Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal …

Quality control (QC) of marine data is a critical aspect in ensuring the usability of oceanic
data. In this paper, we propose a novel QC method for time-series ocean wave data, which …

Self-supervised learning has demonstrated state-of-the-art performance on various anomaly
detection tasks. Learning effective representations by solving a supervised pretext task with …

Detecting abnormal network events is an important activity of Internet Service Providers
particularly when running critical applications (eg, ultra low-latency applications in mobile …

时间序列异常检测作为时间序列研究的重要组成部分, 已经引起学术界和工业界的广泛关注和
研究. 针对时间序列数据中蕴含的深层局部特征和复杂的前后依赖关系, 提出一种融合双重注意 …

As the application of Industrial Robots (IRs) scales and related participants increase, the
demands for intelligent Operation and Maintenance (O&M) and multi-tenant collaboration …

Continuous learning faces the challenge of catastrophic forgetting. Our research findings
indicate that in unsupervised federated continual learning (UFCL), the limited model …

Electricity theft emerges as a critical concern within non-technical losses in power systems,
resulting in substantial economic losses and posing a substantial threat to the power grid's …

In machine learning (ML) problems, it is widely believed that more training samples lead to
improved predictive accuracy but incur higher computational costs. Consequently, achieving …

Detecting time-series anomalies is extremely intricate due to the rarity of anomalies and
imbalanced sample categories, which often result in costly and challenging anomaly …