6,023 research outputs found
Mobile Device Background Sensors: Authentication vs Privacy
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
An agent-based approach for energy-efficient sensor networks in logistics
As part of the fourth industrial revolution, logistics processes are augmented with connected information systems to improve their reliability and sustainability. Above all, customers can analyse process data obtained from the networked logistics operations to reduce costs and increase margins. The logistics of managing liquid goods is particularly challenging due to the strict transport temperature requirements involving monitoring via sensors attached to containers. However, these sensors transmit much redundant information that, at times, does not provide additional value to the customer, while consuming the limited energy stored in the sensor batteries. This paper aims to explore and study alternative approaches for location tracking and state monitoring in the context of liquid goods logistics. This problem is addressed by using a combination of data-driven sensing and agent-based modelling techniques. The simulation results show that the longest life span of batteries is achieved when most sensors are put into sleep mode yielding an increase of ×21.7 and ×3.7 for two typical routing scenarios. However, to allow for situations in which high quality sensor data is required to make decisions, agents need to be made aware of the life cycle phase of individual containers. Key contributions include (1) an agent-based approach for modelling the dynamics of liquid goods logistics to enable monitoring and detect inefficiencies (2) the development and analysis of three sensor usage strategies for reducing the energy consumption, and (3) an evaluation of the trade-offs between energy consumption and location tracking precision for timely decision making in resource constrained monitoring systems
On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse
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
Equity and spatial accessibility of healthcare resources in online health community network
Introduction: This study investigates the geographical distribution and fractal characteristics of the medical service network in China, using the “Good Doctor website” as a case study.Methods: Data for this study were extracted from the Good Doctor website Health Community. A two-tiered hierarchical network model was developed to analyze the geographical distribution and fractal characteristics of the medical service network in China.Results: Results unveil the hierarchical nature of hospital distribution and the interconnectivity among healthcare institutions. Shandong Province as a central node within the national hospital network, and networks of secondary hospitals show significant self-similarity and scale-free properties.Discussion: The small world and fractal characteristics shed light on the rapid dissemination of medical information and the robustness of the healthcare network. The results offer a novel perspective for understanding and optimizing the distribution of medical resources, and help improve the efficiency of healthcare services supply
Multidisciplinary perspectives on Artificial Intelligence and the law
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
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
Dissecting structural and biochemical features of DNA methyltransferase 1
DNA methylation is an epigenetic modification found in every branch of life. An essential enzyme for the maintenance of DNA methylation patterns in mammals is DNA methyltransferase 1 (DNMT1). Its recruitment is regulated through its large N-terminus, which contains six annotated domains. Although most of these have been assigned a function, we are still lacking a holistic understanding of the enzyme's spatio-temporal regulation. Interestingly, a large segment of the N-terminus is devoid of any known domain and appears to be disordered in its sequence. Over the past years, such disordered sequences have increasingly gained attention, due to their role in forming biomolecular condensates through liquid-liquid phase separation (LLPS). These liquid compartments offer specific environmental conditions distinct from the surrounding that can enhance protein recruitment and function.
In this work, we explore a potential role for the intrinsically disordered domain (IDR) in the recruitment of DNMT1. Taking an evolutionary approach, we uncover that structural features of the region that are key for IDR function are highly conserved. Moreover, we find conserved biochemical signatures compatible with a role in LLPS. Using a reconstitution assay and an opto-genetic approach in cells, we for the first time show that the DNMT1 IDR is capable of undergoing LLPS in vitro and in vivo. In addition, we define a novel region of interest (ROI) of about 120 amino acids in the IDR that appears to have been inserted in the ancestor of eutherian mammals. Although the ROI has a distinct biochemical signature, we find no effect on the LLPS behavior of the IDR. Therefore, we discuss other potential roles of the ROI related to DNA methylation, for example, imprinting.
Finally, we lay the foundation for investigating a biological function of the IDR and establish a system for screening DNMT1 mutant phenotypes in mouse embryonic stem cells. Swift depletion of the endogenous protein is enabled by degron-mediated degradation, while our optimized construct design and efficient derivation strategy ensure the robust expression of the large transgenes. In combination with different methods for DNA methylation read-out, this system can now be used to study the role of the IDR and ROI in maintaining the steady-state level of DNA methylation against mechanisms of passive and active demethylation, but also for studying phenotypes affecting the efficiency of DNMT1 recruitment in the future
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