668 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    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

    A Survey of Sequential Pattern Based E-Commerce Recommendation Systems

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    E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems

    Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach

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    Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other. This achieves economies of scale, thus reducing cost, greenhouse gas emissions and road congestion. But which carrier should partner with whom, and how much should each carrier be compensated? Traditional game theoretic solution concepts are expensive to calculate as the characteristic function scales exponentially with the number of agents. This would require solving the vehicle routing problem (NP-hard) an exponential number of times. We therefore propose to model this problem as a coalitional bargaining game solved using deep multi-agent reinforcement learning, where - crucially - agents are not given access to the characteristic function. Instead, we implicitly reason about the characteristic function; thus, when deployed in production, we only need to evaluate the expensive post-collaboration vehicle routing problem once. Our contribution is that we are the first to consider both the route allocation problem and gain sharing problem simultaneously - without access to the expensive characteristic function. Through decentralised machine learning, our agents bargain with each other and agree to outcomes that correlate well with the Shapley value - a fair profit allocation mechanism. Importantly, we are able to achieve a reduction in run-time of 88%.Comment: Final, published version can be found here: https://www.sciencedirect.com/science/article/pii/S0968090X2300366

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Redefiniendo el consentimiento informado en investigación biomédica

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    Esta tesis se enmarca en el proyecto europeo i-CONSENT (H2020, GA 741856) y se enfoca en el consentimiento informado (CI) en el contexto de la investigación biomédica. Su objetivo principal es analizar la problemática actual del CI y proponer recomendaciones que mejoren su comprensibilidad y adaptabilidad a las necesidades y preferencias de la población objetivo. La tesis consta de 6 artículos que abarcan diversas actividades llevadas a cabo durante los casi 4 años de duración del proyecto, brindándonos una visión integral de su evolución y proporcionando una idea de la experiencia de participar en él. Además, la tesis incluye una sección dedicada a las principales vivencias y aprendizajes como coordinador técnico del proyecto. Desde una perspectiva académica, los artículos abordan distintos aspectos relacionados con el CI en investigación, combinando metodologías teóricas, como la revisión sistemática de la literatura y los textos legales, con metodologías participativas que dan voz a las principales partes interesadas, como grupos nominales o design thinking. Esta combinación de enfoques permite recopilar información relevante que facilita una mejor comprensión de las complejidades y desafíos asociados con el CI. Dentro de la tesis, se examinan diversos aspectos, como la percepción de los potenciales participantes sobre la comprensión del CI, las expectativas de participación de los pacientes, el asentimiento en menores y las perspectivas de género. También se analiza el contenido del asentimiento informado desde diferentes perspectivas, incluyendo la legislación y la literatura científica, con el objetivo de comprender las diferencias en la percepción de legisladores, investigadores, padres y menores. Otro tema abordado es el uso e impacto de las herramientas digitales en el CI. Además, se proporciona una visión general del proyecto y se presentan guías con recomendaciones para mejorar el CI, destacando los factores clave identificados durante la investigación. Se evalúa la idoneidad de las recomendaciones a través de la opinión de expertos representativos de las distintas partes interesadas, y se muestra la implementación práctica de las guías para la elaboración de materiales de asentimiento. En resumen, la tesis ofrece un análisis exhaustivo del CI en investigación, abordando aspectos específicos y enfatizando la importancia de la inclusión, al mismo tiempo que presenta recomendaciones para mejorar el proceso del CI.This PhD thesis is part of the European i-CONSENT project (H2020, GA 741856) and focuses on informed consent (IC) in the context of biomedical research. Its main objective is to analyse the current problems of IC and propose recommendations to improve its comprehensibility and adaptability to the needs and preferences of the target population. The thesis consists of 6 articles covering various activities carried out during the almost 4 years of the project, giving us a comprehensive view of its evolution and providing an insight into the experience of participating in it. In addition, the thesis includes a section dedicated to the main experiences and lessons learned as technical coordinator of the project. From an academic perspective, the articles address different aspects related to CI in research, combining theoretical methodologies, such as the systematic review of literature and legal texts, with participatory methodologies that give voice to the main stakeholders, such as focus groups or design thinking. This combination of approaches allows for the collection of relevant information that facilitates a better understanding of the complexities and challenges associated with IC. Within the thesis, various aspects are examined, such as potential participants' perceptions of understanding IC, patients' expectations of participation, assent in minors and gender perspectives. The content of informed consent is also analysed from different perspectives, including legislation and scientific literature, with the aim of understanding the differences in the perceptions of legislators, researchers, parents and minors. Another topic addressed is the use and impact of digital tools in IC. In addition, an overview of the project is provided and guidelines with recommendations for improving IC are presented, highlighting the key factors identified during the research. The appropriateness of the recommendations is assessed through the opinion of experts representing different stakeholders, and the practical implementation of the guidelines for the development of assent materials is shown. In summary, the research thesis provides a comprehensive analysis of IC in research, addressing specific aspects and emphasising the importance of inclusiveness, while presenting recommendations for improving the IC process

    Human-centered Information Security and Privacy: Investigating How and Why Social and Emotional Factors Affect the Protection of Information Assets

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    Information systems (IS) are becoming increasingly integrated into the fabric of our everyday lives, for example, through cloud-based collaboration platforms, smart wearables, and social media. As a result, nearly every aspect of personal, social, and professional life relies on the constant exchange of information between users and online service providers. However, as users and organizations entrust more and more of their personal and sensitive information to IS, the challenges of ensuring information security and privacy become increasingly pressing, particularly given the rise of cybercrime and microtargeting capabilities. While the protection of information assets is a shared responsibility between technology providers, legislation, organizations, and individuals, previous research has emphasized the pivotal role of the user as the last line of defense. Whereas prior works on human-centered information security and privacy have primarily studied the human aspect from a cognitive perspective, it is important to acknowledge that security and privacy phenomena are deeply embedded within users’ social, emotional, and technological environment. Therefore, individual decision-making and organizational phenomena related to security and privacy need to be examined through a socio-emotional lens. As such, this thesis sets out to investigate how and why socio-emotional factors influence information security and privacy, while simultaneously providing a deeper understanding of how these insights can be utilized to design effective security and privacy-enhancing tools and interventions. This thesis includes five studies that have been published in peer-reviewed IS outlets. The first strand of this thesis investigates individual decision-making related to information security and privacy. Daily information disclosure decisions, such as providing login credentials to a phishing website or giving apps access to one’s address book, crucially affect information security and privacy. In an effort to support users in their decision-making, research and practice have begun to develop tools and interventions that promote secure and privacy-aware behavior. However, our knowledge on the design and effectiveness of such tools and interventions is scattered across a diverse research landscape. Therefore, the first study of this thesis (article A) sets out to systematize this knowledge. Through a literature review, the study presents a taxonomy of user-oriented information security interventions and highlights crucial shortcomings of current approaches, such as a lack of tools and interventions that provide users with long-term guidance and an imbalance regarding cyber attack vectors. Importantly, the study confirms that prior works in this field tend to limit their scope to a cognitive processing perspective, neglecting the influence of social and emotional factors. The second study (article B) examines how users make decisions on disclosing their peers’ personal information, a phenomenon referred to as privacy interdependence. Previous research has shown that users tend to have a limited understanding of the social ramifications of their decisions to share information, that is, the impact of their disclosure decisions on others’ privacy. The study is based on a theoretical framework that suggests that for a user, recognizing and respecting others’ privacy rights is heavily influenced by the perceived salience of others within their own socio-technical environment. The study introduces an intervention aimed at increasing the salience of others’ personal data during the decision-making process, resulting in a significant decrease of interdependent privacy infringements. These findings indicate that current interfaces do not allow users to make informed decisions about their peers’ privacy – a problem that is highly relevant for policymakers and regulators. Shifting the focus towards an organizational context of individual security decision-making, the third study (article C) investigates employees’ underlying motives for reporting cyber threats. With the aim to maximize employees’ adoption of reporting tools, the study examines the effect of two tool design features on users’ utilitarian and hedonic motivation to report information security incidents. The findings suggest that reporting tools that elicit a sense of warm glow, that is, a boost of self-esteem and personal satisfaction after performing an altruistic act, result in higher tool adoption compared to those that address solely users’ utilitarian motivation. This unlocks a new perspective on organizational information security as a whole and showcases new ways in which organizations can engage users in promoting information security. The second strand of this thesis focuses on the context of organizational information security. Beyond individual decision-making, organizations face the challenge of maintaining an information security culture, including, for example, employees’ awareness of security risks, top management commitment, and interdepartmental collaboration with regard to security issues. The fourth study (article D) presents a measurement instrument to assess employees’ security awareness. Complementary to the predominant method of self-reported surveys, the study introduces an index based on employees’ susceptibility to simulated social engineering attacks. As such, it presents a novel way to measure security awareness that closes the intention-behavior gap and enables information security officers to nonintrusively monitor human vulnerabilities in real-time. Furthermore, the findings indicate that security education, training and awareness (SETA) programs not only increase employees’ awareness of information security risks, but also improve their actual security behavior. Finally, the fifth study (article E) investigates the influence of external socio-emotional disruption on information security culture. Against the backdrop of the COVID-19 pandemic, the longitudinal study reveals novel inhibitors and facilitators of information security culture that emerged in the face of global socially and emotionally disruptive change over the course of 2020. Specifically, the study demonstrates that such disruptive events can influence information security culture negatively, or – counterintuitively – positively, depending on prerequisites such as digital maturity and economic stability. Overall, this thesis highlights the importance of considering socio-emotional factors in protecting information assets by providing a more comprehensive understanding of why and how such factors affect human behavior related to information security and privacy. By doing so, this thesis answers calls for research that urge scholars to consider security and privacy issues in a larger social and emotional context. The studies in this thesis contribute to IS research on information security and privacy by (1) uncovering social and emotional motives as hitherto largely neglected drivers of users decision-making, (2) demonstrating how tools and interventions can leverage these motives to improve users’ protection of information assets, and (3) revealing the importance of external socio-emotional factors as a thus far under-investigated influence on organizational information security. In practice, this thesis offers actionable recommendations for designers building tools and interventions to support decision-making with regard to information security and privacy. Likewise, it provides important insights to information security officers on how to build a strong and resilient information security culture, and guides policymakers in accounting for socially embedded privacy phenomena

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
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