Michal Muszynski

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—Affective computing is an important research area of computer science, with strong ties with humanities in particular. In this work we detail recent research activities towards determining moments of aesthetic importance in movies, on the basis of the reactions of multiple spectators. These reactions correspond to the multimodal reaction profile of a group(More)
The paper presents data mining methods applied to gene selection for recognition of a particular type of prostate cancer on the basis of gene expression arrays. Several chosen methods of gene selection, including the Fisher method, correlation of gene with a class, application of the support vector machine and statistical hypotheses, are compared on the(More)
Detection of highlights in movies is a challenge for the affective understanding and implicit tagging of films. Under the hypothesis that synchronization of the reaction of spectators indicates such highlights, we define a synchronization measure between spectators that is capable of extracting movie highlights. The intuitive idea of our approach is to(More)
Affective computing has strong ties with literature and film studies, e.g. text sentiment analysis, affective tagging of movies. In this work we report on recent findings towards identifying highlights in movies on the basis of the synchronization of physiological and behavioral signals of people. The proposed architecture is utilizing dynamic time warping(More)
Detection of emotional and aesthetic highlights is a challenge for the affective understanding of movies. Our assumption is that synchronized spectators' physiological and behavioral reactions occur during these highlights. We propose to employ the periodicity score to capture synchronization among groups of spectators' signals. To uncover the periodicity(More)
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