Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Smart Cities: Challenges and Opportunities
Mohammad Ilyas
(pages: 1-6)

Bridging the Gap: Communicating to Increase the Visibility and Impact of Your Academic Work
Erin Ryan
(pages: 7-12)

Cross-Cultural Online Networking Based on Biomedical Engineering to Motivate Transdisciplinary Communication Skills
Shigehiro Hashimoto
(pages: 13-17)

Interdisciplinary Approaches to Learning Informatics
Masaaki Kunigami
(pages: 18-22)

The Impact of Artificial Intelligence and the Importance of Transdisciplinary Research
R. Cherinka, J. Prezzama, P. O'Leary
(pages: 23-28)

Emotional Communication as Complex Phenomenon in Musical Interpretation – Proposal for a Systemic Model That Promotes a Transdisciplinary Process of Self-Formation and Reflection Around Expressiveness as a Lived Experience
Fuensanta Fernández de Velazco, Eduardo Carpinteyro-Lara, Saúl Rodríguez-Luna
(pages: 29-33)

A Multi-Disciplinary Cybernetic Approach to Pedagogic Excellence
Russell Jay Hendel
(pages: 34-41)

The Ethics of Artificial Intelligence in the Era of Generative AI
Vassilka D. Kirova, Cyril S. Ku, Joseph R. Laracy, Thomas J. Marlowe
(pages: 42-50)

Trans-Disciplinary Communication: Context and Semantics
Maurício Vieira Kritz
(pages: 51-57)

A Brave New World: AI as a Nascent Regime?
Jasmin Cowin, Birgit Oberer, Cristo Leon
(pages: 58-66)

The Role of Art and Science – Relational Dynamics in Human Ecology
Giorgio Pizziolo, Rita Micarelli
(pages: 67-75)

Advancing Entrepreneurship Education: An Integrated Approach to Empowering Future Innovators
Birgit Oberer, Alptekin Erkollar
(pages: 76-81)

Harmonizing Horizons: The Symphony of Human-Machine Collaboration in the Age of AI
Birgit Oberer, Alptekin Erkollar
(pages: 82-86)

How Do Students Learn Artificial Intelligence in Interdisciplinary Field of Biomedical Engineering?
Shigehiro Hashimoto
(pages: 87-91)

What is ChatGPT and its Present and Future for Artificial Intelligence in Trans-Disciplinary Communications?
Richard Segall
(pages: 92-98)


 

Abstracts

 


ABSTRACT


An Ad Hoc Adaptive Hashing Technique forNon-Uniformly Distributed IP Address Lookup in Computer Networks

Christopher Martinez, Wei-Ming Lin


Hashing algorithms long have been widely adopted to design a fast address look-up process which involves a search through a large database to find a record associated with a given key. Hashing algorithms involve transforming a key inside each target data to a hash value hoping that the hashing would render the database a uniform distribution with respect to this new hash value. The close the final distribution is to uniform, the less search time would be required when a query is made. When the database is already key-wise uniformly distributed, any regular hashing algorithm, such as bit-extraction, bit-group XOR, etc., would easily lead to a statistically perfect uniform distribution after the hashing. On the other hand, if records in the database are instead not uniformly distributed as in almost all known practical applications, then even different regular hash functions would lead to very different performance. When the target database has a key with a highly skewed distributed value, performance delivered by regular hashing algorithms usually becomes far from desirable. This paper aims at designing a hashing algorithm to achieve the highest probability in leading to a uniformly distributed hash result from a non-uniformly distributed database. An analytical pre-process on the original database is first performed to extract critical information that would significantly benefit the design of a better hashing algorithm. This process includes sorting on the bits of the key to prioritize the use of them in the XOR hashing sequence, or in simple bit extraction, or even a combination of both. Such an ad hoc hash design is critical to adapting to all real-time situations when there exists a changing (and/or expanding) database with an irregular non-uniform distribution. Significant improvement from simulation results is obtained in randomly generated data as well as real data.

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