Volume of video content surpass all other content types in internet. As per the reports from different sources, video traffic had acquired 82% of internet usage in 2022. Video is going to be more important in the year...
Volume of video content surpass all other content types in internet. As per the reports from different sources, video traffic had acquired 82% of internet usage in 2022. Video is going to be more important in the years to come for user engagement, advertisement & marketing, news, education etc. Video information retrieval becomes an important problem to solve in this context. An accurate and fast video tagging system can aid a good content recommendation to the end users. It helps to audit the content automatically thereby platforms can control the contents which are politically and morally harmful. there are not many faster or cost-effective mechanisms to tag user generated videos at this moment. Manual tagging is a costly and highly time taking task. A delay in indexing the videos like news, sports etc., shall reduce its freshness and relevancy. Deep learning techniques have reached its maturity in the contents like text and images, but it is not the case with videos. Deep learning models need more resources to deal with videos due to its multi-modality nature, and temporal behavior. Apart fromthat, there are not many large-scale video datasets available at this moment. Youtube-8M is the largest dataset which is publicly available as of now. Much research works happened over Youtube-8M dataset. from our study, all these have a potential limitation. For example, in Youtube-8M, Video labels are only around 3.8K which are not covering all real-world tags. It is not covering the new domains which are created along withthe surge in the content traffic. this study aims to handle this problem of tag creation through different methods available thereby enhancing the labels to a much wider set. this work also aims to produce a scalable tagging pipeline which uses multiple retrieval mechanisms, combine their results. the work aims to standardize the retrieved tokens across languages. this work creates a dataset as an outcome from ‘Wikidata’, which can be used for any NLP
Large Language models (LLMs) are increasingly utilized in educational settings, raising questions about their efficacy in standardized testing contexts. this study evaluates the performance of popular LLMs, including ...
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ISBN:
(数字)9798350356250
ISBN:
(纸本)9798350356267
Large Language models (LLMs) are increasingly utilized in educational settings, raising questions about their efficacy in standardized testing contexts. this study evaluates the performance of popular LLMs, including GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Flash, Llama-3.1-70B, Mistral Large 2, DeepSeek-V2, and Gemma-2-27B, in answering data structure and algorithm design questions fromthe Iranian university entrance exams for master’s programs in computer science and information technology. the research analyzes the accuracy of responses, the length of answers, required reading time, and vocabulary levels across the 2022,2023, and 2024 exams. Questions were categorized into five main areas: Algorithm Analysis and Complexity, Sorting and Searching, Graph Algorithms, data Structures, and Advanced Topics and NP-completeness. the study compares the LLMs’ performance in both Persian and English contexts, providing a comprehensive evaluation of their capabilities and limitations in this domain. Results indicate that GPT-4o achieved the highest average accuracy ($\mathbf{7 5. 0 \%}$), followed by Claude 3.5 Sonnet (67.2%) and Mistral Large 2 (64.1%). the majority of the models performed better on English questions compared to Persian, with GPT-4o showing the largest performance gap (81.3% vs $\mathbf{6 8. 8 \%}$). the findings offer insights into the practical applications of LLMs in educational assessments and contribute to the ongoing discourse on the role of AI in academia, particularly in non-English speaking regions.
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