Data-Driven Intent Understanding and Cognitive Intelligence
Special Issue Information
Dear Colleagues,
We have entered an era of "smart change" from data to knowledge. With the further integration of artificial intelligence and big data, the construction of pathways from perceptual intelligence to cognitive intelligence will become faster and faster. The era of artificial intelligence empowering all walks of life is coming. However, this phenomenon is largely due to the massive data generated by the Internet and the improvement of large-scale computing capabilities. There are still many obstacles on the road from perceptual intelligence to cognitive intelligence. Therefore, in this Special Issue, we aim to use leading intelligence technologies such as personalized recommendation, knowledge tracing, sentiment analysis, intelligent question and answer, video understanding, and pose estimation to mine and understand these multiple modalities of data for accurate intent understanding and intelligent cognitive services.
This special issue solicits original and high-quality papers that address emerging research challenges in different areas of intent understanding and cognitive intelligence. We invite authors to submit original research, new developments, experimental works, and surveys in the fields of intent understanding, cognitive intelligence, human–robot interaction, intelligent education, and vision-based engineering applications. The topics of interest of this Special Issue include, but are not limited to the following:
Intent Understanding in industrial application/human–computer interaction/e-learning/sports technique analysis;
Combining vision with other modalities (e.g., texts, audio, biosignals) for human intent understanding;
Cognitive intelligence application and innovation (conversation systems, sentiment analysis, intelligent question and answer, etc.);
Intent understanding by Large Language Models;
ChatGPT technology for cognitive intelligence;
Face, gesture and pose analysis for human–computer interaction/e-learning/sports technique analysis;
User intention understanding based on big data analysis;
Recommendation algorithm using context information / social network information / knowledge graph;
Representation learning based on knowledge graph/graph neural network/transformer/diffusion model etc.
Knowledge tracing (dataset construction, cognitive theory, network structure innovation, etc.)
Interactive behavior analysis including human–computer interaction and sports technique analysis etc.;
Image processing, video understanding, speech emotion recognition and signal processing for human-robot Interaction//e-learning/sports technique analysis;
Vision-based head pose estimation, facial expression recognition, gaze estimation and human pose estimation;
Databases and open-source tools for human–computer interaction/e-learning/sports technique analysis;
Virtual reality and augmented reality for human–computer interaction/e-learning/sports technique analysis;
Dr. Duantengchuan Li
Dr. Yufan Wang
Dr. Jing Wang
Guest Editors