167 research outputs found

    Deep visual generation for automotive design upgrading and market optimising

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    The rising levels of homogeneity of modern cars in terms of price and functions has made exterior styling increasingly vital for market success. Recently, researchers have attempted to apply deep learning, especially deep generative models, to automotive exterior design, which has enabled machines to deliver diverse novel designs from large-scale data. In this thesis, we argue that recent advancements in deep learning techniques, particularly in deep generation, can be utilised to facilitate different aspects of automotive exterior design, including design generation, evaluation, and market profit predicting. We conducted three independent studies, each providing tailored solutions to specific automotive design scenarios. These include: a study focused on adapting the latest deep generative model to achieve regional modifications in existing designs, and evaluating these adjustments in terms of design aesthetic and prospective profit changes; another study dedicated to developing a predictive model to assess the modernity of existing designs regarding the future fashion trends; and a final study aiming to incorporate the distinctive shape characteristics of a cheetah into the side view designs of cars. This thesis has four main contributions. First, the developed DVM-CAR dataset is the first large-scale automotive dataset containing designs and marketing data over 10 years. It can be used for different types of research needs from multiple disciplines. Second, given the inherent constraints in automotive design, such as the need to maintain “family face”, and the fact that unconstrained design generation can be seen as a special form of regional modification, our research distinctively focuses on the regional modifications to existing designs, a departure from existing studies. Third, our studies are the first works that integrate the design modules with market profit optimisation. This reforms the traditional product design optimisation frameworks by replacing the abridged design profiles with graphical designs. Finally, the proposed data-driven measures offer effective approaches for automotive aesthetic evaluation and market forecasting, including approaches that can make assessments from a dynamic perspective by examining the evolving fashion trends

    The Ephemeral City : Songs for the Ghost Quarters

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    The towers of the Stockholm skyline twine with radio transmissions, flying out over the city, drifting down through the streets and sinking into the underground telephone system below. Stockholm has buildings that have been there for centuries, but is also full of modern and contemporary architectures, all jostling for their place in parallel collective memory. In taking the city up as a subject, this artistic PhD project in music expands allegories to these architectural instruments into the world of the mechanical and the electrical. By taking up and transforming the materials of the cityscape, this project spins ephemeral cities more subtle than the colossal forces transforming the cityscape. The aim is to empower urban dwellers with another kind of ownership of their city.The materials in the project are drawn around themes of urban memory and transformation, psychogeography and the ghosts of the imagined city. There are three questions the artistic works of this project reflect on and address. The first is about the ability of city-dwellers to regain or create some sense of place, history or belonging through the power of their imaginations. The second reflects on the possibility for imagined alternatives to re-empower a sense of place for the people who encounter them. The third seeks out the points where stories, memories, or alternative futures are collective, at what point are they wholly individual, and how the interplay between them plays out in listening.There is an improvisatory practice in how we relate to urban environments: an ever-transforming inter-play between the animate and inanimate. Each individual draws phantoms of memory and imagination onto the cityscape, and this yields subtle ways people can be empowered in their surroundings. The artistic works of this project are made to illuminate those subtleties, centering around a group of compositions, improvisations, artistic collaborations and sound installations in music and sound, utilizing modular synthesizers, field recordings, pipe organs, multi-channel settings; PureData and SuperCollider programs, string ensembles with hurdy-gurdy and nyckelharpa or violin, and sound installations. This choice of instruments is as an allegory to the architecture of Stockholm. The final result is a collection of music and sound works, made to illuminate the imagined city. Taken as a whole, the works of the project create an imaginary city–The Ephemeral City–in order to argue that this evocation of ephemeral space is a way to empower urban dwellers through force of imagination, immune to the vast forces tearing through the fabric of Stockholm life by virtue of the ghostly, transitory and mercurial, as compelling to the inner eye as brick and mortar to the outer life

    Fortifying robustness: unveiling the intricacies of training and inference vulnerabilities in centralized and federated neural networks

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    Neural network (NN) classifiers have gained significant traction in diverse domains such as natural language processing, computer vision, and cybersecurity, owing to their remarkable ability to approximate complex latent distributions from data. Nevertheless, the conventional assumption of an attack-free operating environment has been challenged by the emergence of adversarial examples. These perturbed samples, which are typically imperceptible to human observers, can lead to misclassifications by the NN classifiers. Moreover, recent studies have uncovered the ability of poisoned training data to generate Trojan backdoored classifiers that exhibit misclassification behavior triggered by predefined patterns. In recent years, significant research efforts have been dedicated to uncovering the vulnerabilities of NN classifiers and developing defenses or mitigations against them. However, the existing approaches still fall short of providing mature solutions to address this ever-evolving problem. The widely adopted defense mechanisms against adversarial examples are computationally expensive and impractical for certain real-world applications. Likewise, the practical black-box defense against Trojan backdoors has failed to achieve state-of-the-art performance. More concerning is the limited exploration of these vulnerabilities within the context of cooperative attack or Federated learning, leaving NN classifiers exposed to unknown risks. This dissertation aims to address these critical gaps and refine our understanding of these vulnerabilities. The research conducted within this dissertation encompasses both the attack and defense perspectives, aiming to shed light on future research directions for vulnerabilities in NN classifiers

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Fundamentals

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    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

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    Convergence of Intelligent Data Acquisition and Advanced Computing Systems

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    This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions
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