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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinghui Ni Hui Cui Lihai Zhao Fengling Li Xiaohui Han Lijuan Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Quan Cheng Laboratory Shandong Open University University of Technology Sydney

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA.

关键词： Training Data privacy Adaptation models Source coding Semantics Image retrieval Signal processing Protection Speech processing Knowledge transfer

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Research on Wireless Channel Modeling for Complex Water Surface Environments

Research on Wireless Channel Modeling for Complex Water Surf...

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2023 International Conference on Intelligent Communication and networking, ICN 2023

作者： Li, Xujie Ge, Tianzhang Wei, Baochao Li, Haotian Xu, Ning Jiangsu Health Development Research Center Nanjing210036 China Nhc Contraceptives Adverse Reaction Surveillance Center Nanjing210036 China Jiangsu Provincial Medical Key Laboratory of Fertility Protection and Health Technology Assessment Nanjing210036 China Hohai University College of Computer and Information Nanjing210098 China

ISBN: (纸本)9798350357677

Accurate and efficient channel modeling has always been a critical foundation for the deployment and application of 5G communication technology. To meet the requirements of continuous and extensive coverage in 5G, higher demands have been placed on wireless channel characteristics across various scenarios. However, the propagation of wireless signals over water may involve more complex reflection and scattering phenomena. Additionally, the presence of vegetation on water surface further complicates the channel modeling. Addressing these challenges, we assess relevant theories and representative signal attenuation models, and propose a channel model for complex water-based scenarios based on the open water channel model. To ascertain the precision of the proposed model, we establish an experimental platform and conduct measurements on the attenuation of signals across a water pool populated with various obstacles. The results demonstrate that the suggested model more accurately corresponds to the distinctive patterns of wireless signal attenuation encountered within complex water surface environments. © 2023 IEEE.

关键词： 5G mobile communication systems

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UAV Swarm Path Planning for Sensor Data Collection via Double Pre-Partitioned Deep Q-network

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IEEE Sensors Journal 2025年

作者： Zhang, Heng Gao, Yuan Wang, Xin He, Yunpeng Yang, Ming Wu, Xiaoming Yang, Chaoqun Cao, Xianghui Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China Jiangsu Ocean University School of Computer Engineering Lianyungang222000 China Southeast University School of Automation Nanjing210096 China

Collision-free path planning is a significant challenge in deploying unmanned aerial vehicle (UAV) swarms for sensor data collection tasks, particularly in uncertain environments. This paper addresses the problem of designing flight strategies for energy-constrained UAV swarms using deep reinforcement learning. We propose a locally-aware double pre-partitioned deep Q-network (DPPDQN) for flight planning of multiple UAVs in large-scale uncertain industrial environments. This algorithm aims to collect data from ground sensors with communication constraints while avoiding mutual collisions. In the proposed algorithm, we first introduce a node pre-partitioning scheme using an improved clustering algorithm, which efficiently reduces the number of access points for UAVs through effective data aggregation. Subsequently, we employ an area pre-partitioning algorithm that segments the detection area into several subregions based on the number of UAVs available for operation, thereby preventing collisions among the UAV swarm during task execution. Finally, we propose a deep Q-network algorithm incorporating a convolutional neural network, which utilizes the local perceptions of UAVs as state inputs to address the 'curse of dimensionality"occurring in large-scale environments. Systematic simulation experiments validate the effectiveness and advantages of the proposed method. © 2025 IEEE.

关键词： Convolutional neural networks

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Investigation of Solar Noise in Underwater Wireless Optical Communication Systems

Investigation of Solar Noise in Underwater Wireless Optical ...

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2023 International Conference on Intelligent Communication and networking, ICN 2023

作者： Li, Xujie Wei, Baochao Ge, Tianzhang Li, Haotian Xu, Ning Jiangsu Health Development Research Center Nanjing210036 China Nhc Contraceptives Adverse Reaction Surveillance Center Nanjing210036 China Jiangsu Provincial Medical Key Laboratory of Fertility Protection and Health Technology Assessment Nanjing210036 China Hohai University College of Computer and Information Nanjing210098 China

ISBN: (纸本)9798350357677

Solar noise as an innegligible interference in underwater wireless optical communication (UWOC) systems is a significant concern. But there are no studies on the Channel Impulse Response (CIR) in the presence of solar noise, and there are no functions available for fitting the CIR curve in this scenario. To address this problem, this paper employs Monte Carlo method to simulate the channel impulse response (CIR) of underwater optical communication systems with solar noise. Additionally, we propose a method that utilizes the composite exponential function (CEF) to fit the CIR curve of the turbid water under sunlight. We verify that the introduction of sunlight aggravates waveform broadening, and our newly proposed model has a better accuarcy of numerical fitting in turbid *** results lay the foundation for subsequent theoretical analysis of system performance. © 2023 IEEE.

关键词： Exponential functions

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Lightweight Federated Learning Secure Aggregation Protocols

Lightweight Federated Learning Secure Aggregation Protocols

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International Conference on networking and network Applications (NaNA)

作者： Xianyu Mu Youliang Tian Zhou Zhou Jinbo Xiong State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

With the increasing awareness of user privacy protection and the improvement of domestic and international privacy protection laws, coalitional learning has become the current machine learning framework to protect user privacy. However, there are still specific security threats in coalitional learning, i.e., semi-honest servers obtain the corresponding privacy information by launching reverse inference attacks on the user’s local model gradient. Although effective against the attacks of semi-honest adversaries, previous research schemes resulted in huge computational costs and poor robustness. Therefore, in this paper, we propose a federated learning security aggregation protocol based on lightweight cryptography algorithms, which can significantly reduce the computational costs at the user’s end and is robust to users offline. First, this paper constructs an efficient secure aggregation protocol based on lightweight cryptographic algorithms, which significantly reduces the computational costs required on the user side. Second, an effective reconfiguration algorithm is designed in order to prevent the aggregation failure caused by the auxiliary computing nodes going offline. Finally, this paper demonstrates the security of the protocol through security analysis. In addition, this paper shows through experimental results that the scheme achieves a significant efficiency improvement, and the number of required communication rounds is reduced to two.

关键词： Privacy Protocols Runtime Federated learning Computational modeling Inference algorithms Robustness

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A Triangular Stable Node network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Parallel Pattern Matching over Brotli Compressed network Traffic

Parallel Pattern Matching over Brotli Compressed Network Tra...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xiuwen Sun Guangzheng Zhang Di Wu Qingying Yu Jie Cui Hong Zhong School of Computer Science and Technology Anhui University Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Pattern matching is a crucial technique for network traffic detection applications. As a fundamental computation model used by pattern matching, the finite state automata execute sequential matching due to the state dependence among transitions. Meanwhile, most services tend to compress their data to improve transmission or storage efficiency. The increased compressed data challenges the straightforward method of matching the whole decompressed data and incurs data dependence among the compression encodings. The related approaches either leverage techniques to break the state dependence of matching uncompressed data or accelerate matching compressed data in a single-threaded manner without considering the state and data dependence. None of them can perform parallel matching over compressed data. This paper provides PETALS, a parallel pattern matching method over Brotli compressed network traffic. PETALS partitions the original compressed traffic into fixed- length blocks for parallel matching and patches the broken compression encodings crossing blocks to break the data dependence. Then, it merges the compressed traffic matching method into path fusion, an enumerative parallelization of finite state automata, to present parallel matching over compressed traffic. Evaluation using real-world network traffic and regular expressions shows that PETALS can raise the speedup from 1.53x to 3.53x of the state-of-the-art parallelization schemes on a 56-cores machine.

关键词： Privacy Instruction sets Computational modeling Automata Telecommunication traffic Programming Parallel processing

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A DoS-attack Resilient Control Approach for AIOT-enabled Systems

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Cao, Xiaoya Chen, Zhenya Liu, Weisheng Liu, Yuanlong Wang, Wenting Wang, Xin Yang, Ming Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350312270

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-Service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy. © 2023 IEEE.

关键词： System stability

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FM-FCN:A Neural network with Filtering Modules for Accurate Vital Signs Extraction

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Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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Regularization Mixup Adversarial Training: A Defense Strategy for Membership Privacy with Model Availability Assurance

Regularization Mixup Adversarial Training: A Defense Strateg...

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Big Data and Privacy Computing (BDPC), International Conference on

作者： Zehua Ding Youliang Tian Guorong Wang Jinbo Xiong State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China

ISBN: (数字)9798350307597

ISBN: (纸本)9798350307603

Neural network models face two highly destructive threats in real-world applications: membership inference attacks (MIAs) and adversarial attacks (AAs). One compromises the model's confidentiality, leading to membership privacy breaches, while the other disrupts the model's availability, rendering it dysfunctional. Recent work has shown that adversarial examples can pose dual threats to neural network models, both MIAs and AAs, so that these two different types of attacks have a certain degree of intersection. However, existing defense methods typically focus on defending against one of the two attacks and cannot simultaneously address both. To address the above issues, we propose a defense method called regularization mixup adversarial training (RMAT), aiming to protect model membership privacy while ensuring model availability. The core idea of RMAT is to use a continuous augmentation strategy during training to generate mixup adversarial examples, weakening attackers' understanding of membership data, and updating the model with a new membership-weighted loss strategy to improve its generalization ability for handling unknown samples. This approach can further protect the model's membership privacy and ensure model availability. Experimental results demonstrate that compared to single defense strategies, RMAT can simultaneously protect the model's membership privacy and availability.

关键词： Training Privacy Data privacy Neural networks computer architecture Rendering (computer graphics) Privacy breach

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