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arXiv 2025年

作者： Xiao, Wei Wang, Huiying Zhou, Qifeng Department of Automation Xiamen University Xiamen361005 China School of Engineering Westlake University Hangzhou China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making Xiamen University Xiamen China

Sequential recommendation is a key area in the field of recommender systems aiming to model user interest based on historical interaction sequences with irregular intervals. While previous recurrent neural network-based and attention-based approaches have achieved significant results, they have limitations in capturing system continuity due to the discrete characteristics. In the context of continuous-time modeling, state space model (SSM) offers a potential solution, as it can effectively capture the dynamic evolution of user interest over time. However, existing SSM-based approaches ignore the impact of irregular time intervals within historical user interactions, making it difficult to model complexed user-item transitions in sequences. To address this issue, we propose a hybrid SSM-based model called SS4Rec for continuous-time sequential recommendation. SS4Rec integrates a time-aware SSM to handle irregular time intervals and a relation-aware SSM to model contextual dependencies, enabling it to infer user interest from both temporal and sequential perspectives. In the training process, the time-aware SSM and the relation-aware SSM are discretized by variable stepsizes according to user interaction time intervals and input data, respectively. This helps capture the continuous dependency from irregular time intervals and provides time-specific personalized recommendations. Experimental studies on five benchmark datasets demonstrate the superiority and effectiveness of SS4Rec. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Wireless Communication with Flexible Reflector: Joint Placement and Rotation Optimization for Coverage Enhancement

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IEEE Transactions on Wireless Communications 2025年

作者： Lu, Haiquan Yu, Zhi Zeng, Yong Ma, Shaodan Jin, Shi Zhang, Rui Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China Purple Mountain Laboratories Nanjing211111 China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore

Passive metal reflectors for communication enhancement have appealing advantages such as ultra low cost, zero energy expenditure, maintenance-free operation, long life span, and full compatibility with legacy wireless systems. To unleash the full potential of passive reflectors for wireless communications, this paper proposes a new passive reflector architecture, termed flexible reflector (FR), for enabling the flexible adjustment of beamforming direction via the FR placement and rotation optimization. We consider the multi-FR aided area coverage enhancement and aim to maximize the minimum expected receive power over all locations within the target coverage area, by jointly optimizing the placement positions and rotation angles of multiple FRs. To gain useful insights, the special case of movable reflector (MR) with fixed rotation is first studied to maximize the expected receive power at a target location, where the optimal single-MR placement positions for electrically large and small reflectors are derived in closed-form, respectively. It is shown that the reflector should be placed at the specular reflection point for electrically large reflector. While for area coverage enhancement, the optimal placement is obtained for the single-MR case and a sequential placement algorithm is proposed for the multi-MR case. Moreover, for the general case of FR, joint placement and rotation design is considered for the single-/multi-FR aided coverage enhancement, respectively. Numerical results are presented which demonstrate significant performance gains of FRs over various benchmark schemes under different practical setups in terms of receive power enhancement. © 2002-2012 IEEE.

关键词： Beamforming

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A Secure and Efficient Privacy data Aggregation Mechanism 18th

A Secure and Efficient Privacy Data Aggregation Mechanism

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Ma, Wenshuo Liu, Xinru Yu, Kan Luo, Chuanwen Wang, Guopeng Liu, Xiaowu School of Artificial Intelligence and Big Data Zibo Vocational Institute Zibo China Weifang Engineering Vocational College Weifang China School of Computer Science and Engineering Macau University of Science and Technology Taipa China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information Science and Technology Beijing Forestry University Beijing China School of Computer Science Qufu Normal University Rizhao China Zhejiang Ocean University Zhoushan China The Open University of China Beijing China

ISBN: (纸本)9783031714665

data aggregation (DA) plays an important role in conserving the limited resources and extending the network lifetime of Wireless Sensor Networks (WSNs). Privacy preservation has garnered considerable attention and is considered one of the most viable schemes to address issues such as communication eavesdropping, information leakage, and unauthorized access in WSNs employing DA. In this paper, we propose an improved Secure Privacy-preserving data Aggregation (iSECPDA) mechanism. It dynamically selects cluster heads through an enhanced Stable Election Protocol (SEP), which considers the residual energy of sensor nodes to enhance network lifetime. In our proposed SEPPDA, we integrate interference information into the sensing data and ensure data transmission privacy through a proposed slicing mechanism. Theoretical analysis and simulation experiments demonstrate that iSECPDA exhibits significant performance improvements in terms of communication overhead and privacy preservation levels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data aggregation

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Causality-inspired Unsupervised Domain Adaptation with Target Style Imitation for Medical Image Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Song, Jincai Chen, Haipeng Lyu, Yingda Nie, Weizhi Liu, An-An Jilin University College of Computer Science and Technology Jilin Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China Jilin University Public Computer Education and Research Center Jilin Changchun130012 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China

Deep learning performance may decrease substantially with unseen heterogeneous data. While most unsupervised domain adaptation (UDA) methods seek to address this through image alignment, they often ignore uncertainty style fluctuations within the target domain. When testing image styles vary in both direction and intensity, such models may fail to adapt. Furthermore, existing UDA methods tend to over-reliance on domain-level entire feature alignment, resulting in potentially over-exploiting semantic content-independent cues (e.g., intensity) as shortcut features. To address these limitations, this paper introduces an innovative and model-agnostic Causality-inspired Representation Learning Based on Target Style Imitation method for UDA. Specifically, we propose a novel Target Style Imitation (TSI) data augmentation approach to diversify the training data and align training and unseen target testing image styles. TSI constructs a Gaussian distribution for the target domain style and simulates unseen testing style variations through random sampling. Additionally, inspired by the stable and generalizable causal mechanism, we propose Causality-inspired Representation Learning (CRL) based on TSI method to enforce feature representations to adhere to causal properties (i.e., Separation and Independence) essential for robust UDA, thus fostering the model to focus on the domain-invariant semantic features. Our method surpasses state-of-the-art methods on two cross-modality medical image segmentation datasets. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Emergency Scheduling of Aerial Vehicles via Graph Neural Neighborhood Search

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Guo, Tong Mei, Yi Du, Wenbo Lv, Yisheng Li, Yumeng Song, Tao Beihang University State Key Laboratory of CNS/ATM School of Electronic and Information Engineering Beijing100191 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Wellington New Zealand Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Jinan250353 China University of the Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Beijing Institute of Technology School of Aerospace Engineering Beijing China

The thriving advances in autonomous vehicles and aviation have enabled the efficient implementation of aerial last-mile delivery services to meet the pressing demand for urgent relief supply distribution. Variable Neighborhood Search (VNS) is a promising technique for aerial emergency scheduling. However, the existing VNS methods usually exhaustively explore all considered neighborhoods with a prefixed order, leading to an inefficient search process and slow convergence speed. To address this issue, this paper proposes a novel graph neural neighborhood search algorithm, which includes an online reinforcement learning (RL) agent that guides the search process by selecting the most appropriate low-level local search operators based on the search state. We develop a dual-graph neural representation learning method to extract comprehensive and informative feature representations from the search state. Besides, we propose a reward-shaping policy learning method to address the decaying reward issue along the search process. Extensive experiments conducted across various benchmark instances demonstrate that the proposed algorithm significantly outperforms the state-of-the-art approaches. Further investigations validate the effectiveness of the newly designed knowledge guidance scheme and the learned feature representations. © 2020 IEEE.

关键词： Reinforcement learning

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SMT(LIA) Sampling with High Diversity

arXiv

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arXiv 2025年

作者： Lai, Yong Li, Junjie Luo, Chuan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Software Beihang University Beijing100191 China

Satisfiability Modulo Linear Integer Arithmetic, SMT(LIA) for short, is pivotal across various critical domains. Previous research has primarily focused on SMT solving techniques. However, in practical applications such as software and hardware testing, there is a need to generate a diverse set of solutions for use as test inputs. We have developed the first sampling framework that integrates local search with CDCL(T) techniques, named HighDiv, capable of generating a highly diverse set of solutions for constraints under linear integer theory. Initially, in the local search phase, we introduced a novel operator called boundary-aware movement. This operator performs random moves by considering the current state’s constraints on variables, thereby enhancing the diversity of variables during the search process. Furthermore, we have conducted an in-depth study of the preprocessing and variable initialization mechanisms within the framework, which significantly enhances the efficiency of subsequent local searches. Lastly, we use the solutions obtained from local search sampling as additional constraints to further explore the solution space using the stochastic CDCL(T) method. Experimental results demonstrate that HighDiv generates solutions with greater diversity compared to the state-of-the-art SMT(LIA) sampling tool, MeGASampler. Copyright © 2025, The Authors. All rights reserved.

关键词： Software testing

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data

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Intelligent Oncology 2025年第1期1卷 17-30页

作者： Yang Luo Yilin Li Mengjie Fang Shuo Wang Lizhi Shao Ruiyang Zou Di Dong Zhenyu Liu Jingwei Wei Jie Tian Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing 100190 China Beijing Key Laboratory of Molecular Imaging Beijing 100190 China School of Life Science and Technology Xidian University Xi’an Shaanxi 710126 China School of Public Health Anhui Medical University Hefei Anhui 230032 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing 100191 China School of Engineering Medicine Beihang University Beijing 100191 China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology Beihang University Beijing 100191 China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi’an Shaanxi 710126 China

The advent of multi-omics approaches has revolutionized the field of oncology by enabling a comprehensive understanding of cancer biology through the integration of diverse biological data. This review aims to explore the synergy between three key omics domains: radiomics, genoproteomics, and pathomics. Radiomics involves extracting high-dimensional data from medical images, providing valuable insights into tumor heterogeneity and treatment response. Genoproteomics, encompassing both genomic and proteomic analyses, delves into the molecular mechanisms driving cancer progression and therapeutic resistance. Pathomics leverages advanced digital pathology techniques to quantitatively analyze tissue architecture and cellular morphology. We provide an in-depth overview of the methodologies and tools employed in each omics field, highlighting their specific applications in oncology, including cancer diagnosis, biomarker discovery, and prediction of treatment outcomes. Furthermore, we discuss the integration of multi-omics data, addressing the challenges and innovative solutions for harmonizing these complex datasets. Through an examination of recent advancements and case studies, we underscore the critical role of multi-omics in advancing our understanding of cancer and paving the way for more effective and personalized therapeutic strategies.

关键词： Oncology Multi-omics Radiomics Genoproteomics Pathomics

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Resource Allocation for Underwater Acoustic Sensor Networks With Partial Spectrum Sharing: When Optimization Meets Deep Reinforcement Learning

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IEEE Transactions on Network and Service Management 2025年

作者： Tang, Rui Zhang, Ruizhi Xu, Yongjun Liu, Chuan Huang, Chongwen Yuen, Chau China West Normal University School of Electronic Information Engineering Nanchong637002 China China West Normal University of Sichuan Province Key Laboratory of Optimization Theory and Applications Nanchong637002 China Internet of Things Perception and Big Data Analysis Key Laboratory of Nanchong City Nanchong637002 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400064 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Nanyang Technological University School of Electrical and Electronics Engineering Singapore639798 Singapore

To utilize the limited acoustic spectrum while combating the harsh underwater propagation, we incorporate partial spectrum sharing into an underwater acoustic sensor network and aim to maximize the minimum data collection rate among all underwater sensor nodes through joint power allocation and spectrum assignment. To cope with the non-convex optimization problem, we propose a Hybrid Model-based and data-based Resource Allocation (HMDRA) scheme: 1) Under any given spectrum assignment strategy, we analyze the impact of the partial spectrum sharing and imperfect successive interference cancellation on baseband signal processing, and formulate a power allocation problem that is solved by the bisection method and Lagrange dual theory. 2) Based on the optimal power allocation strategy, the gradient-free genetic algorithm (GA) is first adopted to approach the optimal solution of the model-less spectrum assignment problem by nearly enumerating the solution space. To reduce complexity, we further propose a deep reinforcement learning (DRL)-based algorithm and obtain an efficient solution by traversing a deep neural network-based policy learned from the training stage. Simulation results show that compared with the GA-based algorithm, the average execution time of the DRL-based algorithm is substantially reduced by 5 orders of magnitude to 0.7076 seconds at the cost of approximately 6 percent performance loss. © 2004-2012 IEEE.

关键词： Deep reinforcement learning

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AuthUp: A Privacy-Preserving V2I Authentication Scheme for Vehicular Ad Hoc Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Xincheng Yin, Xinchun Ning, Jianting Zhang, Tianwei Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology the Zhejiang Key Laboratory of Digital Fashion and Data Governance Hangzhou310018 China Jiangxi Vocational College of Science and Technology Nanchang330200 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science China College of Computer and Cyber Security Fuzhou350117 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Vehicular ad hoc networks (VANETs) play a crucial role in enhancing modern transportation systems, leading to the development of various authentication schemes to meet the stringent security and privacy requirements of vehicular communications. Many existing schemes rely on tamper- proof devices (TPDs) that store extensive secret parameters for identity authentication, making them susceptible to Sybil and side-channel attacks. This paper introduces a sophisticated privacy-preserving authentication protocol, referred to as Authentication and Update (AuthUp), specifically designed to solve the problem. A trusted authority (TA) issues one-time access tokens to vehicles during the registration process. After each vehicle-to-infrastructure (V2I) authentication process, cryptographic key material is updated without compromising privacy. Consequently, Sybil attacks are promptly identified and thwarted, as manipulated tokens fail authentication. Additionally, adversaries are prevented from accessing sensitive data stored in TPDs via side-channel attacks because of the dynamic updating of secret keys. A formal security proof validates the robustness of AuthUp, and extensive evaluations demonstrate its superior efficiency compared to alternative schemes, highlighting its suitability for VANET deployment. © 1967-2012 IEEE.

关键词： Side channel attack

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