New Trends in Knowledge Creation and Retention

A special issue of Knowledge (ISSN 2673-9585).

Deadline for manuscript submissions: 31 July 2024 | Viewed by 6053

Special Issue Editor


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Guest Editor
Business and Management Faculty, Regents University London, London NW1 4NS, UK
Interests: creative enterprise; knowledge management; organisational learning; personal development

Special Issue Information

Dear Colleagues,

With the transformation of the world due to new technologies such as digital twinning, satellites, 3-D printing, drone technology, Artificial Intelligence, and the widespread use of webinars due to COVID-19 and a changed working lifestyle for many, there continues to be challenges and opportunities regarding the creation of retention of knowledge in effective ways. This is true for the privileged and wealthy in society and the underprivileged and weak. The world also consists of complexities that are worth reflecting on and considering with the creation and retention of knowledge in mind. For example, whilst technological solutions can create great opportunities for wealth creation through massification, on the one hand, the value of traditional skills and knowledge of artisans is desired for the creation of tailored products and/or services, and the retention of such knowledge is seen of significant importance.

This Special Issue is designed to include a series of papers that highlight some new themes and/or approaches in the creation and retention of knowledge in society, organisations, and nations in this rapidly changing technological landscape in a world that continues to be blighted by wars, disease, and poverty.

Dr. Peter Sharp
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Knowledge is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • knowledge creation
  • knowledge retention
  • new technologies
  • traditional knowledge and skills

Published Papers (2 papers)

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Research

30 pages, 7913 KiB  
Article
Evaluation of the Omni-Secure Firewall System in a Private Cloud Environment
by Salman Mahmood, Raza Hasan, Nor Adnan Yahaya, Saqib Hussain and Muzammil Hussain
Knowledge 2024, 4(2), 141-170; https://doi.org/10.3390/knowledge4020008 - 2 Apr 2024
Viewed by 713
Abstract
This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation [...] Read more.
This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions. Full article
(This article belongs to the Special Issue New Trends in Knowledge Creation and Retention)
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33 pages, 729 KiB  
Article
ChatGPT and the Generation of Digitally Born “Knowledge”: How Does a Generative AI Language Model Interpret Cultural Heritage Values?
by Dirk H. R. Spennemann
Knowledge 2023, 3(3), 480-512; https://doi.org/10.3390/knowledge3030032 - 18 Sep 2023
Cited by 12 | Viewed by 4885
Abstract
The public release of ChatGPT, a generative artificial intelligence language model, caused wide-spread public interest in its abilities but also concern about the implications of the application on academia, depending on whether it was deemed benevolent (e.g., supporting analysis and simplification of tasks) [...] Read more.
The public release of ChatGPT, a generative artificial intelligence language model, caused wide-spread public interest in its abilities but also concern about the implications of the application on academia, depending on whether it was deemed benevolent (e.g., supporting analysis and simplification of tasks) or malevolent (e.g., assignment writing and academic misconduct). While ChatGPT has been shown to provide answers of sufficient quality to pass some university exams, its capacity to write essays that require an exploration of value concepts is unknown. This paper presents the results of a study where ChatGPT-4 (released May 2023) was tasked with writing a 1500-word essay to discuss the nature of values used in the assessment of cultural heritage significance. Based on an analysis of 36 iterations, ChatGPT wrote essays of limited length with about 50% of the stipulated word count being primarily descriptive and without any depth or complexity. The concepts, which are often flawed and suffer from inverted logic, are presented in an arbitrary sequence with limited coherence and without any defined line of argument. Given that it is a generative language model, ChatGPT often splits concepts and uses one or more words to develop tangential arguments. While ChatGPT provides references as tasked, many are fictitious, albeit with plausible authors and titles. At present, ChatGPT has the ability to critique its own work but seems unable to incorporate that critique in a meaningful way to improve a previous draft. Setting aside conceptual flaws such as inverted logic, several of the essays could possibly pass as a junior high school assignment but fall short of what would be expected in senior school, let alone at a college or university level. Full article
(This article belongs to the Special Issue New Trends in Knowledge Creation and Retention)
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