4,269 research outputs found

    Mobile heritage practices. Implications for scholarly research, user experience design, and evaluation methods using mobile apps.

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    Mobile heritage apps have become one of the most popular means for audience engagement and curation of museum collections and heritage contexts. This raises practical and ethical questions for both researchers and practitioners, such as: what kind of audience engagement can be built using mobile apps? what are the current approaches? how can audience engagement with these experience be evaluated? how can those experiences be made more resilient, and in turn sustainable? In this thesis I explore experience design scholarships together with personal professional insights to analyse digital heritage practices with a view to accelerating thinking about and critique of mobile apps in particular. As a result, the chapters that follow here look at the evolution of digital heritage practices, examining the cultural, societal, and technological contexts in which mobile heritage apps are developed by the creative media industry, the academic institutions, and how these forces are shaping the user experience design methods. Drawing from studies in digital (critical) heritage, Human-Computer Interaction (HCI), and design thinking, this thesis provides a critical analysis of the development and use of mobile practices for the heritage. Furthermore, through an empirical and embedded approach to research, the thesis also presents auto-ethnographic case studies in order to show evidence that mobile experiences conceptualised by more organic design approaches, can result in more resilient and sustainable heritage practices. By doing so, this thesis encourages a renewed understanding of the pivotal role of these practices in the broader sociocultural, political and environmental changes.AHRC REAC

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    Privacy Nicks: How the Law Normalizes Surveillance

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    Privacy law is failing to protect individuals from being watched and exposed, despite stronger surveillance and data protection rules. The problem is that our rules look to social norms to set thresholds for privacy violations, but people can get used to being observed. In this article, we argue that by ignoring de minimis privacy encroachments, the law is complicit in normalizing surveillance. Privacy law helps acclimate people to being watched by ignoring smaller, more frequent, and more mundane privacy diminutions. We call these reductions “privacy nicks,” like the proverbial “thousand cuts” that lead to death.Privacy nicks come from the proliferation of cameras and biometric sensors on doorbells, glasses, and watches, and the drift of surveillance and data analytics into new areas of our lives like travel, exercise, and social gatherings. Under our theory of privacy nicks as the Achilles heel of surveillance law, invasive practices become routine through repeated exposures that acclimate us to being vulnerable and watched in increasingly intimate ways. With acclimation comes resignation, and this shift in attitude biases how citizens and lawmakers view reasonable measures and fair tradeoffs.Because the law looks to norms and people’s expectations to set thresholds for what counts as a privacy violation, the normalization of these nicks results in a constant re-negotiation of privacy standards to society’s disadvantage. When this happens, the legal and social threshold for rejecting invasive new practices keeps getting redrawn, excusing ever more aggressive intrusions. In effect, the test of what privacy law allows is whatever people will tolerate. There is no rule to stop us from tolerating everything. This article provides a new theory and terminology to understand where privacy law falls short and suggests a way to escape the current surveillance spiral

    Platelet-activating factor receptor in health and disease.

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    Background Platelet-activating factor receptor (PAFR) expression has been linked to anthropogenic particulate matter (PM). Traffic-related air pollution (TRAP) now accounts for the majority of this PM. PAFR expression has also been linked to an increased risk of infection from Streptococcus pneumoniae (S. pneumoniae). Children with asthma and sickle cell disease (SCD) have a significantly increased risk of morbidity and mortality from invasive pneumococcal disease (IPD). PAFR expression has not yet been investigated in relation to TRAP-generated PM, nor has constitutive expression been investigated in these children at increased risk of IPD. Methods PM10 was collected from roadside traffic using the Cyclone device. A549 cells were exposed to the collected PM10 and flow cytometry was undertaken to measure PAFR expression by median fluorescence intensity (MFI). Exposed A549 cells also underwent assays to determine bacterial adhesion (colony-forming units, CFU) using D39 S. pneumoniae species. In both experiments, Dulbecco’s phosphate buffered saline (DPBS) was used as a control. In a separate study, children aged 1 – 17 years were recruited into 4 groups: 2 disease groups (children with asthma, and those with SCD); and 2 control groups (healthy children, and children with atopy but not asthma). Nasal epithelial cells were collected and PAFR expression (MFI) measured by flow cytometry. 24-hour PM10 pollution (ÎŒg/m3) data were also collected for each participant. Results TRAP-related PM caused a significant increase in PAFR expression in A549 cells when exposed to a concentration of 10 ug/ml (p < 0.05). Bacterial adhesion (CFU) was significantly raised in A549 cells exposed to TRAP PM verses the control wells (p < 0.05). In children, PAFR expression in SCD was notably raised when compared to all other groups (p < 0.001). There was no 7 significant difference in the PAFR expression in those with asthma versus the control groups. 24% of the children within the study demonstrated exposure to PM10 levels above the WHO daily safety limit. Conclusion PAFR expression and subsequent bacterial adhesion is increased following exposure to TRAP. PAFR is shown to be constitutively raised in those with SCD and this may explain some of the reported risk from IPD. Air pollution levels in London remain above safe limits despite public health initiatives trying to decrease them

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    Taser and Social, Ethnic and Racial Disparities research programme

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    Report from the 'Taser use and its association with social, ethnic and racial disparities in policing (TASERD)' research project. The research project was initiated by the National Police Chiefs’ Council and commissioned by the College of Policing, after their Officer and Staff Safety Review (OSSR) in 2019 found there was growing evidence to suggest that Tasers were being used disproportionately in society. It was carried out by researchers from Keele University, UCL, The University of Exeter and Staffordshire University.National Police Chiefs’ CouncilLondon’s Mayor's Office for Policing and Crime (MOPAC

    Rights on news : expanding copyright on the internet

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    Defence date: 18 February 2020Examining Board: Prof. Giovanni Sartor, EUI (Supervisor); Prof. Pier Luigi Parcu, EUI; Prof. Lionel Bently, University of Cambridge; Prof. Christophe Geiger, University of StrasbourgThe internet and digital technologies have irreversibly changed the way we find and consume news. Legacy news organisations, publishers of newspapers, have moved to the internet. In the online news environment, however, they are no longer the exclusive suppliers of news. New digital intermediaries have emerged, search engines and news aggregators in particular. They select and display links and fragments of press publishers’ content as a part of their services, without seeking the news organisations’ prior consent. To shield themselves from exploitation by digital intermediaries, press publishers have begun to seek legal protection, and called for the introduction of a new right under the umbrella of copyright and related rights. Following these calls, the press publishers’ right was introduced into the EU copyright framework by the Directive on Copyright in the Digital Single Market in 2019
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