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Title and abstract
Evolving Deep Neural Networks with Cultural Algorithms for Real-Time Industrial Applications

Abstract— The goal of this talk is to investigate the applicability of evolutionary algorithms to the design of real-time industrial controllers. Present-day ‘deep learning’ (DL) is firmly established as a useful tool for addressing many practical problems. This has spurred the development of neural architecture search (NAS) methods in order automate the model search activity. CATNeuro is a NAS algorithm based on the graph evolution concept devised by Neuroevolution of Augmenting Topologies (NEAT) but propelled by Cultural Algorithms (CA) as the evolutionary driver. The CA is a network-based, stochastic optimization framework inspired by problem solving in human cultures. Knowledge distribution across the network of graph models is a key to problem solving success in CAT systems. Two alternative mechanisms for knowledge distribution across the network are employed. One supports cooperation (CAT-NEURO) in the network and the other competition (WM).

To test the viability of each configuration prior to use in the industrial setting they were applied to the design of a real-time controller for a 2D fighting game. While both were able to beat the AI program that came with the fighting came but the cooperative method performed statistically better. As a result, it was used to track the motion of a trailer (in lateral and vertical directions) using a camera mounted on the tractor vehicle towing the trailer. In this second real-time application (trailer motion) the CAT-NEURO configuration was compared to the original NEAT (elitist) method of evolution. CATNEURO is found to perform statistically better than NEAT in many aspects of the design including model training loss; model parameter size; and overall model structure consistency. In both scenarios the performance improvements were attributed to the increased model diversity due to the interaction of CA knowledge sources both cooperatively and competitively.

Biography

Dr. Robert G. Reynolds received his Ph.D. in Computer Science, specializing in Artificial Intelligence from the University of Michigan, Ann Arbor. He is currently a Professor of Computer Science and director of the Artificial Intelligence Laboratory at Wayne State University. He is a Senior Member of the IEEE. At the University of Michigan-Ann Arbor, Professor Reynolds is a Visiting Research Scientist with the Museum of Anthropology, and a member of the Complex Systems Group. His interests are in the development of computational models of cultural evolution for use in the simulation of complex organizations, computer gaming, and virtual world applications.

Dr. Reynolds produced a framework called Cultural Algorithms, to express and computationally test various theories of social evolution using multi-agent simulation models. He has authored or co-authored seven books in the area. His most recent books are “Cultural Algorithms: Tools for the Engineering of Social Intelligence into Complex Cultural Systems, 2020, Wiley-IEEE Press”, and “Culture on the Edge of Chaos” publisheef by Springer-Verlag in 2018. In additional he has written over 250 papers. Currently, Dr. Reynolds along with his students, are developing a toolkit for testing Cultural Algorithms in dynamic environments; the Cultural Algorithm Toolkit (CAT). His research group has produced award winning game controller software for several international competitions using the Cultural Algorithms toolkit. In 2017, a software system based upon Cultural Algorithms came in second in the IEEE Single Real Valued Function Optimization competition held in conjunction with the IEEE Congress on Evolutionary Computation.

Title and abstract
Chip-off Forensics: A multi-disciplinary approach

Law enforcement agencies need to develop new reverse engineering methods continuously, which is time and resource-consuming. Reverse engineering is an indispensable method for law enforcement to correctly interpret the system structure, security features, file systems, and other software details for the purpose of evidence acquisition and tool testing. Moreover, law enforcement agencies can hardly compete with new security by default solutions included in mobile devices and operating systems.
Chip‐off is a destructive technique that entails removing the flash memory chip from the printed circuit board (PCB). Removing the flash memory entails cutting the PCB and grinding the PCB allowing the chip contacts to be exposed. Our research and testing provide the forensic community with an understanding of the capabilities and limitations of Chip‐Off reverse engineering techniques.

Biography

Dr Ameer Al-Nemrat - Directory for the Cyber security and Ai Centre - UEL

Dr Ameer Al-Nemrat is the director for the Professional Doctorate in Information Security & the MSc Information Security and Digital Forensics programmes. He is the founder and the director of the Chip Forensics & the Electronic Evidence laboratories, UEL, where he closely working with government and Law Enforcement agencies on cyber security research projects. He personally involved in the professionalisation of Initial police training in Africa, South America, and Middle East. His experience and opinion were directly required to advise and help to maintain International cyber security

Title and abstract
Trustworthy autonomous vehicle

Presentation Abstract: Modern vehicles are increasingly becoming complex, intelligent systems that use various digital technologies to offer smart features such as automated driving, smart/adaptive infotainment, maintenance/support in full integration with the offboard digital infrastructures. These features provide higher levels of automation to relieve the human driver from tedious tasks. However, as the automation level increases, gaining the trust of the users and other stakeholders in the underlying technologies becomes increasingly complex and crucial for the successful adoption of future intelligent vehicles. Trust is a multifaceted concept, one of them being resilience, i.e., the system’s ability to operate safely when something goes wrong. A crucial enabler of resilience is automated integrity monitoring to recognise the internal system faults or unacceptable performance by various system elements due to external factors. To this end, the focus of this presentation is on automated integrity monitoring, elaboration of its challenges and the discussion of the potential approaches that can be adopted in intelligent vehicles. Finally, some of the specific relevant research projects that focus on various challenges of integrity monitoring in the University of Warwick will be briefly introduced.

Biography

Professor Mehrdad Dianati is the Head of Intelligent Vehicles Research Department and the Technical Research Lead in Networked Intelligent Systems at the Warwick Manufacturing Group (WMG), the University of Warwick, UK. He is also the Director of Warwick's Centre for Doctoral Training on Future Mobility Technologies, training doctoral researchers in intelligent and electrified mobility systems in collaboration with the experts in the field of electrification from the Department of Engineering of the University of Warwick. He is a Fellow of Alan Turing Institute and the Field Chief Editor of Frontiers in Future Transportation. His research focuses on the application of Digital Technologies (Information and Communication Technologies and Artificial Intelligent) for the development of future mobility and transport systems. He has over 29 years of combined industrial and academic experience 20 years in various leadership roles in multi-disciplinary collaborative R&D projects. He works closely with the Automotive and ICT industries as the primary application domains of his research. In the past, he has served as an Editor for the IEEE Transactions on Vehicular Technology and several other international journals, including IET Communications.

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