489 research outputs found

    BilVideo: Design and implementation of a video database management system

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    With the advances in information technology, the amount of multimedia data captured, produced, and stored is increasing rapidly. As a consequence, multimedia content is widely used for many applications in today's world, and hence, a need for organizing this data, and accessing it from repositories with vast amount of information has been a driving stimulus both commercially and academically. In compliance with this inevitable trend, first image and especially later video database management systems have attracted a great deal of attention, since traditional database systems are designed to deal with alphanumeric information only, thereby not being suitable for multimedia data. In this paper, a prototype video database management system, which we call BilVideo, is introduced. The system architecture of BilVideo is original in that it provides full support for spatio-temporal queries that contain any combination of spatial, temporal, object-appearance, external-predicate, trajectory-projection, and similarity-based object-trajectory conditions by a rule-based system built on a knowledge-base, while utilizing an object-relational database to respond to semantic (keyword, event/activity, and category-based), color, shape, and texture queries. The parts of BilVideo (Fact-Extractor, Video-Annotator, its Web-based visual query interface, and its SQL-like textual query language) are presented, as well. Moreover, our query processing strategy is also briefly explained. © 2005 Springer Science + Business Media, Inc

    Proceedings of the 2nd Computer Science Student Workshop: Microsoft Istanbul, Turkey, April 9, 2011

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    Bilkent News Portal : a system with new event detection and tracking capabilities

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    Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 65-71.News portal services such as browsing, retrieving, and filtering have become an important research and application area as a result of information explosion on the Internet. In this work, we give implementation details of Bilkent News Portal that contains various novel features ranging from personalization to new event detection and tracking capabilities aiming at addressing the needs of news-consumers. The thesis presents the architecture, data and file structures, and experimental foundations of the news portal. For the implementation and evaluation of the new event detection and tracking component, we developed a test collection: BilCol2005. The collection contains 209,305 documents from the entire year of 2005 and involves several events in which eighty of them are annotated by humans. It enables empirical assessment of new event detection and tracking algorithms on Turkish. For the construction of our test collection, a web application, ETracker, is developed by following the guidelines of the TDT research initiative. Furthermore, we experimentally evaluated the impact of various parameters in information retrieval (IR) that has to be decided during the implementation of a news portal that provides filtering and retrieval capabilities. For this purpose, we investigated the effects of stemming, document length, query length, and scalability issues.Öcalan, Hüseyin ÇağdaşM.S

    Benchmarking Arabic AI with Large Language Models

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    With large Foundation Models (FMs), language technologies (AI in general) are entering a new paradigm: eliminating the need for developing large-scale task-specific datasets and supporting a variety of tasks through set-ups ranging from zero-shot to few-shot learning. However, understanding FMs capabilities requires a systematic benchmarking effort by comparing FMs performance with the state-of-the-art (SOTA) task-specific models. With that goal, past work focused on the English language and included a few efforts with multiple languages. Our study contributes to ongoing research by evaluating FMs performance for standard Arabic NLP and Speech processing, including a range of tasks from sequence tagging to content classification across diverse domains. We start with zero-shot learning using GPT-3.5-turbo, Whisper, and USM, addressing 33 unique tasks using 59 publicly available datasets resulting in 96 test setups. For a few tasks, FMs performs on par or exceeds the performance of the SOTA models but for the majority it under-performs. Given the importance of prompt for the FMs performance, we discuss our prompt strategies in detail and elaborate on our findings. Our future work on Arabic AI will explore few-shot prompting, expand the range of tasks, and investigate additional open-source models.Comment: Foundation Models, Large Language Models, Arabic NLP, Arabic Speech, Arabic AI, , CHatGPT Evaluation, USM Evaluation, Whisper Evaluatio
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