6,261 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Mobile Device Background Sensors: Authentication vs Privacy

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    The increasing number of mobile devices in recent years has caused the collection of a large amount of personal information that needs to be protected. To this aim, behavioural biometrics has become very popular. But, what is the discriminative power of mobile behavioural biometrics in real scenarios? With the success of Deep Learning (DL), architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), have shown improvements compared to traditional machine learning methods. However, these DL architectures still have limitations that need to be addressed. In response, new DL architectures like Transformers have emerged. The question is, can these new Transformers outperform previous biometric approaches? To answers to these questions, this thesis focuses on behavioural biometric authentication with data acquired from mobile background sensors (i.e., accelerometers and gyroscopes). In addition, to the best of our knowledge, this is the first thesis that explores and proposes novel behavioural biometric systems based on Transformers, achieving state-of-the-art results in gait, swipe, and keystroke biometrics. The adoption of biometrics requires a balance between security and privacy. Biometric modalities provide a unique and inherently personal approach for authentication. Nevertheless, biometrics also give rise to concerns regarding the invasion of personal privacy. According to the General Data Protection Regulation (GDPR) introduced by the European Union, personal data such as biometric data are sensitive and must be used and protected properly. This thesis analyses the impact of sensitive data in the performance of biometric systems and proposes a novel unsupervised privacy-preserving approach. The research conducted in this thesis makes significant contributions, including: i) a comprehensive review of the privacy vulnerabilities of mobile device sensors, covering metrics for quantifying privacy in relation to sensitive data, along with protection methods for safeguarding sensitive information; ii) an analysis of authentication systems for behavioural biometrics on mobile devices (i.e., gait, swipe, and keystroke), being the first thesis that explores the potential of Transformers for behavioural biometrics, introducing novel architectures that outperform the state of the art; and iii) a novel privacy-preserving approach for mobile biometric gait verification using unsupervised learning techniques, ensuring the protection of sensitive data during the verification process

    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

    Due Diligence in EU Institutions' Own-Account Procurement:Rules and Practices

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    This study, commissioned by the European Parliament’s Committee on Budgetary Control (CONT), investigates whether EU institutions implement human rights and sustainability due diligence when they purchase goods and services. Based on documentary analysis and interviews, this study finds that sustainability due diligence is lacking in procurement carried out by the European Parliament, the European Commission and the EU agencies. Accordingly, it makes recommendations to promote better integration of due diligence into the procurement of goods and services by the EU institutions

    LIMITS OF ALGORITHMIC FAIR USE

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    In this article, we apply historical copyright principles to the evolving state of text-to-image generation and explore the implications of emerging technological constructs for copyright’s fair use doctrine. Artificial intelligence (“AI”) is frequently trained on copyrighted works, which usually involves extensive copying without owners’ authorization. Such copying could constitute prima facie copyright infringement, but existing guidance suggests fair use should apply to most machine learning contexts. Mark Lemley and Bryan Casey argue that training machine learning (“ML”) models on copyrighted material should generally be permitted under fair use when the model’s outputs transcends the purpose of its inputs. Their arguments are compelling in the domain of AI, generally. However, contemporary AI’s capacity to generate new works of art (“generative AI”) presents a unique case because it explicitly attempts to emulate the expression copyright intends to protect. Jessica Gillotte concludes that generative AI does not illicit copyright infringement because judicial guidance requires adherence to the constitutional imperative to promote the creation of new works when technological change blurs copyright’s boundaries. Even if infringement does occur, Gillotte finds that fair use would serve as a valid defense because training an AI model transforms the original work and is unlikely to damage the original artist’s market for the copyrighted work. Our paper deviates from prior scholarship by exploring specific generative AI use cases in technological detail. Ultimately, we argue that fair use’s first factor, the purpose of the use, and its fourth factor, the impact on the market for the copyrighted work, both weigh against a finding of fair use in generative AI use cases. However, even if text-to-image models aren’t found to be transformative, we argue that the potential for market usurpation alone sufficiently negates fair use. There is presently little specific guidance from courts as to whether using copyrighted works to build generative AI models constitutes either infringement or fair use, although several related lawsuits are currently pending. Text-to-art generative AIs present several scenarios that threaten substantial harm to the market for the copyrighted original, which tends to undercut the case for fair use. For example, a generative AI trained on copyrighted works has already enabled users to create works “in the style of” individual artists, which has allegedly caused business and reputational losses for the emulated copyright holder. Furthermore, past analyses have ignored the potential for a model to be non-transformative when its intended output has the same purpose and is of the same nature as its copyrighted inputs. This article contributes to the discussion by shining a technical light on text-to-art AI use cases to explore whether some uses normatively fail to qualify as fair uses. First, we examine whether text-to-image models present a prima facie infringement claim. We then distinguish text-to-image generative AIs from non-image focused AIs. In doing so, we argue that when the nature of the copyrighted work and the purpose of the infringing use are the same, it is more likely that the original artist will experience market harm. This tilts the overall analysis against a finding of fair use

    The Potential of Electrospinning to Enable the Realization of Energy-Autonomous Wearable Sensing Systems

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    The market for wearable electronic devices is experiencing significant growth and increasing potential for the future. Researchers worldwide are actively working to improve these devices, particularly in developing wearable electronics with balanced functionality and wearability for commercialization. Electrospinning, a technology that creates nano/microfiber-based membranes with high surface area, porosity, and favorable mechanical properties for human in vitro and in vivo applications using a broad range of materials, is proving to be a promising approach. Wearable electronic devices can use mechanical, thermal, evaporative and solar energy harvesting technologies to generate power for future energy needs, providing more options than traditional sources. This review offers a comprehensive analysis of how electrospinning technology can be used in energy-autonomous wearable wireless sensing systems. It provides an overview of the electrospinning technology, fundamental mechanisms, and applications in energy scavenging, human physiological signal sensing, energy storage, and antenna for data transmission. The review discusses combining wearable electronic technology and textile engineering to create superior wearable devices and increase future collaboration opportunities. Additionally, the challenges related to conducting appropriate testing for market-ready products using these devices are also discussed

    Graduate Catalog of Studies, 2023-2024

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    Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

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    Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices.Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies.Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman’s rank correlation coefficient (R) of 0.56 and −0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics.Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements

    Reliable indoor optical wireless communication in the presence of fixed and random blockers

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    The advanced innovation of smartphones has led to the exponential growth of internet users which is expected to reach 71% of the global population by the end of 2027. This in turn has given rise to the demand for wireless data and internet devices that is capable of providing energy-efficient, reliable data transmission and high-speed wireless data services. Light-fidelity (LiFi), known as one of the optical wireless communication (OWC) technology is envisioned as a promising solution to accommodate these demands. However, the indoor LiFi channel is highly environment-dependent which can be influenced by several crucial factors (e.g., presence of people, furniture, random users' device orientation and the limited field of view (FOV) of optical receivers) which may contribute to the blockage of the line-of-sight (LOS) link. In this thesis, it is investigated whether deep learning (DL) techniques can effectively learn the distinct features of the indoor LiFi environment in order to provide superior performance compared to the conventional channel estimation techniques (e.g., minimum mean square error (MMSE) and least squares (LS)). This performance can be seen particularly when access to real-time channel state information (CSI) is restricted and is achieved with the cost of collecting large and meaningful data to train the DL neural networks and the training time which was conducted offline. Two DL-based schemes are designed for signal detection and resource allocation where it is shown that the proposed methods were able to offer close performance to the optimal conventional schemes and demonstrate substantial gain in terms of bit-error ratio (BER) and throughput especially in a more realistic or complex indoor environment. Performance analysis of LiFi networks under the influence of fixed and random blockers is essential and efficient solutions capable of diminishing the blockage effect is required. In this thesis, a CSI acquisition technique for a reconfigurable intelligent surface (RIS)-aided LiFi network is proposed to significantly reduce the dimension of the decision variables required for RIS beamforming. Furthermore, it is shown that several RIS attributes such as shape, size, height and distribution play important roles in increasing the network performance. Finally, the performance analysis for an RIS-aided realistic indoor LiFi network are presented. The proposed RIS configuration shows outstanding performances in reducing the network outage probability under the effect of blockages, random device orientation, limited receiver's FOV, furniture and user behavior. Establishing a LOS link that achieves uninterrupted wireless connectivity in a realistic indoor environment can be challenging. In this thesis, an analysis of link blockage is presented for an indoor LiFi system considering fixed and random blockers. In particular, novel analytical framework of the coverage probability for a single source and multi-source are derived. Using the proposed analytical framework, link blockages of the indoor LiFi network are carefully investigated and it is shown that the incorporation of multiple sources and RIS can significantly reduce the LOS coverage blockage probability in indoor LiFi 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
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