2025 International Seminar Series on Emerging Technologies in Cyber-Physical Systems (CPS)

Presentation

Presentation

Presentation

Biography: Hi, I’m Vishal. I have over six years of university-level teaching experience in the UK and more than 16 years of overall teaching expertise. I hold a PhD in Cybersecurity, an MSc in Software Engineering, and a BE in Information Science. Currently, I serve as a Lecturer at Nottingham Trent University, following the Teaching and Research (T&R) pathway.

My core research interests include Cryptography, Information Security, Cybersecurity, and IoT deployment, with additional expertise in Computer Networks, the Internet of Things (IoT), SQL, MS Power BI, Algorithm Design, Data Structures, and AI/ML. I also enjoy coding in C, C++, and Python.

I have published numerous papers in prestigious journals, including IEEE Access, Elsevier, and Springer.

Title: Lightweight Cryptography for Resource Constrained IoT Devices

Abstract: The increasing adoption of IoT in safety-critical areas, such as smart factories and connected vehicles, presents significant security risks, making system dependability crucial. Traditional cryptographic methods are effective for resource-rich devices but unsuitable for constrained IoT environments, necessitating a lightweight alternative known as Lightweight Cryptography (LWC). Achieving an optimal trade-off between cost, performance, and security remains a challenge. The proposed AUM algorithm addresses this by incorporating a novel 5-bit S-box for strong security, a transpose-based permutation for efficiency, and an optimized key generation technique. Comparative analysis on the ASIC platform and cryptanalysis demonstrate that AUM outperforms existing models.

Biography: Professor Matthew Montebello is a distinguished academic and the Head of the Department of Artificial Intelligence at the University of Malta, where he plays a pivotal role in shaping the future of AI education and research. With a career that seamlessly blends computer science and education, he has developed a unique interdisciplinary expertise, specializing in the application of artificial intelligence to e-learning. His international influence is evident through his visiting academic status and adjunct professorship at the University of Illinois, Urbana-Champaign, where he collaborated on cutting-edge projects with both the Computer Science department and the College of Education. A pioneer in education technology, Professor Montebello has developed a next-generation Virtual Learning Environment (VLE) that integrates social media concepts, revolutionizing how education is delivered in the digital age. His commitment to professional development is reflected in his active involvement with the University of Malta’s Web Editorial Board, Digital Education Committee, and other key committees. Since November 2022, he has been at the forefront of addressing the impact of Generative AI and Large-Language Models on higher education, co-authoring guidelines and facilitating workshops to prepare academics and students for the evolving educational landscape. With a robust portfolio of publications and research in AI, e-learning, and computer science, he is widely recognized as a thought leader in these fields. Additionally, his extensive teaching experience, dating back to his early career in secondary schools, has allowed him to mentor countless students and junior academics, shaping the next generation of professionals in ICT and AI.

Title: The Role of Generative AI in Academia: Transforming Teaching, Assessment, Research, and Writing

Abstract: The talk will equip participants with the knowledge and skills to leverage Generative AI in their academic research and writing. Participants will explore AI-powered tools for literature reviews, references management, academic writing, and research dissemination while addressing ethical considerations, academic integrity, and limitations. The session includes practical advice, links to hands-on activities, and recommendation on integrating AI responsibly into their academic journey.

Biography: Prof. Dr. Imre Felde (Engineer of Information Technology) completed his PhD in 2007. He has been working for Bay Zoltán Foundation for Applied Research (BZAKA, Budapest, Hungary) as research fellow between 1995 and 2009. He was General Director of BZAKA between 2010 and 2012. He has been working as Vice Dean (2012-2018) at Obuda Univesity and as Vice rector (2019-2024). He was appointed Full Professor at the John von Neumann Faculty of informatics in 2022. He published more than 240 scientific and technical papers in field of engineering, computer science, mathemathics and process modeling.

Title: Analysis of mobility customs in urban area by processing mobile network data

Abstract: Mobile phones and even more, smart-phones, are now fundamental parts of our life, they are practically, always with us, wherever we go, almost as if they were a part of our body. The continuous communication between a device and the Mobile Phone Networks leaves traces at the Operator’s system of our mobility habits. Via these devices, the Mobile Phone Network can ‘sense’ our movements, which is the basis of the “Smart City” concept. In the last two decades show the potential of the human mobility characterization using Call Detail Records (CDR) in epidemiology, sociology and urban planning. The analysis of the human movement patterns on the basis of the CDR data, that makes it possible to examine a very large population cost-effectively resulted a number of discoveries about human dynamics.  In addition to, by mixing mobility indicators with external data, the hidden relationship between the daily customs and Socio-Economic Status (SES) of the dwellers could be evaulated.

Biography: Tom Gedeon is the Human-Centric Advancements Chair in AI at Curtin University and was recently the Optus Chair in AI. Prior to this, he was Professor of Computer Science and former Deputy Dean of the College of Engineering and Computer Science at the Australian National University. He gained his BSc (Hons) and PhD from the University of Western Australia.
Tom’s main research areas are Responsive and Responsible AI, and generative AI. His focus is on the development of automated systems for information extraction, from eye gaze and physiological data, as well as textual and other data, and for the synthesis of the extracted information into humanly useful information resources, primarily using neural/deep networks and fuzzy logic methods, and delivered in real, augmented and virtual environments.
Tom has over 400 publications, and has run multiple international conferences. He is a former president of the Asia-Pacific Neural Network Assembly, and former President of the Computing Research and Education Association of Australasia. He has been General Chair for the International Conference on Neural Information Processing (ICONIP) three times. He has been nominated for VC’s awards for postgraduate supervision at three Universities. He was a member of the Australian Research Council’s College of Experts 2018-2021, and continues from 2024-2026. He is an associate editor of the IEEE Transactions on Fuzzy Systems, and a member of the Governing Boards of the IEEE Systems Man and Cybernetics and the Asia-Pacific Neural Network Society.

Title: Responsive AI and Responsible AI for useful and privacy protected AI systems

Abstract: With the availability of low-cost sensors in today’s environment, we are increasingly cnapturing data directly from individuals’ behaviours via wearables and cameras. This enables us to create AI tools capable of reading human actions and reactions to the outcomes created by our AI systems, allowing us to enhance or adjust their outputs, and be responsive to the human. This closely resembles the nonverbal cues used by people during a conversation. Responsive AI refers to cutting-edge AI that responds to human actions and reactions to predict subtle emotional states. In practice today, these sensors include wearable devices that detect skin conductance, heart rate, muscular activation, and skin temperature, among other signals. Cameras are used to track eye gaze behaviours, capture videos, record thermal images, or utilise hyperspectral technology, and with a long term additional goal of replacing wearable sensors. The deployment of AI to discern nuanced human internal states introduces new privacy concerns in addition to the expected privacy implications associated with video cameras. These risks can be alleviated through the adoption of privacy-preserving techniques referred to as Responsible AI. This is a privacy-by-design method for managing the use of private and personal data. In practice, this means using adversarial generative algorithms to remove personally identifying data from sensor data streams and videos. We will discuss previous work in these areas to show how fully utilising Responsive AI needs the incorporation of Responsible AI principles.

Biography: Dr. Tamás István Józsa is a lecturer (equivalent to assistant professor) at the Centre for Computational Engineering Sciences at Cranfield University specialised in modelling and simulation (M&S). Cranfield University is an associate partner of GEMINI (https://dth-gemini.eu) with Tamás as institutional lead. This EU flagship project targets the advancement of stroke diagnosis and treatment based on M&S. Beyond biomechanics, Tamás collaborates with industrial and academic partners to tackle problems in internal and external aerodynamics, such as the optimisation of supersonic combustors and hypersonic waveriders, and the safety assessment of cryogenic hydrogen tanks and jet blasts. His educational activities spread across the Computational Fluid Dynamics (CFD), Aerospace Computational Engineering, and Computational Software Techniques in Engineering MSc courses.

Between 2022 and 2023, Tamás was a Career Bridging Fellow at Amsterdam University Medical Centres. Previosuly, he worked as a doctoral adviser and postdoctoral researcher at the University of Oxford. He completed his PhD at the University of Edinburgh in 2018 and earned an MSc in CFD at Cranfield University in 2014. He studied mechanical engineering at the Budapest University of Technology and Economics between 2008 and 2013 (BSc & MSc).

Title: Computational modelling of perfusion in health and ischaemic stroke

Abstract: Stroke claims approximately 6.5 million lives each year and more than 50 million people live with stroke-related disabilities worldwide. About 25% of stroke patients are less 65 years old. Average clinical trials associated with stroke treatment development are estimated to take three years and cost £25 million with success rate about 15%. Such trials pioneered chemical (thrombolysis) and mechanical (thrombectomy) procedures which are combined to remove the blood clots causing ischaemic strokes. However, treatment optimisation remains an outstanding challenge. For example, it is unclear whether certain patient groups could benefit from blood pressure medication, or from single instead of combined treatment.

Modelling and simulation methods are the workhorse of product engineering in every sector except medical device and drug development. Simulations emerge as a promising approach to reduce the resources needed for clinical trials, to optimise, and to personalise stroke treatments but several issues need to be resolved first. The presentation will provide the necessary anatomical and pathophysiological details to appreciate the difficulties of stroke modelling and treatment simulations. Thereafter, the key components of a comprehensive stroke simulation pipeline will be discussed. Focus will be placed on a porous continuum model of the entire human brain. Details of clinical data integration will be discussed which enables the simulation of hundreds of stroke patients. Verification, validation, uncertainty and error quantification activities will be shown.