6,104 research outputs found

    A review of abnormal behavior detection in activities of daily living

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    Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

    Colour technologies for content production and distribution of broadcast content

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    The requirement of colour reproduction has long been a priority driving the development of new colour imaging systems that maximise human perceptual plausibility. This thesis explores machine learning algorithms for colour processing to assist both content production and distribution. First, this research studies colourisation technologies with practical use cases in restoration and processing of archived content. The research targets practical deployable solutions, developing a cost-effective pipeline which integrates the activity of the producer into the processing workflow. In particular, a fully automatic image colourisation paradigm using Conditional GANs is proposed to improve content generalisation and colourfulness of existing baselines. Moreover, a more conservative solution is considered by providing references to guide the system towards more accurate colour predictions. A fast-end-to-end architecture is proposed to improve existing exemplar-based image colourisation methods while decreasing the complexity and runtime. Finally, the proposed image-based methods are integrated into a video colourisation pipeline. A general framework is proposed to reduce the generation of temporal flickering or propagation of errors when such methods are applied frame-to-frame. The proposed model is jointly trained to stabilise the input video and to cluster their frames with the aim of learning scene-specific modes. Second, this research explored colour processing technologies for content distribution with the aim to effectively deliver the processed content to the broad audience. In particular, video compression is tackled by introducing a novel methodology for chroma intra prediction based on attention models. Although the proposed architecture helped to gain control over the reference samples and better understand the prediction process, the complexity of the underlying neural network significantly increased the encoding and decoding time. Therefore, aiming at efficient deployment within the latest video coding standards, this work also focused on the simplification of the proposed architecture to obtain a more compact and explainable model

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    Target kinematic state estimation with passive multistatic radar

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    Guidelines for the Management of Cultural Heritage using 3D models for the insertion of heterogeneous data

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    The Management of Cultural Heritage (MCH) is a very complex operation aimed at protecting the physical integrity of CH assets, while promoting their historical value and development of tourism industry. In recent years, the use of digital technologies has become an essential part of the MCH delicate process, but the use of 3D models is still limited to few academic research to date. Furthermore, very few supra-national standard guidelines regulating their use are available to date and the operator who decides to use a 3D model as a basis for management is faced with the scarcity and fragmentation of standards and guidelines. The aim of the PhD research is to develop guidelines to produce 3D models for MCH, with the purpose to efficiently entry, store and manage digital data. The here provided guidelines investigate every aspect of the process leading from data acquisition to cataloguing and archiving, processing and creation of a simplified information system for the management. In order to elaborate guidelines that could be suitable for as many typologies of CH as possible an international approach was chosen, developing the thesis in joint supervision under the University of Bologna and the Universitat Politècnica de València, and by applying the state-of-the-art technologies of acquisition, processing and use of 3D models to a variety of case studies. The investigation, by highlighting the problems inherent to the MCH, made it possible to identify the main open issues that need to be explored in future lines of research, such as the application of standards to a large number of cultural assets; the automatic classification of raw data; the processing of collected data for the creation of relations, strategies and methods for the classification, integration and optimisation of heterogeneous data.La Gestione del Patrimonio Culturale (GPC) è un’operazione molto complessa mirata alla conservazione dell’integrità fisica dei Beni Culturali e alla contemporanea divulgazione dei valori storici e fruizione del Patrimonio. Nel corso degli ultimi anni l’applicazione delle tecnologie digitali ai Beni Culturali è diventata una parte imprescindibile nella GPC, ma l’uso dei modelli 3D per la gestione è finora limitato ad alcune ricerche e applicazioni accademiche. Inoltre, ad oggi sono pochi gli standard con carattere sovranazionale che guidano gli Enti nel procedimento di creazione e uso dei modelli tridimensionali per la GPC. Il gestore che decide di utilizzare un modello 3D come base per la gestione si scontra con la scarsezza e la frammentarietà di standard e indicazioni in tale ambito. L’obiettivo della tesi è quello di elaborare linee guida per la produzione di modelli 3D per un’efficace gestione, inserimento e conservazione dei dati. Tali linee guida indagano ogni aspetto del processo che porta dall’acquisizione del dato, alla sua catalogazione e archiviazione, al suo processamento e alla creazione di un sistema informativo semplificato per la gestione. Per arrivare all’elaborazione di linee guida che si adattino a più tipologie possibili di Beni Culturali è stato scelto un approccio interdisciplinare e internazionale, sviluppando la tesi in regime di cotutela tra l’Università di Bologna e l’Universitat Politècnica de València, e applicando lo stato dell'arte delle tecnologie di acquisizione, elaborazione e utilizzo dei modelli 3D ad una varietà di casi studio. Il percorso di ricerca ha permesso di individuare le principali questioni aperte che devono essere approfondite in futuro, come l’applicazione di standard a un alto numero di Beni Culturali; la ricerca di sistemi per la classificazione automatica dei dati grezzi; l’elaborazione dei dati raccolti per la creazione di relazioni, strategie e metodi per la classificazione, l’integrazione e l’ottimizzazione di dati eterogenei.La Gestión del Patrimonio Cultural (GPC) es una operación muy compleja cuyo objetivo es la conservación de la integridad física del Patrimonio y la difusión de los valores históricos. En los últimos años, la aplicación de las tecnologías digitales se ha convertido en una parte indispensable de la GPC, pero el uso de modelos 3D para la gestión se limita a algunas investigaciones y aplicaciones académicas. Además, hasta la fecha existen pocas normas y directrices de carácter supranacional que guíen a las instituciones en el proceso de creación y uso de modelos 3D para la GPC. El objetivo de la tesis es desarrollar directrices para la producción de modelos 3D del Patrimonio Cultural con el fin de gestionar, introducir y preservar eficazmente los datos. Estas directrices investigan todos los aspectos del proceso que va desde la adquisición de datos, pasando por su catalogación y archivo, hasta su tratamiento y la creación de un sistema de información simplificado para su gestión. Se ha optado por un enfoque interdisciplinar e internacional con el fin de elaborar directrices que se adapten al mayor número posible de tipos de Bienes Culturales, desarrollando la tesis en el marco de un acuerdo de cotutela entre la Universidad de Bolonia y la Universitat Politècnica de València, y aplicando las tecnologías más avanzadas para la adquisición, procesamiento y uso de modelos 3D a una variedad de casos de estudio. La investigación ha permitido identificar las principales cuestiones abiertas que se deben explorar en futuras líneas de investigación, como la aplicación de estándares a un gran número de Bienes Culturales; la búsqueda de sistemas para la clasificación automática de los datos brutos; el tratamiento de los datos recogidos para la creación de relaciones, estrategias y métodos de clasificación, integración y optimización de datos heterogéneos

    Robust outlier detection by de-biasing VAE likelihoods

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    Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that DGM likelihoods are unreliable and can be easily biased by simple transformations to input data. Here, we examine outlier detection with variational autoencoders (VAEs), among the simplest of DGMs. We propose novel analytical and algorithmic approaches to ameliorate key biases with VAE likelihoods. Our bias corrections are sample-specific, computationally inexpensive, and readily computed for various decoder visible distributions. Next, we show that a well-known image pre-processing technique -- contrast stretching -- extends the effectiveness of bias correction to further improve outlier detection. Our approach achieves state-of-the-art accuracies with nine grayscale and natural image datasets, and demonstrates significant advantages -- both with speed and performance -- over four recent, competing approaches. In summary, lightweight remedies suffice to achieve robust outlier detection with VAEs.Comment: To appear at CVPR 2022. 20 pages and 19 figure

    A Genealogy of Consumer Surveillance: From the First Public Market to Eatons Department Store to Amazon

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    Consumer surveillance has intensified over time and across differing forms of consumption space and spatial arrangement, which in turn raises the question of what explains the historical changes in the modalities of consumer surveillance. Contemporary surveillance literatures focus primarily on the current phenomenon with little consideration of the historical processes upon which the changes in the scope and intensity of the modalities of consumer surveillance were made possible. My study employs Foucauldian genealogical methodology as a system of inquiry to map the historical transformation in the modalities of consumer surveillance, by utilizing archival records, across three different consumption spaces in key stages of retail development: the first regulatory public market in the Town of York during the pre-industrial period, Eatons department store in the industrial economy, and Amazon that coincided with the rise of information economy. Conversely, contemporary theories of surveillance generally approach the intensification question by focusing on the surveillance-space axis or surveillance-consumption axis, and the spatiality of consumer surveillance is reduced to Foucauldian disciplinary panopticon. Utilizing Foucaults theories of power and governmentality and his intriguing account of the role of space in the exercise of power, my genealogical project examines the intersection of surveillance-space-consumption to understand the intensification of consumer surveillance over time across the three spaces under study. In my genealogical project, I identify five key moments pertaining to differing modalities of consumer surveillance: marketization of space, standardization of consuming bodies, statistification of consumers, virtualization of consumption, and AI inhabitation in consumer spaces. My genealogical project demonstrates that spatiality and spatialization are a recurring issue in differing modalities of consumer surveillance over time. Yet, the spatial techniques have changed and become more complex to augment the scope and intensity of monitoring and gaining of new knowledge about consumers and consumption, as part of long-standing efforts to manage the unpredictable dynamics of consumer behaviour by attaining control over all aspects of consumers life

    Impacts of higher education assessment and feedback policy and practice on students: A review of the literature 2016-2021

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    Research Report for Advance HE following a Systematic literature review of assessment and feedback policy and practice on student

    The Evolution of Smart Buildings: An Industrial Perspective of the Development of Smart Buildings in the 2010s

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    Over the course of the 2010s, specialist research bodies have failed to provide a holistic view of the changes in the prominent reason (as driven by industry) for creating a smart building. Over the 2010s, research tended to focus on remaining deeply involved in only single issues or value drivers. Through an analysis of the author’s peer reviewed and published works (book chapters, articles, essays and podcasts), supplemented with additional contextual academic literature, a model for how the key drivers for creating a smart building have evolved in industry during the 2010s is presented. The critical research commentary within this thesis, tracks the incremental advances of technology and their application to the built environment via academic movements, industrial shifts, or the author’s personal contributions. This thesis has found that it is demonstrable, through the chronology and publication dates of the included research papers, that as the financial cost and complexity of sensors and cloud computing reduced, smart buildings became increasingly prevalent. Initially, sustainability was the primary focus with the use of HVAC analytics and advanced metering in the early 2010s. The middle of the decade saw an economic transformation of the commercial office sector and the driver for creating a smart building was concerned with delivering flexible yet quantifiably used space. Driven by society’s emphasis on health, wellbeing and productivity, smart buildings pivoted their focus towards the end of the 2010s. Smart building technologies were required to demonstrate the impacts of architecture on the human. This research has evidenced that smart buildings use data to improve performance in sustainability, in space usage or for humancentric outcomes
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