1,057 research outputs found

    Inter-individual variation of the human epigenome & applications

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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

    Online semi-supervised learning in non-stationary environments

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    Existing Data Stream Mining (DSM) algorithms assume the availability of labelled and balanced data, immediately or after some delay, to extract worthwhile knowledge from the continuous and rapid data streams. However, in many real-world applications such as Robotics, Weather Monitoring, Fraud Detection Systems, Cyber Security, and Computer Network Traffic Flow, an enormous amount of high-speed data is generated by Internet of Things sensors and real-time data on the Internet. Manual labelling of these data streams is not practical due to time consumption and the need for domain expertise. Another challenge is learning under Non-Stationary Environments (NSEs), which occurs due to changes in the data distributions in a set of input variables and/or class labels. The problem of Extreme Verification Latency (EVL) under NSEs is referred to as Initially Labelled Non-Stationary Environment (ILNSE). This is a challenging task because the learning algorithms have no access to the true class labels directly when the concept evolves. Several approaches exist that deal with NSE and EVL in isolation. However, few algorithms address both issues simultaneously. This research directly responds to ILNSEā€™s challenge in proposing two novel algorithms ā€œPredictor for Streaming Data with Scarce Labelsā€ (PSDSL) and Heterogeneous Dynamic Weighted Majority (HDWM) classifier. PSDSL is an Online Semi-Supervised Learning (OSSL) method for real-time DSM and is closely related to label scarcity issues in online machine learning. The key capabilities of PSDSL include learning from a small amount of labelled data in an incremental or online manner and being available to predict at any time. To achieve this, PSDSL utilises both labelled and unlabelled data to train the prediction models, meaning it continuously learns from incoming data and updates the model as new labelled or unlabelled data becomes available over time. Furthermore, it can predict under NSE conditions under the scarcity of class labels. PSDSL is built on top of the HDWM classifier, which preserves the diversity of the classifiers. PSDSL and HDWM can intelligently switch and adapt to the conditions. The PSDSL adapts to learning states between self-learning, micro-clustering and CGC, whichever approach is beneficial, based on the characteristics of the data stream. HDWM makes use of ā€œseedā€ learners of different types in an ensemble to maintain its diversity. The ensembles are simply the combination of predictive models grouped to improve the predictive performance of a single classifier. PSDSL is empirically evaluated against COMPOSE, LEVELIW, SCARGC and MClassification on benchmarks, NSE datasets as well as Massive Online Analysis (MOA) data streams and real-world datasets. The results showed that PSDSL performed significantly better than existing approaches on most real-time data streams including randomised data instances. PSDSL performed significantly better than ā€˜Staticā€™ i.e. the classifier is not updated after it is trained with the first examples in the data streams. When applied to MOA-generated data streams, PSDSL ranked highest (1.5) and thus performed significantly better than SCARGC, while SCARGC performed the same as the Static. PSDSL achieved better average prediction accuracies in a short time than SCARGC. The HDWM algorithm is evaluated on artificial and real-world data streams against existing well-known approaches such as the heterogeneous WMA and the homogeneous Dynamic DWM algorithm. The results showed that HDWM performed significantly better than WMA and DWM. Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM. In both drift and real-world streams, significance tests and post hoc comparisons found significant differences between algorithms, HDWM performed significantly better than DWM and WMA when applied to MOA data streams and 4 real-world datasets Electric, Spam, Sensor and Forest cover. The seeding mechanism and dynamic inclusion of new base learners in the HDWM algorithms benefit from the use of both forgetting and retaining the models. The algorithm also provides the independence of selecting the optimal base classifier in its ensemble depending on the problem. A new approach, Envelope-Clustering is introduced to resolve the cluster overlap conflicts during the cluster labelling process. In this process, PSDSL transforms the centroidsā€™ information of micro-clusters into micro-instances and generates new clusters called Envelopes. The nearest envelope clusters assist the conflicted micro-clusters and successfully guide the cluster labelling process after the concept drifts in the absence of true class labels. PSDSL has been evaluated on real-world problem ā€˜keystroke dynamicsā€™, and the results show that PSDSL achieved higher prediction accuracy (85.3%) and SCARGC (81.6%), while the Static (49.0%) significantly degrades the performance due to changes in the users typing pattern. Furthermore, the predictive accuracies of SCARGC are found highly fluctuated between (41.1% to 81.6%) based on different values of parameter ā€˜kā€™ (number of clusters), while PSDSL automatically determine the best values for this parameter

    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 (ā€˜AIā€™) 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 ā€“ 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 CatĆ³lica 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

    Inter-individual variation of the human epigenome & applications

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    Genome-wide association studies (GWAS) have led to the discovery of genetic variants influencing human phenotypes in health and disease. However, almost two decades later, most human traits can still not be accurately predicted from common genetic variants. Moreover, genetic variants discovered via GWAS mostly map to the non-coding genome and have historically resisted interpretation via mechanistic models. Alternatively, the epigenome lies in the cross-roads between genetics and the environment. Thus, there is great excitement towards the mapping of epigenetic inter-individual variation since its study may link environmental factors to human traits that remain unexplained by genetic variants. For instance, the environmental component of the epigenome may serve as a source of biomarkers for accurate, robust and interpretable phenotypic prediction on low-heritability traits that cannot be attained by classical genetic-based models. Additionally, its research may provide mechanisms of action for genetic associations at non-coding regions that mediate their effect via the epigenome. The aim of this thesis was to explore epigenetic inter-individual variation and to mitigate some of the methodological limitations faced towards its future valorisation.Chapter 1 is dedicated to the scope and aims of the thesis. It begins by describing historical milestones and basic concepts in human genetics, statistical genetics, the heritability problem and polygenic risk scores. It then moves towards epigenetics, covering the several dimensions it encompasses. It subsequently focuses on DNA methylation with topics like mitotic stability, epigenetic reprogramming, X-inactivation or imprinting. This is followed by concepts from epigenetic epidemiology such as epigenome-wide association studies (EWAS), epigenetic clocks, Mendelian randomization, methylation risk scores and methylation quantitative trait loci (mQTL). The chapter ends by introducing the aims of the thesis.Chapter 2 focuses on stochastic epigenetic inter-individual variation resulting from processes occurring post-twinning, during embryonic development and early life. Specifically, it describes the discovery and characterisation of hundreds of variably methylated CpGs in the blood of healthy adolescent monozygotic (MZ) twins showing equivalent variation among co-twins and unrelated individuals (evCpGs) that could not be explained only by measurement error on the DNA methylation microarray. DNA methylation levels at evCpGs were shown to be stable short-term but susceptible to aging and epigenetic drift in the long-term. The identified sites were significantly enriched at the clustered protocadherin loci, known for stochastic methylation in neurons in the context of embryonic neurodevelopment. Critically, evCpGs were capable of clustering technical and longitudinal replicates while differentiating young MZ twins. Thus, discovered evCpGs can be considered as a first prototype towards universal epigenetic fingerprint, relevant in the discrimination of MZ twins for forensic purposes, currently impossible with standard DNA profiling. Besides, DNA methylation microarrays are the preferred technology for EWAS and mQTL mapping studies. However, their probe design inherently assumes that the assayed genomic DNA is identical to the reference genome, leading to genetic artifacts whenever this assumption is not fulfilled. Building upon the previous experience analysing microarray data, Chapter 3 covers the development and benchmarking of UMtools, an R-package for the quantification and qualification of genetic artifacts on DNA methylation microarrays based on the unprocessed fluorescence intensity signals. These tools were used to assemble an atlas on genetic artifacts encountered on DNA methylation microarrays, including interactions between artifacts or with X-inactivation, imprinting and tissue-specific regulation. Additionally, to distinguish artifacts from genuine epigenetic variation, a co-methylation-based approach was proposed. Overall, this study revealed that genetic artifacts continue to filter through into the reported literature since current methodologies to address them have overlooked this challenge.Furthermore, EWAS, mQTL and allele-specific methylation (ASM) mapping studies have all been employed to map epigenetic variation but require matching phenotypic/genotypic data and can only map specific components of epigenetic inter-individual variation. Inspired by the previously proposed co-methylation strategy, Chapter 4 describes a novel method to simultaneously map inter-haplotype, inter-cell and inter-individual variation without these requirements. Specifically, binomial likelihood function-based bootstrap hypothesis test for co-methylation within reads (Binokulars) is a randomization test that can identify jointly regulated CpGs (JRCs) from pooled whole genome bisulfite sequencing (WGBS) data by solely relying on joint DNA methylation information available in reads spanning multiple CpGs. Binokulars was tested on pooled WGBS data in whole blood, sperm and combined, and benchmarked against EWAS and ASM. Our comparisons revealed that Binokulars can integrate a wide range of epigenetic phenomena under the same umbrella since it simultaneously discovered regions associated with imprinting, cell type- and tissue-specific regulation, mQTL, ageing or even unknown epigenetic processes. Finally, we verified examples of mQTL and polymorphic imprinting by employing another novel tool, JRC_sorter, to classify regions based on epigenotype models and non-pooled WGBS data in cord blood. In the future, we envision how this cost-effective approach can be applied on larger pools to simultaneously highlight regions of interest in the methylome, a highly relevant task in the light of the post-GWAS era.Moving towards future applications of epigenetic inter-individual variation, Chapters 5 and 6 are dedicated to solving some of methodological issues faced in translational epigenomics.Firstly, due to its simplicity and well-known properties, linear regression is the starting point methodology when performing prediction of a continuous outcome given a set of predictors. However, linear regression is incompatible with missing data, a common phenomenon and a huge threat to the integrity of data analysis in empirical sciences, including (epi)genomics. Chapter 5 describes the development of combinatorial linear models (cmb-lm), an imputation-free, CPU/RAM-efficient and privacy-preserving statistical method for linear regression prediction on datasets with missing values. Cmb-lm provide prediction errors that take into account the pattern of missing values in the incomplete data, even at extreme missingness. As a proof-of-concept, we tested cmb-lm in the context of epigenetic ageing clocks, one of the most popular applications of epigenetic inter-individual variation. Overall, cmb-lm offer a simple and flexible methodology with a wide range of applications that can provide a smooth transition towards the valorisation of linear models in the real world, where missing data is almost inevitable. Beyond microarrays, due to its high accuracy, reliability and sample multiplexing capabilities, massively parallel sequencing (MPS) is currently the preferred methodology of choice to translate prediction models for traits of interests into practice. At the same time, tobacco smoking is a frequent habit sustained by more than 1.3 billion people in 2020 and a leading (and preventable) health risk factor in the modern world. Predicting smoking habits from a persistent biomarker, such as DNA methylation, is not only relevant to account for self-reporting bias in public health and personalized medicine studies, but may also allow broadening forensic DNA phenotyping. Previously, a model to predict whether someone is a current, former, or never smoker had been published based on solely 13 CpGs from the hundreds of thousands included in the DNA methylation microarray. However, a matching lab tool with lower marker throughput, and higher accuracy and sensitivity was missing towards translating the model in practice. Chapter 6 describes the development of an MPS assay and data analysis pipeline to quantify DNA methylation on these 13 smoking-associated biomarkers for the prediction of smoking status. Though our systematic evaluation on DNA standards of known methylation levels revealed marker-specific amplification bias, our novel tool was still able to provide highly accurate and reproducible DNA methylation quantification and smoking habit prediction. Overall, our MPS assay allows the technological transfer of DNA methylation microarray findings and models to practical settings, one step closer towards future applications.Finally, Chapter 7 provides a general discussion on the results and topics discussed across Chapters 2-6. It begins by summarizing the main findings across the thesis, including proposals for follow-up studies. It then covers technical limitations pertaining bisulfite conversion and DNA methylation microarrays, but also more general considerations such as restricted data access. This chapter ends by covering the outlook of this PhD thesis, including topics such as bisulfite-free methods, third-generation sequencing, single-cell methylomics, multi-omics and systems biology.<br/

    Digitalization and Development

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    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

    Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation

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    Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system's life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society.Comment: 30 pages, 5 figures, under second revie

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well

    Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

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    A quantitative evaluation of kinetic parameters, the jointā€™s range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the deviceā€™s positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a userā€™s vital signs directly from the body in an accurate and nonā€“invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coachā€™s subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during postā€“operative rehabilitation and athletesā€™ training, and to present evidence that supports the efļ¬cacy of this technology for healthcare applications. First, a classiļ¬cation of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the userā€™s health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for deļ¬ning the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties
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