry less discriminant information than expressive ones. This issue will be a key for designing a tuning step in further research.
Caribbean Academy Of Science
Deshaies, France

hal-01408029
https://hal.univ-antilles.fr/hal-01408029

Éditeur(s) : HAL CCSD Springer
Résumé : International audience
This paper presents a spiking neural network simulator suitablefor biologically plausible large neural networks, named DAMNEDfor “Distributed And Multi-threaded Neural Event-Driven”. The simulatoris designed to run efficiently on a variety of hardware. DAMNEDmakes use of multi-threaded programming and non-blocking communicationsin order to optimize communications and computations overlap.This paper details the even-driven architecture of the simulator. Someoriginal contributions are presented, such as the handling of a distributedvirtual clock and an efficient circular event queue taking into accountspike propagation delays. DAMNED is evaluated on a cluster of computersfor networks from 103 to 105 neurons. Simulation and networkcreation speedups are presented. Finally, scalability is discussed regardingnumber of processors, network size and activity of the simulated NN.
Engineering Applications of Neural Networks: 11th International Conference, EANN 2009
London, United Kingdom

hal-01437753
https://hal.archives-ouvertes.fr/hal-01437753
DOI : 10.1007/978-3-642-03969-0_33