检索结果-内蒙古大学图书馆

Poisoning as a Post-Protection: Mitigating Membership Privacy Leakage From Gradient and Prediction of Federated Models

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Liu, Gaoyang Xu, Tianlong Yang, Yang Abdelmoniem, Ahmed M. Wang, Chen Liu, Jiangchuan Huazhong University of Science and Technology Hubei Key Laboratory of Internet of Intelligence School of Electronic Information and Communications Wuhan430074 China Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada Ministry of Education Key Laboratory of Intelligent Sensing System and Security China Hubei University School of Artificial Intelligence Wuhan430062 China Queen Mary University of London School of Electronic Engineering and Computer Science United Kingdom

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to train a unified model without sharing their private data. However, recent works demonstrate that FL models are vulnerable to membership inference attacks (MIAs), which can infer whether a data sample was used to train a given FL model. Existing countermeasures either require far-reaching modifications of FL training process or enforce extra processing in prediction phase, yielding them unlikely to be applied well in practice. In this paper, we design a post-protection mechanism, dubbed P2-Protection, which degrades the inference performance of MIAs by simultaneously poisoning the prediction and gradient of the target FL model to reduce the privacy leakage of training data while keeping the model prediction accuracy. P2-Protection only involves one additional training round to embed the poisoned prediction and gradient into the target FL model, without requiring model retraining or training process modification. We evaluate P2-Protection and compare it with two state-of-the-art defenses against three MIAs on five realistic datasets. Experimental results show that P2-Protection outperforms the existing defenses by offering limited implement overhead and improved utility-privacy trade-off. © 2004-2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A data acquisition system of CNC based on Zigbee and GPRS technology

A data acquisition system of CNC based on Zigbee and GPRS te...

引用

2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Xiao Bin CAO Xiang Li ZHANG Hong Mei ZHANG Key Laboratory of Cloud Computing & Complex System Guilin University of Electronic Technology

ISBN: (纸本)9781509001668

A data acquisition and monitoring system of machine tools is designed,which is based on Zigbee and GPRS wireless communication *** monitoring and alerting about the safety status of the machine tools operates via the acquisition of such physical parameters as temperature and *** hardware such as the CC2530 RF circuit and power module is *** on the CC2530 module and SIM900 module,the wireless transmission and SMS alarm can be *** detailed design process of the software is also given in this paper,besides,the data monitoring is carried out on the PC software *** turns out to be a success of this security monitoring system which integrates the data acquisition and transmission of machine tool and alarm system under the industrial *** software platform overcomes the shortcomings such as the poor realtime performance of traditional CNC machine tools monitoring system and the difficulty of data transmission,and shows an extensive range of applicability.

关键词： CNC monitoring realtime performance Zigbee GPRS

来源：评论

学校读者我要写书评

暂无评论

Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways

引用

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Zhang, Xuejian He, Ruisi Yang, Mi Ding, Jianwen Chen, Ruifeng Gao, Shuaiqi Qi, Ziyi Zhang, Zhengyu Ai, Bo Zhong, Zhangdui Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Henan High Speed Railway Operation and Maintenance Engineering Research Center Zhengzhou451460 China China Academy of Railway Sciences Corporation Ltd. Institute of Computing Technology Beijing100081 China

5 G for Railways (5G-R) is globally recognized as a promising next-generation railway communication system designed to meet increasing demands. Channel modeling serves as foundation for communication system design, with tapped delay line (TDL) models widely utilized in system simulations due to their simplicity and practicality and serves as a crucial component of various standards like 3GPP. However, existing TDL models applicable to 5G-R systems are limited. Most fail to capture non-stationarity, a critical characteristic of railway communications, while others are unsuitable for the specific frequency bands and bandwidths of 5G-R. In this paper, a channel measurement campaign for 5G-R dedicated network is carried out, resulting in a measurement-based 5-tap TDL model utilizing a first-order two-state Markov chain to represent channel non-stationarity. key model parameters, including number of taps, statistical distribution of amplitude, phase and Doppler shift, and state transition probability matrix, are extracted. The correlation between tap amplitudes is also obtained. Finally, the accuracy of model is validated through comparisons with measurement data and 3GPP model. These findings are expected to offer valuable insights for design and link-level simulation of 5G-R systems. © 2025 IEEE.

关键词： Markov chains

来源：评论

学校读者我要写书评

暂无评论

Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

引用

Journal of Computer Science & technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

来源：评论

学校读者我要写书评

暂无评论

HCOpt: An automatic optimizer for configuration parameters of Hadoop 2nd

HCOpt: An automatic optimizer for configuration parameters o...

引用

2nd International Conference on Human Centered computing, HCC 2016

作者： Zhang, Xiong Zeng, Linxi Shi, Xuanhua Wu, Song Xie, Xia Jin, Hai Services Computing Technology and System Laboratory Big Data Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783319318530

MapReduce is an efficient tool for data-intensive applications. Hadoop, an open-source implementation of MapReduce, has been widely adopted and experienced by some enterprises and scientific computing communities. However, when users intend to run a MapReduce program in Hadoop, they have to set a number of configuration parameters to make sure the program runs efficiently. Users often run into performance problems because they are unaware of how to set these parameters. To address these performance problems, we focus on the optimization opportunities presented by the high configurability of Hadoop, and propose an automation tool named HCOpt for performance optimization of Hadoop configuration parameters. HCOpt uses a Profile Engine to collect monitoring information from running MapReduce programs, a Prediction Engine to estimate the performance of a given Hadoop configuration and a genetic-based search algorithm to find an optimized configuration in the large search space. Our evaluation shows that HCOpt reduces the job completion time of Hadoop applications by up to 20 % when compared to applications run with configuration parameters that suggested by the rule-based optimization. © Springer International Publishing Switzerland 2016.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

Prescribed-time synchronization for multiweighted complex networks: A switching control scheme

引用

systems & Control Letters 2025年 203卷

作者： Linlong Xu Xiwei Liu Department of Computer Science and Technology Tongji University Shanghai 201804 PR China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai 201804 PR China

This letter deals with an interesting intersection phenomenon of prescribed-time stability (PTSta) for dynamical systems, and develops a novel switching control scheme to investigate prescribed-time synchronization (PTSyn) for multiweighted and directly coupled complex networks. Different from most previous works that scholars only pay attention to designing the regulation function to ensure PTSta, we aim to select various parameters and uncover the mathematical mechanism of intersecting system state curves. We rigorously prove that, if the settling time is larger than 1, then no matter what the initial value is, the intersection exists only once before the settling time; otherwise, there is no intersection. Moreover, an energy consumption evaluation function is also put forward for PTSta, whose exact value is also calculated via exponential integrals. Then, this intersection theory is applied on the PTSyn of multiweighted complex networks, which is beneficial to construct the switching control scheme or choose the optimal parameters to reduce the energy cost. The rearranging variables’ order technique is utilized to conduct the multiweighted complex networks and obtain the synchronization criterion. Finally, four simulations are presented to verify theoretical results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Geometric Algebra Multi-order Graph Neural Network for Traffic Prediction

引用

IEEE Transactions on Big Data 2024年第3期11卷 1-15页

作者： Zang, Di Cui, Zhe Wang, Zengqiang Lei, Juntao Ding, Yongjie Wei, Chenguang Zhang, Junqi Department of Computer Science and Technology and the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Accurate traffic prediction is crucial for urban traffic management. Spatial-temporal graph neural networks, which combine graph neural networks with time series processing, have been extensively employed in traffic prediction. However, traditional graph neural networks only capture pairwise spatial relationships between road network nodes, neglecting high-order interactions among multiple nodes. Meanwhile, most work for extracting temporal dependencies suffers from implicit modeling and overlooks the internal and external dependencies of time series. To address these challenges, we propose a Geometric Algebraic Multi-order Graph Neural Network (GA-MGNN). Specifically, in the temporal dimension, we design a convolution kernel based on the rotation matrix of geometric algebra, which not only learns internal dependencies between different time steps in time series but also external dependencies between time series and convolution kernels. In the spatial dimension, we construct a tokenized hypergraph and integrate dynamic graph convolution with attention hypergraph convolution to comprehensively capture multi-order spatial dependencies. Additionally, we design a segmented loss function based on traffic periodic information to further improve prediction accuracy. Extensive experiments on seven real-world datasets demonstrate that GAMGNN outperforms state-of-the-art baselines IEEE

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Helix Scan:A Scan Design for Diagnosis

引用

Tsinghua Science and technology 2007年第S1期12卷 83-88页

作者：王飞胡瑜李晓维 Graduate School of Chinese Academy of Sciences Key Laboratory of Computer System and Architecture Institute of Computing TechnologyChinese Academy of Sciences

Scan design is a widely used design-for-testability technique to improve test quality and efficiency. For the scan-designed circuit, test and diagnosis of the scan chain and the circuit is an important process for silicon debug and yield learning. However, conventional scan designs and diagnosis methods abort the subsequent diagnosis process after diagnosing the scan chain if the scan chain is faulty. In this work, we propose a design-for-diagnosis scan strategy called helix scan and a diagnosis algorithm to address this issue. Unlike previous proposed methods, helix scan has the capability to carry on the diagnosis process without losing information when the scan chain is faulty. What is more, it simplifies scan chain diagnosis and achieves high diagnostic resolution as well as accuracy. Experimental results demonstrate the effectiveness of our design.

关键词： test diagnosis scan chain diagnosis design for diagnosis (DFD)

来源：评论

学校读者我要写书评

暂无评论

SIS model of epidemic spreading on dynamical networks with community

引用

中国高等学校学术文摘·计算机科学 2009年第3期3卷 361-365页

作者： Chengyi XIA Shiwen SUN Feng RAO Junqing SUN Jinsong WANG Zengqiang CHEN Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System of Ministry of Education Tianjin University of Technology Tianjin 300191 Department of Automation Nankai University Tianjin 300071 China

We present a new epidemic Susceptible-Infected-Susceptible (SIS) model to investigate the spreading behav-ior on networks with dynamical topology and community structure. Individuals in the model are mobile agents who are allowed to perform the inter-community (i.e., long-range) motion with the probability p. The mean-field theory is uti-lized to derive the critical threshold (λ_C) of epidemic spread-ing inside separate communities and the influence of the long-range motion on the epidemic spreading. The results indicate that λ_C is only related with the population density within the community, and the long-range motion will make the original disease-free community become the endemic state. Large-scale numerical simulations also demonstrate the theoretical approximations based on our new epidemic model. The current model and analysis will help us to fur-ther understand the propagation behavior of real epidemics taking place on social networks.

关键词： Susceptible-Infected-Susceptible (SIS) model disease propagation complex network dynamical topology community behavior

来源：评论

学校读者我要写书评

暂无评论

Human behavior recognition based on axonometric projections and PHOG feature

引用

Journal of Computational Information systems 2014年第8期10卷 3455-3463页

作者： Shen, Xiaoxia Zhang, Hua Gao, Zan Xue, Yanbin Xu, Guangping Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China

Human behavior recognition has become a hot research topic in computer vision. In this paper, we propose an effective method to recognize human behaviors from sequences of depth maps, which provide additional body shape and motion information for behavior recognition. In our approach, we construct a novel difference motion history image, and propose axonometric projection to capture the target motion process, after that, pyramid histogram of orientated gradients is extracted for each view to describe the target motion. Large scale experimental results on challenging and public MSR-action3D behavior dataset demonstrate that the performances of our difference motion history image and our descriptors are satisfying, what's more, our proposed axonometric projection approach further improves the performance, which significantly outperforms the state-of-the-art methods. © 2014 Binary Information Press.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：