Analyzing the Visual Road Scene for Driver Stress Estimation

Research on Driver Stress Estimation Based on Visual Road Scenes Academic Background Driver stress is a significant factor contributing to traffic accidents, injuries, and fatalities. Studies show that 94% of traffic accidents are related to drivers, with inattention, internal and external distractions, and improper speed control all closely linked...

Effects of Algorithmic Transparency on User Experience and Physiological Responses

The Impact of Algorithmic Transparency on User Experience and Physiological Responses Academic Background With the rapid development of Affective Computing technology, Affect-aware Task Adaptation systems have gradually become a research hotspot. These systems recognize users’ psychological states through various measurements (e.g., physiological s...

Theory of Mind Abilities Predict Robot’s Gaze Effects on Object Preference

Academic Background In human social interactions, gaze is one of the most important ways to convey information. Research has shown that human gaze can influence others’ attention, cognition, and even preferences. For example, when a person looks at an object, the observer tends to believe that the object is attractive to the gazer, which in turn in...

TFAGL: A Novel Agent Graph Learning Method Using Time-Frequency EEG for Major Depressive Disorder Detection

A Novel Method for Depression Detection Based on Time-Frequency EEG: TFAGL Academic Background Major Depressive Disorder (MDD) is a common mental illness worldwide, characterized by symptoms such as sadness, guilt, and low self-esteem, accompanied by loss of interest, diminished enthusiasm for life, and disruptions in sleep or appetite. According t...

LineConvGraphs: Line Conversation Graphs for Effective Emotion Recognition Using Graph Neural Networks

A New Approach to Emotion Recognition in Conversations Based on Graph Neural Networks Research Background Emotion recognition (ER) is an important component of human-computer interaction (HCI), aiming to identify human emotional states by analyzing multimodal data such as speech, text, and video. This technology has broad application prospects in f...

CSE-GResNet: A Simple and Highly Efficient Network for Facial Expression Recognition

An Efficient Expression Recognition Network Based on Gabor Convolution: CSE-GResNet Academic Background Facial Expression Recognition (FER) is an important research direction in the field of computer vision, with wide applications in social robots, healthcare, social psychology, customer service, and psychoanalysis. Facial expressions are natural a...

Phonetically-Anchored Domain Adaptation for Cross-Lingual Speech Emotion Recognition

Phonetic-Anchored Domain Adaptation in Cross-Lingual Speech Emotion Recognition Academic Background Speech Emotion Recognition (SER) has broad application prospects in intelligent agents, social robots, voice assistants, and automated call center systems. With the development of globalization, the demand for cross-lingual SER is increasing. However...

Facial 3D Regional Structural Motion Representation Using Lightweight Point Cloud Networks for Micro-Expression Recognition

3D Regional Structural Motion Representation Using Lightweight Point Cloud Networks for Micro-Expression Recognition Academic Background Micro-expressions (MEs) are brief and subtle facial expressions in human emotional expression, typically lasting between 1⁄25 and 1⁄5 of a second. Due to their spontaneity, rapidity, and difficulty to control, mic...

Multi-scale Hyperbolic Contrastive Learning for Cross-subject EEG Emotion Recognition

Cross-Subject EEG Emotion Recognition Research Based on Multi-Scale Hyperbolic Contrastive Learning Academic Background Electroencephalography (EEG), as a physiological signal, plays an important role in the field of affective computing. Compared with traditional non-physiological cues (such as facial expressions or voice), EEG signals have higher ...

Multimodal Sentiment Analysis with Mutual Information-Based Disentangled Representation Learning

Disentangled Representation Learning in Multimodal Sentiment Analysis Using Mutual Information: An Innovative Study Academic Background With the rapid development of social media, the amount of user-generated multimedia content (such as tweets and videos) has increased dramatically. These multimedia data typically include three modalities: visual (...