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The text is a foreword for a special issue of the Journal of Information Science & Engineering on Combinatorial Mathematics and Computational Theory. It explains that these fields have a significant impact on computer science, mathematics, and science in general. Combinatorial Mathematics involves the study of discrete objects and has applications in various areas, while Computational Theory focuses on studying algorithms and their efficiency. The text also mentions a symposium that was held to promote academic exchanges and advance research in these fields. The special issue includes four papers on topics such as opinion optimization, local diagnosis, enumerating furthest pairs, and determining the 2-tuple total domination number of a graph. The authors express gratitude to the reviewers and authors for their contributions to the special issue. [Extracted from the article]

Academic genealogy (AG) provides valuable insights into the transmission of knowledge from mentors to mentees, revealing the evolution of knowledge within the academic community. This study explores the intricate dynamics of knowledge evolution within academic genealogies, utilizing on a dataset comprising 16,852 computer science researchers, 613,277 papers, and 11,988 mentorship relationships. By focusing on small-scale knowledge units, our analysis aims to uncover patterns of knowledge inheritance and mutation across different subfields of computer science and highlights several aspects of knowledge evolution in computer science. Firstly, computer science is characterized by strong mentorship ties, indicating the significance of knowledge transmission within the field. Additionally, there is a mix of foundational and developing areas, suggesting a field that is growing and diversifying rather than declining, as indicated by linear regression outcomes. Secondly, our research reveals a surge in collaborative knowledge exchange in computer science since 2000, with fields such as Computer-Communication Networks and Software Engineering leading in terms of output and impact. Furthermore, areas like Computer Graphics and Artificial Intelligence stand out for their depth and novelty. Thirdly, we categorize researchers into three types: roots, branches, and leaves, reflecting their role in knowledge transmission. Branch researchers tend to innovate, while leaf researchers show a combination of traditional knowledge uptake and new contributions, illustrating the dynamic flow of ideas within the field. Future research endeavors are encouraged to embrace larger datasets and further fortify our understanding of the topic. [ABSTRACT FROM AUTHOR]

Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing demand on Nash equilibrium computation, especially for Internet economics and multi-agent learning. This paper reviews various algorithms computing the Nash equilibrium and its approximation solutions in finite normal-form games from both theoretical and empirical perspectives. For the theoretical part, we classify algorithms in the literature and present basic ideas on algorithm design and analysis. For the empirical part, we present a comprehensive comparison on the algorithms in the literature over different kinds of games. Based on these results, we provide practical suggestions on implementations and uses of these algorithms. Finally, we present a series of open problems from both theoretical and practical considerations. • A classification on Nash equilibrium algorithms in the literature with basic ideas on design and analysis presented. • A comprehensive comparison on Nash equilibrium algorithms in the literature over different kinds of games. • Practical suggestions on implementations and uses of Nash equilibrium algorithms. [ABSTRACT FROM AUTHOR]

The Boolean satisfiability problem, a renowned NP-complete challenge in computer science, has recently garnered interest in the Discrete Hopfield Neural Network - Satisfiability model. This model adeptly integrates logical rules into Hopfield networks, excelling in locating global minima for traditional SAT problems. However, it faces efficiency challenges when dealing with SAT problems characterized by dynamic evolution constraints due to its static network architecture. During dynamic evolution iterations, there is a significant exponential increase in the computational costs due to redundant and repetitive computations. In order to address this challenge, this paper introduces a dynamic evolution variant of the Discrete Hopfield Neural Network - Satisfiability model. In extensive simulation experiments, we progressively augmented the number of constraint clauses from 1 to 1500, seeking global minima for CNF problems. The proposed model exhibited congruent performance with the traditional model, achieving a Global Minimum Ratio of 1 and a Hamming distance of 0. Crucially, the proposed model minimized CPU utilization and neared zero error metrics, while the traditional model experienced exponential CPU and error metric escalation. These outcomes affirm the proposed model's robust global search capabilities and high precision, aligning with the traditional model. Furthermore, owing to this model's incorporation of not only temporal constraint increment operator but also innovative real-time learning techniques and clever integration methods, along with the establishment of a novel real-time decision mechanism, the proposed model effectively addresses the issues of redundancy and repeated calculations inherent in traditional models. This results in a stable and significantly improved computational speed. Additionally, this model's dynamic evolution network architecture is specifically designed to accommodate arbitrary and efficient extensions of dynamic constraints, while also featuring a built-in preprocessing function to filter out duplicate variables. In the future, as a dynamic evolution processor, this model exhibits immense potential for solving large-scale SAT problems with dynamic evolution constraints and holds broad application prospects in emerging fields such as automated reasoning, intelligent recommendation systems, and machine learning. [ABSTRACT FROM AUTHOR]

• 32 identified articles concerning game-based approach in computer science in primary education. • There is no standardized methodology for designing games. • The focus should be put on designing a game as a pedagogical strategy. • Only few computer science topics are covered through game-based approach. • More data to evaluate the effectiveness of the game-based approach are needed. This paper reviews the current situation concerning the implementation of game-based learning in computer science in primary education, providing insight into current trends, identifying strengths and potential research topics. Articles published in four databases from 2017 to 2021 are included in the analysis and an in-depth analysis of 32 articles is done. Different types of games, implemented in various educational contexts, are presented in these articles. Most of them describe implemented methodology, game-based environment or are evaluating the effectiveness of the created game or the approach. The possibility of implementing a game-based approach while learning other computer science topics or measuring the effectiveness of learning by designing a game as a pedagogical strategy are some areas for future research. [ABSTRACT FROM AUTHOR]