1,332 research outputs found

    The Individual And Their World

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    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    The Public Performance Of Sanctions In Insolvency Cases: The Dark, Humiliating, And Ridiculous Side Of The Law Of Debt In The Italian Experience. A Historical Overview Of Shaming Practices

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    This study provides a diachronic comparative overview of how the law of debt has been applied by certain institutions in Italy. Specifically, it offers historical and comparative insights into the public performance of sanctions for insolvency through shaming and customary practices in Roman Imperial Law, in the Middle Ages, and in later periods. The first part of the essay focuses on the Roman bonorum cessio culo nudo super lapidem and on the medieval customary institution called pietra della vergogna (stone of shame), which originates from the Roman model. The second part of the essay analyzes the social function of the zecca and the pittima Veneziana during the Republic of Venice, and of the practice of lu soldate a castighe (no translation is possible). The author uses a functionalist approach to apply some arguments and concepts from the current context to this historical analysis of ancient institutions that we would now consider ridiculous. The article shows that the customary norms that play a crucial regulatory role in online interactions today can also be applied to the public square in the past. One of these tools is shaming. As is the case in contemporary online settings, in the public square in historic periods, shaming practices were used to enforce the rules of civility in a given community. Such practices can be seen as virtuous when they are intended for use as a tool to pursue positive change in forces entrenched in the culture, and thus to address social wrongs considered outside the reach of the law, or to address human rights abuses

    Exhibiting the Past

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    With respect to public issues, history matters. With the worldwide interest for historical issues related with gender, religion, race, nation, and identity, public history is becoming the strongest branch of academic history. This volume brings together the contributions from historians of education about their engagement with public history, ranging from musealisation and alternative ways of exhibiting to new ways of storytelling

    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)

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    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!

    Musiktheorie als interdisziplinäres Fach: 8. Kongress der Gesellschaft für Musiktheorie Graz 2008

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    Im Oktober 2008 fand an der Universität für Musik und darstellende Kunst Graz (KUG) der 8. Kongress der Gesellschaft für Musiktheorie (GMTH) zum Thema »Musiktheorie als interdisziplinäres Fach« statt. Die hier vorgelegten gesammelten Beiträge akzentuieren Musiktheorie als multiperspektivische wissenschaftliche Disziplin in den Spannungsfeldern Theorie/Praxis, Kunst/Wissenschaft und Historik/Systematik. Die sechs Kapitel ergründen dabei die Grenzbereiche zur Musikgeschichte, Musikästhetik, zur Praxis musikalischer Interpretation, zur kompositorischen Praxis im 20. und 21. Jahrhundert, zur Ethnomusikologie sowie zur Systematischen Musikwissenschaft. Insgesamt 45 Aufsätze, davon 28 in deutscher, 17 in englischer Sprache, sowie die Dokumentation einer Podiumsdiskussion zeichnen in ihrer Gesamtheit einen höchst lebendigen und gegenwartsbezogenen Diskurs, der eine einzigartige Standortbestimmung des Fachs Musiktheorie bietet.The 8th congress of the Gesellschaft für Musiktheorie (GMTH) took place in October 2008 at the University for Music and Dramatic Arts Graz (KUG) on the topic »Music Theory and Interdisciplinarity«. The collected contributions characterize music theory as a multi-faceted scholarly discipline at the intersection of theory/practice, art/science and history/system. The six chapters explore commonalties with music history, music aesthetics, musical performance, compositional practice in twentieth- and twenty-first-century music, ethnomusicology and systematic musicology. A total of 45 essays (28 in German, 17 in English) and the documentation of a panel discussion form a vital discourse informed by contemporaneous issues of research in a broad number of fields, providing a unique overview of music theory today. A comprehensive English summary appears at the beginning of all contributions

    Visual Place Recognition in Changing Environments Utilising Sequence-Based Filtering and Extremely JPEG Compressed Images

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    Visual Place Recognition (VPR), part of Simultaneous Localisation and Mapping (SLAM), is an essential task for the localisation process, where each robotic platform is required to successfully navigate through its environment using visual information gathered from the on-board camera. Despite the recent efforts of the research community, VPR remains an improving process. To this end, a large number of deep-learning-based and handcrafted VPR techniques (also referred as learnt and non-learnt VPR techniques) have been proposed to overcome the challenges in this field, such as viewpoint, illumination and seasonal variations. While Convolutional Neural Network (CNN)-based VPR techniques have significant computational requirements that may restrict their applicability on resource-constrained platforms, handcrafted VPR techniques struggle with appearance changes. In this thesis, two mainly unexplored avenues of research are investigated, namely sequence-based filtering and JPEG compression. To overcome the previously mentioned challenges, this thesis proposes a handcrafted VPR technique based on HOG descriptors, paired with an adaptive sequence-based filtering schema to perform VPR in scenarios where the appearance of the environment drastically changes upon different traversals. The technique entitled ConvSequential-SLAM is capable of achieving comparable place matching performance with state-of-the-art VPR techniques at reduced computational costs. The approach utilised for matching sequences of images in the above technique has been employed to investigate the improvement in VPR performance and the computational effort required to execute VPR when utilising a sequence-based filtering approach. As CNNs are computationally demanding, this thesis shows that VPR can be performed more efficiently using lightweight techniques. Furthermore, this thesis also investigates the effects of JPEG compression for VPR applications, where important reductions in both transmission and storage requirements can be achieved. As the VPR performance is drastically reduced, especially for high compression ratios, this thesis shows how a fine-tuned CNN can achieve more consistent VPR performance on highly JPEG compressed data (i.e. above 90% JPEG compression). Sequence-based filtering is introduced to overcome the performance loss due to JPEG compression. This thesis shows that the size of a JPEG compressed image is often smaller than the size of the image descriptor, and therefore should be transferred instead. Furthermore, our experiments also show that the amount of data required for transfer is reduced with an increase in JPEG compression, even when requiring an increased number of images in a sequence. This thesis also analyses the effects of image resolution on the performance of handcrafted techniques, to enable efficient deployment of VPR solutions on commercial products. The analysis performed in this thesis confirms that local feature descriptors are unable to operate on low-resolution images, as no keypoints (salient information) are detected. Moreover, this thesis also shows that the time required to perform VPR is reduced with a decrease in image resolution

    Estrategias de visión por computador para la estimación de pose en el contexto de aplicaciones robóticas industriales: avances en el uso de modelos tanto clásicos como de Deep Learning en imágenes 2D

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    184 p.La visión por computador es una tecnología habilitadora que permite a los robots y sistemas autónomos percibir su entorno. Dentro del contexto de la industria 4.0 y 5.0, la visión por ordenador es esencial para la automatización de procesos industriales. Entre las técnicas de visión por computador, la detección de objetos y la estimación de la pose 6D son dos de las más importantes para la automatización de procesos industriales. Para dar respuesta a estos retos, existen dos enfoques principales: los métodos clásicos y los métodos de aprendizaje profundo. Los métodos clásicos son robustos y precisos, pero requieren de una gran cantidad de conocimiento experto para su desarrollo. Por otro lado, los métodos de aprendizaje profundo son fáciles de desarrollar, pero requieren de una gran cantidad de datos para su entrenamiento.En la presente memoria de tesis se presenta una revisión de la literatura sobre técnicas de visión por computador para la detección de objetos y la estimación de la pose 6D. Además se ha dado respuesta a los siguientes retos: (1) estimación de pose mediante técnicas de visión clásicas, (2) transferencia de aprendizaje de modelos 2D a 3D, (3) la utilización de datos sintéticos para entrenar modelos de aprendizaje profundo y (4) la combinación de técnicas clásicas y de aprendizaje profundo. Para ello, se han realizado contribuciones en revistas de alto impacto que dan respuesta a los anteriores retos
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