245 results
Search Results
2. South African research contributions to Lecture Notes in Computer Science, 1973-2022.
- Author
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Naudé, Filistéa and Kroeze, Jan H.
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COMPUTER science ,ARTIFICIAL intelligence ,RESEARCH personnel ,AUTHORSHIP collaboration ,PERIODICAL articles - Abstract
Lecture Notes in Computer Science (LNCS) is a globally recognised publication outlet for the field of Computer Science, including in South Africa. In this study, spanning from 1973 to 2022, we investigated the research participation of South African based authors in LNCS. The publication output and citation impact of these authors were compared to the global Computer Science and LNCS output. The authorship patterns and collaborative behaviour of South African LNCS papers were explored, and a keyword or topic analysis also conducted. Of the total of 518 662 LNCS papers published globally between 1973 and 2022, South African based researchers contributed 1150 papers (0.22%). The LNCS papers from South Africa exhibit a strong collaborative publication culture, with 1043 (91%) co-authored and 107 (9%) singleauthored works. Local LNCS researchers prefer institutional collaboration (43%), followed by international (37%) and national collaboration (11%). Europe emerged as the most significant collaboration partner for LNCS researchers in South Africa. Of the 1150 papers, 836 (73%) had received citations, while 314 (27%) had not. On average, papers published by South African based authors received 6.05 citations, compared to the global LNCS average of 9.49 citations per paper. A keyword analysis revealed that the majority of papers by South African authors focus on artificial intelligence. The results indicate that, although LNCS serves as a reputable dissemination platform for Computer Science research output both globally and locally, South African authors should consider publishing more journal articles to build and improve their researcher profiles. Significance: * The study shows that LNCS is the most frequent publication outlet for Computer Science researchers, globally and in South Africa. * The study offers insight into the publication output, authorship patterns, collaborative behaviour and citation impact of South African based Computer Science researchers. [ABSTRACT FROM AUTHOR]
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- 2024
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3. Preface of the Special Issue Dedicated to Selected Papers from IWOCA 2022.
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Bazgan, Cristina and Fernau, Henning
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WATERMARKS ,COMPUTER science ,DATA structures ,INDEPENDENT sets ,BIPARTITE graphs - Abstract
The 33rd International Workshop On Combinatorial Algorithms (IWOCA 2022) was held at the University of Trier in Germany. This workshop covers a wide range of topics related to combinatorial algorithms. The special issue of the journal Algorithmica contains extended versions of selected papers from IWOCA 2022, which were nominated by the Program Committee and underwent a rigorous reviewing process. The special issue includes nine papers on various topics such as perfect matchings, algorithmic questions, and winner determination algorithms. One paper was chosen as the Best Paper of IWOCA 2022 and another as the Best Student Paper. The special issue is recommended for readers interested in exploring more papers from IWOCA 2022. [Extracted from the article]
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- 2024
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4. Text-based paper-level classification procedure for non-traditional sciences using a machine learning approach.
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Moctezuma, Daniela, López-Vázquez, Carlos, Lopes, Lucas, Trevisan, Norton, and Pérez, José
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MACHINE learning ,COMPUTER science ,INFORMATION science ,CLASSIFICATION ,CARTOGRAPHY - Abstract
Science as a whole is organized into broad fields, and as a consequence, research, resources, students, etc., are also classified, assigned, or invited following a similar structure. Some fields have been established for centuries, and some others are just flourishing. Funding, staff, etc., to support fields are offered if there is some activity on it, commonly measured in terms of the number of published scientific papers. How to find them? There exist well-respected listings where scientific journals are ascribed to one or more knowledge fields. Such lists are human-made, but the complexity begins when a field covers more than one area of knowledge. How to discern if a particular paper is devoted to a field not considered in such lists? In this work, we propose a methodology able to classify the universe of papers into two classes; those belonging to the field of interest, and those that do not. This proposed procedure learns from the title and abstract of papers published in monothematic or "pure" journals. Provided that such journals exist, the procedure could be applied to any field of knowledge. We tested the process with Geographic Information Science. The field has contacts with Computer Science, Mathematics, Cartography, and others, a fact which makes the task very difficult. We also tested our procedure and analyzed its results with three different criteria, illustrating its power and capabilities. Interesting findings were found, where our proposed solution reached similar results as human taggers also similar results compared with state-of-the-art related work. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Using Debugging as a Platform for Transdisciplinary Learning
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Nicole Panorkou, Toni York, and Erell Germia
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In this paper we discuss the types of knowledge used by six middle school students as they engaged with a debugging task designed to integrate ideas from computer science, mathematics and science. Our findings show that the computational thinking practice of debugging is a rich source of opportunities to integrate these different disciplines. The analysis illustrates how the types of knowledge the students did and did not use at each step of the debugging process were related to their ability to succeed at each step. Our work contributes to theory and practice by uncovering implications for studying debugging through two refined frameworks and for designing debugging tasks to support transdisciplinary learning.
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- 2024
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6. Benefits of international collaboration in computer science: a case study of China, the European Union, and the United States
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Gómez-Espés, Alberto, Färber, Michael, and Jatowt, Adam
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- 2024
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7. Guest Editorial: Special Issue on Semantic Computing.
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D'Auria, Daniela
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SPARQL (Computer program language) ,KNOWLEDGE graphs ,SEMANTIC computing ,MOBILE computing ,QUESTION answering systems ,COMPUTER science - Abstract
The International Journal of Semantic Computing has published a special issue on Semantic Computing, which includes five selected papers from the 17th IEEE International Conference on Semantic Computing. Semantic Computing focuses on the derivation, description, integration, and use of semantics for various resources. The papers in the special issue cover topics such as intent detection, constructing probabilistic models from knowledge graphs, unsupervised estimation of subjective content descriptions, the influence of noisy labels on question answering systems, and automatic domain-adaptive sentiment analysis. Each paper presents innovative approaches and findings in their respective areas of research. [Extracted from the article]
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- 2024
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8. Clinical Pearl: The Clinical Relevance of Neonatal Informatics.
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Falciglia, Gustave H., Hageman, Joseph R., Hussain, Walid, Alkureishi, Lolita Alcocer, Shah, Kshama, and Goldstein, Mitchell
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MEDICAL logic ,CRITICALLY ill ,PATIENTS ,ARTIFICIAL intelligence ,NEONATAL intensive care units ,ACUTE kidney failure in children ,COMPUTER science ,NEONATAL intensive care ,HOSPITAL nurseries ,INFORMATION science ,ELECTRONIC health records ,WATER-electrolyte balance (Physiology) ,QUALITY assurance ,ALGORITHMS ,CHILDREN - Abstract
The article focuses on the importance of clinical informatics in neonatal care, highlighting its potential to provide critical resources for clinicians. Topics include the specialized data needed for neonatal care, the challenges in transitioning from paper to electronic health records, and the impact of informatics on real-time patient management and research.
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- 2024
9. A Multifunctional, Low Cost and Sustainable Neonatal Database System.
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Pinheiro, Joaquim M. B., Fisher, Marilyn, Munshi, Upender K., Khalak, Rubia, Tauber, Kate A., Cummings, James J., Cerone, Jennifer B., Monaco-Brown, Meredith, Geis, Gina, Chowdhry, Rehman, Fay, Mary, Paul, Anshu A., Levine, Carolyn, Pan, Phillip, and Horgan, Michael J.
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DATABASES ,DATA quality ,NEONATAL intensive care ,NEONATAL intensive care units ,COMPUTER science ,DATABASE management ,PERINATAL death ,QUALITY assurance ,DECISION making ,INFORMATION science ,NEONATOLOGY ,ELECTRONIC health records ,ECONOMICS - Abstract
Continuous improvement in the clinical performance of neonatal intensive care units (NICU) depends on the use of locally relevant, reliable data. However, neonatal databases with these characteristics are typically unavailable in NICUs using paper-based records, while in those using electronic records, the inaccuracy of data and the inability to customize commercial data systems limit their usability for quality improvement or research purposes. We describe the characteristics and uses of a simple, neonatologist-centered data system that has been successfully maintained for 30 years, with minimal resources and serving multiple purposes, including quality improvement, administrative, research support and educational functions. Structurally, our system comprises customized paper and electronic components, while key functional aspects include the attending-based recording of diagnoses, integration into clinical workflows, multilevel data accuracy and validation checks, and periodic reporting on both data quality and NICU performance results. We provide examples of data validation methods and trends observed over three decades, and discuss essential elements for the successful implementation of this system. This database is reliable and easily maintained; it can be developed from simple paper-based forms or used to supplement the functionality and end-user customizability of existing electronic medical records. This system should be readily adaptable to NICUs in either high- or limited-resource environments. [ABSTRACT FROM AUTHOR]
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- 2024
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10. Introduction to the Special Issue: Resources for Undergraduate Cryptology.
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Boersma, Stuart, Christensen, Chris, and Millichap, Christian
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CRYPTOGRAPHY ,COMPUTATIONAL mathematics ,UNDERGRADUATES ,NUMBER theory - Abstract
This editorial introduces the special issue, Resources for Undergraduate Cryptology. We begin by describing possible roles for cryptology in the undergraduate mathematics curriculum together with a brief overview of the subject. We conclude with a brief preview of each paper included in this issue. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Is ChatGPT making scientists hyper-productive? The highs and lows of using AI
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Prillaman, McKenzie
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- 2024
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12. Maximum Matching Sans Maximal Matching: A New Approach for Finding Maximum Matchings in the Data Stream Model.
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Feldman, Moran and Szarf, Ariel
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TRIANGLES ,DATA modeling ,COMBINATORIAL optimization ,GREEDY algorithms ,COMPUTER science ,ALGORITHMS - Abstract
The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains 1 / 2 -approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continues this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work Konrad and Naidu (Approximation, randomization, and combinatorial optimization. Algorithms and techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol 207, pp 19:1–19:18, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than 1 / 2 -approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms were previously known, and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs. The main significance of our results is not in the numerical improvements, but in demonstrating the potential of non-maximal-matching-first algorithms. [ABSTRACT FROM AUTHOR]
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- 2024
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13. The nearest point problems in fuzzy quasi-normed spaces.
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Wu, Jian-Rong and Liu, He
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CONVEX sets ,MATHEMATICAL optimization ,FUZZY sets ,POINT set theory ,COMPUTER science ,NORMED rings - Abstract
Motivated by the fact that the fuzzy quasi-normed space provides a suitable framework for complexity analysis and has important roles in discussing some questions in theoretical computer science, this paper aims to study the nearest point problems in fuzzy quasi-normed spaces. First, by using the theory of dual space and the separation theorem of convex sets, the properties of the fuzzy distance from a point to a set in a fuzzy quasi-normed space are studied comprehensively. Second, more properties of the nearest point are given, and the existence, uniqueness, characterizations, and qualitative properties of the nearest points are obtained. The results obtained in this paper are of great significance for expanding the application fields of optimization theory. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Wasserstein GAN-based architecture to generate collaborative filtering synthetic datasets.
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Bobadilla, Jesús and Gutiérrez, Abraham
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DEEP learning ,GENERATIVE adversarial networks ,RECOMMENDER systems ,COMPUTER vision ,COMPUTER science - Abstract
Currently, generative applications are reshaping different fields, such as art, computer vision, speech processing, and natural language. The computer science personalization area is increasingly relevant since large companies such as Spotify, Netflix, TripAdvisor, Amazon, and Google use recommender systems. Then, it is rational to expect that generative learning will increasingly be used to improve current recommender systems. In this paper, a method is proposed to generate synthetic recommender system datasets that can be used to test the recommendation performance and accuracy of a company on different simulated scenarios, such as large increases in their dataset sizes, number of users, or number of items. Specifically, an improvement in the state-of-the-art method is proposed by applying the Wasserstein concept to the generative adversarial network for recommender systems (GANRS) seminal method to generate synthetic datasets. The results show that our proposed method reduces the mode collapse, increases the sizes of the synthetic datasets, improves their ratings distributions, and maintains the potential to choose the desired number of users, number of items, and starting size of the dataset. Both the baseline GANRS and the proposed Wasserstein-based WGANRS deep learning architectures generate fake profiles from dense, short, and continuous embeddings in the latent space instead of the sparse, large, and discrete raw samples that previous GAN models used as a source. To enable reproducibility, the Python and Keras codes are provided in open repositories along with the synthetic datasets generated to test the proposed architecture (https://github.com/jesusbobadilla/ganrs.git). [ABSTRACT FROM AUTHOR]
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- 2024
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15. Taking Flight for a Greener Planet: How Swarming Could Help Monitor Air Pollution Sources.
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Baumgart, Jan, Mikołajewski, Dariusz, and Czerniak, Jacek M.
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AIR pollution monitoring ,POLLUTION monitoring ,AIR pollution ,SUSTAINABILITY ,COMMERCIAL drones - Abstract
As the world grapples with the pressing challenge of environmental sustainability, the need for innovative solutions to combat air pollution has become paramount. Air pollution is a complex issue that necessitates real-time monitoring of pollution sources for effective mitigation. This paper explores the potential of swarm algorithms applied as a novel and efficient approach to address this critical environmental concern. Swarm algorithms offer a promising framework for coordinating fleets of drones to collaboratively monitor and analyze air pollution sources. The unique capabilities of drones, including their agility, accessibility, and versatility, make them ideal candidates for aerial data collection. When harnessed in a swarm, these drones can create a dynamic and adaptable network that provides a more comprehensive and fine-grained understanding of air pollution dynamics. This paper delves into the conceptual foundations of using swarm algorithms in drone-based air pollution monitoring. [ABSTRACT FROM AUTHOR]
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- 2024
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16. Tools and Methods of Program Analysis : 6th International Conference, TMPA 2021, Tomsk, Russia, November 25–27, 2021, Revised Selected Papers
- Author
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Rostislav Yavorskiy, Ana Rosa Cavalli, Anna Kalenkova, Rostislav Yavorskiy, Ana Rosa Cavalli, and Anna Kalenkova
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- Software engineering, Artificial intelligence, Application software, Computer networks, Computer science, Programming languages (Electronic computers)
- Abstract
This book constitutes the refereed proceedings of the 6th International Conference on Tools and Methods of Program Analysis, TMPA 2021, held in Tomsk, Russia, during November 25–27, 2021.The 15 full papers and 3 short papers included in this book were carefully reviewed and selected from 45 submissions. They focus on various aspects of application of modern methods of data science to the analysis of software quality.
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- 2024
17. Agents and Artificial Intelligence : 15th International Conference, ICAART 2023, Lisbon, Portugal, February 22–24, 2023, Revised Selected Papers
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Ana Paula Rocha, Luc Steels, Jaap van den Herik, Ana Paula Rocha, Luc Steels, and Jaap van den Herik
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- Artificial intelligence, Computer science, Computers, Computers, Special purpose, Computer networks, Image processing—Digital techniques, Computer vision
- Abstract
This book contains the revised and extended versions of selected papers from the 15th International Conference on Agents and Artificial Intelligence, ICAART 2023, held in Lisbon, Portugal, during February 22–24, 2023. The 23 full papers included in this book were carefully reviewed and selected from 306 submissions. The conference was organized in 2 tracks as follows: One track focuses on Agents, Multi-Agent Systems and Software Platforms, Distributed Problem Solving and Distributed AI in general. The other track focuses mainly on Artificial Intelligence, Knowledge Representation, Planning, Learning, Scheduling, Perception Reactive AI Systems, and Evolutionary Computing and other topics related to Intelligent Systems and Computational Intelligence.
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- 2024
18. Graph Drawing and Network Visualization : 31st International Symposium, GD 2023, Isola Delle Femmine, Palermo, Italy, September 20–22, 2023, Revised Selected Papers, Part I
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Michael A. Bekos, Markus Chimani, Michael A. Bekos, and Markus Chimani
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- Computer science, Computer science—Mathematics, Discrete mathematics, Image processing—Digital techniques, Computer vision, Data structures (Computer science), Information theory, Application software, Signal processing
- Abstract
This two-volume set LNCS 14465-14466 constitutes the proceedings of the 31st International Symposium on Graph Drawing and Network Visualization, GD 2023, held in Isola delle Femmine, Palermo, Italy, in September 2023. The 31 full papers, 7 short papers, presented together with 2 invited talks, and one contest report, were thoroughly reviewed and selected from the 100 submissions. The abstracts of 11 posters presented at the conference can be found in the back matter of the volume. The contributions were organized in topical sections as follows: beyond planarity; crossing numbers; linear layouts; geometric aspects; visualization challenges; graph representations; graph decompositions; topological aspects; parameterized complexity for drawings; planar graphs; frameworks; algorithmics.
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- 2024
19. Dynamic Logic. New Trends and Applications : 5th International Workshop, DaLí 2023, Tbilisi, Georgia, September 15–16, 2023, Revised Selected Papers
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Nina Gierasimczuk, Fernando R. Velázquez-Quesada, Nina Gierasimczuk, and Fernando R. Velázquez-Quesada
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- Computer science, Mathematical logic, Logic programming, Software engineering, Computer networks
- Abstract
This book constitutes the revised selected papers of the 5th International Workshop on Dynamic Logic. New Trends and Applications, DaLí 2023, held in Tbilisi, Georgia, during September 15–16, 2023. The 8 full papers in this book were carefully reviewed and selected from 10 submissions. They deal with new trends and applications in the area of Dynamic Logic.
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- 2024
20. How to Navigate the Pitfalls of AI Hype in Health Care.
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Suran, Melissa and Hswen, Yulin
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DATA privacy ,ARTIFICIAL intelligence ,MEDICAL care ,COMPUTER science ,COLLEGE teachers - Abstract
In this Medical News article, Arvind Narayanan, PhD, a professor of computer science at Princeton University, discusses the benefits of using artificial intelligence in research and clinical settings while remaining cautious of hype, biases, and data privacy issues. [ABSTRACT FROM AUTHOR]
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- 2024
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21. Virtual reality application on mental health: A review on functionality.
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Hamzah, Irna and Salwana, Ely
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DATABASES ,MENTAL health ,ELECTRONICS engineers ,SCIENCE databases ,COMPUTER science - Abstract
Virtual reality (VR) has grown and become more prevalent as technology advances. However, there is a smaller amount of study focusing on the functionality of VR applications in mental health. Therefore, to close the gap, research has been conducted with the aim of reviewing the functionality of VR applications for mental health. The research has performed a systematic literature review (SLR) to investigate the growth of VR applications for mental health. Three significant databases in computer science were involved in the search process, specifically, Scopus, the Institute of Electrical and Electronics Engineers (IEEE), and World of Science (WoS). The result indicates seventy-six papers (76) in three databases. The research analyzed the papers, and the result suggested reviewing four papers. The result also shows that the functionality of virtual reality (VR) applications for mental health is beneficial to the medical sector. However, less research has been conducted specifically on the application functionality. We believe there will be more research on the field in the future. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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22. Advances in Computer Games : 18th International Conference, ACG 2023, Virtual Event, November 28–30, 2023, Revised Selected Papers
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Michael Hartisch, Chu-Hsuan Hsueh, Jonathan Schaeffer, Michael Hartisch, Chu-Hsuan Hsueh, and Jonathan Schaeffer
- Subjects
- Computer science, User interfaces (Computer systems), Human-computer interaction, Artificial intelligence, Computer vision, Computer science—Mathematics
- Abstract
This book constitutes the refereed post proceedings of the 18th International Conference on Advances in Computer Games, ACG 2023, held online, during November 28–30, 2023.The 14 full papers included in this book were carefully reviewed and selected from 29 submissions. They were organized in topical sections as follows: Chess and its Variants, Solving Games, Board Games, Card Games, Player Investigation, Math, Games, and Puzzles.
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- 2024
23. A Bibliometric Review of the Ordered Weighted Averaging Operator.
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Figuerola-Wischke, Anton, Merigó, José M., Gil-Lafuente, Anna M., and Boria-Reverter, Josefa
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BIBLIOMETRICS ,DATABASES ,COMPUTER science ,AGGREGATION operators ,DATA visualization ,SOFTWARE measurement - Abstract
The ordered weighted averaging (OWA) operator was proposed by Yager back in 1988 and constitutes a parameterized family of aggregation functions between the minimum and the maximum. The purpose of this paper is to perform a bibliometric review of this aggregation operator during the last 35 years through the Web of Science (WoS) Core Collection database and the Visualization of Similarities (VOS) viewer software. The results show that the OWA operator is an increasingly popular aggregation operator, especially in Computer Science. The results also allow the assertion that Yager, as expected, is still the most influential and productive author. Moreover, the study reveals that institutions from over 80 countries have contributed to OWA research, highlighting the high presence of Chinese universities and the emergence of Pakistani ones. Other interesting findings are presented to provide a comprehensive and up-to-date analysis of the OWA operator literature. [ABSTRACT FROM AUTHOR]
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- 2024
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24. Inverse design in photonic crystals.
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Deng, Ruhuan, Liu, Wenzhe, and Shi, Lei
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PHOTONIC crystals ,ELECTROMAGNETIC fields ,WORK design ,COMPUTER science - Abstract
Photonic crystals are periodic dielectric structures that possess a wealth of physical characteristics. Owing to the unique way they interact with the light, they provide new degrees of freedom to precisely modulate the electromagnetic fields, and have received extensive research in both academia and industry. At the same time, fueled by the advances in computer science, inverse design strategies are gradually being used to efficiently produce on-demand devices in various domains. As a result, the interdisciplinary area combining photonic crystals and inverse design emerges and flourishes. Here, we review the recent progress for the application of inverse design in photonic crystals. We start with a brief introduction of the background, then mainly discuss the optimizations of various physical properties of photonic crystals, from eigenproperties to response-based properties, and end up with an outlook for the future directions. Throughout the paper, we emphasize some insightful works and their design algorithms, and aim to give a guidance for readers in this emerging field. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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25. TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network.
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Al-Saadawi, Hussein Farooq Tayeb and Das, Resul
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GRAPH neural networks ,EMOTION recognition ,NATURAL language processing ,ARTIFICIAL intelligence ,AFFECTIVE computing ,COMPUTER science ,MULTIMODAL user interfaces - Abstract
Affective computing is a multidisciplinary field encompassing artificial intelligence, natural language processing, linguistics, computer science, and social sciences. This field aims to deepen our comprehension and capabilities by deploying inventive algorithms. This article presents a groundbreaking approach, the Cumulative Attribute-Weighted Graph Neural Network, which is innovatively designed to integrate trimodal textual, audio, and visual data from the two multimodal datasets. This method exemplifies its effectiveness in performing comprehensive multimodal sentiment analysis. Our methodology employs vocal inputs to generate speaker embeddings trimodal analysis. Using a weighted graph structure, our model facilitates the efficient integration of these diverse modalities. This approach underscores the interrelated aspects of various emotional indicators. The paper's significant contribution is underscored by its experimental results. Our novel algorithm achieved impressive performance metrics on the CMU-MOSI dataset, with an accuracy of 94% and precision, recall, and F1-scores above 92% for Negative, Neutral, and Positive emotion categories. Similarly, on the IEMOCAP dataset, the algorithm demonstrated its robustness with an overall accuracy of 93%, where exceptionally high precision and recall were noted in the Neutral and Positive categories. These results mark a notable advancement over existing state-of-the-art models, illustrating the potential of our approach in enhancing Sentiment Recognition through the synergistic use of trimodal data. This study's comprehensive analysis and significant results demonstrate the proposed algorithm's effectiveness in nuanced emotional state recognition and pave the way for future advancements in affective computing, emphasizing the value of integrating multimodal data for improved accuracy and robustness. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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26. CSIM: A Fast Community Detection Algorithm Based on Structure Information Maximization.
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Liu, Yiwei, Liu, Wencong, Tang, Xiangyun, Yin, Hao, Yin, Peng, Xu, Xin, and Wang, Yanbin
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TIME complexity ,GREEDY algorithms ,ALGORITHMS ,EXPECTATION-maximization algorithms ,COMPUTER science ,SOCIAL media - Abstract
Community detection has been a subject of extensive research due to its broad applications across social media, computer science, biology, and complex systems. Modularity stands out as a predominant metric guiding community detection, with numerous algorithms aimed at maximizing modularity. However, modularity encounters a resolution limit problem when identifying small community structures. To tackle this challenge, this paper presents a novel approach by defining community structure information from the perspective of encoding edge information. This pioneering definition lays the foundation for the proposed fast community detection algorithm CSIM, boasting an average time complexity of only O (n log n) . Experimental results showcase that communities identified via the CSIM algorithm across various graph data types closely resemble ground truth community structures compared to those revealed via modularity-based algorithms. Furthermore, CSIM not only boasts lower time complexity than greedy algorithms optimizing community structure information but also achieves superior optimization results. Notably, in cyclic network graphs, CSIM surpasses modularity-based algorithms in effectively addressing the resolution limit problem. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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27. Approximate Sampling of Graphs with Near-P-Stable Degree Intervals.
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Erdős, Péter L., Mezei, Tamás Róbert, and Miklós, István
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MARKOV processes ,SOCIAL networks ,COMPUTER science - Abstract
The approximate uniform sampling of graph realizations with a given degree sequence is an everyday task in several social science, computer science, engineering etc. projects. One approach is using Markov chains. The best available current result about the well-studied switch Markov chain is that it is rapidly mixing on P-stable degree sequences (see DOI:10.1016/j.ejc.2021.103421). The switch Markov chain does not change any degree sequence. However, there are cases where degree intervals are specified rather than a single degree sequence. (A natural scenario where this problem arises is in hypothesis testing on social networks that are only partially observed.) Rechner, Strowick, and Müller–Hannemann introduced in 2018 the notion of degree interval Markov chain which uses three (separately well studied) local operations (switch, hinge-flip and toggle), and employing on degree sequence realizations where any two sequences under scrutiny have very small coordinate-wise distance. Recently, Amanatidis and Kleer published a beautiful paper (DOI:10.4230/LIPIcs.STACS.2023.7), showing that the degree interval Markov chain is rapidly mixing if the sequences are coming from a system of very thin intervals which are centered not far from a regular degree sequence. In this paper, we substantially extend their result, showing that the degree interval Markov chain is rapidly mixing if the intervals are centered at P-stable degree sequences. [ABSTRACT FROM AUTHOR]
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- 2024
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28. Review of the grey wolf optimization algorithm: variants and applications.
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Liu, Yunyun, As'arry, Azizan, Hassan, Mohd Khair, Hairuddin, Abdul Aziz, and Mohamad, Hesham
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OPTIMIZATION algorithms ,GREY Wolf Optimizer algorithm ,WOLVES ,SWARM intelligence ,COMPUTER science ,CHEMICAL engineering - Abstract
One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its effective application in diverse fields for solving optimization issues. Consequently, the GWO has rapidly garnered substantial research interest and a broad audience across numerous areas. To better understand the literature on this algorithm, this review paper aims to consolidate and summarize research publications that utilized the GWO. The paper begins with a concise introduction to the GWO, providing insight into its natural establishment and conceptual framework for optimization. It then lays out the theoretical foundation and key procedures involved in the GWO, following which it comprehensively examines the most recent iterations of the algorithm and categorizes them into parallel, modified, and hybridized variations. Subsequently, the primary applications of the GWO are thoroughly explored, spanning various fields such as computer science, engineering, energy, physics and astronomy, materials science, environmental science, and chemical engineering, among others. This review paper concludes by summarizing the key arguments in favour of GWO and outlining potential lines of inquiry in the future research. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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29. On the d-Claw Vertex Deletion Problem.
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Hsieh, Sun-Yuan, Le, Hoang-Oanh, Le, Van Bang, and Peng, Sheng-Lung
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BIPARTITE graphs ,PLANAR graphs ,COMPLETE graphs ,COMBINATORICS ,GRAPH algorithms ,COMPUTER science - Abstract
Let d-claw (or d-star) stand for K 1 , d , the complete bipartite graph with 1 and d ≥ 1 vertices on each part. The d-claw vertex deletion problem, d -claw-vd, asks for a given graph G and an integer k if one can delete at most k vertices from G such that the resulting graph has no d-claw as an induced subgraph. Thus, 1 -claw-vd and 2 -claw-vd are just the famous vertex cover problem and the cluster vertex deletion problem, respectively. In this paper, we strengthen a hardness result recently proved in Jena and Subramani (in: Du, Du, Wu, and Zhu (eds) Theory and applications of models of computation - 17th annual conference, TAMC 2022, Tianjin, China, September 16–18, 2022, Proceedings, 2022), by showing that cluster vertex deletion remains NP -complete even when restricted to planar bipartite graphs of maximum degree 3 and arbitrary large girth. Moreover, for every d ≥ 3 , we show that d -claw-vd is NP -complete even when restricted to planar bipartite graphs of maximum degree d. These hardness results are optimal with respect to degree constraint. By extending the hardness result in Bonomo-Braberman et al (in: Computing and combinatorics - 26th international conference, COCOON 2020, Proceedings, Lecture Notes in Computer Science, vol 12273, Springer, 2020, pp 14–26, 2020), we show that, for every d ≥ 3 , d -claw-vd is NP -complete even when restricted to split graphs without (d + 1) -claws, and split graphs of diameter 2. On the positive side, we prove that d -claw-vd is polynomially solvable on what we call d-block graphs, a class properly contains all block graphs. This result extends the polynomial-time algorithm in Cao et al (Theor Comput Sci, 2018) for 2 -claw-vd on block graphs to d -claw-vd for all d ≥ 2 and improves the polynomial-time algorithm proposed by Bonomo-Brabeman et al. for (unweighted) 3 -claw-vd on block graphs to 3-block graphs. [ABSTRACT FROM AUTHOR]
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- 2024
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30. Bit-Close: a fast incremental concept calculation method.
- Author
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Ke, Yunfeng, Li, Jinhai, and Li, Shen
- Subjects
COGNITIVE learning ,COMPUTER science ,CONCEPT learning ,DATA mining ,ALGORITHMS - Abstract
The theory of Formal Concept Analysis (FCA) finds diverse applications in fields like knowledge extraction, cognitive concept learning and data mining. The construction of a concept lattice significantly influences the effectiveness of formal concept analysis; hence, the development of high-performance algorithms for concept construction is crucial. In this paper, we introduce a novel algorithm called "Bit-Close" for formal concept construction. Bit-Close leverages bit representation and operations, fundamental to computer science, to enhance the In-Close algorithm. Furthermore, we explore the parallel method of Bit-Close. Our experimental results, obtained from multiple public and random datasets, demonstrate that Bit-Close outperforms In-Close by approximately 20% and is significantly better than other competing algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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31. Editorial of Applied Geometric Algebras in Computer Science and Engineering (AGACSE 21).
- Author
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Vašík, Petr, Hitzer, Eckhard, and Lavor, Carlile
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COMPUTER science ,COMPUTER engineering ,ALGEBRA ,COMPUTER engineers ,QUANTUM cryptography ,MEASUREMENT errors ,CLIFFORD algebras - Abstract
This document is an editorial summarizing the Applied Geometric Algebras in Computer Science and Engineering (AGACSE) conference held in Brno, Czech Republic in September 2021. The conference aimed to promote the use of geometric algebra in fields such as image processing, robotics, and quantum computing. The conference proceedings were published in the journal Mathematical Methods in the Applied Sciences. The editorial provides a list of accepted papers, covering topics such as applied geometry, technological applications, algebra, and quantum phenomena. One specific paper explores the use of geometric algebra in teaching rotations through neural networks. The document is a compilation of research papers showcasing the applications of geometric algebra in various fields, including robotics, control systems, image processing, cryptography, and physics. Each paper presents a specific problem or application and proposes a unique approach or solution using geometric algebra. The authors compare their methods with existing techniques and provide mathematical analysis to support their claims. Overall, the papers demonstrate the versatility and effectiveness of geometric algebra in different domains. [Extracted from the article]
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- 2024
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32. Machine learning based file type classifier designing in IoT cloud.
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Sharma, Puneet, Kumar, Manoj, and Sharma, Ashish
- Subjects
MACHINE learning ,INTERNET of things ,RANDOM forest algorithms ,COMPUTER science - Abstract
With the increase interest and number of the users in Social media, the file handling has also increased. To manage the load, cloud servers are being used by the service providers. To identify and cluster file is a difficult task that is important in the domain of computer science. Various traditional approaches for identification exists that uses design features. The problem with these methods is get that they can be easily spoofed. To resolve the issue, in this paper, a hybrid algorithm combining the features of Random Forest with AdaBoost is proposed. The algorithm Internet of Thing (IoT) data file formatting (IDFF) classifies data as (text, image, audio and video) and gives better accuracy. Our proposed research obtains better Accuracy (93%), Precision (95%), Recall (95%), F-Measure (95%), and G-Mean (96%). [ABSTRACT FROM AUTHOR]
- Published
- 2024
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33. Fractal feature selection model for enhancing high-dimensional biological problems.
- Author
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Alsaeedi, Ali Hakem, Al-Mahmood, Haider Hameed R., Alnaseri, Zainab Fahad, Aziz, Mohammad R., Al-Shammary, Dhiah, Ibaida, Ayman, and Ahmed, Khandakar
- Subjects
FEATURE selection ,ARTIFICIAL intelligence ,MACHINE learning ,STANDARD deviations ,COMPUTER science - Abstract
The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%. [ABSTRACT FROM AUTHOR]
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- 2024
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34. Graph-Based Covert Transaction Detection and Protection in Blockchain.
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Guo, Zhenyu, Li, Xin, Liu, Jiamou, Zhang, Zijian, Li, Meng, Hu, Jingjing, and Zhu, Liehuang
- Abstract
Covert communication is an method that plays an important role in secure data transmission. The technology embeds covert information into data and propagates it through covert channels. The communication quality depends on the choice of channel and data embedding techniques. Recently, blockchain has emerged to become the preferred channel to carry out covert communication for its decentralization and anonymity features. Existing covert transaction methods are constructed transaction-by-transaction, which makes them immune to text analysis-based detection methods. However, it is easy to expose their features on the transaction graph level. Unfortunately, there is yet no method to detect covert transactions by the features of transaction graph. In this paper, we propose a covert transaction detection method based on graph structure. By analyzing the statistical features of graph structure for addresses, we can infer whether they are the participants of covert transactions. Furthermore, we design a protection method of covert transactions based on graph generation networks. By adjusting the structural features between different addresses, our method enhances the security of multiple interrelated covert transactions. Experimental analysis on the Bitcoin Testnet verifies the security and the efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
- View/download PDF
35. Quantum 4x4 Tic-Tac-Toe.
- Author
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Vinay, Keya
- Subjects
GAME theory ,DECISION making ,TIC-tac-toe (Game) ,SUPERPOSITION principle (Physics) ,SYSTEM analysis - Abstract
Game theory studies interactive decision-making, where the outcome for each participant or player depends on the actions of all. Quantum game theory introduces new strategies and outcomes that are impossible in classical game theory, hence it is generally advantageous over classical game theory. One of the games in which quantum features add advantages to strategies and winning possibilities is the 3x3 tic-tac-toe. Here, we study the quantum 4x4 tic-tac-toe, a new tic-tac-toe model that incorporates quantum strategies into the classical 4x4 tic-tac-toe. This paper reviews some winning strategies and properties of the quantum 4x4 tic-tac-toe. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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36. Progressive Collapse Analysis of the Champlain Towers South in Surfside, Florida.
- Author
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Pellecchia, Cosimo, Cardoni, Alessandro, Cimellaro, Gian Paolo, Domaneschi, Marco, Ansari, Farhad, and Khalil, Ahmed Amir
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BUILDING failures ,PROGRESSIVE collapse ,STRUCTURAL failures ,COLUMNS ,CIVIL engineering ,COMPUTER science - Abstract
Since the Ronan Point collapse in the UK in 1968, the progressive collapse analysis of residential buildings has gradually drawn the attention of civil engineers and the scientific community. Recent advances in computer science and the development of new numerical methodologies allow us to perform high-fidelity collapse simulations. This paper assesses different scenarios that could have hypothetically caused the collapse of the Champlain Tower South Condo in Surfside, Florida, in 2021, one of the most catastrophic progressive collapse events that has ever occurred. The collapse analysis was performed using the latest developments in the Applied Element Method (AEM). A high-fidelity numerical model of the building was developed according to the actual structural drawings. Several different collapse hypotheses were examined, considering both column failures and degradation scenarios. The analyses showed that the failure of deep beams at the pool deck level, directly connected to the perimeter columns of the building, could have led to the columns' failure and subsequent collapse of the eastern wing of the building. The simulated scenario highlights the different stages of the collapse sequence and appears to be consistent with what can be observed in the footage of the actual collapse. To improve the performance of the structure against progressive collapse, two modifications to the original design of the building were introduced. From the analyses, it was found that disconnecting the pool deck beam from the perimeter columns could have been effective in preventing the local collapse of the pool deck slab from propagating to the rest of the building. Moreover, these analyses indicate that enhancing the torsional strength and stiffness of the core could have prevented the collapse of the eastern part of the building, given the assumptions and initiation scenarios considered. Building catastrophic collapses can cause significant lives and economic losses. Poor design and maintenance, in combination with aging, will more likely increase, in the next years, the number of buildings potentially vulnerable to the risk of collapse, due to either seismic, accidental, or degradation actions. This research focuses on the analysis of the Champlain Tower South condo collapse, which occurred in Surfside, Florida, in 2021. Different hypothetical collapse scenarios were simulated, comparing the analysis results with the actual evidence of the collapse. The analyses have shown that the degradation of the pool deck slab, due to corrosion, may have contributed to the collapse of the building. Finally, two different minor revisions of the original design of the building were analyzed to reduce the risk of failure and understand how the collapse of similar residential buildings could be prevented. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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37. Software reengineering: Effects on software reliability and stability: Review.
- Author
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Vistro, Daniel Mago and Hassan, Taimoor
- Subjects
SOFTWARE reliability ,SYSTEMS software ,COMPUTER software ,LEGACY systems ,COMPUTER science - Abstract
Now a day's software reengineering plays a great role in the industry of computer science. There are a lot of different companies that runs different type of software's but most of them have old/legacy software systems. These systems need some improvements and reconstruct, but if we develop a new system for the specific company then that is much expensive, and a lot of risk involve in it. These different situations face, we are reengineering the software based on customer requirements and time demand, that is much helpful and low expensive. In this paper, we review different relevant papers and describe it in a definite way and show it that after software reengineering, software system will be more stable and reliable. In this also discuss the overall software reengineering process and their different interrelated steps. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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38. Studies from School of Computer Science and Technology Provide New Data on Applied Sciences (GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception)
- Subjects
Algorithm ,Food processing machinery industry ,Computer science ,Algorithms - Abstract
2024 MAR 1 (NewsRx) -- By a News Reporter-Staff News Editor at Science Letter -- A new study on applied sciences is now available. According to news reporting out of [...]
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- 2024
39. Fundamentals of text-to-image synthesis - A survey.
- Author
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Manoj, Oswalt, Dharika, Venkatesan, and Sarath, Vishnu
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IMAGE reconstruction ,MAGNETIC resonance imaging ,COMPUTER science ,CRIME scenes ,PRIVATE sector - Abstract
Text-to-Image Synthesis is the method of creating occurrences (images, animation) from a given text depiction. The text depiction is additionally combined, utilizing the discourse of the client. Image synthesizing has consistently been an eye-catching business sector for nearly ten years now. Blending pictures dependent on the depiction given by the client will be more client-driven than more frameworks created, which causes individuals to comprehend the impression of others. Text-to-picture Synthesis is utilized to create fake countenances - producing counterfeit faces that look practically indistinguishable from genuine individuals - Coloring pictures, MRI image reconstruction, and crime scene visualization. In this paper, we review the technique for text-to-picture Synthesis and its applications in different fields. These regions incorporate medical applications, Forensics applications, and cultural applications. Our work centers around giving a synopsis on quite possibly the most exceptionally encouraging areas of Computer Science for people and organizations keen on making an ideal world. We envision our review to acquire more significant and better applications contributing to the advancement of humankind. [ABSTRACT FROM AUTHOR]
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- 2024
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40. The utilization of machine learning on studying Hadith in Islam: A systematic literature review
- Author
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Sulistio, Bambang, Ramadhan, Arief, Abdurachman, Edi, Zarlis, Muhammad, and Trisetyarso, Agung
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- 2024
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41. Reliability and validity of a single-item computer science identity instrument
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Wu, Rongxiu, Sunbury, Susan, Sadler, Philip, and Sonnert, Gerhard
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- 2024
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42. 'Inclusion We Stand, Divide We Fall': Digital Inclusion from Different Disciplines for Scientific Collaborations
- Author
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Fang, Wei, Zhou, Lihong, Shi, Qinggong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sserwanga, Isaac, editor, Joho, Hideo, editor, Ma, Jie, editor, Hansen, Preben, editor, Wu, Dan, editor, Koizumi, Masanori, editor, and Gilliland, Anne J., editor
- Published
- 2024
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43. A survey on IoT trust model frameworks.
- Author
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Ferraris, Davide, Fernandez-Gago, Carmen, Roman, Rodrigo, and Lopez, Javier
- Subjects
TRUST ,INTERNET of things ,COMPUTER science - Abstract
Trust can be considered as a multidisciplinary concept, which is strongly related to the context and it falls in different fields such as Philosophy, Psychology or Computer Science. Trust is fundamental in every relationship, because without it, an entity will not interact with other entities. This aspect is very important especially in the Internet of Things (IoT), where many entities produced by different vendors and created for different purposes have to interact among them through the internet often under uncertainty. Trust can overcome this uncertainty, creating a strong basis to ease the process of interaction among these entities. We believe that considering trust in the IoT is fundamental, and in order to implement it in any IoT entity, it is fundamental to consider it through the whole System Development Life Cycle. In this paper, we propose an analysis of different works that consider trust for the IoT. We will focus especially on the analysis of frameworks that have been developed in order to include trust in the IoT. We will make a classification of them providing a set of parameters that we believe are fundamental in order to properly consider trust in the IoT. Thus, we will identify important aspects to be taken into consideration when developing frameworks that implement trust in the IoT, finding gaps and proposing possible solutions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. Predicting and optimizing marketing performance in dynamic markets.
- Author
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Guhl, Daniel, Paetz, Friederike, Wagner, Udo, and Wedel, Michel
- Subjects
MARKETING ,OPERATIONS research ,BRAND loyalty ,ECONOMIC change ,COMPUTER science ,NEW product development - Abstract
Our world is turbulent: ecological, social, political, technological, economic, and competitive business environments change constantly. Consumers have changing preferences, learn, build trust in brands, adopt new products, and are persuaded by advertising. Firms innovate and engage in and respond to competition. Exogenous events, such as changes in economic conditions and regulations, as well as human crises, also cause major shifts in markets. This special issue focuses on novel Marketing data and modern methodologies from different fields (e.g., Operations Research (OR), Statistics, Econometrics, and Computer Science), which help firms understand, utilize, and respond to market dynamics more efficiently. Here we propose a framework comprising analytical methods and data for dynamic markets that is useful for structuring research in this domain. Next, we summarize the history of the Marketing/OR interface. We highlight studies at the Marketing/OR interface from the last decade focusing specifically on dynamic markets and use our proposed framework to identify trends and gaps in the extant literature. After that, we present and summarize the papers of the current special issue and their contributions to the field against the backdrop of our framework and the trends in the literature. Finally, we conclude and discuss which future Marketing/OR research could tackle important issues in dynamic markets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Automated Quality Evaluation of Large-Scale Benchmark Datasets for Vision-Language Tasks.
- Author
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Zhao, Ruibin, Xie, Zhiwei, Zhuang, Yipeng, and L. H. Yu, Philip
- Subjects
COMPUTER science ,ARTIFICIAL intelligence ,DEEP learning ,UNIVERSITY research ,BENCHMARK problems (Computer science) - Abstract
Large-scale benchmark datasets are crucial in advancing research within the computer science communities. They enable the development of more sophisticated AI models and serve as "golden" benchmarks for evaluating their performance. Thus, ensuring the quality of these datasets is of utmost importance for academic research and the progress of AI systems. For the emerging vision-language tasks, some datasets have been created and frequently used, such as Flickr30k, COCO, and NoCaps, which typically contain a large number of images paired with their ground-truth textual descriptions. In this paper, an automatic method is proposed to assess the quality of large-scale benchmark datasets designed for vision-language tasks. In particular, a new cross-modal matching model is developed, which is capable of automatically scoring the textual descriptions of visual images. Subsequently, this model is employed to evaluate the quality of vision-language datasets by automatically assigning a score to each 'ground-truth' description for every image picture. With a good agreement between manual and automated scoring results on the datasets, our findings reveal significant disparities in the quality of the ground-truth descriptions included in the benchmark datasets. Even more surprising, it is evident that a small portion of the descriptions are unsuitable for serving as reliable ground-truth references. These discoveries emphasize the need for careful utilization of these publicly accessible benchmark databases. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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46. Informational Equivalence but Computational Differences? Herbert Simon on Representations in Scientific Practice.
- Author
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Waszek, David
- Subjects
DATA structures ,MENTAL representation ,COGNITIVE psychology ,COMPUTER science ,PROBLEM solving - Abstract
To explain why, in scientific problem solving, a diagram can be "worth ten thousand words," Jill Larkin and Herbert Simon (1987) relied on a computer model: two representations can be "informationally" equivalent but differ "computationally," just as the same data can be encoded in a computer in multiple ways, more or less suited to different kinds of processing. The roots of this proposal lay in cognitive psychology, more precisely in the "imagery debate" of the 1970s on whether there are image-like mental representations. Simon (1972, 1978) hoped to solve this debate by thoroughly reducing the differences between forms of mental representations (e.g., between images and sentences) to differences in computational efficiency; to carry out this reduction, he borrowed from computer science the concepts of data type and of data structure. I argue that, in the end, his account amounted to nothing more than characterizing representations by the fast operations on them. This analysis then allows me to assess what Simon's approach actually achieves when transported from psychology to the study of scientific representations, as in Larkin and Simon (1987): it allows comparing, not representations in and of themselves, but rather the computational roles they play in particular problem-solving processes—that is, representations together with a particular way of using them. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. An improved clustering method using particle swarm optimization algorithm and mitochondrial fusion model (PSO-MFM).
- Author
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Nasef, Mohammed M., El Kafrawy, Passent M., and Hashim, Amal
- Subjects
TIME complexity ,MITOCHONDRIA ,DISTRIBUTED computing ,PARTICLE swarm optimization ,COMPUTER science ,PARALLEL programming ,LOTKA-Volterra equations - Abstract
Computational models are foundational concepts in computer science; many of these models such as P systems are based on natural biological processes. P systems represent a wide framework for a variety of concepts of data mining, as models of data clustering approaches. Data clustering is a technique for analyzing data based on its structure that is widely utilized for many applications. In this paper, the proposed model (PSO-MFM) has combined the Particle Swarm Optimization algorithm (PSO) with Mitochondrial Fusion Model to overcome some constraints of clustering techniques. The solving of clustering problem based on particle swarm is investigated in the proposed model when mutual dynamic rules are used. It can find the best cluster centers for a data set and improve clustering performance by utilizing the distributed parallel computing concept of mutual dynamic rules of mitochondrial fusion model. The comparative results demonstrate that the proposed strategy outperforms competition models when it comes to clustering accuracy, stability and the most efficient in time complexity. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. Design of a personalised adaptive ubiquitous learning system.
- Author
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Adewale, O. S., Agbonifo, O. C., Ibam, E. O., Makinde, A. I., Boyinbode, O. K., Ojokoh, B. A., Olabode, O., Omirin, M. S., and Olatunji, S. O.
- Subjects
TECHNOLOGICAL innovations ,UBIQUITOUS computing ,CONTROL groups ,COMPUTER science ,LEARNING - Abstract
With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to meet individual learner's requirements and promote the effectiveness and performance of the learning process. Furthermore, the study used an experimental control group to ascertain the significance of learners' learning styles and preferences in improving learning performance. The model considers different modules such as personalisation, location support, learning plan and context awareness while relying on the proper classification of learners' learning styles. The system was experimented using 127 learners of a Computer Science Course. The result obtained using 10-fold cross-validation on various algorithms shows better performance in ROC, Kappa and Accuracy, which were all used to evaluate the quality of classification outcome and compared with learners' performances in assessments. During classification, Naïve Bayes Model has the highest average ROC value of 0.979, TP Rate of 0.951 and FP Rate of 0.045. The results obtained show an improvement in learning. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Beyond teaching computational thinking: Exploring kindergarten teachers’ computational thinking and computer science curriculum design considerations
- Author
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Kim, Jiyoung, Leftwich, Anne, and Castner, Daniel
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- 2024
- Full Text
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50. Introduction to informatics in a Peruvian penitentiary using cs unplugged: from university to penitentiary.
- Author
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Díaz-León, Jose Alfredo, Arbelaitz, Olatz, and Arruarte, Ana
- Subjects
CONCENTRATION camps ,COMPUTER science ,TREATMENT programs ,SOCIAL skills education ,INSTITUTIONALIZED persons - Abstract
Within the field of social reintegration and re-education, this paper presents an educational experience carried out at the Iquitos Penitentiary Center, Lima, Peru, with the aim of providing an introduction to informatics to 25 inmates who volunteered to take part in the project. Twenty students and a teacher from the Scientific University of the South also in Peru, were responsible for initiating the transmission of knowledge from the university to inmates, with the collaboration and participation of the penitentiary coordinator. The main objectives of the case study were to validate both the suitability of the CS unplugged proposal and the adaptability of the L2T2L pedagogic strategy to the transmission of knowledge to adults, specifically penitentiary inmates. This strategy had been originally designed to transmit informatics knowledge from university to primary school. The validity and effectiveness of the experience was assessed using surveys. Results confirm that inmates achieved a good level of understanding when endeavoring to resolve most of the CS unplugged assignments designed for them. It was also seen that L2T2L is adaptable and valid for different scenarios other than those for which it was initially designed. Indeed, it was proven to be valid for transmitting knowledge to the prison population. Finally, it should be pointed out that the experience is easily replicable and that it brings an opportunity to introduce informatics into education programs in prisons, something which can contribute enormously to social reintegration and re-education, facilitating the subsequent reentry of inmates into the community once their period of imprisonment has ended. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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