The Centre for Digital Citizens (CDC) will address emerging challenges of digital citizenship, taking an inclusive, participatory approach to the design and evaluation of new technologies and services that support 'smart', 'data-rich' living in urban, rural and coastal communities. Core to the Centre's work will be the incubation of sustainable 'Digital Social Innovations' (DSI) that will ensure digital technologies support diverse end-user communities and will have long-lasting social value and impact beyond the life of the Centre. Our technological innovations will be co-created between academic, industrial, public and third sector partners, with citizens supporting co-creation and delivery of research. Through these activities, CDC will incubate user-led social innovation and sustainable impact for the Digital Economy (DE), at scale, in ways that have previously been difficult to achieve. The CDC will build on a substantial joint legacy and critical mass of DE funded research between Newcastle and Northumbria universities, developing the trajectory of work demonstrated in our highly successful Social Inclusion for the Digital Economy (SIDE) hub, our Digital Civics Centre for Doctoral Training and our Digital Economy Research Centre (DERC). The CDC is a response to recent research that has challenged simplified notions of the smart urban environment and its inhabitants, and highlighted the risks of emerging algorithmic and automated futures. The Centre will leverage our pioneering participatory design and co-creative research, our expertise in digital participatory platforms and data-driven technologies, to deliver new kinds of innovation for the DE, that empowers citizens. The CDC will focus on four critical Citizen Challenge areas arising from our prior work: 'The Well Citizen' addresses how use of shared personal data, and publicly available large-scale data, can inform citizens' self-awareness of personal health and wellbeing, of health inequalities, and of broader environmental and community wellbeing; 'The Safe Citizen' critically examines online and offline safety, including issues around algorithmic social justice and the role of new data technologies in supporting fair, secure and equitable societies; 'The Connected Citizen' explores next-generation citizen-led digital public services, which can support and sustain civic engagement and action in communities, and engagement in wider socio-political issues through new sustainable (openly managed) digital platforms; and 'The Ageless Citizen' investigates opportunities for technology-enhanced lifelong learning and opportunities for intergenerational engagement and technologies to support growth across an entire lifecourse. CDC pilot projects will be spread across the urban, rural and costal geography of the North East of England, embedded in communities with diverse socio-economic profiles and needs. Driving our programme to address these challenges is our 'Engaged Citizen Commissioning Framework'. This framework will support citizens' active engagement in the co-creation of research and critical inquiry. The framework will use design-led 'initiation mechanisms' (e.g. participatory design workshops, hackathons, community events, citizen labs, open innovation and co-production platform experiments) to support the co-creation of research activities. Our 'Innovation Fellows' (postdoctoral researchers) will engage in a 24-month social innovation programme within the CDC. They will pilot DSI projects as part of highly interdisciplinary, multi-stakeholder teams, including academics and end-users (e.g. Community Groups, NGO's, Charities, Government, and Industry partners). The outcome of these pilots will be the development of further collaborative bids (Research Council / Innovate UK / Charity / Industry funded), venture capital pitches, spin-outs and/or social enterprises. In this way the Centre will act as a catalyst for future innovation-focused DE activity.

1) To create the next generation of Natural Language Processing experts, stimulating the growth of NLP in the public and private sectors domestically and internationally. A pool of NLP talent will provide incentives for (existing) companies to expand their operations in the UK and lead to start-ups and new products. 2) To deliver a programme which will have a transformative effect on the students that we train and on the field as a whole, developing future leaders and producing cutting-edge research in both methodology and applications. 3) To give students a firm grounding in the challenge of working with language in a computational setting and its relevance to critical engineering and scientific problems in our modern world. The Centre will also train them in the key programming, engineering, and machine learning skills necessary to solve NLP problems. 4) To attract students from a broad range of backgrounds, including computer science, AI, maths and statistics, linguistics, cognitive science, and psychology and provide an interdisciplinary cohort training approach. The latter involves taught courses, hands-on laboratory projects, research-skills training, and cohort-based activities such as specialist seminars, workshops, and meetups. 5) To train students with awareness of user design, ethics and responsible research in order to design systems that improve user statisfaction, treat users fairly, and increase the uptake of NLP technology across cultures, social groups and languages.

As our software systems grow in size and complexity, increasingly diverse users have different wants and needs from their languages: the right language for a statistician (e.g. R) is different from that of someone who formally verifies safety properties (e.g. OCaml), which is different again from someone creating user-facing apps (e.g. Javascript). However, different languages inhabit different silos and interactions between them are crude and slow. Language composition has long been touted as the solution to this problem, allowing languages to be used together in a fine-grained way, but has traditionally struggled to match this promise. In the Lecture Fellowship, my team and I showed that large, messy, real-world languages can be composed together, even allowing different languages to be intermingled within a single line of code. We were able to make the performance of such multi-lingual programs close to their mono-language constituents, showing that language composition's promise is real. However, in the course of this research, an unexpected problem became apparent: Virtual Machines (VMs), the systems used to make many languages run fast (and which are crucial to the good performance of language composition), do not perform as expected. In the largest VM experiment to date, we showed that VMs perform incorrectly in around 60% of cases. Attempts to fix existing VMs have largely failed, because the problems are so deeply embedded that they cannot be teased out, even after careful examination. This is a significant problem for language composition, for which VMs are a foundational pillar. This Fellowship Extension thus aims to show that VMs can have good, predictable performance and that they are a suitable foundational pillar for language composition. However, we cannot expect to create a traditional VM, which often consume tens, hundreds, or thousands of person years of effort. Instead, my team and I will create a new meta-tracing VM system, since history shows that these can be created in a small number of person years. Fortunately for us, meta-tracing has also been shown as the fastest way to run multi-lingual programs, so it is a natural fit. We will rigorously benchmark the new meta-tracing system we create from the beginning of, and throughout, its development. This will enable us to observe performance regressions soon after they occur, allowing us to fix them quickly. We will also take the opportunity to address one of meta-tracing's biggest weaknesses: its slow warmup, that is the time between a program starting, and JIT compilation completing. Tracing currently involves a software interpreter interpreting a software interpreter, with a 100-200x overhead when a loop is traced. We will use the Processor Trace (PT) feature found in recent x86 chips to move the software part of meta-tracing into hardware, giving a roughly 100x speed-up to this critical phase of the system. That will also allow us to be more aggressive in optimising other parts of the tracer that currently cause poor warm-up. At the end of this Fellowship Extension, alongside traditional research papers, we will produce an open-source release of our new meta-tracing system. This will allow others to build on our work, be that for language composition, or simply to make individual languages run fast.