4,111 research outputs found

    Mobile heritage practices. Implications for scholarly research, user experience design, and evaluation methods using mobile apps.

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    Mobile heritage apps have become one of the most popular means for audience engagement and curation of museum collections and heritage contexts. This raises practical and ethical questions for both researchers and practitioners, such as: what kind of audience engagement can be built using mobile apps? what are the current approaches? how can audience engagement with these experience be evaluated? how can those experiences be made more resilient, and in turn sustainable? In this thesis I explore experience design scholarships together with personal professional insights to analyse digital heritage practices with a view to accelerating thinking about and critique of mobile apps in particular. As a result, the chapters that follow here look at the evolution of digital heritage practices, examining the cultural, societal, and technological contexts in which mobile heritage apps are developed by the creative media industry, the academic institutions, and how these forces are shaping the user experience design methods. Drawing from studies in digital (critical) heritage, Human-Computer Interaction (HCI), and design thinking, this thesis provides a critical analysis of the development and use of mobile practices for the heritage. Furthermore, through an empirical and embedded approach to research, the thesis also presents auto-ethnographic case studies in order to show evidence that mobile experiences conceptualised by more organic design approaches, can result in more resilient and sustainable heritage practices. By doing so, this thesis encourages a renewed understanding of the pivotal role of these practices in the broader sociocultural, political and environmental changes.AHRC REAC

    Self-supervised learning for transferable representations

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    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    Metro systems : Construction, operation and impacts

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    Peer reviewedPublisher PD

    Sound Event Detection by Exploring Audio Sequence Modelling

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    Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing a sound recognition system are, which portion of a sound event should the system analyse, and what proportion of a sound event should the system process in order to claim a confident detection of that particular sound event. While the classification of sound events has improved a lot in recent years, it is considered that the temporal-segmentation of sound events has not improved in the same extent. The aim of this thesis is to propose and develop methods to improve the segmentation and classification of everyday sound events in SED models. In particular, this thesis explores the segmentation of sound events by investigating audio sequence encoding-based and audio sequence modelling-based methods, in an effort to improve the overall sound event detection performance. In the first phase of this thesis, efforts are put towards improving sound event detection by explicitly conditioning the audio sequence representations of an SED model using sound activity detection (SAD) and onset detection. To achieve this, we propose multi-task learning-based SED models in which SAD and onset detection are used as auxiliary tasks for the SED task. The next part of this thesis explores self-attention-based audio sequence modelling, which aggregates audio representations based on temporal relations within and between sound events, scored on the basis of the similarity of sound event portions in audio event sequences. We propose SED models that include memory-controlled, adaptive, dynamic, and source separation-induced self-attention variants, with the aim to improve overall sound recognition

    Intelligent ultrasound hand gesture recognition system

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    With the booming development of technology, hand gesture recognition has become a hotspot in Human-Computer Interaction (HCI) systems. Ultrasound hand gesture recognition is an innovative method that has attracted ample interest due to its strong real-time performance, low cost, large field of view, and illumination independence. Well-investigated HCI applications include external digital pens, game controllers on smart mobile devices, and web browser control on laptops. This thesis probes gesture recognition systems on multiple platforms to study the behavior of system performance with various gesture features. Focused on this topic, the contributions of this thesis can be summarized from the perspectives of smartphone acoustic field and hand model simulation, real-time gesture recognition on smart devices with speed categorization algorithm, fast reaction gesture recognition based on temporal neural networks, and angle of arrival-based gesture recognition system. Firstly, a novel pressure-acoustic simulation model is developed to examine its potential for use in acoustic gesture recognition. The simulation model is creating a new system for acoustic verification, which uses simulations mimicking real-world sound elements to replicate a sound pressure environment as authentically as possible. This system is fine-tuned through sensitivity tests within the simulation and validate with real-world measurements. Following this, the study constructs novel simulations for acoustic applications, informed by the verified acoustic field distribution, to assess their effectiveness in specific devices. Furthermore, a simulation focused on understanding the effects of the placement of sound devices and hand-reflected sound waves is properly designed. Moreover, a feasibility test on phase control modification is conducted, revealing the practical applications and boundaries of this model. Mobility and system accuracy are two significant factors that determine gesture recognition performance. As smartphones have high-quality acoustic devices for developing gesture recognition, to achieve a portable gesture recognition system with high accuracy, novel algorithms were developed to distinguish gestures using smartphone built-in speakers and microphones. The proposed system adopts Short-Time-Fourier-Transform (STFT) and machine learning to capture hand movement and determine gestures by the pretrained neural network. To differentiate gesture speeds, a specific neural network was designed and set as part of the classification algorithm. The final accuracy rate achieves 96% among nine gestures and three speed levels. The proposed algorithms were evaluated comparatively through algorithm comparison, and the accuracy outperformed state-of-the-art systems. Furthermore, a fast reaction gesture recognition based on temporal neural networks was designed. Traditional ultrasound gesture recognition adopts convolutional neural networks that have flaws in terms of response time and discontinuous operation. Besides, overlap intervals in network processing cause cross-frame failures that greatly reduce system performance. To mitigate these problems, a novel fast reaction gesture recognition system that slices signals in short time intervals was designed. The proposed system adopted a novel convolutional recurrent neural network (CRNN) that calculates gesture features in a short time and combines features over time. The results showed the reaction time significantly reduced from 1s to 0.2s, and accuracy improved to 100% for six gestures. Lastly, an acoustic sensor array was built to investigate the angle information of performed gestures. The direction of a gesture is a significant feature for gesture classification, which enables the same gesture in different directions to represent different actions. Previous studies mainly focused on types of gestures and analyzing approaches (e.g., Doppler Effect and channel impulse response, etc.), while the direction of gestures was not extensively studied. An acoustic gesture recognition system based on both speed information and gesture direction was developed. The system achieved 94.9% accuracy among ten different gestures from two directions. The proposed system was evaluated comparatively through numerical neural network structures, and the results confirmed that incorporating additional angle information improved the system's performance. In summary, the work presented in this thesis validates the feasibility of recognizing hand gestures using remote ultrasonic sensing across multiple platforms. The acoustic simulation explores the smartphone acoustic field distribution and response results in the context of hand gesture recognition applications. The smartphone gesture recognition system demonstrates the accuracy of recognition through ultrasound signals and conducts an analysis of classification speed. The fast reaction system proposes a more optimized solution to address the cross-frame issue using temporal neural networks, reducing the response latency to 0.2s. The speed and angle-based system provides an additional feature for gesture recognition. The established work will accelerate the development of intelligent hand gesture recognition, enrich the available gesture features, and contribute to further research in various gestures and application scenarios

    Evaluating the sustainability and resiliency of local food systems

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    With an ever-rising global population and looming environmental challenges such as climate change and soil degradation, it is imperative to increase the sustainability of food production. The drastic rise in food insecurity during the COVID-19 pandemic has further shown a pressing need to increase the resiliency of food systems. One strategy to reduce the dependence on complex, vulnerable global supply chains is to strengthen local food systems, such as by producing more food in cities. This thesis uses an interdisciplinary, food systems approach to explore aspects of sustainability and resiliency within local food systems. Lifecycle assessment (LCA) was used to evaluate how farm scale, distance to consumer, and management practices influence environmental impacts for different local agriculture models in two case study locations: Georgia, USA and England, UK. Farms were grouped based on urbanisation level and management practices, including: urban organic, peri-urban organic, rural organic, and rural conventional. A total of 25 farms and 40 crop lifecycles were evaluated, focusing on two crops (kale and tomatoes) and including impacts from seedling production through final distribution to the point of sale. Results were extremely sensitive to the allocation of composting burdens (decomposition emissions), with impact variation between organic farms driven mainly by levels of compost use. When composting burdens were attributed to compost inputs, the rural conventional category in the U.S. and the rural organic category in the UK had the lowest average impacts per kg sellable crop produced, including the lowest global warming potential (GWP). However, when subtracting avoided burdens from the municipal waste stream from compost inputs, trends reversed entirely, with urban or peri-urban farm categories having the lowest impacts (often negative) for GWP and marine eutrophication. Overall, farm management practices were the most important factor driving environmental impacts from local food supply chains. A soil health assessment was then performed on a subset of the UK farms to provide insight to ecosystem services that are not captured within LCA frameworks. Better soil health was observed in organically-farmed and uncultivated soils compared to conventionally farmed soils, suggesting higher ecosystem service provisioning as related to improved soil structure, flood mitigation, erosion control, and carbon storage. However, relatively high heavy metal concentrations were seen on urban and peri-urban farms, as well as those located in areas with previous mining activity. This implies that there are important services and disservices on farms that are not captured by LCAs. Zooming out from a focus on food production, a qualitative methodology was used to explore experiences of food insecurity and related health and social challenges during the COVID-19 pandemic. Fourteen individuals receiving emergency food parcels from a community food project in Sheffield, UK were interviewed. Results showed that maintaining food security in times of crisis requires a diverse set of individual, household, social, and place-based resources, which were largely diminished or strained during the pandemic. Drawing upon social capital and community support was essential to cope with a multiplicity of hardship, highlighting a need to develop community food infrastructure that supports ideals of mutual aid and builds connections throughout the food supply chain. Overall, this thesis shows that a range of context-specific solutions are required to build sustainable and resilient food systems. This can be supported by increasing local control of food systems and designing strategies to meet specific community needs, whilst still acknowledging a shared global responsibility to protect ecosystem, human, and planetary health

    “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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    Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts

    The Value of Self-Reflexivity for Learning: A Study of Self-Reflexive Practice in Photography Criticism

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    This study is an exploration of the learning value of self-reflexivity within practices of interpretation, undertaken in the context of photography criticism. Reflexivity, understood in this study as the practice of making the researcher’s presence explicit within a research-based interpretation, is an established part of research methodology in a number of disciplines, but is rarely employed in writing about photography and is seldom discussed in the discipline’s theoretical literature. The study addresses this apparent ‘gap’ by exploring self-reflexivity’s potential to produce metacognitive and other learning gains, and personal growth, both for researchers/practitioners of photography criticism and for readers/audiences of criticism. The study has found that self-reflexivity based on critical self-reflection and disclosure can reveal the pervasive entanglement of thinking and feeling in critical interpretation, and the links between thinking/feeling and the researcher/practitioner’s life history. These links, which indicate that criticality can have an affective and biographical basis, are not generally recognised explicitly in photography writing, and the findings support the case for a new, ‘postcritical’ form of criticism – one interested in subjectivity and the operations of feeling – that is beginning to emerge within the discipline and cognate fields. More broadly, the study has developed an expanded conception of self-reflection as a mechanism for producing learning and growth when used within a self-reflexive interpretive framework. The study has found that by revealing links between thinking, feeling and life history, self-reflection can generate transformed, enriched and extended forms of understanding about photographs, ourselves and the world, and forms of personal growth, in such a way that suggests it can lead to a ‘new’ or largely unrecognised threshold concept, understood as the most powerful type of learning concept, within practices of criticism. The capacity of self-reflection to lead to alteration and enrichment of interpretation in fields such as visual culture seems to be underexplored in practical education research, and the study thus makes a significant contribution to reflective practice studies as well as to threshold concepts research, with direct implications for how critical studies curricula within arts-based higher education are designed and how the subject might be taught and learned. The study was undertaken as a self-reflexive, practice-led research inquiry, first by the researcher (a practising photography critic and lecturer) working independently, and later with a group of eight academic colleagues acting as research participants. To produce its findings, the study has pioneered an innovative nine-step protocol for engaging in self-reflexive interpretation. Practical guidelines and protocols for undertaking reflexive research seem to be rare in any field and are thought to be unprecedented in photography criticism and related disciplines. The study’s self-reflexive analytic protocol therefore represents a substantial contribution to interpretive research methodology, as well as an effective practical learning tool that is ready to use in formal education settings and beyond. The protocol is presented here, however, as the first iteration of a methodological tool that is likely to continue to evolve with further use

    Archaeological palaeoenvironmental archives: challenges and potential

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    This Arts and Humanities Research Council (AHRC) sponsored collaborative doctoral project represents one of the most significant efforts to collate quantitative and qualitative data that can elucidate practices related to archaeological palaeoenvironmental archiving in England. The research has revealed that archived palaeoenvironmental remains are valuable resources for archaeological research and can clarify subjects that include the adoption and importation of exotic species, plant and insect invasion, human health and diet, and plant and animal husbandry practices. In addition to scientific research, archived palaeoenvironmental remains can provide evidence-based narratives of human resilience and climate change and offer evidence of the scientific process, making them ideal resources for public science engagement. These areas of potential have been realised at an imperative time; given that waterlogged palaeoenvironmental remains at significant sites such as Star Carr, Must Farm, and Flag Fen, archaeological deposits in towns and cities are at risk of decay due to climate change-related factors, and unsustainable agricultural practices. Innovative approaches to collecting and archiving palaeoenvironmental remains and maintaining existing archives will permit the creation of an accessible and thorough national resource that can service archaeologists and researchers in the related fields of biology and natural history. Furthermore, a concerted effort to recognise absences in archaeological archives, matched by an effort to supply these deficiencies, can produce a resource that can contribute to an enduring geographical and temporal record of England's biodiversity, which can be used in perpetuity in the face of diminishing archaeological and contemporary natural resources. To realise these opportunities, particular challenges must be overcome. The most prominent of these include inconsistent collection policies resulting from pressures associated with shortages in storage capacity and declining specialist knowledge in museums and repositories combined with variable curation practices. Many of these challenges can be resolved by developing a dedicated storage facility that can focus on the ongoing conservation and curation of palaeoenvironmental remains. Combined with an OASIS + module designed to handle and disseminate data pertaining to palaeoenvironmental archives, remains would be findable, accessible, and interoperable with biological archives and collections worldwide. Providing a national centre for curating palaeoenvironmental remains and a dedicated digital repository will require significant funding. Funding sources could be identified through collaboration with other disciplines. If sufficient funding cannot be identified, options that would require less financial investment, such as high-level archive audits and the production of guidance documents, will be able to assist all stakeholders with the improved curation, management, and promotion of the archived resource

    ENGINEERING HIGH-RESOLUTION EXPERIMENTAL AND COMPUTATIONAL PIPELINES TO CHARACTERIZE HUMAN GASTROINTESTINAL TISSUES IN HEALTH AND DISEASE

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    In recent decades, new high-resolution technologies have transformed how scientists study complex cellular processes and the mechanisms responsible for maintaining homeostasis and the emergence and progression of gastrointestinal (GI) disease. These advances have paved the way for the use of primary human cells in experimental models which together can mimic specific aspects of the GI tract such as compartmentalized stem-cell zones, gradients of growth factors, and shear stress from fluid flow. The work presented in this dissertation has focused on integrating high-resolution bioinformatics with novel experimental models of the GI epithelium systems to describe the complexity of human pathophysiology of the human small intestines, colon, and stomach in homeostasis and disease. Here, I used three novel microphysiological systems and developed four computational pipelines to describe comprehensive gene expression patterns of the GI epithelium in various states of health and disease. First, I used single cell RNAseq (scRNAseq) to establish the transcriptomic landscape of the entire epithelium of the small intestine and colon from three human donors, describing cell-type specific gene expression patterns in high resolution. Second, I used single cell and bulk RNAseq to model intestinal absorption of fatty acids and show that fatty acid oxidation is a critical regulator of the flux of long- and medium-chain fatty acids across the epithelium. Third, I use bulk RNAseq and a machine learning model to describe how inflammatory cytokines can regulate proliferation of intestinal stem cells in an experimental model of inflammatory hypoxia. Finally, I developed a high throughput platform that can associate phenotype to gene expression in clonal organoids, providing unprecedented resolution into the relationship between comprehensive gene expression patterns and their accompanying phenotypic effects. Through these studies, I have demonstrated how the integration of computational and experimental approaches can measurably advance our understanding of human GI physiology.Doctor of Philosoph
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