arXiv subjects

Xiangyang Wu

Publications and source records attributed to Xiangyang Wu.

7 recordsLinked to original sources

arXiv2026

SAVVY: Student Attention Visualization for Video-based Learning Analysis

Video-Based Learning (VBL) has become a popular delivery medium of education in the past decade, ranging from online education to hybrid learning. Students' rising expectations for video quality have motivated teachers to enhance the design of instructional videos before releasing them. Analyzing the attention of pilot cohorts in advance has become a conventional optimization strategy to guide course improvement. However, existing attention quantification algorithms are highly susceptible to noise in real-world environments, degrading estimation accuracy. Moreover, even when attention data are available, teachers must still invest substantial effort in empirical revision attempts, limiting practical feasibility. To address these challenges, we first propose a novel attention modeling framework based on multimodal brain signals that enables stable tracking of student attention levels. We then develop SAVVY, a novel interactive visual analytics system that integrates visual and auditory attention to support top-down exploration of student attention variations. SAVVY comprises three coordinated visualization modules. These modules incorporate multi-level information, including course content structure, audiovisual information density, and attentional resource allocation, and provide multi-temporal-resolution attention trajectories of individual students, enabling teachers to comprehensively analyze the underlying causes of attention fluctuations and inform their subsequent instructional video improvement. We evaluate SAVVY through quantitative experiments, two case studies, and expert interviews. The results demonstrate the effectiveness and usability of SAVVY in intuitively identifying student attention variations and supporting instructional video optimization.

cs.HC↗

arXiv2026

GraphQAG: A Knowledge-Graph-Guided Visual Analytics Framework for Question-Answer Pairs Generation

Question-answer (QA) pairs are widely used in knowledge base construction, question-answering systems, and the post-training of large language models (LLMs). However, important knowledge in long documents is often distributed across multiple paragraphs and connected through complex entity relationships. Such fragmented and relational knowledge poses substantial challenges for existing QA generation methods, which often fail to adequately cover core document content, cross-paragraph semantic connections, and multi-entity relationships. We present GraphQAG, a knowledge graph-guided visual analytics framework for generating high-quality QA pairs from long documents. GraphQAG follows a three-stage workflow. First, it constructs a document knowledge graph by segmenting the document into paragraphs and extracting salient entities and relations. Second, it builds a graph-based generation space from entities, relations, and multi-hop paths to constrain and guide LLM-based QA generation. Third, it uses the knowledge graph as an interactive visual representation, enabling users to explore document knowledge structures, inspect the coverage and evidence provenance of generated QA pairs, and iteratively refine the QA pair set through graph-based interactions. We evaluated GraphQAG through a user study with 16 participants, two case studies, and expert interviews. The results indicate that GraphQAG effectively supports users in identifying knowledge coverage gaps, examining generated QA pairs, and refining the QA pair set. These findings demonstrate the usefulness of combining knowledge graphs, LLM-based generation, and visual analytics for producing more comprehensive and trustworthy QA pairs from long documents.

cs.HC↗

arXiv2026

AniMaster: From Story Texts to Animated Videos via Cinematic Script Generation and Interactive Authoring

Recent advances in Video Generation Models (VGMs) have demonstrated strong capabilities in producing short video clips. However, it is still challenging for everyday creators to leverage these models to produce polished long-form animated videos from brief story texts. Informed by a formative study with both novice creators and film experts, we identify two major challenges of interactive video authoring: (1) the lack of expertise in translating free-form story texts to professional cinematic scripts and finally high-quality animated videos, and (2) the absence of effective ways to convey video design intents to key variables of visual storytelling, such as shot composition, camera controls and shot sequencing. Drawing on narratology and film studies, we propose a three-layer design framework that defines the key design dimensions across three layers (i.e., story texts, cinematic scripts, and animated videos) as well as the translation between them. Built on this framework, we present AniMaster, a VGM-powered authoring tool to enable everyday creators to easily produce smooth animated videos from free-form story texts. AniMaster automatically expands brief story texts to detailed cinematic scripts, and further translates cinematic scripts into polished videos by following professional visual storytelling principles. It also allows users to interactively edit the scripts and refine the generated videos via text instructions and intuitive interactions. We extensively evaluated AniMaster through an in-depth user study with 16 participants, two case studies, and expert interviews with 2 film professionals. The results demonstrate the effectiveness and usability of AniMaster in helping everyday creators create polished animated videos from free-form story texts.

cs.HC↗

arXiv2025

Remodeling Semantic Relationships in Vision-Language Fine-Tuning

Vision-language fine-tuning has emerged as an efficient paradigm for constructing multimodal foundation models. While textual context often highlights semantic relationships within an image, existing fine-tuning methods typically overlook this information when aligning vision and language, thus leading to suboptimal performance. Toward solving this problem, we propose a method that can improve multimodal alignment and fusion based on both semantics and relationships.Specifically, we first extract multilevel semantic features from different vision encoder to capture more visual cues of the relationships. Then, we learn to project the vision features to group related semantics, among which are more likely to have relationships. Finally, we fuse the visual features with the textual by using inheritable cross-attention, where we globally remove the redundant visual relationships by discarding visual-language feature pairs with low correlation. We evaluate our proposed method on eight foundation models and two downstream tasks, visual question answering and image captioning, and show that it outperforms all existing methods.

cs.CV↗

arXiv2025

Quantitative Damping Calculation and Compensation Method for Global Stability Improvement of Inverter-Based Systems

Small-signal stability issues-induced broadband oscillations pose significant threats to the secure operation of multi-inverter systems, attracting extensive research attention. Researches revealed that system instability is led by the lacking of positive damping, yet it has not been clearly specified how much the exact amount of damping compensation required to sufficiently ensure system global stability. This paper presents a feasible solution for quantitative damping calculation and compensation to enhance the global stability of inverter-based systems. First, based on the system nodal admittance model, a quantitative damping calculation algorithm is presented, which can suggest the required damping compensation as well as compensation location for sufficient stability improvement. Then, we propose a specific AD with output current feedforward control strategy, which make the AD be quasi-pure resistive and can effectively enhance system damping efficiency. Finally, a testing system with three inverters is used as case study, showing that the proposed method provides a promising solution to efficiently enhance the global stability improvement of inverter-based systems. Simulations and experiments validate the proposed method.

eess.SY↗

arXiv2024

Self-Adaptive Active Damping Method for Stability Enhancement of Systems With Black-Box Inverters Considering Operating Points

Due to the black-box nature of inverters and the wide variation range of operating points, it is challenging to on-line predict and adaptively enhance the stability of inverter-based systems. To solve this problem, this paper provides a feasible self-adaptive active damping method to eliminate potential small-signal instability of systems with black-box inverters under multiple operating points. First, the framework that includes grid impedance estimation, inverters' admittance identification, and self-adaptive strategy is presented. Second, a widely-applicable and engineering-friendly method for inductive-resistive grid impedance estimation is studied, in which a frequency-integral-based dq-axis aligning method is presented to avoid the inaccuracy resulting from the disturbance theta. Then, to make the system have a sufficient stable margin under different operating points, a self-adaptive active damper (SAD) as well as its control strategy with lag compensator modification is proposed, in which the SAD's damping compensation mechanism for the system's stability enhancement is investigated and revealed. Finally, the mapping between system's parameter variations and SAD's parameters is established based on the artificial neural network (ANN) technique, serving as a computationally light model surrogate that is favorable for on-line parameter-tuning for SAD to compensate the system's damping according to operating points. The effectiveness of the proposed method is verified by simulations in PSACD/EMTDC and experiments in RT-Lab platforms.

eess.SY↗

arXiv2020

ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

cs.HC↗