7,088 research outputs found

    Automated Mapping of Adaptive App GUIs from Phones to TVs

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    With the increasing interconnection of smart devices, users often desire to adopt the same app on quite different devices for identical tasks, such as watching the same movies on both their smartphones and TV. However, the significant differences in screen size, aspect ratio, and interaction styles make it challenging to adapt Graphical User Interfaces (GUIs) across these devices. Although there are millions of apps available on Google Play, only a few thousand are designed to support smart TV displays. Existing techniques to map a mobile app GUI to a TV either adopt a responsive design, which struggles to bridge the substantial gap between phone and TV or use mirror apps for improved video display, which requires hardware support and extra engineering efforts. Instead of developing another app for supporting TVs, we propose a semi-automated approach to generate corresponding adaptive TV GUIs, given the phone GUIs as the input. Based on our empirical study of GUI pairs for TV and phone in existing apps, we synthesize a list of rules for grouping and classifying phone GUIs, converting them to TV GUIs, and generating dynamic TV layouts and source code for the TV display. Our tool is not only beneficial to developers but also to GUI designers, who can further customize the generated GUIs for their TV app development. An evaluation and user study demonstrate the accuracy of our generated GUIs and the usefulness of our tool.Comment: 30 pages, 15 figure

    Coworking through the Pandemic: Flexibly Yours

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    Coworking can be defined as a paid for service (usually) providing shared workspace and amenities to users. When the pandemic hit, owing to the business model’s in-person foundations of physical proximity and shared amenities, the coworking industry was expected to be seriously impacted. Yet fast forward, and as the pandemic has played out, coworking businesses are uniquely positioned in this uncertain and changing workscape. This dissertation presents one of the first academic explorations into how independent coworking businesses fared in the initial year of the pandemic. Specifically, the research explores the following questions: 1. How did independent coworking businesses manage and adapt to the pandemic? 2. What is virtual coworking and what are the experiences of workers in these virtual coworking spaces? 3. How does coworking flexibility affect social support and connection? Using a critically interpretive poststructural approach, this ethnography included virtual fieldwork and interviews. Sixty hours of virtual participant observation and 30 loosely structured interviews were conducted with coworking stakeholders (i.e., owner-operators, managers, and users) over videoconferencing platforms. Secondary data included written fieldnotes and coworking documents. Results capture the strategies used by coworking business owner-operators and managers to sustain their businesses and the attendant relationships with coworking users, irrespective of whether or not a physical location could be provided under pandemic lockdowns. Given the expansion of coworking businesses into virtual service offerings, a key contribution of my research is the finding that co-location in a physical coworking space is not necessary to cultivate vibes and a sense of community. By removing the physical infrastructure of coworking, the virtual coworking product in which I participated points to both a reinforcement of and an emphasis on the centrality of social connection, support, and community. By de-centering the priority of a physical co-location, I conceptualize coworking businesses as commodified support infrastructures—affective atmospheres produced through the entanglement of human bodies, other living things, objects, and technologies in a space. In viewing coworking businesses as fluid affective atmospheres of support, my research adds to the emerging coworking scholarship that attends to the atmospheric qualities of coworking, the role of affective labour, and the possibilities of encounters and interactions as bodies, objects, and technologies interconnect. My results reinforce the deep ambivalence of coworking, capturing tensions between productivity and sociality, and a blurring of boundaries between professional and private, and work and leisure. The analysis also suggests that the inherent flexibility, informality, turnover, and autonomy in coworking practices can make creating stable social connections and support difficult. Finally, the COVID-19 crisis brought to light how coworking lies primarily outside the scope of current employment legislation, which includes occupational health and safety, employment standards, and workers’ compensation. In the absence of well-defined policy directions, coworking business owner-operators and managers made individualized decisions, thereby ultimately downloading further risk and responsibility onto their coworking users

    Self-supervised learning techniques for monitoring industrial spaces

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    Dissertação de mestrado em Matemática e ComputaçãoEste documento é uma Dissertação de Mestrado com o título ”Self-Supervised Learning Techniques for Monitoring Industrial Spaces”e foi realizada e ambiente empresarial na empresa Neadvance - Machine Vision S.A. em conjunto com a Universidade do Minho. Esta dissertação surge de um grande projeto que consiste no desenvolvimento de uma plataforma de monitorização de operações específicas num espaço industrial, denominada SMARTICS (Plataforma tecnoló gica para monitorização inteligente de espaços industriais abertos). Este projeto continha uma componente de investigação para explorar um paradigma de aprendizagem diferente e os seus métodos - self-supervised learning, que foi o foco e principal contributo deste trabalho. O supervised learning atingiu um limite, pois exige anotações caras e dispendiosas. Em problemas reais, como em espaços industriais nem sempre é possível adquirir um grande número de imagens. O self-supervised learning ajuda nesses problemas, ex traindo informações dos próprios dados e alcançando bom desempenho em conjuntos de dados de grande escala. Este trabalho fornece uma revisão geral da literatura sobre a estrutura de self-supervised learning e alguns métodos. Também aplica um método para resolver uma tarefa de classificação para se assemelhar a um problema em um espaço industrial.This document is a Master’s Thesis with the title ”Self-Supervised Learning Techniques for Monitoring Industrial Spaces” and was carried out in a business environment at Neadvance - Machine Vision S.A. together with the University of Minho. This dissertation arises from a major project that consists of developing a platform to monitor specific operations in an industrial space, named SMARTICS (Plataforma tecnológica para monitorização inteligente de espaços industriais abertos). This project contained a research component to explore a different learning paradigm and its methods - self-supervised learning, which was the focus and main contribution of this work. Supervised learning has reached a bottleneck as they require expensive and time-consuming annotations. In real problems, such as in industrial spaces it is not always possible to require a large number of images. Self-supervised learning helps these issues by extracting information from the data itself and has achieved good performance in large-scale datasets. This work provides a comprehensive literature review of the self supervised learning framework and some methods. It also applies a method to solve a classification task to resemble a problem in an industrial space and evaluate its performance

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Voice-Based Gender Recognition Model Using FRT and Light GBM

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    Voice-based gender recognition is vital in many computer-aided voice analysis applications like Human-Computer Interaction, fraudulent call identification, etc. A powerful feature is needed for training the machine learning model to discriminate a gender as male or female from a voice signal. This work proposes the use of a gradient boosting model in conjunction with a novel Cumulative Point Index (CPI) feature computed by Forward Rajan Transform (FRT) for gender recognition from voice signals. Firstly, voice signals are preprocessed to remove the nonsignificant silence period and are further framed and windowed to make them stationary. Then CPI is computed using the first coefficients of FRT and concatenated to form a feature set, and it is used to train the Light Gradient Boosting Machine (LightGBM) to recognize the gender. This approach provides better accuracy and faster training compared with the state of the art techniques. Experimental results show the primacy of the FRTCPI over other standard features used in the literature. It is also shown that the proposed features, in combination with LightGBM, provide better accuracy of 95.26% with a less computational time of 2.25 s over the challenging large datasets like Speech Accent Archive, Voice Gender Dataset, Common Voice, and Texas Instruments/Massachusetts Institute of Technology corpus

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Deep learning-powered vision-based energy management system for next-gen built environment

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    Heating, ventilation and air-conditioning (HVAC) systems provide thermally comfortable spaces for occupants, and their consumption is strongly related to how occupants utilise the building. The over- or under-utilisation of spaces and the increased adoption of flexible working hours lead to unnecessary energy usage in buildings with HVAC systems operated using static or fixed schedules during unoccupied periods. Demand-driven methods can enable HVAC systems to adapt and make timely responses to dynamic changes in occupancy. Approaches central to the implementation of a demand-driven approach are accurate in providing real-time information on occupancy, including the count, localisation and activity levels. While conventional occupancy sensors exist and can provide information on the number and location of occupants, their ability to detect and recognise occupancy activities is limited. This includes the operation of windows and appliances, which can impact the building’s performance. Artificial intelligence (AI) has recently become a critical tool in enhancing the energy performance of buildings and occupant satisfaction and health. Recent studies have shown the capabilities of AI methods, such as computer vision and deep learning in detecting and recognising human activities. The recent emergence of deep learning algorithms has propelled computer vision applications and performance. While several studies used deep learning and computer vision to recognise human motion or activity, there is limited work on integrating these methods with building energy systems. Such methods can be used to obtain accurate and real-time information about the occupants for assisting in the operation of HVAC systems. In this research, a demand-driven deep learning framework was proposed to detect and recognise occupancy behaviour for optimising the operation of building HVAC systems. The computer vision-based deep learning algorithm, convolutional neural network (CNN), was selected to develop the vision-based detector to recognise common occupancy activities such as sitting, standing, walking and opening and closing windows. A dataset consisting of images of occupants in buildings performing different activities was formed to perform the training the model. The trained model was deployed to an AI-powered camera to perform real-time detection within selected case study building spaces, which include university tutorial rooms and offices. Two main types of detectors were developed to show the capabilities of the proposed approach; this includes the occupancy activity detector and the window opening detector. Both detectors were based on the Faster R-CNN with Inception V2 model, which was trained and tested using the same approach. In addition, the influence of different parameters on the performance, such as the training data size, labelling method, and how real-time detection was conducted in different indoor spaces was evaluated. The results have shown that a single response 'people detector’ can accurately understand the number of people within a detected space. The ‘occupancy activity detector’ could provide data towards the prediction of the internal heat emissions of buildings. Furthermore, window detectors were formed to recognise the times when windows are opened, providing insights into the potential ventilation heat losses through this type of ventilation strategy employed in buildings. The information generated by the detector is then outputted as profiles, which are called Deep Learning Influence Profiles (DLIP). Building energy simulation (BES) was used to assess the potential impact of the use of detection and recognition methods on building performance, such as ventilation heat loss and energy demands. The generated DLIPs were inputted into the BES tool. Comparisons with static or scheduled occupancy profiles, currently used in conventional HVAC systems and building energy modelling were made. The results showed that the over- or under-estimation of the occupancy heat gains could lead to inaccurate heating and cooling energy predictions. The deep learning detection method showed that the occupancy heat gains could be represented more accurately compared to static office occupancy profiles. A difference of up to 55% was observed between occupancy DLIP and static heat gain profile. Similarly, the window detection method enabled accurate recognition of the opening and closing of windows and the prediction of ventilation heat losses. BES was conducted for various scenario-based cases that represented typical and/or extreme situations that would occur within selected case study buildings. Results showed that the detection methods could be useful for modulating heating and cooling systems to minimise building energy losses while providing adequate indoor air quality and thermal conditions. Based on the developed individual detectors, combined detectors were formed and also assessed during experimental tests and analysis using BES. The vision-based technique’s integration with the building control system was discussed. A heat gain prediction and optimisation strategy were proposed along with a hybrid controller that optimises energy use and thermal comfort. This should be further developed in future works and assessed in real building installations. This work also discussed the limitations and practical challenges of implementing the proposed technology. Initial results of survey-based questionnaires highlighted the importance of informing occupants about the framework approach and how DLIPs were formed. In all, preference is towards a less intrusive and effective approach that could meet the needs of optimising building energy loads for the next-gen built environment
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