751 research outputs found

    30th European Congress on Obesity (ECO 2023)

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    This is the abstract book of 30th European Congress on Obesity (ECO 2023

    Exploring Compassion-Driven Interaction: Bridging Buddhist Theory and Contemplative Practice Through Arts-led Research-through-Design

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    Compassion cultivation focuses on developing a genuine concern for others and a willingness to alleviate their suffering. As understandings of the benefits of compassion cultivation on wellbeing have evolved, an increasing interest in designing technologies for this context have followed. However, while scientific research focuses on measuring and evaluating compassion, designerly understandings of compassion informing human-computer interaction have been less explored. We are currently confronted with huge global challenges and our entanglement with technology brings paradoxes and existential tensions related to wellbeing and human flourishing. Viewing technologies as mediators of values and morality, human-computer interaction has a stake in shaping our possible futures. A shift in the field to welcoming a plurality of worldviews, invites opportunities to authentically integrate knowledge from ancient wisdom traditions into how and why we design. This research aims to advance understandings of compassion cultivation for designing technologies by developing novel approaches to research inspired by Buddhist philosophy and practice. This thesis draws upon an arts-led research-through-design approach and spiritual practice. The findings and insights from the studies contribute primarily to the areas of soma design, first-person research and design for wellbeing. The main contributions to knowledge are design guidelines emerging from three case studies: Understanding Tonglen, Wish Happiness, and Inner Suchness comprising one autoethnography and two concept-driven design artefacts for public exhibition. While in the act of researching, the contemplative practitioner-researcher, a research persona, emerged to support authentic engagement and embodied understandings of the dynamic unfolding processes of the practice. A contemplative framework to train self-observation and the concept of designerly gaze were developed to help investigate the phenomenon

    Summer/Fall 2023

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    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

    A comparative analysis of the skilled use of automated feedback tools through the lens of teacher feedback literacy

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    Effective learning depends on effective feedback, which in turn requires a set of skills, dispositions and practices on the part of both students and teachers which have been termed feedback literacy. A previously published teacher feedback literacy competency framework has identified what is needed by teachers to implement feedback well. While this framework refers in broad terms to the potential uses of educational technologies, it does not examine in detail the new possibilities of automated feedback (AF) tools, especially those that are open by offering varying degrees of transparency and control to teachers. Using analytics and artificial intelligence, open AF tools permit automated processing and feedback with a speed, precision and scale that exceeds that of humans. This raises important questions about how human and machine feedback can be combined optimally and what is now required of teachers to use such tools skillfully. The paper addresses two research questions: Which teacher feedback competencies are necessary for the skilled use of open AF tools? and What does the skilled use of open AF tools add to our conceptions of teacher feedback competencies? We conduct an analysis of published evidence concerning teachers’ use of open AF tools through the lens of teacher feedback literacy, which produces summary matrices revealing relative strengths and weaknesses in the literature, and the relevance of the feedback literacy framework. We conclude firstly, that when used effectively, open AF tools exercise a range of teacher feedback competencies. The paper thus offers a detailed account of the nature of teachers’ feedback literacy practices within this context. Secondly, this analysis reveals gaps in the literature, signalling opportunities for future work. Thirdly, we propose several examples of automated feedback literacy, that is, distinctive teacher competencies linked to the skilled use of open AF tools

    Machine learning in portfolio management

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    Financial markets are difficult learning environments. The data generation process is time-varying, returns exhibit heavy tails and signal-to-noise ratio tends to be low. These contribute to the challenge of applying sophisticated, high capacity learning models in financial markets. Driven by recent advances of deep learning in other fields, we focus on applying deep learning in a portfolio management context. This thesis contains three distinct but related contributions to literature. First, we consider the problem of neural network training in a time-varying context. This results in a neural network that can adapt to a data generation process that changes over time. Second, we consider the problem of learning in noisy environments. We propose to regularise the neural network using a supervised autoencoder and show that this improves the generalisation performance of the neural network. Third, we consider the problem of quantifying forecast uncertainty in time-series with volatility clustering. We propose a unified framework for the quantification of forecast uncertainty that results in uncertainty estimates that closely match actual realised forecast errors in cryptocurrencies and U.S. stocks

    Copyright Damages Need To Have a Sufficient Punitive Element To Successfully Deter Infringement

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    Enforcement issues have been very much to the fore in recent years, with increasing emphasis on the need to deter infringing conduct. this thesis will concentrate on the provisions relating to damages, as set out in section 97 of the Copyright, Designs and Patents Act 1988 (CDPA 1988) and will consider the research question, which is whether those provisions contain a sufficient punitive element to successfully deter copyright infringement. The thesis will initially set out the conceptual framework, which identifies the issues that arise with compensation only damages, then go on to consider the purpose and effect of of punitive damages as a deterrent. it will also consider the concept of deterrence. the research explores the international framework that governs an award of copyright damages, to include the TRIPS Agreement and the Trade and Cooperation Agreement 2020, as well as any potential Free Trade Agreements (FTA’s) that already apply, or which may apply to the UK following Brexit. the provisions of the Enforcement Directive 2004/48/EC will be explored in some detail. There will be an analysis of the existing domestic framework of compensatory damages in the UK, followed by consideration of the existing framework of punitive damages. By comparison, it will be exploring the availability and application of punitive damages in Australia and the United States and finally, it will address the human rights issues which arise from the chilling effect on free speech by the application of punitive damages in that context. the work has doctrinal legal research as well as comparative methods, which have focused upon the existing law and whether and to what extent, either the US or Australia can provide appropriate guidance for a reform of section 97 of the CDPA 1988. The conclusions will show that the UK should explicitly provide for the availability of punitive damages, as they are necessary to deter copyright infringement, but also that any punitive provisions could be clearly set out in the legislation, along with the clear guidance as to their application. The findings of this research may provide a normative basis for reform of the damages provisions for copyright infringement in the UK and will therefore constitute a contribution to knowledge

    Automatic Essay Scoring Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses

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    Deep-learning based Automatic Essay Scoring (AES) systems are being actively used in various high-stake applications in education and testing. However, little research has been put to understand and interpret the black-box nature of deep-learning-based scoring algorithms. While previous studies indicate that scoring models can be easily fooled, in this paper, we explore the reason behind their surprising adversarial brittleness. We utilize recent advances in interpretability to find the extent to which features such as coherence, content, vocabulary, and relevance are important for automated scoring mechanisms. We use this to investigate the oversensitivity (i.e., large change in output score with a little change in input essay content) and overstability (i.e., little change in output scores with large changes in input essay content) of AES. Our results indicate that autoscoring models, despite getting trained as “end-to-end” models with rich contextual embeddings such as BERT, behave like bag-of-words models. A few words determine the essay score without the requirement of any context making the model largely overstable. This is in stark contrast to recent probing studies on pre-trained representation learning models, which show that rich linguistic features such as parts-of-speech and morphology are encoded by them. Further, we also find that the models have learnt dataset biases, making them oversensitive. The presence of a few words with high co-occurrence with a certain score class makes the model associate the essay sample with that score. This causes score changes in ∌95% of samples with an addition of only a few words. To deal with these issues, we propose detection-based protection models that can detect oversensitivity and samples causing overstability with high accuracies. We find that our proposed models are able to detect unusual attribution patterns and flag adversarial samples successfully

    BYU Journal of Public Law Volume 37 Number 1

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