1,285 research outputs found

    Displacement and the Humanities: Manifestos from the Ancient to the Present

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    This is the final version. Available on open access from MDPI via the DOI in this recordThis is a reprint of articles from the Special Issue published online in the open access journal Humanities (ISSN 2076-0787) (available at: https://www.mdpi.com/journal/humanities/special_issues/Manifestos Ancient Present)This volume brings together the work of practitioners, communities, artists and other researchers from multiple disciplines. Seeking to provoke a discourse around displacement within and beyond the field of Humanities, it positions historical cases and debates, some reaching into the ancient past, within diverse geo-chronological contexts and current world urgencies. In adopting an innovative dialogic structure, between practitioners on the ground - from architects and urban planners to artists - and academics working across subject areas, the volume is a proposition to: remap priorities for current research agendas; open up disciplines, critically analysing their approaches; address the socio-political responsibilities that we have as scholars and practitioners; and provide an alternative site of discourse for contemporary concerns about displacement. Ultimately, this volume aims to provoke future work and collaborations - hence, manifestos - not only in the historical and literary fields, but wider research concerned with human mobility and the challenges confronting people who are out of place of rights, protection and belonging

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Protecting Privacy in Indian Schools: Regulating AI-based Technologies' Design, Development and Deployment

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    Education is one of the priority areas for the Indian government, where Artificial Intelligence (AI) technologies are touted to bring digital transformation. Several Indian states have also started deploying facial recognition-enabled CCTV cameras, emotion recognition technologies, fingerprint scanners, and Radio frequency identification tags in their schools to provide personalised recommendations, ensure student security, and predict the drop-out rate of students but also provide 360-degree information of a student. Further, Integrating Aadhaar (digital identity card that works on biometric data) across AI technologies and learning and management systems (LMS) renders schools a ‘panopticon’. Certain technologies or systems like Aadhaar, CCTV cameras, GPS Systems, RFID tags, and learning management systems are used primarily for continuous data collection, storage, and retention purposes. Though they cannot be termed AI technologies per se, they are fundamental for designing and developing AI systems like facial, fingerprint, and emotion recognition technologies. The large amount of student data collected speedily through the former technologies is used to create an algorithm for the latter-stated AI systems. Once algorithms are processed using machine learning (ML) techniques, they learn correlations between multiple datasets predicting each student’s identity, decisions, grades, learning growth, tendency to drop out, and other behavioural characteristics. Such autonomous and repetitive collection, processing, storage, and retention of student data without effective data protection legislation endangers student privacy. The algorithmic predictions by AI technologies are an avatar of the data fed into the system. An AI technology is as good as the person collecting the data, processing it for a relevant and valuable output, and regularly evaluating the inputs going inside an AI model. An AI model can produce inaccurate predictions if the person overlooks any relevant data. However, the state, school administrations and parents’ belief in AI technologies as a panacea to student security and educational development overlooks the context in which ‘data practices’ are conducted. A right to privacy in an AI age is inextricably connected to data practices where data gets ‘cooked’. Thus, data protection legislation operating without understanding and regulating such data practices will remain ineffective in safeguarding privacy. The thesis undergoes interdisciplinary research that enables a better understanding of the interplay of data practices of AI technologies with social practices of an Indian school, which the present Indian data protection legislation overlooks, endangering students’ privacy from designing and developing to deploying stages of an AI model. The thesis recommends the Indian legislature frame better legislation equipped for the AI/ML age and the Indian judiciary on evaluating the legality and reasonability of designing, developing, and deploying such technologies in schools

    Communicating a Pandemic

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    This edited volume compares experiences of how the Covid-19 pandemic was communicated in the Nordic countries – Denmark, Finland, Iceland, Norway, and Sweden. The Nordic countries are often discussed in terms of similarities concerning an extensive welfare system, economic policies, media systems, and high levels of trust in societal actors. However, in the wake of a global pandemic, the countries’ coping strategies varied, creating certain question marks on the existence of a “Nordic model”. The chapters give a broad overview of crisis communication in the Nordic countries during the first year of the Covid-19 pandemic by combining organisational and societal theoretical perspectives and encompassing crisis response from governments, public health authorities, lobbyists, corporations, news media, and citizens. The results show several similarities, such as political and governmental responses highlighting solidarity and the need for exceptional measures, as expressed in press conferences, social media posts, information campaigns, and speeches. The media coverage relied on experts and was mainly informative, with few critical investigations during the initial phases. Moreover, surveys and interviews show the importance of news media for citizens’ coping strategies, but also that citizens mostly trusted both politicians and health authorities during the crisis. This book is of interest to all who are looking to understand societal crisis management on a comprehensive level. The volume contains chapters from leading experts from all the Nordic countries and is edited by a team with complementary expertise on crisis communication, political communication, and journalism, consisting of Bengt Johansson, Øyvind Ihlen, Jenny Lindholm, and Mark Blach-Ørsten. Publishe

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Secure storage systems for untrusted cloud environments

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    The cloud has become established for applications that need to be scalable and highly available. However, moving data to data centers owned and operated by a third party, i.e., the cloud provider, raises security concerns because a cloud provider could easily access and manipulate the data or program flow, preventing the cloud from being used for certain applications, like medical or financial. Hardware vendors are addressing these concerns by developing Trusted Execution Environments (TEEs) that make the CPU state and parts of memory inaccessible from the host software. While TEEs protect the current execution state, they do not provide security guarantees for data which does not fit nor reside in the protected memory area, like network and persistent storage. In this work, we aim to address TEEs’ limitations in three different ways, first we provide the trust of TEEs to persistent storage, second we extend the trust to multiple nodes in a network, and third we propose a compiler-based solution for accessing heterogeneous memory regions. More specifically, • SPEICHER extends the trust provided by TEEs to persistent storage. SPEICHER implements a key-value interface. Its design is based on LSM data structures, but extends them to provide confidentiality, integrity, and freshness for the stored data. Thus, SPEICHER can prove to the client that the data has not been tampered with by an attacker. • AVOCADO is a distributed in-memory key-value store (KVS) that extends the trust that TEEs provide across the network to multiple nodes, allowing KVSs to scale beyond the boundaries of a single node. On each node, AVOCADO carefully divides data between trusted memory and untrusted host memory, to maximize the amount of data that can be stored on each node. AVOCADO leverages the fact that we can model network attacks as crash-faults to trust other nodes with a hardened ABD replication protocol. • TOAST is based on the observation that modern high-performance systems often use several different heterogeneous memory regions that are not easily distinguishable by the programmer. The number of regions is increased by the fact that TEEs divide memory into trusted and untrusted regions. TOAST is a compiler-based approach to unify access to different heterogeneous memory regions and provides programmability and portability. TOAST uses a load/store interface to abstract most library interfaces for different memory regions

    Optimising delivery of the Childsmile nursery supervised toothbrushing programme in Scotland

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    Background. Supervised toothbrushing in nurseries, delivered as a component of Childsmile, Scotland’s national oral health improvement programme for children, is associated with reduced caries experience and cost savings in prevented dental treatments. There is also evidence that it is effective in reducing oral health inequalities, with greater improvements in oral health observed among children living in the most deprived areas. However Childsmile process evaluation data indicate that the nursery supervised toothbrushing programme does not take place as intended in all nursery settings. This highlighted the need to undertake further research to optimise its delivery, to maximise the gains for children’s oral health and contribute to reducing oral health inequalities. Aims: The overarching aim of the research is to optimise delivery of the nursery supervised toothbrushing programme, which is achieved by: further developing its Theory of Change; assessing the fidelity of its implementation compared with the Theory of Change; identifying the barriers and facilitators to its implementation; and identifying implementation strategies to overcome those barriers. It is intended that findings will be fed back into the Childsmile programme to inform ongoing improvement of the nursery supervised toothbrushing programme component. Methods: The research was framed within the paradigm of pragmatism and utilised a mixed-methods approach, informed by a programme theory approach and implementation science methods, making it the first study of its kind to utilise this approach to investigate the implementation of a complex toothbrushing intervention delivered in educational settings. The researcher explicated the programme’s Theory of Change via documentary review, to identify its key components (the inputs, activities and outcomes); and qualitative interviews and focus groups with programme stakeholders, to discuss and agree the Theory of Change, which was depicted in a logic model. Using a mixed methods approach, the researcher undertook national, crosssectional surveys of nurseries, qualitative interviews with programme stakeholders and extracted data from ongoing Childsmile process evaluation, to assess fidelity of implementation and identify barriers and facilitators to delivery. Delivery-in-reality was assessed in comparison with the intended model (per the logic model developed in the previous stage of the research). The researcher used the Consolidated Framework for Implementation Research to categorise the barriers and facilitators identified and mapped these to the Expert Recommendations for Implementing Change compilation of implementation strategies to identify potential methods and techniques to overcome barriers to programme delivery. Results: This novel study identified that optimising the Childsmile nursery supervised toothbrushing programme requires a shared vision to be developed and strengthened among partners involved in its implementation, supported by developing a formal implementation blueprint and further work to increase nursery staff’s buy-in, such as local champions and enhanced training. The fidelity of programme delivery should continue to be monitored and evaluated using the methodology and logic model developed via this research. The inputs, activities and outcomes comprising the Theory of Change of the nursery supervised toothbrushing programme were specified, with consensus on those reached among programme stakeholders. This included stating the primary aim of the programme: 100% of children brush their teeth in nursery, every day they attend. However, national survey results showed that this target was not met, with 92% of eligible children brushing in nurseries on the day of the survey and variation in percentages of children brushing across geographical health boards. Nurseries with 100% toothbrushing rates were more likely to have fewer children attending, only have a single age group attending and were situated in certain geographical health board areas and not others. Using a mixed methods approach highlighted inconsistencies between these quantitative data on nurseries’ participation and qualitative findings on stakeholders’ perceptions about nurseries’ participation. There were variations between health boards in the extent to which delivery-in-reality matched what was intended. This included the content and frequency of training provided to nursery staff to support their delivery of the programme, with no standardised training package available nationally. Relationships between Childsmile teams and local authorities’ education departments were identified as important although these required careful management and communication. Barriers and facilitators influencing programme implementation before and during the Covid19 pandemic were identified and the Consolidated Framework for Implementation Research provided good coverage of these (encompassing all five domains and 14 out of 26 constructs associated with intervention implementation). Relevant constructs included: ‘Complexity’, in relation to fitting toothbrushing in to nursery routines and perceptions about it being too time-consuming; ‘Patient Needs and Resources’, in terms of children’s ability to perform the required actions as well as their reluctance to participate in toothbrushing instead of other available activities; and ‘External Policies and Incentives’, which related to the interpretation of early years policies which conflicted with directing children to participate in activities, including toothbrushing. An overarching theme related to the prioritisation of the nursery supervised toothbrushing programme by nursery staff, including the extent to which other activities were given precedence over it; and nursery staff’s willingness to accommodate toothbrushing flexibly within nursery schedules. The Covid-19 pandemic disrupted delivery of the programme due to nursery closures in 2020 and 2021, as well as creating additional pressures for nursery staff once establishments reopened. This affected the extent to which they engaged with efforts to restart the toothbrushing programme. Conclusions: This research has explicated the Theory of Change for Childsmile’s nursery supervised toothbrushing programme, from the perspective of programme stakeholders. There is scope for further specification of core, ‘essential’ programme components and adaptable, peripheral components, to identify an acceptable level of delivery which will allow progress towards outcomes. There are also opportunities to work with stakeholders from other organisations, aside from Childsmile, to identify changes to the Theory of Change to enhance its fit with their needs and priorities. In assessing the fidelity of programme implementation, it was found that aspects were delivered as intended; however, most logic model activities had components that were not being delivered with fidelity, including that less than 100% of children brushed their teeth every day they attended nursery. It was identified that the nursery context in which the programme is delivered was complex and fluctuating, with competing demands on nursery staff’s time. This indicated a need to accept that the programme has to fit within overall nursery provision, to ensure it is given enough priority. This requires identifying implementation strategies to find ways to help it fit alongside other priorities, including strategies to enhance engagement among nursery staff while taking their perspectives into account. A number of recommendations are made to support and optimise programme delivery going forward. These include supplementing the programme’s ongoing work in fostering relationships with partners with a focused communications campaign, targeted at stakeholders in individual nurseries and local authority education departments, which demonstrates how the programme fits within the wider nursery curriculum and its contribution to children’s health and wellbeing alongside information (tailored to stakeholders’ roles) that clarifies what is involved in programme delivery. It is also recommended that a knowledge exchange and support network should be established among nurseries, led by champions (invited to undertake this role among nursery staff with an interest in oral health working in nurseries identified to deliver the programme well) who support and mentor their peers to overcome challenges to delivering the toothbrushing programme. This could include enhanced training, tailored to individual nurseries’ needs, to provide practical solutions to overcome challenges encountered. To encourage participation among local authorities’ education departments and individual establishments’ head teachers and managers, it is recommended that further, supportive dialogue takes place between the Childsmile programme, the Scottish Government and local authority education departments

    2017 GREAT Day Program

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    SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp

    Perceptions and Practicalities for Private Machine Learning

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    data they and their partners hold while maintaining data subjects' privacy. In this thesis I show that private computation, such as private machine learning, can increase end-users' acceptance of data sharing practices, but not unconditionally. There are many factors that influence end-users' privacy perceptions in this space; including the number of organizations involved and the reciprocity of any data sharing practices. End-users emphasized the importance of detailing the purpose of a computation and clarifying that inputs to private computation are not shared across organizations. End-users also struggled with the notion of protections not being guaranteed 100\%, such as in statistical based schemes, thus demonstrating a need for a thorough understanding of the risk form attacks in such applications. When training a machine learning model on private data, it is critical to understand the conditions under which that data can be protected; and when it cannot. For instance, membership inference attacks aim to violate privacy protections by determining whether specific data was used to train a particular machine learning model. Further, the successful transition of private machine learning theoretical research to practical use must account for gaps in achieving these properties that arise due to the realities of concrete implementations, threat models, and use cases; which is not currently the case
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