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Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection [article]

Zeyu You, Yichu Zhou, Tao Yang, Wei Fan
2021 arXiv   pre-print
In this work, we target the textual anomaly detection problem and propose a deep anomaly-injected support vector data description (AI-SVDD) framework.  ...  Anomaly detection or outlier detection is a common task in various domains, which has attracted significant research efforts in recent years.  ...  Acknowledgments We would like to show our special thanks to Xingyuan Pan, who is a Ph.D. graduated from University of Utah and now working as an applied scientist in Amazon.  ... 
arXiv:2110.14729v1 fatcat:iw42w6wr4vhark663chove646m

CloudDet: Interactive Visual Analysis of Anomalous Performances in Cloud Computing Systems [article]

Ke Xu, Yun Wang, Leni Yang, Yifang Wang, Bo Qiao, Si Qin, Yong Xu, Haidong Zhang, Huamin Qu
2019 arXiv   pre-print
are visualized in our system to indicate the occurrences of anomalies.  ...  A novel unsupervised anomaly detection algorithm is developed to identify anomalies based on the specific temporal patterns of the given metrics data (e.g., the periodic pattern), the results of which  ...  , from data centers to their sub-level data clusters; the temporal overview ( Fig. 4(2) ), displaying the anomaly distributions over time for data filtering (T1); the rank view ( Fig. 4(3) ), showing  ... 
arXiv:1907.13187v1 fatcat:fai2vqpxrfdb7gahlovxiwivsi

Hyperspectral Anomaly Detection via Dual Collaborative Representation

Guoyun Zhang, Nanying Li, Bing Tu, Zhuolang Liao, Yishu Peng
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Index Terms-Collaborative representation, density peak (DP) clustering, hyperspectral anomaly detection, low-rank and sparse matrix decomposition (LRSMD).  ...  Then, the density peak clustering algorithm is applied to the low-rank background matrix to calculate the density information of the pixels in a sliding dual window.  ...  In matrix completion, a low-rank matrix can be quickly recovered from a small amount of randomly sampled data.  ... 
doi:10.1109/jstars.2020.3009324 fatcat:stvf7x2tw5bnvm532jwoey76wu

Data stream anomaly detection through principal subspace tracking

Pedro Henriques dos Santos Teixeira, Ruy Luiz Milidiú
2010 Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10  
We consider the problem of anomaly detection in multiple co-evolving data streams. In this paper, we introduce FRAHST (Fast Rank-Adaptive row-Householder Subspace Tracking).  ...  It automatically learns the principal subspace from N numerical data streams and an anomaly is indicated by a change in the number of latent variables.  ...  In data centers, an anomaly is a short-lived deviation from its normal operation.  ... 
doi:10.1145/1774088.1774434 dblp:conf/sac/TeixeiraM10 fatcat:m6oui6ezojhvxihyoorkd7gq6a

Tracking System Behaviour from Resource Usage Data [article]

Niyazi Sorkunlu, Varun Chandola, Abani Patra
2017 arXiv   pre-print
Results are shown for data collected for 2013 from the Lonestar4 system at the Texas Advanced Computing Center (TACC)  ...  in the system logs.  ...  Additionally, the authors thank researchers at Texas Advanced Computing Center for access to the Lonestar4 data and related support.  ... 
arXiv:1705.10756v1 fatcat:q724ertflbdkfbhvy23jmsuvdi

Seasonal influence of ENSO on the Atlantic ITCZ and equatorial South America

M. Münnich, J. D. Neelin
2005 Geophysical Research Letters  
Data Sets and Methods [6] We analyze satellite based monthly data for precipitation (Climate Prediction Center Merged Analysis of Precipitation ''CMAP'' [Xie and Arkin, 1997] ), SST (National Oceanic  ...  and Atmospheric Administration ''OI.v2'' [Reynolds et al., 2002] ) and sea level height (AVISO/altimetry data center ''DT-MSLA'' [Ducet et al., 2000] ) which start in 1979, 1982 and 1993, respectively  ... 
doi:10.1029/2005gl023900 fatcat:ppuguoeh45bevnja6n47s3cwya

Comparison of 1997–98 U.S. Temperature and Precipitation Anomalies to Historical ENSO Warm Phases

Shawn R. Smith, David M. Legler, Mylene J. Remigio, James J. O'Brien
1999 Journal of Climate  
For 1997 and 1998, daily precipitation totals and temperature means are obtained from the sum- mary of the day (SOD) data available through the Na- tional Climatic Data Center.  ...  Ranking the anomalies versus nine historical warm phases identifies extreme 1998 sea- sonal anomalies and places these anomalies in a his- torical context.  ... 
doi:10.1175/1520-0442(1999)012<3507:cousta>2.0.co;2 fatcat:qnpvs57xq5bvbpjhyodg4afejm

Page 1852 of Journal of Climate Vol. 12, Issue 6 [page]

1999 Journal of Climate  
The temporal separation, Af, is ex- pressed in kilometers using _ time difference in days X spatial radius in km At ea temporal radius in days (4) The resulting cluster centers were then ranked first in  ...  An SST anomaly for each observation in each of the two in situ datasets is then obtained for each of the six climatologies, resulting in 12 anomaly datasets. An anomaly, SST!.  ... 

K-Means-based isolation forest

Paweł Karczmarek, Adam Kiersztyn, Witold Pedrycz, Ebru Al
2020 Knowledge-Based Systems  
There may be incorrect data present in the database, e.g., mistakenly inserted by users. Also the distant points located far from cluster centers could be regarded as anomalies.  ...  these rankings of points in a context of anomaly score, see Fig. 12 and Fig. 13 .  ... 
doi:10.1016/j.knosys.2020.105659 fatcat:m4hd345oengw5nbpo7c4frj7ly

Evaluation of IPCC Models' Performance in Simulating Late-Twentieth-Century Climatologies and Weather Patterns over North America

Valentina Radić, Garry K. C. Clarke
2011 Journal of Climate  
Most of the models are successful in simulating the frequencies of daily anomaly patterns from the 20-yr-average daily pattern.  ...  However, the model ranking is sensitive to the choice of climate variable.  ...  Flato generously provided the NARR data. Furthermore, we thank A. Rasmussen, A. Werner, and the three anonymous reviewers for their valuable comments.  ... 
doi:10.1175/jcli-d-11-00011.1 fatcat:tiv3yzouqfhvzcv23cvrguepre

Flow-based Anomaly Detection [article]

Łukasz Maziarka, Marek Śmieja, Marcin Sendera, Łukasz Struski, Jacek Tabor, Przemysław Spurek
2020 arXiv   pre-print
Experiments show that the proposed model outperforms related methods on real-world anomaly detection problems.  ...  We propose OneFlow - a flow-based one-class classifier for anomaly (outliers) detection that finds a minimal volume bounding region.  ...  In a similar spirit, Chen, Qian, and Saligrama (2013) apply Ranking SVM based on rankings created from pairwise comparison of nominal data.  ... 
arXiv:2010.03002v2 fatcat:4wjue5yuunewbpalw5yxx56oyy

Event detection using customer care calls

Yi-Chao Chen, Gene Moo Lee, Nick Duffield, Lili Qiu, Jia Wang
2013 2013 Proceedings IEEE INFOCOM  
., thousands of categories in our dataset). In this paper, we propose a systematic method for detecting events in a major cellular network using customer care call data.  ...  We show the effectiveness of our approach using data from a large cellular service provider in the US.  ...  Acknowledgements: This work is supported in part by NSF Grants CNS-0916309 and CNS-1117009.  ... 
doi:10.1109/infcom.2013.6566966 dblp:conf/infocom/ChenLDQW13 fatcat:vpzhxdujnjhhtajxkroqybogny

A data-driven approach to detect passenger flow anomaly under station closure

Yuhang Wu, Baojing Huang, Xue Li, Yingnan Zhang, Xinyue Xu
2020 IEEE Access  
The Wilcoxon signed rank test requires data pairing.  ...  Since the travel time data may be subject to different distributions, the Wilcoxon signed rank test has a wider scope of application than the T-test in travel time anomaly testing applicability.  ... 
doi:10.1109/access.2020.3016398 fatcat:tttigiuehbbkfpetbq7uhy5snq

Chimbuko: A Workflow-Level Scalable Performance Trace Analysis Tool [article]

Sungsoo Ha, Wonyong Jeong, Gyorgy Matyasfalvi, Cong Xie, Kevin Huck, Jong Youl Choi, Abid Malik, Li Tang, Hubertus Van Dam, Line Pouchard, Wei Xu, Shinjae Yoo (+2 others)
2020 arXiv   pre-print
This work introduces Chimbuko, a performance analysis framework that provides real-time, distributed, in situ anomaly detection.  ...  Data volumes are reduced for human-level processing without losing necessary details.  ...  Then, the scientist switched to Rank 0 and wanted to check how "MD FORCES" may affect other ranks. He found that Rank 0 mainly suffered anomalies in "MD FINIT" (Fig. 11 and "CF CMS" (Fig. 12 ).  ... 
arXiv:2008.13742v1 fatcat:wc2ltfxpvbddvfycsgquysapwi

Flow-based SVDD for anomaly detection [article]

Marcin Sendera, Marek Śmieja, Łukasz Maziarka, Łukasz Struski, Przemysław Spurek, Jacek Tabor
2021 arXiv   pre-print
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools.  ...  In the latent space, FlowSVDD finds the center point c and radius R to enclose (1 − ν) percentage of data inside the ball B(c; R).  ...  Observe that, unlike the density-based flow models, FlowSVDD does not transform data into Gaussian distribution in a latent space. Benchmark data for anomaly detection.  ... 
arXiv:2108.04907v1 fatcat:lyye2mtn7jeafjisft4medb4rq
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