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Anomaly Detection Models Based on Context-aware Sequential Long Short-Term Memory Learning

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Anomaly Detection Models Based on Context-aware Sequential L...

IEEE Global Communications Conference

作者： Lu Xu Da Ye Zhongzhi Luan Carol Fung Depei Qian Sino-German Joint Software Institute Beihang University Beijing China Computer Science Department Virginia Commonwealth University Richmond USA Beijing municipal key laboratory of network technology Beihang University Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

For a large and complex system that provides services to users, an exception can cause cascading failures if it is not detected and handled in time. System monitoring and anomaly detection can be used to identify system malfunctioning. However, as the size and the complexity of the online service system increases, anomaly detection becomes a challenging problem. This is because the size, complexity and correlation among the data bring great difficulties to anomaly detection process. To address the above challenges, we propose three context-aware sequential Long Short-Term Memory (LSTM) learning models for multi-dimensional anomaly detection, namely, LastLSTM model, AvgLSTM model and CirclLSTM model. In particular, the CirclLSTM model is a period-related LSTM model that can integrate cyclical system historical information into anomaly learning. We evaluated our methods based on three real-world datasets. Our experimental results show that our method can achieve a higher accuracy than other baseline methods such as the Gaussian Naive Bayes (GaussianNB) model, k-nearest neighbors (KNN) algorithm and Logistic Regression (LR) model.

关键词： Anomaly detection Context modeling Monitoring Correlation Recurrent neural networks Computational modeling Data models

Improving Cluster Resource Efficiency with Oversubscription

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Improving Cluster Resource Efficiency with Oversubscription

IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Jie Chen Chun Cao Ying Zhang Xiaoxing Ma Haiwei Zhou Chengwei Yang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing China iQiyi Inc.

ISBN: (纸本)9781538626672

Volumes of studies on resource scheduling are proposed to improve the efficiency of computing clusters. As users usually overestimate the resource requirements for their jobs, further, most schedulers ignore the dynamic variation of jobs' runtime resource usage, the utilization of real-world clusters is significantly limited. In this paper, we argue that resource oversubscription, which allocates more resources than the physical capacity, is a necessary complement to existing systems. To alleviate resource contention, we augment oversubscription with lightweight prediction and dynamic CPU throttling. We implemented our approach called Datom, which is an extension module of the Apache Mesos cluster manager. We evaluated Datom with real-world video transcoding workloads and simulations with Google cluster trace. The results show that comparing to original Mesos, Datom increased CPU utilization, memory utilization and overall task throughput by up to 22%, 23%, 20% respectively, and shortened jobs' complete time by up to 20%, at the expenses of moderate penalty on job execution.

关键词： Task analysis Resource management Transcoding Throughput Estimation Processor scheduling Dynamic scheduling

Uncertainty Relations in the Presence of Quantum Memory for Mutually Unbiased Measurements

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arXiv 2018年

作者： Wang, Kun Wu, Nan Song, Fangmin State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210093 China

In [1], uncertainty relations in the presence of quantum memory was formulated for mutually unbiased bases using conditional collision entropy. In this paper, we generalize their results to the mutually unbiased measurements. Our primary result is an equality between the amount of uncertainty for a set of measurements and the amount of entanglement of the measured state, both of which are quantified by the conditional collision entropy. Implications of this equality relation are discussed. We further show that similar equality relation can be obtained for generalized symmetric informationally complete measurements. We also derive an interesting equality for arbitrary orthogonal basis of the space of Hermitian, traceless operators. Copyright © 2018, The Authors. All rights reserved.

关键词： Entropy

Entanglement detection via direct-sum majorization uncertainty relations

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arXiv 2018年

作者： Wang, Kun Wu, Nan Song, Fangmin State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210093 China

In this paper we investigate the relationship between direct-sum majorization formulation of uncertainty relations and entanglement, for the case of two and many observables. Our primary results are entanglement detection methods based on direct-sum majorization uncertainty relations. These nonlinear detectors provide a set of necessary conditions for detecting entanglement whose number grows with the dimension of the state being detected. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum entanglement

HPC AI500: A Benchmark Suite for HPC AI Systems 1st

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HPC AI500: A Benchmark Suite for HPC AI Systems

1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Jiang, Zihan Gao, Wanling Wang, Lei Xiong, Xingwang Zhang, Yuchen Wen, Xu Luo, Chunjie Ye, Hainan Lu, Xiaoyi Zhang, Yunquan Feng, Shengzhong Li, Kenli Xu, Weijia Zhan, Jianfeng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Dover DE United States Beijing Academy of Frontier Sciences and Technology Beijing China State University of New York Buffalo United States Department of Computer Science and Engineering The Ohio State University Columbus United States National Supercomputing Center in Shenzhen Shenzhen China National Supercomputing Center in Changsha Changsha China National Supercomputing Center in Jinan Jinan China Texas Advanced Computing Center The Texas University at Austin Austin United States

ISBN: (纸本)9783030328122

In recent years, with the trend of applying deep learning (DL) in high performance scientific computing, the unique characteristics of emerging DL workloads in HPC raise great challenges in designing, implementing HPC AI systems. The community needs a new yard stick for evaluating the future HPC systems. In this paper, we propose HPC AI500—a benchmark suite for evaluating HPC systems that running scientific DL workloads. Covering the most representative scientific fields, each workload from HPC AI500 is based on real-world scientific DL applications. Currently, we choose 14 scientific DL benchmarks from perspectives of application scenarios, data sets, and software stack. We propose a set of metrics for comprehensively evaluating the HPC AI systems, considering both accuracy, performance as well as power and cost. We provide a scalable reference implementation of HPC AI500. The specification and source code are publicly available from http://***/HPCAI500/***. Meanwhile, the AI benchmark suites for datacenter, IoT, Edge are also released on the BenchCouncil web site. © 2019, Springer Nature Switzerland AG.

关键词： Benchmarking

Distributed symmetry breaking in sampling (optimal distributed randomly coloring with fewer colors)

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arXiv 2018年

作者： Feng, Weiming Hayes, Thomas P. Yin, Yitong Department of Computer Science and Technology Nanjing University Department of Computer Science University of New Mexico State Key Laboratory for Novel Software Technology Nanjing University

We examine the problem of almost-uniform sampling proper q-colorings of a graph whose maximum degree is Δ. A famous result, discovered independently by Jerrum [31] and Salas and Sokal [39], is that, assuming q > (2+δ)Δ, the Glauber dynamics (a.k.a. single-site dynamics) for this problem has mixing time O(n log n), where n is the number of vertices, and thus provides a nearly linear time sampling algorithm for this problem. A natural question is the extent to which this algorithm can be parallelized. Previous work [15] has shown that a O(Δlog n) time parallelized algorithm is possible, and that Ω(log n) time is necessary. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

Correction to: A digital rights management system based on a scalable blockchain

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Peer-to-Peer Networking and Applications 2021年第4期14卷 1869-1869页

作者： Garba, Abba Dwivedi, Ashutosh Dhar Kamal, Mohsin Srivastava, Gautam Tariq, Muhammad Hasan, M. Anwar Chen, Zhong Key Laboratory of High Confidence Software Technologies Peking University MoE Beijing China Department of Computer Science and Technology EECS Peking University Beijing China Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark Department of Electrical Engineering National University of Computer and Emerging Sciences Peshawar Pakistan Department of Mathematics and Computer Science Brandon University Brandon Canada Research Center for Interneural Computing China Medical University Taichung Republic of China Princeton University Princeton USA Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

A Correction to this paper has been published: https://***/10.1007/s12083-021-01098-2

关键词：

Bi-directional mission offloading for agile space-air-ground integrated networks

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arXiv 2019年

作者： Zhou, Sheng Wang, Guangchao Zhang, Shan Niu, Zhisheng Shen, Xuemin Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Beihang Univesity Beijing100191 China State Key Laboratory of Software Development Environment Beijing Key Laboratory of Computer Networks Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada

Space-air-ground integrated networks (SAGIN) provide great strengths in extending the capability of ground wireless networks. On the other hand, with rich spectrum and computing resources, the ground networks can also assist space-air networks to accomplish resource-intensive or power-hungry missions, enhancing the capability and sustainability of the space-air networks. Therefore, bi-directional mission offloading can make full use of the advantages of SAGIN and benefits both space-air and ground networks. In this article, we identify the key role of network reconfiguration in coordinating heterogeneous resources in SAGIN, and study how network function virtualization (NFV) and service function chaining (SFC) enable agile mission offloading. A case study validates the performance gain brought by bi-directional mission offloading. Future research issues are outlooked as the bi-directional mission offloading framework opens a new trail in releasing the full potentials of SAGIN. Copyright © 2019, The Authors. All rights reserved.

关键词： Network function virtualization

Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends

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MedComm

MedComm 2025年第6期6卷 e70247页

作者： Wang, Ruoyun Chen, Fanxuan Chen, Haoman Lin, Chenxing Shuai, Jincen Wu, Yutong Ma, Lixiang Hu, Xiaoqu Wu, Min Wang, Jin Zhao, Qi Shuai, Jianwei Pan, Jingye Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China Wenzhou Medical University Wenzhou China UCSC Baskin School of Engineering University of California Santa Cruz CA United States Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China Department of Medicine Harvard Medical School and Brigham and Women's Hospital Boston MA United States Joint Research Centre on Medicine The Affiliated Xiangshan Hospital of Wenzhou Medical University Ningbo China Stony Brook University Stony Brook NY United States School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou China Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization Wenzhou China

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly higher radiation doses, and increased reading times. Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. This article reviews the application and outlook of DL in DBT-based breast cancer screening. First, the fundamentals and challenges of DBT technology are introduced. The applications of DL in DBT are then grouped into three categories: diagnostic classification of breast diseases, lesion segmentation and detection, and medical image generation. Additionally, the current public databases for mammography are summarized in detail. Finally, this paper analyzes the main challenges in the application of DL techniques in DBT, such as the lack of public datasets and model training issues, and proposes possible directions for future research, including large language models, multisource domain transfer, and data augmentation, to encourage innovative applications of DL in medical imaging. © 2025 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.

关键词： deep learning digital breast tomosynthesis early breast cancer screening medical image analysis public database