6,667 research outputs found

    Financial and Economic Review 22.

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    Knowledge Distillation and Continual Learning for Optimized Deep Neural Networks

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    Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations. Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally learning new tasks, the model performance on old tasks significantly drops. The ability to accommodate new knowledge while retaining previously learned knowledge is called continual learning. Since the realworld environments in which the model operates are always evolving, a robust neural network needs to have this continual learning ability for adapting to new changes

    Religion, Education, and the ‘East’. Addressing Orientalism and Interculturality in Religious Education Through Japanese and East Asian Religions

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    This work addresses the theme of Japanese religions in order to rethink theories and practices pertaining to the field of Religious Education. Through an interdisciplinary framework that combines the study of religions, didactics and intercultural education, this book puts the case study of Religious Education in England in front of two ‘challenges’ in order to reveal hidden spots, tackle unquestioned assumptions and highlight problematic areas. These ‘challenges’, while focusing primarily on Japanese religions, are addressed within the wider contexts of other East Asian traditions and of the modern historical exchanges with the Euro-American societies. As result, a model for teaching Japanese and other East Asian religions is discussed and proposed in order to fruitfully engage issues such as orientalism, occidentalism, interculturality and critical thinking

    Reinforcing nature-based solutions through tools providing social-ecological-technological integration

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    While held to be a means for climate change adaptation and mitigation, nature-based solutions (NbS) themselves are vulnerable to climate change. To find ways of compensating for this vulnerability we combine a focused literature review on how information technology has been used to strengthen positive social-ecological-technological feedback, with the development of a prototype decision-support tool. Guided by the literature review, the tool integrates recent advances in using globally available remote sensing data to elicit information on functional diversity and ecosystem service provisioning with information on human service demand and population vulnerability. When combined, these variables can inform climate change adaptation strategies grounded in local social-ecological realities. This type of integrated monitoring and packaging information to be actionable have potential to support NbS management and local knowledge building for context-tailored solutions to societal challenges in urban environments.Peer reviewe

    Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

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    In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources

    Cyberbullying in educational context

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    Kustenmacher and Seiwert (2004) explain a man’s inclination to resort to technology in his interaction with the environment and society. Thus, the solution to the negative consequences of Cyberbullying in a technologically dominated society is represented by technology as part of the technological paradox (Tugui, 2009), in which man has a dual role, both slave and master, in the interaction with it. In this respect, it is noted that, notably after 2010, there have been many attempts to involve artificial intelligence (AI) to recognize, identify, limit or avoid the manifestation of aggressive behaviours of the CBB type. For an overview of the use of artificial intelligence in solving various problems related to CBB, we extracted works from the Scopus database that respond to the criterion of the existence of the words “cyberbullying” and “artificial intelligence” in the Title, Keywords and Abstract. These articles were the subject of the content analysis of the title and, subsequently, only those that are identified as a solution in the process of recognizing, identifying, limiting or avoiding the manifestation of CBB were kept in the following Table where we have these data synthesized and organized by years

    Calibrating trust between humans and artificial intelligence systems

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    As machines become increasingly more intelligent, they become more capable of operating with greater degrees of independence from their users. However, appropriate use of these autonomous systems is dependent on appropriate trust from their users. A lack of trust towards an autonomous system will likely lead to the user doubting the capabilities of the system, potentially to the point of disuse. Conversely, too much trust in a system may lead to the user overestimating the capabilities of the system, and potentially result in errors which could have been avoided with appropriate supervision. Thus, appropriate trust is trust which is calibrated to reflect the true performance capabilities of the system. The calibration of trust towards autonomous systems is an area of research of increasing popularity, as more and more intelligent machines are introduced to modern workplaces. This thesis contains three studies which examine trust towards autonomous technologies. In our first study, in Chapter 2, we used qualitative research methods to explore how participants characterise their trust towards different online technologies. In focus groups, participants discussed a variety of factors which they believed were important when using digital services. We had a particular interest in how they perceived social media platforms, as these services rely upon users continued sharing of their personal information. In our second study, in Chapter 3, using our initial findings we created a human-computer interaction experiment, where participants collaborated with an Autonomous Image Classifier System. In this experiment, we were able to examine the ways that participants placed trust in the classifier during different types of system performance. We also investigated whether users’ trust could be better calibrated by providing different displays of System Confidence Information, to help convey the system’s decision making. In our final study, in Chapter 4, we built directly upon the findings of Chapter 3, by creating an updated version of our human-computer interaction experiment. We provided participants with another cue of system decision making, Gradient-weighted Class Activation Mapping, and investigated whether this cue could promote greater trust towards the classifier. Additionally, we examined whether these cues can improve participants’ subjective understanding of the system’s decision making, as a way of exploring how to improve the interpretability of these systems. This research contributes to our current understanding of calibrating users’ trust towards autonomous systems, and may be particularly useful when designing Autonomous Image Classifier Systems. While our results were inconclusive, we did find some support for users preferring the more complicated interfaces we provided. Users also reported greater understanding of the classifier’s decision making when provided with the Gradient-weighted Class Activation Mapping cue. Further research may clarify whether this cue is an appropriate method of visualising the decision-making of Autonomous Image Classifier Systems in real-world settings

    Interdisciplinarity as a political instrument of governance and its consequences for doctoral training

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    UK educational policies exploit interdisciplinarity as a marketing tool in a competitive educational world by building images of prosperous futures for society, the economy, and universities. Following this narrative, interdisciplinary science is promoted as superior to disciplinary forms of research and requires the training of future researchers accordingly, with interdisciplinary doctoral education becoming more established in universities. This emphasis on the growth of interdisciplinary science polarises scholars’ views on the role of academic research between the production of knowledge on the one hand and knowledge as an economic resource at the other end of the spectrum. This research asks: what is the rationale behind the perceived value of interdisciplinary research and training, and how does it affect graduate students’ experiences of their PhD? Based on a practice theory perspective for its suitability in generating insights into how university’s social life is organised, reproduced and transformed, the doctorate is conceptualised as sets of interconnected practices that are observable as they happen. This current study, therefore, comprised two stages of data collection and analysis; the examination of documents to elucidate educational policy practices and an educational ethnography of an interdisciplinary doctoral programme. This study found interdisciplinary doctoral training is hindered by the lack of role models and positive social relationships, which are crucial to the way interdisciplinary students learn. Furthermore, it is argued that interdisciplinarity is sometimes applied to research as a label to fit with funders’ requirements. Specifically, in this case, medical optical imaging is best seen as an interdiscipline as it does not exhibit true interdisciplinary integration. Further insights show that while interdisciplinarity is promoted in policy around promises and expectations for a better future, it is in tension with how it is organisationally embedded in higher education. These insights form the basis for a list of practical recommendations for institutions. Overall, interdisciplinary doctoral training was observed to present students with difficulties and to leave policy concerns unaddressed

    An onto-epistemological (re)framing and (re)connecting of organisations as praxeological multi-capital value systems

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    Organisation stands as one of humankind’s greatest inventions, and reconceptualising organisations to meet the ever-diversifying needs of the modern stakeholder community one of its most significant challenges. Historically, scientific management principles simplified the challenge through a profitable operations practice imperative, which reinforced a creation and destruction value dualism, and causal and value dead ends. However, value is contingent upon meeting needs, demanding that organisations leverage a wider and connected set of capitals to meet the diverse needs of modernity. This research seeks to understand how praxeologically inert legacy organisations can generate value by (re)connecting capitals and (re)framing as multi-capital value systems. The study’s setting is the university-led Made Smarter Leadership Development programme which provided an insightful longitudinal case study over the two-year programme life-cycle. The research surfaced rich qualitative insights on participant sense-making journeys across a diverse set of participant-researcher touchpoints, and also collected associated quantitative survey data. Analysis was conducted in three streams, and iteratively built up a complementary organisational model ontology. Stream one, a qualitative ethnographic study utilised grounded theory analysis to surface the prâxis (re)framing priorities of organisations. Analysis of such priorities yielded an onto-epistemological perspective of an organisation, and novel insights were generated on prâxis (re)framing strategies, organisational maturity, and how prâxes and frames combine as a relational onto-epistemological duality. Stream two’s quantitative analysis of respondent data identified the 20 significant prâxis-elements that form six systemically correlated and causally related capital factors. Findings indicate how multiple capitals connect as an organisational structure which orchestrates value flows between capital factors. Stream three elaborated on the prior two streams’ empirically-grounded foundations through sensemaking systems dynamics theory. This modelling produced both empirical findings and a generalisable methodology to reconceptualise organisations as a connected praxeological multi-capital value system. Specifically, findings informed how means-ends dynamics orchestrate complex capital interactions, which form pan-organisational value journeys, and ultimately form generalisable value archetypes. In summary, the research confirmed an organisation is a connected multi-capital praxeological value system, this outcome enabled by the discovery of a novel onto-epistemological perspective of organisations
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