[Background] Individuals unfamiliar with sign language are often unsure if it is universal. In fact, sign language is not a universal language: each country, (and sometimes different regions within countries) has its own sign language. In mainland Europe, which is characterised by landlocked countries, International Sign is used during meetings. In the UK and Japan, which are island nations, research has focused more on regional sign language than on contact between different sign languages. In Deaf people's folklore, when deaf individuals who use different sign languages encounter one another, they are capable of engaging in basic conversation after around 30 minutes. This is likely because sign language is a highly visual language and deaf people are skilled in visual communication. This phenomenon has recently been labelled 'cross-signing', and has attracted considerable attention in the fields of cognitive linguistics and communication (Zeshan, 2015; Byun et al., 2017). [Target] This study focuses on how deaf people from different regions and different countries participate in translanguaging when cross-signing, which is a specific feature of communications among deaf people, and analyses improvisations and changes in sign language communication styles in the context of video conferencing systems. [Research Methods] We will perform surveys, and language and interaction analyses. Also, a next-generation sign language corpus will be devised, with movements detected using AI techniques for annotation purposes. Interviews will be performed to clarify deaf people's attitudes towards video conferencing communications, and a large-scale questionnaire will be distributed to determine the time spent using video conferencing systems, the number of people communicated with via this modality, and the type of sign language used (according to region and generation) before and after the COVID-19 pandemic. This research will focus on changes in sign language expressions due to language contact during video conferences, considering translanguaging and cross-signing practices. The interaction analysis will consider basic CA concepts, such as turn-taking and repair sequences. [Scientific Significance] The scientific significance of this research is to understand how sign language users have been affected by the major changes in the communication environment precipitated by the COVID-19 pandemic. This situation is a rare example of rapid penetration of information technology into a community using a specific language, and has significance in terms of cultural anthropology and linguistic anthropology, especially given that humanity may encounter a similar situation in the future.

Imagine a future where autonomous systems are widely available to improve our lives. In this future, autonomous robots unobtrusively maintain the infrastructure of our cities, and support people in living fulfilled independent lives. In this future, autonomous software reliably diagnoses disease at early stages, and dependably manages our road traffic to maximise flow and minimise environmental impact. Before this vision becomes reality, several major limitations of current autonomous systems need to be addressed. Key among these limitations is their reduced resilience: today's autonomous systems cannot avoid, withstand, recover from, adapt, and evolve to handle the uncertainty, change, faults, failure, adversity, and other disruptions present in such applications. Recent and forthcoming technological advances will provide autonomous systems with many of the sensors, actuators and other functional building blocks required to achieve the desired resilience levels, but this is not enough. To be resilient and trustworthy in these important applications, future autonomous systems will also need to use these building blocks effectively, so that they achieve complex technical requirements without violating our social, legal, ethical, empathy and cultural (SLEEC) rules and norms. Additionally, they will need to provide us with compelling evidence that the decisions and actions supporting their resilience satisfy both technical and SLEEC-compliance goals. To address these challenging needs, our project will develop a comprehensive toolbox of mathematically based notations and models, SLEEC-compliant resilience-enhancing methods, and systematic approaches for developing, deploying, optimising, and assuring highly resilient autonomous systems and systems of systems. To this end, we will capture the multidisciplinary nature of the social and technical aspects of the environment in which autonomous systems operate - and of the systems themselves - via mathematical models. For that, we have a team of Computer Scientists, Engineers, Psychologists, Philosophers, Lawyers, and Mathematicians, with an extensive track record of delivering research in all areas of the project. Working with such a mathematical model, autonomous systems will determine which resilience- enhancing actions are feasible, meet technical requirements, and are compliant with the relevant SLEEC rules and norms. Like humans, our autonomous systems will be able to reduce uncertainty, and to predict, detect and respond to change, faults, failures and adversity, proactively and efficiently. Like humans, if needed, our autonomous systems will share knowledge and services with humans and other autonomous agents. Like humans, if needed, our autonomous systems will cooperate with one another and with humans, and will proactively seek assistance from experts. Our work will deliver a step change in developing resilient autonomous systems and systems of systems. Developers will have notations and guidance to specify the socio-technical norms and rules applicable to the operational context of their autonomous systems, and techniques to design resilient autonomous systems that are trustworthy and compliant with these norms and rules. Additionally, developers will have guidance to build autonomous systems that can tolerate disruption, making the system usable in a larger set of circumstances. Finally, they will have techniques to develop resilient autonomous systems that can share information and services with peer systems and humans, and methods for providing evidence of the resilience of their systems. In such a context, autonomous systems and systems of systems will be highly resilient and trustworthy.