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Burst-Sensitive Traffic Forecast Via Multi-Property Personalized Fusion in Federated Learning

IEEE Transactions on Mobile Computing 2025年

作者： Xue, Jingjing Sun, Sheng Liu, Min Wang, Yuwei Meng, Xuying Wang, Jingyuan Zhang, JunBo Xu, Ke Chinese Academy of Sciences University of Chinese Academy of Sciences Institute of Computing Technology Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beihang University School of Computer Science and Engineering China Beihang University MIIT Key Laboratory of Data Intelligence and Management Beijing China JD Technology JD iCity China JD Intelligent Cities Research China Tsinghua University Department of Computer Science and Technology Beijing China Zhongguancun Laboratory Beijing China

For distributed network traffic prediction with data localization and privacy protection, Federated Learning (FL) enables collaborative training without raw data exchange across Base Stations (BSs). Nevertheless, traffic across BSs exhibit inherently heterogeneous trend burst and smooth fluctuation properties, but existing FL methods model single-scale series from only one view, which cannot simultaneously capture diverse trend and fluctuation properties, especially distinct burst distributions. In this paper, we propose Personalized Federated Forecasting with Multi-property Self-fusion (P2FMS), which can represent multi-scale traffic properties from different views. With precise multi-property representations, a fusion-level prediction decision is learned for each client in a personalized manner to promptly sense traffic bursts and improve forecasting performance in non-IID settings. Specifically, P2FMS decomposes the traffic series into distinct time scales, based on which, we effectively extract closeness, period, and trend properties from different views. The closeness and period are embedded through global-view representations with spatial correlations, while non-stationary trends are individually fitted from the client-side view. Furthermore, a personalized combiner is designed to accurately quantify the proportion of general fluctuation raws (i.e., closeness and period) and specific trend property in predictions, which enables multi-property self-fusion for each client to accommodate heterogeneous traffic patterns and enhance prediction accuracy. Besides, an alternant training mechanism is introduced to optimize property representation and fusion modules with the convergence guarantee. Extensive experiments on real-world datasets show that P2FMS outperforms status quo methods in both prediction performance and convergence time. © 2002-2012 IEEE.

关键词： Weather forecasting

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Delving into Invisible Semantics for Generalized One-shot Neural Human Rendering

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IEEE Transactions on Visualization and Computer Graphics 2025年

作者： Lin, Yihong Xu, Xuemiao Zhang, Huaidong Xu, Cheng Li, Weijie Xie, Yi Qin, Jing He, Shengfeng South China University of Technology Guangdong China Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China South China University of Technology China Hong Kong Polytechnic University Centre for Smart Health Hong Kong CAS-Hong Kong Joint Laboratory for Multimodal Medical Molecular Imaging Hong Kong Singapore Management University School of Computing and Information Systems Singapore

Traditional human neural radiance fields often overlook crucial body semantics, resulting in ambiguous reconstructions, particularly in occluded regions. To address this problem, we propose the Super-Semantic Disentangled Neural Renderer (SSD-NeRF), which employs rich regional semantic priors to enhance human rendering accuracy. This approach initiates with a Visible-Invisible Semantic Propagation module, ensuring coherent semantic assignment to occluded parts based on visible body segments. Furthermore, a Region-Wise Texture Propagation module independently extends textures from visible to occluded areas within semantic regions, thereby avoiding irrelevant texture mixtures and preserving semantic consistency. Additionally, a view-aware curricular learning approach is integrated to bolster the model's robustness and output quality across different viewpoints. Extensive evaluations confirm that SSD-NeRF surpasses leading methods, particularly in generating quality and structurally semantic reconstructions of unseen or occluded views and poses. © 2025 IEEE.

关键词： Semantics

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Exploring the potential of saponins from Dicotyledonous plants in sustainable pest management: Innovations and challenges: A review

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Journal of Natural Pesticide Research 2025年 11卷

作者： Hameed, Muhammad Salman Urooj, Nida Basit, Abdul Abbas, Anam Noreen Qasim, Muhammad State Key Laboratory of Green Pesticide International Joint Research Center for Intelligent Biosensor Technology and Health Central China Normal University Wuhan 430079 China Department of Business Administrative Bahaudin Zakriya University Multan Pakistan Guizhou Provincial Key Laboratory for Agricultural Pest Management of the Mountainous Region Scientific Observing and Experimental Station of Crop Pest in Guiyang Ministry of Agriculture and Rural Affairs Institute of Entomology Guizhou University Guiyang 550025 China Institute of Insect Ecology College of Plant Protection Fujian Agriuclture and Forestry University Fuzhou China Key Laboratory of Oasis Agricultural Pest Management and Plant Protection Utilization College of Agriculture Shihezi University Xinjiang Shihezi 832002 China

Plants produce a diverse array of secondary metabolites, including saponins, which play significant roles in defense against herbivores. This review investigates the potential of saponins from dicotyledonous plants, particularly within the Fabaceae (Leguminosae) family—encompassing species such as alfalfa, grass peas, lupins, lentils, chickpeas, and soybeans—as tools for sustainable pest management. We provide a comparative analysis with other natural pest control agents, emphasizing the unique advantages and limitations of saponins. The molecular and biochemical mechanisms by which saponins affect pest physiology are explored, alongside discussions on their synergistic effects with other pest control substances. Recent field trials and case studies are reviewed to assess the practical applications, effectiveness, and challenges encountered. The environmental impact, safety considerations, and economic feasibility of saponin use are critically examined. Innovations in saponin extraction, formulation, and application methods are highlighted. Perspectives on integrating saponins into integrated pest management systems and addressing regulatory hurdles are also discussed. Further research is needed to explore the development of cost-effective extraction methods, the potential for resistance development among pests, and the scalability of saponin-based solutions in large-scale agricultural systems. This review offers a comprehensive overview of how saponins can contribute to sustainable agriculture, highlighting key areas for future research and innovation. © 2025 The Authors

关键词： Fabacear and Biodegradability Pest management Saponins Sustainable Agriculture

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Edge-Enhanced Cascaded MRF for SAR Image Segmentation

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Mengmeng Shang, Ronghua Liu, Kang Feng, Jie Wang, Chao Xu, Songhua Li, Yangyang Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Shaanxi Xi’an710071 China Xidian University School of Computer Science and Technology Shaanxi Xi’an710071 China Research Center for Data Hub and Security Zhejiang Lab Hangzhou311100 China The Second Affiliated Hospital of Xi’an Jiaotong University Department of Health Management Institute of Medical Artificial Intelligence Xi’an710004 China

Markov random fields (MRFs) effectively capture local contextual information by modeling the spatial dependencies between pixels, which helps highlight details and enhances segmentation smoothness. To fully exploit MRF for synthetic aperture radar (SAR) image segmentation, we propose a novel edge-enhanced cascaded MRF (ECMRF) approach. Specifically, we introduce multiple edge-constrained filters to emphasize SAR image boundaries and provide relatively clean features. Building on this, we present a cascaded MRF framework that sequentially integrates region-level and pixel-level segmentations with feature perturbation and fusion to generate the final segmentation output. The framework comprises four key components: 1) a region-level MRF (RMRF), regulated by edge features, to achieve precise region segmentation;2) a pixel-level MRF (PMRF) with selective label smoothing to refine edges and reduce noise clusters;3) equal-channel feature perturbation (EC-FP) to increase feature diversity;and 4) a random probability-based feature fusion (RP-FF) scheme to merge the input features. The experimental results demonstrate that our ECMRF outperforms six state-of-the-art comparable methods, underscoring its competitive performance. © 1980-2012 IEEE.

关键词： Image segmentation

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A novel construction of certificateless aggregate signcryption scheme for smart healthcare

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Expert Systems with Applications 2025年 285卷

作者： Liu, Xianxiang Zhou, Yanwei Zhu, Yasi Xia, Zhe School of Computer Science Shaanxi Normal University Xi'an China Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu China School of Computer Science and Artificial intelligence Wuhan University of Technology Wuhan China

The rapid advancement of smart medical technology has undoubtedly enhanced the quality of human life, yet it faces numerous security challenges. In particular, the sensors in smart healthcare are limited by computational resources, leading to security vulnerabilities and inefficiencies. These limitations not only threaten user privacy but also impose significant burdens on system functionality. To address the above issues, researchers have predominantly adopted signcryption technology to enhance communication security. Despite notable progress in recent years, several challenges remain unresolved. Many existing schemes rely heavily on bilinear pairing operations, which can introduce substantial computational costs for medical devices and create security risks that may compromise user privacy. Therefore, we propose an efficient certificateless signcryption aggregation scheme (CLAS) to address these challenges. Firstly, our proposed scheme eliminates the need for bilinear pairing computations, significantly improving efficiency, and our proposal reduces ciphertext length and communication cost by aggregating transaction ciphertexts. Furthermore, we use signcryption approach to ensure the security of transmission data with varying lengths. Through rigorous formal security proofs, efficiency analyses, and performance evaluations, we prove that our scheme achieves confidentiality, unforgeability, and better efficiency in computation and communication. © 2025 Elsevier Ltd

关键词： Ciphertext

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NCCMF:Non-Collaborative Continuous Monitoring Framework for Container-Based Cloud Runtime Status

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Computers, Materials & Continua 2024年第10期81卷 1687-1701页

作者： Tao Zheng Wenyi Tang Xingshu Chen Changxiang Shen School of Cyber Science and Engineering Sichuan UniversityChengdu610065China Intelligent Policing and National Security Risk Management Laboratory Luzhou646000China Cyber Science Research Institute Sichuan UniversityChengdu610065China Key Laboratory of Data Protection and Intelligent Management(Sichuan University) Ministry of EducationChengdu610065China

The security performance of cloud services is a key factor influencing users’selection of Cloud Service Providers(CSPs).Continuous monitoring of the security status of cloud services is ***,existing research lacks a practical framework for such ongoing *** address this gap,this paper proposes the first NonCollaborative Container-Based Cloud Service Operation State Continuous Monitoring Framework(NCCMF),based on relevant *** operates without the CSP’s collaboration by:1)establishing a scalable supervisory index system through the identification of security responsibilities for each role,and 2)designing a Continuous Metrics Supervision Protocol(CMA)to automate the negotiation of supervisory *** framework also outlines the supervision process for cloud services across different deployment *** results demonstrate that NCCMF effectively monitors the operational state of two real-world IoT(Internet of Things)cloud services,with an average supervision error of less than 15%.

关键词： Container-based cloud non-collaborative continuous monitor runtime status

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BSDSGANet: Bidirectional Skip-stored Dual-Stream Gated Attention Network for multivariate time series classification

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Knowledge-Based Systems 2025年 318卷

作者： Yi, Yugen Zhao, Panpan Sheng, Hui Liu, Min Li, Shicheng Dai, Jiangyan Kong, Jun Qiao, Shaojie School of Software Research Center for Management Science and Engineering Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing Jiangxi Normal University Nanchang330022 China School of Artificial Intelligence and Big Data Jiangxi University of Technology Nanchang330098 China School of Computer Engineering Weifang University Weifang261061 China College of Information Science and Technology Northeast Normal University Changchun130117 China School of Software Engineering Chengdu University of Information Technology Chengdu610225 China

Echo State Networks (ESNs) are popular in Time Series Classification (TSC) due to their high-dimensional stochastic projections. However, due to the echo state at the current step is susceptible to the preceding step, the existing ESNs struggle to capture the long-term temporal dependencies. Furthermore, most of them only employ the unidirectional storage to capture the sequence information, which fails to fully consider the impact of the past and future moments simultaneously. To address these issues, we propose an advanced method called Bidirectional Skip-stored Dual-Stream Gated Attention Network (BSDSGANet) for Multivariate Time Series Classification (MTSC). Specifically, to roundly exploit the sequence information, a Bidirectional Skip-Stored Encoder (BSSE) is designed to enrich the temporal context by integrating past and future information. Then, a Dual-Stream Gated Attention Learner (DSGAL) mechanism is incroporated, which employs a Gated Recurrent Self-Attention (GRSA) module to learn the long-term dependencies and variable relationships. Meanwhile, a Multi-Scale Gated Attention (MSGA) module is developed to extract more important features from multiple scales. Extensive experiments conducted on eighteen benchmark MTSC datasets show that BSDSGANet outperforms five traditional methods and twelve deep learning models. © 2025 Elsevier B.V.

关键词： Convolutional neural networks

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CIRSM-Net: A Cyclic Registration Network for SAR and Optical Images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Wang, Peng Liu, Yuzhou Liang, Xiao Zhu, Daiyin Gong, Xunqiang Ye, Yuanxin Lee, Harry F. Huang, Bo Nanjing University of Aeronautics and Astronautics Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing210016 China Optoelectronic Measurement and Intelligent Perception Zhongguancun Open Laboratory Beijing100190 China City University of Macau Faculty of Data Science 999078 China Beijing Institute of Control Engineering Beijing100190 China East China University of Technology Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake Ministry of Natural Resources Nanchang330013 China Southwest Jiaotong University Faculty of Geosciences and Environmental Engineering Chengdu611756 China Chinese University of Hong Kong Department of Geography and Resource Management SAR Hong Kong Hong Kong University of Hong Kong Department of Geography SAR Hong Kong Hong Kong

The registration of synthetic aperture radar (SAR) and optical images is critical in multimodal remote sensing image fusion. In recent years, deep learning-based registration networks have been continuously introduced. However, owing to the significant disparities in viewing angles and radiometric properties between SAR and optical images, current deep learning methods struggle to fully exploit the physical properties of radar imaging. In addition, many existing matching networks typically perform only a forward pass, resulting in suboptimal model performance. This article proposes a cyclic iterative registration SAR mechanism network (termed as CIRSM-Net) for the registration of SAR and optical images. First, we design a learning module that integrates the radar equation with a microwave scattering model to capture deep features from SAR images, and design a corresponding scattering feature loss to aid in better generalization across various radar images. Then, to explore optimization methods for matching networks, this study proposes a strategy of multiple iterative optimizations within the matching network. Specifically, it integrates speeding-up radiation-variation insensitive feature transform (RIFT2) supervision in the backend matching network and iteratively optimizes the final output. Finally, during the iteration process, we propose an innovative matching loss function that combines the rotation invariance supervision of RIFT2 with iterative optimization techniques to enhance feature matching accuracy. Experimental results on both public and our own datasets additionally confirm the effectiveness and superiority of the proposed approach, demonstrating its significant potential for practical applications. © 2025 IEEE.

关键词： Radar imaging

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KubeFuzzer:Automating RESTful API Vulnerability Detection in Kubernetes

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Computers, Materials & Continua 2024年第10期81卷 1595-1612页

作者： Tao Zheng Rui Tang Xingshu Chen Changxiang Shen School of Cyber Science and Engineering Sichuan UniversityChengdu610065China Cyber Science Research Institute Sichuan UniversityChengdu610065China Key Laboratory of Data Protection and Intelligent Management(Sichuan University) Ministry of EducationChengdu610065China

RESTful API fuzzing is a promising method for automated vulnerability detection in Kubernetes *** tools struggle with generating lengthy,high-semantic request sequences that can pass Kubernetes API gateway *** address this,we propose KubeFuzzer,a black-box fuzzing tool designed for Kubernetes RESTful *** utilizes Natural Language Processing(NLP)to extract and integrate semantic information from API specifications and response messages,guiding the generation of more effective request *** evaluation of KubeFuzzer on various Kubernetes clusters shows that it improves code coverage by 7.86%to 36.34%,increases the successful response rate by 6.7%to 83.33%,and detects 16.7%to 133.3%more bugs compared to three leading *** identified over 1000 service crashes,which were narrowed down to 7 unique *** tested these bugs on 10 real-world Kubernetes projects,including major providers like AWS(EKS),Microsoft Azure(AKS),and Alibaba Cloud(ACK),and confirmed that these issues could trigger service *** have reported and confirmed these bugs with the Kubernetes community,and they have been addressed.

关键词： Kubernetes RESTful APIs API fuzzing black-box fuzzing

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Detecting APT-Exploited Processes through Semantic Fusion and Interaction Prediction

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Computers, Materials & Continua 2024年第2期78卷 1731-1754页

作者： Bin Luo Liangguo Chen Shuhua Ruan Yonggang Luo School of Cyber Science and Engineering Sichuan UniversityChengdu610065China Key Laboratory of Data Protection and Intelligent Management(Sichuan University) Ministry of EducationChengdu610065China Cyber Science Research Institute Sichuan UniversityChengdu610065China

Considering the stealthiness and persistence of Advanced Persistent Threats(APTs),system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a ***-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks,and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection,which requires lots of manual efforts to locate attack *** paper proposes an APT-exploited process detection approach called ThreatSniffer,which constructs the benign provenance graph from attack-free audit logs,fits normal system entity interactions and then detects APT-exploited processes by predicting the rationality of entity ***,ThreatSniffer understands system entities in terms of their file paths,interaction sequences,and the number distribution of interaction types and uses the multi-head self-attention mechanism to fuse these ***,based on the insight that APT-exploited processes interact with system entities they should not invoke,ThreatSniffer performs negative sampling on the benign provenance graph to generate non-existent edges,thus characterizing irrational entity interactions without requiring APT attack *** last,it employs a heterogeneous graph neural network as the interaction prediction model to aggregate the contextual information of entity interactions,and locate processes exploited by attackers,thereby achieving fine-grained APT *** results demonstrate that anomaly-based detection enables ThreatSniffer to identify all attack *** to the node-level APT detection method APT-KGL,ThreatSniffer achieves a 6.1%precision improvement because of its comprehensive understanding of entity semantics.

关键词： Advanced persistent threat provenance graph multi-head self-attention graph neural network

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