arXiv subjects

Siqing Wei

Publications and source records attributed to Siqing Wei.

3 recordsLinked to original sources

arXiv2026

ONOTE: Hypergraph-Grounded Omnimodal Reasoning for Computational Music Science

Omnimodal notation processing, centered on sheet music, is a controlled scientific setting in which auditory, visual, symbolic, and physical representations must encode the same musical events. Yet existing work remains fragmented across recognition and transcription, rarely testing structural consistency across notation systems. Western-staff bias and underspecified model judges further conceal errors in pitch, timing, ordering, and instrument-specific constraints. We introduce ONOTE, a unified framework that treats music as a scientifically structured domain of measurable cross-representation correspondences. Its test-only benchmark draws on a diverse collection of musical sources covering staff, Jianpu, and tablature across varied genres, instruments, and structural conditions, with aligned multimodal derivatives. Four complementary tasks cover score understanding, notation conversion, audio transcription, and symbolic generation, testing pitch and duration ordering, output syntax, and disclosed instrument-specific constraints. ONOTE also constructs a provenance-bearing proposition hypergraph from external music-theory materials for entity- and hyperedge-based evidence retrieval. Deterministic validity checks, disclosed structural-compliance SMG scoring, and controlled RAG comparisons reveal gaps between visual recognition and structure-preserving outputs. Results separate perception from music-theory application and structural or physical constraint satisfaction. ONOTE provides an auditable framework for studying representation invariance and knowledge-grounded intervention in computational music science.

cs.SD↗

arXiv2024

Theorizing neuro-induced relationships between cognitive diversity, motivation, grit and academic performance in multidisciplinary engineering education context

Nowadays, engineers need to tackle many unprecedented challenges that are often complex, and, most importantly, cannot be exhaustively compartmentalized into a single engineering discipline. In other words, most engineering problems need to be solved from a multidisciplinary approach. However, conventional engineering programs usually adopt pedagogical approaches specifically tailored to traditional, niched engineering disciplines, which become increasingly deviated from the industry needs as those programs are typically designed and taught by instructors with highly specialized engineering training and credentials. To reduce the gap, more multidisciplinary engineering programs emerge by systematically stretching across all engineering fibers, and challenge the sub-optimal traditional pedagogy crowded in engineering classrooms. To further advance future-oriented pedagogy, in this work, we hypothesized neuro-induced linkages between how cognitively different learners are and how the linkages would affect learners in the knowledge acquisition process. We situate the neuro-induced linkages in the context of multidisciplinary engineering education and propose possible pedagogical approaches to actualize the implications of this conceptual framework. Our study, based on the innovative concept of brain fingerprint, would serve as a pioneer model to theorize key components of learner-centered multidisciplinary engineering pedagogy which centers on the key question: how do we motivate engineering students of different backgrounds from a neuro-inspired perspective?

cs.CY↗

arXiv2023

Exploring the Efficacy of ChatGPT in Analyzing Student Teamwork Feedback with an Existing Taxonomy

Teamwork is a critical component of many academic and professional settings. In those contexts, feedback between team members is an important element to facilitate successful and sustainable teamwork. However, in the classroom, as the number of teams and team members and frequency of evaluation increase, the volume of comments can become overwhelming for an instructor to read and track, making it difficult to identify patterns and areas for student improvement. To address this challenge, we explored the use of generative AI models, specifically ChatGPT, to analyze student comments in team based learning contexts. Our study aimed to evaluate ChatGPT's ability to accurately identify topics in student comments based on an existing framework consisting of positive and negative comments. Our results suggest that ChatGPT can achieve over 90\% accuracy in labeling student comments, providing a potentially valuable tool for analyzing feedback in team projects. This study contributes to the growing body of research on the use of AI models in educational contexts and highlights the potential of ChatGPT for facilitating analysis of student comments.

cs.HC↗