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20th ieee Int symposium on Parallel and Distributed Processing with Applicat / 15th ieee Int Conf on Social Comp and Networking / 12th ieee Int Conf on Big data and Cloud Comp / 12th ieee Int Conf on Sustainable Comp and Commun

作者： Xu, Zhibin Jiang, Zhaoxue Li, Tong You, Junling Wu, Bingzhen Li, Liangxiong Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyher Secur Beijing 100049 Peoples R China

ISBN: (纸本)9781665464970

With the development of Artificial Intelligence for IT Operations(AIOps), an increasing number of log analysis approaches have been proposed to guarantee the stability and reliability of large-scale systems. However, traditional approaches still exist some limitations. First, they cannot adapt to the changing log statements. Second, most of them are based on large-scale labeled data. Third, most of their frames are fixed. To overcome these limitations, we propose OpenLog based on meta-learning and few-shot learning. Experimental results show that OpenLog can achieve an accuracy of 95.8% on detecting unknown anomaly classes, and an accuracy of 93.3% in anomaly classification on unbalanced logs with unknown anomaly classes. In the few-shot classification, OpenLog can still achieve an accuracy over 80%.

关键词： Anomaly classification log analysis meta-learning few-shot learning AIOPs

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A Visual Analytics Approach for Exploratory Causal analysis: Exploration, Validation, and Applications

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第2期27卷 1448-1458页

作者： Xie, Xiao Du, Fan Wu, Yingcai Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Adobe Res San Jose CA 95110 USA

Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals.

关键词： Exploratory causal analysis correlation and causation causal graph

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A Survey of visualization and analysis in High-Resolution Connectomics

A Survey of Visualization and Analysis in High-Resolution Co...

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24th Eurographics/ieee VGTC Conference on visualization (EuroVis)

作者： Beyer, Johanna Troidl, Jakob Boorboor, Saeed Hadwiger, Markus Kaufman, Arie Pfister, Hanspeter Harvard Univ Visual Comp Grp Cambridge MA 02138 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA King Abdullah Univ Sci & Technol Visual Comp Ctr Thuwal Saudi Arabia

The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high-resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state-of-the-art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open-source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.

关键词： Pipelines

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DEPCOMM: Graph Summarization on System Audit Logs for Attack Investigation 43

DEPCOMM: Graph Summarization on System Audit Logs for Attack...

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43rd ieee symposium on Security and Privacy (SP)

作者： Xu, Zhiqiang Fang, Pengcheng Liu, Changlin Xiao, Xusheng Wen, Yu Meng, Dan Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA

ISBN: (数字)9781665413169

ISBN: (纸本)9781665413169

Causality analysis generates a dependency graph from system audit logs, which has emerged as an important solution for attack investigation. In the dependency graph, nodes represent system entities (e.g., processes and files) and edges represent dependencies among entities (e.g., a process writing to a file). Despite the promising early results, causality analysis often produces a large graph (> 100, 000 edges) and it is a daunting task for security analysts to inspect such a large graph for attack investigation. To address challenges in attack investigation, we propose DEPCOMM, a graph summarization approach that generates a summary graph from a dependency graph by partitioning a large graph into process-centric communities and presenting summaries for each community. Specifically, each community consists of a set of intimate processes that cooperate with each other to accomplish certain system activities (e.g., file compression), and the resources (e.g., files) accessed by these processes. Within a community, DEPCOMM further identifies redundant edges caused by less-important and repetitive system activities, and perform compression on these edges. Finally, DEPCOMM generates the summary for each community using the InfoPaths that represent the information flows across communities. These InfoPaths are more likely to capture a set of attack-related processes that work together to achieve certain malicious goals. Our evaluations on real attacks (similar to 150 million events) demonstrate that DEPCOMM generates 18.4 communities on average for a dependency graph, which is similar to 70x smaller than the original graph. Our compression further reduces the edges in each community to 32.1 on average. Compared with the 9 state-of-the-art community detection algorithms, on average, DEPCOMM achieves a 2.29x better F1-score than these algorithms in detecting communities. Through cooperating with the automatic techniques HOLMES, DEPCOMM can identify attackrelated communities b

关键词： attack investigation system auditing graph summarization community detection

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An analysis and visualization of Best Practices for Police data Transparency

An Analysis and Visualization of Best Practices for Police D...

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2021 ieee Systems and Information Engineering Design symposium, SIEDS 2021

作者： Arango, Maria Clara Hogue, Andrew Williams, Karyne University of Virginia School of Data Science CharlottesvilleVA United States

ISBN: (纸本)9781665412506

The ability to hold police accountable for the actions of officers is contingent upon independent review of data surrounding misconduct. This research identifies gaps in information and useful features to build a guide for policing data collection and analysis for public accountability, based on the practices of other cities around the US. Our primary concern is to obtain actionable and transparent data on police activities and identify recording practices to serve Charlottesville as examples to follow. Prominent challenges include sourcing usable data, judging whether the data were comprehensive, and inconsistencies across datasets that prevented joining, and in turn, limited meaningful data analysis. Our guide also advises Charlottesville of the ethical and methodological considerations when analyzing policing data. The final guide includes: recommendations for sharing data with the public through dashboard visualization, which was informed by a multi-city scrub of open policing data portals;recommendations for feature variables to collect for meaningful analysis;and a multi-city proof-of-concept data dashboard. © 2021 ieee.

关键词： Transparency

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A Distributed Training Fault Diagnosis Method for Big data in Rail Transit System

A Distributed Training Fault Diagnosis Method for Big Data i...

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2023 CAA symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： He, Qichao Cai, Yifei Wen, Tao Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China China Academy of Railway Sciences Corporation Limited Locomotive & Car Research Institute Beijing100094 China

ISBN: (纸本)9798350337754

After decades of rapid development, our rail transit system has entered a period of frequent maintenance of equipment. It is faced with challenges of various equipment models, complex working conditions, a huge amount of fault data, etc. To meet the requirement of real-time fault diagnosis of the rail transit system to guarantee safety, a distributed training architecture is proposed to accelerate the training of a large-scale data fault diagnosis model. On the other hand, rail transit fault data is highly confidential and private, while the traditional centralized data fault diagnosis has a high risk of data leakage. Therefore, the Federated Learning (FL) paradigm is adopted in this paper and it can effectively alleviate the "data island"problem. From the experimental results of this paper, on the one hand, we successfully applied the FL paradigm to the field of rail transit fault diagnosis, ensuring the privacy and security of fault data. On the other hand, the distributed training architecture has been greatly improved in terms of training speed and computing resource utilization. © 2023 ieee.

关键词： Failure analysis

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Comparison of Offline Monitoring Algorithms for Production data of Workshop Equipment Based on DTW 3

Comparison of Offline Monitoring Algorithms for Production D...

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3rd International symposium on Intelligent Robotics and Systems, ISoIRS 2023

作者： Jia, Tang Yi, Su Wuxi Automobile Engineering Branch of Jiangsu Union Technical Wuxi China Wuxi Vocational Institute of Commerce Wuxi China

ISBN: (纸本)9798350339963

Current data acquisition and analysis algorithms based on electrical signals are generally solved using distance similarity algorithms, for which a Dynamic Time Warping (DTW) based algorithm is proposed. The similarity distance generally needs to calculate the distance between sampling points and points, and then carry out subsequent calculations. For the problem that the similarity distance of the existing basic electrical signal cannot determine the threshold, a dynamic time-based regularization algorithm is given. Firstly, the similarity distance was calculated by using Euclidean and Fréchet algorithms for different time periods after preprocessing, and the similarity threshold was found to be 86. Then the dynamic time regularization algorithm is introduced to obtain the similarity distance threshold of 40. Finally, aligning the time domain and current amplitude domain of the algorithm can improve the similarity distance accuracy of anomaly identification under offline monitoring. The experimental results show that the Euclidean distance is susceptible to the influence of extreme data in a more complex workshop environment, and the Fréchet algorithm will have a large error, which is suitable for a more standard and uniform model of each component, while the proposed improved algorithm can accurately calibrate the threshold value from 40 to 35. © 2023 ieee.

关键词： Time domain analysis

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Federated analysis of Neuroimaging data Using Coinstac

Federated Analysis of Neuroimaging Data Using Coinstac

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ieee International symposium on Biomedical Imaging

作者： Sunitha Basodi Ross Kelly Javier Romero Dylan Martin Sandeep Panta Sergey Plis Anand D. Sarwate Vince D. Calhoun TReNDS Center Georgia State University Georgia Institute of Technology Emory University Atlanta USA Department of Electrical & Computer Engineering Rutgers University NJ USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Federated analysis can help perform large-scale analyses using neuroimaging datasets across various research groups overcoming the limitations of institutional data-sharing poli-cies, privacy or regulatory concerns as it requires no data sharing. In this work, we showcase our federated platform, COINSTAC, a fully secure, privacy-preserving neuroinfor-matics platform that allows large-scale analysis of federated neuroimaging data (no sharing) and offers several feder-ated algorithms with results on par with centralized analysis (shared/pooled). All federated computations (applications) currently existing in the COINSTAC are trained and tested to ensure that the results of these federated methods perform similarly to their corresponding centralized methods.

关键词： Neuroimaging data privacy Magnetic resonance imaging Computational modeling Biological system modeling data models Biomedical imaging

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GraphFederator: Federated Visual analysis for Multi-party Graphs

GraphFederator: Federated Visual Analysis for Multi-party Gr...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Dongming Han Haiyang Zhu Wei Chen Rusheng Pan Yijing Liu Jiehui Zhou Haozhe Feng Tianye Zhang Xumeng Wang Minfeng Zhu Jianrong Tao Changjie Fan Xiaolong Luke Zhang State Key Lab of CAD&CG Zhejiang University China Hithink RoyalFlush Information Network Co. Ltd. China Wuchan Zhongda Digital Technology Co. Ltd. China Ministry of Education Laboratory of Art and Archaeology Image (Zhejiang University) China College of Computer Science Nankai University China Zhejiang University China Fuxi AI Lab Netease China Pennsylvania State University USA

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

This paper presents GraphFederator, a novel approach to construct federated representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for federated modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a Federated Graph Representation Model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization tools for federated visualization, exploration, and analysis of multi-party graphs. Experimental results on two datasets demonstrate the effectiveness of our approach.

关键词： visualization Analytical models Federated learning data visualization data models Encryption

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Bearing Fault Diagnosis and Localization Method Based on WDCNN and Unity3D

Bearing Fault Diagnosis and Localization Method Based on WDC...

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2023 Panda Forum on Power and Energy, PandaFPE 2023

作者： Liu, Haoming Chen, Jianjun Zhu, Jiaoyiling Luo, Shihua Fan, Haoyang Xi, Xianpeng School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350321173

In large power generation enterprises with extensive land coverage and diverse equipment, traditional Electric Motor (EM) condition monitoring methods result in high labor costs and heavy workloads for field inspection personnel. This paper proposes a method using Deep Learning (DL) for fault diagnosis of EM bearings and displays the fault information on the frontend web page through a Unity WebGL application. Firstly, a Wide Convolutional Deep Neural Network (WDCNN) model is employed to pre-train and perform fault classification on a typical bearing sample dataset. The classified results are then stored in a local database. A Unity3D WebGL application, built on a frontend platform based on the *** framework, creates a 3D visualization model of the EM and displays the fault classification results on the interactive 3D model, achieving a visual presentation of real-time data and fault diagnosis results. Experimental results of displaying fault data under different load conditions in the dataset indicate that this method can accurately locate bearing faults, verifying the feasibility of visualizing bearing datasets through DL models for fault identification and 3D visualization. © 2023 ieee.

关键词： Failure analysis

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