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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]

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]

This small scale study aims to analyze the question papers of Board of Intermediate and Secondary Education in the subject of computer science with reference to item analysis and Bloom's taxonomy. Data were collected from 100 students of Grade 9th and 10th from the schools of Lahore city using convenient sampling technique. Data collected on the papers developed by Board of Intermediate and Secondary Education for the year of 2015 and 2016. Item analyses were performed using Conquest software. Findings of the study shows that in the question papers conducted by Board of Intermediate and Secondary Education the majority questions were measuring the student abilities of knowledge and comprehension and only few questions were given to measure the student abilities to analyze, synthesize and evaluate, and this can be very helpful for the policy makers. Result of item analysis shows that many questions were not in the acceptable range of item difficulty and item discrimination. Items in the question papers were either too easy or too difficult. Findings revealed that the papers conducted and administered by Board of Intermediate and Secondary Education were not up to the mark, with reference to Bloom's taxonomy. The researcher recommended to train the assessment committee/panel developing the items. [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.

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]

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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 [...]

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]

In our post-pandemic world, where the majority of higher education institutions have transitioned back to in-person classes, this paper argues that we must not return to pre-COVID teaching practices. Instead, we have the obligation and opportunity to create an educational experience and environment that better facilitates learning and instruction. This paper presents post-COVID best practices for employing technology in higher education based on an original survey and follow-up interviews of seventeen computing instructors at our institution. After a literature review, we describe four general categories of practices that enhance the post-COVID classroom: online student activities, digital instructor notes, remote classroom participation and collaboration, and a paperless classroom. For each of these categories, we provide vignettes to illustrate scope and intent. We also offer recommendations for addressing digital dishonesty, required infrastructure, institutional support, and being prepared to seamlessly return to a blended or fully remote environment in the event of another crisis. Finally, we identify additional emerging technological challenges and opportunities that require further effort. Overall, this paper emphasizes the need for a shift towards improved practices in the classroom rather than just a return to pre-pandemic norms. We believe implementing these recommendations will result in a more flexible, accessible, and robust post-COVID educational experience.