Deep Learning has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning (MMDL) is to create models …

Online continual learning (online CL) studies the problem of learning sequential tasks from
an online data stream without task boundaries, aiming to adapt to new data while alleviating …

This paper focuses on the active interpretability for deep learning-based speech emotion
recognition (SER). To achieve this, we propose an explicit feature constrained model, the …

In multi-label learning, each instance can be associated with multiple and non-exclusive
labels. Previous studies assume that all the labels in the learning process are fixed and …

Recognising and monitoring emotional states play a crucial role in mental health and well-
being management. Importantly, with the widespread adoption of smart mobile and …

In recent years, a number of new research papers have emerged on the application of
neural networks in affective computing. One of the newest trends observed is the utilization …

Purpose The aim of this study is to investigate subject independent emotion recognition
capabilities of EEG and peripheral physiological signals namely: electroocoulogram (EOG) …

Investigating the functional connectivities in the brain networks of individuals with attention
deficit hyperactivity disorder (ADHD) has long intrigued researchers. ADHD individuals have …

Abstract Mobility as a Service (MaaS) is an innovative transport concept, anticipated to
provide travelers with different kinds of travel services, more sustainable than a private car …

Sound synthesis refers to the creation of original acoustic signals with broad applications in
artistic innovation, such as music creation for games and videos. Nonetheless, machine …