Jingchao Wang

JBHI Journal 2026 Journal Article

An OBS-WA Algorithm for Pose Optimization of a Tooth Preparation Robot End-Effector in Confined Spaces

• Jingang Jiang
• Zhonghao Xue
• Jianpeng Sun
• Chunrui Wang
• Jingchao Wang
• Jie Pan
• Tao Shen

Most traditional instrument pose planning algorithms focus on optimizing the pose of vertical instruments in open spaces. However, there is a lack of research on pose planning for cantilevered instruments in confined environments. In this paper, we propose an innovative method to optimizing instrument pose under multi-objective constraints. The method introduces the concept of a personalized outer outer bounding sphere and defines the safe feasible region for intraoperative instruments based on the Euclidean distance. For optimizing handle orientation during surgery, we propose an algorithm based on a center search strategy, which ensures that the handle orientation solution set avoids interference with adjacent teeth. Additionally, we introduce an improved scheme based on the outer bounding sphere weighted average (OBS-WA) algorithm to optimize robotic arm joint angles, considering multi-objective constraints. One contribution of this study is the development of an improved skeleton-based instrument collision detection method that addresses the limitations of traditional triangular mesh detection in real-time performance. Another innovation lies in solving the multi-objective optimization problem within the oral cavity. By establishing a test system on an experimental platform, this study demonstrates compliance control and safety planning during tooth preparation.

JBHI Journal 2026 Journal Article

CAM-Interacted Vision GNN for Multi-Label Medical Images

• Jingchao Wang
• Baoyao Yang
• Siqi Liu
• Xiaoqi Zheng
• Wenbin Yao
• Junxiang Chen

Vision Graph Neural Network (ViG) is designed to recognize different objects through graph-level processing. However, ViG constructs graphs with appearance-level neighbors and neglects the category semantic. The oversight results in the unintentional connection of patches that belong to different objects, thus affecting the distinctiveness of categories in multi-label medical image learning. Since the pixel-level annotations for images are not easily available, category-aware graphs can not be directly built. To solve this problem, we consider localizing category-specific regions using Class Activation Maps (CAMs), an effective way to highlight regions belonging to each category without requiring manual annotations. Specifically, we propose a CAM-interacted Vision GNN (CiV-GNN), in which category-aware graphs are formed to perform intra-category graph processing. CIV-GNN includes a Class-activated Patch Division (CAPD) module, which introduces CAMs as guidance for category-aware graph building. Furthermore, we develop a Multi-graph Interactive Processing (MIP) module to model the relations between category-aware graphs, promoting inter-category interaction learning. Experimental results show that CiV-GNN performs well in surgical tool localization and multi-label medical image classification. Specifically, for m2cai16-localization, CiV-GNN exhibits a 1. 43% and 7. 02% improvement in mAP50 and mAP50-95, respectively, compared to YOLOv8.

AAAI Conference 2025 Conference Paper

Contradicted in Reliable, Replicated in Unreliable: Dual-Source Reference for Fake News Early Detection

• Yifan Feng
• Weimin Li
• Yue Wang
• Jingchao Wang
• Fangfang Liu
• Zhongming Han

Early detection of fake news is crucial to mitigate its negative impact. Current research in fake news detection often utilizes the difference between real and fake news regarding the support degree from reliable sources. However, it has overlooked their different semantic outlier degrees among unreliable source information during the same period. Since fake news often serves idea propaganda, unreliable sources usually publish a lot of information with the same propaganda idea during the same period, making it less likely to be a semantic outlier. To leverage this difference, we propose the Reliable-Unreliable Source Reference (RUSR) Fake News Early Detection Method. RUSR introduces the publication background for detected news, which consists of related news with common main objects of description and slightly earlier publication from both reliable and unreliable sources. Furthermore, we develop a strongly preference-driven support degree evaluation model and a two-hop semantic outlier degree evaluation model, which respectively mitigate the interference of news with weak validation effectiveness and the tightness degree of semantic cluster. The designed redistribution module and expanding range relative time encoding are adopted by both models, respectively optimizing early checkpoint of training and expressing the relevance of news implied by their release time gap. Finally, we present a multi-model mutual benefit and collaboration framework that enables the multi-model mutual benefit of generalization in training and multi-perspective prediction of news authenticity in inference. Experiments on our newly constructed dataset demonstrate the superiority of RUSR.

IJCAI Conference 2025 Conference Paper

Imagination-Limited Q-Learning for Offline Reinforcement Learning

• Wenhui Liu
• Zhijian Wu
• Jingchao Wang
• Dingjiang Huang
• Shuigeng Zhou

Offline reinforcement learning seeks to derive improved policies entirely from historical data but often struggles with over-optimistic value estimates for out-of-distribution (OOD) actions. This issue is typically mitigated via policy constraint or conservative value regularization methods. However, these approaches may impose overly constraints or biased value estimates, potentially limiting performance improvements. To balance exploitation and restriction, we propose an Imagination-Limited Q-learning (ILQ) method, which aims to maintain the optimism that OOD actions deserve within appropriate limits. Specifically, we utilize the dynamics model to imagine OOD action-values, and then clip the imagined values with the maximum behavior values. Such design maintains reasonable evaluation of OOD actions to the furthest extent, while avoiding its over-optimism. Theoretically, we prove the convergence of the proposed ILQ under tabular Markov decision processes. Particularly, we demonstrate that the error bound between estimated values and optimality values of OOD state-actions possesses the same magnitude as that of in-distribution ones, thereby indicating that the bias in value estimates is effectively mitigated. Empirically, our method achieves state-of-the-art performance on a wide range of tasks in the D4RL benchmark.

EAAI Journal 2024 Journal Article

A comprehensive wind speed prediction system based on intelligent optimized deep neural network and error analysis

• Yagang Zhang
• Xue Kong
• Jingchao Wang
• Siqi Wang
• Zheng Zhao
• Fei Wang

IS Journal 2024 Journal Article

A Text-Enhanced Transformer Fusion Network for Multimodal Knowledge Graph Completion

• Jingchao Wang
• Xiao Liu
• Weimin Li
• Fangfang Liu
• Xing Wu
• Qun Jin

Multimodal knowledge graphs (MKGs) organize multimodal facts in the form of entities and relations, and have been successfully applied to several downstream tasks. As most MKGs are incomplete, the MKG completion task has been proposed to address this problem, which aims to complete missing entities in MKGs. Most of the previous works obtain reasoning ability by capturing the correlation between target triplets and related images, but they ignore contextual semantic information and the reasoning process is not easily explainable. To address these issues, we propose a novel text-enhanced transformer fusion network, which converts the context path between head and tail entities into natural language text and fuses multimodal features from both coarse and fine granularities through a multigranularity fuser. It not only effectively enhances text semantic information but also improves the interpretability of the model by introducing paths. Experimental results on benchmark datasets demonstrate the effectiveness of our model.

IS Journal 2024 Journal Article

Group Behavior Prediction and Evolution in Social Networks

• Jingchao Wang
• Xinyi Zhang
• Weimin Li
• Xiao Yu
• Fangfang Liu
• Qun Jin

Group behavior prediction and evolution in social networks aims to accurately predict and model trends and patterns of group behavior through detailed analysis of massive user data, which is of great significance to the formulation of marketing strategies, user experience, and business strategies. Therefore, experts in various fields are actively exploring the potential of social network data to develop more accurate group behavior prediction and evolution models. This article provides an overview of these studies and explores the challenges and opportunities faced by group behavior prediction and evolution in social networks.

JBHI Journal 2023 Journal Article

QGD-Net: A Lightweight Model Utilizing Pixels of Affinity in Feature Layer for Dermoscopic Lesion Segmentation

• Jingchao Wang
• Guoheng Huang
• Guo Zhong
• Xiaochen Yuan
• Chi-Man Pun
• Jie Deng

Response: Pixels with location affinity, which can be also called “pixels of affinity, ” have similar semantic information. Group convolution and dilated convolution can utilize them to improve the capability of the model. However, for group convolution, it does not utilize pixels of affinity between layers. For dilated convolution, after multiple convolutions with the same dilated rate, the pixels utilized within each layer do not possess location affinity with each other. To solve the problem of group convolution, our proposed quaternion group convolution uses the quaternion convolution, which promotes the communication between to promote utilizing pixels of affinity between channels. In quaternion group convolution, the feature layers are divided into 4 layers per group, ensuring the quaternion convolution can be performed. To solve the problem of dilated convolution, we propose the quaternion sawtooth wave-like dilated convolutions module (QS module). QS module utilizes quaternion convolution with sawtooth wave-like dilated rates to effectively leverage the pixels that share the location affinity both between and within layers. This allows for an expanded receptive field, ultimately enhancing the performance of the model. In particular, we perform our quaternion group convolution in QS module to design the quaternion group dilated neutral network (QGD-Net). Extensive experiments on Dermoscopic Lesion Segmentation based on ISIC 2016 and ISIC 2017 indicate that our method has significantly reduced the model parameters and highly promoted the precision of the model in Dermoscopic Lesion Segmentation. And our method also shows generalizability in retinal vessel segmentation.