Special Section Overview
Affective Computing, as a core technology of Human-Computer Interaction (HCI) and intelligent services, is rapidly transitioning from the laboratory to the real world at an unprecedented pace, profoundly reshaping and enabling the ubiquitous deployment of numerous critical fields. Its essence lies in endowing machines with the capability to recognize, understand, process, and simulate human emotions, thereby constructing more empathetic, adaptive, and natural interactive experiences. This technology has deeply penetrated key cloud-driven application scenarios such as mental health services (e.g., assisted diagnosis of mood disorders, AI-powered psychological companionship), immersive entertainment (e.g., emotion-driven content generation in VR/AR, emotional responses of game characters), and intelligent education (e.g., learning emotion analysis, personalized tutoring). It has become a key driver in enhancing service intelligence and user experience.
However, with the explosive growth of multi-modal affective data (physiological signals, behavioral data) on edge devices, Affective Computing faces significant challenges including high privacy sensitivity, high computational heterogeneity, and weak adaptability to dynamic environments. Traditional centralized affective analysis frameworks struggle to meet the requirements for real-time processing, user privacy protection, and collaboration among resource-constrained edge devices.
This special session aims to focus on the critical direction of cloud-edge-device collaborative architecture, exploring novel paradigms urgently needed for the ubiquitous deployment of Affective Computing. We solicit prospective and practical research contributions.
Topics of Interest
We invite high-quality, original research submissions on topics including, but not limited to:
- Acquisition and Fusion of Multimodal Affective Signals (Physiological Signals, Behavioral Data)
- Federated Learning for Physiological Signals and Behavioral Data Acquired from Ubiquitous Edge Devices (e.g., Wearable Sensors, Smartphones)
- Generalization Capability of Affective Computing Models for Universalized Scenarios (e.g., Adaptation to Cross-Cultural, Cross-Age, and Cross-Individual Differences)
- Ethics and Fairness in Affective Computing
- Secure Handling of Privacy-Sensitive and Highly Personalized Affective Data
- Dynamic Environment Adaptability (e.g., How to Ensure Model Robustness in Scenarios like Cross-Domain?)
- Multimodal Fusion with Low-Quality Data (e.g., Data Missing, Poor Data Quality)
- Building Trustworthy Affective AI
- Human-Computer Interaction in Cloud-Edge Collaboration
- Trustworthy Affective Computing Models in Cloud-Edge Collaborative Environments
Important Dates
- Paper Submission: September 30, 2025
- Notification of Acceptance: October 15, 2025
- Camera-Ready: October 30, 2025
- Special Section Date: November 16, 2025 (TBC)
Submission Guidelines
Authors are invited to submit original, unpublished research papers. Submissions should not exceed 8 pages, including tables, figures, and references in IEEE CS format. The template files for LATEX or WORD can be downloaded from the IEEE site https://www.ieee.org/conferences/publishing/templates.html. Submission must be made in PDF format only with savable text and embedded fonts.
The review process will be doubly blind, and so a submission should not include any information that may identify the authors of the manuscripts. Technical content of the camera-ready manuscript must be identical to the submitted version except for changes made to address TPC review comments.
For each accepted submission, at least one of the co-authors must have a full conference registration and present the work in person.
Special Section Chairs (alphabetical order)
- Chengsheng Mao, Northwestern University, USA (chengsheng.mao@northwestern.edu)
- Minqiang Yang, Lanzhou University, China (yangmq@lzu.edu.cn)
- Ricardo Jorge da Silva Santos, Polytechnic of Porto, Portugal (rjs@estg.ipp.pt)
- Rui Liu, Inner Mongolia University, China (imucslr@imu.edu.cn)
- Yongfeng Tao, Lanzhou University, China (taoyongfeng@lzu.edu.cn)
- Yuan Zong, Southeast University, China (xhzongyuan@seu.edu.cn)
Contact Information
For any inquiries about the section, please contact:
Email: taoyongfeng@lzu.edu.cn
Keynote Speaker
Dr. Ruifeng Xu
Professor and Deputy Director of the College of Artficial Intelligence
Harbin Institute of Technology (Shenzhen), China
Stance Detection in the LLM AGE
Abstract
With the rapid development of social media platforms, an increasing number of users express their opinions, viewpoints, or stances through these platforms. The dissemination of such information online can profoundly influence social cognition. Stance detection refers to the use of techniques such as text mining and natural language processing to automatically identify the stance of authors toward a specific topic. The emergence of large models has introduced new opportunities for stance detection tasks. This report primarily explores the research on stance detection from three perspectives in the era of LLMs: background knowledge enhancement, multimodal stance detection, stance detection debias and multimodal stance detection, as well as the application of stance detection in social media situational awareness.
Biography
Professor Ruifeng Xu, is the Deputy Director of the College of Artficial Intelligence, at Harbin Institute of Technology (Shenzhen). He serves Chair of the Asian Language Resources Committee under the Asian Federation for Natural Language Processing, Council Member and Deputy Director of the Natural Language Understanding Committee of the Chinese Association for Artificial Intelligence, Council Member and Deputy Director of the Affective Computing Committee, Deputy Director of the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies. His research areas include natural language processing, large langueage models, affective computing, and social media mining. He has authored 4 academic books and published over 250 papers in international journals and conferences. With over 11,000 citations on Google Scholar. Prof. Xu is awarded the First Prize of the Qian Weichang Science and Technology Award for Chinese Information Processing and the Second Prize of the Science and Technology Progress Award by the Ministry of Education, Heilongjiang Province, and Guangdong Province, respectively. He is the supervisor of two outstanding PhD Thesis award issueed by the Chinese Association for Artificial Intelligence and the Chinese Information Processing Society.