DARTIGUES-PALLEZ , Christel

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The research of Human Action Recognition (HAR) has made a lot of progress in recent years, and the research based on RGB images is the most extensive. However , there are two main shortcomings: the recognition accuracy is insufficient, and the time consumption of the algorithm is too large. In order to improve these issues our project attempts...

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One of the main problems encountered when predicting student success, as a tool to aid students, is the lack of data used to model each student. This lack of data is due in part to the small number of students in each university course and also, the limited number of features that describe the educational background for each student. In this...

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses...

During their studies students receive written notes and comments from their professors assessing their grades, attitudes, qualities, and lacuna. These characteristics reflect a more subjective approach as opposed to the typical grading system. This paper, through topic modelling and word vectorization approaches, uses textual data to predict...

A lot of research has been done for human activity recognition. But most of it uses a static and immutable set of sensors known beforehand. This approach does not work when applied to a ubiquitous or mobile system, since we cannot know which sensors will be available in the users' surroundings. This is why we consider here an opportunistic...

Un grand nombre de recherches existent pour la reconnaissance d'activité humaine. Cependant, la plupart d'entre elles utilisent un ensemble statique et immuable de capteurs connus par avance. Cette approche ne fonctionne pas lorsqu'elle est appliquée à un système ubiquitaire, car nous ne connaissons alors pas par avance quels capteurs seront...

To overcome short text classification issues due to shortness and sparseness, the enrichment process is classically proposed: topics (word clusters) are extracted from external knowledge sources using Latent Dirichlet Allocation. All the words, associated to topics which encompass short text words, are added to the initial short text content....

Using traditional Random Forests in short text classification revealed a performance degradation compared to using them for standard texts. Shortness, sparseness and lack of contextual information in short texts are the reasons of this degradation. Existing solutions to overcome these issues are mainly based on data enrichment. However, data...

Human action recognition is a challenging field that have been addressed with many different classification techniques such as SVM or Random Decision Forests and by considering many different kinds of information joints, key poses, joints rotation matrix, angles for example. This paper presents our approach for action recognition that considers...