12,318 research outputs found

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Examples of works to practice staccato technique in clarinet instrument

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    Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır. Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur. Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir. Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır. Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır

    Brand logos versus brand names: A comparison of the memory effects of textual and pictorial brand elements placed in computer games

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    While a plethora of studies on gamification of advertising exists, little is known about how consumers process different types of brand elements (logos and names) placed in computer games, and whether differences in information processing lead to variations in brand memory. This gap is addressed by conducting three rigorous experiments. In Study 2 we find that, in general, brand logos lead to stronger memory than brand names – something known as the picture superiority effect. Study 3 examines the condition where the picture superiority effect is neutralized. We find that when the speed of a computer game is reduced, names and logos develop similar memory. Finally, in Study 4, we examine whether the picture superiority effect can be neutralized also in the context of high-speed games. We find that in fast games if the physical distinctiveness of the brand elements is increased, both logos and names yield in similar memory

    The determinants of value addition: a crtitical analysis of global software engineering industry in Sri Lanka

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    It was evident through the literature that the perceived value delivery of the global software engineering industry is low due to various facts. Therefore, this research concerns global software product companies in Sri Lanka to explore the software engineering methods and practices in increasing the value addition. The overall aim of the study is to identify the key determinants for value addition in the global software engineering industry and critically evaluate the impact of them for the software product companies to help maximise the value addition to ultimately assure the sustainability of the industry. An exploratory research approach was used initially since findings would emerge while the study unfolds. Mixed method was employed as the literature itself was inadequate to investigate the problem effectively to formulate the research framework. Twenty-three face-to-face online interviews were conducted with the subject matter experts covering all the disciplines from the targeted organisations which was combined with the literature findings as well as the outcomes of the market research outcomes conducted by both government and nongovernment institutes. Data from the interviews were analysed using NVivo 12. The findings of the existing literature were verified through the exploratory study and the outcomes were used to formulate the questionnaire for the public survey. 371 responses were considered after cleansing the total responses received for the data analysis through SPSS 21 with alpha level 0.05. Internal consistency test was done before the descriptive analysis. After assuring the reliability of the dataset, the correlation test, multiple regression test and analysis of variance (ANOVA) test were carried out to fulfil the requirements of meeting the research objectives. Five determinants for value addition were identified along with the key themes for each area. They are staffing, delivery process, use of tools, governance, and technology infrastructure. The cross-functional and self-organised teams built around the value streams, employing a properly interconnected software delivery process with the right governance in the delivery pipelines, selection of tools and providing the right infrastructure increases the value delivery. Moreover, the constraints for value addition are poor interconnection in the internal processes, rigid functional hierarchies, inaccurate selections and uses of tools, inflexible team arrangements and inadequate focus for the technology infrastructure. The findings add to the existing body of knowledge on increasing the value addition by employing effective processes, practices and tools and the impacts of inaccurate applications the same in the global software engineering industry

    Data-to-text generation with neural planning

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    In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structures as input and produces textual output. The inputs can take the form of database tables, spreadsheets, charts, and so on. The main application of data-to-text generation is to present information in a textual format which makes it accessible to a layperson who may otherwise find it problematic to understand numerical figures. The task can also automate routine document generation jobs, thus improving human efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or its variants. These models generate fluent (but often imprecise) text and perform quite poorly at selecting appropriate content and ordering it coherently. This thesis focuses on overcoming these issues by integrating content planning with neural models. We hypothesize data-to-text generation will benefit from explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our generator are tables (with records) in the sports domain. And the output are summaries describing what happened in the game (e.g., who won/lost, ..., scored, etc.). We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records should be mentioned and in which order, and then generate the document while taking the micro plan into account. We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the records corresponding to the entities by using hierarchical attention at each time step. We then combine planning with the high level organization of entities, events, and their interactions. Such coarse-grained macro plans are learnt from data and given as input to the generator. Finally, we present work on making macro plans latent while incrementally generating a document paragraph by paragraph. We infer latent plans sequentially with a structured variational model while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document

    TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH

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    Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)

    Um modelo para suporte automatizado ao reconhecimento, extração, personalização e reconstrução de gráficos estáticos

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    Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others. A well constructed data chart leads to an intuitive understanding of its underlying data and in the same way, when data charts have wrong design choices, a redesign of these representations might be needed. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Computer Vision techniques for image classification and object detection are widely used for the problem of recognizing charts, but only in images without any disturbance. Other features in real-world images that can make this task difficult are not present in most literature works, like photo distortions, noise, alignment, etc. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. The task of reconstructing data is straightforward, as long the data is available the visualization can be reconstructed, but the scenario of reconstructing it on the same context is complex. Using a Visualization Grammar for this scenario is a key component, as these grammars usually have extensions for interaction, chart layers, and multiple views without requiring extra development effort. This work presents a model for automated support for custom recognition, and reconstruction of charts in images. The model automatically performs the process steps, such as reverse engineering, turning a static chart back into its data table for later reconstruction, while allowing the user to make modifications in case of uncertainties. This work also features a model-based architecture along with prototypes for various use cases. Validation is performed step by step, with methods inspired by the literature. This work features three use cases providing proof of concept and validation of the model. The first use case features usage of chart recognition methods focused on documents in the real-world, the second use case focus on vocalization of charts, using a visualization grammar to reconstruct a chart in audio format, and the third use case presents an Augmented Reality application that recognizes and reconstructs charts in the same context (a piece of paper) overlaying the new chart and interaction widgets. The results showed that with slight changes, chart recognition and reconstruction methods are now ready for real-world charts, when taking time, accuracy and precision into consideration.Os gráficos de dados são amplamente utilizados na nossa vida diária, estando presentes nos meios de comunicação regulares, tais como jornais, revistas, páginas web, livros, e muitos outros. Um gráfico bem construído leva a uma compreensão intuitiva dos seus dados inerentes e da mesma forma, quando os gráficos de dados têm escolhas de conceção erradas, poderá ser necessário um redesenho destas representações. Contudo, na maioria dos casos, estes gráficos são mostrados como uma imagem estática, o que significa que os dados originais não estão normalmente disponíveis. Portanto, poderiam ser aplicados métodos automáticos para extrair os dados inerentes das imagens dos gráficos, a fim de permitir estas alterações. A tarefa de reconhecer os gráficos e extrair dados dos mesmos é complexa, em grande parte devido à variedade de tipos de gráficos e às suas características visuais. As técnicas de Visão Computacional para classificação de imagens e deteção de objetos são amplamente utilizadas para o problema de reconhecimento de gráficos, mas apenas em imagens sem qualquer ruído. Outras características das imagens do mundo real que podem dificultar esta tarefa não estão presentes na maioria das obras literárias, como distorções fotográficas, ruído, alinhamento, etc. Duas técnicas de visão computacional que podem ajudar nesta tarefa e que têm sido pouco exploradas neste contexto são a deteção e correção da perspetiva. Estes métodos transformam um gráfico distorcido e ruidoso em um gráfico limpo, com o seu tipo pronto para extração de dados ou outras utilizações. A tarefa de reconstrução de dados é simples, desde que os dados estejam disponíveis a visualização pode ser reconstruída, mas o cenário de reconstrução no mesmo contexto é complexo. A utilização de uma Gramática de Visualização para este cenário é um componente chave, uma vez que estas gramáticas têm normalmente extensões para interação, camadas de gráficos, e visões múltiplas sem exigir um esforço extra de desenvolvimento. Este trabalho apresenta um modelo de suporte automatizado para o reconhecimento personalizado, e reconstrução de gráficos em imagens estáticas. O modelo executa automaticamente as etapas do processo, tais como engenharia inversa, transformando um gráfico estático novamente na sua tabela de dados para posterior reconstrução, ao mesmo tempo que permite ao utilizador fazer modificações em caso de incertezas. Este trabalho também apresenta uma arquitetura baseada em modelos, juntamente com protótipos para vários casos de utilização. A validação é efetuada passo a passo, com métodos inspirados na literatura. Este trabalho apresenta três casos de uso, fornecendo prova de conceito e validação do modelo. O primeiro caso de uso apresenta a utilização de métodos de reconhecimento de gráficos focando em documentos no mundo real, o segundo caso de uso centra-se na vocalização de gráficos, utilizando uma gramática de visualização para reconstruir um gráfico em formato áudio, e o terceiro caso de uso apresenta uma aplicação de Realidade Aumentada que reconhece e reconstrói gráficos no mesmo contexto (um pedaço de papel) sobrepondo os novos gráficos e widgets de interação. Os resultados mostraram que com pequenas alterações, os métodos de reconhecimento e reconstrução dos gráficos estão agora prontos para os gráficos do mundo real, tendo em consideração o tempo, a acurácia e a precisão.Programa Doutoral em Engenharia Informátic
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