The FinTech industry is one of the major growth industries in the United Kingdom. These companies create new, cheaper and faster services, utilizing the latest technologies such as cloud, mobile and blockchain. To succeed they need to gain the trust of customers in a period that society's trust in the financial industry is still impacted by the mortgage crisis almost a decade ago. They need to gain this trust while technologies are changing rapidly and data breaches are continuously in the news. FinTrust will research the issue of trust in FinTech, identifying the generic research challenges and establishing fundamental research results. A particular focus will be on increased automation through the use of machine learning algorithms, which may have implications that affect consumer trust in the new services. For the first time, research results as derived in FinTrust will be made available as a Trust Engineering Tool Kit, allowing service provider, regulators and consumer organizations to assess levels of trust, quantify the extent to which trust matters to consumers and trust attitude differentiates consumers, assess bias in automated advice, establish whether algorithms may lead to financial exclusion or financial distress and protect services from erroneous algorithmic results. In our work we balance the commercial interest with society's interests, addressing issues such as financial exclusion through algorithmic bias. FinTrust offers a focused pathway to academic, societal and economic impact. Starting from the co-creation effort with Atom Bank that resulted in the proposal, we grow the Stakeholder Panel to include customer organizations, regulators and other stakeholders. We then establish a joint-up academic community in a FinTech Network, bringing together ICT, EPSRC and other research and intertwine this with existing research networks in finance and business research. We will make the software tools available freely and to a wide audience, training the research staff in interacting and public engagement to optimize the impact from our research.

Biometrics has been widely utilized in the past two decades in many areas such as healthcare, banking, surveillance, and security control. Given the increased uptake of internet and mobile computing globally, many companies have been turning to biometric privacy and security to ensure secure communication. However, biometric verification over third-party or public network servers may be abusively exploited in an unauthorized way. To protect the privacy and improve the security, it has been advocated to carry out biometric verification in encrypted or transformed domains, where privacy and security can be more effectively guaranteed. The basic idea behind the project is that the biometrics in the irreversible encrypted/transformed domains contains exactly the same amount of information as its original one, and hence one can establish a pattern recognition methodology to determine/extract useful information from chaotic signals in encrypted/transformed domains. This First Grant Scheme project aims to investigate how to discover and evaluate the information from chaotic signals for discriminative power, and develop robust pattern recognition schemes for biometric/multi-biometric verification in encrypted/transformed domains. The proposed methods/schemes will be vigorously validated over typical wild face/speech/gait datasets, and two practical demo systems (biometric banking and pedestrian profiling) will be designed and tested in real world environments. The project will focus on both theoretical understanding of chaotic information and application-specific exploitation of chaotic pattern recognition. Considering multiple data structures hidden beneath a set of given chaotic signals, I will develop a robust way to find out the underlying various data structures for data understanding, clustering and classification. On the other side, given a specific issue such as encrypted/transformed biometric verification, one need to examine the generic theoretic findings in this specific topic and develop a robust scheme for biometric human identification. The work of this project is within the areas of signal processing, machine learning and pattern analysis. The research on encryted/transformed biometric verification has come from the practical new needs of the UK's emerging new businesses. The project will provide the understanding needed to allow the future development of robust biometric verification methods with novel applications.

Biometrics has been widely utilized in the past two decades in many areas such as healthcare, banking, surveillance, and security control. Given the increased uptake of internet and mobile computing globally, many companies have been turning to biometric privacy and security to ensure secure communication. However, biometric verification over third-party or public network servers may be abusively exploited in an unauthorized way. To protect the privacy and improve the security, it has been advocated to carry out biometric verification in encrypted or transformed domains, where privacy and security can be more effectively guaranteed. The basic idea behind the project is that the biometrics in the irreversible encrypted/transformed domains contains exactly the same amount of information as its original one, and hence one can establish a pattern recognition methodology to determine/extract useful information from chaotic signals in encrypted/transformed domains. This First Grant Scheme project aims to investigate how to discover and evaluate the information from chaotic signals for discriminative power, and develop robust pattern recognition schemes for biometric/multi-biometric verification in encrypted/transformed domains. The proposed methods/schemes will be vigorously validated over typical wild face/speech/gait datasets, and two practical demo systems (biometric banking and pedestrian profiling) will be designed and tested in real world environments. The project will focus on both theoretical understanding of chaotic information and application-specific exploitation of chaotic pattern recognition. Considering multiple data structures hidden beneath a set of given chaotic signals, I will develop a robust way to find out the underlying various data structures for data understanding, clustering and classification. On the other side, given a specific issue such as encrypted/transformed biometric verification, one need to examine the generic theoretic findings in this specific topic and develop a robust scheme for biometric human identification. The work of this project is within the areas of signal processing, machine learning and pattern analysis. The research on encryted/transformed biometric verification has come from the practical new needs of the UK's emerging new businesses. The project will provide the understanding needed to allow the future development of robust biometric verification methods with novel applications.

Our vision, as a consortium of nine Universities and six Industrial Partners, is to shape an actively inclusive culture in the Engineering and Physical Sciences (EPS) community that supports, drives and sustains greater equality for all, including traditionally under-represented groups (e.g., women, disabled people, LGBT+, and Black, Asian and Minority Ethnic (BAME) researchers). Having identified seven critical challenges to this vision (lack of role models; a leaky pipeline; inequity in opportunities; lack of synergy in initiatives; lack of understanding of barriers by some senior leaders; poor data; and poor analysis of progress), we outline six activities designed to address them: shared-characteristic mentoring; reverse mentoring; an on-line networking platform; leadership and networking development; collaboration with industry; better data capture; and better analysis. The critical feature of this bid is that we will pool opportunities and experience across our consortium to better meet the needs of groups underrepresented in EPS. As a result of this inclusive culture, a wider pool of talented individuals will be able to successfully progress within the EPS community and these resulting diverse perspectives will ultimately produce better science to address complex and important global challenges. HEI partners: Durham University, Lancaster University, Leeds Beckett University, Newcastle University, Northumbria University, Teesside University, University of Huddersfield, University of Hull, and Leeds University. Industrial Partners: Atom Bank, Stanley Black and Decker, IBM, GTN Ltd., Northumbrian Water Ltd. and SAGE. Our consortium of nine HEIs, from a wide base but all based in the North of England, and six industrial partners, from a range of EPS-facing domains, are all committed to continuous improvement with regard to Equality, Diversity and Inclusion (EDI) in EPS. Our aim is to take a collaborative approach, drawing together resources, opportunities, experience and best practice to build a consolidated arena within which to influence change. By pooling our resources we will be able to meet the needs of groups under-represented in EPS more effectively. By the end of the project we will have: - a better understanding and awareness of the barriers and challenges facing under-represented groups within the EPS community. - an assessment of interventions which aim to improve the representation of groups, currently under-represented within EPS. Critically this will also include a robust evaluation to understand the conditions under which things work well and don't work well for different communities. - Our proposal is that these interventions will result in groups under-represented in EPS having: a greater willingness to act as mentors; a greater motivation to apply for promotion and grants; a desire to take on leadership roles; a great awareness of role models; a stronger sense of belonging within the North of England's EPS community, and the EPS community in general; and better links with industry. - established and shared best practice with regard to developing inclusive EPS communities from HEIs and industry (and beyond) with HEIs, research councils, industry and policy makers through seminars, publications and an online platform. - This will result in HEIs and industries sharing, changing and introducing policies to adopt this best practice. Longer term legacy impact will be seen in: changes to practices (such as training) and policies within the consortium and beyond; establishment of best practice which will be disseminated so that other key stakeholders adopt it; a more inclusive environment within the EPS community, demonstrated by a more diverse staff and student base including within senior roles; and adoption of this best practice beyond the EPS community.

The online world is a curious but uncertain world. It enriches many facets of life but at the same time exposes citizens to a variety of threats that may cause harm to them, their loved ones and to wider society. Many of these harms result from a complex interaction of societal processes driven by diverse stakeholders-we call these Complex Harms. Consider for example smart homes, with devices that manage energy usage, CCTV cameras for the garage and increasingly integrated IT components throughout the house. With such technology, the dynamics in families may change, for instance offering monitoring capabilities. This may results in harms that may include domestic violence, loss of privacy and gathering of disproprotionately large sets of population data by large industries. This raises a number of questions: What is the role of the individual, friends and family to mitigate potential harms? How can one work with the device provider to minimize harm? Should the law interfere? The AGENCY project will use a number of case studies to answer these questions: HealthTech, Identity Management, Smart Homes and Online Disinformation. Complex harms such as above tend to happen to citizens, and, in most cases, they are not purposely caused or easily controlled by citizens. The AGENCY project believes firmly that establishing citizen agency is an absolutely necessary ingredient for any transformative approaches that resolve these complex harms. Citizens need to be empowered through agency-enhanching technologies, behaviours and processes to gain a sense of control, ownership, security, and consequently trust in their online activities. Protecting against complex harms is a wicked problem because so many stakeholder are involved, and because many harms are unintended consequences of the practical use and evolution of technology. Therefore, mitigating complex harms requires interdisciplinary co-design principles, technology foundations and collaborative governance procedures to assure online citizen agency in the presence of multiple stakeholder interests. The project brings together computer science, user-centered design, business, psychology, sociology, legal and ethical experts. If AGENCY succeeds, it will provide a profound understanding of the role of online agency in protecting citizens and will deliver collaborative methods, technological building blocks and scientifically grounded best practices for our society to provide more proactive and structured approaches to protecting citizens online.