Confirmed Workshops
We are pleased to advise that the following Workshops are confirmed for the 2024 programme and we would like to thank the organisers for the time they took to put forward and manage these sessions.
| No. | Workshop Title | Runtime (hours) | Workshop Organiser |
|---|---|---|---|
| 1 | The 17th International Workshop on Artificial Intelligence and Cybersecurity (AICS2024) | 3 | Ian Welch & Tao Ban |
| 2 | AI Education | 3 | Michael Watts, Ranpreet Kaur & Akbar Ghobakhlou |
| 3 | Neural Models of Infants and Child Development | 3 | Alistair Knott, Annette Henderson & Florian Bednarski |
| 4 | Privacy Compliant Health Data As A Service For AI Development | 4.5 | Mufti Mahmud & Antti Arrola |
The 17th International Workshop on Artificial Intelligence and Cybersecurity (AICS2024)
Date: 3 December
Part 1: Session 1D
Time: 11.00 - 12.30
Location: WG126
Part 2: Session 2D
Time: 13.30 - 15.00
Location: WG126
Led by Dr. Ian Welch (Victoria University of Wellington), Dr. Tao Ban (National Institute of Information and Communications Technology)
The purpose of the 17th International Artificial Intelligence and Cyber Security Workshop (AICS2024) is to raise awareness of cybersecurity, promote the potential of industrial applications, and give young researchers exposure to the main issues related to the topic and ongoing works in this area. AICS2024 will provide a forum for researchers, security experts, engineers, and research students to demonstrate new technologies, present the latest research works, share ideas, and discuss future directions in the fields of artificial intelligence and cybersecurity.
Speaker 1: 13.30 - 14.00 Kazushi Ikeda (NAIST, Japan) - Theoretical background of deep learning
Speaker 2: 14.00 - 14.30 Richard Kenyon (Datapay AI Labs, New Zealand) - Securing AI Chatbots in Enterprise Applications: Cybersecurity challenges for GenAIApplications in compliance driven industries like Payroll, Tax, and Employment
AI Education
Date: 3 December
Part 1: Session 1C
Time: 11.00 - 12.30
Location: WG308
Part 2: Session 2C
Time: 13.30 - 15.00
Location: WG308
Led by Dr. Michael Watts, Ranpreet Kaur (Media Design School), Dr. Akbar Ghobakhlou (Auckland University of Technology)
There has been an explosion in the applications of Artificial Intelligence (AI). While Large Language Models such as ChatGPT have garnered much of the attention, other AI technologies have also found wide application, such as the predictive keyboards on mobile devices, and facial recognition systems in supermarkets. Some technology venture capitalists have reported that 80% of the funding pitches they receive involve AI. Many business owners believe that AI is going to put them out of business, unless they adapt to the technology. Others are desperately searching for ways to get onto the AI bandwagon. This surge in interest in AI has led to a worldwide shortage of AI engineers. Furthermore, the inappropriate application of AI, whether through the use of biased data or unethical applications, has also led to social and economic fallout.
The increased public awareness of AI technologies has also led to a proliferation of media commentary, of varying degrees of competence, and governmental regulation. Some students have taken to using AI tools to assist in their assignments, while others have changed their career pathways due to a perception that AI is going to destroy their future job prospects.
There is, therefore, a need for education about AI. This need spans nearly all levels of education, from primary school through to postgraduates. At primary and secondary level so that people enter the working world with the basic knowledge of AI and how it affects their lives. At tertiary undergraduate and postgraduate level so that we have a steady supply of engineers and developers who can utilise AI in an appropriate and ethical manner.
This all raises a fundamental question: How is this education being done?
This special session is intended to attract papers dealing with all aspects of AI education. Topics of interest include, but are not limited to:
- Incorporating AI into teaching curricula at all levels of education
- The design and implementation of AI-specialist teaching curricula
- Technologies used to teach AI
- Teaching the ethics of AI
- Policy making around AI education
- The teaching of specialist topics within AI
Paper 1: 11.00 - 11.15 Zhenyu Xu, Victor S. Sheng, Kun Zhang - Logic Error Localization in Student Programming Assignments Using Pseudocode and Graph Neural Networks
Paper 2: 11.15 - 11.30 Kirill Krinkin, Tatiana Berlenko - Flipped University: LLM-Assisted Lifelong Learning Environment
Speaker 1: 11.30 - 12.00 Michael Witbrock (University of Auckland, New Zealand) - AI and the End of Useful Skills
Speaker 2: 12.00 - 12.30 Irwin King (The Chinese University of Hong Kong, Hong Kong) - The Critical Role of AI in Learning Analytics and Assessment in the Future of Education
Speaker 3: 13.30 - 14.00 Vithya Yogarajan (University of Auckland) - Embracing AI in Tertiary Teaching
Speaker 4: 14.00 - 14.30 Jonathan Chan (King Mongkut’s University of Technology, Thailand) - Balancing AI and Human Interaction in Education
Speaker 5: 14.30 - 15.00 Mufti Mahmud (King Fahd University of Petroleum and Minerals, Saudi Arabia) - AI in Provisioning Personalised Learning Through Engagement Detection
Neural models of infants and child development
Date: 4 December
Part 1: Session 4D
Time: 11.00 - 12.30
Location: WG126
Part 2: Session 5D
Time: 13.30 - 15.00
Location: WG126
Led by Prof. Alistair Knott (Victoria University of Wellington), Prof. Annette Henderson (University of Auckland), Florian Bednarski (University of Auckland)
The dramatic advances of neural AI methods we have seen in the last few years are loosely based on the brain's distributed mode of computation, but are distinctively unhumanlike in the way they develop. LLMs, for instance, begin learning directly on vast quantities of unembodied mature adult language; it is only at a late stage that their learning is interactively shaped (by alignment) or becomes 'multimodal' (through interfaces with vision or action). By contrast, human infants' learning is fundamentally embodied: from birth, infants must learn to engage with the physical world, by meaningfully deploying their sensory and motor apparatus (Smith and Gasser, 2005). Infants' learning is also fundamentally staged, beginning with the acquisition of basic sensorimotor concepts and abilities, along with conceptions of close caregivers, and building on these (Vygotsky, 1994). Infants' learning is also interactive, driven by targeted real-time input from caregivers (Bornstein et al., 2008), but equally self-guided, driven by infants' own curiosity and experiences (Oudeyer et al., 2007).
There is a growing awareness that computational models of infant development may offer ways of augmenting the current generation of high-performing AI models. The session we propose will bring together researchers working on neural models of infant cognitive development, focussing on embodied learning, learning through interaction, self-guided learning, and staged learning. Crucially, the session will also invite participation from developmental psychologists. The work of psychologists studying development in human infants and children is newly relevant to work in AI, and their voices are increasingly heard in discussions about how AI should progress (see e.g. Smith, 2023; Gopnik and Chiang, 2024).
Speaker 1: 11:00-11:30 Mark Sagar (University of Auckland) - An introduction to BabyX
Speaker 2: 11:30-12:00 Alistair Knott: (Victoria University of Wellington) - Events and cognitive modes in BabyX
Speaker 3: 12:00-12:30 Florian Bednarski (University of Auckland) - Evaluating interactions with BabyX
Speaker 4: 1:30-2:30 Alison Gopnik (UC Berkeley): Causal Learning as Empowerment - Infant contingency learning as a model for AI
Speaker 5: 2:30-3:00 Martin Takac (Comenius University, Bratislava) - Under the hood of BabyX: cognitive architecture, emotions, active inference
Privacy Compliant Health Data As A Service For AI Development
Date: 5 December
Part 1: Session 7C
Time: 11.00 - 12.30
Location: WG208
Part 2: Session 8C
Time: 13.30 - 15.00
Location: WG308
Part 3: Session 9C
Time: 15.30 - 17.00
Location: WG308
Led by Dr. Mufti Mahmud (King Fahd University of Petroleum and Minerals, Saudi Arabia), Dr. Antti Arrola (University of Turku, Finland)
Artificial intelligence (AI) enables data-driven innovations in health care. AI systems, which process vast amounts of data quickly and in detail, show promise both as a tool for preventive health care and clinical decision-making. However, the distributed storage and limited access to health data form a barrier to innovation, as developing trustworthy AI systems require large datasets for training and validation. Furthermore, the availability of anonymous datasets would increase the adoption of AI-powered tools by supporting health technology assessments and education. Secure, privacy compliant data utilization is key for unlocking the full potential of AI and data analytics. In this project we have been developing a solution that enables analyst to utilize encryption-in-use technologies (secure multi-party computation, fully homomorphic encryption and federated learning) to run analytics and build better machine learning models by accessing more data. We have been working on advancing the current state-of-the-art data synthesis methods towards a more generalized approach of synthetic data generation, and also developing metrics for testing and validation, as well as protocols that enable synthetic data generation without access to real-world data (through multi-party computation). These have been put together as a combined effort from 20 partners from 10 European countries and funded by the European Commission under the Horizon Europe Programme.
The workshop will introduce the audience to the project and its approaches to achieving a next-generation healthcare ecosystem in Europe through secure, privacy-preserving AI models as a service and synthetic healthcare data as a service.
Speaker 1: 11:00 – 11:05 Mufti Mahmud (King Fahd University of Petroleum and Minerals, Saudi Arabia) - Introduction to the ‘Privacy Compliant Health Data As A Service For AI Development’ Technologies Session 1
Speaker 2: 11:05 – 11:20 Antti Airola (Assoc. Prof., University of Turku, Finland) - Introduction to the PHASE IV AI project
Speaker 3: 11:20 – 11:40 Erkay Savas (Sabancı University, Türkiye) - Federated Learning over Encrypted Data
Speaker 4: 11:40 – 12:00 Artur Rocha (INESC TEC, Portugal) - Data privacy methods and tools
Speaker 5: 12:00 – 12:20 Mariya Georgieva (Tune Insight, Switzerland) - Balancing Data Privacy and Utility: Introduction to Privacy-Enhancing Technologies (PETs)
Speaker 6: 14:00 – 14:20 13:30 – 13:40 Antti Airola (Assoc. Prof., University of Turku, Finland) - Summarisation of the Morning Session and Introduction to the afternoon session
Speaker 7: 13:40 – 14:00 Irfan Khan (Turku University of Applied Science, Finland) - Synthetic healthcare data generation
Speaker 8: 14:00 – 14:20 Tunc Asuroglu (VTT, Finland) - Synthetic data, Data quality measures
Speaker 9: 14:20 – 14:40 Ibrahim Sabra (University of Vienna, Austria) - AI-generated Synthetic Data: Legal Standing and Ethical Implications
Speaker 10: 15:30 – 15:55 David Brown (Nottingham Trent University, UK) - Prediction of people at high risk of lung cancer from EHR
Speaker 11: 15:55 – 16:20 Hélder Oliveira (INESC TEC, FCUP, Portugal) - Accurate image-based lung Cancer Characterization Using Machine Learning
Speaker 12: 16:20 – 16:45 Christos Chatzichristos (Post-doctoral researcher, KU Leuven, Belgium) - AI-based prediction of lymph node dissection