Mental fatigue is a psychophysical condition with a significant adverse effect on daily life,
compromising both physical and mental wellness. We are experiencing challenges in this …

Emotion recognition using multi-modal physiological signals is an emerging field in affective
computing that significantly improves performance compared to unimodal approaches. The …

Emotion recognition based on multimodal physiological signals is attracting more and more
attention. However, how to deal with the consistency and heterogeneity of multimodal …

Physiological signals serve as indispensable clues for understanding various physiological
states of human bodies. Most existing works have focused on a single type of physiological …

Sleep stage classification has important clinical significance for the diagnosis of sleep-
related diseases. To pursue more accurate sleep stage classification, multi-channel sleep …

Self-supervised learning has emerged as a promising method for training neural networks
without needing annotated data. In this paper, we present a self-supervised learning method …

Despite the promising performance of existing visual models on public benchmarks, the
critical assessment of their robustness for real-world applications remains an ongoing …

For diagnosing mental health conditions and assessing sleep quality, the classification of
sleep stages is essential. Although deep learning-based methods are effective in this field …

Multivariate time series (MTS) classification is widely applied in fields such as industry,
healthcare, and finance, aiming to extract key features from complex time series data for …

Detecting marine objects inshore presents challenges owing to algorithmic intricacies and
complexities in system deployment. We propose a difficulty-aware edge-cloud collaborative …