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Current research shows that the detection of semantic concepts (e.g., animal, bus, person, dancing, etc.) in multimedia documents such as videos, requires the use of several types of complementary descriptors in order to achieve good results. In this work, we explore strategies for combining dozens of complementary content descriptors (or ˵experts″) in an efficient way, through the use of late fusion approaches, for concept detection in multimedia documents. We explore two fusion approaches that share a common structure: both start with a clustering of experts stage, continue with an intra-cluster fusion and finish with an inter-cluster fusion, and we also experiment with other state-of-the-art methods. The first fusion approach relies on a priori knowledge about the internals of each expert to group the set of available experts by similarity. The second approach automatically obtains measures on the similarity of experts from their output to group the experts using agglomerative clustering, and then combines the results of this fusion with those from other methods. In the end, we show that an additional performance boost can be obtained by also considering the context of multimedia elements. [ABSTRACT FROM AUTHOR]

We describe the main features of the Dynamic Dictionary of Mathematical Functions (version 1.5). It is a website consisting of interactive tables of mathematical formulas on elementary and special functions. The formulas are automatically generated by computer algebra routines. The user can ask for more terms of the expansions, more digits of the numerical values, or proofs of some of the formulas. [ABSTRACT FROM AUTHOR]

The detection of the state open or closed of mouth is an important information in many applications such as hypo-vigilance analysis, face features segmentation or emotions recognition. In this work we propose a supervised classification method for mouth state detection based on retina filtering and cortex analysis inspired by the human visual system. The first stage of the method is the learning of reference signatures (Log Polar Spectrums) from some open and closed mouth images manually classified. The signatures are constructed by computing the amplitude log-polar spectrum of the retina filtered images. Principal Components Analysis (PCA) is then performed using the Log Polar Spectrum as feature vectors to reduce the number of dimension by keeping 95 % of the total variance. Finally a binary SVM classifier is trained using the projections the principal components given by the PCA in order to classify the mouth. [ABSTRACT FROM AUTHOR]

This paper presents a driver simulator, which takes into account information about the user's state of mind (level of attention, fatigue state, stress state). The user's state of mind analysis is based on video data and biological signals. Facial movements such as eyes blinking, yawning, head rotations... are detected on video data: they are used in order to evaluate the fatigue and attention level of the driver. The user's electrocardiogram and galvanic skin response are recorded and analyzed in order to evaluate the stress level of the driver. A driver simulator software is modified so that the system is able to appropriately react to these critical situations of fatigue and stress: some audio and visual messages are sent to the driver, wheel vibrations are generated and the driver is supposed to react to the alert messages. A multi threaded system is proposed to support multi messages sent by different modalities. Strategies for data fusion and fission are also provided. [ABSTRACT FROM AUTHOR]