By Richard Dapoigny, Patrick Barlatier (auth.), Patrick Brézillon, Patrick Blackburn, Richard Dapoigny (eds.)
This ebook constitutes the lawsuits of the eighth overseas and Interdisciplinary convention on Modeling and utilizing Context, CONTEXT 2013, held in Annecy, France, in October/November 2013. The 23 complete papers and nine brief papers awarded have been rigorously reviewed and chosen from a number of submissions. furthermore the publication includes keynote speeches and nine poster papers. They hide state of the art effects from the big variety of disciplines involved in context, together with: Cognitive Sciences (Linguistics, Psychology, machine technological know-how, Neuroscience), and desktop technological know-how (artificial intelligence, logics, ubiquitous and pervasive computing, context-awareness systems), and the Social Sciences and Organizational Sciences, in addition to the arts and all software parts, together with medication and Law.
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Sample text
One RE corresponded to the RE present in the corpus and produced by a person and the other RE corresponded to the top 1 RE produced by our system. We asked the judges to select the RE that would be more useful to identify the target in the scene. That is, to select it from among the other objects in the stimulus pictures. Our goal is to show that even if the RE generated by our algorithm does not coincide with the RE produced by a human in the corpus collection, it can be judged as good or even better than the REs generated by humans.
Using linear regression we are able to learn interesting characteristics of the domain. To start with, it learns known facts such that the saliency of a color depends strongly on whether the target object is of that color, and it does not depend on its discrimination power in the model. , large and small) are used more frequently when it has a higher discriminative power which confirms a previous finding reported in [20]. , facing left and facing right) are used as a last resource, when it is necessary to identify the target uniquely.
The input to the algorithm will be a relational model M = Δ, ||·|| , where Δ is the non-empty domain of objects in the scene, and || · || is an interpretation 1 Of course, if we are only interested in a referring expression for a given target we can stop the procedure as soon as the target is the only element of some of the classes. Evaluation of a Refinement Algorithm for the GRE 33 function that assigns to all properties in the scene its intended extension. For example, the scene shown in Figure 1 could be represented by the model M = Δ, || · || shown in Figure 2.