Filters








1,473 Hits in 6.0 sec

Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization [article]

Ye Zheng, Xiang Wang, Rui Deng, Tianpeng Bao, Rui Zhao, Liwei Wu
2021 arXiv   pre-print
To facilitate the learning with only normal images, we propose a new pretext task called non-contrastive learning for the fine alignment stage.  ...  The essence of unsupervised anomaly detection is to learn the compact distribution of normal samples and detect outliers as anomalies in testing.  ...  Supplementary Material for Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for AnomalyDetection and Localization Details on Experiments Implementation Details of two Coarse Alignment  ... 
arXiv:2110.04538v1 fatcat:s5iueshbwvhi7noqaeldqkuskm

Keeping CALM: When Distributed Consistency is Easy [article]

Joseph M. Hellerstein, Peter Alvaro
2019 arXiv   pre-print
This theoretical result has practical implications for developers of distributed applications.  ...  We also discuss ways that monotonic thinking can influence distributed systems design, and how new programming language designs and tools can help developers write consistent, coordination-free code.  ...  Acknowledgments The authors thank Eric Brewer, Jose Faleiro, Pat Helland, Frank Neven, Chris Ré and Jan Van den Bussche for their feedback and encouragement.  ... 
arXiv:1901.01930v2 fatcat:5pfitzrjpnetziol2jup3ualna

WILDS: A Benchmark of in-the-Wild Distribution Shifts [article]

Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David (+11 others)
2021 arXiv   pre-print
This gap remains even with models trained by existing methods for tackling distribution shifts, underscoring the need for new methods for training models that are more robust to the types of distribution  ...  for tumor identification; across camera traps for wildlife monitoring; and across time and location in satellite imaging and poverty mapping.  ...  Acknowledgements Many people generously volunteered their time and expertise to advise us on Wilds.  ... 
arXiv:2012.07421v3 fatcat:bsohmukpszajxeadeo25oxmbs4

Performance Anomaly Detection and Bottleneck Identification

Olumuyiwa Ibidunmoye, Francisco Hernández-Rodriguez, Erik Elmroth
2015 ACM Computing Surveys  
It is posted here by permission of ACM for your personal use. Not for redistribution.  ...  Our approach provides an overview of anomaly detection and bottleneck identification research as it relates to the performance of computing systems.  ...  in a distributed system and how the association is used for detecting anomalies. in the range 1 to +1.  ... 
doi:10.1145/2791120 fatcat:yajvevdzl5h6tc6ii5sjd7qzlm

Incremental anomaly detection using two-layer cluster-based structure

Elnaz Bigdeli, Mahdi Mohammadi, Bijan Raahemi, Stan Matwin
2018 Information Sciences  
Anomaly detection algorithms face several challenges, including processing speed and dealing with noise in data.  ...  In this thesis, a two-layer clusterbased anomaly detection structure is presented which is fast, noise-resilient and incremental.  ...  . • To create coarse and fine-level clusters: -The coarse and fine levels are created to speed up the updating procedure as discussed in Chapter 7.  ... 
doi:10.1016/j.ins.2017.11.023 fatcat:fisgkfropjh7fj3tpxoxdqszxa

Analysis of Anomalies in the Internet Traffic Observed at the Campus Network Gateway [article]

Veronica del Carmen Estrada
2017 arXiv   pre-print
A considerable portion of the machine learning literature applied to intrusion detection uses outdated data sets based on a simulated network with a limited environment.  ...  We focus on a topic rarely investigated: the characterization of anomalies in a large network environment.  ...  However, a fine-grained approach is needed to focus on threats such as botnets and distributed attacks.  ... 
arXiv:1706.03206v1 fatcat:2cjqlogednf3leibr2lewusmwi

Intelligent Traffic Management in Next-Generation Networks

Ons Aouedi, Kandaraj Piamrat, Benoît Parrein
2022 Future Internet  
The research community has advocated the application of ML/DL in softwarized environments for network traffic management, including traffic classification, prediction, and anomaly detection.  ...  At the same time, machine learning (ML) and especially deep learning (DL) methods have also been deployed to solve complex problems without explicit programming.  ...  Unlike the centralized architecture, distributed SDN can deploy multiple IDS for the active detection of attacks. In contrast to the other IDS approaches, Shu et al.  ... 
doi:10.3390/fi14020044 fatcat:dx6leecgtfegdlao5aq6p6ouci

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali, Abbes Amira
2020 Cognitive Computation  
machine learning algorithms and can effectively detect anomalous patterns.  ...  This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model.  ...  In this context, a convolutional architecture based on autoencoder and LSTM is used to identify local anomaly events via learning raw image sequences and detected edges.  ... 
doi:10.1007/s12559-020-09764-y fatcat:op4ochnzizdvbmhczeroopxt7q

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Haitao Yuan, Guoliang Li
2021 Data Science and Engineering  
Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting).  ...  Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression.  ...  In particular, they use the CNN model to locally detect the extreme events, while designing a pixel recursive super-resolution model to recover coarse climate data.  ... 
doi:10.1007/s41019-020-00151-z fatcat:nnnnxnpo3bgk3l4hpr7kk2n4xa

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.  ...  Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience  ...  data mining Luo and Nagarajany [348] Distributed WSN anomaly detection AE SGD Employs distributed anomaly detection techniques to offload computations from the cloud Other Heydari et al  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.  ...  Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics.  ...  data mining Luo and Nagarajany [345] Distributed WSN anomaly detection AE SGD Employs distributed anomaly detection techniques to offload computations from the cloud Other Heydari et al  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Community sense and response systems

Matthew Faulkner, MingHei Cheng, Andreas Krause, Robert Clayton, Thomas Heaton, K. Mani Chandy, Monica Kohler, Julian Bunn, Richard Guy, Annie Liu, Michael Olson
2014 Communications of the ACM  
In contrast, we focus on the decentralized case with binary channel noise, and provide theoretical guarantees that hold even in the non-asymptotic regime.  ...  While results apply only in the centralized setting, they support the idea of using GMMs for anomaly detection could be extended to learn, for each phone, a GMM that adapts to non-stationary sources of  ...  , y 1 , · · · , y t . where c i0,i1,i2,i3 is a constant that depends only on i 0 , . . . , i 3 , and equals to zero for all except terms of the summation.  ... 
doi:10.1145/2622633 fatcat:zexy7ku2qrej5b3bhq65vgbvva

Session reports for SIGCOMM 2010

Shailesh Agrawal, Immanuel Ilavarasan Thomas, Arun Vishwanath, Tianyin Xu, Fang Yu, Kavitha Athota, Pramod Bhatotia, Piyush Goyal, Phani Krisha, Kirtika Ruchandan, Nishanth Sastry, Gurmeet Singh (+1 others)
2011 Computer communication review  
-one should check DECnet and CLNP for this. The Boston hospital example: the company providing the switches used one huge bridged network.  ...  With bridges we did such a good job and it was so plug-and-play that you didn't have to think about them, so people are still taking large networks and doing bridges.  ...  to be anomaly-free Problem Statement: Can we detect anomalies without having to learn what is normal?  ... 
doi:10.1145/1925861.1925873 fatcat:jpaoo55h75hprb5k2vlkin5zzi

Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data

Maram El-Nadry, Wenzhao Li, Hesham El-Askary, Mohamed A. Awad, Alaa Ramadan Mostafa
2019 Remote Sensing  
This approach can improve the knowledge of air pollutant variability and intensity in the MENA region for decision makers to operate proper mitigation strategies.  ...  (MODIS, MISR, OMI, and MERRA-2) to predict the best usable AOD product as compared to AERONET data.  ...  Acknowledgments: The authors would like to thank the AERONET team for calibrating and maintaining instrumentation and processing these data.  ... 
doi:10.3390/rs11182096 fatcat:wxfozv3qyndz5mcfgdwgca4sia

Mini-UAV-based Remote Sensing: Techniques, Applications and Prospectives [article]

Tian-Zhu Xiang, Gui-Song Xia, Liangpei Zhang
2018 arXiv   pre-print
, etc., which make it an effective complement to other remote-sensing platforms and a cost-effective means for remote sensing.  ...  Finally, some prospects for future work are discussed.  ...  Large data sets are in demand to train deep learning models with good generalization, both for fine-tuning models and for training networks from scratch.  ... 
arXiv:1812.07770v2 fatcat:l4tdfpr55jbjvhxzi47lwrs2re
« Previous Showing results 1 — 15 out of 1,473 results