648 research outputs found

    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

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    Digital support for alcohol moderation and smoking cessation in cancer survivors

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    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    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

    The 2023 wearable photoplethysmography roadmap

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    Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

    Anwendungen maschinellen Lernens für datengetriebene Prävention auf Populationsebene

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    Healthcare costs are systematically rising, and current therapy-focused healthcare systems are not sustainable in the long run. While disease prevention is a viable instrument for reducing costs and suffering, it requires risk modeling to stratify populations, identify high- risk individuals and enable personalized interventions. In current clinical practice, however, systematic risk stratification is limited: on the one hand, for the vast majority of endpoints, no risk models exist. On the other hand, available models focus on predicting a single disease at a time, rendering predictor collection burdensome. At the same time, the den- sity of individual patient data is constantly increasing. Especially complex data modalities, such as -omics measurements or images, may contain systemic information on future health trajectories relevant for multiple endpoints simultaneously. However, to date, this data is inaccessible for risk modeling as no dedicated methods exist to extract clinically relevant information. This study built on recent advances in machine learning to investigate the ap- plicability of four distinct data modalities not yet leveraged for risk modeling in primary prevention. For each data modality, a neural network-based survival model was developed to extract predictive information, scrutinize performance gains over commonly collected covariates, and pinpoint potential clinical utility. Notably, the developed methodology was able to integrate polygenic risk scores for cardiovascular prevention, outperforming existing approaches and identifying benefiting subpopulations. Investigating NMR metabolomics, the developed methodology allowed the prediction of future disease onset for many common diseases at once, indicating potential applicability as a drop-in replacement for commonly collected covariates. Extending the methodology to phenome-wide risk modeling, elec- tronic health records were found to be a general source of predictive information with high systemic relevance for thousands of endpoints. Assessing retinal fundus photographs, the developed methodology identified diseases where retinal information most impacted health trajectories. In summary, the results demonstrate the capability of neural survival models to integrate complex data modalities for multi-disease risk modeling in primary prevention and illustrate the tremendous potential of machine learning models to disrupt medical practice toward data-driven prevention at population scale.Die Kosten im Gesundheitswesen steigen systematisch und derzeitige therapieorientierte Gesundheitssysteme sind nicht nachhaltig. Angesichts vieler verhinderbarer Krankheiten stellt die Prävention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung für ein präventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und Maßnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da für die meisten Krankheiten keine Risikomodelle existieren und sich verfüg- bare Modelle auf einzelne Krankheiten beschränken. Weil für deren Berechnung jeweils spezielle Sets an Prädiktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe Datenmodalitäten, wie Bilder oder -omics- Messungen, systemische Informationen über zukünftige Gesundheitsverläufe, mit poten- tieller Relevanz für viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch für die Risikomod- ellierung unzugänglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier Datenmodalitäten in der Primärpräven- tion zu untersuchen: polygene Risikoscores für die kardiovaskuläre Prävention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- ität wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenüber üblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der Datenmodalität zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores für die kardiovaskuläre Prävention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen über den zukünftigen Ausbruch von Krankheiten. Unter Einsatz einer phänomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prädik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert für deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, präventionsori- entierten Medizin zu verändern

    An Optimized and Privacy-Preserving System Architecture for Effective Voice Authentication over Wireless Network

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    The speaker authentication systems assist in determining the identity of speaker in audio through distinctive voice characteristics. Accurate speaker authentication over wireless network is becoming more challenging due to phishing assaults over the network. There have been constructed multiple kinds of speech authentication models to employ in multiple applications where voice authentication is a primary focus for user identity verification. However, explored voice authentication models have some limitations related to accuracy and phishing assaults in real-time over wireless network. In research, optimized and privacy-preserving system architecture for effective speaker authentication over a wireless network has been proposed to accurately identify the speaker voice in real-time and prevent phishing assaults over network in more accurate manner. The proposed system achieved very good performance metrics measured accuracy, precision, and recall and the F1 score of the proposed model were98.91%, 96.43%, 95.37%, and 97.99%, respectively. The measured training losses on the epoch 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 were 2.4, 2.1, 1.8, 1.5, 1.2, 0.9, 0.6, 0.3, 0.3, 0.3, and 0.2, respectively. Also, the measured testing losses on the epoch of 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 were 2.2, 2, 1.5, 1.4, 1.1, 0.8, 0.8, 0.7, 0.4, 0.1 and 0.1, respectively. Voice authentication over wireless networks is serious issue due to various phishing attacks and inaccuracy in voice identification. Therefore, this requires huge attention for further research in this field to develop less computationally complex speech authentication systems.Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved

    The pharmaco-epidemiology of loop diuretic dispensing and its relationship to the diagnosis of heart failure and to prognosis

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    Heart failure is a major and growing public health problem associated with poor patient outcomes, including reduced quality of life and high hospitalisation and mortality rates. It is a complex clinical syndrome rather than a single disease, which lacks a practical, universal, and standardised definition. Currently, the definition relies on the identification of symptoms and signs of cardiac dysfunction, such as ankle swelling and breathlessness, which are neither specific nor objective. Many patients are only diagnosed once their symptoms and signs are severe enough to require hospitalisation. Pathophysiologically, heart failure can be defined by the presence of salt and water retention, also known as congestion, associated with cardiac dysfunction. Within the United Kingdom, the pharmacological class of loop diuretics is used primarily for the treatment of congestion due to cardiac dysfunction. The aim of this thesis is to investigate the pharmacoepidemiology of loop diuretic dispensing and its relationship to the diagnosis of heart failure, with a particular focus on patient outcomes. The first analysis describes the prevalence of repeated loop diuretic dispensing and/or diagnosis of heart failure within the NHS Greater Glasgow & Clyde Health Board population on 1st January 2012, including patient outcomes over the following five years. This research is thought to be the first population-level investigation into the prevalence of repeated loop diuretic dispensing and its prognostic significance in patients with and without a diagnosis of heart failure. The analysis found that an estimated 3.2% of the population received repeated loop diuretic dispensing, while only 1.3% of the population had a diagnosis of heart failure. Hospitalisation rates were higher in those with a loop diuretic (0.99 admissions per patient-year at risk for those with only repeated loop diuretic dispensing and 1.51 admissions per patient-year at risk for those with both) than those with only a diagnosis of heart failure (0.93 admissions patient-year at risk). All-cause mortality followed a similar pattern; adjusting for age, sex, socioeconomic deprivation and comorbidity status, the 5-year hazard ratio and (95% confidence interval) were 1.8 (1.8 - 1.9) for those with those only repeated loop diuretic dispensing and 2.3 (2.2 - 2.4) for those with both, while only 1.2 (2.2 - 2.4) for those with only a diagnosis of heart failure, implying that the presence of repeated loop diuretic dispensing is a marker of serious disease. The second analysis stepped backwards in ‘patient-time’ to describe the pattern of hospitalisations in the year leading up to the initiation of loop diuretic dispensing or an incident diagnosis of heart failure using network graphs. While the precursors to heart failure are known, this research is thought to be the first to report the common patterns in events leading up to the initiation of loop diuretics. While there was little difference in comorbidity and medication levels 24 months prior, in the year leading up to the initiation, those who received a diagnosis of heart failure were more likely to be admitted for well-recognised contributors to the condition, including ischaemic heart disease in particular, but also atrial fibrillation/flutter and valve disease. In contrast, these patterns were not often seen in those who were only initiated on a loop diuretic, instead with a focus on admissions for non-specific symptoms and signs, most commonly unspecified chest pain. The third analysis starts where the second leaves off. It assesses the prognostic relationship between the initiation of loop diuretic and diagnosis of heart failure on mortality and whether the sequence of these events matters using semi-Markov multi-state modes, a flexible model for use on longitudinal time data where there is an event-related dependence on outcomes. Those on repeated loop diuretic dispensing without a diagnosis of heart failure were majority women (62%). Many with evidence of left atrial dilation (53%), while those with a diagnosis of heart failure without a repeat loop diuretic were majority men (63%). Many had a history of myocardial infarction (51%). Hospitalisations and mortality were higher in those with a repeat loop diuretic (within the first year per patient-year at risk: hospitalisation, 1.44; mortality, 0.20) compared to those with a diagnosis of heart failure without a repeat loop diuretic (within the first year per patient-year at risk: hospitalisation, 1.47; mortality, 0.14). Rates were higher still in those with both loop diuretic and heart failure (where both events occurred together within the first year per patient-year at risk: hospitalisation, 1.74; mortality, 0.16; or where the diagnosis of HF preceded the initiation of loop diuretic, within the first year per patient-year at risk: hospitalisation, 1.68; mortality, 0.20), with the highest being in those who initiated the loop diuretic in advance of receiving a diagnosis of heart failure (within the first year per patient-year at risk: hospitalisation, 2.26; mortality, 0.28). The fourth and final analysis subsets the population to investigate the mortality of the 24,921 patients with ischaemic heart disease according to whether or not they have had a repeat loop diuretic and/or diagnosis of heart failure; of whom, 3,806 had only repeat loop diuretic, 2,384 had only a diagnosis of heart failure, and 3,531 had both. This analysis found that after adjusting for age, sex, and other prognostic markers, mortality was associated with the repeat loop diuretic regardless of the patient’s heart failure status. Those with a repeat loop diuretic without a diagnosis of heart failure experienced substantially higher rates of cardiovascular (an estimated 15%) and all-cause mortality (47%) than those with a diagnosis of heart failure without a repeat loop diuretic (an estimated 8% cardiovascular and 19% all-cause mortality), while rates were highest for those with both (an estimated 25% cardiovascular and 57% all-cause mortality). In conclusion, these analyses found that many more patients are repeatedly treated with loop diuretic than ever receive a diagnosis of heart failure. These patients are at a high risk of hospitalisation and death, and based on their characteristics, many probably have undiagnosed heart failure. From a public health and epidemiological perspective, the current definition of heart failure likely underestimates the true burden on the healthcare system. From the patient’s perspective, with the efficacy of angiotensin receptor-neprilysin inhibitor, sodium-glucose co-transporter-2 inhibitors, and mineralocorticoid receptor antagonistss, a missed diagnosis means a missed opportunity to improve the patient’s outcome and quality of life, regardless of their heart failure phenotype. Even more alarming, if these patients are receiving the loop diuretic inappropriately, the loop diuretic is likely causing these increased hospitalisation and mortality rates. If the loop diuretic can be safely withdrawn, other medications with diuretic properties exist which have good safety profiles. Ultimately, further research is required to determine the optimal strategy for managing these patients

    Optimising multimodal fusion for biometric identification systems

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    Biometric systems are automatic means for imitating the human brain’s ability of identifying and verifying other humans by their behavioural and physiological characteristics. A system, which uses more than one biometric modality at the same time, is known as a multimodal system. Multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to systems based on a single biometric modality. This thesis addresses some issues related to the implementation of multimodal biometric identity verification systems. The thesis assesses the feasibility of using commercial offthe-shelf products to construct deployable multimodal biometric system. It also identifies multimodal biometric fusion as a challenging optimisation problem when one considers the presence of several configurations and settings, in particular the verification thresholds adopted by each biometric device and the decision fusion algorithm implemented for a particular configuration. The thesis proposes a novel approach for the optimisation of multimodal biometric systems based on the use of genetic algorithms for solving some of the problems associated with the different settings. The proposed optimisation method also addresses some of the problems associated with score normalization. In addition, the thesis presents an analysis of the performance of different fusion rules when characterising the system users as sheep, goats, lambs and wolves. The results presented indicate that the proposed optimisation method can be used to solve the problems associated with threshold settings. This clearly demonstrates a valuable potential strategy that can be used to set a priori thresholds of the different biometric devices before using them. The proposed optimisation architecture addressed the problem of score normalisation, which makes it an effective “plug-and-play” design philosophy to system implementation. The results also indicate that the optimisation approach can be used for effectively determining the weight settings, which is used in many applications for varying the relative importance of the different performance parameters
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