This paper addresses the problem of feature fusion between smile, as a visual feature, and text, as a transcription result. The influence of smile over semantic data has been considered before, without investigating multiple approaches for the fusion. This problem is multi-modal, which makes it more difficult. The goal of this article is to investigate how this fusion could increase the current interactivity of a dialogue system by boosting the automatic detection rate of the sentiments expressed by a human user. There are two original propositions in our approach. The first lies in the use of a segmented detection for text data, rather than predicting a single label for every document (video). Second, this paper studies the importance of several features in the process of multi-modal fusion. Our approach uses basic features, such as NGrams, Smile Presence or Valence to find the best fusion approach. Moreover, we test a two level classification approach, using a SVM.

Sentiment analysis and affect detection algorithms are generally based on annotated data, structured into dictionaries, ontologies or word nets. Among other research problems, two issues are considered very important in this field: 1) word sense disambiguation and 2) accuracy of affect detection. Most of the current approaches use annotated resources based on word nets. Their structure, founded on synonymic relations, makes the disambiguation process very difficult. Our model uses contextonyms, which simplify the decision process. Therefore, the disambiguation issue is transformed into a context matching problem. The second focus is on the manual annotation of the data followed by a semantic valence propagation. This approach enables the generation of new affective labels from a set of initial ones, through the expansion process. Unfortunately, this is usually done to the detriment of precision. We use an existing linguistic resource, SentiWordNet, which is one of the largest dictionaries available for sentiment analysis. Using our disambiguation model, we manage to solve all the SentiWordNet ambiguities and inconsistencies, which increases the accuracy of the classification process. This is the first of our major contributions. Second, we manage to reduce the disagreement percentage computed against well known linguistic resources to less than half of the original rate.

This paper presents an indexing support system that suggests for librarians a set of topics and keywords relevant to a pedagogical document. Our method of document indexing uses the Wikipedia category network as a conceptual taxonomy. A directed acyclic graph is built for each document by mapping terms (one or more words) to a concept in the Wikipedia category network. Properties of the graph are used to weight these concepts. This allows the system to extract so called important concepts from the graph and to disambiguate terms of the document. According to these concepts, topics and keywords are proposed. This method has been evaluated by the librarians on a corpus of french pedagogical documents.

In this paper we introduce distributed multiagent simulation and recall some solutions based on HLA protocol. We make a short presentation of peer-to- peer systems. We highlight the architecture of a distributed multiagent system based on a peer-to-peer model. We present some preliminary experimental results to show the performance of this architecture running a distributed version of ants model. We compare this implementation with a non-distributed ants version and with a distributed version on Client/Server architecture.

Classical kernel-based classifiers only use a single kernel, butthe real world applications have emphasized the need to con-sider a combination of kernels also known as a multiple kernel in order to boost the performance. Our purpose isto automatically find the mathematical expression of a multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. Each GP chromosome isa tree encoding the mathematical expression of a multiple kernel. Numerical experiments show that the SVM embedding the evolved multiple kernel performs better than the standard kernels for the considered classification problems.

hybrid model for evolving support vector machine (SVM) kernel functions is developed in this paper. The kernel expression is considered as a parameter of the SVM algorithm and the current approach tries to find the best expression for this SVM parameter. The model is a hybrid technique that combines a genetic programming (GP) algorithm and a support vector machine (SVM) algorithm. Each GP chromosome is a tree encoding the mathematical expression for the kernel function. The evolved kernel is compared to several human-designed kernels and to a previous genetic kernel on several datasets. Numerical experiments show that the SVM embedding our evolved kernel performs statistically better than standard kernels, but also than previous genetic kernel for all considered classification problems.

This paper gives a general presentation of a method for designing, scripting and automatically generating collectives educational activities (CEAs) for distance learning. The CEAs are generated in XML (Extensible Markup Language) and can be integrated in a plate form, as a service. We first present a model which describes the content aggregation. Then, we present briefly the methodology for designing, scripting and generating the CEAs. Finally, we present the tools helping the teachers to design, build and generate the CEAs.