Special Sessions

Papers submission

To submit your papers, choose the special sessions under "Topics" on the submission system (please select only one special session): https://easychair.org/my/conference?conf=cscwd2025

1. Adaptive Collaboration Systems

Organizer

• Haibin Zhu, Nipissing University, haibinz@nipissingu.ca

Description

Adaptability is a common and typical property for natural systems in the real world. It is also an important and desirable property for computer supported artificial distributed intelligent systems. Adaptive collaboration system can be viewed as a set of interacting intelligent agents, real or abstract, forming an integrated system that is able to respond to internal and environmental changes. Feedback is a key feature of adaptive systems, enabling the response to changes. Artificial systems can be made adaptive using feedback to sense new conditions in the environment and adapt accordingly. Distributed adaptive systems can find applications in almost all industrial sectors, particularly in aerospace, automotive, and manufacturing.

2. Applications & Industry

Organizers

• Nada Matta, LIST3N, Université de technologie de Troyes nada.matta@utt.fr
• Domitile Lourdeaux, Heudiasyc, Université de technologie de Compiègne domitile.lourdeaux@hds.utc.fr
• François Rauscher, Infopro-Digital Grou, Francois.RAUSCHER@infopro-digital.com
• Bruno Raymond, Heudiasyc, Université de technologie de Compiègne bruno.ramond@utc.fr

Description

The objective of this session is to show the impact of different cooperative support tools on industrials design activities. Several types of companies will be invited to present as same as cooperative applications, projects and their experience feedback on their use.

3. Collaborative Business Information and Technology

Organizer

• Usharani Hareesh Govindarajan, University of Shanghai for Science and Technology Hareesh.pillai@usst.edu.cn

Description

The Special Session on Collaborative Business Informatics and Technology at the 2025 IEEE 28th International Conference on Computer-Supported Cooperative Work in Design (IEEE CSCWD) aims to bring together researchers, practitioners, and industry experts to explore cutting-edge developments in collaborative technologies and their impact on business informatics and related fields. In an era where digital transformation, global supply chain disruptions, and geopolitical uncertainties demand agile and adaptive business processes, collaboration within and between organizations is becoming increasingly critical. This session will address the evolving role of collaborative computing technologies and their ability to improve business operations, foster innovation, and ensure secure, efficient communication and decision-making.
The session will focus on the intersection of technologies such as Artificial Intelligence, Cloud/Edge Computing, Blockchain, Cyber-Physical Systems, Industry 4.0, digital twins, and the Industrial Metaverse. We aim to explore how these advancements are reshaping changing business environments, manufacturing, supply chains, and sustainability efforts while enabling organizations to adapt to market dynamics, technological shifts, and social changes. Special attention will be given to the role of business informatics in supporting sustainable practices across industries, intelligent forecasting, and the integration of collaborative systems for the betterment of society.
We invite original research and case studies that provide theoretical and practical insights into integrating collaborative computing with business informatics, social change, and technology forecasting. Contributions that address the use of machine learning models, industrial foundation models, intelligent forecasting algorithms in decision-making, and solutions to security and privacy challenges in collaborative systems are especially encouraged. Research examining sustainability efforts in collaborative environments and supply chains is also welcomed.

4. Knowledge-driven Big Data Computing and Its Applications

Organizers

• Ling Wang, Northeast Electric Power University, smile2867ling@neepu.edu.cn
• Keun Ho Ryu, Chungbuk National University, khryu@chungbuk.ac.kr
• Lei Kou, Qilu University of Technology, koulei1991@qlu.edu.cn
• Wei Ding, Shandong Computer Science Center, dingw@sdas.org
• Teng Li, Shandong University, li.teng@sdu.edu.cn

Description

Knowledge Driven is uplevel computing of basic big data, which aims to construct a sustainably knowledge upgraded structure by valuable rules discovery continuously. It can support more complex knowledge decisions through more widely knowledge cross computation, such as understandability interaction, potential valuable relevance mining, interest-aware computing, and etc. This special session will discuss recent advanced in knowledge-driven big data computing and its applications.
The topics of interest include, but are not limited to:
- Knowledge-driven Data Mining and Machine Learning Models
- Potential Knowledge Mining based on Semantic Analysis
- Bioinformatics and EBM Decision Making
- Renewable Energy Models and Prediction
- Spatial Temporal Data Mining and Knowledge Extraction
- Physical Model and Knowledge-driven Integrated Learning
- Sensor-aware Human-Computer Knowledge Interaction
- Knowledge-driven Chat GPT and AI Interaction
- Smart Grid and Microgrids Resilience Computing
- Trustworthy Decision-making Support for Operation and Maintenance of Wind Farm

5. Innovative Digital Collaboration Approaches for Enhancing Higher Education Scientific Skills.

Organizers

• Thierry Gidel, Université de technologie de Compiègne (UTC), thierry.gidel@utc.fr
• Mohammad Khalil, Centre for the Science of Learning & Technology (SLATE), University of Bergen, mohammad.khalil@uib.no
• Cristian Lai, Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna - CRS4 Srl Uninominale (CRS4), cristian.lai@crs4.it

Description

The increasing reliance on digital technologies in scientific education is reshaping the ways students and researchers collaborate, communicate, and develop key research skills. Beyond the technical aspects, these digital technologies also impact the human elements of collaboration, such as team dynamics, cross-disciplinary interaction, and the ability to navigate cultural and communication challenges in a globalized academic environment. This special session will focus on the design, development, implementation, and assessment of digital collaboration approaches (e.g., platforms, tools, methods) that foster research skills, learning, critical thinking, and teamwork in scientific education. Topics of interest include, but are not limited to:
- Virtual, remote and web-based labs,
- Collaborative learning environments,
- AI-driven tools,
- Learning Analytics and Education data mining for exploring and improving collaborative environments
- Multimodal collaborative environments for learning
- Integration of digital technologies into curricula.
In addition to conceptual studies, we strongly encourage the submission of experimental results and case studies. Submissions that offer insights from real-world applications — highlighting challenges, successes, and lessons learned — are especially welcomed. This session aims to build a comprehensive understanding of how innovative digital tools can shape and enhance scientific research skills, both technically and socially, in educational contexts.

6. Collaborative Innovations and Emerging Technologies in Industrial Transformation

Organizers

• Nan Xiang, Xi’an Jiaotong-Liverpool University, Nan.Xiang@xjtlu.edu.cn
• Zhijie Xu, Xi’an Jiaotong-Liverpool University, Zhijie.Xu@xjtlu.edu.cn
• Chengtao Ji, Xi’an Jiaotong-Liverpool University, Chengtao.Ji@xjtlu.edu.cn
• Yushan Pan, Xi’an Jiaotong-Liverpool University, yushan.pan@ieee.org

Description

As the industrial landscape continues to evolve with the integration of advanced technologies, cross-disciplinary collaboration becomes essential for driving innovation and transformation. This special session will explore how interdisciplinary approaches—combining fields such as design science, service science, medical science, and data science—are shaping the next generation of industrial systems. By examining the role of emerging technologies such as artificial intelligence (AI), cyber-physical systems, digital twins, and the industrial metaverse, this session will foster discussions on the challenges, opportunities, and future directions in industrial innovation. This session aims to:
- Increase understanding of how interdisciplinary collaboration can lead to industrial innovation.
- Promote the adoption of emerging technologies such as AI, cyber-physical systems, and digital twins in industrial contexts.
- Inspire new research directions and collaborative projects across disciplines and sectors.
- Provide actionable insights for industry professionals on how to harness innovative technologies for competitive advantage.

7. Embedded Intelligent Systems for Imaging

Organizers

• Uzair Aslam Bhatti, School of information and Communication Engineering, Hainan University, uzair@hainanu.edu.cn
• Tang Hao, School of information and Communication Engineering, Hainan University, melineth@hainanu.edu.cn,
• Muhammad Aamir,College of Computer Science, Huanggang Normal University, aamirshaikh86@hotmail.com
• Waheed Abro, Université d’Artois, wahmed.abro@univ-artois.fr

Description

Embedded intelligent systems are becoming increasingly integral to modern imaging technologies, driven by advancements in artificial intelligence (AI) and the need for real-time, efficient image processing. This proposal aims to explore the development of an embedded intelligent system that can be utilized for advanced imaging tasks, such as object detection, recognition, and image enhancement. By leveraging neural networks, edge computing, and AI-driven optimization algorithms, the system is expected to offer superior accuracy and speed in image analysis. Our approach will involve integrating state-of-the-art sensors with powerful AI models, allowing for real-time performance in areas such as medical imaging, surveillance, autonomous vehicles, and industrial automation. The system will be designed for scalability, ensuring compatibility across various imaging platforms and industries. The results from this research will contribute to the evolution of intelligent embedded systems, paving the way for more efficient, adaptable, and intelligent imaging solutions.

8. Collaborative Anomaly Detection and Fault Diagnosis

Organizers

• Hongwei Wang, Zhejiang University, hongweiwang@zju.edu.cn
• Gongzhuang Peng, Beijing University of Science and Technology, gzpeng@ustb.edu.cn
• Hao Qin, Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, h.qin@giim.ac.cn
• Jiannan Yang, University of York, jiannan.yang@york.ac.uk

Description

The complexity of modern industrial systems has escalated the need for Collaborative Anomaly Detection and Fault Diagnosis (CADFD), which integrates multiple diagnostic resources, data from various sensors, and expertise from different domains to enhance the accuracy and reliability of fault detection and analysis. This special session aims to bring together researchers and practitioners to explore the latest advancements in CADFD. Topics of interest include, but are not limited to:
- Multi-source Heterogeneous Data Collaborative Fault Diagnosis Methods
- Multi-source Domain Adaptation (MDA) for Fault Diagnosis
- Collaborative Fault Diagnosis Methods in Data Imbalance and Long Tail Scenarios
- Federated Learning for Collaborative Fault Diagnosis
- Discussion on Remote Real-time Collaborative Fault Diagnosis Methods
The theories, models, methods and tools for these aspects are of interest to this session.

9. AI-Driven Innovations in Industrial Intelligence for Advancing Industry 4.0 and Sustainable Development

Organizers

• Xu Cheng, Tianjin University of Technology, xu.cheng@ieee.org
• Man Zhu, Wuhan University of Technology, man.zhu.393@whut.edu.cn
• Jianhua Zhang, Tianjin University of Technology, zjh@ieee.org

Description

The rapid development of Industry 4.0 has revolutionized traditional industrial processes, with advanced technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data playing pivotal roles. These innovations have led to the creation of smart factories, autonomous production systems, and data-driven manufacturing, which enable industries to achieve greater efficiency, flexibility, and precision. Industrial Intelligence, powered by AI and machine learning, optimizes operations by facilitating predictive maintenance, real-time decision-making, and process automation. This integration has significantly reshaped industrial landscapes, promoting productivity while reducing operational costs and human error.
In recent years, the focus has expanded to include sustainability and ethical considerations in Industry 4.0, giving rise to what is often called "smart and green manufacturing." Industrial Intelligence now plays a crucial role in energy-efficient production, waste reduction, and resource optimization. However, the widespread implementation of these technologies also presents challenges related to data privacy, cybersecurity, workforce retraining, and ensuring legal compliance. This special issue will explore how AI and Industrial Intelligence can address these challenges while advancing Industry 4.0. The issue aims to present specific research directions and expected outcomes that will foster important discussions and drive innovation in both academic and industrial settings.
The special issue has the following topics (but are not limited to):
- AI-driven predictive maintenance in smart factories
- Real-time decision-making and process automation in Industry 4.0
- Cybersecurity and data privacy in industrial IoT
- Digital twin technologies for industrial process monitoring
- Sustainable manufacturing through AI-driven solutions
- Ethical challenges and workforce adaptation in Industrial Intelligence
- AI-powered industrial safety and risk management

10. Smart Sensor Networks and Internet of Things

Organizers

• Tie Qiu, Tianjin University, qiutie@ieee.org
• Wenbing Zhao, Cleveland State University, w.zhao1@csuohio.edu

Description

With the rapid development of information technology, Internet of Things (IoT) and Intelligent sensing technologies have become hot topic. In the large scale Sensor Networks, the massive sensing data with complex in structures, high dimensional, distributed, and heterogamous are growing rapidly. It brings new opportunities to data owner and challenges to data researchers. These challenges are distinguished and require new computational paradigm. How to build an efficient IoT architecture and get the large scale sensing data has become the emerging research focuses.
This Special Session seeks to original research papers as well as review papers on Sensor Networks and Internet of Things:
- Industrial Internet of Things
- Wireless sensor networks
- Internet of Things
- Social Networks and Mobile Computing.
- Big Data and Cloud Computing
- Cyber-physical Systems
- Intelligent transportation systems