We are Increasingly being immersed in a technology-mediated world, where the omni-presence of data introduces increased needs in mechanisms facilitating in-situ cognition, reasoning and sensemaking [2, 3]. In parallel, edge computing, facilitated by future networks, such as 5G, is transforming the way data is being processed and delivered from millions of devices around the world, bringing computing and analytics close to where the data is created [1]. Building on these synergies, this project will investigate the use of edge-based object recognition using distributed neural networks (DNN), as a mechanism for in-situ registration and data processing for mobile, Web-based Immersive Analytics (IA) in Extended Reality (XR). Object-recognition can provide accurate and real-time registration [1], yet its practical application still faces important challenges. Current object-recognition systems are either self-contained, or cloud-based, yet face low latency and poor user experience respectively. Deep Learning, and DNNs, can provide effective solutions for object detection, and ameliorate these challenges [1]. In addition, they have the potential to provide adaptive MR interfaces, and multimodal sensing capabilities useful for advanced IA experiences [2]. [1] P. Ren, X. Qiao, Y. Huang, L. Liu, S. Dustdar and J. Chen, Edge-Assisted Distributed DNN Collaborative Computing Approach for Mobile Web Augmented Reality in 5G Networks, in IEEE Network, vol. 34, no. 2, pp. 254-261, March/April 2020, doi: 10.1109/MNET.011.1900305. [2] P. W. S. Butcher, N. W. John and P.D. Ritsos, VRIA: A Web-based Framework for Creating Immersive Analytics Experiences, in IEEE Transactions on Visualization and Computer Graphics (Early Access), 2020 doi: 10.1109/TVCG.2020.2965109. [3] J. C. Roberts, P.D. Ritsos, S. K. Badam, D. Brodbeck, J. Kennedy and N. Elmqvist, Visualization beyond the Desktop--the Next Big Thing, in IEEE Computer Graphics and Applications, vol. 34, no. 6, pp. 26-34, Nov.-Dec. 2014, doi: 10.1109/MCG.2014.82.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Quality of residential care for people with severe dementia is poor and in urgent need of improvement. While improving quality of care is important for all residents, there is a risk that people with more severe dementia, many of whom no longer communicate verbally, will be marginalised and fail to have their psychological and social needs met. Carers may assume that people with severe dementia are unaware of their surroundings and of what is happening to them. As a result, carers may focus on getting tasks done without paying much attention to the reactions and responses of the resident. Recent research indicates, however, that people with very severe dementia may still retain a degree of awareness. If care staff can be trained to observe the signs of awareness, this should help them to increase their sensitivity to the needs of the resident and to enhance their care practice. Training for care staff that includes both information provision and ongoing support for implementing new skills can be very effective in improving resident well-being. In this study we aim to develop a measure that staff can use to help them observe subtle signs of awareness, to provide staff with training and support in using this measure, and to evaluate the effects on resident quality of life and on staff attitudes and practices. In developing the measure we will use as a starting-point a similar measure developed for people with severe brain injury, and we will hold focus groups for carers to establish how the measure should be initially adapted. We will then use the measure to observe residents? behaviour and responses, and refine it accordingly. Finally we will carry out an intervention study comparing homes where staff receive training and support in using the new measure with homes where residents simply receive their usual care. We expect to see improvements in resident quality of life and staff attitudes and practice in the homes where staff received training, but not in the homes where residents received usual care. The information we gain from this initial study will allow us to develop a definitive trial of the intervention that will provide sound evidence for practitioners and policy-makers. In the meantime, the findings will influence training provision for care staff, and the new measure of awareness in people with severe dementia will be useful for both practitioners and researchers.

We live in a period of unprecedented data availability, but not all data are equal - with quantitative data sometimes viewed as more reliable, robust and/or useful than qualitative data. This is particularly problematic when conducting the interdisciplinary research necessary to address the most important global challenges, such as climate change and sustainable development. For example, the benefits human's derive from nature are categorized into three types: provisioning services (products obtained from ecosystems; e.g. food), regulating services (benefits obtained from the regulation of ecosystem processes; e.g. regulation of air quality), and cultural services (non-material benefits people obtain from ecosystems; e.g. cultural heritage or spiritual enrichment). Whilst both provisioning and regulating services can be quantified at local and global scales, cultural services are often viewed as 'unquantifiable', being spatially and temporally distinct, intangible, subtle, mutable and intuitive in nature, based on ethical and philosophical perception - thus largely unique to the individual. As such, most nature-based research is dominated by the relatively easily quantified provisioning and regulating services, which are readily monetized to enable comparisons across services. The same is not true of cultural services and how to combine these data to holistically value nature's contributions to people is unknown. We seek to address this here. Using existing data from seven national surveys across Wales (1000 respondents per survey; 3 surveys complete [Jan-Jun 2020], 1 ongoing, 3 planned in Jan-Jun '21). Using Supercomputing Wales, we will use bespoke Natural Language Processing (NLP) to analyse the quantitative data within these surveys, understanding how people's reasons for spending time in greenspace change from before, during and after the ongoing coronavirus crisis. These qualitative data contain free text responses in both English and Welsh, and our cross-language analysis would compare responses to see if there are any specific language differences, as well as differences between genders and socioeconomic groups. Finally, advanced visualization techniques will be developed to enable the comparison of the qualitative free text responses and quantitative survey data, which includes distance travelled and length and regularity of visits. The ability to visualise both quantitative and qualitative data at national-scales may transform sustainable decision-making. [1] Willcock, S., Camp, B. J., & Peh, K. S. H. (2017). A comparison of cultural ecosystem service survey methods within South England. Ecosystem Services, 26, 445-450. [2] W.J. Teahan. 2018. A Compression-Based Toolkit for Modelling and Processing Natural Language Text, Information, Vol. 9, No. 294. MDPI Publishers. doi:10.3390/infoxx010001. [3] Rick Walker, Llyr ap Cenydd, Serban Pop, Helen C Miles, Chris J Hughes, William J Teahan, and Jonathan C Roberts. 2013. Storyboarding for visual analytics. Journal of Information Visualization.