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End date: 2022 × Start date: 2020 × DDC: 004 × Type: Text × Publisher: Amsterdam [u.a.] : Elsevier Science ×

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Now showing 1 - 5 of 5
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    An AI-based open recommender system for personalized labor market driven education
    (Amsterdam [u.a.] : Elsevier Science, 2022) Tavakoli, Mohammadreza; Faraji, Abdolali; Vrolijk, Jarno; Molavi, Mohammadreza; Mol, Stefan T.; Kismihók, Gábor
    Attaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements
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    High order discretization methods for spatial-dependent epidemic models
    (Amsterdam [u.a.] : Elsevier Science, 2022) Takács, Bálint; Hadjimichael, Yiannis
    In this paper, an epidemic model with spatial dependence is studied and results regarding its stability and numerical approximation are presented. We consider a generalization of the original Kermack and McKendrick model in which the size of the populations differs in space. The use of local spatial dependence yields a system of partial-differential equations with integral terms. The uniqueness and qualitative properties of the continuous model are analyzed. Furthermore, different spatial and temporal discretizations are employed, and step-size restrictions for the discrete model’s positivity, monotonicity preservation, and population conservation are investigated. We provide sufficient conditions under which high-order numerical schemes preserve the stability of the computational process and provide sufficiently accurate numerical approximations. Computational experiments verify the convergence and accuracy of the numerical methods.
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    Kafka-ML: Connecting the data stream with ML/AI frameworks
    (Amsterdam [u.a.] : Elsevier Science, 2022) Martín, Cristian; Langendoerfer, Peter; Zarrin, Pouya Soltani; Díaz, Manuel; Rubio, Bartolomé
    Machine Learning (ML) and Artificial Intelligence (AI) depend on data sources to train, improve, and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data to continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we propose Kafka-ML, a novel and open-source framework that enables the management of ML/AI pipelines through data streams. Kafka-ML provides an accessible and user-friendly Web user interface where users can easily define ML models, to then train, evaluate, and deploy them for inferences. Kafka-ML itself and the components it deploys are fully managed through containerization technologies, which ensure their portability, easy distribution, and other features such as fault-tolerance and high availability. Finally, a novel approach has been introduced to manage and reuse data streams, which may eliminate the need for data storage or file systems.
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    On the convergence order of the finite element error in the kinetic energy for high Reynolds number incompressible flows
    (Amsterdam [u.a.] : Elsevier Science, 2021) García-Archilla, Bosco; John, Volker; Novo, Julia
    The kinetic energy of a flow is proportional to the square of the norm of the velocity. Given a sufficient regular velocity field and a velocity finite element space with polynomials of degree , then the best approximation error in is of order . In this survey, the available finite element error analysis for the velocity error in is reviewed, where is a final time. Since in practice the case of small viscosity coefficients or dominant convection is of particular interest, which may result in turbulent flows, robust error estimates are considered, i.e., estimates where the constant in the error bound does not depend on inverse powers of the viscosity coefficient. Methods for which robust estimates can be derived enable stable flow simulations for small viscosity coefficients on comparatively coarse grids, which is often the situation encountered in practice. To introduce stabilization techniques for the convection-dominated regime and tools used in the error analysis, evolutionary linear convection–diffusion equations are studied at the beginning. The main part of this survey considers robust finite element methods for the incompressible Navier–Stokes equations of order , , and for the velocity error in . All these methods are discussed in detail. In particular, a sketch of the proof for the error bound is given that explains the estimate of important terms which determine finally the order of convergence. Among them, there are methods for inf–sup stable pairs of finite element spaces as well as for pressure-stabilized discretizations. Numerical studies support the analytic results for several of these methods. In addition, methods are surveyed that behave in a robust way but for which only a non-robust error analysis is available. The conclusion of this survey is that the problem of whether or not there is a robust method with optimal convergence order for the kinetic energy is still open.
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    The genomic data deficit : On the need to inform research subjects of the informational content of their genomic sequence data in consent for genomic research
    (Amsterdam [u.a.] : Elsevier Science, 2020) Hallinan, Dara
    Research subject consent plays a significant role in the legitimation of genomic research in Europe – both ethically and legally. One key criterion for any consent to be legitimate is that the research subject is ‘informed’. This criterion implies that the research subject is given all relevant information to allow them to decide whether engaging with a genomic research infrastructure or project would be normatively desirable and whether they wish to accept the risks associated with engagement. This article makes the normative argument that, in order to be truly ‘informed’, the research subject should be provided with information on the informational content of their genomic sequence data. Information should be provided, in the first instance, prior to the initial consent transaction, and should include: information on the fact that genomic sequence data will be collected and processed, information on the types of information which can currently be extracted from sequence data and information on the uncertainties surrounding the types of information which may eventually be extractable from sequence data. Information should also be provided, on an ongoing basis, as relevant and necessary, throughout the research process, and should include: information on novel information which can be extracted from sequence data and information on the novel uses and utility of sequence data. The article argues that current elaborations of ‘informed’ consent fail to adequately address the requirements set out in the normative argument and that this inadequacy constitutes an issue in need of a solution. The article finishes with a set of observations as to the fora best suited to deliver a solution and as to the substantive content of a solution. © 2020 The Authors