17,038 research outputs found
Efficient Video Indexing on the Web: A System that Leverages User Interactions with a Video Player
In this paper, we propose a user-based video indexing method, that
automatically generates thumbnails of the most important scenes of an online
video stream, by analyzing users' interactions with a web video player. As a
test bench to verify our idea we have extended the YouTube video player into
the VideoSkip system. In addition, VideoSkip uses a web-database (Google
Application Engine) to keep a record of some important parameters, such as the
timing of basic user actions (play, pause, skip). Moreover, we implemented an
algorithm that selects representative thumbnails. Finally, we populated the
system with data from an experiment with nine users. We found that the
VideoSkip system indexes video content by leveraging implicit users
interactions, such as pause and thirty seconds skip. Our early findings point
toward improvements of the web video player and its thumbnail generation
technique. The VideSkip system could compliment content-based algorithms, in
order to achieve efficient video-indexing in difficult videos, such as lectures
or sports.Comment: 9 pages, 3 figures, UCMedia 2010: 2nd International ICST Conference
on User Centric Medi
Generating indicative-informative summaries with SumUM
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies
Opening Access To Practice-based Evidence in Clinical Decision Support Systems with Natural Query Language
Evidence-based medicine can be effective only if constantly tested against errors in medical practice. Clinical record database summarization supported by a machine allows allow to detect anomalies and therefore help detect the errors in early phases of care. Summarization system is a part of Clinical Decision Support Systems however it cannot be used directly by the stakeholder as long as s/he is not able to query the clinical record database. Natural Query Languages allow opening access to data for clinical practitioners, that usually do not have knowledge about articial query languages. Results: We have developed general purpose reporting system called Ask Data Anything (ADA) that we applied to a particular CDSS implementation. As a result, we obtained summarization system that opens the access for these of clinical researchers that were excluded from the meaningful summary of clinical records stored in a given clinical database. The most significant part of the component - NQL parser - is a hybrid of Controlled Natural Language (CNL) and pattern matching with a prior error repair phase. Equipped with reasoning capabilities due to the intensive use of semantic technologies, our hybrid approach allows one to use very simple, keyword-based (even erroneous) queries as well as complex CNL ones with the support of a predictive editor. By using ADA sophisticated summarizations of clinical data are produced as a result of NQL query execution. In this paper, we will present the main ideas underlying ADA component in the context of CDSS
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