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Xuesong Lu

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

EdGCL: Disentangling Social and Cognitive Homophily in Graph-Based Educational Recommender Systems

Qingqing Liang
Chunyang Wang
Peiwei Xia
Yanan Zeng
Xin Liu
Xuesong Lu
Aoying Zhou

Educational recommendation systems have been a fundamental component for alleviating learning disorientation in self-paced learning. While existing studies mainly leverage cognitive theories to guide learning motivation modeling, they critically overlook the role of social influences. Through empirical analysis, we identify social homophily as an additional driver of learning behaviors, i.e., learners tend to adopt resources validated by their social cohort. However, two challenges impede effective social homophily modeling: (1) the absence and sparsity of predefined social relations in online education, and (2) the deep entanglement of social homophily with cognitive homophily in behavioral data. To tackle these challenges, we propose a graph-based framework EdGCL that explicitly disentangles social homophily and cognitive homophily. EdGCL infers implicit social relations from learners' social behaviors and encodes them via a graph transformer, generating social-view representations. Simultaneously, it constructs a heterogeneous learning graph to model cognitive homophily, which is enhanced by a type-aware aggregator and cognitive diagnosis loss. To ensure the semantic distinctiveness of dual-view homophily modeling, a cross-view contrastive disentanglement mechanism is designed to pull intra-view representations closer while pushing inter-view representations away. Evaluation on two real-world educational datasets demonstrates the superior recommendation performance of EdGCL, highlighting the necessity of dual homophily modeling for understanding the motivations behind learning behaviors.

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JBHI Journal 2024 Journal Article

SSCFormer: Revisiting ConvNet-Transformer Hybrid Framework From Scale-Wise and Spatial-Channel-Aware Perspectives for Volumetric Medical Image Segmentation

Qinlan Xie
Yong Chen
Shenglin Liu
Xuesong Lu

Accurate and robust medical image segmentation is crucial for assisting disease diagnosis, making treatment plan, and monitoring disease progression. Adaptive to different scale variations and regions of interest is essential for high accuracy in automatic segmentation methods. Existing methods based on the U-shaped architecture respectively tackling intra- and inter-scale problem with a hierarchical encoder, however, are restricted by the scope of multi-scale modeling. In addition, global attention and scaling attention in regions of interest have not been appropriately adopted, especially for the salient features. To address these two issues, we propose a ConvNet-Transformer hybrid framework named SSCFormer for accurate and versatile medical image segmentation. The intra-scale ResInception and inter-scale transformer bridge are designed to collaboratively capture the intra- and inter-scale features, facilitating the interaction of small-scale disparity information at a single stage with large-scale from multiple stages. Global attention and scaling attention are cleverly integrated from a spatial-channel-aware perspective. The proposed SSCFormer is tested on four different medical image segmentation tasks. Comprehensive experimental results show that SSCFormer outperforms the current state-of-the-art methods.

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ICRA Conference 2017 Conference Paper

Optimizing guidance for an active shooter event

Sean Gunn
Peter B. Luh
Xuesong Lu
Brock Hotaling

It is unclear what triggers the behavior of active shooters, but their consequences are severe. There is opportunity to implement an automated response system capable of delivery guidance to evacuees' to aide people to safety. Optimizing guidance delivery is challenging because active shooter incidents evolve quickly and are unpredictable. In this paper, we develop a new problem formulation specific for active shooter events by utilizing the structure of each room. To effectively solve this problem, a divide-and-conquer approach is deployed to split evacuees into groups. The egress routes are decomposed and coordinated for each group are optimized using stochastic dynamic programming. Numerical testing and simulation show our solution with a safe room the solution fast relevant to shooting events and effectively.

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