Computing just what you need: Online data analysis and reduction at extreme scales

I Foster, M Ainsworth, B Allen, J Bessac… - Euro-Par 2017: Parallel …, 2017‏ - Springer
A growing disparity between supercomputer computation speeds and I/O rates makes it
increasingly infeasible for applications to save all results for offline analysis. Instead …

Streaming anomaly detection using randomized matrix sketching

H Huang, SP Kasiviswanathan - Proceedings of the VLDB Endowment, 2015‏ - dl.acm.org
Data is continuously being generated from sources such as machines, network traffic,
application logs, etc. Timely and accurate detection of anomalies in massive data streams …

Quantum assimilation-based data augmentation for state of health prediction of lithium-ion batteries with peculiar degradation paths

H Gao, K Lin, Y Cui, Y Chen - Applied Soft Computing, 2022‏ - Elsevier
Lithium-ion batteries with more rapid capacity loss or “peculiar degradation paths” are
usually hard to completely avoid in production given complex electrochemical systems and …

Quantum assimilation-based state-of-health assessment and remaining useful life estimation for electronic systems

Y Cui, J Shi, Z Wang - IEEE Transactions on Industrial …, 2015‏ - ieeexplore.ieee.org
State-of-health (SOH) assessment and remaining useful life (RUL) estimation are among the
key issues in prognostics and health management (PHM) for electronic systems. Unlike …

Using Dirichlet marked Hawkes processes for insider threat detection

P Zheng, S Yuan, X Wu - Digital Threats: Research and Practice …, 2021‏ - dl.acm.org
Malicious insiders cause significant loss to organizations. Due to an extremely small number
of malicious activities from insiders, insider threat is hard to detect. In this article, we present …

Physics-based anomaly detection defined on manifold space

H Huang, H Qin, S Yoo, D Yu - ACM Transactions on Knowledge …, 2014‏ - dl.acm.org
Current popular anomaly detection algorithms are capable of detecting global anomalies but
often fail to distinguish local anomalies from normal instances. Inspired by contemporary …

Diverse power iteration embeddings and its applications

H Huang, S Yoo, D Yu, H Qin - 2014 IEEE International …, 2014‏ - ieeexplore.ieee.org
Spectral Embedding is one of the most effective dimension reduction algorithms in data
mining. However, its computation complexity has to be mitigated in order to apply it for real …

Diverse power iteration embeddings: Theory and practice

H Huang, S Yoo, D Yu, H Qin - IEEE Transactions on …, 2015‏ - ieeexplore.ieee.org
Manifold learning, especially spectral embedding, is known as one of the most effective
learning approaches on high dimensional data, but for real-world applications it raises a …

[PDF][PDF] A Two-Level Approach based on Integration of Bagging and Voting for Outlier Detection.

A Dogan, D Birant - J. Data Inf. Sci., 2020‏ - sciendo.com
Purpose: The main aim of this study is to build a robust novel approach that is able to detect
outliers in the datasets accurately. To serve this purpose, a novel approach is introduced to …

[کتاب][B] Dynamic Fraud Detection via Sequential Modeling

P Zheng - 2020‏ - search.proquest.com
The impacts of information revolution are omnipresent from life to work. The web services
have significantly changed our living styles in daily life, such as Facebook for …