arXiv subjects

Vyron Damasiotis

Publications and source records attributed to Vyron Damasiotis.

3 recordsLinked to original sources

arXiv2024

Effective Data Stewardship in Higher Education: Skills, Competences, and the Emerging Role of Open Data Stewards

The significance of open data in higher education stems from the changing tendencies towards open science, and open research in higher education encourages new ways of making scientific inquiry more transparent, collaborative and accessible. This study focuses on the critical role of open data stewards in this transition, essential for managing and disseminating research data effectively in universities, while it also highlights the increasing demand for structured training and professional policies for data stewards in academic settings. Building upon this context, the paper investigates the essential skills and competences required for effective data stewardship in higher education institutions by elaborating on a critical literature review, coupled with practical engagement in open data stewardship at universities, provided insights into the roles and responsibilities of data stewards. In response to these identified needs, the paper proposes a structured training framework and comprehensive curriculum for data stewardship, a direct response to the gaps identified in the literature. It addresses five key competence categories for open data stewards, aligning them with current trends and essential skills and knowledge in the field. By advocating for a structured approach to data stewardship education, this work sets the foundation for improved data management in universities and serves as a critical step towards professionalizing the role of data stewards in higher education. The emphasis on the role of open data stewards is expected to advance data accessibility and sharing practices, fostering increased transparency, collaboration, and innovation in academic research. This approach contributes to the evolution of universities into open ecosystems, where there is free flow of data for global education and research advancement.

cs.CY↗

arXiv2024

Uncovering Key Trends in Industry 5.0 through Advanced AI Techniques

This article analyzes around 200 online articles to identify trends within Industry 5.0 using artificial intelligence techniques. Specifically, it applies algorithms such as LDA, BERTopic, LSA, and K-means, in various configurations, to extract and compare the central themes present in the literature. The results reveal a convergence around a core set of themes while also highlighting that Industry 5.0 spans a wide range of topics. The study concludes that Industry 5.0, as an evolution of Industry 4.0, is a broad concept that lacks a clear definition, making it difficult to focus on and apply effectively. Therefore, for Industry 5.0 to be useful, it needs to be refined and more clearly defined. Furthermore, the findings demonstrate that well-known AI techniques can be effectively utilized for trend identification, particularly when the available literature is extensive and the subject matter lacks precise boundaries. This study showcases the potential of AI in extracting meaningful insights from large and diverse datasets, even in cases where the thematic structure of the domain is not clearly delineated.

cs.AI↗

arXiv2024

DOLLmC: DevOps for Large Language model Customization

The rapid integration of Large Language Models (LLMs) into various industries presents both revolutionary opportunities and unique challenges. This research aims to establish a scalable and efficient framework for LLM customization, exploring how DevOps practices should be adapted to meet the specific demands of LLM customization. By integrating ontologies, knowledge maps, and prompt engineering into the DevOps pipeline, we propose a robust framework that enhances continuous learning, seamless deployment, and rigorous version control of LLMs. This methodology is demonstrated through the development of a domain-specific chatbot for the agricultural sector, utilizing heterogeneous data to deliver actionable insights. The proposed methodology, so called DOLLmC, not only addresses the immediate challenges of LLM customization but also promotes scalability and operational efficiency. However, the methodology's primary limitation lies in the need for extensive testing, validation, and broader adoption across different domains.

cs.SE↗