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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]

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]

This paper presents a preliminary comparison of citation impact between two types of Best Paper Awards recipients: pre-and post-publication peer-selected best papers. Two hundred and ninety-nine pairs of pre-and post-publication peer-selected best papers from 15 highly prestigious computer science conferences spanning more than two decades were gathered for empirical analysis. Each pair was published in the same proceedings, and their citation information was collected within a 10-year period after their formal publication. A series of pairwise comparisons were performed in order to unbiasedly uncover their differences in citation counts (i.e., a good proxy for citation impact), as well as in citation speed, citation diversity, and citation location distributions (i.e., three characteristic properties of citation impact). The empirical results demonstrate that in computer science, the post-publication peer-selected best papers receive significantly more citations than their pre-publication counterparts, with an estimated probability of up to 0.79 (95% CI: 0.74 ~ 0.83). Meanwhile, they tend to accumulate their citations more quickly in the later (versus earlier) stages—they can scarcely be fast-aging papers, and their accumulated citations tend to cover a broader scope of academic disciplines. Overall, however, the pre-and post-publication peer-selected best papers have very similar citation location distributions. Both of them are most frequently cited in the Background sections, followed by the Methods sections, and finally the Results sections. In addition, the citation impact superiority of the post-publication peer-selected best papers remains consistent across different fields of research and over a long period of time, from 1997 to 2022. [ABSTRACT FROM AUTHOR]

The nonsensical computer-generated articles, spotted years after the problem was first seen, could lead to a wave of retractions. [ABSTRACT FROM AUTHOR]

Decentralization is a pervasive concept found across disciplines, including Economics, Political Science, and Computer Science, where it is used in distinct yet interrelated ways. Here, we develop and publicly release a general pipeline to investigate the scholarly history of the term, analysing 425 , 144 academic publications that refer to (de)centralization. We find that the fraction of papers on the topic has been exponentially increasing since the 1950s. In 2021, 1 author in 154 mentioned (de)centralization in the title or abstract of an article. Using both semantic information and citation patterns, we cluster papers in fields and characterize the knowledge flows between them. Our analysis reveals that the topic has independently emerged in the different fields, with small cross-disciplinary contamination. Moreover, we show how Blockchain has become the most influential field about 10 years ago, while Governance dominated before the 1990s. In summary, our findings provide a quantitative assessment of the evolution of a key yet elusive concept, which has undergone cycles of rise and fall within different fields. Our pipeline offers a powerful tool to analyze the evolution of any scholarly term in the academic literature, providing insights into the interplay between collective and independent discoveries in science. [ABSTRACT FROM AUTHOR]

Large language models can draft abstracts or suggest research directions, but these artificial-intelligence tools are a work in progress. [ABSTRACT FROM AUTHOR]

The tremendous advances in computer science in the last few decades have provided the platform to address and solve complex problems using interdisciplinary research. In this paper, we investigate how the extent of interdisciplinarity in computer science domain (which is further divided into 24 research fields) has changed over the last 50 years. To this end, we collect a massive bibliographic dataset with rich metadata information. We start with quantifying interdisciplinarity of a field in terms of the diversity of topics and citations. We then analyze the effect of interdisciplinary research on the scientific impact of individual fields and observe that highly disciplinary and highly interdisciplinary papers in general have a low scientific impact; remarkably those that are able to strike a balance between the two extremes eventually land up having the highest impact. Further, we study the reciprocity among fields through citation interactions and notice that links from one field to related and citation-intensive fields (fields producing large number of citations) are reciprocated heavily. A systematic analysis of the citation interactions reveals the life trajectory of a research field, which generally undergoes three phases—a growing phase, a matured phase and an interdisciplinary phase. The combination of metrics and empirical observations presented here provides general benchmarks for future studies of interdisciplinary research activities in other domains of science. [ABSTRACT FROM AUTHOR]

Open peer review is a growing trend in academic publications. Public access to peer review data can benefit both the academic and publishing communities. It also serves as a great support to studies on review comment generation and further to the realization of automated scholarly paper review. However, most of the existing peer review datasets do not provide data that cover the whole peer review process. Apart from this, their data are not diversified enough as the data are mainly collected from the field of computer science. These two drawbacks of the currently available peer review datasets need to be addressed to unlock more opportunities for related studies. In response, we construct MOPRD, a multidisciplinary open peer review dataset. This dataset consists of paper metadata, multiple version manuscripts, review comments, meta-reviews, author's rebuttal letters, and editorial decisions. Moreover, we propose a modular guided review comment generation method based on MOPRD. Experiments show that our method delivers better performance as indicated by both automatic metrics and human evaluation. We also explore other potential applications of MOPRD, including meta-review generation, editorial decision prediction, author rebuttal generation, and scientometric analysis. MOPRD is a strong endorsement for further studies in peer review-related research and other applications. [ABSTRACT FROM AUTHOR]

This paper examines how early career-related factors can predict the future research performance of computer and information scientists. Although a few bibliometric studies have previously investigated multiple factors relating to early career scientists that significantly predict their future research performance, there have been limited studies on early career-related factors affecting scientists in the fields of information science and computer science. This study analyzes 4102 scientists whose publishing careers started in the same year. The criteria used to quantify future research performance of the target scientists included the number of publications and citation counts of publications in a 4-year citation window to indicate future research productivity and research impact, respectively. These criteria were regressed on 13 early career-related factors. The results showed that these factors accounted for about 27% and 23% of the future productivity of the target scientists in terms of journal articles and conference papers, respectively; these 13 factors were also responsible for 19% of the future impact of target scientists' journal articles and 19% of the future impact of their conference papers. The factor that most contributed to explaining the future research performance (i.e. publication numbers) and future research impact (i.e. citation counts of publications) was the number of publications (both journal articles and conference papers) produced by the target scientists in their early career years. [ABSTRACT FROM AUTHOR]

Published scientific articles are linked together into a graph, the citation graph, through their citations. This paper explores the notion of similarity based on connectivity alone, and proposes several algorithms to quantify it. Our metrics take advantage of the local neighborhoods of the nodes in the citation graph. Two variants of link-based similarity estimation between two nodes are described, one based on the separate local neighborhoods of the nodes, and another based on the joint local neighborhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on a subgraph of the citation graph of computer science in a retrieval context. The results are compared with text-based similarity, and demonstrate the complementarity of link-based and text-based retrieval. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for generating realistic drawing strokes in real-time that can take on the appearance of pastels, charcoals, or crayons. The similarity between the pigment deposit patterns on paper surfaces produced by pastel strokes and the shadows/shades on illuminated paper surfaces have been investigated. Multiple paper textures have been prepared and the paper surfaces have been ascertained by illumination from various directions to represent strokes in arbitrary directions. These textures have been processed as if they could be used as a height field, and pigments deposited on the paper have been calculated using the height field and tablet inputs. [ABSTRACT FROM AUTHOR]

It is commonly accepted that scientific research or, more precisely, the number of scientific publications, in computer science has greatly increased over the last few years. The reason would appear to be the pressure to publish, coined by the expression 'Publish or perish', which is, among other things, necessary for promotions and applications for grants or projects. In this paper we have conducted a study that covers computer science publications from 1936 to 2010 in order to quantify this increase in publications regarding computing research. We have considered the computing conferences and journals available in the DBLP computer science bibliography (DBLP ) database, including more than 1.5 million papers, and more than 4 million authors (more than 900,000 different people), corresponding to about 1,000 different journals and 3,000 different conferences and workshops. Our study confirms and quantifies these increases with regard to the number of papers, number of authors, number of papers per author, etc. However, it also reaches a surprising conclusion: the real productivity of researchers has decreased throughout history. The reason for this decrease is the average number of authors per paper, which has grown significantly and is currently three. [ABSTRACT FROM AUTHOR]

The research output of India in computer science during 1999-2008 is analyzed in this paper on several parameters including total research output, its growth, rank and global publication share, citation impact, share of international collaborative papers and major collaborative partner countries and patterns of research communication in most productive journals. It also analyses the characteristics of most productive institutions, authors and high-cited papers. The publications output and impact of India is also compared with China, South Korea, Taiwan and Brazil. [ABSTRACT FROM AUTHOR]

Open-science initiatives have gained substantial momentum in computer science, and particularly in software-engineering research. A critical aspect of open-science is the public availability of artifacts (e.g., tools), which facilitates the replication, reproduction, extension, and verification of results. While we experienced that many artifacts are not publicly available, we are not aware of empirical evidence supporting this subjective claim. In this article, we report an empirical study on software artifact papers (SAPs) published at the International Conference on Software Engineering (ICSE), in which we investigated whether and how researchers have published their software artifacts, and whether this had scientific impact. Our dataset comprises 789 ICSE research track papers, including 604 SAPs (76.6 %), from the years 2007 to 2017. While showing a positive trend towards artifact availability, our results are still sobering. Even in 2017, only 58.5 % of the papers that stated to have developed a software artifact made that artifact publicly available. As we did find a small, but statistically significant, positive correlation between linking to artifacts in a paper and its scientific impact in terms of citations, we hope to motivate the research community to share more artifacts. With our insights, we aim to support the advancement of open science by discussing our results in the context of existing initiatives and guidelines. In particular, our findings advocate the need for clearly communicating artifacts and the use of non-commercial, persistent archives to provide replication packages. [ABSTRACT FROM AUTHOR]

The main scientific results of the 16th International Conference on Pattern Recognition and Information Processing (PRIP-2023), Minsk, Republic of Belarus, October 2023, are reviewed and analyzed. The history of this series of conferences is outlined, and its significant role in the development of the theory and practice of image analysis, pattern recognition, and artificial intelligence is indicated. A list of articles in the special issue is provided, prepared from reports selected by the PRIP-2023 Program Committee. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special issue of the journal "Programming & Computer Software" titled "Recent Trends on Advanced Computing: The Converging Technologies." The issue features research and practical implementation results from researchers and industry experts in computer science, engineering, and technology. The papers cover a range of topics including microservices, software quality, user engagement on social media, non-functional requirements, medical software architecture, fallacies in political speeches, intelligent learning environments, and more. The guest editors for this special issue are Prof. Dr. Andrei Tchernykh, Prof. Reyes Juárez Ramírez, Dr. Esteban Mocskos, and Prof. Sergio Nesmachnow. [Extracted from the article]

A number of bibliometric studies point out that the role of conference publications in computer science differs from that in other traditional fields. Thus, it is interesting to identify the relative status of journal and conference publications in different subfields of computer science based on the citation rates categorised by the China Computer Federation (CCF) classifications and venue types. In this research, we construct a dataset containing over 100,000 papers recommended by the CCF catalogue and their citation information. We also investigate some other factors that often influence a paper's citation rate. An experimental study shows that the relative status of journals and conferences varies greatly in different subfields of computer science, and the impact of different publication levels varies according to the citation rate. We also verify that the classification of a publication, number of authors, maximum h-index of all authors of a paper, and average number of papers published by a publication have different effects on the citation rate, although the citation rate may have a different degree of correlation with these factors. [ABSTRACT FROM AUTHOR]

This document is a foreword for a special issue of the journal Microsystem Technologies. The special issue features selected papers from the 11th International Conference on Computing, Communication and Sensor Network (CCSN) held in September 2022 at Utkal University in Bhubaneswar, Odisha, India. The conference covered various interdisciplinary areas such as computing, communication, sensor networks, and circuit designs. The papers included in this special issue have undergone a rigorous review process and were selected for publication. The foreword expresses the hope that readers will find the papers informative. [Extracted from the article]

A world of AI-assisted writing and reviewing might transform the nature of the scientific paper. [ABSTRACT FROM AUTHOR]

Classifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields. [ABSTRACT FROM AUTHOR]

Thousands of research papers are being published every day, and among all these research works, one of the fastest-growing fields is computer science (CS). Thus, learning which research areas are trending in this particular field of study is advantageous to a significant number of scholars, research institutions, and funding organizations. Many scientometric studies have been done focusing on analyzing the current CS trends and predicting future ones from different perspectives as a consequence. Despite the large datasets from this vast number of CS publications and the power of deep learning methods in such big data problems, deep neural networks have not yet been used to their full potential in this area. Therefore, the objective of this paper is to predict the upcoming years' CS trends using long short-term memory neural networks. Accordingly, CS papers from 1940 and their corresponding fields of study from the microsoft academic graph dataset have been exploited for solving this research trend prediction problem. The prediction accuracy of the proposed method is then evaluated using RMSE and coefficient of determination (R2) metrics. The evaluations show that the proposed method outperforms the baseline approaches in terms of the prediction accuracy in all considered time periods. Subsequently, adopting the proposed method's predictions, we investigate future trending areas in computer science research from various viewpoints. [ABSTRACT FROM AUTHOR]

A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI. [ABSTRACT FROM AUTHOR]

In this paper we study the classical problem of throughput maximization. In this problem we have a collection J of n jobs, each having a release time r j , deadline d j , and processing time p j . They have to be scheduled non-preemptively on m identical parallel machines. The goal is to find a schedule which maximizes the number of jobs scheduled entirely in their [ r j , d j ] window. This problem has been studied extensively (even for the case of m = 1 ). Several special cases of the problem remain open. Bar-Noy et al. (Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, May 1–4, 1999, Atlanta, Georgia, USA, pp. 622–631. ACM, 1999, https://doi.org/10.1145/301250.301420) presented an algorithm with ratio 1 - 1 / (1 + 1 / m) m for m machines, which approaches 1 - 1 / e as m increases. For m = 1 , Chuzhoy et al. (42nd Annual Symposium on Foundations of Computer Science (FOCS) 2001, 14–17 October 2001, Las Vegas, Nevada, USA, pp. 348–356. IEEE Computer Society, 2001) presented an algorithm with approximation with ratio 1 - 1 e - ε (for any ε > 0 ). Recently Im et al. (SIAM J Discrete Math 34(3):1649–1669, 2020) presented an algorithm with ratio 1 - 1 / e + ε 0 for some absolute constant ε 0 > 0 for any fixed m. They also presented an algorithm with ratio 1 - O (log m / m) - ε for general m which approaches 1 as m grows. The approximability of the problem for m = O (1) remains a major open question. Even for the case of m = 1 and c = O (1) distinct processing times the problem is open (Sgall in: Algorithms - ESA 2012 - 20th Annual European Symposium, Ljubljana, Slovenia, September 10–12, 2012. Proceedings, pp 2–11, 2012). In this paper we study the case of m = O (1) and show that if there are c distinct processing times, i.e. p j 's come from a set of size c, then there is a randomized (1 - ε) -approximation that runs in time O (n m c 7 ε - 6 log T) , where T is the largest deadline. Therefore, for constant m and constant c this yields a PTAS. Our algorithm is based on proving structural properties for a near optimum solution that allows one to use a dynamic programming with pruning. [ABSTRACT FROM AUTHOR]

The goal of this paper is to estimate object's 6D pose based on the texture-less dataset. The pose of each projection view is obtained by rendering the 3D model of each object, and then the orientation feature of the object is implicitly represented by the latent space obtained from the RGB image. The 3D rotation of the object is estimated by establishing the codebook based on a template matching architecture. To build the latent space from the RGB images, this paper proposes a network based on a variant Adversarial Autoencoder (Makhzani et al. in Computer Science, 2015). To train the network, we use the dataset without pose annotation, and the encoder and decoder do not have a structural symmetry. The encoder is inspired by the existing model (Yang et al. in proceedings of IJCAI, 2018), (Yang et al. in proceedings 11 of CVPR, 2019) that incorporates the function of feature extraction from two different streams. Based on this network, the latent feature vector that implicitly represents the orientation of the object is obtained from the RGB image. Experimental results show that the method in this paper can realize the 6D pose estimation of the object and the result accuracy is better than the advanced method (Sundermeyer et al. in proceedings of ECCV, 2018). [ABSTRACT FROM AUTHOR]

This paper articulates an approach to incorporating instructor feedback in design-based research. Throughout the process of designing and implementing curriculum to support middle school students' debugging practices in a summer computer science workshop, our research and practice team utilized instructor-generated conjecture maps as boundary objects, providing insight into the instructors' reflections on their classroom teaching. We develop an analytic tool for categorizing instructors' reflections on their conjecture maps, attending specifically to how instructors push back on design choices, whether by envisioning new mediating processes, introducing new connections, discussing new design features, articulating confusion/uncertainty, and/or presenting hopes and predictions. The tool is then applied to seven instructors' daily reflections over the course of four weeks of instruction, focused on three conjecture maps. Overall, the paper documents a range of tensions that instructors encounter when aiming to provide sustained debugging support to students and introduces a tool for understanding the detailed ways that instructors critique design conjectures. [ABSTRACT FROM AUTHOR]

In September 2008 Thomson Reuters added to the ISI Web of Science (WOS) the Conference Proceedings Citation Indexes for Science and for the Social Sciences and Humanities. This paper examines how this change affects the publication and citation counts of highly cited computer scientists. Computer science is a field where proceedings are a major publication venue. The results show that most of the highly cited publications of the sampled researchers are journal publications, but these highly cited items receive more than 40% of their citations from proceedings papers. The paper also discusses issues related to double-counting, i.e., when a given work is published both in a proceedings and later on as a journal paper. [ABSTRACT FROM AUTHOR]

Handwritten Text Recognition (HTR) technology is now a mature machine learning tool, becoming integrated in the digitisation processes of libraries and archives, speeding up the transcription of primary sources and facilitating full text searching and analysis of historic texts at scale. However, research into how HTR is changing our information environment is scant. This paper presents a systematic literature review regarding how researchers are using one particular HTR platform, Transkribus, to indicate the domains where HTR is applied, the approach taken, and how the technology is understood. 381 papers from 2015 to 2020 were gathered from Google Scholar, Scopus, and Web of Science, then grouped and coded into categories using quantitative and qualitative approaches. Published research that mentions Transkribus is international and rapidly growing. Transkribus features primarily in archival and library science publications, while a long tail of broad and eclectic disciplines, including history, computer science, citizen science, law and education, demonstrate the wider applicability of the tool. The most common paper categories were humanities applications (67%), technological (25%), users (5%) and tutorials (3%). This paper presents the first overarching review of HTR as featured in published research, while also elucidating how HTR is affecting the information environment. [ABSTRACT FROM AUTHOR]

In most areas of computer science (CS), and in the software domain in particular, international conferences are as important as journals as a venue to disseminate research results. This has resulted in the creation of rankings to provide quality assessment of conferences (specially used for academic promotion purposes) like the well-known CORE ranking created by the Computing Research and Education Association of Australasia. In this paper we analyze 102 CORE-ranked conferences in the software area (covering all aspects of software engineering, programming languages, software architectures and the like) included in the DBLP dataset, an online reference for computers science bibliographic information. We define a suite of metrics focusing on the analysis of the co-authorship graph of the conferences, where authors are represented as nodes and co-authorship relationships as edges. Our aim is to first characterize the patterns and structure of the community of researchers in software conferences. We then try to see if these values depend on the quality rank of the conference justifying this way the existence of the different classifications in the CORE-ranking system. [ABSTRACT FROM AUTHOR]

The study provides an overview of the research activity carried out in the field of cybernetics. To do so, all research papers from 1997 to 2011 (16,445 research papers) under the category of 'Computer Science, Cybernetics' of Web of Science have been processed using our in-house software which is developed specifically for this purpose. Among its multiple capabilities, this software analyses individual and compound keywords, quantifies productivity taking into account the work distribution, estimates the impact of each article and determines the collaborations established at different scales. Keywords analysis identifies the evolution of the most important research topics in the field of cybernetics and their specificity in biological aspects, as well as the research topics with lesser interest. The analysis of productivity, impact and collaborations provides a framework to assess research activity in a specific and realistic context. The geographical and institutional distribution of publications reveals the leading countries and research centres, analysing their relation to main research journals. Moreover, collaborations analysis reveals great differences in terms of internationalization and complexity of research networks. The results of this study may be very useful for the characterization and the decisions made by research in the field of cybernetics. [ABSTRACT FROM AUTHOR]

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]

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]

In computer science, as opposed to many other disciplines, papers published in conference and workshop proceedings count as formal publications when evaluating the scholarship of an academic. We consider the relationship between high quality journals and conferences in the computer vision (CV) subfield of computer science. We determined that 30% of papers in the top-3 CV journals base their work on top-3 conference papers by the same authors (which we call priors (See 'Methods' section for the definition of a prior)). Journal papers with priors are significantly more cited than journal papers without priors. Also the priors themselves are cited more than other papers from the conferences. For a period of 3-5 years after the journal paper publication, the priors receive more citations than the follow-up journal paper. After that period, the journal paper starts receiving most of the citations. Furthermore, we found that having the prior conference paper did not make it any easier (faster) to publish in a journal. We also surveyed journal authors and based on their answers and the priors analysis, we discovered that authors seem to be divided into different groups depending on their preferred method of publication. [ABSTRACT FROM AUTHOR]

Writing is a commonplace activity that individuals partake in regularly. However, the implications behind it are often overlooked. When we write, various psychological factors come into play as the pen creates letters on the paper. Handwriting analysis has long been a subject of study, attracting researchers from diverse disciplines such as graphology, psychology, paleography, neuroscience, criminology, and computer science. Among the promising applications of handwriting analysis is gender classification, where a system can predict the gender of a writer based on their handwriting style. Since each individual's handwriting is unique, and variations exist between the handwriting of different genders, an automatic gender classification system can exploit these differences to make predictions. This paper presents a deep-learning-based gender classification system specifically designed for Urdu handwriting. The proposed approach utilizes a CNN network trained and tested on a self-created dataset contributed by 200 distinct male and 200 female Urdu writers. Through this method, the gender classification system achieved an impressive overall accuracy of 99.63%. The results obtained demonstrate that our technique for Urdu handwriting-based writer identification surpasses existing approaches. In the future, we intend to explore transfer learning techniques to further advance this field. [ABSTRACT FROM AUTHOR]

The growing diffusion of perspectivism within the debate on information system ontologies [ISOs] does not correspond to a thorough analysis of what perspectivism specifically consists of. This paper aims to fill this void. First, I show what supporting perspectivism in information system ontologies [PISO] means in terms of (minimal) claims and implications; then I argue that the definitions of ISO implicitly assume PISO's (minimal) claims or, in other words, that ISOs presuppose and maintain PISO. Section 2 presents the main definitions of ISO. Section 3 specifies what claims are common to all perspectivists in ISO. Sects. 4–7 analyze the implications of those claims. Section 8 explores the chance of multiple perspectivisms within ISOs' domain. Finally, Sects. 9–10 assume that, if PISO's (minimal) claims and (their) implications can be inferred from ISO's definitions, then ISOs are perspectivist, or PISO's minimal claims are assumptions underlying ISOs. [ABSTRACT FROM AUTHOR]

Prior research has introduced the Single-Instruction-Multiple-Data paradigm for DNA computing (SIMD DNA). It offers the potential for storing information and performing in-memory computations on DNA, with massive parallelism. This paper introduces three new SIMD DNA operations: sorting, shifting, and searching. Each is a fundamental operation in computer science. Our implementations demonstrate the effectiveness of parallel pairwise operations with this new paradigm. [ABSTRACT FROM AUTHOR]

Hour of Code is a widely recognized global event that aims to introduce programming to novice users and integrate computer science into education. This paper presents an analysis of the effectiveness of the support system and user interface of Minecraft Adventurer, a serious game designed for the Hour of Code global event. Although previous studies have primarily focused on the educational benefits of Hour of Code games, there has been limited research on their support methods. Therefore, this paper aims to address this gap with an empirical study of the experience of 104 students who played the game for one hour. Student progress was tracked by an administering teacher and after the game session, a questionnaire was administered to collect data on the participant's perceptions of the support system, interface efficiency, and overall experience with Hour of Code. The results of the study reveal significant problems with the aforementioned systems, which apply not only to Minecraft Adventurer but also to several other similar serious games. Additionally, the findings showed a correlation between the utilization of the support system and student performance, indicating that student's comprehension of the support system significantly influences their learning outcomes. This paper concludes by providing potential solutions to address the identified insufficiencies, offering valuable insights for future researchers and game developers on the design and evaluation of serious games for educational purposes. [ABSTRACT FROM AUTHOR]

In the paper, the affinity of quadratic fractional functions and the gradient of pseudolinear quadratic fractional functions are characterized. [ABSTRACT FROM AUTHOR]

The words "Programming is the second literacy" were coined more than 40 years ago [13], but never came to life. The paper develops and details that old slogan by proposing that the mainstream mathematics education in schools should merge with education in computer science/programming. Of course, this means a deep structural reform of school mathematics education. We are not talking about adapting the 20th century mathematics to the 21st century—as it outlined in [10, 19], we mean the 21st century mathematics education for the 21st century mathematics. To the best of our knowledge, this paper is perhaps the first known attempt to start a proper feasibility study for this reform. The scope of the paper does not allow us to touch the delicate socio-political (and financial) sides of the reform, we are looking only at general curricular and didactic aspects and possible directions of the reform. In particular, we indicate approaches to development of a Domain Specifiic Language (DSL) as a basis for all programming aspects of a new course. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCIs), as a promising technology for rehabilitation, have attracted significant interest in the fields such as neurosciences, computer science, and biomedical engineering. Based on the expanded database of Science Citation Index, this paper analyzes bibliometrics in the field of BCI from 1990 to 2020 and discusses its potential research trends and prospects. The work provides a detailed overview of the BCI research status founded on the country, research area, institution, journal, author, citation, and keyword. A total of 7,880 papers suggest that the United States is leading the field of BCI research, followed by China and Germany. University of California system has produced the most publications, while the Graz University of Technology is leading the list for the h-index. Journal of Neural Engineering and IEEE Transactions on Neural Systems and Rehabilitation Engineering were identified to be the most productive journals in BCIs. Keywords analysis reflects that most research has focused on electroencephalography (EEG)-based BCIs to achieve a safe, real-time, stable link between the patient and artificial actuators. We can conclude by analyzing hot articles that new materials applied to neural probes may become the next hot topic. The typical paradigm of the BCI industry is the most urgent problem that can be solved in the standardization process. The rehabilitation applications appear as the initial driving force and ultimate goal for BCIs. This research can offer valuable references to BCI practitioners and provide data support for related studies. [ABSTRACT FROM AUTHOR]

Large language models are transforming scientific writing and publishing. But the productivity boost that these tools bring could have a downside. [ABSTRACT FROM AUTHOR]

Cluster analysis is one of the most fundamental methods in data mining, and it has been widely used in economics, social sciences and computer science. However, with the rapid development of Internet technology, the volume of data required for various web applications has grown rapidly, making the traditional clustering analysis methods face technical challenges. How to obtain useful information in a large amount of data quickly and efficiently is an urgent problem in many industrial fields. With the continuous development of cloud computing technology, large amounts of data can be performed quickly and efficiently. Hadoop is an open source distributed cloud computing platform with HDFS (Digital File System) and MapReduce as its core. HDFS provides massive data storage, while MapReduce uses the MapReduce programming model to achieve parallel processing. Compared with the traditional parallel programming model, it contains basic functions such as data partitioning, task scheduling, and parallel processing, making it possible for users to develop distributed applications on their own without understanding the basics of distributed basics, thus facilitating the design of parallel programs. K-means algorithm is a typical clustering analysis method, which is widely used in industry, but the number of iterations will increase significantly due to the growth of data volume, thus reducing the efficiency of computation. In order to better apply to the cluster analysis of large-scale data, this paper firstly implements a parallelization algorithm based on MapReduce on Hadoop platform using the basic idea of MapReduce and improves the K-means algorithm for the problems of blindness and easy to fall into local optimum when selecting randomly in clusters. [ABSTRACT FROM AUTHOR]

The security that resides in the public-key cryptosystems relies on the presumed computational hardness of mathematical problems behind the systems themselves (e.g. the semiprime factorization problem in the RSA cryptosystem), that is because there is not known any polynomial time (classical) algorithm to solve them. The paper focuses on the computing paradigm of virus machines within the area of Unconventional Computing and Natural Computing. Virus machines, which incorporate concepts of virology and computer science, are considered as number computing devices with the environment. The paper designs a virus machine that solves a generalization of the semiprime factorization problem and verifies it formally. [ABSTRACT FROM AUTHOR]

Handwriting recognition and analysis has been an active area of research in the last two decades. Handwriting analysis is being studied in various fields of science, such as graphology, neurology, psychology, and computer science. Furthermore, automated handwriting analysis has several applications, including forensic, security, medical, and disease prediction. This paper presents the most recent handwriting analysis techniques and advancements available in the literature for age and gender classification/detection. Different steps, including feature extraction and classification, frequently used in the literature for age and gender detection, are discussed, and the presented works are classified according to the applied feature extraction and classification methods. The online and offline benchmark databases are also reviewed. We used a text mining technique to perform a quantitative content analysis of the presented research and better understand the co-occurrence network diagrams of age and gender classification/detection. This study is a valuable resource that provides new research directions to students and researchers interested in this field for further research and investigation. [ABSTRACT FROM AUTHOR]

I The Declining Price Anomaly Is Not Universal in Multi-Buyer Sequential Auctions (but almost is) i , by Vishnu V. Narayan, Enguerrand Prebet and Adrian Vetta, which studies the declining price anomaly, i.e., that the price weakly decreases when multiple copies of an item are sold sequentially over time, and shows that the anomaly is not guaranteed in full-information first-price or second-price sequential auctions with three or more buyers. The main results of the paper concern fractionally subadditive (XOS) bidders in sequential and simultaneous auctions under both the first and second price rule for each item. The Symposium on Algorithmic Game Theory (SAGT) is a well-established annual event, aiming to bring together researchers from Computer Science, Economics and Mathematics, and to present original research at the intersection of Algorithms and Game Theory. [Extracted from the article]

The authors construct a polynomial-time algorithm for the problem on domain size HT ht where the signatures are realizable by "matchgates" over a basis of size 1. The paper "Polynomial-time axioms of choice and polynomial-time cardinality" by Joshua Grochow studies the Axiom of Choice in complexity theory. They also show that the HT ht non-random numbers are the points where the HT ht ergodic theorems do not hold. [Extracted from the article]

In this paper, we investigate the achievable rate of perfect hash code. For an integer q ≥ 2 , a perfect q-hash code C is a block code over [ q ] : = { 1 , ... , q } of length n in which every subset { c 1 , c 2 , ⋯ , c q } of q elements is separated, i.e., there exists i ∈ [ n ] such that { proj i (c 1) , ⋯ , proj i (c q) } = [ q ] , where proj i (c j) denotes the ith position of c j . Finding the maximum size M(n, q) of perfect q-hash codes of length n, for given q and n, is a fundamental problem in combinatorics, information theory, and computer science. In this paper, we are interested in asymptotic behavior of this problem. Precisely speaking, we will focus on the quantity R q : = lim sup n → ∞ log 2 M (n , q) n . A well-known probabilistic argument shows an existence lower bound on R q , namely R q ≥ 1 q - 1 log 2 1 1 - q ! / q q (Fredman and Komlós, SIAM J Algebr Discrete Methods 5(1):61–68, 1984; Körner, SIAM J Algebr Discrete Methods 7(4):560–570, 1986). This is still the best-known lower bound till now except for the case q = 3 (Körner and Marton, Eur J Comb 9(6):523–530, 1988). The improved lower bound of R 3 was discovered in 1988 and there has been no progress on the lower bound of R q for more than 30 years. In this paper we show that this probabilistic lower bound can be improved for q from 4 to 15 and all odd integers between 17 and 25, and all sufficiently largeq. [ABSTRACT FROM AUTHOR]

Homomorphic evaluation of hash functions offers a solution to the challenge of data integrity authentication in the context of homomorphic encryption. The earliest attempt to achieve homomorphic evaluation of SHA-256 hash function was proposed by Mella and Susella (in: Cryptography and coding—14th IMA international conference, IMACC 2013. Lecture notes in computer science, vol 8308. Springer, Heidelberg, pp 28–44, 2013. https://doi.org/10.1007/978-3-642-45239-0%5f3.) based on the BGV scheme. Unfortunately, their implementation faced significant limitations due to the exceedingly high multiplicative depth, rendering it impractical. Recently, a homomorphic implementation of SHA-256 based on the TFHE scheme (Homomorphic evaluation of SHA-256. https://github.com/zama-ai/tfhe-rs/tree/main/tfhe/examples/sha256%5fbool) brings it from theory to reality, however, its current efficiency remains insufficient. In this paper, we revisit the homomorphic evaluation of the SHA-256 hash function in the context of TFHE, further reducing the reliance on gate bootstrapping and enhancing evaluation latency. Specifically, we primarily utilize ternary gates to reduce the number of gate bootstrappings required for logic functions in message expansion and addition of modulo 2 32 in iterative compression. Furthermore, we demonstrate that our optimization techniques are applicable to the Chinese commercial cryptographic hash SM3. Finally, we give specific comparative implementations based on the TFHE-rs library. Experiments demonstrate that our optimization techniques lead to an improvement of approximately 35–50% compared with the state-of-the-art result under different cores. [ABSTRACT FROM AUTHOR]

The unprecedented growth of publications in many research domains brings the great convenience for tracing and analyzing the evolution and development of research topics. Despite the significant contributions made by existing studies, they usually extract topics from the titles of papers, instead of obtaining topics from the authoritative sessions provided by venues (e.g., AAAI, NeurIPS, and SIGMOD). To make up for the shortcoming of existing work, we develop a novel framework namely RTTP(Research Topic Trend Prediction). Specifically, the framework contains the following two components: (1) a topic alignment strategy called TAS is designed to obtain the detailed contents of research topics in each year, (2) an enhanced prediction network called EPN is designed to capture the research trend of known years for prediction. In addition, we construct two real-world datasets of specific research domains in computer science, i.e., database and data mining, computer architecture and parallel programming. The experimental results demonstrate that the problem is well solved and our solution outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%. [ABSTRACT FROM AUTHOR]

The performance of a permissioned blockchain, namely, Hyperledger Fabric, is modeled and analyzed in a quantitative manner in this paper. Various types of nodes contribute to the performance of Hyperledger Fabric, and each of those is modeled and tracked along the operational flow of the permissioned blockchain. There are nodes for endorsement, ordering, and commitment to realize the due decentralized network operations. A quantitative model for each type of nodes has been proposed in Ke and Park (Performance study on various Hyperledger Fabric node types and transaction flow, IEEE BCCA, 2021) along with numerical analysis. Each type of the nodes is characterized in terms of transaction/block queue size and waiting time, and the transaction/block arrival rates and the transaction/block service rates are considered for simulation purposes. The analysis is extended beyond the analysis in Ke and Park (Performance study on various Hyperledger Fabric node types and transaction flow, IEEE BCCA, 2021) in this paper to particularly demonstrate how the arrival rates and the service rates co-influence the performance and how the number of channels impacts the performance, in order to ultimately facilitate a more dynamic way of optimization, taking the co-relation across different types of nodes into account. The major contribution of this paper to the field of computer science by creating a series of queuing models to evaluate the performance of different types of nodes: Chaincode Execution and Endorsement, Block Creation and Delivery, Transaction Validation and Block Committing, and Transaction Processing. [ABSTRACT FROM AUTHOR]