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Target-Oriented WiFi Sensing for Respiratory Healthcare: from Indiscriminate Perception to In-Area Sensing

IEEE network 2024年

作者： Wang, Meng Huang, Jinyang Zhang, Xiang Liu, Zhi Li, Meng Zhao, Peng Yan, Huan Sun, Xiao Dong, Mianxiong Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligence Machine School of Computer and Information Hefei230601 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China Muroran Institute of Technology Department of Sciences and Informatics 0508585 Japan

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, i.e., beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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Social Internet of Tings Trust Management Based on Implicit Social Relationship 5th

Social Internet of Tings Trust Management Based on Implicit ...

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5th International Conference on Security and Privacy in New computing Environments, SPNCE 2022

作者： Zhang, Hongbin Fan, Fan Zhao, Dongmei Liu, Bin Wang, Yanxia Liu, Jian School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China Hebei Geological Workers’ University Shijiazhuang050081 China

ISBN: (纸本)9783031306228

The "Social Internet of Things (SIoT)" is a combination of the Internet of Things (IoT) and social networks to form a new paradigm. The SIoT promotes the development of smart cities, smart transportation, and many other fields. In SIoT, the openness and mobility of objects are enhanced. However, this tends to lead to network data sparsity problems. By distinguishing explicit and implicit social relationships, we introduce an implicit social relationship-based trust management model (IRTM) for reliable service delivery in SIoT. IRTM establishes implicit social relationships among nodes by mining their latent characteristics and trust transitivity. It models SIoT by creating sub-networks for each social relationship as a way to fuse the impact of different types of social relationships on trust management. To address the problem of malicious attacks by malicious nodes in the network to protect their interests, it considers two metrics, node relationship strength, and recommendation reliability, to filter malicious recommendations. Experiments conducted in the presence of data sparsity and malicious objects show that IRTM can improve the accuracy and convergence of trust evaluation compared to other methods that ignore implicit social relationships when computing trust. In addition, our scheme can improve resistance to trust-related attacks. © 2023, ICST institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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Research on the Design of Government data Governance Evaluation System under the Perspective of Digital Government 9

Research on the Design of Government Data Governance Evaluat...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Xu, Zirui Sun, Jinyang Lu, Zhe Han, Delong Qilu University of Technology Shandong Academy of Sciences Information Research Institute Shandong Academy of Sciences Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

In the context of the digital era, government data governance is a key factor in promoting the modernization of government governance, but there is a lack of a complete set of evaluation index system to standardize the content and scope of government data governance. This paper adopts the policy tool theory to code and analyze the 12 policies from the three policy tool dimensions of supply layer, demand layer, and environment layer, and proposes three first-level evaluation indicators of basic environment, actual effectiveness, and safeguard mechanism, on the basis of which the evaluation content is selected in a focused manner based on the integration of policy documents related to data governance. Meanwhile, a comprehensive, scientific and objective evaluation system of government data governance is constructed by adopting assessment means combining subjectivity and objectivity, and quantitative and qualitative. The system is designed to assess and guide the data governance practices of the domestic government, point out the direction for government departments at all levels to carry out data governance, and play an important role in promoting the transparency and efficiency of data management. © 2024 IEEE.

关键词： data governance digital government government information indicator system policy tools

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Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer 14

Parallel Optimization of Computational Fluid Dynamics Applic...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Liu, Anjun Liao, Yin Xu, Xihao Chen, YanYan Zhong, Hongjun Wu, Jianming Kong, Bo Raikov, Alexander Guo, Meng Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Industrial Software Jinan China Zhejiang Yuansuan Technology Co. Ltd Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Hangzhou China Qilu University of Technology Shandong Academy of Sciences Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security China Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Shandong Jinan China

ISBN: (纸本)9798350371024

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1-2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry. © 2023 IEEE.

关键词： Supercomputers

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DAPO-QAOA: An algorithm for solving combinatorial optimization problems by dynamically constructing phase operators

arXiv

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arXiv 2025年

作者： Wang, Yukun Li, ZeYang Wan, Linchun Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing102249 China State Key Lab of Processors Institute of Computing Technology CAS Beijing100190 China School of Computer and Information Science Southwest University Chongqing400715 China

The Quantum Approximate Optimization Algorithm (QAOA) is a well-known hybrid quantum-classical algorithm for combinatorial optimization problems. Improving QAOA involves enhancing its approximation ratio while addressing practical constraints of Noisy Intermediate Scale Quantum (NISQ) devices, such as minimizing the number of two-qubit gates and reducing circuit depth. Although existing research has optimized designs for phase and mixer operators to improve performance, challenges remain, particularly concerning the excessive use of two-qubit gates in the construction of phase operators. To address these issues, we introduce a Dynamic Adaptive Phase Operator (DAPO) algorithm, which dynamically constructs phase operators based on the output of previous layers and neighborhood search approach, optimizing the problem Hamiltonian more efficiently. By using solutions generated by QAOA itself to simplify the problem Hamiltonian at each layer, the algorithm captures the problem’s structural properties more effectively, progressively steering the solution closer to the optimal target. Experimental results on MaxCut and NAE3SAT problems show that DAPO achieves higher approximation ratios and significantly reduces two-qubit RZZ gates, especially in dense graphs. Compared to vanilla QAOA, DAPO uses only 66% of RZZ gates at the same depth while delivering better results, demonstrating its potential for efficient combinatorial optimization in the NISQ era. © 2025, CC BY.

关键词： Hamiltonians

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D4M: dataset Distillation via Disentangled Diffusion Model

arXiv

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arXiv 2024年

作者： Su, Duo Hou, Junjie Gao, Weizhi Tian, Yingjie Tang, Bowen School of Computer Science and Technology UCAS United Kingdom Sino-Danish College UCAS United Kingdom Department of Computer Science NCSU United States School of Economics and Management UCAS United Kingdom Research Center on Fictitious Economy and Data Science CAS China Key Laboratory of Big Data Mining and Knowledge Management CAS China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS United Kingdom Institute of Computing Technology CAS China

dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this, we introduce dataset Distillation via Disentangled Diffusion Model (D4M), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D4M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile, eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments, D4M demonstrates superior performance and robust generalization, surpassing the SOTA methods across most aspects. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback

arXiv

引用

arXiv 2022年

作者： Zhu, Yunchang Pang, Liang Lan, Yanyan Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Cas University of Chinese Academy of Sciences Beijing China Data Intelligence System Research Center Institute of Computing Technology Cas Beijing China Institute for Ai Industry Research Tsinghua University Beijing China Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Cas University of Chinese Academy of Sciences Beijing China

Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents. Existing PRF methods independently treat revised queries originating from the same query but using different numbers of feedback documents, resulting in severe query drift. Without comparing the effects of two different revisions from the same query, a PRF model may incorrectly focus on the additional irrelevant information increased in the more feedback, and thus reformulate a query that is less effective than the revision using the less feedback. Ideally, if a PRF model can distinguish between irrelevant and relevant information in the feedback, the more feedback documents there are, the better the revised query will be. To bridge this gap, we propose the Loss-over-Loss (LoL) framework to compare the reformulation losses between different revisions of the same query during training. Concretely, we revise an original query multiple times in parallel using different amounts of feedback and compute their reformulation losses. Then, we introduce an additional regularization loss on these reformulation losses to penalize revisions that use more feedback but gain larger losses. With such comparative regularization, the PRF model is expected to learn to suppress the extra increased irrelevant information by comparing the effects of different revised queries. Further, we present a differentiable query reformulation method to implement this framework. This method revises queries in the vector space and directly optimizes the retrieval performance of query vectors, applicable for both sparse and dense retrieval models. Empirical evaluation demonstrates the effectiveness and robustness of our method for two typical sparse and dense retrieval models. © 2022, CC BY.

关键词： Feedback

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Ethereum Smart Contract Representation Learning for Robust Bytecode-Level Similarity Detection 34

Ethereum Smart Contract Representation Learning for Robust B...

引用

34th International Conference on Software Engineering and Knowledge Engineering, SEKE 2022

作者： Tian, Zhenzhou Huang, Yaqian Tian, Jie Wang, Zhongmin Chen, Yanping Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Department of Computer Science and Engineering Wright State University DaytonOH United States

ISBN: (纸本)1891706543

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed. However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust. Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code. This leads to a large number of similar contracts with potential security risks. Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum. In this paper, we design a learning-effective and cost-efficient model, called SmartSD, for Ethereum smart contract similarity detection. Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks. The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers. © 2022 Knowledge Systems institute Graduate School. All rights reserved.

关键词： Smart contract

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Adversarial Camouflage for Node Injection Attack on Graphs

arXiv

引用

arXiv 2022年

作者： Tao, Shuchang Cao, Qi Shen, Huawei Wu, Yunfan Hou, Liang Sun, Fei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Node injection attacks on Graph Neural networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates. However, our study indicates that these attacks often fail in practical scenarios, since defense/detection methods can easily identify and remove the injected nodes. To address this, we devote to camouflage node injection attack, making injected nodes appear normal and imperceptible to defense/detection methods. Unfortunately, the non-Euclidean structure of graph data and the lack of intuitive prior present great challenges to the formalization, implementation, and evaluation of camouflage. In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes. Then for implementation, we propose an adversarial CAmouflage framework for Node injection Attack, namely CANA, to improve attack performance under defense/detection methods in practical scenarios. A novel camouflage metric is further designed under the guide of distribution similarity. Extensive experiments demonstrate that CANA can significantly improve the attack performance under defense/detection methods with higher camouflage or imperceptibility. This work urges us to raise awareness of the security vulnerabilities of GNNs in practical applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Sensing-Communication-Computation Integration for Federated Edge Learning with Controllable Model Dropout

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IEEE Internet of Things Journal 2025年

作者： Jiao, Xiang Zhu, Guangxu Jiang, Wei Chen, Li Luo, Wu Wen, Dingzhu State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Shenzhen Research Institute of Big Data Shenzhen China The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China The CAS Key Laboratory of Wireless Optical Communication University of Science and Technology of China Hefei China Network Intelligence Center School of Information Science and Technology ShanghaiTech University Shanghai China

FEderated Edge Learning (FEEL) is an advanced paradigm in edge artificial intelligence, enabling privacy-preserving collaborative model training through periodic communication between edge devices and a central server. FEEL involves three key processes: sensing, computation, and communication for data acquisition, processing, and exchange, respectively. Due to limited system resources, optimizing each process individually may lead to suboptimal learning performance. This challenge has sparked research into Integrated Sensing-Computation-Communication (ISCC) design for enhanced FEEL. While previous work has optimized general learning parameters such as batch size and computing frequency, there is a lack of customized designs considering the neural network architecture as an optimizable variable in ISCC for FEEL. To close this gap, we introduce a novel design where each device generates a sub-model through controllable weight dropout, adding flexibility by directly manipulating the learning process and reducing computation and communication overhead. To guide ISCC resource allocation in this new setting, we present a comprehensive convergence analysis, revealing the tight coupling of sensing, computation, and communication across devices and their impact on FEEL convergence. Building on these theoretical insights, we formulate an ISCC problem aiming to maximize the FEEL convergence rate through joint optimization of variables such as batch size, sensing power, dropout rate, and communication power. This non-convex problem is decomposed into two sub-problems via alternating optimization: one controls batch size using a sorting algorithm, while the other focuses on ISCC device parameters, transformable into a convex problem solved by successive convex approximation. Extensive experiments using human motion recognition datasets demonstrate the superiority of the proposed design over baseline schemes. © 2014 IEEE.

关键词： Federated learning

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