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arXiv 2023年

作者： Zhang, Hangtao Yao, Zeming Zhang, Leo Yu Hu, Shengshan Chen, Chao Liew, Alan Li, Zhetao School of Cyber Science and Engineering Huazhong University of Science and Technology China Swinburne University of Technology Australia Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China RMIT University Australia Xiangtan University China

Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we propose a Flexible Model Poisoning Attack (FMPA) that can achieve versatile attack goals. We consider a practical threat scenario where no extra knowledge about the FL system (e.g., aggregation rules or updates on benign devices) is available to adversaries. FMPA exploits the global historical information to construct an estimator that predicts the next round of the global model as a benign reference. It then fine-tunes the reference model to obtain the desired poisoned model with low accuracy and small perturbations. Besides the goal of causing DoS, FMPA can be naturally extended to launch a fine-grained controllable attack, making it possible to precisely reduce the global accuracy. Armed with precise control, malicious FL service providers can gain advantages over their competitors without getting noticed, hence opening a new attack surface in FL other than DoS. Even for the purpose of DoS, experiments show that FMPA significantly decreases the global accuracy, outperforming six state-of-the-art attacks. © 2023, CC BY.

关键词： Federated learning

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User-level failure detection and auto-recovery of parallel programs in HPC systems

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Frontiers of computer science 2021年第6期15卷 31-42页

作者： Guozhen ZHANG Yi LIU Hailong YANG Jun XU Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China

As the mean-time-between-failures(MTBF)continues to decline with the increasing number of components on large-scale high performance computing(HPC)systems,program failures might occur during the execution period with high *** successful execution of the HPC programs has become an issue that the unprivileged users should be *** the user perspective,if the program failure cannot be detected and handled in time,it would waste resources and delay the progress of program ***,the unprivileged users are unable to perform program state checking due to execution control by the job management system as well as the limited ***,automated tools for supporting user-level failure detection and autorecovery of parallel programs in HPC systems are *** paper proposes an innovative method for the unprivileged user to achieve failure detection of job execution and automatic resubmission of failed *** state checker in our method is encapsulated as an independent job to reduce interference with the user *** addition,we propose a dual-checker mechanism to improve the robustness of our *** implement the proposed method as a tool named automatic re-launcher(ARL)and evaluate it on the Tianhe-2 *** results show that ARL can detect the execution failures effectively on Tianhe-2 *** addition,the communication and performance overhead caused by ARL is *** good scalability of ARL makes it applicable for large-scale HPC systems.

关键词： high performance computing parallel program failure detection failure auto-recovery

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PixelFade: Privacy-preserving Person Re-identification with Noise-guided Progressive Replacement

arXiv

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arXiv 2024年

作者： Zhang, Delong Peng, Yi-Xing Wu, Xiao-Ming Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

Online person re-identification services face privacy breaches from potential data leakage and recovery attacks, exposing cloud-stored images to malicious attackers and triggering public concern. The privacy protection of pedestrian images is crucial. Previous privacy-preserving person re-identification methods are unable to resist recovery attacks and compromise accuracy. In this paper, we propose an iterative method (PixelFade) to optimize pedestrian images into noise-like images to resist recovery attacks. We first give an in-depth study of protected images from previous privacy methods, which reveal that the chaos of protected images can disrupt the learning of recovery models. Accordingly, Specifically, we propose Noise-guided Objective Function with the feature constraints of a specific authorization model, optimizing pedestrian images to normal-distributed noise images while preserving their original identity information as per the authorization model. To solve the above non-convex optimization problem, we propose a heuristic optimization algorithm that alternately performs the Constraint Operation and the Partial Replacement Operation. This strategy not only safeguards that original pixels are replaced with noises to protect privacy, but also guides the images towards an improved optimization direction to effectively preserve discriminative features. Extensive experiments demonstrate that our PixelFade outperforms previous methods in resisting recovery attacks and ReID performance. The code is available at https://***/iSEElaboratory/PixelFade. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Multi-GNSS simulation and its positioning performance for static and dynamic users 6

Multi-GNSS simulation and its positioning performance for st...

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6th International Symposium on Autonomous systems, ISAS 2023

作者： Li, Kun Gao, Mengzhi Zheng, Shuaiyong Wang, Xuanwen Jin, Xiaoqin Yang, Peng Yang, Jianlei Lu, Jing Yao, Gaole Tianjin University of Technology School of Integrated Circuit Science and Engineering Tianjin China Nankai University College of Artificial Intelligence Tianjin China Tianjin University of Technology School of Computer Science and Engineering Tianjin China The 54th Research Institute of China Electronics Technology Group Corporation State Key Laboratory of Satellite Navigation System and Equipment Technology Shijiazhuang China Land Satellite Remote Sensing Application Center Ministry of Natural Resources Beijing China Tianjin AtoN Division The Navigation Guarantee Centre of North China Sea Tianjin China

ISBN: (纸本)9798350316155

Global navigation satellite system plays an important role in military and civil fields, providing global positioning, navigation and timing services in real time. Unfortunately, the services provided by single constellation or double constellations cannot meet the requirements of high-level navigation, and there are still some problems such as low positioning accuracy and low number of visible satellites at specific circumstances to be solved. In this paper, multi-GNSSs are constructed and their global positioning performance is analyzed with respect to static and dynamic users. Results show that with more constellations, the position dilution of precision is decreased, and the number of visible satellites and the positioning accuracy are improved, which reveals that compared with single constellation or double constellations, multi-GNSSs can obviously improve the positioning performance. The methodology proposed can serve as a technical reference for national integrated PNT systems. © 2023 IEEE.

关键词： Navigation

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Fine-grained Scheduling in FPGA-Based Convolutional Neural Networks 5

Fine-grained Scheduling in FPGA-Based Convolutional Neural N...

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5th IEEE International Conference on Cloud computing and Big Data Analytics, ICCCBDA 2020

作者： Zhang, Wei Liao, Xiaofei Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

ISBN: (纸本)9781728160245

FPGA has been considered as a promising solution to accelerate Convolutional Neural Networks (CNNs) for its excellent performance in energy efficiency and programmability. However, prior designs are usually designed for inference only as designers can map pre-trained models to the hardware in a very efficient way. However, those approaches may not be suitable for training CNN models. In this paper, we propose FConv, in which the CPU and FPGA work together in a fine-grained manner. The FPGA accelerator in FConv uses one Winograd-based convolver, which reduces the design complexity and improves performance. We apply double-buffer for output routine to effectively overlap computation and data transfer. We also integrate multiple PEs to improve data parallelism. We propose our analytical model for prediction and use it as a guide in task scheduling. We find the upper limit of performance under the current design based on the analytical model. We evaluate our design on VGG-16 and Densnet-40 on ImageNet and CIFAR-10. We achieve 262.43 GOP/s on the VGG-16 model, which is 2.13 times of the performance compared to FFT-based implementation on the same platform. We also achieve at most 4 times+ performance improvement compared MKL with 20 threads running on 10 core Intel processors. © 2020 IEEE.

关键词： Field programmable gate arrays (FPGA)

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ReSQM: Accelerating Database Operations Using ReRAM-Based Content Addressable Memory

ReSQM: Accelerating Database Operations Using ReRAM-Based Co...

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作者： Li, Huize Jin, Hai Zheng, Long Liao, Xiaofei National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

The huge amount of data enforces great pressure on the processing efficiency of database systems. By leveraging the in-situ computing ability of emerging nonvolatile memory, processing-in-memory (PIM) technology shows great potential in accelerating database operations against traditional architectures without data movement overheads. In this article, we introduce ReSQM, a novel ReCAM-based accelerator, which can dramatically reduce the response time of database systems. The key novelty of ReSQM is that some commonly used database queries that would be otherwise processed inefficiently in previous studies can be in-situ accomplished with massively high parallelism by exploiting the PIM-enabled ReCAM array. ReSQM supports some typical database queries (such as SELECTION, SORT, and JOIN) effectively based on the limited computational mode of the ReCAM array. ReSQM is also equipped with a series of hardware-algorithm co-designs to maximize efficiency. We present a new data mapping mechanism that allows enjoying in-situ in-memory computations for SELECTION operating upon intermediate results. We also develop a count-based ReCAM-specific algorithm to enable the in-memory sorting without any row swapping. The relational comparisons are integrated for accelerating inequality join by making a few modifications to the ReCAM cells with negligible hardware overhead. The experimental results show that ReSQM can improve the (energy) efficiency by $611\times $ ( $193\times $ ), $19\times $ ( $17\times $ ), $59\times $ ( $43\times $ ), and $307\times $ ( $181\times $ ) in comparison to a 10-core Intel Xeon E5-2630v4 processor for SELECTION, SORT, equi-join, and inequality join, respectively. In contrast to state-of-the-art CMOS-based CAM, GPU, FPGA, NDP, and PIM solutions, ReSQM can also offer $2.2\times 39\times $ speedups. © 1982-2012 IEEE.

关键词： Query languages

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Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system

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Digital Communications and Networks 2025年第3期11卷 634-641页

作者： Adeel Akram Muhammad Bilal Khan Najah Abed Abu Ali Qixing Zhang Awais Ahmad Muhammad Shahid Iqbal Syed Atif Moqurrab Laboratory for Intelligent Sensing and Computing Institute of Advanced Technology University of Science and Technology of China Hefei 230031 Anhui China State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230026 Anhui China College of IT United Arab Emirates University Al-Ain 15551 United Arab Emirates Department of Electrical and Computer Engineering COMSATS University Islamabad Attock 43600 Pakistan College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Kingdom of Saudi Arabia Department of Computer Science and Information Technology Women University of AJ&K Bagh Pakistan School of Computing Gachon University 1342 Seongnam-daero Sujeong-gu Seongnam-si 13120 Korea

The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timely intervention can mitigate their adverse effects. In this context, the need for non-invasive, efficient monitoring systems becomes paramount. Although wearable sensors have gained traction for monitoring health activities, they may cause discomfort during prolonged use, especially for the elderly. To address this issue, we present an intelligent, non-invasive Software-Defined Radio Frequency (SDRF) sensing system, tailored red for monitoring elderly people's falls during routine activities. Harnessing the power of deep learning and machine learning, our system processes the Wireless Channel State Information (WCSI) generated during regular and fall activities. By employing sophisticated signal processing techniques, the system captures unique patterns that distinguish falls from normal activities. In addition, we use statistical features to streamline data processing, thereby optimizing the computational efficiency of the system. Our experiments, conducted for a typical home environment while using treadmill, demonstrate the robustness of the system. The results show high classification accuracies of 92.5%, 95.1%, and 99.8% for three Artificial Intelligence (AI) algorithms. Notably, the SDRF-based approach offers flexibility, cost-effectiveness, and adaptability through software modifications, circumventing the need for hardware overhaul. This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring, providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.

关键词： AI Elderly falls Intelligent learning SDRF WCSI

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SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector

arXiv

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arXiv 2024年

作者： Mao, Qianchen Li, Qiang Wang, Bingshu Zhang, Yongjun Dai, Tao Chen, C.L. Philip The School of Software Northwestern Polytechnical University Taicang Campus Suzhou215400 China The School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 China

In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a pathway that preserves high-resolution features of sparse and tiny targets. However, it can result in redundant and expensive computations. To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets. Specifically, to cope with the computational redundancy issue, we employ a new dual-branch sparse decoder to restore the feature map. Firstly, the fast branch directly predicts a sparse map indicating potential small target locations (occupying only 0.5% area of the map). Secondly, the slow branch conducts fine-grained adjustments at the positions indicated by the sparse map. Additionally, we design an lightweight DO-RepEncoder based on reparameterization with the Downsampling Orthogonality, which can effectively reduce memory consumption and inference latency. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models while achieving faster inference speed and fewer parameters. For example, on the IRSTD-1K dataset, SpirDet improves MIoU by 4.7 and has a 7× FPS acceleration compared to the previous state-of-the-art model. The code will be open to the public. © 2024, CC BY.

关键词： Deep learning

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation 14

An Adaptive Decision-Making Approach for Better Selection of...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds. © 2023 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation system

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Chinese Journal of Electronics 2025年第2期34卷 563-575页

作者： Cheng Hongyuan Liu Yining Zhou Fei Tan Zhiyuan Zhang Xianchao School of Computer Science & Engineering Linyi University Linyi 273300 China School of Data Science and Artificial Intelligence Wenzhou University of Electronic Technology Wenzhou 325027 China Guangxi Key Laboratory of Digital Infrastructure Nanning 530023 China School of Computing Engineering & the Built Environment Edinburgh Napier University Edinburgh EH10 5DT UK College of Information Science and Engineering Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing 314001 China

Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been proposed to obtain and recommend reliable platoon head vehicles. These trustworthiness evaluation protocols for PSRS fail to achieve both reliability and fairness. We first provide a reliable trustworthiness evaluation method to ensure that the reputation level of platoon head vehicle can be calculated by cloud service provider (CSP) with the help of key agreement mechanism and truth discovery technology. The semi-trusted entity CSP may maliciously tamper with the reputation level of the platoon head vehicle. We also provide a reputation level confirmation method to ensure the fairness of trustworthiness evaluation. Formal security proof and security analysis are provided to show that our trustworthiness evaluation protocol can achieve the goals of privacy protection, reliability, fairness and resistance to several security attacks. Experiments demonstrate that this protocol can save execution time and achieve reliable and fair trustworthiness evaluation for PSRS.

关键词： Reliability Fairness Privacy protection Trustworthiness evaluation Vehicle platoon

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