Institution profile

Jilin University

Academic institutionasia · cn
Official website
Research library731linked papers
Opportunities0open roles
Selected work

Representative Papers

AccessFixer: Enhancing GUI Accessibility for Low Vision Users With R-GCN Model

Feb 01, 2024IEEE Transactions on Software Engineering

Low-vision users often struggle to perceive mobile GUIs due to excessively small component sizes, insufficient inter-component spacing, and inadequate color contrast. To address this, we propose the first end-to-end collaborative GUI accessibility repair method. Our approach models spatial, semantic, and stylistic relationships among UI components using a Relational Graph Convolutional Network (R-GCN) and jointly optimizes component size, spacing, and color attributes under design-consistency constraints—ensuring uniform color palettes, equidistant layouts, and scalable adaptations—while preventing new accessibility defects. Evaluated on 30 real-world mobile applications, our method resolves 81.2% of identified accessibility issues on average. We contributed pull requests to 10 open-source projects, with 8 accepted or merged. A user study with low-vision participants confirms statistically significant improvements in interface usability and perceived satisfaction.

5 citationsRead paper

Towards Open Diversity-Aware Social Interactions

Feb 17, 2025arXiv.org

In the digital era, the rapid proliferation of diverse populations, perspectives, and knowledge lacks corresponding adaptive mechanisms, leading to superficial social relationships and intensified echo chambers. Method: This study proposes and implements the “We Internet” platform, introducing— for the first time—the Diversity-Aware AI framework, which integrates sociology, ethics, and artificial intelligence. It establishes multidimensional modeling and representation learning methods for social diversity and designs a human-AI collaborative, ethics-driven algorithmic architecture with interpretable matching guidance. Contribution/Results: Empirical validation demonstrates that the framework significantly enhances cross-group understanding, mitigates filter bubbles, and deepens collaborative engagement. It provides both a theoretical foundation and an implementable paradigm for open, inclusive, and trustworthy social AI systems.

3 citationsRead paper

Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing

Jan 08, 2026arXiv.org

This work addresses the high cost, prolonged duration, and user experience disruption associated with online A/B testing in traditional recommender systems, as well as the limitations of existing large language model–based user agents that lack multimodal perception and authentic interactive capabilities, thereby failing to accurately simulate user behavior. To overcome these challenges, the authors propose A/B Agent—a novel user agent framework capable of processing multimodal inputs and supporting multi-page interactions. Within a constructed recommendation sandbox environment, A/B Agent integrates user profiles, action memory, and a fatigue mechanism, enabling, for the first time, visually aware simulation users with cross-page behavioral modeling. Experimental results demonstrate that this framework effectively substitutes real-world A/B testing across model, data, and feature dimensions, and that the synthesized data it generates significantly enhances recommender system performance.

2 citationsRead paper

Event-Driven Online Vertical Federated Learning

Jun 17, 2025International Conference on Learning Representations

This paper addresses practical challenges in vertical federated learning (VFL), where clients possess non-overlapping features, data streams arrive asynchronously, and updates are triggered by local events—scenarios poorly supported by existing synchronous or time-driven online VFL frameworks. Method: We propose the first event-driven online VFL framework. It formally models event-asynchrony in VFL, introduces Dynamic Local Regret (DLR) as a novel performance metric, and establishes the first rigorous convergence theory for non-convex, non-stationary settings. We further design an event-triggered activation mechanism and an asynchronous collaborative optimization algorithm. Contribution/Results: We theoretically prove an upper bound on DLR that converges over time. Experiments demonstrate that our method achieves superior model stability under non-stationary data compared to state-of-the-art online VFL approaches, while significantly reducing communication overhead and computational cost.

1 citationsRead paper

Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language Models

May 19, 2025

To address two key bottlenecks in leveraging large language models (LLMs) for combinatorial optimization problems (COPs)—weak generalization of search direction and high evaluation overhead—this paper proposes a dual-mechanism framework: Core Abstraction Prompting (CAP) and Prototypical Performance Prediction Prompting (PPP). CAP introduces the first abstraction of elite heuristics’ core components into transferable prompt priors; PPP enables zero- or few-shot heuristic performance prediction via semantic similarity, augmented by reliability discrimination and accuracy enhancement modules. Integrated with theoretically grounded prompt optimization, heuristic semantic equivalence identification, and a multi-LLM collaborative evaluation framework, our approach achieves state-of-the-art performance across four COP task categories, five canonical problems, and eight LLMs. Hercules-P, our implementation, significantly reduces evaluation cost. Ablation studies confirm the effectiveness of each component.

1 citationsRead paper
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