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Now showing 1 - 10 of 117
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    Recreational Motion Simulation: A New Frontier for Virtual Worlds Research
    (The Eurographics Association, 2021) Williams, Benjamin; Headleand, Christopher J.; Xu, Kai and Turner, Martin
    Motion simulation is a developing field which continues to grow with the recent incline in commercial virtual reality. Whilst the majority of motion simulation research focuses on flight simulation and training, its utility in recreational settings is often overlooked. Despite this lack of research, the use of motion simulators for recreational purposes spans decades, and is still today one of the most popular applications of motion simulator devices. Furthermore, with the recent development of low-cost motion simulation platforms, consumers have begun to use these devices in the home. Research regarding motion simulation and its effects in recreational experiences is needed now more than ever, and in this position paper we outline several reasons for its importance.
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    Authoring Visualisation of Routinely Collected Data Using LLMs
    (The Eurographics Association, 2024) Hosseini, Amir; Wood, Jo; Elshehaly, Mai; Hunter, David; Slingsby, Aidan
    The integration of routinely collected healthcare data into decision-making processes has the potential to revolutionise patient care and health outcomes. However, the complexity and heterogeneity of these datasets pose significant challenges for effective querying and analysis. Visualisation supports socio-technical processes where data analytics are augmented with human expertise to overcome data complexity. However, the authorship of effective visualisation is a challenging task, especially for users without a technical background, such as commissioners, clinicians and population health experts. This complexity calls for more efforts to develop natural language interfaces (NLIs) to democratise access to and understanding of routine data through visualisation. This short paper presents an innovative approach utilising Large Language Models (LLMs) to facilitate the querying and visualisation of routinely collected healthcare data. We present a preliminary framework for combining natural language queries with visualisation recommendation systems to retrieve and visualise relevant information from electronic health records (EHRs). We propose a human-in-the-loop approach for establishing accurate and efficient LLM-enabled information retrieval. Our preliminary findings suggest that LLMs can significantly streamline the visualisation authoring process, enabling stakeholders and healthcare professionals to access critical information rapidly and accurately. This work underscores the potential of LLM-driven solutions in advancing healthcare data utilisation and paves the way for future research in this promising intersection of artificial intelligence and medical informatics.
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    Automating Visualization Quality Assessment: a Case Study in Higher Education
    (The Eurographics Association, 2021) Holliman, Nicolas S.; Xu, Kai and Turner, Martin
    We present a case study in the use of machine+human mixed intelligence for visualization quality assessment, applying automated visualization quality metrics to support the human assessment of data visualizations produced as coursework by students taking higher education courses. A set of image informatics algorithms including edge congestion, visual saliency and colour analysis generate machine analysis of student visualizations. The insight from the image informatics outputs has proved helpful for the marker in assessing the work and is also provided to the students as part of a written report on their work. Student and external reviewer comments suggest that the addition of the image informatics outputs to the standard feedback document was a positive step. We review the ethical challenges of working with assessment data and of automating assessment processes.
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    EBBVH: A Novel Method for Constructing Bounding Volume Hierarchies
    (The Eurographics Association, 2024) Houghton, Matthew; Spoerer, Kristian; Hunter, David; Slingsby, Aidan
    We present an attempt to improve upon the construction of the most prevalent acceleration structure that is used in ray traced rendering techniques, the Bounding Volume Hierarchy. Our improvement is a novel technique for BVH construction called 'Edge-Based Bounding Volume Hierarchy'. This algorithm uses a hybrid top-down & bottom-up approach to improve performance for raytracing in large scenes, by up to 10x in some scenes.
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    Extended Reality (XR) Immersive Visualisation: Identifying AI project member 'needs' in order to design for a more effective Low-Code Machine Learning Model Development experience
    (The Eurographics Association, 2022) Wheeler, Richard; Carroll, Fiona; Peter Vangorp; Martin J. Turner
    With organisations making machine learning projects more of a priority, issues have been found regarding the presentation of these types of projects and in particular, in explaining how the models that are produced work, not only internally but also to the final user. The following paper discusses the design and development of a novel Extended Reality (XR) solution that enables rapid development, experimentation and clear presentation of complex machine learning models using eXplainable AI (XAI) principles. The paper documents the findings from a short initial feasibility questionnaire study which probed participant's opinions around their current use of XR environments, low-code development platforms, and their experience of working on machine learning model development projects. The findings of that study showed that the proposed solution could be deemed novel especially regarding its use of extended reality, as none of the participants had used this technology for machine learning development productivity or collaboration. The aim of the paper is to highlight the development of a system that uses a low-code development platform for the development of machine learning models and then uses an extended reality environment to not only enable collaboration within development teams but also as a system for presenting a model's output. This paper documents the early phases of the research process (i.e. identifying the need) whilst also sharing ideas on how the issue can be solved.
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    Recognising Specific Foods in MRI Scans Using CNN and Visualisation
    (The Eurographics Association, 2020) Gardner, Joshua; Al-Maliki, Shatha; Lutton, Évelyne; Boué, François; Vidal, Franck; Ritsos, Panagiotis D. and Xu, Kai
    This work is part of an experimental project aiming at understanding the kinetics of human gastric emptying. For this purpose magnetic resonance imaging (MRI) images of the stomach of healthy volunteers have been acquired using a state-of-art scanner with an adapted protocol. The challenge is to follow the stomach content (food) in the data. Frozen garden peas and petits pois have been chosen as experimental proof-of-concept as their shapes are well defined and are not altered in the early stages of digestion. The food recognition is performed as a binary classification implemented using a deep convolutional neural network (CNN). Input hyperparameters, here image size and number of epochs, were exhaustively evaluated to identify the combination of parameters that produces the best classification. The results have been analysed using interactive visualisation. We prove in this paper that advances in computer vision and machine learning can be deployed to automatically label the content of the stomach even when the amount of training data is low and the data imbalanced. Interactive visualisation helps identify the most effective combinations of hyperparameters to maximise accuracy, precision, recall and F1 score, leaving the end-user evaluate the possible trade-off between these metrics. Food recognition in MRI scans through neural network produced an accuracy of 0.97, precision of 0.91, recall of 0.86 and F1 score of 0.89, all close to 1.
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    Immersive WebXR Data Visualisation Tool
    (The Eurographics Association, 2023) Ogbonda, Ebube Glory; Vangorp, Peter; Hunter, David
    This paper presents a study of a WebXR data visualisation tool designed for the immersive exploration of complex datasets in a 3D environment. The application developed using AFrame, D3.js, and JavaScript enables an interactive, device-agnostic platform compatible with various devices and systems. A user study is proposed to assess the tool's usability, user experience, and mental workload using the NASA Task Load Index (NASA TLX). The evaluation is planned to employ questionnaires, task completion times, and open-ended questions to gather feedback and insights. The anticipated results aim to provide insights into the effectiveness of the application in supporting users in understanding and extracting insights from complex data while delivering an engaging and intuitive experience. Future work will refine and expand the tool's capabilities by exploring interaction guidance, visualisation layout optimisation, and long-term user experience assessment. This research contributes to the growing field of immersive data visualisation and informs future tool design.
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    Towards Developing a Digital application for the Five Design-Sheets Methodology
    (The Eurographics Association, 2022) Owen, Aron E.; Roberts, Jonathan C.; Peter Vangorp; Martin J. Turner
    The Five Design-Sheet Methodology is a sketching methodology that helps people ideate different designs; it has been used to develop computer interfaces, games and data visualisations. Traditionally, it is a paper-based process that structures the developer to think about their design solution over five sheets with five sections. However, with the rise of mobile phones and tablets, there is an emerging opportunity to achieve the sketched design ideation process in a digital form. This work investigates the transition of the Five Design-Sheets from a paper-based methodology into a digital sketching application. The paper introduces how we considered the challenge, and have started to develop an application. Currently our application implements the first sheet of the FdS process. We describe the application and present a brief evaluation of the work with designers and developers.
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    Emotion Transfer for 3D Hand Motion using StarGAN
    (The Eurographics Association, 2020) Chan, Jacky C. P.; Irimia, Ana-Sabina; Ho, Edmond S. L.; Ritsos, Panagiotis D. and Xu, Kai
    In this paper, we propose a new data-driven framework for 3D hand motion emotion transfer. Specifically, we first capture highquality hand motion using VR gloves. The hand motion data is then annotated with the emotion type and converted to images to facilitate the motion synthesis process and the new dataset will be available to the public. To the best of our knowledge, this is the first public dataset with annotated hand motions. We further formulate the emotion transfer for 3D hand motion as an Image-to-Image translation problem, and it is done by adapting the StarGAN framework. Our new framework is able to synthesize new motions, given target emotion type and an unseen input motion. Experimental results show that our framework can produce high quality and consistent hand motions.
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    Interweaving Data and Stories: A Case Study on Unveiling the Human Dimension of U.S. Refugee Movements through Narrative Visualisation
    (The Eurographics Association, 2023) Ogbonda, Ebube Glory; Roberts, Jonathan C.; Butcher, Peter W. S.; Vangorp, Peter; Hunter, David
    In response to the escalating global refugee crisis, we present a case-study of developing an advanced tool for interpreting high-dimensional refugee data. Developed using Mapbox and D3.js, our interactive visualisation harmonises geographical and temporal dimensions of U.S. refugee data from the State Department's Refugee Processing Center. Our modular approach and robust data preprocessing enable seamless interactions among diverse visual components. The result is a narrative-driven visualisation that reveals broad immigration trends and individual refugee movements, fostering a nuanced and empathetic understanding of refugee dynamics. This work highlights the power of narrative visualisations in shaping policy decisions and promoting global discourse on the refugee crisis, marking a significant leap in data visualisation for refugee and immigration challenges.