Jie Xing

AAAI Conference 2026 Conference Paper

Multifaceted Scenario-Aware Hypergraph Learning for Next POI Recommendation

• Yuxi Lin
• Yongkang Li
• Jie Xing
• Zipei Fan

Among the diverse services provided by Location-Based Social Networks (LBSNs), Next Point-of-Interest (POI) recommendation plays a crucial role in inferring user preferences from historical check-in trajectories. However, existing sequential and graph-based methods frequently neglect significant mobility variations across distinct contextual scenarios (e.g., tourists versus locals). This oversight results in suboptimal performance due to two fundamental limitations: the inability to capture scenario-specific features and the failure to resolve inherent inter-scenario conflicts. To overcome these limitations, we propose the Multifaceted Scenario-Aware Hypergraph Learning method (MSAHG), a framework that adopts a scenario-splitting paradigm for next POI recommendation. Our main contributions are: (1) Construction of scenario-specific, multi-view disentangled sub-hypergraphs to capture distinct mobility patterns; (2) A parameter-splitting mechanism to adaptively resolve conflicting optimization directions across scenarios while preserving generalization capability. Extensive experiments on three real-world datasets demonstrate that MSAHG consistently outperforms five state-of-the-art methods across diverse scenarios, confirming its effectiveness in multi-scenario POI recommendation.

EAAI Journal 2025 Journal Article

Study on the deformation mode domain of shrink energy-absorbing structures based on curve feature classification method

• Jiaxing He
• Ping Xu
• Jie Xing
• Shuguang Yao
• Bo Wang
• Xin Zheng

Shrink energy-absorbing structures play a key role in engineering applications by absorbing impact energy and ensuring passenger safety. However, inappropriate structural parameters and contact conditions can lead to buckling instability or folding collapse, which reduces the energy absorption efficiency. For this purpose, a deformation mode classification method based on the curve feature was proposed. A Long Short Term Memory (LSTM) network was used to predict the crushing force curve, followed by feature extraction and mode classification to establish the mapping relationships from design parameters to deformation modes. The deformation mode domain was then constructed using the classification model for data expansion, and its boundaries were precisely defined using surface fitting techniques. The critical cone angles of the shrink deformation mode at different friction coefficients were obtained by two-dimensional analysis. In addition, a structural design strategy was also proposed to maximize the specific energy absorption (SEA) of the structure under the shrink deformation mode. The results show that the classification method can effectively predict the deformation modes with 97 % accuracy. Further analysis of the deformation mode domain reveals that the critical cone angle of the shrink deformation mode decreases with the increase of the friction coefficient. Overall, this study predicts the deformation modes of shrink energy-absorbing structures and analyzes the variation of the critical cone angle, providing important guidance for structural optimization and improving energy absorption efficiency.

JBHI Journal 2021 Journal Article

Using BI-RADS Stratifications as Auxiliary Information for Breast Masses Classification in Ultrasound Images

• Jie Xing
• Chao Chen
• Qinyang Lu
• Xun Cai
• Aijun Yu
• Yi Xu
• Xiaoling Xia
• Yue Sun

Breast Ultrasound (BUS) imaging has been recognized as an essential imaging modality for breast masses classification in China. Current deep learning (DL) based solutions for BUS classification seek to feed ultrasound (US) images into deep convolutional neural networks (CNNs), to learn a hierarchical combination of features for discriminating malignant and benign masses. One existing problem in current DL-based BUS classification was the lack of spatial and channel-wise features weighting, which inevitably allow interference from redundant features and low sensitivity. In this study, we aim to incorporate the instructive information provided by breast imaging reporting and data system (BI-RADS) within DL-based classification. A novel DL-based BI-RADS Vector-Attention Network (BVA Net) that trains with both texture information and decoded information from BI-RADS stratifications was proposed for the task. Three baseline models, pre-trained DenseNet-121, ResNet-50 and Residual-Attention Network (RA Net) were included for comparison. Experiments were conducted on a large scale private main dataset and two public datasets, UDIAT and BUSI. On the main dataset, BVA Net outperformed other models, in terms of AUC (area under the receiver operating curve, 0. 908), ACC (accuracy, 0. 865), sensitivity (0. 812) and precision (0. 795). BVA Net also achieved the high AUC (0. 87 and 0. 882) and ACC (0. 859 and 0. 843), on UDIAT and BUSI. Moreover, we proposed a method that integrates both BVA Net binary classification and BI-RADS stratification estimation, called integrated classification. The introduction of integrated classification helped improving the overall sensitivity while maintaining a high specificity.

AAMAS Conference 2003 Conference Paper

Engineering Commitment-Based Multiagent Systems: A Temporal Logic Approach

• Jie Xing
• Munindar P. Singh