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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Kaixuan Chen Wei Luo Shunyu Liu Yaoquan Wei Yihe Zhou Yunpeng Qing Quan Zhang Yong Wang Jie Song Mingli Song State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China State Grid Shandong Electronic Power Company Jinan China

ISBN: (纸本)9798400712456

In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections. Specifically, our proposed approach, named Powerformer, develops a dedicated section-adaptive attention mechanism, separating itself from the self-attention employed in conventional transformers. This mechanism effectively integrates power system states with transmission section information, which facilitates the development of robust state representations. Furthermore, by considering the graph topology of power system and the electrical attributes of bus nodes, we introduce two customized strategies to further enhance the expressiveness: graph neural network propagation and multi-factor attention mechanism. Extensive evaluations are conducted on three power system scenarios, including the IEEE 118-bus system, a realistic China 300-bus system, and a large-scale European system with 9241 buses, where Powerformer demonstrates its superior performance over several popular baseline methods. The code is available at: https://***/Cra2yDavid/Powerformer

关键词： graph neural network

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Oesophageal cancer-specific mortality risk and public health insurance: Prospective cohort study from China

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World Journal of Gastrointestinal Oncology 2025年第4期17卷 274-285页

作者： Xiang-Lin Wu Xiao-Sheng Li Jing-Han Cheng Lin-Xin Deng Zu-Hai Hu Jun Qi Hai-Ke Lei Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer HospitalChongqing 400030China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center Chongqing University Cancer HospitalChongqing 400030China Research Center for Medicine and Social Development Chongqing Medical UniversityChongqing 400016China Department of Health Statistics School of Public HealthChongqing Medical UniversityChongqing 400016China Department of Thoracic Surgery The People’s Hospital of Changshou ChongqingChongqing 401220China

BACKGROUND Oesophageal cancer is a significant health concern worldwide,with high inci-dence and mortality *** China,the disease burden is particularly high,accounting for a substantial proportion of oesophageal cancer cases and related deaths *** To explore the relationship between the mortality rate of oesophageal cancer patients and insurance type,out-of-pocket ratio,and the joint effects of insurance type and out-of-pocket *** The χ^(2) test was used to analyze patients’demographic and clinical *** logistic regression,the Cox proportional hazard model,and the competitive risk model were used to calculate the cumulative hazard ratios(HRs)of all-cause death and oesophageal cancer-specific death among patients with different types of insurance and out-of-pocket *** Compared with patients covered by basic medical insurance for urban and rural residents,patients covered by urban employee basic medical insurance for urban workers(UEBMI)had a 23.30%increased risk of oesophageal cancer-specific death[HR=1.233,95%confidence interval(CI):1.093-1.391,P<0.005].Compared with patients in the low out-of-pocket ratio group,patients in the high out-of-pocket ratio group had a 25.80%reduction in the risk of oesophageal cancer-specific death(HR=0.742,95%CI:0.6555-0.84,P<0.005).With each 10%increase in the out-of-pocket ratio,the risk of oesophageal cancer-specific death decreased by 10.10%in patients covered by ***,the risk of oesophageal cancer-specific death increased by 26.90%in patients in the high out-of-pocket ratio *** This study reveals the relationships of the specific mortality rate of patients with oesophageal cancer with the out-of-pocket ratio and medical insurance types as well as their combined *** study provides practical suggestions and guidance for the formulation of relevant policies in this area.

关键词： Oesophageal cancer Public health Insurance Prognosis Mortality risk

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Unknown Task Selection and Worker Recruitment Using Two-Stage Multi-Armed Bandit in Crowdsensing

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IEEE Internet of Things Journal 2025年

作者： Song, Haoyuan Li, Peng Du, Rui Yu, Hai Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hubei Polytechnic University School of Computer Huangshi435003 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Mobile Crowdsensing (MCS) faces significant challenges in selecting tasks with expected high revenue and recruiting workers with expected high qualities to maximize overall utility. Existing approaches often assume the revenue of posting tasks or the quality of workers is determined, which limits their practical applicability. This paper tackles these challenges by modeling the long-term multi-task, multi-worker selection problem as a Two-Stage Multi-Armed Bandit (TS-MAB) problem under uncertainty. In each round, the MCS platform selects a long-term task from a pool and recruits the necessary workers, determining payments accordingly. We model the task selection phase as a MAB problem, and the worker recruitment phase, influenced by the task selection, as a Combinatorial MAB (CMAB) problem. We propose an extended Upper Confidence Bound (UCB)-based strategy and develop an incentive mechanism based on Auction theory, combined with TS-MAB (i.e., ATS-MAB), for unknown task selection and worker recruitment. Our mechanism theoretically guarantees truthfulness and individual rationality, with a theoretical analysis of its regret bounds. Furthermore, we introduce an adaptive incentive mechanism called AATS-MAB, which improves worker recruitment and quality updates, achieving higher total sensing quality and lower regret. Extensive simulations demonstrate the effectiveness and scalability of the proposed methods. © 2014 IEEE.

关键词： Personnel selection

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FrustumFusionNets: A Three-Dimensional Object Detection Network Based on Tractor Road Scene

arXiv

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arXiv 2025年

作者： Yang, Lili Chang, Mengshuai Guo, Xiao Feng, Yuxin Mei, Yiwen Wu, Caicong College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China

To address the issues of the existing frustum-based methods' underutilization of image information in road three-dimensional object detection as well as the lack of research on agricultural scenes, we constructed an object detection dataset using an 80-line Light Detection And Ranging (LiDAR) and a camera in a complex tractor road scene and proposed a new network called FrustumFusionNets (FFNets). Initially, we utilize the results of image-based two-dimensional object detection to narrow down the search region in the three-dimensional space of the point cloud. Next, we introduce a Gaussian mask to enhance the point cloud information. Then, we extract the features from the frustum point cloud and the crop image using the point cloud feature extraction pipeline and the image feature extraction pipeline, respectively. Finally, we concatenate and fuse the data features from both modalities to achieve three-dimensional object detection. Experiments demonstrate that on the constructed test set of tractor road data, the FrustumFusionNetv2 achieves 82.28% and 95.68% accuracy in the three-dimensional object detection of the two main road objects, cars and people, respectively. This performance is 1.83% and 2.33% better than the original model. It offers a hybrid fusion-based multi-object, high-precision, real-time three-dimensional object detection technique for unmanned agricultural machines in tractor road scenarios. On the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) Benchmark Suite validation set, the FrustumFusionNetv2 also demonstrates significant superiority in detecting road pedestrian objects compared with other frustum-based three-dimensional object detection methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Tractors (truck)

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Adaptive disentangled target representation for unsupervised domain adaptation in remote sensing segmentation

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Lu, Runuo Dong, Shoubin Jia, Jianxin Wang, Xusheng Liu, Kai Chen, Jinsong Guo, Shanxin Zheng, Xiaorou Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou510641 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute EspooFI-02150 Finland Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application Shenzhen518055 China

Due to the significant differences between the source and target domains, semantic segmentation models for remote sensing images trained on the source domain often struggle to generalize effectively to new target domains, leading to performance degradation. Most solutions disentangle features into domain-invariant features shared by both domains and domain-specific features unique to each domain, relying primarily on the domain-invariant features for model construction and inference. However, during this process, critical domain-specific features as essential information are often discarded as noise, leading to a reduction in segmentation accuracy. To address these issues, this study proposes an unsupervised domain adaptation, called ChameleonRS. ChameleonRS separates the domain-invariant features and the domain-specific features by disentanglement learning, and introduces a multi-level feature alignment module to enhance the representation of domain-invariant features. More importantly, it aligns predictions based on domain-invariant features with those based on fused invariant and specific features using contrastive learning, effectively capturing target-specific features. This study explores two cross-domain semantic segmentation tasks: (I) Geographic location variation. (II) Geographic location and imaging mode variation. Results show that under setting (I), ChameleonRS outperforms the state-of-the-art method by 4.79% in Intersection over Union (IoU) and 6.51% in the F1 score. Under setting (II), it achieves improvements of 4.46% and 4.82% in IoU and F1 score. Results demonstrate the superiority and adaptability of ChameleonRS under varying geographical and imaging conditions, effectively mitigating the generalization issues caused by distribution differences, and thereby promoting the development and application of remote sensing technologies in practical scenarios. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection

arXiv

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arXiv 2025年

作者： Hao, Pingting Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University PortlandOR97201 United States

In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and efficiency remains a significant question in MVML. Existing methods often extract information separately from the consistency part and the complementary part, which may result in noise due to unclear segmentation. In this paper, we propose a unified model constructed from the perspective of global-view reconstruction. Additionally, while feature selection methods can discern the importance of features, they typically overlook the uncertainty of samples, which is prevalent in realistic scenarios. To address this, we incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness. Thus, the global-view is reconstructed through the graph structure between samples, sample confidence, and the view relationship. The accurate mapping is established between the reconstructed view and the label matrix. Experimental results demonstrate the superior performance of our method on multi-view datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature Selection

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Optimal Flying Location for Unmanned Aerial Vehicle Equipped with MF-RIS assisted Covert Wireless on Communication

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Jie Li, Yunxiang Tian, Wen Dai, Yuewei Liu, Yinqiu Niyato, Dusit Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Nanjing China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Key Laboratory of Intelligent Support Technology In Complex Environment Ministry of Education Nanjing210044 China Nanyang Technological University College of Computing and Data Science Singapore

In the current information age, covert communication holds significant value owing to its advantages, such as heightened security, enhanced flexibility, and improved concealment, surpassing those of traditional communication methods. However, existing covert communication approaches approximate communication channel models by Line-of-Sight (LoS) links, which is insufficient due to neglecting the impact of multipath effects and applying inaccurate performance assessments. To better adapt to real-world communication channel conditions, we propose a novel covert communication model assisted by Unmanned Aerial Vehicles (UAV) equipped with Multi-Functional Reconfigurable Intelligent Surface (MF-RIS), named UMCCM. Specifically, based on a mixed link of LoS and Non-Line-of-Sight (NLoS), UMCCM leverages MF-RIS as a flexible relay to enhance the signal-to-noise ratio (SNR) and effective throughput. We first derive the mixed channel model expression. Then, we transform covert constraints from incomplete gamma functions to those linked with relative entropy and accordingly optimize the UAV transmission power and block length. Finally, the optimal flying position of the UAV is obtained by analyzing the functional properties of SNR and effective throughput under these refined constraints. Extensive numerical results demonstrate the superiority of our proposed UMCCM for enhancing SNR and effective throughput over the existing works. © 2025 IEEE.

关键词： Aircraft communication

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Multi-Scale Semantic Communication for Object Detection: Single and Cross-Domain Scenarios

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Jie Yin, Hang Song, Bin Chi, Yuhao Zhang, Zhaoyang Yuen, Chau Niyato, Dusit Xidian University State Key Laboratory of Integrated Services Networks Xi’an710071 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore Nanyang Technological University College of Computing and Data Science 639798 Singapore

With the rapid popularity of vision-driven communication applications, object detection has become one of the fundamental techniques for performing practical vision tasks. In traditional communication systems, images are compressed for transmission, reconstructed at the receiver, and then processed by existing object detection algorithms. However, transmitting large amounts of images consumes significant storage and communication resources. To address this challenge, a semantic communication-based image reconstruction scheme has been proposed for object detection, which transmits only the semantic information relevant to image reconstruction. However, this method is prone to losing key information, such as object position and texture details, leading to degraded object detection performance. Additionally, it is sensitive to environmental factors such as weather and lighting, resulting in poor adaptability across multiple scenarios. To address these issues, we propose a multiscale semantic communication framework for object detection that transmits only multi-scale semantic features relevant to the task and employs decoupling at the receiver to separate positional and classification information of target objects without requiring image reconstruction. To improve adaptability across multiple scenarios, we introduce a cross-domain object detection technique that ensures reliable object detection in new scenarios by optimizing the framework’s multi-scale semantic encoder through domain adversarial learning. Numerical results demonstrate that the proposed framework achieves mean average precision improvements of 15.4% ~ 38.5% over the traditional communication framework within low to medium signal-to-noise ratio regions in additive white Gaussian noise and Rayleigh fading channels. © 2002-2012 IEEE.

关键词： Fading channels

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Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Evolutionary Direction Learning With Multivariate Gaussian Probabilistic Model for Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Xianpeng Zhang, Jingchuan Tang, Lixin Liu, Yaxue Ministry of Education National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

In recent years, utilizing data from the evolutionary process of multiobjective evolutionary algorithms (MOEAs) to learn knowledge and guide evolutionary search has become a popular research topic. However, existing knowledge learning (KL) frameworks often suffer from the low quality of collected datasets and the inefficiency of model construction, which significantly limits their effectiveness. To address this issue, this paper proposes a novel evolutionary direction learning (EDL) framework, which aims to learn the evolutionary direction (ED) knowledge for each objective to enhance the population generation of MOEAs. The proposed EDL incorporates an effective data collection method based on objective improvement to generate high-quality datasets, based on which a multivariate Gaussian probabilistic model is employed to learn ED knowledge for each objective through a data fusion modeling approach. Besides, a knowledge assignment method is designed to select the most suitable ED knowledge to guide the evolution of solutions. Experimental results on both synthetic and real-world problems demonstrate that the proposed EDL framework can accelerate the convergence of MOEAs and significantly improve their performance. A comparison of the proposed EDL with three state-of-the-art KL frameworks indicates that EDL is a highly competitive learning framework, achieving superior performance with larger datasets and impressive efficiency. © 1997-2012 IEEE.

关键词： Contrastive Learning

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