Topology-aware deep learning model enhances EEG-based motor imagery decoding

Electroencephalography (EEG) is a fascinating noninvasive technique that measures and records the brain's electrical activity. It detects small electrical signals produced when neurons in the brain ...
The Hacker News

Self-Spreading 'GlassWorm' Infects VS Code Extensions in Widespread Supply Chain Attack

Cybersecurity researchers have discovered a self-propagating worm that spreads via Visual Studio Code (VS Code) extensions on the Open VSX Registry and the Microsoft Extension Marketplace, ...
IEEE

STAD: Self-Supervised Transformer for Anomaly Detection in Multi-Variate Time Series Data

Abstract: Anomaly detection in multivariate time series has gained significant attention in past few years. The rarity of anomalies, considerable data volatility, absence of anomaly labels, and need ...
Scientific Research Publishing

Sonani, R. and Govindarajan, V. (2022) A Hybrid Cloud-Integrated Autoencoder-GNN Architecture for Adaptive, High-Dimensional Anomaly Detection in US Financial Services ...

ABSTRACT: 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 ...
WATTAgNet

New take on water data could boost poultry disease detection

A new approach to the way already existing water consumption data is analyzed on poultry farms could identify changes in bird activity and behavior more quickly. "We can detect the subtle changes ...
Frontiers

Leveraging real-world data for safety signal detection and risk management in pre- and post-market settings

The evolving regulatory landscape has increasingly recognized the value of real-world data (RWD) in enhancing drug safety surveillance across the clinical development lifecycle. Enabled by frameworks ...
IEEE

KDAD: Knowledge Distillation-Based Anomaly Detection for Thermal Infrared Hyperspectral Image

Abstract: Autoencoder (AE) is extensively utilized in Hyperspectral anomaly detection (HAD) tasks owing to the robust feature extraction and image reconstruction capabilities. However, AE lacks ...
Frontiers

HairSentinel: a time-aware anomaly detection framework for forecasting hairfall trends using temporal fusion transformers

1 Analytics Department, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India 2 Department of Data Science, School of Computer Science and Engineering ...