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  1. AU="Demertzi, Vasiliki"
  2. AU="Leonidov, A"
  3. AU="Luo, Suxin"
  4. AU="Thompson, Charlotte A S"
  5. AU="Dubbel, Polly"
  6. AU="Ten Bosch, Nora"
  7. AU="Giménez-Arnau, Ana Maria"
  8. AU=Maul Robert W.
  9. AU="Ivn Prez-MaldonadoauthorLaboratorio de Toxicologa Molecular, Centro de Investigacin Aplicada en Ambiente y Salud (CIAAS), Coordinacin para la Innovacin y Aplicacin de la Ciencia y la Tecnologa (CIACYT), Universidad Autnoma de San Luis Potos, MexicoFacultad de Medicina, Universidad Autnoma de San Luis Potos, San Luis Potos, MexicoFacultad de Enfermera, Universidad Autnoma de Zacatecas, Mexico"
  10. AU="Hansen, Kristian Schultz"
  11. AU="Davenport, Bennett"

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  1. Buch ; Online: An Overview of Privacy Dimensions on Industrial Internet of Things (IIoT)

    Demertzi, Vasiliki / Demertzis, Stavros / Demertzis, Konstantinos

    2023  

    Abstract: Thanks to rapid technological developments, new innovative solutions and practical applications of the Industrial Internet of Things (IIoT) are being created, upgrading the structures of many industrial enterprises. IIoT brings the physical and digital ... ...

    Abstract Thanks to rapid technological developments, new innovative solutions and practical applications of the Industrial Internet of Things (IIoT) are being created, upgrading the structures of many industrial enterprises. IIoT brings the physical and digital environment together with minimal human intervention and profoundly transforms the economy and modern business. Data flowing through IIoT feed artificial intelligence tools, which perform intelligent functions such as performance tuning of interconnected machines, error correction, and preventive maintenance. However, IIoT deployments are vulnerable to sophisticated security threats at various levels of the connectivity and communications infrastructure they incorporate. The complex and often heterogeneous nature of chaotic IIoT infrastructures means that availability, confidentiality and integrity are difficult to guarantee. This can lead to potential mistrust of network operations, concerns about privacy breaches or loss of vital personal data and sensitive information of network end-users. This paper examines the privacy requirements of an IIoT ecosystem in industry standards. Specifically, it describes the industry privacy dimensions of the protection of natural persons through the processing of personal data by competent authorities for the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties. In addition, it presents an overview of the state-of-the-art methodologies and solutions for industrial privacy threats. Finally, it analyses the privacy requirements and suggestions for an ideal secure and private IIoT environment.
    Schlagwörter Computer Science - Cryptography and Security
    Thema/Rubrik (Code) 303
    Erscheinungsdatum 2023-01-15
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  2. Buch ; Online: An Overview of Cyber Threats, Attacks, and Countermeasures on the Primary Domains of Smart Cities

    Demertzi, Vasiliki / Demertzis, Stavros / Demertzis, Konstantinos

    2022  

    Abstract: A smart city is a place where existing facilities and services are enhanced by digital technology to benefit people and companies. The most critical infrastructures in this city are interconnected. Increased data exchange across municipal domains aims to ...

    Abstract A smart city is a place where existing facilities and services are enhanced by digital technology to benefit people and companies. The most critical infrastructures in this city are interconnected. Increased data exchange across municipal domains aims to manage the essential assets, leading to more automation in city governance and optimization of the dynamic offered services. However, no clear guideline or standard exists for modeling these data flows. As a result, operators, municipalities, policymakers, manufac-turers, solution providers, and vendors are forced to accept systems with limited scalability and varying needs. Nonetheless, it is critical to raise awareness about smart city cybersecurity and implement suitable measures to safeguard citizens' privacy and security because the cyber threats seem to be well-organized, diverse, and sophisticated. This study aims to present an overview of cyber threats, attacks, and countermeasures on the primary domains of smart cities (smart government, smart mobility, smart environment, smart living, smart healthcare, smart economy, and smart people) to present information extracted from state-of-the-art to policymakers to perceive the critical situation and, at the same time, to be a valuable resource for the scientific community.
    Schlagwörter Computer Science - Cryptography and Security
    Thema/Rubrik (Code) 303
    Erscheinungsdatum 2022-07-10
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  3. Buch ; Online: A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

    Demertzi, Vasiliki / Demertzis, Konstantinos

    2020  

    Abstract: The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the ... ...

    Abstract The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the pedagogical strategies that adapt to the real individual skills of the students. An important innovation in this direction is the Adaptive Educational Systems (AES) that support automatic modeling study and adjust the teaching content on educational needs and students' skills. Effective utilization of these educational approaches can be enhanced with Artificial Intelligence (AI) technologies in order to the substantive content of the web acquires structure and the published information is perceived by the search engines. This study proposes a novel Adaptive Educational eLearning System (AEeLS) that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience. It is a novel hybrid machine learning system that combines a Semi-Supervised Classification method for ontology matching and a Recommendation Mechanism that uses a hybrid method from neighborhood-based collaborative and content-based filtering techniques, in order to provide a personalized educational environment for each student.
    Schlagwörter Computer Science - Information Retrieval ; Computer Science - Digital Libraries ; Computer Science - Information Theory
    Thema/Rubrik (Code) 302 ; 027
    Erscheinungsdatum 2020-07-29
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  4. Artikel: A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System

    Demertzi, Vasiliki / Demertzis, Konstantinos

    Abstract: The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the ... ...

    Abstract The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the pedagogical strategies that adapt to the real individual skills of the students. An important innovation in this direction is the Adaptive Educational Systems (AES) that support automatic modeling study and adjust the teaching content on educational needs and students' skills. Effective utilization of these educational approaches can be enhanced with Artificial Intelligence (AI) technologies in order to the substantive content of the web acquires structure and the published information is perceived by the search engines. This study proposes a novel Adaptive Educational eLearning System (AEeLS) that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience. It is a novel hybrid machine learning system that combines a Semi-Supervised Classification method for ontology matching and a Recommendation Mechanism that uses a hybrid method from neighborhood-based collaborative and content-based filtering techniques, in order to provide a personalized educational environment for each student.
    Schlagwörter covid19
    Verlag ArXiv
    Dokumenttyp Artikel
    Datenquelle COVID19

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