Dung Manh Nguyen

Sinh Trong Vu, Hieu Trung Pham, Dung Manh Nguyen et al.

Classroom behavior monitoring is a critical aspect of educational research, with significant implications for student engagement and learning outcomes. Recent advancements in Visual Question Answering (VQA) models offer promising tools for automatically analyzing complex classroom interactions from video recordings. In this paper, we investigate the applicability of several state-of-the-art open-source VQA models, including LLaMA2, LLaMA3, QWEN3, and NVILA, in the context of classroom behavior analysis. To facilitate rigorous evaluation, we introduce our BAV-Classroom-VQA dataset derived from real-world classroom video recordings at the Banking Academy of Vietnam. We present the methodology for data collection, annotation, and benchmark the performance of the selected VQA models on this dataset. Our initial experimental results demonstrate that all four models achieve promising performance levels in answering behavior-related visual questions, showcasing their potential in future classroom analytics and intervention systems.

Nam Le Hai, Dung Manh Nguyen, Nghi D. Q. Bui

CodeLLMs have gained widespread adoption for code generation tasks, yet their capacity to handle repository-level code generation with complex contextual dependencies remains underexplored. Our work underscores the critical importance of leveraging repository-level contexts to generate executable and functionally correct code. We present RepoExec, a novel benchmark designed to evaluate repository-level code generation, with a focus on three key aspects: executability, functional correctness through comprehensive test case generation, and accurate utilization of cross-file contexts. Our study examines a controlled scenario where developers specify essential code dependencies (contexts), challenging models to integrate them effectively. Additionally, we introduce an instruction-tuned dataset that enhances CodeLLMs' ability to leverage dependencies, along with a new metric, Dependency Invocation Rate (DIR), to quantify context utilization. Experimental results reveal that while pretrained LLMs demonstrate superior performance in terms of correctness, instruction-tuned models excel in context utilization and debugging capabilities. RepoExec offers a comprehensive evaluation framework for assessing code functionality and alignment with developer intent, thereby advancing the development of more reliable CodeLLMs for real-world applications. The dataset and source code are available at https://github.com/FSoft-AI4Code/RepoExec.