Workshop 1 (11:00 – 13:00) – Towards Human-Centered Intelligent Cockpits for Automated Vehicles

11.00-11.10 Welcome

Professor Cuixia Ma, Institute of Software, Chinese Academy of Sciences; Automotive Software Innovation Center (ChongQing)

Chaired by Professor Shengfeng Qin, Northumbria University, UK

11.10-11.30 Talk 1: Latent Traffic Hazard Notifications cockpits: The Effects on Driver Attention, Trust, and Driving Safety 

Speaker: Dr Qingkun Li, Institute of Software, Chinese Academy of Sciences; Automotive Software Innovation Center (ChongQing)

Chaired by Professor Cuixia Ma, Institute of Software, CAS; Automotive Software Innovation Center (ChongQing)

11.30-11.50 Talk 2: Human-Automated Vehicle Interaction: Virtual Reality Studies

Speaker: Dr Chongfeng Wei, Queen’s University, Belfast

Chaired by Professor Cuixia Ma, Institute of Software, CAS; Automotive Software Innovation Center (ChongQing)

11.50-12.00 Workshop Q&A

Chaired by Dr Qingkun Li, Institute of Software, CAS; Automotive Software Innovation Center (ChongQing)

12.00-12.20 Talk 3: Visual Analytics and Natural Interactions for Multimodal Contents

Speaker: Dr Zeyuan Huang, Institute of Software, CAS

Chaired by Dr Qingkun Li, Institute of Software, CAS;  Automotive Software Innovation Center (ChongQing)

12.20-12.40 Talk 4: Smart Urban Railway Operation Planning Based on Passenger Flow Prediction

Speaker: Professor Jian Zhang, Southwest Jiaotong University, China

 Chaired by Professor Shengfeng Qin, Northumbria University, UK

12.40-13:00 Talk 5: Intelligent cockpit software/hardware demonstration

Speaker: Professor Kang Yue,Institute of Software, CAS; Automotive Software Innovation Center (ChongQing)

Chaired by Dr Qingkun Li, Institute of Software, CAS; Automotive Software Innovation Center (ChongQing)

13.00-13.10 Workshop Q&A

Chaired by Professor Shengfeng Qin, Northumbria University, UK

Workshop 2 (14:00 – 15:45) – Yong professionals in Digital Healthcare Technology

14:00-14:30 Talk 1: Early diagnosis of bowel cancer using small-scale robots

Speaker: Associate Professor Yang Liu, Personal Chair in Dynamics and Control in the Engineering Department at the University of Exeter, UK

Chaired by Dr Chunwei Xia, School of Computing, University of Leeds, Leeds

14:30-15:00 Talk 2: How AI help with Patient-Facing Applications?

Speaker: Dr Lecture Dewei Yi, Senior Lecture at Department of Computing Science, University of Aberdeen, UK

Chaired by Dr Chunwei Xia, School of Computing, University of Leeds, Leeds

15:00-15:30 Talk 3: Explainable Foundation Models for Early Detection of Alzheimer’s Disease and Related Dementia

Speaker: Dr Sam Danso, Senior Lecture at School of Computer Science, Faculty of Technology, University of Sunderland

Chaired by Dr Chunwei Xia, School of Computing, University of Leeds, Leeds

Appendix:

Yang Liu’s Talk:

Title: Early diagnosis of bowel cancer using small-scale robots

Abstract: Bowel cancer causes nearly a million deaths per year, with more than half of cases being fatal. Although early cancer detection can significantly improve patient outcomes, over half of bowel cancer cases in the UK are diagnosed at a late stage. Currently, detection of bowel cancer and pre-cancerous polyps is predominantly performed through either visual inspection of the colonic mucosa during endoscopy (colonoscopy), which is an invasive procedure, or by cross-sectional imaging, which is less reliable for small-sized lesions that are not easily visualised. If such polyps are not detected and removed early, they may become cancerous. In this talk, I will introduce how our lab has addressed this challenge by developing small-scale robots through a “fantastic voyage”, encompassing mathematical modelling, numerical analysis, control and optimisation, experimental investigation, proof-of-concept validation, and ex vivotesting. Our work has focused on fabricating and controlling robots at millimetre and micrometre scales, particularly in recreating a gut-like environment for testing these robots to reduce live animal testing before clinical trials. Finally, I will summarise the key challenges in this work and discuss new directions for future development.

Biography: Yang Liu is a Personal Chair in Dynamics and Control in the Engineering Department at the University of Exeter, the Director of the Exeter Small-Scale Robotics Laboratory, an Honorary Lecturer in the Endoscopy Department at the Royal Devon University Healthcare NHS Foundation Trust, and a Topical Associate Editor of Nonlinear Dynamics (journal). He obtained his B.Eng. degree in Automation from Hunan University, Changsha, China in 2003, an M.Sc. degree in Control Systems from the University of Sheffield, Sheffield, UK in 2005, and a Ph.D. degree in Control Engineering from Staffordshire University, Stafford, UK in 2010. After joining the University of Exeter in 2016, he has been leading the Exeter Small-Scale Robotics Laboratory, working on the development and control of small-scale robots at millimetre and micrometre scales for the early detection of bowel cancer and its metastasis, as well as targeted cancer therapy. He has published more than 100 academic papers including more than 90 high impact peer-reviewed journal papers, in journals such as IEEE Robot Autom. Let., Phil. Trans. R. Soc. A, Int. J. Mech. Sci., Nonlinear Dyn., and J. Sound Vib. Prof. Liu has been listed among Stanford & Elsevier’s Global Top 2% Scientists since 2019. His team received the Lab Science Bursary Award at the 2019 British Society of Gastroenterology Annual Meeting and the Ali H. Hayfeh Prize at the International Nonlinear Dynamics Conference in 2021.

Dewei Yi’s Talk

Title: How AI help with Patient-Facing Applications?

Abstract

With AI’s growing prevalence in healthcare, its applications in patient-facing scenarios are expanding. This talk explores AI’s multifaceted role in such applications through three primary pillars: Sustainability, Efficiency, and Engagement (SEE)：Sustainability: “MyChoice” initiative will be discussed aiming to harness AI to promote healthy and sustainable food choices. Despite existing guidelines and increased awareness, significant progress in improving dietary health and reducing environmental impact remains elusive. The MyChoice project seeks to deepen the understanding of individual and population-level drivers and barriers to sustainable food choices, utilizing AI for dietary assessment and calorie estimation. Efficiency: We will then examine the ASICA platform, developed for melanoma aftercare. This platform faces challenges with poor-quality self-taken images provided by patients. The proposed ASICA+ aims to overcome these issues with a lightweight AI toolkit that evaluates and guides patients in real-time to improve image quality. This involves a semantics-aware contrastive learning model for label-free medical image quality assessment, offering efficient, real-time solutions that run on edge devices. Engagement: Finally, the talk addresses the use of AI to enhance the comprehensibility of cancer multidisciplinary team (MDT) reports for patients. By leveraging tools like ChatGPT, the goal is to reduce clinician burnout and improve patient communication. A case study on using ChatGPT to explain MDT reports reveals both positive feedback and barriers, highlighting the necessity for patient-friendly interfaces and seamless integration into clinical workflows.

This talk underscores AI’s potential to enhance healthcare through sustainable practices, efficient patient management, and improved patient engagement.

Biography: Dr Dewei Yi is Senior Lecturer (associate professor) in the Department of Computing Science at University of Aberdeen (UoA), U.K. He received his PhD degree in the Department of Aeronautical and Automotive Engineering, Loughborough University in 2018. He successfully secured £1M+ grants and PhD scholarships from Fisheries Innovation Scotland (FIS), BBSRC, Cancer Research UK (CRUK), Department for Environment Food and Rural Affairs (DEFRA), Petroleum Technology Development Fund (PTDF), and Tertiary Education Trust Fund (TETF). He has published about 40+ high-quality research papers including IEEE Transactions (10+), Transportation Research Part C: Emerging Technology (TRC), Computer Networks, Knowledge-based System, etc. He also served as associate editor of Multimedia Tools and Applications journal, Track co-chair of IEEE 96th Vehicular Technology Conference (VTC2022), and guest editor of Electronics Journal and Applied Science Journal.

Sam Danso’s Talk:

Title: Explainable Foundation Models for Early Detection of Alzheimer’s Disease and Related Dementia

Abstract:

Alzheimer’s Disease and Related Dementias ADRD is known to develop over two decades before manifestation of signs and symptoms. While over 50million people is currently estimated to be living with ADRD globally, no cure has been found. However, early detection of modifiable risk factors may reduce the risk of ADRD by at least 50 percent.  In this talk, I will discuss an ongoing project which seeks to develop adaptive explainable foundation models for early detection of ADRD. It employs Transfer Learning and Interpretable frameworks with capability to explore personalised interactions of risk factors of ADRD across the lifespan.

Biography:

Dr Sam Danso is a Senior Lecture at the School of Computer Science, Faculty of Technology, University of Sunderland. He holds a PhD in Artificial Intelligence from the University of Leeds, an MSc in Advanced Software Engineering from Bournemouth University, and a BSc (Hons) in Computer Science from the University of Sunderland. His research interest is in applied AI and data science with a focus on brain health. He is also interested in the privacy of AI models within the context of trustworthy AI and data protection. He is a member of a PhD Advisory Committee at Ionian University, Greece.