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arXiv 2023年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Wang, Wen Jiang, Xuefeng Gao, Bo Lu, Jinda The Institute of Computing Technology Chinese Academy of Sciences Beijing China The University of Chinese Academy of Sciences Beijing China The Zhongguancun Laboratory Beijing China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Computer and Information Technology The Engineering Research Center of Network Management Technology for High-Speed Railway Ministry of Education Beijing Jiaotong University Beijing China The School of Information Science and Technology University of Science and Technology of China Hefei China

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end devices in EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across multiple devices without compromising data privacy. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which suffers from unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) allows to update model parameters with logits transfer and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache. Empirical experiments on four datasets demonstrate that FedCache achiev

关键词： Distillation

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Regularly Truncated M-estimators for Learning with Noisy Labels

arXiv

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arXiv 2023年

作者： Xia, Xiaobo Lu, Pengqian Gong, Chen Han, Bo Yu, Jun Yu, Jun Liu, Tongliang The Sydney AI Center School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia The Australian AI Institute Faculty of Engineering and IT The University of Technology Sydney BroadwayNSW2007 Australia The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing Hong Kong Polytechnic University Hong Kong The Department of Computer Science Hong Kong Baptist University Hong Kong The School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China The Department of Automation University of Science and Technology of China Hefei230026 China

The sample selection approach is very popular in learning with noisy labels. As deep networks "learn pattern first", prior methods built on sample selection share a similar training procedure: the small-loss examples can be regarded as clean examples and used for helping generalization, while the large-loss examples are treated as mislabeled ones and excluded from network parameter updates. However, such a procedure is arguably debatable from two folds: (a) it does not consider the bad influence of noisy labels in selected small-loss examples;(b) it does not make good use of the discarded large-loss examples, which may be clean or have meaningful information for generalization. In this paper, we propose regularly truncated M-estimators (RTME) to address the above two issues simultaneously. Specifically, RTME can alternately switch modes between truncated M-estimators and original M-estimators. The former can adaptively select small-losses examples without knowing the noise rate and reduce the side-effects of noisy labels in them. The latter makes the possibly clean examples but with large losses involved to help generalization. Theoretically, we demonstrate that our strategies are label-noise-tolerant. Empirically, comprehensive experimental results show that our method can outperform multiple baselines and is robust to broad noise types and levels. The implementation is available at https://***/xiaoboxia/RTM_LNL. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Pre-Chirp-Domain Index Modulation for Full-Diversity Affine Frequency Division Multiplexing towards 6G

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Guangyao Mao, Tianqi Xiao, Zhenyu Wen, Miaowen Liu, Ruiqi Zhao, Jingjing Basar, Ertugrul Wang, Zhaocheng Chen, Sheng Beihang University School of Electronic and Information Engineering State Key Laboratory of CNS/ATM Beijing100191 China Greater Bay Area Innovation Research Institute of BIT Zhuhai519000 China Zhuhai519088 China South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China ZTE Corporation Wireless and Computing Research Institute Beijing100029 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China Tampere University Department of Electrical Engineering Tampere33720 Finland Koc University Department of Electrical and Electronics Engineering Sariyer Istanbul34450 Turkey Tsinghua University Department of Electronic Engineering Beijing100084 China University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

As a superior multicarrier technique utilizing chirp signals for high-mobility communications, affine frequency division multiplexing (AFDM) is envisioned to be a promising candidate for sixth-generation (6G) wireless networks. AFDM is based on the discrete affine Fourier transform (DAFT) with two adjustable parameters of the chirp signals, termed the pre-chirp and post-chirp parameters, respectively. Whilst the post-chirp parameter complies with stringent constraints to combat the time-frequency doubly selective channel fading, we show that the pre-chirp counterpart can be flexibly manipulated for an additional degree of freedom. Therefore, this paper proposes a novel AFDM scheme with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can implicitly convey extra information bits through dynamic pre-chirp parameter assignment, thus enhancing both spectral and energy efficiency. Specifically, we first demonstrate that the subcarrier orthogonality is still maintained by applying distinct pre-chirp parameters to various subcarriers in the AFDM modulation process. Inspired by this property, we allow each AFDM subcarrier to carry a unique pre-chirp signal according to the incoming bits. By such an arrangement, extra bits can be embedded into the index patterns of pre-chirp parameter assignment without additional energy consumption. We derive asymptotically tight upper bounds on the average bit error probability (BEP) of the proposed schemes with the maximum-likelihood detection, and validate that the proposed AFDM-PIM can achieve full diversity under doubly dispersive channels. Based on the derived result, we further propose an optimal pre-chirp alphabet design to enhance the bit error rate (BER) performance via intelligent optimization algorithms. Simulation results demonstrate that the proposed AFDM-PIM outperforms the classical benchmarks. © 2002-2012 IEEE.

关键词： Chirp modulation

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Heterogeneous influence of individuals’ behavior on mask efficacy in gathering environments

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Frontiers of engineering Management 2022年第4期9卷 550-562页

作者： Haochen SUN Xiaofan LIU Zhanwei DU Ye WU Haifeng ZHANG Xiaoke XU College of Information and Communication Engineering Dalian Minzu UniversityDalian 116600China Web Mining Laboratory Department of Media and CommunicationCity University of Hong KongHong KongChina WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong KongChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkHong KongChina Computational Communication Research Center Beijing Normal University(Zhuhai)Zhuhai 519087China School of Journalism and Communication Beijing Normal UniversityBeijing 100875China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical ScienceAnhui UniversityHefei 230601China

Wearing masks is an easy way to operate and popular measure for preventing *** masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains ***,we aim to investigate the epidemic prevention effect of masks in different real-life gathering *** study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering *** transmission of COVID-19 is simulated using the Monte Carlo simulation *** heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general ***,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to *** masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and ***,the use of masks alone in primary schools and hospitals cannot control *** high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for *** the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be ***,asymptomatic infection has varying effects on the prevention effect of masks in different *** effect can be weakened or eliminated by increasing the usage rate of masks in high schools and ***,the effect on primary schools and hospitals cannot be *** study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.

关键词： COVID-19 masks behavioral heterogeneity asymptomatic infection

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Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning

arXiv

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arXiv 2023年

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Independent Data Scientist and Engineer Al Ain Abu Dhabi United Arab Emirates

COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high *** Codes 68-04, 68T05, 68T07, 68T20 © 2023, CC BY-NC-ND.

关键词： Coronavirus

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Consistency and Diversity induced Human Motion Segmentation

arXiv

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arXiv 2022年

作者： Zhou, Tao Fu, Huazhu Gong, Chen Shao, Ling Porikli, Fatih Ling, Haibin Shen, Jianbing The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of High Performance Computing Agency for Science Technology and Research Singapore Riyadh Saudi Arabia The Research School of Engineering The Australian National University Australia The Department of Computer Science Stony Brook University Strony Brook NY United States The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University United States

Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion analysis

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A Reliable Data-transmission Mechanism using Blockchain in Edge computing Scenarios

arXiv

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arXiv 2022年

作者： Zhang, Peiying Pang, Xue Kumar, Neeraj Aujla, Gagangeet Singh Cao, Haotong Qingdao266580 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology India Department of Computer Science and Information Engineering Asia University Taiwan King Abdul Aziz University Jeddah Saudi Arabia School of Computing Newcastle University United Kingdom Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China

With the advent of the Internet of things (IoT) era, more and more devices are connected to the IoT. Under the traditional cloud-thing centralized management mode, the transmission of massive data is facing many difficulties, and the reliability of data is difficult to be guaranteed. As emerging technologies, blockchain technology and edge computing (EC) technology have attracted the attention of academia in improving the reliability, privacy and invariability of IoT technology. In this paper, we combine the characteristics of the EC and blockchain to ensure the reliability of data transmission in the IoT. First of all, we propose a data transmission mechanism based on blockchain, which uses the distributed architecture of blockchain to ensure that the data is not tampered with;secondly, we introduce the three-tier structure in the architecture in turn;finally, we introduce the four working steps of the mechanism, which are similar to the working mechanism of blockchain. In the end, the simulation results show that the proposed scheme can ensure the reliability of data transmission in the Internet of things to a great extent. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices

arXiv

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arXiv 2024年

作者： Zeng, Hongye Zou, Ke Chen, Zhihao Gao, Yuchong Chen, Hongbo Zhang, Haibin Zhou, Kang Wang, Meng Goh, Rick Siow Mong Liu, Yong Jiang, Chang Zheng, Rui Fu, Huazhu School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China National Key Laboratory of Fundamental Science on Synthetic Vision College of Computer Science Sichuan University Chengdu China College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Department of Orthopedics Zhongshan Hospital Fudan University Shanghai China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) framework to align the style of handheld device data to those of standard devices. The proposed TISA can directly infer handheld device images without extra training and is suited for clinical applications. We show that TISA performs better and more stably in medical detection and segmentation tasks for handheld device data. We further validate TISA as the clinical model for automatic measurements of spinal curvature and carotid intima-media thickness. The automatic measurements agree well with manual measurements made by human experts and the measurement errors remain within clinically acceptable ranges. We demonstrate the potential for TISA to facilitate automatic diagnosis on handheld ultrasound devices and expedite their eventual widespread use. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Hybrid algorithm for detecting intrusion with optical fiber sensor in rainy weather

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Optik 2024年 311卷

作者： Chebaane, Saleh Ben Khalifa, Sana Louati, Ali Bahri, Haythem Saidani, Taoufik Department of Physics College of Science University of Ha'il P.O. Box 2240 Ha'il Saudi Arabia Faculty of Sciences of Monastir University of Monastir Monastir Tunisia Department of Physics College of Science Qassim University P.O.Box 6644 Almolaydah Buraydah51452 Saudi Arabia ESSTHS University of Sousse Sousse4011H Tunisia Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia UGA University of Grenoble Alpes and member in GIPSA Laboratory France Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha91911 Saudi Arabia

Fiber optic intrusion detection is recognized as one of the most effective perimeter defense strategies against intruders and terrorists. Most current infiltration detection systems gather information via optical cables and trace invasions using classification techniques. This research introduces two intrusion detection algorithms and a hybrid approach to address the challenges of unstable and hard-to-extract fiber optic infiltration features, especially during rainy weather. The first method, known as the phase parameter-matching algorithm, calculates the system's disturbance covariance at any position and uses the Frobenius norm to identify intrusions by measuring the difference between disturbance covariances at distinct sites. The second method, the cross-rebuilding algorithm, employs the Kalman filter to reconstruct the output of each point based on the non-infiltration state parameters, detecting intrusions through cross-reconstruction error. Experimental tests were conducted to evaluate both methods. The results indicate that both methods are viable, with the phase parameter-matching algorithm being more robust but slower, and the cross-rebuilding algorithm being faster but less reliable. The hybrid algorithm combines the strengths of both approaches, offering a balanced solution with moderate computation time and enhanced resistance to environmental interference, leading to improved overall detection accuracy and reduced false positive rates. © 2024 Elsevier GmbH

关键词： Intrusion detection

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Contrastive Learning Based Human Activity Recognition Using Body Sensors

Contrastive Learning Based Human Activity Recognition Using ...

引用

International Conference on Big Data computing and Communications (BIGCOM)

作者： Chuanshi Xie Yu Zhou Dandan Yu Shilong Sun Xiao Zhang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Information Center The First Affiliated Hospital of Dalian Medical University Dalian China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Computer Science South-Central Minzu University State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing South-Central Minzu University Wuhan China

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use.

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