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arXiv 2021年

作者： Wu, Zhiyue Xu, Hongzuo Pang, Guansong Yu, Fengyuan Wang, Yijie Jian, Songlei Wang, Yongjun Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology China University of Adelaide Australia

DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been done on DRAM failure prediction mainly due to the lack of public available datasets. This paper presents a comprehensive empirical evaluation of diverse machine learning techniques for DRAM failure prediction using a large-scale multi-source dataset, including more than three millions of records of kernel, address, and mcelog data, provided by Alibaba Cloud through PAKDD 2021 competition. Particularly, we first formulate the problem as a multi-class classification task and exhaustively evaluate seven popular/state-of-the-art classifiers on both the individual and multiple data sources. We then formulate the problem as an unsupervised anomaly detection task and evaluate three state-of-the-art anomaly detectors. Further, based on the empirical results and our experience of attending this competition, we discuss major challenges and present future research opportunities in this task. © 2021, CC BY.

关键词： Web services

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A regularized magnetotelluric inversion with a minimum support gradient constraint

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Earthquake science 2020年第3期33卷 130-140页

作者： Junjun Zhou Xiangyun Hu Tiaojie Xiao Institute of Geophysics and Geomatics China University of GeosciencesWuhan 430000China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China

Magnetotelluric(MT)inversion is an illposed problem and the standard way to address it is through regularization,by adding a stabilizing functional to the data objective functional in order to obtain a stable *** traditional stabilizing functionals,in which a low-order differential operator is used,yield a smooth solution that may not be appropriate when anomalies occur in block *** some cases the focused imaging of a sharp electrical boundary is *** though various experiments have used stabilizing functionals that are suitable to obtain a clear and sharp boundary,such as the minimum support(MS)and the minimum gradient support(MGS)functionals,there are still some limitations in *** this paper,the minimum support gradient(MSG)is proposed as the stabilizing *** the uniform regularization framework,a regularized inversion with a variety of stabilizing functionals is performed and the inversion results are *** study shows that MSG inversion can not only obtain a clearly focused inversion but also a quite stable and robust one.

关键词： magnetotelluric focus inversion sharp boundary regulafization

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Deep learning for social network information cascade analysis: A survey 5

Deep learning for social network information cascade analysi...

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5th IEEE International Conference on Data science in Cyberspace, DSC 2020

作者： Gao, Liqun Zhou, Bin Jia, Yan Tu, Hongkui Wang, Ye Chen, Chenguang Wang, Haiyang Zhuang, Hongwu National University of Defense Technology College of Computer ChangSha China National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory ChangSha China

ISBN: (纸本)9781728195582

The phenomenon of information dissemination in social networks is widespread, and Social Network Information Cascade Analysis (SNICA) aims to acquire valuable knowledge in the process of information dissemination in social networks. As the number, volume, and resolution of social network data increase rapidly, traditional social network data analysis methods, especially the analysis method of social network graph (SNG) data becoming overwhelmed in SNICA. Recently, deep learning models have changed this situation, and it has achieved success in SNICA with its powerful implicit feature extraction capabilities. In this paper, we provide a comprehensive survey of recent progress in applying deep learning techniques for SNICA. We first introduce related concepts and summarize the advantages of deep learning technology in SNICA. Then, we propose a general framework for deep learning technology, which applies to SNICA. Then, different SNICA application scenarios in the framework are classified and discussed according to user behavior analysis, information cascade analysis, rumor detection, and social network event analysis. Finally, we discuss the limitations of current work and suggest future directions. © 2020 IEEE.

关键词： Data mining

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Detection of regions with the least impact on true and fake image classification through reinforcement learning 3

Detection of regions with the least impact on true and fake ...

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2020 3rd International Conference on Computer Information science and Artificial Intelligence, CISAI 2020

作者： Li, Wei Qiao, Peng Dou, Yong Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha410005 China

With the development of artificial intelligence [1-3], convolutional neural networks (CNNs) have made significant progress in image generation and manipulation. The generated images using facial image synthesis methods, e.g., Faceswap [4], Deepfakes [5], Face2face [6], NeuralTextures [7], are very realistic and even difficult to distinguish by human eyes. It poses serious challenges to social and personal security. There are many methods that can detect synthetic facial images with an accuracy rate of over 99%. But the synthetic facial image classification model can only give a true or fake result, and it is impossible to know which part is the discordant area. Therefore, we propose and experimentally verify a method that using reinforcement learning to select the region in the image that has the least impact on the classification model. © Published under licence by IOP Publishing Ltd.

关键词： Image classification

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FAAD:an unsupervised fast and accurate anomaly detection method for a multi-dimensional sequence over data stream

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Frontiers of Information technology & Electronic Engineering 2019年第3期20卷 388-404页

作者： Bin LI Yi-jie WANG Dong-sheng YANG Yong-mou LI Xing-kong MA Science and Technology on Parallel and Distributed Processing Laboratory College of ComputerNational University of Defense Technology Block Chain Research Institute of LianLian Pay

Recently, sequence anomaly detection has been widely used in many fields. Sequence data in these fields are usually multi-dimensional over the data stream. It is a challenge to design an anomaly detection method for a multi-dimensional sequence over the data stream to satisfy the requirements of accuracy and high speed. It is because:(1) Redundant dimensions in sequence data and large state space lead to a poor ability for sequence modeling;(2) Anomaly detection cannot adapt to the high-speed nature of the data stream, especially when concept drift occurs, and it will reduce the detection rate. On one hand, most existing methods of sequence anomaly detection focus on the single-dimension sequence. On the other hand, some studies concerning multi-dimensional sequence concentrate mainly on the static database rather than the data stream. To improve the performance of anomaly detection for a multi-dimensional sequence over the data stream, we propose a novel unsupervised fast and accurate anomaly detection(FAAD) method which includes three algorithms. First, a method called "information calculation and minimum spanning tree cluster" is adopted to reduce redundant dimensions. Second, to speed up model construction and ensure the detection rate for the sequence over the data stream, we propose a method called"random sampling and subsequence partitioning based on the index probabilistic suffix tree." Last, the method called "anomaly buffer based on model dynamic adjustment" dramatically reduces the effects of concept drift in the data stream. FAAD is implemented on the streaming platform Storm to detect multi-dimensional log audit *** with the existing anomaly detection methods, FAAD has a good performance in detection rate and speed without being affected by concept drift.

关键词： Data stream Multi-dimensional sequence Anomaly detection Concept drift Feature selection

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JointCloud Cross-chain Verification Model of Decentralized Identifiers

JointCloud Cross-chain Verification Model of Decentralized I...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Tao Zhong Peichang Shi Junsheng Chang Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Institute of Computer Technology College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665443326

When multiple entities communicate or collaborate in JointCloud, identities are the very prior basis to build trust with each other. Decentralized identifier (DID) can provide a trusted identity with blockchain technology and a complete method of identity verification based on verifiable credentials, which solves problems of conventional centralized identity. However, current DIDs can only conduct verification within a single blockchain, which limits the interoperability of DIDs on different blockchains. Network isolation hinders the verification of DIDs on different blockchains and thus there is a need to break the barrier between blockchains. In this paper, we propose a model to conduct cross-chain verification of DIDs. We build a system of credit evaluation to describe the credibility of DIDs in a unified way and deploy smart contracts to implement cross-chain verification of DIDs. Experimental results verifies the feasibility of the model, which realizes cross-chain verification of DIDs in the networks of blockchain.

关键词： Couplings Computational modeling Scalability Conferences Smart contracts Prototypes Data structures

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture

Optimizing Batched Small Matrix Multiplication on Multi-core...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Xiaohan Zuo Chunhua Xiao Qinglin Wang Chen Shi College of Computer Science Chongqing University China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance Computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x.

关键词： Performance evaluation Scientific computing High performance computing Digital signal processors Redundancy Batch production systems Signal processing algorithms Computer architecture Machine learning Matrix decomposition

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MixedCache: Enabling Flow Directed Rule-Caching Scheme based on Heterogeneous Cache for OpenFlow 3

MixedCache: Enabling Flow Directed Rule-Caching Scheme based...

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2020 3rd International Conference on Computer Information science and Artificial Intelligence, CISAI 2020

作者： Yang, Jialun Yang, Yi Liu, Jiahao Liu, Rulin Qiao, Peng Wang, Baosheng College of Computer Science and Technology National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

OpenFlow switches in SDN use Multiple Flow Tables (MFTs) for fine-grained flow control. Commodity switches integrate hardware storage resources such as SRAM and TCAM to store flow tables to achieve high-speed lookups. Many increased flow tables are rapidly exhausting these hardware storage resources, which makes the switches have to balance high-speed search and massive storage. The rule-caching scheme is a popular method to solve this problem, which caches the most commonly used rules into hardware storage resources. The existing rule-caching schemes are based on single hardware storage resources, and they cannot flexibly adjust the caching strategy according to the traffic characteristics. Simultaneously, the deployed commodity switches face the problem of difficulty in changing the size of SRAM and TCAM. This paper innovatively proposes the MixedCache scheme, which makes full use of the hardware storage resources in the switch according to the skewed characteristics of network traffic. MixedCache stores the large flows in SRAM by exact match and stores the small flows in the TCAM by wildcard match. MixedCache does not need to change the size of the deployed switch hardware storage resources, but makes full use of existing resources. Compared with the rule-caching scheme based on the exact match, the cache hit rate can increase by up to 15.61%. Compared with the rule-caching scheme based on the wildcard match, the cache hit rate can increase by up to 29.69%. © Published under licence by IOP Publishing Ltd.

关键词： Static random access storage

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Neutronic calculations of the China dual-functional lithium–lead test blanket module with the parallel discrete ordinates code Hydra

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Nuclear science and Techniques 2020年第8期31卷 1-12页

作者： Guang-Chun Zhang Jie Liu Liang-Zhi Cao Hong-Chun Wu Xian-Bao Yuan Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China School of Nuclear Science and Technology Xi’an Jiaotong UniversityXi’an 710049China College of Mechanical and Power Engineering China Three Gorges UniversityYichang 443002China

The China dual-functional lithium–lead test blanket module(DFLL-TBM) is a liquid Li Pb blanket concept developed by the Institute of Nuclear Energy Safety technology of the Chinese Academy of sciences for testing in ITER to validate relevant tritium breeding and shielding technologies. In this study, neutronic calculations of DFLL-TBM were carried out using a massively parallel three-dimensional transport code, Hydra, with the Fusion Evaluated Nuclear Data Library/MG. Hydra was developed by the Nuclear Engineering Computational Physics Lab based on the discrete ordinates method and has been devoted to neutronic analysis and shielding evaluation for nuclear facilities. An in-house Monte Carlo code(MCX) was employed to verify the discretized calculation model used by Hydra for the DFLL-TBM calculations. The results showed two key aspects:(1) In most material zones,Hydra solutions are in good agreement with the reference MCX results within 1%, and the maximal relative difference of the neutron flux is merely 3%, demonstrating the correctness of the calculation model;(2) while the current DFLL-TBM design meets the operation shielding requirement of ITER for 4 years, it does not satisfy the tritium self-sufficiency requirement. Compared to the two-step approach, Hydra produces higher accuracies as it does not rely on the homogenization technique during the calculation process. The parallel efficiency tests of Hydra using the DFLL-TBM model also showed that this code maintains a high parallel efficiency on O(100) processors and, as a result, is able to significantly improve computing performance through parallelization. Parameter studies have been carried out by varying the thickness of the beryllium armor layer and the tritium breeding zone to understand the influence of the beryllium layer and breeding zone thickness on tritium breeding performance. This establishes a foundation for further improvement in the tritium production performance of DFLL-TBM.

关键词： Discrete ordinates method DFLL-TBM Neutronic analysis Tritium breeding performance

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NH-CIL: A Nested Hierarchy Algorithm for Class Incremental Learning

NH-CIL: A Nested Hierarchy Algorithm for Class Incremental L...

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International Conference on Computer Supported Cooperative Work in Design

作者： Mingyu Liu Yijie Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R.China

ISBN: (纸本)9781728165981

Class incremental learning is widely applied in the classification scenarios as the number of classes is usually dynamically changing. However, the existing algorithms increase computational cost to implement class incremental learning in order to increase classification quality. In this paper, we propose a nested hierarchy algorithm based on OCSVM for class incremental learning, called NH-CIL. We reuse support vectors to eliminate redundant instances and catch the key ones to replace the whole model because of the generalization ability of OCSVM. When a new class arrives, NH-CIL adopts OCSVM on the new class and the old classes respectively to get the corresponding sketching supports vectors. Then NH-CIL reuses these two kinds of sketching support vectors to build a binary sub-classifier. These two steps are repeatedly nested to form a hierarchy classification model in a bottom-up manner while the number of classes increases. On the contrary, the testing phase is in a top-down manner. NH-CIL can be used as a flexible approach in the classification scenarios during the collaborative information processing. We conduct the experiments on 8 real-world benchmark datasets to compare NH-CIL with some other class incremental learning algorithms, e.g. SD-CIL, HS-CIL and OP-CIL. The experiment results show that NH-CIL averagely achieves more than 5.1%, 8.6% and 11.6% accuracy improvement and 39.8%, 24.7% and 12.6% efficiency improvement over SD-CIL, HS-CIL and OP-CIL, respectively.

关键词： Machine learning algorithms Computational modeling Heuristic algorithms Conferences Support vector machine classification Information processing Benchmark testing

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