1,643 research outputs found
Displacement and the Humanities: Manifestos from the Ancient to the Present
This is the final version. Available on open access from MDPI via the DOI in this recordThis is a reprint of articles from the Special Issue published online in the open access journal Humanities (ISSN 2076-0787) (available at: https://www.mdpi.com/journal/humanities/special_issues/Manifestos Ancient Present)This volume brings together the work of practitioners, communities, artists and other researchers from multiple disciplines. Seeking to provoke a discourse around displacement within and beyond the field of Humanities, it positions historical cases and debates, some reaching into the ancient past, within diverse geo-chronological contexts and current world urgencies. In adopting an innovative dialogic structure, between practitioners on the ground - from architects and urban planners to artists - and academics working across subject areas, the volume is a proposition to: remap priorities for current research agendas; open up disciplines, critically analysing their approaches; address the socio-political responsibilities that we have as scholars and practitioners; and provide an alternative site of discourse for contemporary concerns about displacement. Ultimately, this volume aims to provoke future work and collaborations - hence, manifestos - not only in the historical and literary fields, but wider research concerned with human mobility and the challenges confronting people who are out of place of rights, protection and belonging
Self-supervised learning for transferable representations
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
Sound Event Detection by Exploring Audio Sequence Modelling
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
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The impact of enterprise social networking on knowledge sharing between academic staff in higher education
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHigher education institutions have always considered knowledge sharing critical for research excellence and finding proper methods for sharing knowledge across academic staff has therefore been a major issue for universities and knowledge management research. Recent evidence shows that many universities have embraced enterprise social networking tools to improve communication, relationships, partnerships, and knowledge sharing. To date, there is little understanding of the critical factors for online knowledge sharing behaviour between academic staff, and the impact of these factors on work benefits for academic staff which differ between consumptive users and contributive users in higher education. This study employed the extended unified theory of acceptance and use of technology (UTAUT) to examine factors affecting knowledge sharing about the consumptive use and contributive use of enterprise social network (ESN) behaviour. The study adopts a critical realism philosophical approach and employed a grounded theory mixed methods. The conceptual model was validated through structural equation modelling based on an online survey of 254 academic staff using enterprise social networking as a part of their work in the United Kingdom. The findings have significant theoretical and practical implications for researchers and policy makers. The research has developed a cohesive ESN use model by extending and modifying the unified theory of acceptance and use of technology. The findings indicate significant differences around factors affecting consumptive and contributive usage patterns within ESNs. Due to advances in communication technologies, this research argues that a previous model suggested by Venkatesh et al. (2003) is no longer fit for purpose and the new communication tools can lead to improved knowledge in higher education. This research also makes valuable contributions to universities from a managerial viewpoint, suggesting that universities could help their scholars find a more comprehensive range of funding sources matching scholars' ideas
AI: Limits and Prospects of Artificial Intelligence
The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence
A study of the inter-relationship of identity and urban heritage in Chiang Mai Old City, Thailand
The urban heritage identity of historical cities has received growing attention due to the weakening of their urban identity. For this reason, urban identity has been identified as a preliminary study of this research. Forty years ago, many researchers attempted to explain a broader understanding of urban heritage identity, which is relevant to human factors that affect urban, place, and built environment relationships. This involved the three interrelated concepts of identity: distinctiveness; urban heritage; and place attachment. These establish a balance between people and their identification with places.
Urban heritage identity is associated a place's physicality and heritage attributes that reflect socio-cultural values. It can be concluded that urban heritage identity becomes significant through concepts of environmental psychology. Distinctiveness theory, as a part of identity theory, has been used in this study to describe the genuine perception of local participants and is a fundamental part of defining place identity. Furthermore, the definition of place attachment has been used to explain the relationship of distinct places on time of residence, frequency of use, emotional, physical, social, and activities. The study also explores Chiang Mai Old City’s built environment, which especially analyses the façade and streetscape characteristics that reflect the city's socio-cultural value. The research concludes with suggestions for preserving the city's urban heritage characteristics.
Chiang Mai Old City has unprecedented diversity and cultural dynamics related to its intangible and tangible urban heritage. Moreover, the city is in the critical stage of being nominated as a new World Heritage Site by UNESCO, with the city's distinctiveness and place attachment being significant in supporting further heritage management strategies. The research mainly focuses on how local people interpret and understand the urban heritage identity of Chiang Mai Old City. This has been achieved through surveys of four hundred participants living in the Old City, two-way focus groups with five participants in each group, in-depth interviews with twenty-five participants, and ten architects drawing suggestions for further built environment management strategies. The results are described through seven aspects that explore the distinctiveness and place attachment theories of Chiang Mai Old City.
The findings can be described in seven aspects: historical value; cultural activities; a particular character; landmark; identity; community; and everyday life. The results reveal that there are five distinct places in the city: Pra Singha Temple; Chedi Luang Temple; Three Kings monument square; Tha-Pare gate square; and Chiang Mai Old City's Moat. The results can also be used to develop an assessment indicator for defining the distinctiveness of other historic cities through the engagement of local people.
The study repeatedly employs distinct places to describe in-place attachment theory. The results reveal positivity, emotion, and the spiritual anchor of place attached to local people in social engagement, explicitly divulging the rootedness of religion, culture, and community activities through the length of time. All five distinct places have an inseparable ability to display tangible heritage value and such a positive emotion to places is crucial in contributing to urban heritage characteristics. Moreover, the time or length of residency is a vital aspect to people’s perception of the city's distinctiveness; however, the value of the physical setting itself can increase the sense of belonging of newcomers.This research used a mixed methods approach in defining place identity process and socio-cultural values in distinctive streetscapes scenes in the city. This study strongly believes that the findings demonstrate that local people can help to develop the management of the city. The results presented suggest that the heritage value of streetscapes is related to historical attributes, natural objects, people, and cultural events in the scenes that explain the meanings ascribed to places associated with social and cultural values. The built environment characteristics and heritage value can be assumed from human experience. The study can be a new perspective for local authorities, urban designers, and heritage teams to determine whether projects will strengthen the existing urban heritage identity.
Most importantly, this research has revealed new perspectives on urban heritage identity and practical study methods whilst also contributing to management strategies. In addition, continuing research into urban heritage identity will significantly improve knowledge development, practical support, and collaboration with local people and architects to establish and maintain cherished distinct places and living environments for urban residents
A critical sociolinguistic study of diasporization among Hungarians in Catalonia
This thesis investigates how contemporary diasporas evolve, how diasporization takes place under the conditions of late modernity, and how language features in this process. By diasporization, I refer to the process(es) in which diasporic groups emerge and individuals start to engage in certain diasporic practices, i.e., social practices that are associated with their ethnic or national origin or with their imagined homeland, or with boundary management in the host-land. The research was an ethnographically informed critical sociolinguistic study of first-generation Hungarians in Catalonia that drew on collaborative methodologies in order to include the emic perspectives of the participants. To capture these perspectives, the research combined many data generating techniques, such as ethnographic field notes, biographical interviews, online focus groups, collection of material evidence, and collaborative interpretation with the key participants in the research.La tesis investiga cómo evolucionan las diásporas contemporáneas y de qué modo se produce la diasporización en las condiciones de la modernidad tardía. Con diasporización me refiero al proceso, o procesos, en los que surgen los grupos diaspóricos y los individuos comienzan a llevar a cabo ciertas prácticas diaspóricas, es decir, prácticas sociales que se asocian a su origen étnico o nacional, su patria imaginada o la gestión de las fronteras en el país de acogida. La tesis toma la forma de estudio crítico informado etnográficamente en personas húngaras en Cataluña de primera generación y se basa en metodologías colaborativas para incluir las perspectivas émicas de las personas participantes. Con el fin de captar estas perspectivas, el estudio combina múltiples técnicas de generación de datos, como por ejemplo las notas de campo etnográficas, las entrevistas biográficas, los grupos focales en línea, la recopilación de rastros materiales y la interpretación colaborativa con las personas participantes clave en el estudio.La tesi investiga com evolucionen les diàspores contemporànies i de quina manera es produeix la diasporització en les condicions de la modernitat tardana. Amb diasporització em refereixo al procés, o processos, en què sorgeixen els grups diaspòrics i els individus comencen a dur a terme certes pràctiques diaspòriques, és a dir, pràctiques socials que s'associen al seu origen ètnic o nacional, la seva pàtria imaginada o la gestió de les fronteres al país d'acollida. La tesi pren forma d'estudi crític informat etnogràficament en persones hongareses a Catalunya de primera generació i es basa en metodologies col·laboratives per incloure les perspectives èmiques de les persones que hi participen. Per captar aquestes perspectives, l'estudi combina múltiples tècniques de generació de dades, com ara les notes de camp etnogràfiques, les entrevistes biogràfiques, els grups focals en línia, la recopilació de rastres materials i la interpretació col·laborativa amb les persones participants clau en l'estudi.Societat de la informació i el coneixemen
Semi-automated learning strategies for large-scale segmentation of histology and other big bioimaging stacks and volumes
Labelled high-resolution datasets are becoming increasingly common and necessary in different areas of biomedical imaging. Examples include: serial histology and ex-vivo MRI for atlas building, OCT for studying the human brain, and micro X-ray for tissue engineering. Labelling such datasets, typically, requires manual delineation of a very detailed set of regions of interest on a large number of sections or slices. This process is tedious, time-consuming, not reproducible and rather inefficient due to the high similarity of adjacent sections.
In this thesis, I explore the potential of a semi-automated slice level segmentation framework and a suggestive region level framework which aim to speed up the segmentation process of big bioimaging datasets. The thesis includes two well validated, published, and widely used novel methods and one algorithm which did not yield an improvement compared to the current state-of the-art.
The slice-wise method, SmartInterpol, consists of a probabilistic model for semi-automated segmentation of stacks of 2D images, in which the user manually labels a sparse set of sections (e.g., one every n sections), and lets the algorithm complete the segmentation for other sections automatically. The proposed model integrates in a principled manner two families of segmentation techniques that have been very successful in brain imaging: multi-atlas segmentation and convolutional neural networks.
Labelling every structure on a sparse set of slices is not necessarily optimal, therefore I also introduce a region level active learning framework which requires the labeller to annotate one region of interest on one slice at the time. The framework exploits partial annotations, weak supervision, and realistic estimates of class and section-specific annotation effort in order to greatly reduce the time it takes to produce accurate segmentations for large histological datasets.
Although both frameworks have been created targeting histological datasets, they have been successfully applied to other big bioimaging datasets, reducing labelling effort by up to 60−70% without compromising accuracy
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!
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