Zobrazeno 1 - 10
of 11 533
pro vyhledávání: '"Madjid, A."'
Autor:
Maredj, Azze-Eddine, Sadallah, Madjid
In the rapidly evolving landscape of digital content, the task of summarizing multimedia documents, which encompass textual, visual, and auditory elements, presents intricate challenges. These challenges include extracting pertinent information from
Externí odkaz:
http://arxiv.org/abs/2412.19133
Autor:
Sadallah, Madjid
Delivering high-quality content is crucial for effective reading comprehension and successful learning. Ensuring educational materials are interpreted as intended by their authors is a persistent challenge, especially with the added complexity of mul
Externí odkaz:
http://arxiv.org/abs/2412.11944
Autor:
Sultanow, Eldar, Selimllari, Fation, Dutta, Siddhant, Reese, Barry D., Tehrani, Madjid, Buchanan, William J
Data poisoning attacks on machine learning models aim to manipulate the data used for model training such that the trained model behaves in the attacker's favor. In classical models such as deep neural networks, large chains of dot products do indeed
Externí odkaz:
http://arxiv.org/abs/2410.05145
Real-time object detection in indoor settings is a challenging area of computer vision, faced with unique obstacles such as variable lighting and complex backgrounds. This field holds significant potential to revolutionize applications like augmented
Externí odkaz:
http://arxiv.org/abs/2409.01871
Autor:
Myers, John M., Madjid, Hadi
Although quantum states nicely explain experiments, the outcomes of experiments are not states. Instead, outcomes correspond to probability distributions. Twenty years ago we proved categorically that probability distributions leave open a choice of
Externí odkaz:
http://arxiv.org/abs/2405.20788
Over the last decade, Artificial Intelligence (AI) has become increasingly popular, especially with the use of chatbots such as ChatGPT, Gemini, and DALL-E. With this rise, large language models (LLMs) and Generative AI (GenAI) have also become more
Externí odkaz:
http://arxiv.org/abs/2403.08701
In the era of personalized education, the provision of comprehensible explanations for learning recommendations is of a great value to enhance the learner's understanding and engagement with the recommended learning content. Large language models (LL
Externí odkaz:
http://arxiv.org/abs/2403.03008
In the age of artificial intelligence (AI), providing learners with suitable and sufficient explanations of AI-based recommendation algorithm's output becomes essential to enable them to make an informed decision about it. However, the rapid developm
Externí odkaz:
http://arxiv.org/abs/2402.07910
Autor:
Abu-Rasheed, Hasan, Dornhöfer, Mareike, Weber, Christian, Kismihók, Gábor, Buchmann, Ulrike, Fathi, Madjid
Publikováno v:
2023 IEEE International Conference on Advanced Learning Technologies (ICALT)
Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models a
Externí odkaz:
http://arxiv.org/abs/2401.13609
Student commitment towards a learning recommendation is not separable from their understanding of the reasons it was recommended to them; and their ability to modify it based on that understanding. Among explainability approaches, chatbots offer the
Externí odkaz:
http://arxiv.org/abs/2401.08517