Title:An overview of how disruptive technologies will transform our lives!
Professor Abdalla is currently the Provost and academic lead of the University of East London. Prior to that, he was Pro Vice-Chancellor for Learning and Teaching and the College of Technology and Innovation; and Executive Dean of the School of Architecture, Computing and Engineering. He is an international authority in the field of Artificial Intelligence, Smart/Future Cities, Brain Computer Interface (BCI), and Autonomous Systems with over 25 years’ experience in both academia and industry.
Professor Abdalla is a Principal Fellow of the British Higher Education Academy and a Fellow of the British Royal Society for Arts (FRSA). He is a member of the Editorial Board of several international journals. He won several awards nationally and internationally in recognition of his original contribution to research, academia and industry.
Professor Abdalla has been leading major research projects nationally and internationally in partnership with world-class companies such as, Siemens, IBM, AWS and Ford Motor Company. He published over 150 papers world-wide, supervised over 50 PhDs and secured several millions of pounds of research grants from the Research Council, European Commission, Governmental Bodies and Industry.
Professor Abdalla served on the Technology Strategy (TSB) Advisory Board and the Research Council (EPSRC) Assessment Panel and Peer Review College. He acted as an external examiner for many universities including Sheffield, Brunel, Bradford, Cranfield, Birmingham, Nottingham Trent, De Montfort, and Dublin City. He chairs a number of Professorial Panels in the UK and overseas. He has been proactively engaged in various governmental debates at Westminster with regards to the Industrial Strategy, Artificial Intelligence, and graduates’ skills for economic growth.
There is no doubt that disruptive technologies such as, Artificial intelligence, virtual/ augmented reality VR/AR), internet of things, blockchain technology, and e-commerce are significantly influencing our future. These technologies will affect the normal operation of industry and displaces a well-established product or technology, creating a new market or industry globally. For example, artificial intelligence continues to transform the world we live in. AI is becoming responsible for everything from medical breakthroughs in cancer research to cutting-edge climate change research. It has the ability to harness massive amounts of data and use their learned intelligence to make optimal decisions and discoveries in fractions of the time that it would take humans.
The transformative impact of these disruptive technologies on our society will have far-reaching economic, legal, political and regulatory implications that we need to be preparing for!
The Keynote will discuss opportunities and challenges of the application of disruptive technologies in Healthcare, automation, industry, Banking, transportation, cities, education, media, cybersecurity and customer service.
Title:Data-driven Machine Learning Precision Livestock Farming Technologies and Applications
Professor of Broadband Networks, graduated with a BSc and PhD in Electronic and Electrical Engineering from the University of Strathclyde. He has edited two books, authored/co-authored six book chapters, over 380 journal and conference papers and secured funding in excess of £15M. He was Topical Editor for the ‘IEEE Transactions on Communications’, Technical Programme Co-Chair for the ‘IEEE International Conference in Communications (ICC07)’, co-founder, Director and Chief Technology Officer of Kamelian Ltd., a technology start-up focusing on the manufacture of advanced semi-conductor devices and Silent Herdsman Ltd a company providing a range of cloud-based precision livestock health services.
Professor Andonovic’s research interests center on the development of broadband networks, distributed wireless sensor systems, Internet-of-Things and data-driven applications/services.
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The Food and Agriculture Organisation (FAO) of the United Nations predicts that the global population will grow to around 10billion by 2050 and consequently food production must increase by up to 70% to meet that need. The target has to be achieved in spite of the limited availability of arable lands, the increasing need for fresh water (agriculture consumes 70% of the world’s fresh water supply) and other less predictable factors, such as the impact of climate change which leads to variations to seasonal events in the life cycle of plant and animals. Furthermore, agriculture faces a range of additional challenges from new pests and diseases that compromise output quality, generate harmful residues (drugs within the food chain) which in turn necessitate increased pollution management. In parallel, the growing trend of increasing farm sizes, and taking into account the remoteness of the farming community, translates into a migration from established practices based on visual inspection to more automatic techniques capturing data from plants/animals dispersed across a wide spectrum of locations.
Precision Livestock Farming (PLF) is thus core to satisfying the ever increasing world-wide demand for good quality products whilst heavily reducing environmental load and resource use. The pressing need to secure food supplies ensures that the adoption of technology-enabled solutions and applications/services will continue to gather pace. Increasingly on-farm dairy systems (heat detection, milk analysis, feed management etc) are being deployed through Cloud-based implementations, releasing the potential to provision a range of applications/services that bring benefit throughout the entire supply chain. The integration of multiple data streams yields significantly more value because a single indicator may return to normal over a period of time. The integrated data can be analysed to determine correlations between input/output parameters for each individual animal forming the basis for a range of services informed by the relationship between both and dissemination of alerts through multiple channels.
The paper details the features of a platform that implements PLF management strategies for the dairy industry. The platform elements comprise robust, high node count sensor networks gathering data from individual animals and a cloud based software environment that manages on-farm data and pro-actively alerts the farmer, real time, of key operational and management interventions. The principle is that if the needs of animals at the individual level are properly defined and met, then the needs of farmers and downstream stakeholders including consumers follow. The more precisely that needs are met, the less waste there is in the system, resulting in greater economic and environmental benefits. In turn, the creation of new business models based on provisioning a range of services to livestock farmers becomes possible, promoting the easy uptake of technology to all in the supply chain.
The platform is scalable in terms of handling multiple data streams, able to manage farms increasing in size including hybrid environments and support remote farms harnessing the growth of the connected world. The platform captures data from cows located in different areas, collates this information and presents it to a mix including the farmers/herdsmen, veterinarians, feed specialists over various mediums, such as smartphone, home computer or within the parlour.
Keywords: Data-Driven Applications/Services; Distributed Intelligent Sensor Networks and Applications; Internet of Things Technologies; Low Power Wireless Connectivity; Edge Computing; Machine Learning; Accelerometers; Precision Farming
Title:Taxonomy of Fraud based on Morphological Analysis and Attribute Listing
Dr. Cyril Onwubiko is a Senior Member IEEE, Distinguished Speaker (DVP), & Board of Governor IEEE Computer Society, Past Secretary, IEEE United Kingdom and Ireland, Past Founding Chair, IEEE United Kingdom and Ireland Blockchain Group. As an active IEEE member for over 15 years, he has served in several other roles: Executive Member of the IEEE UK and Ireland Computer Society Chapter, Member of the European Public Policy Committee (EPPC) Working Group on ICT, responsible to the IEEE Board of Directors for the coordination of public policy activities, Reviewer to the IEEE Security & Privacy, IEEE Intelligence & Security Informatics (ISI), Young Professionals, Women in Engineering etc. He is also a Trustee, Board Member and Volunteer to other Charity organisations.
Cyril has over 20 years of experience in Enterprise Security Architecture, Cyber Situational Awareness, Cyber Security, Artificial Intelligence & Blockchain. Currently, he is Director, Enterprise Security Architecture at Pearson Plc, the world’s learning company. He is also Director, Artificial Intelligence, Blockchain & Cyber Security at Research Series Limited, where he directs strategy and governance in AI, Blockchain & Cyber Security. Prior to Pearson Plc, he had worked in the Financial Services, Telecommunication, Health, Government and Public Services Sectors.
He holds a PhD in Computer Network Security from Kingston University, London, UK; MSc in Internet Engineering, and BSc, first class honours, in Computer Science & Mathematics. He has authored and edited several books (8) and published over 40 peer-reviewed articles in leading and prestigious academic journals and conferences. He is the Editor of the Cyber Science series, Editor-in-Chief of the International Journal on Cyber Situational Awareness (IJCSA), and Founder of the Centre for Multidisciplinary Research, Innovation and Collaboration (C-MRiC), a not for profit and nongovernmental organisation dedicated to the advancement of outstanding research and innovation through collaboration (https://www.c-mric.com). For more information, please visit https://www.c-mric.com/cyril
Abstract— A comprehensive taxonomy of fraud is presented based on morphological analysis, attribute listing and matrix analysis. Fraud matrix and tree classification frameworks are presented and discussed. They are then utilized to classify and explain a number of the different types of frauds, shown as products, in the fraud matrix classification framework. First, triangular attributes of fraud are formulated, followed by fraud channels and elementary fraud features. Several well-known fraud types are identified using the proposed fraud classification framework. Further, new fraud types are discovered using the framework, for example, transactional frauds, automated frauds, synchronized fraud, unwitting accomplice, and ‘Robin Hood’ fraud. The importance of the taxonomy is that it can be used to classify both existing and newly identified fraud types in a way and manner that has not been previously reported; that is, it offers understanding to the different classes of frauds, the inherent threat actors behind such frauds, their capabilities, intent and the resulting nature of the frauds. This taxonomy has potential to offer insight in how appropriate countermeasures to mitigating the different types of frauds could be formulated.
Title:A Conversational AI Approach to Detecting Deception and Tackling Insurance Fraud
Dr Julie Wall is a Reader in Computer Science, Director of Impact and Innovation for the School of Architecture, Computing and Engineering and leads the Intelligent Systems Research Group at the University of East London. Her current research focuses on developing machine learning and deep learning approaches for speech enhancement, natural language processing and natural language understanding and she maintains collaborative R&D links with industry. This has led to the successful acceptance of two Innovate UK grants with a combined total value of £2,273,177. Since starting her PhD in 2006, Julie has been exploring the overarching research area of designing intelligent systems for processing and modelling temporal data. This primarily involves investigating the architectures and learning algorithms of neural networks for a variety of data sources.
Speech and natural language technology have advanced at a rapid pace in recent years. This advance, a facet of the industry 4.0 era, has been driven in part by GPU hardware and the deep learning frameworks that use them, and by the adoption of open-source software by the academic and commercial AI community alike. The spirit of cooperation among researchers in the academic and commercial worlds has resulted in claims of human parity in speech recognition models, and the emergence of numerous architectures based on decision trees, DNNs, CNNs, RNNs and Transformers, to mention but a few. These developments have markedly impacted the way in which humans communicate with computers and are currently driving numerous commercial products that rely on speech, natural language processing and natural language understanding, loosely termed Conversational AI. This talk will present a real-world case study in the insurance domain that exploits speech and language to produce an explainable pipeline that identifies and justifies the behavioural elements of a fraudulent claim during a telephone report of an insured loss.
To detect the behavioural features of speech for deception detection, we have curated a robust set of acoustic and linguistic markers that potentially indicate deception in a conversation. Statistical measures and machine learning were used to identify these linguistic markers in the right context. The explainable pipeline means that the output of the decision-making element of the system provides transparent decision explainability, overcoming the “black-box” challenge of traditional AI systems. This patent-pending technology, made possible through the support of funding from UK Research and Innovation (UKRI), is now part of a real-world commercial system, called LexiQal. This talk will outline the LexiQal approach to address the need for an efficient data-driven deep learning transparent approach (Explainable AI) to call analytics, an automated approach to forensic statement analysis, where there is a need to interpret the context of the spoken utterances accurately.