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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Evaluation of Multiple Exemplar Training Plus Discrimination Training On Promoting Generalization of Response Variability

Contreras, Bethany P. 01 May 2017 (has links)
Typically developing children learn from play. For example, play serves as a foundation for children to acquire early language and social skills. Children with autism tend to have deficits in play, and often engage in rigid or repetitive behaviors during play. Such rigid play behavior can limit opportunities for these children to learn from play. Researchers have shown that it is possible to increase the variety of play behaviors that children with autism engage in. But, research has not yet shown whether these gains in play behavior will transfer to other play environments and situations. Therefore, the purpose of this study was to investigate methods for promoting the transfer of varied and appropriate play to other play situations with three children with autism. In this study, we increased varied play behavior by providing rewards for playing in a varied manner (and not providing rewards for playing in an inappropriate or rigid manner). We did this with multiple different play situations to help the participants learn to engage in varied play in different situations. We then tested to see if the participants would vary their play with completely new play situations. We found that, following some modifications, our procedures were successful at increasing varied play behavior for all three participants, and that their varied play transferred to other play situations.
2

Reconnaissance d'actions en temps réel à partir d'exemples / Real time actions recognition from examplars

Barnachon, Mathieu 22 April 2013 (has links)
Le développement de l'image numérique et des outils associés ces dernières années a entraîné une évolution dans les attentes des utilisateurs et des changements dans leurs habitudes de travail. Cette évolution apporte de nouvelles possibilités d'utilisation ouvrant l'usage à un public très large, allant des interactions gestuelles aux jeux vidéo, en passant par le suivi d'activités à domicile, la surveillance, ... Pour qu'elles puissent être performantes et attractives, ces nouvelles technologies nécessitent la mise en œuvre d'outils de reconnaissance et d'interprétation des gestes humains, par des méthodes efficaces, rapides et ouvertes. Actuellement, les méthodes proposées en reconnaissance d'actions peuvent être regroupées en trois catégories principales : les approches de type apprentissage automatique (Machine Learning), les modélisations stochastique ou encore les méthodes utilisant le paradigme des examplars. Les travaux développés dans cette thèse se rattachent à cette dernière catégorie : " méthodes à base d'exemples " (examplar-based) où l'apprentissage peut être fait à partir de quelques instances représentatives. Nous avons fait le choix d'une démarche qui limite le recours à des grandes bases de données, et qui permet la reconnaissance d'action de façon anticipée, c'est-à-dire avant que cette dernière ne soit finie. Pour ce faire, nos travaux ont été menés selon deux visions complémentaires, avec le soucis constant d'aboutir à des traitements qui soient temps réel, précis et ouverts à la reconnaissance de nouvelles actions / With the success of new interactive solution, like the Wii-Remote or the Sony Eyetoy, and more recently the Microsoft Kinect, we work on new interactions between game and gamers, with a video-based system. The motion recognition will be used to control the game character or the interaction inside a game, an application, etc. My subject concerns interaction between real and virtual characters. We try to enlarge game actions, with movements - spontaneous or not - from gamers, for example. We working on two points. First, we release constraint on the learning of action, i.e. an action has to be learnt quickly (one shot learning), even in uncontrolled environment: person's living room, cybercafes, etc. Second is understanding motions with new solutions. The more motion capture techniques are reliable, the more new metaphors could be invented linking real actions to virtual ones. These new interactions will allow access to gestural applications by an larger public, usually not interested in, or not familiar with. We propose new interaction video-based: full body motion capture in uncontrolled environment; motion understanding; intention transfer to an avatar and new controls production. The possibilities will be wider than only video games or home entertainment

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