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This paper proposes that Wikipedia can effectively be used in order to lessen the negative effects of data sparsity on the accuracy of recommendations produced by Recommender Systems, provided that domain resources available for recommendation can successfully be mapped to Wikipedia articles. Under the assumption that hyperlinks between Wikipedia articles convey latent semantic relationships between...
This paper presents a region-based approach towards semantic image retrieval. Combining segmentation and the popular Bag-of-Words model, a visual vocabulary of the most common “region types” is first constructed using the database images. The visual words are consistent image regions, extracted through a k-means clustering process. The regions are described with color and texture features, and a ”model...
In this paper, we present a multi-form image representation and adaptive weighting approach for image browsing and retrieval systems. To support the development of the multi-form representation with understanding on users' image searching behavior, we conducted an interview study with users of various image browsing and retrieval systems. Based on the insights gained from the user study we propose...
This paper presents a new scheme for detecting humans' falls in highly dynamic house environments. The scheme distinguishes falls from other humans' activities, like sitting, walking, lying, under (a) sudden and abrupt illumination changes (b) non-periodic/significant motions in the background (chairs, curtains, tables), (c) humans' movements towards all possible directions across camera. In particular,...
Current work focuses on user modeling in terms of affective analysis that could in turn be used in intelligent personalized interfaces and systems, dynamic profiling and context-aware multimedia applications. The analysis performed within this work comprises of statistical processing and classification of automatically extracted gestural and head pose expressivity features. Computational formulation...
The complexity of current mixed software/hardware custom digital systems, has led to the requirement for automated high-level synthesis of the system from abstract, executable specifications, in order to drastically reduce the long development cycles. This paper introduces an approach to validate the hardware compilation process of a formal, high-level synthesizer, using provably-correct logic programming...
We propose a method for user-driven recognition of events in audio streams, aiming to assist journalists towards easily annotate unedited audiovisual content. Nonlocal information provided by the user, as for example that the sound of applause exists within the video, is used for adapting the audio event classifiers so as to detect the exact position of these events in the video. Towards this end,...
The problem of bridging the semantic gap can be approached by dividing all types of metadata extracted from multimedia content into three levels - low, mid and high - according to their levels of semantic abstraction and try to define the mapping between them. This paper proposes a scheme for extracting high-level semantic information out of mid-level features, which can be applied in dealing with...
Recommender systems aim at solving the problem of information overload by selecting items (commercial products, educational assets, TV programs, etc) that match the users' interests. Recently, there have been approaches to drive the recommendations by the information stored in electronic health records, for which the traditional strategies applied in e-commerce, e-learning, entertainment and other...
Adaptation and Personalization systems can benefit with the incorporation of ontological knowledge, especially when it comes to modeling users. In this paper we present a cognitive approach to Web Personalization based on an ontology that contains users' cognitive factors. Accordingly, a human factors' ontology has been designed and developed using RDFa, and could be used in any Web-based application...
Two influential strands in Recommender systems (RS) are the collaborative filtering and content based filtering that by taking into account user communities or interaction history suggest to the active user interesting items. However, the aforementioned approaches do not work well when confronted with new users with few interactions; or with the addition of new items. In such cases, the guidance of...
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