Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...

A Local-Scale Dataset of Annual Spatiotemporal Maps of Physical Vulnerability in the Cyclone-Impacted Coastal Khurushkul Community (Bangladesh) and Mudslide-Affected Freetown (Sierra Leone) (2016–2023 ...

Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul 34349, Turkey Lab for Innovative Drugs (Lab4IND), ...

Covid-19 broke the charts. Decades from now, the pandemic will be visible in the historical data of nearly anything measurable today: an unmistakable spike, dip or jolt that officially began for ...

This repository provides the implementation of the AEGAE method for community detection in attributed graphs. AEGAE integrates Laplacian regularization and a graph autoencoder to generate robust node ...

The growing microbial resistance to traditional medicines necessitates in-depth analysis of medicine-microbe interactions (MMIs) to develop new therapeutic strategies. Widely used artificial ...

Abstract: Skeleton-based human action recognition (HAR) is being utilized in various fields like action classification and abnormal behavior detection. The accurate coordinates of the human joints are ...

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...

Abstract: Considering the personalized characteristics and behavior patterns of users, quickly and accurately identifying abnormal users is crucial in the realm of E-commerce applications. However, ...

1 School of Computer Science and Technology, Xidian University, Xi'an, China 2 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Electronic ...