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International Conference on computer Supported Cooperative Work in Design

作者： Yi Zhao Chunyu Hu Xiaodong Yang Lin Yuan Xiang Tian Tianlei Gao Yiqiang Chen 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) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China Shandong Artificial Intelligence Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Wearable activity recognition is crucial for ubiquitous computing, enhancing human-machine interaction, medical monitoring, and personalized services. As wearable devices collect user activity data that often contain personal privacy information, federated learning (FL) is increasingly applied to protect user data privacy. However, in real-world scenarios, users' data are commonly exhibit heterogeneity, manifesting as non-independent and identically distributed (non-IID) characteristics, which presents challenges for FL methods. Traditional FL client selection approaches with heterogeneous data can cause global model drift, reducing the accuracy of activity recognition models. In this paper, we propose Data Contribution-Based Federated Learning (DCBFL) method, an adaptive FL training approach by selecting clients to counter the problem caused by heterogeneous data. Specifically, we first utilize a conditional generator on the server to construct an auxiliary dataset, which is used to train an auxiliary model as a benchmark to measure the degree of heterogeneity in each client's data. Furthermore, we reasonably differentiate the data contributions of clients based on the degree of data heterogeneity and select suitable clients for FL training, effectively utilizing heterogeneous data information, mitigating global model drift. The comprehensive experiments are conducted on five public activity recognition datasets under non-IID conditions in this work. The experimental results show that DCBFL outperforms existing baseline methods, showcasing superior performance.

关键词： Training Adaptation models Data privacy Accuracy Federated learning Activity recognition Ubiquitous computing Data models Biomedical monitoring Wearable devices

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Mixture of Experts for Intelligent networks: A Large Language Model-enabled Approach

Mixture of Experts for Intelligent Networks: A Large Languag...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongyang Du Guangyuan Liu Yijing Lin Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Automation Guangdong University of Technology and Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

关键词： Wireless communication Energy consumption Navigation Computational modeling Logic gates Probability Throughput

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Secure Integrated Sensing and SWIPT via Active IRS

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Jinlei Zhang, Jifa Liu, Mingqian Zhao, Nan Al-Dhahir, Naofal Wang, Xianbin Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Xidian University State Key Laboratory of Integrated Service Networks Shanxi Xian710071 China University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75083 United States Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

To achieve sustainable communication and sensing, simultaneous wireless information and power transfer (SWIPT) has been introduced into integrated sensing and communication (ISAC). However, this combination brings significant security challenges due to signal multiplexing and spectrum sharing. In this paper, an active intelligent reflecting surface (IRS) assisted secure integrated sensing and SWIPT system is proposed with the power splitting (PS) model adopted. To maximize the harvested power while satisfying the constraints of sidelobe level ratio and secrecy rate, a problem is formulated to jointly optimize the transmit beamforming, artificial noise (AN) vectors, PS ratios, and amplification factors and phase shifts of active IRS, which is difficult to solve due to the coupled variables. To this end, we decompose it into two sub-problems, and propose two alternating optimization (AO) algorithms to solve them. First, an AO algorithm based on semi-definite relaxation (SDR) is developed. Specifically, we develop a two-layer algorithm to obtain the transmit beamforming matrix, AN covariance matrix and PS ratios, and utilize the penalty-based method to design the coefficients of active IRS. To reduce the complexity caused by the high-dimensional matrix operation of SDR, an AO algorithm based on successive convex approximation (SCA) is proposed, which can approximate the original problem as a sequence of convex counterparts via the first-order Taylor expansion. Simulation results show that the SCA-based AO algorithm can achieve the performance close to that of SDR with lower complexity. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Impact of Hardware Impairments on Covert Cooperative Backscatter Communications

Impact of Hardware Impairments on Covert Cooperative Backsca...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Huan Li Yinghui Ye Lu Lv Guangyue Lu Naofal Al-Dhahir Shaanxi information communication network and security Laboratory Xi'an University of Posts and Telecommunications Xi'an China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Department of Electrical and Computer Engineering The University of Texas at Dallas Richardson TX USA

In this paper, we investigate a covert cooperative backscatter communication (BackCom) network under residual hardware impairments (RHIs) that are unavoidable in practical radio frequency transceivers and may have an impact on the communication covertness and reliability. By exploiting the availability of statistical channel state information, we derive analytic expressions for the eavesdropper's detection error probability (DEP) and system outage probability (SOP) to evaluate the system covertness and communication reliability, respectively. Using the derived results, we prove that RHIs can improve the communication covertness but significantly degrade the outage performance. We also show that under RHIs and extremely high transmit signal-to-noise levels, the system outage performance has a non-zero asymptote, while the DEP still approaches one, similar to the scenario with perfect hardware. computer simulations corroborate our analytical results.

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Text information Mining in Cyberspace: An information Extraction Method Based on T5 and keyBERT

Text Information Mining in Cyberspace: An Information Extrac...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Kuan Liu Yumei Li Yuanhua Qi Ning Qi Mengran Zhai Information Research Institute of Shandong Academy of Sciences Qilu University of Technology (Shandong Academy of Sciences) Jinan China 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) Shandong Institute of Economy and Informatization Development Jinan China 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Shandong Institute of Economy and Informatization Development Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the development of the digital economy and the advent of the big data era, the rapid growth of textual information in cyberspace has placed higher demands on information processing technology. This paper proposes an information extraction technique based on the Text-to-Text Transfer Transformer, known as T5, and keyBERT models, aiming to efficiently distill the key content from textual information in cyberspace. This method combines automatic summarization and keyword extraction to form information briefs. The experiments selected representative policy documents related to data elements as input data and employed information entropy and ROUGE scores as metrics to evaluate the automatic summarization model. The results were compared with the outputs of similar models. Experimental results indicate that the T5 model outperforms other models in terms of summarization effectiveness, and the proposed information extraction method shows significant advantages in readability and processing efficiency, demonstrating practical application value.

关键词： Measurement Biological system modeling Cyberspace Text summarization information processing information retrieval Transformers Data models Data mining information entropy

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$k$kTCQ: Achieving Privacy-Preserving $k$k-Truss Community Queries Over Outsourced Data

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IEEE Transactions on Dependable and Secure Computing 2023年第4期21卷 2750-2765页

作者： Yunguo Guan Rongxing Lu Songnian Zhang Yandong Zheng Jun Shao Guiyi Wei School of Information Security and Applied Computing Eastern Michigan University Ypsilanti MI USA Faculty of Computer Science University of New Brunswick Fredericton NB Canada State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Zhejiang Gongshang University Hangzhou China

Community search over graphs, which is believed as a powerful tool for locating subgraphs of closely related vertices, has received considerable attention in recent years, and $k$ -truss is such a popular community search metric to obtain subgraphs in which every edge forms $(k-2)$ triangles. In this paper, we particularly consider $k$ -truss community query services, which will return all $k$ -truss communities containing a given query vertex. As is known, when the size of graph grows, for achieving better performance, it is natural for a service provider to outsource the services to a powerful cloud. However, this stresses the need for privacy-preserving $k$ -truss community query services, as the cloud server is not fully trustable. Over the past years, many schemes focusing on privacy-preserving graph computation have been put forth, but none of them can well support privacy-preserving $k$ -truss community queries. Aiming at this challenge, we first propose a privacy-preserving $k$ -truss community query scheme ( $k$ TCQ) by constructing boolean circuits with homomorphic encryption technique and a table-based index. After that, we also design an efficiency-enhanced version ( $k$ TCQ+) based on a stream cipher scheme to reduce the encrypted index's size and improve the query efficiency. Detailed security analysis shows that both $k$ TCQ and $k$ TCQ+ can well preserve data privacy and access pattern privacy, and extensive experimental results also demonstrate that $k$ TCQ+ can observably reduce the size of encrypted index and the query time by $12\times$ and $5.9\times$ , respectively.

关键词： Indexes Servers Cryptography Privacy Social networking (online) Homomorphic encryption Data privacy

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TSdetector: Temporal-Spatial Self-correction Collaborative Learning for Colonoscopy Video Detection

arXiv

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arXiv 2024年

作者： Wang, Kai-Ni Wang, Haolin Zhou, Guang-Quan Wang, Yangang Yang, Ling Chen, Yang Li, Shuo School of Biological Science and Medical Engineering Southeast University Nanjing China Jiangsu Key Laboratory of Biomaterials and Devices Southeast University Nanjing China Laboratory of Image Science and Technology Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China TUGE Healthcare Nanjing China Institute of Medical Technology Peking University Health Science Center China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University United States

CNN-based object detection models that strike a balance between performance and speed have been gradually used in polyp detection tasks. Nevertheless, accurately locating polyps within complex colonoscopy video scenes remains challenging since existing methods ignore two key issues: intra-sequence distribution heterogeneity and precision-confidence discrepancy. To address these challenges, we propose a novel Temporal-Spatial self-correction detector (TSdetector), which first integrates temporal-level consistency learning and spatial-level reliability learning to detect objects continuously. Technically, we first propose a global temporal-aware convolution, assembling the preceding information to dynamically guide the current convolution kernel to focus on global features between sequences. In addition, we designed a hierarchical queue integration mechanism to combine multi-temporal features through a progressive accumulation manner, fully leveraging contextual consistency information together with retaining long-sequence-dependency features. Meanwhile, at the spatial level, we advance a position-aware clustering to explore the spatial relationships among candidate boxes for recalibrating prediction confidence adaptively, thus eliminating redundant bounding boxes efficiently. The experimental results on three publicly available polyp video dataset show that TSdetector achieves the highest polyp detection rate and outperforms other state-of-the-art methods. The code can be available at https://***/soleilssss/TSdetector. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Asynchronous Tag-based Physical-Layer Authentication in Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Tan, Haijun Xu, Yuntao Du, Jiewei Xie, Ning Li, Hongbin Peng Cheng Laboratory Control and Network Foundation Laboratory Frontier Research Center Shenzhen518107 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China National Innovation Center of High Speed Train Qingdao266111 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Tag-based physical-layer authentication (PLA) has gained significant research interest due to its high security and low complexity compared to traditional upper-layer authentication mechanisms. However, conventional tag-based PLA schemes often require strict synchronization between the tag and the source message before transmission, which may limit their practicality and effectiveness. To address this limitation, we propose an asynchronous tag-based PLA scheme, where the tag and the source message are transmitted asynchronously with an intentional time delay introduced at the transmitter. The proposed scheme enhances both compatibility and security without compromising robustness. We provide a theoretical analysis of the proposed scheme, and derive closed-form expressions to characterize its performance. Furthermore, theoretical comparisons with existing methods yield insightful conclusions regarding the advantages of the proposed approach. The scheme is implemented and extensively evaluated through simulations, where the theoretical results exhibit strong agreement with the simulation outcomes, thereby validating its effectiveness. © 2025 IEEE.

关键词： asynchronous closed form intentional timing delay Physical-layer authentication tag-based

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A Simulation Model for Proactive Services: A Case Study on Evading Dangerous Areas for LNG Ships 16th

A Simulation Model for Proactive Services: A Case Study on...

引用

16th International Conference on Service Science, ICSS 2023

作者： Shi, Yongpeng Wang, Guiling Zhang, Jiayu Li, Zhenyu Yu, Jian School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China Department of Computer Science Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819944019

Proactive service is a kind of service that can be automatically provided based on logical judgment without human intervention. The traditional approach to evaluating proactive services involves conducting quantitative analyses through public datasets. However, this method can only evaluate the effectiveness of the specific algorithms and has limitations in evaluating the effectiveness of real-world applications as a whole. Determining a more appropriate evaluation approach for proactive services is a challenging problem. To address this issue, this paper proposes a method to evaluate the effectiveness of proactive services from a simulation perspective with a case of LNG (Liquefied Natural Gas) shipping as an example. Using the multi-agent simulation method, we design and implement a simulation model for LNG ships navigating at sea while avoiding dangerous areas by AnyLogic, which is capable of interacting with the deep learning model for predicting ships’ locations. Our simulation model also defines a set of metrics for evaluating the effectiveness of proactive services in the application case. We present a simulation demonstration on evading dangerous areas for LNG ships and conduct simulation experiments. The experimental results show that the simulation model can serve as an effective evaluation method for proactive services. Additionally, the simulation model can also be used to analyze differences in the application effects of proactive services implemented under different prediction algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning

arXiv

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arXiv 2023年

作者： Zhang, Zheng Zhang, Xiaolei Qi, Yaolei Yang, Guanyu LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China Dept. of Diagnostic Radiology Jinling Hospital Medical School of Nanjing University Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing210096 China Strasbourg France

Coronary artery segmentation on coronary-computed tomography angiography (CCTA) images is crucial for clinical use. Due to the expertise-required and labor-intensive annotation process, there is a growing demand for the relevant label-efficient learning algorithms. To this end, we propose partial vessels annotation (PVA) based on the challenges of coronary artery segmentation and clinical diagnostic characteristics. Further, we propose a progressive weakly supervised learning framework to achieve accurate segmentation under PVA. First, our proposed framework learns the local features of vessels to propagate the knowledge to unlabeled regions. Subsequently, it learns the global structure by utilizing the propagated knowledge, and corrects the errors introduced in the propagation process. Finally, it leverages the similarity between feature embeddings and the feature prototype to enhance testing outputs. Experiments on clinical data reveals that our proposed framework outperforms the competing methods under PVA (24.29% vessels), and achieves comparable performance in trunk continuity with the baseline model using full annotation (100% vessels). Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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