780 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

    Face Emotion Recognition Based on Machine Learning: A Review

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    Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions

    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

    A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation

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    Body language (BL) refers to the non-verbal communication expressed through physical movements, gestures, facial expressions, and postures. It is a form of communication that conveys information, emotions, attitudes, and intentions without the use of spoken or written words. It plays a crucial role in interpersonal interactions and can complement or even override verbal communication. Deep multi-modal learning techniques have shown promise in understanding and analyzing these diverse aspects of BL. The survey emphasizes their applications to BL generation and recognition. Several common BLs are considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and Talking Head (TH), and we have conducted an analysis and established the connections among these four BL for the first time. Their generation and recognition often involve multi-modal approaches. Benchmark datasets for BL research are well collected and organized, along with the evaluation of SOTA methods on these datasets. The survey highlights challenges such as limited labeled data, multi-modal learning, and the need for domain adaptation to generalize models to unseen speakers or languages. Future research directions are presented, including exploring self-supervised learning techniques, integrating contextual information from other modalities, and exploiting large-scale pre-trained multi-modal models. In summary, this survey paper provides a comprehensive understanding of deep multi-modal learning for various BL generations and recognitions for the first time. By analyzing advancements, challenges, and future directions, it serves as a valuable resource for researchers and practitioners in advancing this field. n addition, we maintain a continuously updated paper list for deep multi-modal learning for BL recognition and generation: https://github.com/wentaoL86/awesome-body-language

    Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

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    [Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

    A review of natural language processing in contact centre automation

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    Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    The Democratization of News - Analysis and Behavior Modeling of Users in the Context of Online News Consumption

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    Die Erfindung des Internets ebnete den Weg fĂŒr die Demokratisierung von Information. Die Tatsache, dass Nachrichten fĂŒr die breite Öffentlichkeit zugĂ€nglicher wurden, barg wichtige politische Versprechen, wie zum Beispiel das Erreichen von zuvor uninformierten und daher oft inaktiven BĂŒrgern. Diese konnten sich nun dank des Internets tagesaktuell ĂŒber das politische Geschehen informieren und selbst politisch engagieren. WĂ€hrend viele Politiker und Journalisten ein Jahrzehnt lang mit dieser Entwicklung zufrieden waren, Ă€nderte sich die Situation mit dem Aufkommen der sozialen Online-Netzwerke (OSN). Diese OSNs sind heute nahezu allgegenwĂ€rtig – so beziehen inzwischen 67%67\% der Amerikaner zumindest einen Teil ihrer Nachrichten ĂŒber die sozialen Medien. Dieser Trend hat die Kosten fĂŒr die Veröffentlichung von Inhalten weiter gesenkt. Dies sah zunĂ€chst nach einer positiven Entwicklung aus, stellt inzwischen jedoch ein ernsthaftes Problem fĂŒr Demokratien dar. Anstatt dass eine schier unendliche Menge an leicht zugĂ€nglichen Informationen uns klĂŒger machen, wird die Menge an Inhalten zu einer Belastung. Eine ausgewogene Nachrichtenauswahl muss einer Flut an BeitrĂ€gen und Themen weichen, die durch das digitale soziale Umfeld des Nutzers gefiltert werden. Dies fördert die politische Polarisierung und ideologische Segregation. Mehr als die HĂ€lfte der OSN-Nutzer trauen zudem den Nachrichten, die sie lesen, nicht mehr (54%54\% machen sich Sorgen wegen Falschnachrichten). In dieses Bild passt, dass Studien berichten, dass Nutzer von OSNs dem Populismus extrem linker und rechter politischer Akteure stĂ€rker ausgesetzt sind, als Personen ohne Zugang zu sozialen Medien. Um die negativen Effekt dieser Entwicklung abzumildern, trĂ€gt meine Arbeit zum einen zum VerstĂ€ndnis des Problems bei und befasst sich mit Grundlagenforschung im Bereich der Verhaltensmodellierung. Abschließend beschĂ€ftigen wir uns mit der Gefahr der Beeinflussung der Internetnutzer durch soziale Bots und prĂ€sentieren eine auf Verhaltensmodellierung basierende Lösung. Zum besseren VerstĂ€ndnis des Nachrichtenkonsums deutschsprachiger Nutzer in OSNs, haben wir deren Verhalten auf Twitter analysiert und die Reaktionen auf kontroverse - teils verfassungsfeindliche - und nicht kontroverse Inhalte verglichen. ZusĂ€tzlich untersuchten wir die Existenz von Echokammern und Ă€hnlichen PhĂ€nomenen. Hinsichtlich des Nutzerverhaltens haben wir uns auf Netzwerke konzentriert, die ein komplexeres Nutzerverhalten zulassen. Wir entwickelten probabilistische Verhaltensmodellierungslösungen fĂŒr das Clustering und die Segmentierung von Zeitserien. Neben den BeitrĂ€gen zum VerstĂ€ndnis des Problems haben wir Lösungen zur Erkennung automatisierter Konten entwickelt. Diese Bots nehmen eine wichtige Rolle in der frĂŒhen Phase der Verbreitung von Fake News ein. Unser Expertenmodell - basierend auf aktuellen Deep-Learning-Lösungen - identifiziert, z. B., automatisierte Accounts anhand ihres Verhaltens. Meine Arbeit sensibilisiert fĂŒr diese negative Entwicklung und befasst sich mit der Grundlagenforschung im Bereich der Verhaltensmodellierung. Auch wird auf die Gefahr der Beeinflussung durch soziale Bots eingegangen und eine auf Verhaltensmodellierung basierende Lösung prĂ€sentiert

    Understanding Founders’ and Successors’ Expectations of British Higher Education Related to the Chinese Family Business: An Extrapolative Expectation Perspective

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    The importance of family business and thus family business succession is well supported in the literature. As part of their succession plan, Chinese family businesses tend to send the prospective successor to study in an overseas university. However, there is little attention paid to the effectiveness and efficiency of the successors’ overseas education and its impact on the family business succession. In particular there is a lack of attention on the expectation of the founders and successors’. This thesis is about exploring and explaining the similarities and differences in expectation of successors’ overseas education between founders and successors of family businesses in China. 60 informants comprising 30 pairs of successors (who were studying a business course) and business founders completed identical questionnaires separately. This was then followed by in-depth one-to-one interviews with respondents. Adopting extrapolative expectation theory, which holds that expectations are caused by prior experience, a comprehensive conceptual framework is developed, followed by corresponding hypotheses. The findings support the hypothesis that the business founders’ expectations about the business-related factors of a business degree course are significantly higher than the expectations of successors themselves. On the other hand, the expectations of founders and successors for non-business-related factors were similar, for instance: for the development of English skills. A follow-up in-depth qualitative research in the form of interviews was conducted with the business founders and successors. Qualitative data analysis helps to reveal that while there are some interesting differences associated with respondents’ background, gender and personality, the result of the analysis shows that work experience plays a key role in explaining the difference in expectations of the founders and successors. In addition, four competing theories (intention, relationship, gender and personality) failed to explain such differences. The thesis makes a significant contribution to knowledge in understanding the expectations of the family business on successors’ overseas education. It shows that, due to differences in work experience, the expectation of business founders and their successors differ significantly in many aspects related to the successors’ overseas education. This is important as literature tends to consider the multiple influences of various individuals within a family on major decisions as one decision-making unit. The differences in expectation have major implications in their choice of courses and satisfaction of successors’ overseas education, which in turn affects the effectiveness and efficiency of the longer-term family business succession process. The findings of this thesis help the family business, policymakers and researchers to see a potential issue in the family business succession process that is largely ignored. It is suggested that more resources can be put by policymakers into raising awareness of the different expectations that are identified. Family businesses should work on the differences in expectations including enhancing both understanding and the successors’ solid work experience before studying abroad. The findings of this thesis highlight an important but unresearched area for researchers to be further explored. Finally, the project design, which adopts a complementary mixed-method approach and compares the views from business founders and successors, is unique and helpful to understand the research question through different lenses
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