8 papers
A Spatio-Temporal Expert Prefetching Framework for Efficient MoE-based LLM Inference
Yingnan Zhao, Razvan Bunescu, Ahmed Louri +2
Mixture-of-Experts (MoE) based large language models (LLMs), such as Qwen and DeepSeek, have recently emerged as an effective approach to improving model capacity without proportio…
Reasoning Trajectories for Socratic Debugging of Student Code: From Misconceptions to Contradictions and Updated Beliefs
Erfan Al-Hossami, Razvan Bunescu
In Socratic debugging, instructors guide students towards identifying and fixing a bug on their own, instead of providing the bug fix directly. Most novice programmer bugs are caus…
Automatic Classification of Pedagogical Materials against CS Curriculum Guidelines
Erik Saule, Kalpathi Subramanian, Razvan Bunescu
Professional societies often publish curriculum guidelines to help programs align their content to international standards. In Computer Science, the primary standard is published b…
McMining: Automated Discovery of Misconceptions in Student Code
Erfan Al-Hossami, Razvan Bunescu
When learning to code, students often develop misconceptions about various programming language concepts. These can not only lead to bugs or inefficient code, but also slow down th…
A Survey of Affective Recommender Systems: Modeling Attitudes, Emotions, and Moods for Personalization
Tonmoy Hasan, Razvan Bunescu
Affective Recommender Systems are an emerging class of intelligent systems that aim to enhance personalization by aligning recommendations with users' affective states. Reflecting…
A Text-Based Recommender System that Leverages Explicit Affective State Preferences
Tonmoy Hasan, Razvan Bunescu
The affective attitude of liking a recommended item reflects just one category in a wide spectrum of affective phenomena that also includes emotions such as entranced or intrigued,…