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Weimin Li

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8 papers

2 author rows

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.

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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.

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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.

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JBHI Journal 2022 Journal Article

Automatic Lung Nodule Segmentation and Intra-Nodular Heterogeneity Image Generation

Jiangdian Song
Shih-Cheng Huang
Brendan Kelly
Guanqun Liao
Jingyun Shi
Ning Wu
Weimin Li
Zaiyi Liu

Automatic segmentation of lung nodules on computed tomography (CT) images is challenging owing to the variability of morphology, location, and intensity. In addition, few segmentation methods can capture intra-nodular heterogeneity to assist lung nodule diagnosis. In this study, we propose an end-to-end architecture to perform fully automated segmentation of multiple types of lung nodules and generate intra-nodular heterogeneity images for clinical use. To this end, a hybrid loss is considered by introducing a Faster R-CNN model based on generalized intersection over union loss in generative adversarial network. The Lung Image Database Consortium image collection dataset, comprising 2, 635 lung nodules, was combined with 3, 200 lung nodules from five hospitals for this study. Compared with manual segmentation by radiologists, the proposed model obtained an average dice coefficient (DC) of 82. 05% on the test dataset. Compared with U-net, NoduleNet, nnU-net, and other three models, the proposed method achieved comparable performance on lung nodule segmentation and generated more vivid and valid intra-nodular heterogeneity images, which are beneficial in radiological diagnosis. In an external test of 91 patients from another hospital, the proposed model achieved an average DC of 81. 61%. The proposed method effectively addresses the challenges of inevitable human interaction and additional pre-processing procedures in the existing solutions for lung nodule segmentation. In addition, the results show that the intra-nodular heterogeneity images generated by the proposed model are suitable to facilitate lung nodule diagnosis in radiology.

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ICML Conference 2021 Conference Paper

SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

Xiangjun Wang
Junxiao Song
Penghui Qi
Peng Peng
Zhenkun Tang
Wei Zhang
Weimin Li
Xiongjun Pi

AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms and systems, and especially the tremendous amount of computation needed are big obstacles for the community to conduct further research in this direction. We propose a deep reinforcement learning agent, StarCraft Commander (SCC). With order of magnitude less computation, it demonstrates top human performance defeating GrandMaster players in test matches and top professional players in a live event. Moreover, it shows strong robustness to various human strategies and discovers novel strategies unseen from human plays. In this paper, we’ll share the key insights and optimizations on efficient imitation learning and reinforcement learning for StarCraft II full game.

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IROS Conference 2007 Conference Paper

A novel power control strategy of series hybrid electric vehicle

Zhancheng Wang
Weimin Li
Yangsheng Xu

Because of the inherent advantages of increased fuel economy, reduced harmful emissions and better vehicle performance, hybrid electric vehicles (HEV) powered by internal combustion engine (ICE) and energy storage, are being given more and more attention. In this paper, we present a novel approach to the problem of power control strategy for series hybrid electric vehicles (SHEVs). We define 3 different SHEV operation modes and a cost function. After the support vector machine (SVM) training process, we generate a classifier to determine which operation mode should be chosen during driving cycles based on the road situation data, battery state of charge (SOC) data and vehicle speed data. The approach does not need models of SHEV devices, costs less computationally and is more efficient. These distinguished advantages make the approach more practicable in real-time operation. Simulation study proves the feasibility of the approach.

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IROS Conference 2006 Conference Paper

A Novel On-board Temperature Monitoring Approach in the Reflow Soldering Process

Zhancheng Wang
Weimin Li
Hang Tong
Yangsheng Xu

The goal of this paper is to monitor in-process on-board data as a means of indicating product quality and to be able to respond quickly to unexpected process disturbances. Due to pending environmental legislation and market requirements, lead-free soldering is widely used by the electronics industry. As the margin between the higher melting temperatures of lead-free solders and the heat-resistant temperatures of electronic components becomes narrower than lead solders, a more precise control of the temperature is required. Traditionally, the control processes of the on-board temperature are open loop because it is difficult to monitor the temperature on a PCB board. In this paper, we establish a method to determine the real process temperatures at any point on a PCB board in the furnace. We develop the method based on support vector machines (SVM) with multiple-input single-output strategies to learn relationship between the temperatures near the PCB board and the on-board temperature. The method is the only one which has been commercially utilized to predict the on-board temperature because of its low cost and high accuracy

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IROS Conference 2006 Conference Paper

Study on Kinematics Decoupling for Parallel Manipulator with Perpendicular Structures

Jianjun Zhang 0003
Weimin Li
Xiaohui Wang
Feng Gao 0011

Kinematics decoupling characteristics (KDCs) for parallel manipulators simplify the kinematics models, and make them easier in calibration and control. This paper focuses on KDCs for parallel manipulators which are described in detail. The relationship between input and output of a system with KDCs is discussed and the characteristics of its transfer matrix are educed. Then, the kinematics of two parallel manipulators, a 6-PSS parallel micro-manipulator with perpendicular structures (PMMWPS) and a 6-PP+S parallel manipulator with perpendicular structures (PMWPS), is analyzed. Also, the kinematics models are obtained. In the course of the analysis, conditional decoupling is defined. The results show that the two proposed PMWPSs have KDCs. The KDCs for PMWPSs would offer a new idea in the area of parallel manipulators.

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