Browsing by Author "Carroll, Fiona"

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    Chladni Plate Visualisation
    (The Eurographics Association, 2022) Dashti, Sarah; Prakash, Edmond; Navarro-Newball, Andres Adolfo; Hussain, Fiaz; Carroll, Fiona; Peter Vangorp; Martin J. Turner
    The creation of images made out of sound is an ancient discovery from many civilisations, called Cymatics. Cymatics can be referred to as the science of visualising audio frequencies through the Chladni plate. Over the past several years, many scientists, artists and designers have tried to visually and physically represent sound. Physicalising sound was through using liquids and particles as a medium with sound energy to deform and reform the medium aesthetically, creating a unique texture. In the visual arts of computer graphics, the texture is the perceived surface quality showing details of the surface model and colour. The use of texture in computer graphics for modelling and gaming industries is still growing, opening new possibilities for new complex textures yet simple to apply. The paper explores methods of integrating art and science, showing the practices of contemporary Chladni visualisation from an artist's perspective in 3D modelling. The paper also introduces the technique of using computer graphics to compare procedural textures with Chladni's plate representing visual aspects of our novel approach.
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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.