Exploiting the user interaction context for automatic task detection

We have performed three laboratory experiments for evaluating the influence on our ontology-based user task detection approach of the following factors: (i) the used clas- sifier,

We aim to constrain the chemical content of the Horsehead mane with a millimeter unbiased line survey at two positions, namely the photo-dissociation region (PDR) and the

NO2 cross sections, using the signal-to-noise ratio obtained during the abra retrieval for a spectrum recorded with a star elevation of-2.0 ø.. 25,390 RENARD ET AL.'

Starting from low-level contextual attention meta- data captured from the user’s desktop, we utilize rule-based, information extraction and machine learning approaches to

This section presents the feature extraction methodology proposed in the context of this paper. The extracted features are able to quantify the degree of anomaly of each MOAS

The expression quantitative trait loci (eQTL) analysis revealed a significant gender effect of the FoxP3 promoter polymorphism rs3761548A/C on miR-221, miR-222 and miR-532

manufactured achieving a pressure range of 90 to 125 mmHg, an addendum to the current clinical trials will be submitted, so that a larger patient poll will exist

Among the nine available data (three axes for each of the three sensors), we chose to use only 2 of them: the accelerometer z-axis and the gyroscope z-axis.. A