2,738 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

    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

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

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    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹ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 modiïŹed Proportional ConïŹ‚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 classiïŹ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, identiïŹ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 classiïŹ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 classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    Taylor University Catalog 2023-2024

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    The 2023-2024 academic catalog of Taylor University in Upland, Indiana.https://pillars.taylor.edu/catalogs/1128/thumbnail.jp

    Vagueness Markers in Italian

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    Moving from a broad socio-pragmatic perspective, this study analyses how speakers of different ages use a class of items and constructions that codify intentional vagueness in Italian. Items as un po’ ‘a bit’, tipo ‘kind’, diciamo ‘let us say’, così ‘so’, e cose del genere ‘and things like that’, or cosa ‘thing’ constitute a class of linguistically heterogeneous means that often function in conversation as vagueness markers, i.e. elements by which speakers signal that their knowledge or communication are somehow only tentative, approximate and vague. Their use does not depend on language systemic factors, but is the result of a, more or less conscious, choice of speakers to enhance conversation for different reasons, which include facilitating the flow of conversation, signifying a vague categorization, and, eventually, being polite. Operating at the pragmatic level, vagueness markers represent elements that are readily available to speakers’ choices and contribute to characterize individual and generational discourse styles. Through a corpus-based analysis of listeners’ phone-ins to a Milan radio station, this study investigates how vagueness markers are used by speakers of different ages in 1976 and in 2010, and how Italian discourse styles have evolved in the last forty years

    Data ethics : building trust : how digital technologies can serve humanity

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    Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century: For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car, from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad, for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world? How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication. The authors and institutions come from all continents. The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust! The book is a continuation of the volume “Cyber Ethics 4.0” published in 2018 by the same editors

    Teaching Unknown Objects by Leveraging Human Gaze and Augmented Reality in Human-Robot Interaction

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    Roboter finden aufgrund ihrer außergewöhnlichen Arbeitsleistung, PrĂ€zision, Effizienz und Skalierbarkeit immer mehr Verwendung in den verschiedensten Anwendungsbereichen. Diese Entwicklung wurde zusĂ€tzlich begĂŒnstigt durch Fortschritte in der KĂŒnstlichen Intelligenz (KI), insbesondere im Maschinellem Lernen (ML). Mit Hilfe moderner neuronaler Netze sind Roboter in der Lage, Objekte in ihrer Umgebung zu erkennen und mit ihnen zu interagieren. Ein erhebliches Manko besteht jedoch darin, dass das Training dieser Objekterkennungsmodelle, in aller Regel mit einer zugrundeliegenden AbhĂ€ngig von umfangreichen DatensĂ€tzen und der VerfĂŒgbarkeit großer Datenmengen einhergeht. Dies ist insbesondere dann problematisch, wenn der konkrete Einsatzort des Roboters und die Umgebung, einschließlich der darin befindlichen Objekte, nicht im Voraus bekannt sind. Die breite und stĂ€ndig wachsende Palette von Objekten macht es dabei praktisch unmöglich, das gesamte Spektrum an existierenden Objekten allein mit bereits zuvor erstellten DatensĂ€tzen vollstĂ€ndig abzudecken. Das Ziel dieser Dissertation war es, einem Roboter unbekannte Objekte mit Hilfe von Human-Robot Interaction (HRI) beizubringen, um ihn von seiner AbhĂ€ngigkeit von Daten sowie den EinschrĂ€nkungen durch vordefinierte Szenarien zu befreien. Die Synergie von Eye Tracking und Augmented Reality (AR) ermöglichte es dem als Lehrer fungierenden Menschen, mit dem Roboter zu kommunizieren und ihn mittels des menschlichen Blickes auf Objekte hinzuweisen. Dieser holistische Ansatz ermöglichte die Konzeption eines multimodalen HRI-Systems, durch das der Roboter Objekte identifizieren und dreidimensional segmentieren konnte, obwohl sie ihm zu diesem Zeitpunkt noch unbekannt waren, um sie anschließend aus unterschiedlichen Blickwinkeln eigenstĂ€ndig zu inspizieren. Anhand der Klasseninformationen, die ihm der Mensch mitteilte, war der Roboter daraufhin in der Lage, die entsprechenden Objekte zu erlernen und spĂ€ter wiederzuerkennen. Mit dem Wissen, das dem Roboter durch diesen auf HRI basierenden Lehrvorgang beigebracht worden war, war dessen FĂ€higkeit Objekte zu erkennen vergleichbar mit den FĂ€higkeiten modernster Objektdetektoren, die auf umfangreichen DatensĂ€tzen trainiert worden waren. Dabei war der Roboter jedoch nicht auf vordefinierte Klassen beschrĂ€nkt, was seine Vielseitigkeit und AnpassungsfĂ€higkeit unter Beweis stellte. Die im Rahmen dieser Dissertation durchgefĂŒhrte Forschung leistete bedeutende BeitrĂ€ge an der Schnittstelle von Machine Learning (ML), AR, Eye Tracking und Robotik. Diese Erkenntnisse tragen nicht nur zum besseren VerstĂ€ndnis der genannten Felder bei, sondern ebnen auch den Weg fĂŒr weitere interdisziplinĂ€re Forschung. Die in dieser Dissertation enthalten wissenschaftlichen Artikel wurden auf hochrangigen Konferenzen in den Bereichen Robotik, Eye Tracking und HRI veröffentlicht.Robots are becoming increasingly popular in a wide range of environments due to their exceptional work capacity, precision, efficiency, and scalability. This development has been further encouraged by advances in Artificial Intelligence (AI), particularly Machine Learning (ML). By employing sophisticated neural networks, robots are given the ability to detect and interact with objects in their vicinity. However, a significant drawback arises from the underlying dependency on extensive datasets and the availability of substantial amounts of training data for these object detection models. This issue becomes particularly problematic when the specific deployment location of the robot and the surroundings, including the objects within it, are not known in advance. The vast and ever-expanding array of objects makes it virtually impossible to comprehensively cover the entire spectrum of existing objects using preexisting datasets alone. The goal of this dissertation was to teach a robot unknown objects in the context of Human-Robot Interaction (HRI) in order to liberate it from its data dependency, unleashing it from predefined scenarios. In this context, the combination of eye tracking and Augmented Reality (AR) created a powerful synergy that empowered the human teacher to seamlessly communicate with the robot and effortlessly point out objects by means of human gaze. This holistic approach led to the development of a multimodal HRI system that enabled the robot to identify and visually segment the Objects of Interest (OOIs) in three-dimensional space, even though they were initially unknown to it, and then examine them autonomously from different angles. Through the class information provided by the human, the robot was able to learn the objects and redetect them at a later stage. Due to the knowledge gained from this HRI based teaching process, the robot’s object detection capabilities exhibited comparable performance to state-of-the-art object detectors trained on extensive datasets, without being restricted to predefined classes, showcasing its versatility and adaptability. The research conducted within the scope of this dissertation made significant contributions at the intersection of ML, AR, eye tracking, and robotics. These findings not only enhance the understanding of these fields, but also pave the way for further interdisciplinary research. The scientific articles included in this dissertation have been published at high-impact conferences in the fields of robotics, eye tracking, and HRI

    Northeastern Illinois University, Academic Catalog 2023-2024

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    https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp

    Examining the Relationships Between Distance Education Students’ Self-Efficacy and Their Achievement

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    This study aimed to examine the relationships between students’ self-efficacy (SSE) and students’ achievement (SA) in distance education. The instruments were administered to 100 undergraduate students in a distance university who work as migrant workers in Taiwan to gather data, while their SA scores were obtained from the university. The semi-structured interviews for 8 participants consisted of questions that showed the specific conditions of SSE and SA. The findings of this study were reported as follows: There was a significantly positive correlation between targeted SSE (overall scales and general self-efficacy) and SA. Targeted students' self-efficacy effectively predicted their achievement; besides, general self- efficacy had the most significant influence. In the qualitative findings, four themes were extracted for those students with lower self-efficacy but higher achievement—physical and emotional condition, teaching and learning strategy, positive social interaction, and intrinsic motivation. Moreover, three themes were extracted for those students with moderate or higher self-efficacy but lower achievement—more time for leisure (not hard-working), less social interaction, and external excuses. Providing effective learning environments, social interactions, and teaching and learning strategies are suggested in distance education
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