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cs.IR updates on arXiv.org

From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora Personalized Benchmarking: Evaluating LLMs by Individual Preferences Modular Representation Compression: Adapting LLMs for Efficient and Effective 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Tutoring System for Dance Learning
Rajkumar Kannan, Frederic Andres, Balakrishnan Ramadoss · 2010-01-04 · via cs.IR updates on arXiv.org

Recent advances in hardware sophistication related to graphics display, audio and video devices made available a large number of multimedia and hypermedia applications. These multimedia applications need to store and retrieve the different forms of media like text, hypertext, graphics, still images, animations, audio and video. Dance is one of the important cultural forms of a nation and dance video is one such multimedia types. Archiving and retrieving the required semantics from these dance media collections is a crucial and demanding multimedia application. This paper summarizes the difference dance video archival techniques and systems. Keywords: Multimedia, Culture Media, Metadata archival and retrieval systems, MPEG-7, XML.

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