The lack of a formal model of events hinders interoperability in distributed event-based systems. In this paper, we present a formal model of events, called EventModel-F. The model is based on the foundational ontology DOLCE+DnS Ultralite (DUL) and provides comprehensive support to represent time and space, objects and persons, as well as mereological, causal, and correlative relationships between events. In addition, the Event-Model-F provides a flexible means for event composition, modeling event causality and event correlation, and representing different interpretations of the same event. The Event-Model-F is developed following the pattern-oriented approach of DUL, is modularized in different ontologies, and can be easily extended by domain specific ontologies.

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Abstract. Eye tracking information can be used to assign given tags to image regions in order to describe the depicted scene more detailed. We introduce and compare two novel eye-tracking-based measures for conducting such assignments: The segmentation measure uses automatically computed image segments and selects the one segment the users fixate longest. The heat map measure is based on traditional gaze heat maps and sums up the users' fixation durations per pixel. Both measures are applied on gaze data obtained for a set of social media images, which have manually labeled objects as ground truth. We have determined a maximum average precision of 65% at which the segmentation measure points to the correct region in the image. The best coverage of the segments is obtained for the segmentation measure with a F-measure of 35%. Overall, both newly introduced gaze-based measures deliver better results than baseline measures that selects a segment based on the golden ratio of photography or the center position in the image. The eye-tracking-based segmentation measure significantly outperforms the baselines for precision and F-measure.