151 research outputs found

    'A hidden art form' the value of sound in UK television idents (1982-2022)

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    Television idents are hidden in plain sight. Their creativity is often undervalued by industry practitioners and viewers alike, designated a ‘hidden art form’ by creative executive Charlie Mawer (2020). The sound worlds of idents are doubly overlooked, often ignored in visually-centric discourse on idents in industry journals and in media and cultural studies. In the production process, composers are often peripheral to the project, involved only towards the end. This thesis inverts such hierarchies and adopts a sound-oriented perspective towards idents. The approach brings together previously disparate strands across musicology, art and design history, and media studies, aiming to highlight the value of sound in idents as well as the hitherto-neglected creative labour of composers in the promotion of television channels. The scope is confined mainly to the UK, examining idents produced for broadcasters and streaming platforms between 1982 and 2022. This thesis addresses a central question: What is the value of music and sound in television idents? To answer this question, it combines textual analyses of idents with evidence from practitioner interviews. Musicological concepts and theories are employed in the analysis of idents, highlighting the aesthetic character and functions of the music and sounds. The method of reflexive thematic analysis (RTA) applied to the interviews produced new insights into the working environments of the composers and their creative colleagues, exploring themes of identity, collaboration, creative process, and artistic value. The first three chapters set out the aim of this thesis, academic contexts, and methodological approach respectively. Chapter 4 contains a musicological analysis of idents, tracing transformations in the aesthetic character and roles of sound in connection with the changing experience of watching television between 1982 and 2022. Chapter 5 expands on the arguments set out in Chapter 4 by focussing on production contexts, unpacking themes derived from the qualitative analysis of the interviews. Chapter 6 synthesises the conclusions and findings from Chapters 4 and 5 and discusses the commercial, artistic, and cultural value of the music and sound of idents. This thesis culminates with an exploration of future avenues of research and the implications of this research for practitioners and educators. In sum, this thesis argues that the artistic labour of ident production and the valuable role of musical creativity within this commercial and temporally constraining context deserve greater recognition and attention

    Value Creation with Extended Reality Technologies - A Methodological Approach for Holistic Deployments

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    Mit zunehmender Rechenkapazität und Übertragungsleistung von Informationstechnologien wächst die Anzahl möglicher Anwendungs-szenarien für Extended Reality (XR)-Technologien in Unternehmen. XR-Technologien sind Hardwaresysteme, Softwaretools und Methoden zur Erstellung von Inhalten, um Virtual Reality, Augmented Reality und Mixed Reality zu erzeugen. Mit der Möglichkeit, Nutzern Inhalte auf immersive, interaktive und intelligente Weise zu vermitteln, können XR-Technologien die Produktivität in Unternehmen steigern und Wachstumschancen eröffnen. Obwohl XR-Anwendungen in der Industrie seit mehr als 25 Jahren wissenschaftlich erforscht werden, gelten nach wie vor als unausgereift. Die Hauptgründe dafür sind die zugrundeliegende Komplexität, die Fokussierung der Forschung auf die Untersuchung spezifische Anwendungsszenarien, die unzu-reichende Wirtschaftlichkeit von Einsatzszenarien und das Fehlen von geeigneten Implementierungsmodellen für XR-Technologien. Grundsätzlich wird der Mehrwert von Technologien durch deren Integration in die Wertschöpfungsarchitektur von Geschäftsmodellen freigesetzt. Daher wird in dieser Arbeit eine Methodik für den Einsatz von XR-Technologien in der Wertschöpfung vorgestellt. Das Hauptziel der Methodik ist es, die Identifikation geeigneter Einsatzszenarien zu ermöglichen und mit einem strukturierten Ablauf die Komplexität der Umsetzung zu beherrschen. Um eine ganzheitliche Anwendbarkeit zu ermöglichen, basiert die Methodik auf einem branchen- und ge-schäftsprozessunabhängigen Wertschöpfungsreferenzmodell. Dar-über hinaus bezieht sie sich auf eine ganzheitliche Morphologie von XR-Technologien und folgt einer iterativen Einführungssequenz. Das Wertschöpfungsmodell wird durch ein vorliegendes Potential, eine Wertschöpfungskette, ein Wertschöpfungsnetzwerk, physische und digitale Ressourcen sowie einen durch den Einsatz von XR-Technologien realisierten Mehrwert repräsentiert. XR-Technologien werden durch eine morphologische Struktur mit Anwendungsmerk-malen und erforderlichen technologischen Ressourcen repräsentiert. Die Umsetzung erfolgt in einer iterativen Sequenz, die für den zu-grundeliegenden Kontext anwendbare Methoden der agilen Soft-wareentwicklung beschreibt und relevante Stakeholder berücksich-tigt. Der Schwerpunkt der Methodik liegt auf einem systematischen Ansatz, der universell anwendbar ist und den Endnutzer und das Ökosystem der betrachteten Wertschöpfung berücksichtigt. Um die Methodik zu validieren, wird der Einsatz von XR-Technologien in zwei industriellen Anwendungsfällen unter realen wirtschaftlichen Bedingungen durchgeführt. Die Anwendungsfälle stammen aus unterschiedlichen Branchen, mit unterschiedlichen XR-Technologiemerkmalen sowie unterschiedlichen Formen von Wert-schöpfungsketten, um die universelle Anwendbarkeit der Methodik zu demonstrieren und relevante Herausforderungen bei der Durch-führung eines XR-Technologieeinsatzes aufzuzeigen. Mit Hilfe der vorgestellten Methodik können Unternehmen XR-Technologien zielgerichtet in ihrer Wertschöpfung einsetzen. Sie ermöglicht eine detaillierte Planung der Umsetzung, eine fundierte Auswahl von Anwendungsszenarien, die Bewertung möglicher Her-ausforderungen und Hindernisse sowie die gezielte Einbindung der relevanten Stakeholder. Im Ergebnis wird die Wertschöpfung mit wirtschaftlichem Mehrwert durch XR-Technologien optimiert

    Machine Learning for Classical Planning: Neural Network Heuristics, Online Portfolios, and State Space Topologies

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    State space search solves navigation tasks and many other real world problems. Heuristic search, especially greedy best-first search, is one of the most successful algorithms for state space search. We improve the state of the art in heuristic search in three directions. In Part I, we present methods to train neural networks as powerful heuristics for a given state space. We present a universal approach to generate training data using random walks from a (partial) state. We demonstrate that our heuristics trained for a specific task are often better than heuristics trained for a whole domain. We show that the performance of all trained heuristics is highly complementary. There is no clear pattern, which trained heuristic to prefer for a specific task. In general, model-based planners still outperform planners with trained heuristics. But our approaches exceed the model-based algorithms in the Storage domain. To our knowledge, only once before in the Spanner domain, a learning-based planner exceeded the state-of-the-art model-based planners. A priori, it is unknown whether a heuristic, or in the more general case a planner, performs well on a task. Hence, we trained online portfolios to select the best planner for a task. Today, all online portfolios are based on handcrafted features. In Part II, we present new online portfolios based on neural networks, which receive the complete task as input, and not just a few handcrafted features. Additionally, our portfolios can reconsider their choices. Both extensions greatly improve the state-of-the-art of online portfolios. Finally, we show that explainable machine learning techniques, as the alternative to neural networks, are also good online portfolios. Additionally, we present methods to improve our trust in their predictions. Even if we select the best search algorithm, we cannot solve some tasks in reasonable time. We can speed up the search if we know how it behaves in the future. In Part III, we inspect the behavior of greedy best-first search with a fixed heuristic on simple tasks of a domain to learn its behavior for any task of the same domain. Once greedy best-first search expanded a progress state, it expands only states with lower heuristic values. We learn to identify progress states and present two methods to exploit this knowledge. Building upon this, we extract the bench transition system of a task and generalize it in such a way that we can apply it to any task of the same domain. We can use this generalized bench transition system to split a task into a sequence of simpler searches. In all three research directions, we contribute new approaches and insights to the state of the art, and we indicate interesting topics for future work

    Machine learning for classical planning : neural network heuristics, online portfolios, and state space topologies

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    State space search solves navigation tasks and many other real world problems. Heuristic search, especially greedy best-first search, is one of the most successful algorithms for state space search. We improve the state of the art in heuristic search in three directions. In Part I, we present methods to train neural networks as powerful heuristics for a given state space. We present a universal approach to generate training data using random walks from a (partial) state. We demonstrate that our heuristics trained for a specific task are often better than heuristics trained for a whole domain. We show that the performance of all trained heuristics is highly complementary. There is no clear pattern, which trained heuristic to prefer for a specific task. In general, model-based planners still outperform planners with trained heuristics. But our approaches exceed the model-based algorithms in the Storage domain. To our knowledge, only once before in the Spanner domain, a learning-based planner exceeded the state-of-the-art model-based planners. A priori, it is unknown whether a heuristic, or in the more general case a planner, performs well on a task. Hence, we trained online portfolios to select the best planner for a task. Today, all online portfolios are based on handcrafted features. In Part II, we present new online portfolios based on neural networks, which receive the complete task as input, and not just a few handcrafted features. Additionally, our portfolios can reconsider their choices. Both extensions greatly improve the state-of-the-art of online portfolios. Finally, we show that explainable machine learning techniques, as the alternative to neural networks, are also good online portfolios. Additionally, we present methods to improve our trust in their predictions. Even if we select the best search algorithm, we cannot solve some tasks in reasonable time. We can speed up the search if we know how it behaves in the future. In Part III, we inspect the behavior of greedy best-first search with a fixed heuristic on simple tasks of a domain to learn its behavior for any task of the same domain. Once greedy best- first search expanded a progress state, it expands only states with lower heuristic values. We learn to identify progress states and present two methods to exploit this knowledge. Building upon this, we extract the bench transition system of a task and generalize it in such a way that we can apply it to any task of the same domain. We can use this generalized bench transition system to split a task into a sequence of simpler searches. In all three research directions, we contribute new approaches and insights to the state of the art, and we indicate interesting topics for future work.Viele Alltagsprobleme können mit Hilfe der Zustandsraumsuche gelöst werden. Heuristische Suche, insbesondere die gierige Bestensuche, ist einer der erfolgreichsten Algorithmen für die Zustandsraumsuche. Wir verbessern den aktuellen Stand der Wissenschaft bezüglich heuristischer Suche auf drei Arten. Eine der wichtigsten Komponenten der heuristischen Suche ist die Heuristik. Mit einer guten Heuristik findet die Suche schnell eine Lösung. Eine gute Heuristik für ein Problem zu modellieren ist mühsam. In Teil I präsentieren wir Methoden, um automatisiert gute Heuristiken für ein Problem zu lernen. Hierfür generieren wird die Trainingsdaten mittels Zufallsbewegungen ausgehend von (Teil-) Zuständen des Problems. Wir zeigen, dass die Heuristiken, die wir für einen einzigen Zustandsraum trainieren, oft besser sind als Heuristiken, die für eine Problemklasse trainiert wurden. Weiterhin zeigen wir, dass die Qualität aller trainierten Heuristiken je nach Problemklasse stark variiert, keine Heuristik eine andere dominiert, und es nicht vorher erkennbar ist, ob eine trainierte Heuristik gut funktioniert. Wir stellen fest, dass in fast allen getesteten Problemklassen die modellbasierte Suchalgorithmen den trainierten Heuristiken überlegen sind. Lediglich in der Storage Problemklasse sind unsere Heuristiken überlegen. Oft ist es unklar, welche Heuristik oder Suchalgorithmus man für ein Problem nutzen sollte. Daher trainieren wir online Portfolios, die für ein gegebenes Problem den besten Algorithmus vorherzusagen. Die Eingabe für das online Portfolio sind bisher immer von Menschen ausgewählte Eigenschaften des Problems. In Teil II präsentieren wir neue online Portfolios, die das gesamte Problem als Eingabe bekommen. Darüber hinaus können unsere online Portfolios ihre Entscheidung einmal korrigieren. Beide Änderungen verbessern die Qualität von online Portfolios erheblich. Weiterhin zeigen wir, dass wir auch gute online Portfolios mit erklärbaren Techniken des maschinellen Lernens trainieren können. Selbst wenn wir den besten Algorithmus für ein Problem auswählen, kann es sein, dass das Problem zu schwierig ist, um in akzeptabler Zeit gelöst zu werden. In Teil III zeigen wir, wie wir von dem Verhalten einer gierigen Bestensuche auf einfachen Problemen ihr Verhalten auf schwierigeren Problemen der gleichen Problemklasse vorhersagen können. Dieses Wissen nutzen wir, um die Suche zu verbessern. Zuerst zeigen wir, wie man Fortschrittszustände erkennt. Immer wenn gierige Bestensuche einen Fortschrittszustand expandiert, wissen wir, dass es nie wieder einen Zustand mit gleichem oder höheren heuristischen Wert expandieren wird.Wir präsentieren zwei Methoden, die diesesWissen verwenden. Aufbauend auf dieser Arbeit lernen wir von einem Problem, wie man jegliches Problem der gleichen Problemklasse in eine Reihe von einfacheren Suchen aufteilen kann

    Multi-Robot Systems: Challenges, Trends and Applications

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    This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

    The Responsibility of Science

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    This open access book provides an overview of issues of scientific responsibility. The volume comprises three types of contributions: first, analyses of the responsibility of science; second, analyses of the structural conditions for science and its responsibility; and third, normative versions of scientific responsibility. The questions and problems dealt with include science as a profession, ambivalence of research and dual-use, innovation vs. precaution, notions of responsibility, the role of science within society and its relation to human rights, as well as scientific and public discourses. The book addresses scholars in the fields of Science Studies and Research Policy. This is an open access book

    Johanssonian Investigations

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    In the last decades, Ingvar Johansson has made a formidable contribution to the development of philosophy and particularly that of metaphysics. This volume consists of original papers written by 50 philosophers from all over the world to celebrate his 70th birthday. The papers cover traditional issues in metaphysics and the philosophy of mind, applied ethics, applied metaphysics, the nature of human rights, the philosophy of economics and sports

    Transforming urban green space governance in China under ecological civilisation: an institutional analysis

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    Facing expeditious urbanisation and climate change impacts, how has China governed urban green spaces? This thesis establishes urban green spaces as an essential part of urban social ecological systems critical for overall stability, including climate resilience, health and wellbeing. This thesis turns to the common-pool resource theory to understand urban green space governance. The theoretical framework convenes that non-excludable but highly subtractable goods can be governed more sustainably in small scales and through collectively designed rules by actors that contain well-defined property rights, monitoring, and sanctions appropriate to respective levels and scales. The thesis selects three empirical cases and uses the Institutional Analysis Development framework to structure a case study-based qualitative content analysis and a Multi-Criteria Assessment informed by in-depth interviews and urban green space policies and plans. This research finds that land property rights are critical factors for participation in urban green space governance, and urban green spaces in China are still governed primarily as land resources. Conceptualising urban green spaces as common-pool resources reveals that they should contain property rights different from urban land resources for more sustainable governance. Besides, China's urban green space governance has gradually formalised ecological functions, including the potential to cope with climate change, into institutional arrangements in the past two decades and is mostly in line with the common characteristics of successful common-pool resource governance regimes. China's urban green space governance can be improved by striking a better equivalence between benefits and costs for all actors and broaden the extent of collective-choice arrangements. Furthermore, Guangzhou's urban green space governance attunes with the national environmental governance framework Ecological Civilisation through conducting both the means and ends of institutional change. Finally, despite substantial progress under Ecological Civilisation, three main institutional barriers remained in Guangzhou's urban green space governance: the lack of legal foundations for regular ecological status assessments, low awareness of local state actors on climate change impacts and the ecological potential of urban green spaces, and the lack of long-term commitment for a more ecosystem-based approach to urban green space governance. The findings indicate that urban green spaces as essential part of the complex urban social ecological system should not be governed simply as land resources. To attach importance to Urban Green Space Governance in China - Institutional Analysis 4 the ecosystem services and ecological values, it is necessary to define an exclusive and clear set of property rights for urban green spaces. The common-pool resource theory also tells us that institutional arrangements for long-term sustainable resource governance should enable individual and collective actors to participate in the process thoroughly and achieve the end goals, such as good health, wellbeing, and climate resilience. This research helps policymakers in Chinese cities understand why some urban green space governance in the past failed even with great technical planning expertise. Besides, it provides policymakers with practical suggestions on institutional arrangements helpful to promote urban green space governance and to achieve Ecological Civilisation. Finally, the researcher presents several recommendations for policymakers for better practices in the future and future research directions.Enfrentado pela urbanização acelerada e pelos impactos das alterações climáticas, como a China governou os espaços verdes urbanos? Os espaços verdes urbanos são um ponto de entrada em que as ações e os resultados são importantes para a saúde e o bem-estar de todos os cidadãos urbanos e a resiliência climática independentemente dos contextos sociais, econômicos e políticos. A China tem uma enorme responsabilidade e potencial devido às escalas da sua economia, a população e a pegada de carbono total e tem visto uma forte determinação política para agir nos desafios climáticos e ambientais enquanto as constantes necessidades de urbanização e desenvolvimento econômico. Então, como é que as cidades chinesas abordaram o planeamento e a governança dos espaços verdes urbanos? Que facilitações ou desafios institucionais enfrentaram ao planear espaços verdes urbanos? Como é que os governos locais conseguiram melhorar os espaços verdes urbanos e implementar mais Nature-based solutions? Quais são as boas práticas a serem partilhadas? Além disso, por quê alguns desafios persistiram, apesar do sistema de governo centralizado e a forte determinação política? Esta tese propõe-se a estudar três casos sobre o planeamento e a governança dos espaços verdes urbanos em Guangzhou, uma cidade costeira altamente populosa, compacta e vulnerável no sudeste da China. Os objetivos eram compreender as dinâmicas institucionais, os facilitadores e as barreiras subjacentes que podem infetar o planeamento e a governança dos espaços verdes urbanos, examinar a extensão e as abordagens para melhorar os espaços verdes urbanos, analisar os custos e benefícios levados em consideração e, compreender as barreiras institucionais relacionadas ao valores intrínsecos, o que é essencial para desenhar soluções mais genuinamente baseadas na Natureza e do ecossistema. Com base nos insights da revisão da literatura das teorias e práticas chinesas e ocidentais de planeamento urbano, e da governança urbana da perspectiva institucional, esta tese estabelece os espaços verdes urbanos como uma parte essencial dos sistemas socioecológicos urbanos (urban social-ecological systems, ou urban SES) essenciais para a estabilidade geral, incluindo a resiliência climática, a saúde e o bem-estar, e vira-se para a teoria de recursos comuns (common-pool resource), o qual foi desenvolvida pela cientista política norte-americana Elinor Ostrom, para compreender a governança dos espaços verdes urbanos. A estrutura teórica convoca que, os bens não excludentes, mas altamente subtraíveis, podem ser governados de forma mais sustentável em pequenas escalas e por meio de regras projetadas coletivamente Urban Green Space Governance in China - Institutional Analysis com os direitos de propriedade bem definidos, os mecanismos de monitorização e sanção apropriados aos respectivos níveis e escalas. Foi selecionado três casos empíricos de estudo e utilizado a ferramenta de Institutional Analysis Development (IAD) framework para estruturar uma análise de conteúdo qualitativo e uma Avaliação Multi-Critérios informadas pelas entrevistas em profundidade e políticas e planos de espaços verdes urbanos. Esta pesquisa mostra que os direitos de propriedade de solo são fatores críticos para a participação na governança dos espaços verdes urbanos na China, e os espaços verdes urbanos ainda são governados principalmente como recursos de solo urbano. A conceituação dos espaços verdes urbanos como recursos comuns (common-pool resources) revela que eles devem conter direitos de propriedade diferentes do que os recursos de solo urbano. Além disso, a governança dos espaços verdes urbanos da China formalizou gradualmente as funções ecológicas, incluindo o potencial para lidar com as alterações climáticas nas últimas duas décadas e está principalmente em linha com as características dos regimes de governança de bens comuns bem-sucedidos. A governança dos espaços verdes urbanos da China pode ser melhorada, alcançando uma melhor equivalência entre benefícios e custos para todos os atores e ampliando as práticas de escolha coletiva. Além disso, a governança dos espaços verdes urbanos de Guangzhou está em sintonia com a estrutura de governança ambiental nacional de Civilização Ecológica (Ecological Civilisation) por meio da condução ambos dos meios e dos fins da mudança institucional. Finalmente, apesar do progresso substancial sob a Civilização Ecológica, este estudo encontrou três barreiras institucionais principais remanescentes na governança dos espaços verdes urbanos em Guangzhou: a falta de fundamentos legais para avaliações regulares do estado ecológico, a baixa consciência dos atores locais do estado sobre os impactos das alterações climáticas e o potencial ecológico dos espaços verdes urbanos, e, a falta de compromisso de longo prazo na abordagem mais baseada no ecossistema. A falta de fundamentos legais para avaliações regulares do estado ecológico é uma barreira institucional que impede a coordenação institucional multinível. A baixa consciência dos atores locais do estado sobre os impactos das mudanças climáticas e o potencial ecológico dos espaços verdes urbanos é uma rigidez institucional que limita a interação horizontal dentro dos governos locais que requer soluções institucionais. A falta de compromisso de longo prazo para governar os espaços verdes urbanos com base no reconhecimento dos valores e potenciais ecológicos é uma outra rigidez institucional que implica objetivos conflitantes, tensões e compensações nas dimensões políticas. Os resultados indicam que os espaços verdes urbanos como uma parte essencial do sistema socioecológico urbano complexo, não devem ser governados simplesmente como recursos de solo urbano. Para atribuir mais importância aos serviços ecossistêmicos e aos valores ecológicos, é necessário definir um conjunto exclusivo e claro de direitos de propriedade para os espaços verdes urbanos. A teoria de recursos comuns também indica que os arranjos institucionais para a governança de recursos sustentáveis de longo prazo devem permitir que os atores individuais e coletivos participem do processo (means) e atinjam os objetivos finais (ends), como a saúde, o bem-estar, e a resiliência climática. Esta pesquisa tem potencial em ajudar os formuladores de políticas nas cidades chinesas a entender por que alguns casos de governança dos espaços verdes urbanos falharam no passado, mesmo com grande perícia técnica no planeamento. Além disso, tem fornecido aos formuladores de políticas sugestões práticas para melhorar a governança dos espaços verdes urbanos e para se aproximar mais aos ideais da Civilização Ecológica. Finalmente, foi apresentado várias direções para pesquisas futuras

    What Influences the Adoption of Innovations in Healthcare in Wales

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    In a time of limited resources, but increased complexity and demand, innovation presents a pathway to improve quality and efficiency in the provision of healthcare in the National Health Service (NHS) in Wales. There is little scarcity in the availability of quality innovations to the NHS, but there is a clear gap in the ability of the organisation to effectively adopt and spread these innovations into wider use. Research into the adoption of innovations across multiple disciplines has been extensive. Numerous influences have been investigated via mainly quantitative approaches that utilise an array of technology adoption theories. This study explored the adoption of innovation in healthcare in Wales via a pragmatic mixed methods approach using the Technology-Organisation-Environment framework as a theoretical basis. Semi-structured open-ended interviews were conducted with participants experienced in healthcare innovation in Wales. Findings were analysed by a combination coding approach and content analysis. Forty-four factors of influence were discovered, including sixteen novel factors that were not identified in relevant literature. The high importance of individuals and the interactions between people was easily apparent. Therefore, the conceptual framework of ‘People-Organisation-Environment-Technology’, or the ‘POET’ framework, was developed. Theoretical support for this was provided by the Socio-technical systems theory, which acknowledges the importance of people in the social subsystem of an organisation. The POET framework builds upon previous theory by adding the relative levels of importance of and overlap between the four contexts. Second stage analysis assessed the relative importance of factors, their interrelationships, and their propensity to act as barriers or enablers to adoption. The POET framework embraces the complexity in innovation adoption in Wales and is effective for investigating and analysing cases in this setting, and has the potential for generalisability. The findings indicate that NHS Wales should invest in and investigate the influence of people to support innovation adoption
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