The authors regret that because of the condensed notation in Eq. (21), we failed to keep track of the dependence of the correction term [Formula presented] on the parameters of interest [Formula presented] entering through [Formula presented] in Section 5 and Appendix B. The following equations were incorrect in the original submission; thus, we provide updated versions below. [Formula presented] [Formula presented] Following Eq. (42), we note that [Formula presented] is a constant. However, it depends on [Formula presented] and has the following shape: [Formula presented] [Formula presented] [Formula presented] [Formula presented] [Formula presented] [Formula presented] [Formula presented] [Formula presented] [Formula presented] [Formula presented] [Formula presented] This mistake did not significantly influence the numerical results presented in Section 7. Furthermore, the second to last term in Eq. (93) should have been [Formula presented] rather than [Formula presented]. For completeness, we present the corrected versions of the affected Figs. 1–5 and Figs. 7–11. Finally, the dependence on the data [Formula presented] of the approximate posterior in Section 6 should have been made explicit. The updated equations are as follows: [Formula presented] [Formula presented] For the fully updated version of this article, we refer the reader to arXiv:2112.06794. The authors would like to apologise for any inconvenience caused

Simulations - utilizing computers to solve complicated science and engineering problems - are a key ingredient of modern science. The U.S. Department of Energy (DOE) is a world leader in the development of high-performance computing (HPC), the development of applied math and algorithms that utilize the full potential of HPC platforms, and the application of computing to science and engineering problems. An interesting general question is whether the DOE can strategically utilize its capability in simulations to advance innovation more broadly. In this article, I will argue that this is certainly possible.

Computer science is a relatively young discipline combining science, engineering, and mathematics. The main flavors of computer science research involve the theoretical development of conceptual models for the different aspects of computing and the more applicative building of software artifacts and assessment of their properties. In the computer science publication culture, conferences are an important vehicle to quickly move ideas, and journals often publish deeper versions of papers already presented at conferences. These peculiarities of the discipline make computer science an original research field within the sciences, and, therefore, the assessment of classical bibliometric laws is particularly important for this field. In this paper, we study the skewness of the distribution of citations to papers published in computer science publication venues (journals and conferences). We find that the skewness in the distribution of mean citedness of different venues combines with the asymmetry in citedness of articles in each venue, resulting in a highly asymmetric citation distribution with a power law tail. Furthermore, the skewness of conference publications is more pronounced than the asymmetry of journal papers. Finally, the impact of journal papers, as measured with bibliometric indicators, largely dominates that of proceeding papers.

Motivated by an intention to remedy current complications with Dutch terminology concerning informatics, the term informaticology is positioned to denote an academic counterpart of informatics where informatics is conceived of as a container for a coherent family of practical disciplines ranging from computer engineering and software engineering to network technology, data center management, information technology, and information management in a broad sense. Informaticology escapes from the limitations of instrumental objectives and the perspective of usage that both restrict the scope of informatics. That is achieved by including fiction science in informaticology and by ranking fiction science on equal terms with computer science and data science, and framing (the study of) game design, evelopment, assessment and distribution, ranging from serious gaming to entertainment gaming, as a chapter of fiction science. A suggestion for the scope of fiction science is specified in some detail. In order to illustrate the coherence of informaticology thus conceived, a potential application of fiction to the ontology of instruction sequences and to software quality assessment is sketched, thereby highlighting a possible role of fiction (science) within informaticology but outside gaming.

Digitalization and technologization affect numerous domains, promising advantages but also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance implement these technological advances—especially Artificial Intelligence—regulators prescribe high levels of transparency, assuring the traceability of decisions for third parties. Explainable Artificial Intelligence (XAI) is of tremendous importance in this context. We provide an overview of current research on XAI in Finance with a systematic literature review screening 2,022 articles from leading Finance, Information Systems, and Computer Science outlets. We identify a set of 60 relevant articles, classify them according to the used XAI methods and goals that they aim to achieve, and provide an overview of XAI methods used in different Finance areas. Areas like risk management, portfolio optimization, and applications around the stock market are well-researched, while anti-money laundering is understudied. Researchers implement both transparent models and post-hoc explainability, while they recently favored the latter.

Data mining is an important research concept that has a vast scope in future. Data mining is used to find the unseen information from the data. In cluster, main half is feature choice. It involves recognition of a set of options of a set, because feature choice is taken into account as a necessary method. They additionally produce the approximate and according requests with the initial set of options employed in this kind of approach. The most construct on the far side this paper is to relinquish the end result of the bunch options. This paper conveys the cluster and the clustering process. The processing of large datasets the nature of clustering where some more concepts are more helpful and important in a clustering process. In clustering methodology many concepts are very useful. The feature selection algorithm which affects the entire process of clustering is the map-reduce concept. Here time needed to seek out the effective options, options of quality subsets is capable of providing effectiveness. The paper discussed map-reduce feature selection approach, its algorithm and framework of implementation.

The random-phase approximation (RPA) as an approach for computing the electronic correlation energy is reviewed. After a brief account of its basic concept and historical development, the paper is devoted to the theoretical formulations of RPA, and its applications to realistic systems. With several illustrating applications, we discuss the implications of RPA for computational chemistry and materials science. The computational cost of RPA is also addressed which is critical for its widespread use in future applications. In addition, current correction schemes going beyond RPA and directions of further development will be discussed.

The problem of synthesis in computer sciences, including cybernetics, artificial intelligence and system analysis, is analyzed. Main methods of realization this problem are discussed. Ways of search universal method of creation universal synthetic science are represented. As example of such universal method polymetric analysis is given. Perspective of further development of this research, including application polymetric method for the resolution main problems of computer sciences, is analyzed too.

: In last few years, there has been an advancement in programming languages due to different libraries that are introduced. All the developers in this modern era prefer programming language that provides a built-in module/library which can make their work easy. This paper describes the advancement of one such language “Python” and it’s increasing popularity through different statistical data and graphs. In this paper, we explore all the built-in libraries for all different computer science domains such as Data Science, Machine Learning, Image Processing, Deep Learning, Natural Language Processing, Data Visualization, Cloud Computing, Speech recognition, etc. We have also included Memory management in Python. Different frameworks for Python which can make the front-end work easier are also mentioned.

Three novel applications of computational topology in the field of fusion science are developed. A procedure for the automatic classification of the orbits of magnetic field lines into topologically distinct classes using Vietoris-Rips persistent homology is presented and tested for a toy model of a perturbed tokamak. A method for estimating the distribution of the size of islands in the phase space of a Hamiltonian system or area-preserving map by sub-level set persistent homology is explored. This method is used to analyse the case of an accelerator mode island in the phase space of Chirikov's standard map and the possibility of detecting the self-similar island hierarchy responsible for anomalous transport in this model is investigated. Finally, it is suggested that TDA provides a toolset for the detection and characterisation of renormalisation group transformations which leave structures in Hamiltonian phase spaces invariant. The specific example of detecting the transform which leaves the neighborhood of a hyperbolic fixed point of the perturbed pendulum invariant is investigated using two different TDA approaches. Both of which are found to be partially sensitive to the symmetry in question but only weakly.

In the growth of computer science blockchain technology has emerged as a disruptive force that is enhancing various area of software and the way data is managed, stored, and safeguarded. This essay offers a thorough examination of the uses and difficulties of blockchain technology in computer science. In this essay, blockchain technology has shown itself to be a game-changing innovation with numerous computer science applications. It has the enormous potential to completely transform sectors including finance, supply chain management, healthcare, voting systems, and IoT devices. The software technology is completely achieved with blockchain technology and the main security issue is solved with energy use and scalability. The new opportunities are examined and addressed with this innovation sector for creating effective solutions.

The aim of attending an educational institution is learning, which in turn is sought after for the reason of independence of thoughts, ideologies as well as physical and material independence. This physical and material independence is gotten from working in the industry, that is, being a part of the independent working population of the country. There needs to be a way by which students upon graduation can easily adapt to the real world with necessary skills and knowledge required. This problem has been a challenge in some computer science departments, which after effects known after the student begins to work in an industry. The objectives of this project include: Designing a web based chat application for the industry and computer science department, Develop a web based chat application for the industry and computer science and Evaluate the web based chat application for the industry and computer science department. Waterfall system development lifecycle is used in establishing a system project plan, because it gives an overall list of processes and sub-processes required in developing a system. The descriptive research method applied in this project is documentary analysis of previous articles. The result of the project is the design, software a web-based chat application that aids communication between the industry and the computer science department and the evaluation of the system. The application is able to store this information which can be decided to be used later. Awareness of the software to companies and universities, implementation of the suggestions made by the industry in the computer science curriculum, use of this software in universities across Nigeria and use of this not just in the computer science field but in other field of study

Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). Foundational models such as the Generative Pre-trained Transformer (GPT) and LLaMA series have set strong baseline performances in various NL and PL tasks. Additionally, several models have been fine-tuned specifically for code generation, showing significant improvements in code-related applications. Both foundational and fine-tuned models are increasingly used in education, helping students write, debug, and understand code. We present a comprehensive systematic literature review to examine the impact of LLMs in computer science and computer engineering education. We analyze their effectiveness in enhancing the learning experience, supporting personalized education, and aiding educators in curriculum development. We address five research questions to uncover insights into how LLMs contribute to educational outcomes, identify challenges, and suggest directions for future research.

In computational science and in computer science, research software is a central asset for research. Computational science is the application of computer science and software engineering principles to solving scientific problems, whereas computer science is the study of computer hardware and software design. The Open Science agenda holds that science advances faster when we can build on existing results. Therefore, research software has to be reusable for advancing science. Thus, we need proper research software engineering for obtaining reusable and sustainable research software. This way, software engineering methods may improve research in other disciplines. However, research in software engineering and computer science itself will also benefit from reuse when research software is involved. For good scientific practice, the resulting research software should be open and adhere to the FAIR principles (findable, accessible, interoperable and repeatable) to allow repeatability, reproducibility, and reuse. Compared to research data, research software should be both archived for reproducibility and actively maintained for reusability. The FAIR data principles do not require openness, but research software should be open source software. Established open source software licenses provide sufficient licensing options, such that it should be the rare exception to keep research software closed. We review and analyze the current state in this area in order to give recommendations for making computer science research software FAIR and open. We observe that research software publishing practices in computer science and in computational science show significant differences.

Geometric Algebra (GA) has proven to be an advanced language for mathematics, physics, computer science, and engineering. This review presents a comprehensive study of works on Quaternion Algebra and GA applications in computer science and engineering from 1995 to 2020. After a brief introduction of GA, the applications of GA are reviewed across many fields. We discuss the characteristics of the applications of GA to various problems of computer science and engineering. In addition, the challenges and prospects of various applications proposed by many researchers are analyzed. We analyze the developments using GA in image processing, computer vision, neurocomputing, quantum computing, robot modeling, control, and tracking, as well as improvement of computer hardware performance. We believe that up to now GA has proven to be a powerful geometric language for a variety of applications. Furthermore, there is evidence that this is the appropriate geometric language to tackle a variety of existing problems and that consequently, step-by-step GA-based algorithms should continue to be further developed. We also believe that this extensive review will guide and encourage researchers to continue the advancement of geometric computing for intelligent machines.

Computer Science education has been evolving over the years to reflect applied realities. Until about a decade ago, theory of computation, algorithm design and system software dominated the curricula. Most courses were considered core and were hence mandatory; the programme structure did not allow much of a choice or variety. This column analyses why this changed Circa 2010 when elective subjects across scores of topics become part of mainstream education to reflect the on-going lateral acceleration of Computer Science. Fundamental discoveries in artificial intelligence, machine learning, virtualization and cloud computing are several decades old. Many core theories in data science are centuries old. Yet their leverage exploded only after Circa 2010, when the stage got set for people-centric problem solving in massive scale. This was due in part to the rush of innovative real-world applications that reached the common man through the ubiquitous smart phone. AI/ML modules arrived in popular programming languages; they could be used to build and train models on powerful - yet affordable - compute on public clouds reachable through high-speed Internet connectivity. Academia responded by adapting Computer Science curricula to align it with the changing technology landscape. The goal of this experiential piece is to trigger a lively discussion on the past and future of Computer Science education.

Artificial Intelligence (AI) has emerged as a cornerstone of modern computer science, exerting a profound influence on diverse sectors of society. This article offers a comprehensive overview of the evolution and impact of AI in computer science. Beginning with its historical roots and development, the article traces AI's trajectory from its inception to its current state of advancement. Moreover, the article elucidates the techniques of AI in computer science, offering illuminating insights into a spectrum of methodologies including machine learning, deep learning, natural language processing, computer vision, knowledge representation and reasoning, recommender systems, and optimization techniques. In this context, the article explores the current applications of AI across various sectors, including engineering, medical fields, technology, military affairs, economy, education, entertainment, transportation, emphasizing its role in enhancing efficiency, productivity, and decision-making. In doing so, the article delves into the potential future impact of AI, envisioning a world where AI-powered technologies continue to revolutionize human-computer interaction, automation, and artificial general intelligence. To sum up, the article underscores the pivotal role of AI in shaping the future of computer science and society at large, advocating for continued research, development, and ethical stewardship of AI technologies.

Computational philosophy is the use of mechanized computational techniques to unearth philosophical insights that are either difficult or impossible to find using traditional philosophical methods. Computational metaphysics is computational philosophy with a focus on metaphysics. In this paper, we (a) develop results in modal metaphysics whose discovery was computer assisted, and (b) conclude that these results work not only to the obvious benefit of philosophy but also, less obviously, to the benefit of computer science, since the new computational techniques that led to these results may be more broadly applicable within computer science. The paper includes a description of our background methodology and how it evolved, and a discussion of our new results.