With the rise of technology and the continued economic growth evident in modern society,
acts of fraud have become much more prevalent in the financial industry, costing institutions …

Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …

Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …

This report describes the physics case, the resulting detector requirements, and the evolving
detector concepts for the experimental program at the Electron-Ion Collider (EIC). The EIC …

The next wave of wireless technologies is proliferating in connecting things among
themselves as well as to humans. In the era of the Internet of Things (IoT), billions of …

The field of Explainable Artificial Intelligence (XAI) has garnered considerable research
attention in recent years, aiming to provide interpretability and confidence to the inner …

As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …

Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …