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IEEE International conference on Data Mining Workshops (ICDM Workshops)

作者： Mengxin Wang Liming Fang Kuiqi Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute Shenzhen China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China

Federated learning (FL) is a decentralized machine learning framework that prioritizes privacy by allowing clients to train statistical models without sharing their private data, thus eliminating the impact of data fortresses. However, the presence of Byzantine attacks, such as data poisoning and backdoor attack, threatens the robustness of FL schemes. Currently, existing mainstream defense methods are susceptible to multiple adaptive attacks, some of which even violate the privacy principle of FL. Furthermore, these defense schemes become less robust when subjected to targeted poisoning attacks with highly non-IID data distributions. In this work, we propose FedNAT, a novel Byzantine-robust FL framework for whittling away these limitations mentioned above. Specifically, FedNAT first performs a privacy-respecting attention refinement on the activation layer outputs of the local uploads. Then, the server scores the local attentions by calculating their Wasserstein distances and clusters them through the k-median algorithm for global attention aggregation, thus rejecting poisoned local attentions for untargeted attacks. After this process, the global attention is transferred to local attention through the FedNAT loss function, which erases backdoors through the distillation concept. We conduct a comprehensive experimental evaluation to demonstrate that FedNAT significantly outperforms existing robust FL schemes in defending against Byzantine poisoning attacks under both IID and highly non-IID data proportions.

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License Plate Detection methods Based on OpenCV

License Plate Detection Methods Based on OpenCV

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Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Winter), ACIS International Winter conference on

作者： Lin Xu Wenqian Shang Weiguo Lin Wei Huang School of Computer Science and Cybersecurity Communication University of China Beijing China

With the popularization of automobile and the progress of computer vision detection technology, intelligent license plate detection technology has gradually become an important part of intelligent traffic management. License plate detection is used to segment vehicle image and obtain license plate area for follow-up recognition system to screen. It is widely used in intelligent traffic management, vehicle video monitoring and other fields. In this paper, two license plate detection methods are studied, one is based on Sobel edge detection and the other is based on morphological gradient detection. Basing on OpenCV and visual studio 2012 under Windows system, two methods of license plate detection are implemented, and the two algorithms are compared in detail from the aspects of license plate detection accuracy. These methods have high efficiency and good interactivity, which provide a reference for later license plate recognition.

关键词： Visualization image edge detection Transportation Licenses Optical character recognition software Monitoring Software engineering

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New parallel and distributed Tools and Algorithms for Life Sciences

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 112卷 1174-1176页

作者： Carretero, Jesus Krefting, Dagmar Univ Carlos III Madrid Madrid Spain Univ Med Ctr Goettingen Gottingen Germany Univ Appl Sci Berlin Berlin Germany

Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerging methods rely heavily on large scale computational resources. These new methods need to manage Tbytes or Pbytes of data with large-scale structural and functional relationships, TFlops or PFlops of computing power for simulating highly complex models, or many-task processes and workflows for processing and analyzing data. Today, many areas in Life Sciences are facing these challenges. This special issue contains papers showing existing solutions and latest developments in Life Sciences and Computing Sciences to collaboratively explore new ideas and approaches to successfully apply distributed IT-systems in translational research, clinical intervention, and decision-making. (C) 2020 Published by Elsevier B.V.

关键词： Bioinformatics Biomedicine Health Genomics Life Sciences Cloud computing Workflows

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Design and Implementation of Multi-core DSP parallel Compiler Based on Otsu Method 4

Design and Implementation of Multi-core DSP Parallel Compile...

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4th International conference on Advances in image processing, ICAIP 2020

作者： Zhang, Tianxu Meng, Fanchen Wuhan Institute of Technology China Huazhong University of Science and Technology China Wuhan Institute of Technology China

ISBN: (纸本)9781450388368

This paper introduces the principle of the three classical and widely applied local value methods, including Otsu method, maximum entropy method and iterative method. It runs on VS2010 (Microsoft Visual Studio 2010) platform, compares and analyzes it. And then, selects Otsu method with relatively good results to transplant in standard C language on CCS (Code Composer Studio) platform. A multi-core DSP (Digital Signal Processor) is established. After the TMS320C6678 environment, the OpenMP framework is used for parallel processing to optimize the Otsu method for fork-Join mode is used for parallel computing. Two cores, four cores and eight cores are used for fast processing of the Otsu method, summarize the law of speed increase. The results show that the parallel implementation of the digital image processing algorithm based on multi-core DSP in this paper can effectively improve the running speed on the basis of ensuring the accuracy of the Otsu method. © 2020 ACM.

关键词： Iterative methods

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High-Performance parallel Fault Simulation for Multi-Core Systems

High-Performance Parallel Fault Simulation for Multi-Core Sy...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Masoomeh Karami Mohammad-hashem Haghbayan Masoumeh Ebrahimi Hamid Nejatollahi Hannu Tenhunen Juha Plosila University of Turku Turku Finland Royal Institute of Technology (KTH) Kista Sweden. University of California Irvine CA USA.

Fault simulation is a time-consuming process that requires customized methods and techniques to accelerate it. Multi-threading and Multi-core approaches are two promising techniques that can be exploited to accelerate the fault simulation process by using different parts of the hardware at the same time. However, an efficient parallelization is obtained only by the refinement of software with respect to the hardware platform. In this paper, a parallel multi-thread fault simulation technique is proposed to accelerate the simulation process on multi-core platforms. In this approach, the gate input values are independently assigned to each thread. Each input value carries the information of several parallel simulation processes. This provides a multithread parallel fault simulation environment. The experimental results show that the proposed technique can efficiently use the hardware platform. In a single-core platform, the proposed technique can reduce the time by 25% while in a dual-core increasing the thread approximately halves the execution time.

关键词： Multicore processing Instruction sets Very large scale integration Logic gates Hardware Software Circuit faults

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Communication-efficient decentralised algorithms for seismic tomography with sensor networks

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INTERNATIONAL JOURNAL OF parallel EMERGENT AND distributed SYSTEMS 2020年第5期35卷 550-570页

作者： Zhao, Liang Univ South Carolina Upstate Div Math & Comp Sci Spartanburg SC 29303 USA

We consider the decentralised consensus optimisation problem arising from in-situ seismic tomography in large-scale sensor networks. Unlike traditional seismic imaging performed in a centralised location, each node in this setting privately holds an objective function and partial data. The goal of each node is to obtain the optimal solution of the whole seismic image, while communicating only with its immediate neighbours. We present a fast decentralised gradient descent method and prove that this novel method can reach optimal convergence rate ofO(1/k(2))wherekis the number of communication/iteration rounds. A gossip-based asynchronous version is also proposed which is preferable when there is a divergence on the processing speed of the nodes. Extensive numerical experiments on synthetic and real-world sensor network seismic data demonstrate that the proposed algorithms significantly outperform existing methods. [GRAPHICS] .

关键词： Big data decentralised computing in-network processing seismic tomography sensor network

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EDDNet: An Efficient and Accurate Defect Detection Network for the Industrial Edge Environment

EDDNet: An Efficient and Accurate Defect Detection Network f...

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IEEE International conference on Software Quality, Reliability and Security (QRS)

作者： Runbing Qin Ningjiang Chen Yihui Huang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Guangxi Colleges and Universities Key Laboratory of Parallel and Distributed Computing Nanning China

Defect detection aims to locate the accurate position of defects in images, which is of great significance to quality inspection in the industrial product manufacturing. Currently, many defect detection methods rely on deep neural networks to extract features. Although the accuracy of these methods is relatively high, it is computationally intensive, making the methods difficult to deploy in resource-limited edge devices. In order to solve these problems, a lightweight defect detection model for the industrial edge environment is proposed, termed the efficient defect detection network (EDDNet). EfficientNet-B0 is used as the feature extraction backbone, extracting feature maps from feature layers of different depths of the network and fusing multilevel features by multilevel feature fusion (MFF). To obtain more information, we redesign the attention mechanism in MBConv blocks, taking the encoding space (ES) attention mechanism as a new module, which solves the problem that the defective image spatial information is ignored. The experimental results on the NEU-DET and DAGM2007 datasets and PCB defect datasets demonstrate the effectiveness of the proposed EDDNet and its possibility for application in industrial edge device.

关键词： image edge detection Computational modeling Neural networks Software quality Object detection Feature extraction Transformers

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TCM Constitution Analysis Method Based on parallel FP-Growth Algorithm in Hadoop Framework

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JOURNAL OF HEALTHCARE ENGINEERING 2022年第1期2022卷 9006096页

作者： Li, Mingzheng Lv, Xiaojuan Liu, Ye Wang, Lin Song, Jianqiang Henan Univ Sci & Technol Sch Informat Engn Luoyang 471003 Peoples R China Lushi Chinese Med Hosp Sanmenxia 472100 Lushi Cty Peoples R China PLA Rocket Force Characterist Med Ctr Informat Dept Beijing 100120 Peoples R China Henan Qunzhi Informat Technol Co Ltd Luoyang 471003 Peoples R China

This work is devoted to establishing a comparatively accurate classification model between symptoms, constitutions, and regimens for traditional Chinese medicine (TCM) constitution analysis to provide preliminary screening and decision support for clinical diagnosis. However, for the analysis of massive distributed medical data in a cloud platform, the traditional data mining methods have the problems of low mining efficiency and large memory consumption, and long tuning time, an association rules method for TCM constitution analysis (ARA-TCM) is proposed that based on FP-growth algorithm and the open-source distributed file system in Hadoop framework (HDFS) to make full use of its powerful parallel processing capability. Firstly, the proposed method was used to explore the association rules between the 9 kinds of TCM constitutions and symptoms, as well as the regimen treatment plans, so as to discover the rules of typical clinical symptoms and treatment rules of different constitutions and to conduct an evidence-based medical evaluation of TCM effects in constitution-related chronic disease health management. Secondly, experiments were applied on a self-built TCM clinical records database with a total of 30,071 entries and it is found that the top three constitutions are mid constitution (42.3%), hot and humid constitution (31.3%), and inherited special constitution (26.2%), respectively. What is more, there are obvious promotions in the precision and recall rate compared with the Apriori algorithm, which indicates that the proposed method is suitable for the classification of TCM constitutions. This work is mainly focused on uncovering the rules of "disease symptoms constitution regimen" in TCM medical records, but tongue image and pulse signal are also very important to TCM constitution analysis. Therefore, this additional information should be considered into further studies to be more in line with the actual clinical needs.

关键词： Association rules

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DOS-GAN: A distributed Over-Sampling Method Based on Generative Adversarial Networks for distributed Class-Imbalance Learning 20th

DOS-GAN: A Distributed Over-Sampling Method Based on Generat...

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20th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Guan, Hongtao Ma, Xingkong Shen, Siqi Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9783030602482;9783030602475

Class-imbalance Learning is one of the hot research issues in machine learning. In the practical application of distributed class-imbalance learning, data continues to arrive, which often leads to classimbalance situations. The imbalance problem in the distributed scenario is particular: the imbalanced state of different nodes may be complementary. The imbalanced states of different nodes may be complementary. Using this complementary relationship to do oversampling to change the imbalanced state is a valuable method. However, the data island limits data sharing in this case between the nodes. To this end, we propose DOS-GAN, which can take turns to use the data of one same class data on multiple nodes to train the global GAN model, and then use this GAN generator to oversampling the class without the original data being exchanged. Extensive experiments confirm that DOS-GAN outperforms the combination of traditional methods and achieves classification accuracy closes to the method of data aggregating.

关键词： Class-imbalance learning distributed learning Generative adversarial networks Data isolated islands

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AMP: automatically finding model parallel strategies with heterogeneity awareness 22

AMP: automatically finding model parallel strategies with he...

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Proceedings of the 36th International conference on Neural Information processing Systems

作者： Dacheng Li Hongyi Wang Eric Xing Hao Zhang Carnegie Mellon University Mohamed Bin Zayed University of Artificial Intelligence and Carnegie Mellon University and Petuum Inc. University of California Berkeley

ISBN: (纸本)9781713871088

Scaling up model sizes can lead to fundamentally new capabilities in many machine learning (ML) tasks. However, training big models requires strong distributed system expertise to carefully design model-parallel execution strategies that suit the model architectures and cluster setups. In this paper, we develop AMP, a framework that automatically derives such strategies. AMP identifies a valid space of model parallelism strategies and efficiently searches the space for high-performed strategies, by leveraging a cost model designed to capture the heterogeneity of the model and cluster specifications. Unlike existing methods, AMP is specifically tailored to support complex models composed of uneven layers and cluster setups with more heterogeneous accelerators and bandwidth. We evaluate AMP on popular models and cluster setups from public clouds and show that AMP returns parallel strategies that match the expert-tuned strategies on typical cluster setups. On heterogeneous clusters or models with heterogeneous architectures, AMP finds strategies with 1.54× and 1.77× higher throughput than state-of-the-art model-parallel systems, respectively.

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