857 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

    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

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

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields 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 modified Proportional Conflict 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 classifiers, 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, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. 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 classification, and hybrid techniques mixing deep learning with belief functions as well

    Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

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    Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    Towards Object-Centric Scene Understanding

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    Visual perception for autonomous agents continues to attract community attention due to the disruptive technologies and the wide applicability of such solutions. Autonomous Driving (AD), a major application in this domain, promises to revolutionize our approach to mobility while bringing critical advantages in limiting accident fatalities. Fueled by recent advances in Deep Learning (DL), more computer vision tasks are being addressed using a learning paradigm. Deep Neural Networks (DNNs) succeeded consistently in pushing performances to unprecedented levels and demonstrating the ability of such approaches to generalize to an increasing number of difficult problems, such as 3D vision tasks. In this thesis, we address two main challenges arising from the current approaches. Namely, the computational complexity of multi-task pipelines, and the increasing need for manual annotations. On the one hand, AD systems need to perceive the surrounding environment on different levels of detail and, subsequently, take timely actions. This multitasking further limits the time available for each perception task. On the other hand, the need for universal generalization of such systems to massively diverse situations requires the use of large-scale datasets covering long-tailed cases. Such requirement renders the use of traditional supervised approaches, despite the data readily available in the AD domain, unsustainable in terms of annotation costs, especially for 3D tasks. Driven by the AD environment nature and the complexity dominated (unlike indoor scenes) by the presence of other scene elements (mainly cars and pedestrians) we focus on the above-mentioned challenges in object-centric tasks. We, then, situate our contributions appropriately in fast-paced literature, while supporting our claims with extensive experimental analysis leveraging up-to-date state-of-the-art results and community-adopted benchmarks

    The Public Performance Of Sanctions In Insolvency Cases: The Dark, Humiliating, And Ridiculous Side Of The Law Of Debt In The Italian Experience. A Historical Overview Of Shaming Practices

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    This study provides a diachronic comparative overview of how the law of debt has been applied by certain institutions in Italy. Specifically, it offers historical and comparative insights into the public performance of sanctions for insolvency through shaming and customary practices in Roman Imperial Law, in the Middle Ages, and in later periods. The first part of the essay focuses on the Roman bonorum cessio culo nudo super lapidem and on the medieval customary institution called pietra della vergogna (stone of shame), which originates from the Roman model. The second part of the essay analyzes the social function of the zecca and the pittima Veneziana during the Republic of Venice, and of the practice of lu soldate a castighe (no translation is possible). The author uses a functionalist approach to apply some arguments and concepts from the current context to this historical analysis of ancient institutions that we would now consider ridiculous. The article shows that the customary norms that play a crucial regulatory role in online interactions today can also be applied to the public square in the past. One of these tools is shaming. As is the case in contemporary online settings, in the public square in historic periods, shaming practices were used to enforce the rules of civility in a given community. Such practices can be seen as virtuous when they are intended for use as a tool to pursue positive change in forces entrenched in the culture, and thus to address social wrongs considered outside the reach of the law, or to address human rights abuses

    From Vision-Language Multimodal Learning Towards Embodied Agents

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    To build machine agents with intelligent capabilities mimicking human perception and cognition, vision and language stand out as two essential modalities and foster computer vision and natural language processing. Advances in such realms stimulate research in vision-language multimodal learning that allows optical and linguistic inputs and outputs. Due to the innate difference between the two modalities and the lack of large-scale fine-grained annotations, multimodal agents tend to inherit unimodal shortcuts. In this thesis, we develop various solutions to intervene unimodal shortcuts for multimodal generation and reasoning. For visual shortcuts, we introduce a linguistic prior and devise a syntax-aware action targeting module for dynamic description to rectify the correlation between subject and object in a sentence. We apply concept hierarchy and propose a visual superordinate abstraction framework for unbiased concept learning to reduce the correlation among different attributes of an object. For linguistic shortcuts, we disentangle the topic and syntax to reduce the repetition in generated paragraph descriptions for a given image. With the ubiquity of large-scale pre-trained models, we leverage self-supervised learning in finetuning process to increase the robustness of multimodal reasoning. The rapid development in multimodal learning promises embodied agents capable of interacting with physical environments. This thesis studies the typical embodied task vision-and-language navigation in discrete scenarios and proposes an episodic scene memory (ESceme) mechanism to balance generalization and efficiency. We figure out one desirable instantiation of the mechanism, namely candidate enhancing, and validate its superiority in various settings. Without extra time and computational cost before inference, ESceme improves performance in unseen environments by a large margin. We hope our findings can inspire more practical explorations on episodic memory in embodied AI
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