408 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

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    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

    SUTMS - Unified Threat Management Framework for Home Networks

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    Home networks were initially designed for web browsing and non-business critical applications. As infrastructure improved, internet broadband costs decreased, and home internet usage transferred to e-commerce and business-critical applications. Today’s home computers host personnel identifiable information and financial data and act as a bridge to corporate networks via remote access technologies like VPN. The expansion of remote work and the transition to cloud computing have broadened the attack surface for potential threats. Home networks have become the extension of critical networks and services, hackers can get access to corporate data by compromising devices attacked to broad- band routers. All these challenges depict the importance of home-based Unified Threat Management (UTM) systems. There is a need of unified threat management framework that is developed specifically for home and small networks to address emerging security challenges. In this research, the proposed Smart Unified Threat Management (SUTMS) framework serves as a comprehensive solution for implementing home network security, incorporating firewall, anti-bot, intrusion detection, and anomaly detection engines into a unified system. SUTMS is able to provide 99.99% accuracy with 56.83% memory improvements. IPS stands out as the most resource-intensive UTM service, SUTMS successfully reduces the performance overhead of IDS by integrating it with the flow detection mod- ule. The artifact employs flow analysis to identify network anomalies and categorizes encrypted traffic according to its abnormalities. SUTMS can be scaled by introducing optional functions, i.e., routing and smart logging (utilizing Apriori algorithms). The research also tackles one of the limitations identified by SUTMS through the introduction of a second artifact called Secure Centralized Management System (SCMS). SCMS is a lightweight asset management platform with built-in security intelligence that can seamlessly integrate with a cloud for real-time updates

    Fuzzy Natural Logic in IFSA-EUSFLAT 2021

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    The present book contains five papers accepted and published in the Special Issue, “Fuzzy Natural Logic in IFSA-EUSFLAT 2021”, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference “The 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferences”, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IF–THEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications

    Artificial Intelligence for the Edge Computing Paradigm.

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    With modern technologies moving towards the internet of things where seemingly every financial, private, commercial and medical transaction being carried out by portable and intelligent devices; Machine Learning has found its way into every smart device and application possible. However, Machine Learning cannot be used on the edge directly due to the limited capabilities of small and battery-powered modules. Therefore, this thesis aims to provide light-weight automated Machine Learning models which are applied on a standard edge device, the Raspberry Pi, where one framework aims to limit parameter tuning while automating feature extraction and a second which can perform Machine Learning classification on the edge traditionally, and can be used additionally for image-based explainable Artificial Intelligence. Also, a commercial Artificial Intelligence software have been ported to work in a client/server setups on the Raspberry Pi board where it was incorporated in all of the Machine Learning frameworks which will be presented in this thesis. This dissertation also introduces multiple algorithms that can convert images into Time-series for classification and explainability but also introduces novel Time-series feature extraction algorithms that are applied to biomedical data while introducing the concept of the Activation Engine, which is a post-processing block that tunes Neural Networks without the need of particular experience in Machine Leaning. Also, a tree-based method for multiclass classification has been introduced which outperforms the One-to-Many approach while being less complex that the One-to-One method.\par The results presented in this thesis exhibit high accuracy when compared with the literature, while remaining efficient in terms of power consumption and the time of inference. Additionally the concepts, methods or algorithms that were introduced are particularly novel technically, where they include: • Feature extraction of professionally annotated, and poorly annotated time-series. • The introduction of the Activation Engine post-processing block. • A model for global image explainability with inference on the edge. • A tree-based algorithm for multiclass classification

    The assessment and development of methods in (spatial) sound ecology

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    As vital ecosystems across the globe enter unchartered pressure from climate change industrial land use, understanding the processes driving ecosystem viability has never been more critical. Nuanced ecosystem understanding comes from well-collected field data and a wealth of associated interpretations. In recent years the most popular methods of ecosystem monitoring have revolutionised from often damaging and labour-intensive manual data collection to automated methods of data collection and analysis. Sound ecology describes the school of research that uses information transmitted through sound to infer properties about an area's species, biodiversity, and health. In this thesis, we explore and develop state-of-the-art automated monitoring with sound, specifically relating to data storage practice and spatial acoustic recording and data analysis. In the first chapter, we explore the necessity and methods of ecosystem monitoring, focusing on acoustic monitoring, later exploring how and why sound is recorded and the current state-of-the-art in acoustic monitoring. Chapter one concludes with us setting out the aims and overall content of the following chapters. We begin the second chapter by exploring methods used to mitigate data storage expense, a widespread issue as automated methods quickly amass vast amounts of data which can be expensive and impractical to manage. Importantly I explain how these data management practices are often used without known consequence, something I then address. Specifically, I present evidence that the most used data reduction methods (namely compression and temporal subsetting) have a surprisingly small impact on the information content of recorded sound compared to the method of analysis. This work also adds to the increasing evidence that deep learning-based methods of environmental sound quantification are more powerful and robust to experimental variation than more traditional acoustic indices. In the latter chapters, I focus on using multichannel acoustic recording for sound-source localisation. Knowing where a sound originated has a range of ecological uses, including counting individuals, locating threats, and monitoring habitat use. While an exciting application of acoustic technology, spatial acoustics has had minimal uptake owing to the expense, impracticality and inaccessibility of equipment. In my third chapter, I introduce MAARU (Multichannel Acoustic Autonomous Recording Unit), a low-cost, easy-to-use and accessible solution to this problem. I explain the software and hardware necessary for spatial recording and show how MAARU can be used to localise the direction of a sound to within ±10˚ accurately. In the fourth chapter, I explore how MAARU devices deployed in the field can be used for enhanced ecosystem monitoring by spatially clustering individuals by calling directions for more accurate abundance approximations and crude species-specific habitat usage monitoring. Most literature on spatial acoustics cites the need for many accurately synced recording devices over an area. This chapter provides the first evidence of advances made with just one recorder. Finally, I conclude this thesis by restating my aims and discussing my success in achieving them. Specifically, in the thesis’ conclusion, I reiterate the contributions made to the field as a direct result of this work and outline some possible development avenues.Open Acces

    Machine learning based anomaly detection for industry 4.0 systems.

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    223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

    Context-aware and user bahavior-based continuous authentication for zero trust access control in smart homes

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    Orientador: Aldri Luiz dos SantosDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 24/02/2023Inclui referências: p. 96-106Área de concentração: Ciência da ComputaçãoResumo: Embora as casas inteligentes tenham se tornado populares recentemente, as pessoas ainda estão muito preocupadas com questões de segurança, proteção e privacidade. Estudos revelaram que questões de privacidade das pessoas geram prejuízos fisiológicos e financeiros porque as casas inteligentes são ambientes de convivência íntima. Além disso, nossa pesquisa revelou que os ataques de impersonificação são uma das ameaças mais graves contra casas inteligentes porque comprometem a confidencialidade, autenticidade, integridade e não repúdio. Normalmente, abordagens para construir segurança para Sistemas de Casas Inteligentes (SHS) requerem dados históricos para implementar controle de acesso e Sistemas de Detecção de Intrusão (IDS), uma vulnerabilidade à privacidade dos habitantes. Além disso, a maioria dos trabalhos depende de computação em nuvem ou recursos na nuvem para executar tarefas de segurança, que os invasores podem atacar para atingir a confidencialidade, integridade e disponibilidade. Além disso, os pesquisadores não consideram o uso indevido de SHS ao forçar os usuários a interagir com os dispositivos por meio de seus smartphones ou tablets, pois eles costumam interagir por qualquer meio, como assistentes virtuais e os próprios dispositivos. Portanto, os requisitos do sistema de segurança para residências inteligentes devem compreender percepção de privacidade, resposta de baixa latência, localidade espacial e temporal, extensibilidade de dispositivo, proteção contra impersonificação, isolamento de dispositivo, garantia de controle de acesso e levar em consideração a verificação atualizada com um sistema confiável. Para atender a esses requisitos, propomos o sistema ZASH (Zero-Aware Smart Home) para fornecer controle de acesso para as ações do usuário em dispositivos em casas inteligentes. Em contraste com os trabalhos atuais, ele aproveita a autenticação contínua com o paradigma de Confiança Zero suportado por ontologias configuradas, contexto em tempo real e atividade do usuário. A computação de borda e a Cadeia de Markov permitem que o ZASH evite e mitigue ataques de impersonificação que visam comprometer a segurança dos usuários. O sistema depende apenas de recursos dentro de casa, é autossuficiente e está menos exposto à exploração externa. Além disso, funciona desde o dia zero sem a exigência de dados históricos, embora conte com o passar do tempo para monitorar o comportamento dos usuários. O ZASH exige prova de identidade para que os usuários confirmem sua autenticidade por meio de características fortes da classe Something You Are. O sistema executa o controle de acesso nos dispositivos inteligentes, portanto, não depende de intermediários e considera qualquer interação usuário-dispositivo. A princípio, um teste inicial de algoritmos com um conjunto de dados sintético demonstrou a capacidade do sistema de se adaptar dinamicamente aos comportamentos de novos usuários, bloqueando ataques de impersonificação. Por fim, implementamos o ZASH no simulador de rede ns-3 e analisamos sua robustez, eficiência, extensibilidade e desempenho. De acordo com nossa análise, ele protege a privacidade dos usuários, responde rapidamente (cerca de 4,16 ms), lida com a adição e remoção de dispositivos, bloqueia a maioria dos ataques de impersonificação (até 99% com uma configuração adequada), isola dispositivos inteligentes e garante o controle de acesso para todas as interações.Abstract: Although smart homes have become popular recently, people are still highly concerned about security, safety, and privacy issues. Studies revealed that issues in people's privacy generate physiological and financial harm because smart homes are intimate living environments. Further, our research disclosed that impersonation attacks are one of the most severe threats against smart homes because they compromise confidentiality, authenticity, integrity, and non-repudiation. Typically, approaches to build security for Smart Home Systems (SHS) require historical data to implement access control and Intrusion Detection Systems (IDS), a vulnerability to the inhabitant's privacy. Additionally, most works rely on cloud computing or resources in the cloud to perform security tasks, which attackers can exploit to target confidentiality, integrity, and availability. Moreover, researchers do not regard the misuse of SHS by forcing users to interact with devices through their smartphones or tablets, as they usually interact by any means, like virtual assistants and devices themselves. Therefore, the security system requirements for smart homes should comprehend privacy perception, low latency in response, spatial and temporal locality, device extensibility, protection against impersonation, device isolation, access control enforcement, and taking into account the refresh verification with a trustworthy system. To attend to those requirements, we propose the ZASH (Zero-Aware Smart Home) system to provide access control for the user's actions on smart devices in smart homes. In contrast to current works, it leverages continuous authentication with the Zero Trust paradigm supported by configured ontologies, real-time context, and user activity. Edge computing and Markov Chain enable ZASH to prevent and mitigate impersonation attacks that aim to compromise users' security. The system relies only on resources inside the house, is self-sufficient, and is less exposed to outside exploitation. Furthermore, it works from day zero without the requirement of historical data, though it counts on that as time passes to monitor the users' behavior. ZASH requires proof of identity for users to confirm their authenticity through strong features of the Something You Are class. The system enforces access control in smart devices, so it does not depend on intermediaries and considers any user-device interaction. At first, an initial test of algorithms with a synthetic dataset demonstrated the system's capability to dynamically adapt to new users' behaviors withal blocking impersonation attacks. Finally, we implemented ZASH in the ns-3 network simulator and analyzed its robustness, efficiency, extensibility, and performance. According to our analysis, it protects users' privacy, responds quickly (around 4.16 ms), copes with adding and removing devices, blocks most impersonation attacks (up to 99% with a proper configuration), isolates smart devices, and enforces access control for all interactions
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