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Context: The retraction of research papers, for whatever reason, is a growing phenomenon. However, although retracted paper information is publicly available via publishers, it is somewhat distributed and inconsistent. Objective: The aim is to assess: (i) the extent and nature of retracted research in Computer Science (CS) (ii) the post-retraction citation behaviour of retracted works and (iii) the potential impact upon systematic reviews and mapping studies. Method: We analyse the Retraction Watch database and take citation information from the Web of Science and Google scholar. Results: We find that of the 33,955 entries in the Retraction watch database (16 May 2022), 2,816 are classified as CS, i.e., ≈ 8%. For CS, 56% of retracted papers provide little or no information as to the reasons. This contrasts with 26% for other disciplines. There is also some disparity between different publishers, a tendency for multiple versions of a retracted paper to be available beyond the Version of Record (VoR), and for new citations long after a paper is officially retracted (median = 3; maximum = 18). Systematic reviews are also impacted with ≈ 30% of the retracted papers having one or more citations from a review. Conclusions: Unfortunately, retraction seems to be a sufficiently common outcome for a scientific paper that we as a research community need to take it more seriously, e.g., standardising procedures and taxonomies across publishers and the provision of appropriate research tools. Finally, we recommend particular caution when undertaking secondary analyses and meta-analyses which are at risk of becoming contaminated by these problem primary studies. [ABSTRACT FROM AUTHOR]

Reflective practice is an essential catalyst through which the benefits of teaching and learning can be reaped. Through it, weaknesses and strengths can be identified in a way that helps raise the level of addressing challenges that may arise as well as overcome them. This paper presents the critical reflective practices among computer science secondary school teachers from their point of view in Riyadh, Saudi Arabia. To this extent, the study aims to determine the degree of critical reflective practices among computer science secondary school teachers in Riyadh from their perspective. The paper also seeks to investigate the effects of variables such as gender, qualifications and experience on the perceptions of the aforementioned teachers, towards the critical reflective practices among computer science secondary school teachers. The study tool is a questionnaire which consisted of two dimensions and was distributed to a population of 739 participants. From this, the study sample comprised (223) computer science teachers working in secondary school in Riyadh. The findings revealed that there is no significant difference in the estimation degree concerning the critical reflective practices due to the gender. From the results, it was also established that there is no significant difference in the degree of estimation in relation to the critical reflective practices due to educational qualification variables. On the contrary however, there is a significant difference in the degree of estimation in regard to the critical reflective practices due to the years of experience variable. These differences were evident in a group of those with more than 10 years of experience. The other findings produced by the study highlight that the participants are in agreement about the importance of critical reflective practices. The degree of reflective practice, which is from the participants' point of view, is considered to be of a high value. The majority of the subjects opted to agree with the practice of reflection after a training session. It was determined from the results that some of the most common strategies favored by practitioners involved the communal practice of mind reflection with individuals from outside the school.

This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network. This paper investigated the differences between conference papers and journal papers in the field of computer science based on Bayesian network, a knowledge-representative framework that can model relationships among all variables in the network. We defined the variables required for Bayesian networks modeling, calculated the values of each variable based Aminer dataset (a literature data set in the field of computer science), learned the Bayesian network and derived some findings based on network inference. The study found that conferences are more attractive to senior scholars, the academic impact of conference papers is slightly higher than journal papers, and it is uncertain whether conference papers are more innovative than journal papers. The study was limited to the field of computer science and employed Aminer dataset as the sample. Further studies involving more diverse datasets and different fields could provide a more complete picture of the matter. By demonstrating that Bayesian networks can effectively analyze issues in Scientometrics, the study offers valuable insights that may enhance researchers' understanding of the differences between journal and conference in computer science. Academic conferences play a crucial role in facilitating scholarly exchange and knowledge dissemination within the field of computer science. Several studies have been conducted to examine the distinctions between conference papers and journal papers in terms of various factors, such as authors, citations, h-index and others. Those studies were carried out from different (independent) perspectives, lacking a systematic examination of the connections and interactions between multiple perspectives. This paper supplements this deficiency based on Bayesian network modeling. [ABSTRACT FROM AUTHOR]

In this article, we test the hypothesis that computer science papers asking questions (i.e., those with a question mark at the end of their title) are cited more frequently than those that do not have this property. To this end, we analyze a data set of almost two million records on computer science papers indexed in the Web of Science database and focus our investigation on the mean number of citations per paper of its specific subsets. The main finding is that the average number of citations per paper of the so-called “asking papers” is greater by almost 20% than that of other papers, and that this difference is statistically significant.

Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity , clarity , novelty , and significance , based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions. • Define four criteria for quality evaluation of scientific papers: integrity, clarity, novelty, and significance. • Propose a framework for quality evaluation of scientific papers based on coarse features and knowledge entity network. • An effective algorithm for measuring the novelty and significance of scientific papers based on knowledge entity networks. • Create and release a rigorous dataset, which could serve as the gold standard for quality evaluation of scientific papers. • Conduct extensive experiments to validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Introduction functions as a showcase in research articles (RAs). It motivates the reader to read the rest of the paper. However, writing a well-crafted introduction is a complex task, mainly when the writer generates the manuscript in another language. This study investigated the rhetorical differences/similarities employed in the introductions of RAs published in Iranian and international ISI journals in Computer Sciences (CS) using Swales (2004) CARS model. Two sets of CS RAs (30 each) were randomly selected. Frequency and non-parametric tests were used to examine the differences between the two groups of introductions. The results indicated that M 1 S 1 (Generalizing the topic), M2 1A (Indicating the gap), M3 S1 (Describing the research), M3 S4 (Methods Summary), and M 3 S 6 (Stating research advantages) were used with high frequencies. M 2 S 2 (Announcing positive justification) was absent, and the others were in low preferences. Also, the Analysis illustrated a statistically significant variation between the introductions concerning the use of M3S7 (Demarcating the Research Organization). Findings support genre-based pedagogy in scientific writing classes to make the graduate CS students aware of these rhetorical structures conventional to introductions in CS RAs.

Purpose: The number of citations has been widely used to measure the significance of a paper. However, there is a need in introducing another index to determine superiority or inferiority of papers with the same number of citations. We determine superiority or inferiority of papers by using the ranking based on the number of citations and PageRank. Design/methodology/approach: We show the positive linear correlation between Citation Rank (the ranking of the number of citation) and PageRank. On this basis, we identify high-quality, prestige, emerging, and popular papers. Findings: We found that the high-quality papers belong to the subjects of biochemistry and molecular biology, chemistry, and multidisciplinary sciences. The prestige papers correspond to the subjects of computer science, engineering, and information science. The emerging papers are related to biochemistry and molecular biology, as well as those published in the journal "Cell." The popular papers belong to the subject of multidisciplinary sciences. Research limitations: We analyze the Science Citation Index Expanded (SCIE) from 1981 to 2015 to calculate Citation Rank and PageRank within a citation network consisting of 34,666,719 papers and 591,321,826 citations. Practical implications: Our method is applicable to forecast emerging fields of research subjects in science and helps policymakers to consider science policy. Originality/value: We calculated PageRank for a giant citation network which is extremely larger than the citation networks investigated by previous researchers. [ABSTRACT FROM AUTHOR]

Copyright of Digital Studies / Champ Numérique is the property of Open Library of Humanities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we continue describing the project and the experimentation of "Ricerca Operativa Applicazioni Reali" (ROAR; in English, Real Applications of Operations Research), a three-year project for higher secondary schools, introduced. ROAR is composed of three teaching units, addressed to Grades 10, 11, and 12, respectively, having the main aim to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. In a previous paper, we reported on the design and implementation of the first unit, started in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy), in a Grade-10 class. Here, we focus on the second unit, carried out in Winter/Spring 2022 with the same students, now in a Grade-11 class. In particular, we describe objectives, prerequisites, topics and methods, the organization of the lectures, digital technologies used, and a challenging final project. Moreover, we analyze the feedback from students and teachers involved in the experimentation.

The International Institute of Knowledge Innovation and Invention (IIKII) is an institute that promotes the exchange of innovations and inventions, and establishes a communication platform for international innovations and researches. In 2019, IIKII cooperated with the Institute of Electrical and Electronics Engineers (IEEE) Tainan Section Sensors Council to hold IEEE conferences such as IEEE ICIASE 2019, IEEE ECBIOS 2019, IEEE ICKII 2019, ICUSA-GAME 2019, and IEEE ECICE 2019. This Special Issue entitled “Selected Papers from IIKII 2019 conferences” aims to select excellent papers from IIKII 2019 conferences, including symmetry in physics, chemistry, biology, mathematics, and computer science, etc. It selected 21 excellent papers from 750 papers presented in IIKII 2019 conferences on the topic of symmetry. The main goals of this Special Issue are to encourage scientists to publish their experimental and theoretical results in as much detail as possible, and to discover new scientific knowledge relevant to the topic of symmetry.

Background: Peer evaluation is the cornerstone of science evaluation. In this paper, we analyze whether or not a form of peer evaluation, the pre-publication selection of the best papers in Computer Science (CS) conferences, is better than random, when considering future citations received by the papers. Methods: Considering 12 conferences (for several years), we collected the citation counts from Scopus for both the best papers and the non-best papers. For a different set of 17 conferences, we collected the data from Google Scholar. For each data set, we computed the proportion of cases whereby the best paper has more citations. We also compare this proportion for years before 2010 and after to evaluate if there is a propaganda effect. Finally, we count the proportion of best papers that are in the top 10% and 20% most cited for each conference instance. Results: The probability that a best paper will receive more citations than a non best paper is 0.72 (95% CI = 0.66, 0.77) for the Scopus data, and 0.78 (95% CI = 0.74, 0.81) for the Scholar data. There are no significant changes in the probabilities for different years. Also, 51% of the best papers are among the top 10% most cited papers in each conference/year, and 64% of them are among the top 20% most cited. Discussion: There is strong evidence that the selection of best papers in Computer Science conferences is better than a random selection, and that a significant number of the best papers are among the top cited papers in the conference. [ABSTRACT FROM AUTHOR]

Few high schools offer Information Technology (IT)) as a subject in South Africa. This paper used Bourdieu's conception of cultural capital to investigate the reasons why many public schools are shunning away from including IT in their curriculum. Data were collected using a questionnaire and analysed using ANOVA, to determine whether cultural capital influences choosing IT as one of the subjects in the school curriculum. The results showed that public high school principals view IT as a subject that should be done in schools where there is medium-to-high cultural capital status. It is a challenge for schools with low cultural capital to offer IT among their subjects as it is not practiced by the community or society in which the schools exist. The paper concludes by recommending that there is room for the so-called 'low cultural capital' schools to offer IT among their subjects despite the challenges they are facing. Cultural capital does not hinder students' performance. Students from low cultural capital communities can achieve good grades even though they learn under difficult circumstances of inadequate educational resources.

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]

In the November 2021 issue of Spectroscopy, we published a review paper titled 'H-Classic Papers in Atomic Spectroscopy: An Integrative Literature Review' (1). This paper caused quite a bit of [...]

This paper presents results of topic modeling and network models of topics using the ICCS corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of ICCS, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the authors’ keywords. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization(NMF) topic modeling algorithm to discover topics and labeled and grouped results hierarchically. We used Gephi to study static networks of topics, and an R library called DyA to analyze the dynamic networks of topics. [ABSTRACT FROM AUTHOR]

In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is the intelligence exhibited by machines or software. It is the subfield of computer science. Artificial intelligence is becoming a popular field in computer science as it has enhanced the human life in many areas. Artificial intelligence in the last two decades has greatly improved performance of the manufacturing, service sector and so in the field of education. Study in the field of artificial intelligence has given rise to the rapidly growing technology known as expert system. Application areas of artificial intelligence is heaving a huge impact on various fields of life as expert system is widely used in these days to solve the complex problems in various areas as education, engineering, business, medicine, weather forecasting etc. The areas employing the technology of artificial intelligence have seen an increase in the quality and efficiency. This paper gives an overview of this technology and the scope of artificial intelligence in different areas with special reference to the use of this technology in the field of education along with its meaning, searching techniques, inventions and future. [ABSTRACT FROM AUTHOR]

In this article, we test the hypothesis that computer science papers asking questions (i.e., those with a question mark at the end of their title) are cited more frequently than those that do not have this property. To this end, we analyze a data set of almost two million records on computer science papers indexed in the Web of Science database and focus our investigation on the mean number of citations per paper of its specific subsets. The main finding is that the average number of citations per paper of the so-called "asking papers" is greater by almost 20% than that of other papers, and that this difference is statistically significant. [ABSTRACT FROM AUTHOR]

This paper presents a rhetorical analysis of the Introductions of Computer Science (CS) research articles from a specialized corpus with reference to “Announcement of Principal Findings” and “Statement of Value” steps within the framework of the “Create A Research Space” (CARS) model (Swales, 2004), conducted through corpus-based techniques. The results show that discoursal practices in CS are result-oriented and in various ways highlight the writers’ contribution. The results are explicitly described with embedded “value” statements in the elaborate explanations of the nature of the present research. The common linguistic indicators used for this purpose are “contribution”, “efficient” and “novel”. The discussion concludes with the suggestion to amend the CARS model for CS writers.

The paper discusses a certain type of competitions based on distance interaction of a participant with simulation models of concepts from discrete mathematics and computer science. One of them is the "Construct, Test, Explore" (CTE) competition, developed by the authors, the other is the Olympiad in Discrete Mathematics and Theoretical Informatics (DM&TI). The tasks presented in this paper are generally devoted to the concept of a graph isomorphism. Most of the tasks are verified automatically.

The importance of updating, expanding and improving what is taught in cybersecurity curricula is increasing as the security threat landscape becomes more dangerous, breaches become more frequent, and the number of deployed Internet of Things (IoT) devices, known for their security challenges, grows exponentially. This paper argues that a profile of "T-shaped" skills, which is known to be desirable in many consulting and design professions, is being reflected in the latest manifestations of cybersecurity curriculum design and accreditation. A model of learning that yields "T-shaped" professionals combines the ability to apply knowledge across domains (breadth) with the ability to apply functional and disciplinary skills (depth). We present the design of a junior- or senior-level cybersecurity course in which the horizontal stroke of the "T" (representing breadth) spans knowledge areas that cut across the people, process and technology triad. The vertical stroke of the "T" (representing depth) is provided by two aspects of the course design: first, learning the foundational principles of cybersecurity, including practical examples from cryptography and network security; and second, applying the principles of cybersecurity to a semester project, allowing students to expand the core "T" of the course to satisfy their own passions and interests. Our paper concludes with student and instructor reflections on the implementation of this cybersecurity course, as well as broader implications of the lessons learned after the initial offering of this course.

Collaborative learning has numerous benefits such as enhancing learners' critical thinking, developing social skills, and improving learning gains. While engaging in this interactive process, learners' satisfaction toward their partners plays a crucial role in defining the success of the collaboration. However, detecting learners' satisfaction during an ongoing collaboration remains challenging, and there are no automatic techniques to predict learners' satisfaction. In this paper, we propose a multimodal approach to automatically predict peer satisfaction for co-located collaboration with features extracted from 44 middle school learners' collaborative dialogues. We investigated three types of features extracted from learners' dialogues: 1) linguistic features indicating semantics and sentiment; 2) acoustic-prosodic features including energy and pitch; and 3) visual features including eye gaze, head pose, facial action units, and body pose. We then trained several regression models with each of those features to predict the peer satisfaction scores that learners received from their partners. The results revealed that head position and body location were significant indicators of peer satisfaction: lower head and body distances between partners were associated with more positive peer satisfaction. Next, we investigated the influence of multimodal feature fusion methods on peer satisfaction prediction accuracy: early fusion versus late fusion. We report the comparison results between models trained with (1) best-performing unimodal features, (2) multimodal features combined by early fusion, and (3) multimodal features combined by late fusion. This line of research reveals how multimodal features from collaborative dialogues are associated with peer satisfaction, and represents a step toward the development of real-time intelligent systems that support collaborative learning.

In this article we present a bibliometric study of 1.9 million computer science papers published from 1945 to 2014 and indexed in Web of Science. We analyze both the quantity and the impact of these publications according to document types, languages, disciplines, countries, institutions, and publication sources. The most frequent author keywords, cited references, and cited papers as well as the distribution of the number of references and citations per paper and of the age of cited references are also explored. Since conference proceedings play a tremendous role in this scientific field, we investigate the time and place of computer science conferences in terms of the most prolific months and locations. And, last but not least, the production of journal articles and conference papers over the whole time period and the level of collaboration in different computer science disciplines are inspected. One of the main results is the finding that “Artificial Intelligence” is the most productive subfield of computer science, but “Interdisciplinary Applications” has the highest relative impact.

The action required to stem the environmental and social implications of climate change depends crucially on how humankind shapes technology, economy, lifestyle and policy. With transport CO2 emissions accounting for about a quarter of the total, we examine the contribution of CO2 output by scientific travel. Thankfully for the reputation of the scientific community, CO2 emissions associated with the trips required to present a paper at a scientific conference account for just 0.003% of the yearly total. However, with CO2 emissions for a single conference trip amounting to 7% of an average individual’s total CO2 emissions, scientists should lead by example by demonstrating leadership in addressing the issue. [ABSTRACT FROM AUTHOR]

Contributions: The underrepresentation of women in computer science (CS) majors has long been a focus of attention in many academic documents, the majority of them from the United States and Europe. There is, however, a lack of information about educational interventions (EIs) for women in computing in Latin America. The contribution of this article is to cover this gap and describe what researchers in Latin American countries have been publishing about the recruitment and retention of women in the CS field. Background: Many EIs targeting female students at different educational levels--K-12, undergraduate, and graduate--have been undertaken to increase the participation of women in computing in Latin America. However, descriptions of these activities rarely are included in international academic databases. Research Questions: This literature mapping addresses two main research questions (RQ) about the topic of women in computing in academic publications in Latin American countries: RQ1) what are the characteristics of the publications about women in computing in Latin America? and RQ2) what are the published interventions to recruit and retain women in computing in Latin America? To answer RQ1, six subquestions were created covering year, language, country of origin, document type, and professional track. Furthermore, for RQ2, two subquestions were created involving educational level and the use of software development with a female theme. Methodology: This investigation used the systematic literature mapping process. To achieve a broad coverage of papers, the following sources were included: Scopus, Web of Science, Google Scholar, EBSCO, the proceedings of the Latin American Women in Computing Conference (LAWCC), and those of the Women in Technology (WIT) workshop colocated with the annual conference of the Brazilian Computer Society (SBC). The included papers were published in the last decade (2010-2020) and written in English, Portuguese, or Spanish. Findings: The literature mapping encompasses 197 academic documents, 48.2% of which were written in Portuguese, 28.7% in English, and 23.1% in Spanish. The papers originated from 15 of the 20 Latin American countries. Brazil and Costa Rica have the highest number of publications overall. The documents describe initiatives to increase the participation of women in computing majors that cover the entire educational spectrum, from K-12 to graduate programs, but papers targeting populations in higher education have started to appear recently.

Aims: This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results. Method: An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied. Results: Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs. Conclusion: It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs. [ABSTRACT FROM AUTHOR]

Abstract: Image acquisition has great influence on the performance of any computer vision application. Different methods can be utilized to acquire the digital image of a paper, whilst scanning scheme is among the most attractive methods. This attractiveness is because of the fewer types of potential deformations and the low cost of the scanning devices, e.g. flatbed scanners. However, paper is commonly placed imperfectly on the scanner. This slight rotation is not usually based on a pivot around the paper''s geometrical center (the well known regular rotation) but instead it is based on a pivot placed at the corner of the paper. Thus, the result is a digital image that is deformed with an “irregular rotation”. The characteristic of this deformation phenomenon is currently unknown to computer vision scientists. In this paper we provide an extensive investigation of this deformation. In addition, a new set of equations that sway and measure the transformation is proposed. Our investigation leads to the conclusion that the “irregular rotation” phenomenon produces a shear transformation. Furthermore, the experimental results confirm the theoretical findings. [Copyright &y& Elsevier]

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

The present study aims to measure the contributions of Prof. Anurag Kumar, who served as the Director at the Indian Institute of Science (IISc) in Bengaluru between August 2014 to July 2020. He has contributed 181 publications during his 36 years of career. He has published his majority of publications 171 (94.48%) in collaboration; most active collaborators with Prof. Anurag Kumar are Altman E. with (17) publications, Kuri Joy (15), Anand (14), and Singh (12). His highest Collaboration Coefficient found is 0.73 in 2013, and the highest Modified Collaboration Coefficient is 1.33 in the year 2018. The highest degree of collaboration observed for Prof. Anurag Kumar was 1.00. The highest Collaborative Index of Prof. Anurag found is 4.75 in the year 2016. His publications received 2595 citations so far, with 14.3 citations for each publication and an average citation of 72.08 per year. He has produced most of his publications in the field of computer science (45.9%). He preferred conference papers as his communication channel, where he published 100 (55.24%) papers out of 181. Prof. Anurag Kumar has contributed the highest (62) publication between 2005-2010, and he has published his first publication at the age of 30. [ABSTRACT FROM AUTHOR]

There is widespread debate on whether to anonymize author identities in peer review. The key argument for anonymization is to mitigate bias, whereas arguments against anonymization posit various uses of author identities in the review process. The Innovations in Theoretical Computer Science (ITCS) 2023 conference adopted a middle ground by initially anonymizing the author identities from reviewers, revealing them after the reviewer had submitted their initial reviews, and allowing the reviewer to change their review subsequently. We present an analysis of the reviews pertaining to the identification and use of author identities. Our key findings are: (I) A majority of reviewers self-report not knowing and being unable to guess the authors' identities for the papers they were reviewing. (II) After the initial submission of reviews, 7.1% of reviews changed their overall merit score and 3.8% changed their self-reported reviewer expertise. (III) There is a very weak and statistically insignificant correlation of the rank of authors' affiliations with the change in overall merit; there is a weak but statistically significant correlation with respect to change in reviewer expertise. We also conducted an anonymous survey to obtain opinions from reviewers and authors. The main findings from the 200 survey responses are: (i) A vast majority of participants favor anonymizing author identities in some form. (ii) The "middle-ground" initiative of ITCS 2023 was appreciated. (iii) Detecting conflicts of interest is a challenge that needs to be addressed if author identities are anonymized. Overall, these findings support anonymization of author identities in some form (e.g., as was done in ITCS 2023), as long as there is a robust and efficient way to check conflicts of interest. [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]

Abstract: The need for applications that are developed especially for Computational Science and Engineering (CSE) has been growing rapidly in the recent years. These applications are often a prerequisite for research and have to be evolved and maintained for considerable periods of time. However, CSE researchers have traditionally put focus on achieving better computational performance and results rather than the software’s comprehensibility, maintainability and extensibility. This paper first presents two case studies on two different CSE research projects, where common and specific problems are identified. Second we propose solutions that intend to apply software engineering methodologies and tools to improve the CSE research software development. [ABSTRACT FROM AUTHOR]

Copyright of Iberica is the property of Asociacion Europea de Lenguas para Fines Especificos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper describes global research in the field of augmented reality (22078) as indexed in Scopus database during 1992-2019, using a series of bibliometric indicators. The augmented reality (AR) research registered high 54.23% growth, averaged citation impact of 8.90 citations per paper. Nearly 1% of global output in the subject (226 papers) registered high-end citations (100+) per paper. The top 15 countries accounted for 87.05% of global publications output in the subject. The USA is in leadership position for its highest publications productivity (19.25% global share). The U.K. leads the world on relative citation index (2.05). International collaboration has been a major driver of AR research pursuits; between 11.89% and 44.04% of national share of top 15 countries in AR research appeared as international collaborative publications. AR research productivity by application types was the largest across sectors, such as education, industry and medical. Computer science has emerged as the most popular areas in AR research pursuits. Technical University of Munich, Germany and Osaka University, Japan have been the most productive organizations and Nara Institute of S&T, Japan (66.55 and 7.48) and Imperial College, London, U.K. (57.14 and 6.42) have been the most impactful organizations. M. Billinghurst and N. Navab have been the most productive authors and S. Feiner and B. MacIntyre have been the most impactful authors. IEEE Transactions on Visualization & Computer Graphics, Multimedia Tools & Applications and Virtual Reality topped the list of most productive journals. [ABSTRACT FROM AUTHOR]

Access Foundation Programmes are a widening-participation initiative designed to encourage engagement in higher education among underrepresented groups, including those with socioeconomic and educational disadvantage. In particular, mature students enrolled in these programmes experience greater difficulties making the transition to tertiary education, especially when they opt to study disciplines traditionally considered difficult. Computer programming is perceived as a traditionally difficult subject with typically lower pass rates and progression rates than other subjects. This paper describes the first of a three-cycle action research study examining the perceived effects of a structured Peer Assisted Learning (PAL) Programme for mature students enrolled in a computer science programming module for an Access Foundation Programme in an Irish University. The focus of this qualitative study was to evaluate the perceived effect of a PAL programme on learning and whether it offered a positive learning support structure. Findings from our study suggest that PAL programmes have an overall positive effect on subject comprehension as well as enhanced learner confidence for mature Access Foundation students. Furthermore, PAL sessions offered students a support structure that helped with their transition and acculturation to tertiary education. This study also highlights the importance the PAL leader's role has on the perceived effectiveness of the PAL sessions as well as the impact of the students' shared history on the near-peer bond. The study concludes that the implementation of PAL programmes for Access Foundation Programmes has the potential to offer mature students a supportive learning environment and to improve their learning experience.

Highlights: [•] Granular computing aspects of natural computing. [•] Review of different granular soft computing research. [•] Biological motivation, design principles, application areas, open research problems and challenging issues of these models. [Copyright &y& Elsevier]

This paper describes results of a scientometric study of "Robotics" research publications during the period of 10 years i.e. (2009-2018). The raw data was collected from leading citation database i.e. Scopus. The study examines and analysis various scientometrics parameter and after the analysis it has been found that the out of total 4325 research paper, the highest documents were published in 2018 i.e. 791 (18.29%) while the minimum 134 (3.1%) of research papers were found in the year 2009 and the annual growth rate of publications was in fluctuating trends. The maximum (1.23) relative growth rate was found in 2010; the highest doubling time (3.43) was recorded in 2018; the maximum papers were written by more than three authors i.e. 1657 research papers. The average degree of author's collaborations was (0.93). Krishna, K.M. was the most productive author with (51) research papers contribution. Out of a total 16670 citations, 2718 citations were recorded in 2010 and a total 4325 publications, 2704 (62.52%) of records were conference paper. The highest publications came from computer science subject and the highest publications were published in ACM international conference proceeding series while the maximum 2508 'Robotics keyword was used by the authors during the period of study. [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]

In a course of the degree of computer science, the programming project has changed from individual to teamed work, tentatively in couples (pair programming). Students have full freedom to team up with minimum intervention from teachers. The analysis of the working groups made indicates that students do not tend to associate with students with a similar academic performance, perhaps because general cognitive parameters do not drive the choice of academic partners. Pair programming seems to give great results, so the efforts of future research in this field should focus precisely on how these pairs are formed, underpinning the mechanisms of human social interactions. [Paper presented at the International Conference on University Teaching and Innovation (CIDUI): Learning and Teaching Innovation Impacts (9th, Bellaterra, Barcelona, Spain, Jul 5-7 2016.)]

Introduction: Studies have indicated that users' text highlighting behaviour can be further manipulated to improve the relevance of retrieved results. This article reports on a study that examined users' text highlight frequency, length and users' copy-paste actions. Method: A binary voting mechanism was employed to determine the weights for the feedback, which were then used to re-rank the original search results. A search engine prototype was built using the Communications of the ACM test collection, with the well-known BM25 acting as the baseline model. Analysis: The proposed enhanced model's performance was evaluated using the mean average precisions and F-score metrics, and results were compared at the top 5, 10 and 15. Additionally, comparisons were also made based on the number of terms used in a query, that is single, double and triple terms. Results: The findings show that the enhanced model significantly outperformed BM25, and the rest of the models at all document levels. To be specific, the enhanced model showed significant improvements over the frequency model. Additionally, retrieval relevance was found to be the best when the query length is two. Conclusions: Users' post-click behaviour may serve as a significant indicator of their interests, and thus can be used to improve the relevance of the retrieved results. Future studies could look into further extending this model by including other post-click behaviour such as printing or saving. [Paper presented at the Information Seeking in Context (ISIC): The Information Behaviour Conference, Part 1 (11th, Zadar, Croatia, September 20-23, 2016).]

Copyright of Canadian Journal of Information & Library Sciences is the property of Canadian Association for Information Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: We present Open Review, a web-based platform aimed at stimulating executable papers by means of post-publication peer-review. Its goal is to bring computer science researchers to collaboratively build their work upon previous research results, in such a way that transparency, reproducibility and sustainability of research results are greatly improved. The main design goals of the platform are clarity, conciseness, and reproducibility. Its main features are to: (i) provide incentives for making research communities to participate, (ii) make papers executable by means of boards’ annotations, without necessarily involving the authors of an article, and (iii) give snapshots of the current research state on any given article. [Copyright &y& Elsevier]

The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations. We utilize a modified Sigmoid function to handle the fat-tail distributed citation counts. We also remove the target paper in calculating the contribution of co-citations. Following previous studies, we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society (APS) and the Microsoft Academic Graph (MAG) dataset to test the accuracy of our proposed method (NCCAS). In addition, we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS. Compared with the state-of-the-art methods, NCCAS gives the most accurate prediction of Nobel laureates. Furthermore, the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors. The results by NCCAS are also more robust to malicious manipulation. Finally, we perform ablation studies to show the contribution of different components in our methods. Due to limited ground truth on the true leading author of a work, the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers. NCCAS is successfully applied to a large number of publications, demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders. Compared with existing methods, NCCAS not only identifies the leading author of a paper more accurately, but also makes the deification more distinguishable and more robust, providing a new tool for related studies. [ABSTRACT FROM AUTHOR]

EMOTIONAL INTELLIGENCE AND THE SOUL: LIMITATIONS TO STRONG AI. ETHAN WIDEN, FAULKNER UNIVERSITY. Limitations in the reach of programming beyond strict logic as well as weaknesses in the current tests [...]

Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

Scientific progress, or scientific change, has been an important topic in the philosophy and history of science. Previous work has developed quantitative approaches to characterize the progression of science in different fields, but how individual scientists make progress through their careers is not well understood at a comprehensive scale. We characterize the regularity in the temporal dynamics of scientists' publishing behavior with computational algorithms that predict the historical emerging order of publications from individual scientists. We discover that scientists publish in ways following the processes of chaining that mirror those observed in historical word meaning extension, whereby novel ideas emerge by connecting to existing ideas that are proximal in semantic space. We report findings for predominant exemplar-based chaining that reconstructs the emerging order in the publications of 1,164 award-winning and random-sampled scientists from the fields of Physics, Chemistry, Medicine, Economics, and Computer Science over the past century. Our work provides large-scale evidence that scientists across different fields tend to share similar publishing behavior over time by taking incremental steps that build on their past research outputs. [ABSTRACT FROM AUTHOR]

Background: The coronavirus pandemic has caused a shift in how many teaching, learning and research activities are conducted internationally. Lockdowns compelled all education sectors, including higher education (HE), to adopt a variety of online learning practices at short notice. As these changes in practice have implications beyond the pandemic, more needs to be understood about settings across the globe where the move to online may pose particular challenges. This study brings a focus to how lecturers in a college of education in Nigeria responded to the professional demands of lockdowns and developed strategies for managing educational activities that have relevance during and beyond the pandemic itself. Purpose: The aim of the study reported in this paper was to examine lecturers' perceptions of the management of teaching, learning and research activities during a lockdown, drawing out longer term implications. Methods: Data were collected through in-depth, one-to-one interviews with six lecturers from a college of education in Nigeria. Interview data were analysed qualitatively. Findings: Detailed analysis indicated that lecturers' strategies included encouraging students to engage in self-directed learning, as they uploaded learning materials on messaging groups and the college's e-learning platform. However, lecturers felt that the quality of teaching and assessment was compromised by factors including: irregular power supply, poor internet connection, high data costs and some students' limited digital skills. A particular concern was students' lack of interaction on the online platforms, which lecturers perceived to result in a more passive teaching and learning process. Lecturers also experienced difficulties undertaking research activities. A range of strategies and implications for the ongoing management of educational activities was discussed. Conclusions: The study offers insights into how some of the challenges encountered could be addressed to support sustainable online and blended teaching and learning in the longer term, in settings internationally where there are barriers to accessing online teaching and learning. Crucial aspects include strengthening educational technology training for lecturers and students, enhancing e-learning platforms for teaching practical courses, and enabling lecturers to develop beneficial and valuable online resources.

We introduce "Ricerca Operativa Applicazioni Reali" (ROAR; in English, "Real Applications of Operations Research"), a three-year project for higher secondary schools. Its main aim is to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. ROAR offers examples and problems closely connected with students' everyday life or with the industrial reality, balancing mathematical modeling and algorithmics. The project is composed of three teaching units, addressed to grades 10, 11, and 12. The implementation of the first teaching unit took place in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy). In particular, in this paper, we provide a full description of this first teaching unit in terms of objectives, prerequisites, topics and methods, organization of the lectures, and digital technologies used. Moreover, we analyze the feedback received from students and teachers involved in the experimentation, and we discuss advantages and disadvantages related to distance learning that we had to adopt because of the COVID-19 pandemic.