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The present study looks at the composition and growth of world publications output on Library 2.0. The total world output on Library 2.0 during 2004-14 cumulated to 186 papers; and the world output witnessed 6.67% quinquennial growth from 2004-2009 to 2010-14, There were 1183 citations to 186 papers since their publication. In all 65.31% publications received 1 to 30+ citations per paper during 2004-14. Top 10 most productive countries, (out of forty) contributed 80.1% publication share and 94.77% citation share. Social sciences accounted for the highest publications share (79.57%), followed by computer science (46.77%), business, management & accounting, arts & humanities, engineering and medicine and decision science (less than 5% share each) during 2004-14. Top 31 most productive organizations (out of 163) and top 34 most productive authors (out of 180) contributed 39.78% and 39.25% publications share respectively and their citations share was 40.41% and 32.97% respectively during 2004-14. Amongst 186 global publications on Library 2.0, 151 had appeared in 74 journals during 2004-14. Among the 30 highly cited publications (citations per paper from 10 to 139), the largest number (14) came from the USA, 6 from the U.K., 3 from Spain, 2 each from India and China, and 1 each from Finland, Slovenia, Swaziland, Australia, Germany, Norway and Pakistan. These 30 highly cited publications involved 65 authors, 41 organizations and were published in 23 journals. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to explore a possible strategic role for computing ethics and investigate how they might align with corporate values and higher education (HE) strategy making. Design/methodology/approach: A longitudinal study of a university in the South East of England is used to examine qualitative findings and develop a formative discussion. The findings, discussion and conceptual framework draw upon documents analysis and 14 semi-structured interviews with senior informants involved in strategy making and implementation within a HE case study setting. Findings: Findings are discussed in terms of: first, dealing with everyday computing ethical issues facing HE, such as common information technology (IT) threats and data protection; second, responding to ethical opportunities, dilemmas and challenges associated with the adoption of new information and communication technology in areas such as eLearning; and third, harnessing aligned IT opportunities, computing ethics and organizational values for long-term strategy development. Research limitations/implications: This paper is important for strategic decision makers as they consider the joined-up nature of computing ethics and organizational strategy. Explicating hidden ethical opportunity and threat dimensions of eLearning, computing networks and organizational design should be an area for future research. The authors are limited by the use of a single case study, and generalizability of findings. Originality/value: The contribution of the paper is a macro-analytical and conceptual approach that explores tentative links between computing ethics, corporate values and strategy making, while supporting future empirical studies between traditionally disparate research domains.

While gamification is gaining ground in business, marketing, corporate management, and wellness initiatives, its application in education is still an emerging trend. This article presents a study of the published empirical research on the application of gamification to education. The study is limited to papers that discuss explicitly the effects of using game elements in specific educational contexts. It employs a systematic mapping design. Accordingly, a categorical structure for classifying the research results is proposed based on the extracted topics discussed in the reviewed papers. The categories include gamification design principles, game mechanics, context of applying gamification (type of application, educational level, and academic subject), implementation, and evaluation. By mapping the published works to the classification criteria and analyzing them, the study highlights the directions of the currently conducted empirical research on applying gamification to education. It also indicates some major obstacles and needs, such as the need for proper technological support, for controlled studies demonstrating reliable positive or negative results of using specific game elements in particular educational contexts, etc. Although most of the reviewed papers report promising results, more substantial empirical research is needed to determine whether both extrinsic and intrinsic motivation of the learners can be influenced by gamification.

To date, girls and women are significantly underrepresented in computer science and technology. Concerns about this underrepresentation have sparked a wealth of educational efforts to promote girls' participation in computing, but these programs have demonstrated limited impact on reversing current trends. This paper argues that this is, in part, because these programs tend to take a narrow view of their purpose, ignoring important factors that shape girls' identities and education/career choices--not least broader narratives around gender, race, and sexuality. This paper focuses on the issue of sexuality--that is, how sexuality discourses are shaping a diverse range of girls' experiences with technology, their perceptions of themselves, and their ultimate educational and career choices. The paper makes the case for considering these important connections, bringing together research in two disparate areas: (1) sociological research in gender, diversity, and technology and (2) critical cultural studies research in youth sexualities and schooling.

Purpose: The purpose of this study is to examine important work on MOOCs by researchers in the form of major contributing universities, sources of funding, authors, journals, keywords, and citation data. Design/Methodology/Approach: In this study, the researchers assessed by conducting a systematic review of MOOC literature, as other researchers have rightly said that reviewing entails learning from experience. In this study, patterns of research publications within particular topics and historical periods were described using bibliometric analysis, quantitative analysis, and statistical analysis. Findings: The results of this study's cluster analysis revealed the areas where future academics might focus their efforts on MOOCs for future research. There were a total of 199 entries in the notifications, which were then sorted into seven clusters. Every cluster contains different numbers of items. According to the study's findings, education, e-learning, online learning, massive open online courses, and data mining are the most often used keywords. Mostly, Social sciences and computer sciences articles have the highest contribution to research. Research Limitation: The current study only makes use of the Scopus database. The study's focus was taken into consideration when selecting the keywords. Only three keywords were used in this study. The sole language employed in this study was English. Managerial Implications: Tthe paper would help the policymakers Future studies can analyse actual challenges faced by higher education institutions and suggest methods for incorporating MOOCs into the teaching environment to address them. In-depth reporting on real-world case studies can be helpful to academics as well as practitioners. Originality/Value: The evaluation of selected papers yielded a major contribution and food for thought for both scholars and policymakers. [ABSTRACT FROM AUTHOR]

Diversity has become increasingly relevant in computer science and technology development, both in terms of inclusive design teams and as a concept used to design "diversity-aware" technologies. The latter part has received less attention and notions of diversity leveraged to design technologies remain understudied. This paper critically examines diversity concepts employed by computer scientists and designers by reviewing 120 articles in two subfields of computer science: personalized recommendations and human–computer interaction. Drawing on Black feminism and critical race theory-inspired literature, I offer a critique of demographic and cultural user representations and underscore the shortcomings of diversity as a strategy to increase user satisfaction and fairness. Particularly concerning is the unreflected use of binary, static, and individual-level diversity concepts in the design of technologies. Such concepts render structural inequalities between and within groups of users invisible and thereby risk reinforcing existing injustices. The paper closes with considerations on whether and how diversity concepts can be leveraged by designers in a social justice–oriented manner. [ABSTRACT FROM AUTHOR]

Modern cloud services are monitored by numerous multidomain and multivendor monitoring tools, which generate massive numbers of alerts and events that are not actionable. These alerts usually carry isolated messages that are missing service contexts. Administrators become inundated with tickets caused by such alert events when they are routed directly to incident management systems. Noisy alerts increase the risk of crucial warnings going undetected and leading to service outages. One of the feasible ways to cope with the above problems involves revealing the correlations behind a large number of alerts and then aggregating the related alerts according to their correlations. Based on these guidelines, AlertInsight, a framework for alert event reduction, is proposed in this paper. In AlertInsight, the correlations among event sources are found by mining a sequence of historical events. Then, event correlation knowledge is employed to build an online detector targeting the correlated events that are hidden in the event stream. Finally, the correlated events are aggregated into a single high-level event for alert reduction. Because of theweaknesses of the commonly used pairwise correlation analysis methods in complex environments, an innovative approach for multiple correlation mining, which overcomes computational complexity challenges by scanning panoramic views of historical episodes from the perspective of holism, is proposed in this paper. In addition, a neural network-based correlated event detector that can learn the event correlation knowledge generated from correlation mining and then detect the correlated events in a sequence online is proposed. Experiments are conducted to test the effectiveness of AlertInsight. The experimental results (precision=0.92, recall=0.93, and F1-score=0.93) demonstrate the performance of AlertInsight for the recognition of multiple correlated alerts and its competence for alert reduction. [ABSTRACT FROM AUTHOR]

The article discusses the intersection of machine learning and the law, highlighting how machine learning is revolutionizing social life and impacting legal processes. It explores various research topics, such as law as data, predictive policing, and machine learning decision-aids for judicial decision-making. The article also examines the advantages and limitations of using machine learning in legal research, including the reproducibility of results and the potential for bias in algorithms. Overall, the symposium aimed to foster interdisciplinary exchange and explore the ways in which machine learning can contribute to legal scholarship and empirical legal research. [Extracted from the article]

Bitcoin, Litecoin, Dogecoin, et al "cryptocurrencies" have enjoyed a meteoric rise in popularity and use as a way of performing transactions on the Internet and beyond. While gaining market valuations of billions of dollars and generating much popular press in doing so, little has been academically published on the Computer Science/Information Systems (CS/IS) foundations of this phenomena. This paper describes these foundations. In doing so, it is hoped that the success of the cryptocurrency payment systems can be used to demonstrate to CS/IS students how computer theory can be integrated into other disciplines with dramatic results.

The cybersecurity curriculum has grown dramatically over the past decade: once it was just a couple of courses in a computer science graduate program. Today cybersecurity is introduced at the high school level, incorporated into undergraduate computer science and information systems programs, and has resulted in a variety of cybersecurity-specific graduate programs. However, is that even enough? Is cybersecurity so broad that education needs to be more specialized? Employers want graduates who can hit the ground running: not in the broad field of cybersecurity but in some very specific areas. This paper is structured as follows. First, we will provide a brief overview of the current approaches to cybersecurity education including government standards bodies such as the National Initiative for Cybersecurity Education (NICE) framework, the upcoming changes in the National Information Assurance (IA) Education and Training Programs (NIETP) Center of Academic Excellence (CAE) designation requirements, and the Department of Labor competency model. Second, we will present a framework for curriculum changes, which we use to determine the viability of information technology/information systems (IS/IT) curriculum changes to our departmental educational offerings. We examine relationships with other departments and how cybersecurity is enhanced by other domain knowledge. Then we discuss the three specialties we plan to introduce in the cybersecurity graduate curriculum: cybersecurity data analysis, cyber intelligence, and health care information security and privacy. Finally, the future cybersecurity curriculum directions are presented and discussed.

Learning computer programming is one of the main requirements of many educational study plans in higher education. Research has shown that many students face difficulties acquiring reasonable programming skills during their first year of college. In Saudi Arabia, there are twenty-three state-owned universities scattered around the country that provide free college education for Saudi students. Through a quick glance at the website of these universities, we found out that almost all of them offer the same course description and requirements for the computer-programming course. The poor performance of students in these programming courses, however, is always a hot topic during educational gathering held on the country; it is always mentioned that students' performances differ from a university to another and between different regions of the country. The main objective of this research is to investigate the reasons behind the major discrepancies in the performance of students in computer programming course in three main regions of Saudi Arabia: East, West and North. For the purpose of this research, we designed a questionnaire to be filled by a random sample of students from each region considered in this study. The questions on this questioner are related to a set of indices such as socio-economic indices, learning environments indices, and the quality of teaching offered indexes. This paper presents our findings on the difficulties students face while learning computer programming in some regions of Saudi Arabia.

Purpose – In many research areas, there are a variety of different types of academic publications, including journals, magazines and conferences, which provide outlets for researchers to present their findings. Generally speaking, although there are differences in the reviewing criteria and publication processes of different publication types, in the same research area, there is certainly overlap in terms of the problems addressed and the audience for different publication types. Therefore, the research impacts of different publication types in the same research area should be moderately or highly correlated. The paper aims to discuss these issues. Design/methodology/approach – To prove this hypothesis, the authors examine the correlation coefficient of citation impacts for different types of publications, in seven research areas of computer science, from 2000 to 2013. In particular, four related citation statistics are examined for each publication type, which are average citations per paper, average citations per year, average annual increase in individual h-index, and h-index. Findings – The analysis results show only a partial correlation in terms of several specific citation measures for different publication types in the same research area. Moreover, the level of correlation of the citation impact between different publication types is different, depending on the research area. Originality/value – The contribution of this paper is to investigate whether the research impact of different types of publications in the same area is correlated. The findings can help guide researchers and academics choose the most appropriate publication outlets. [ABSTRACT FROM AUTHOR]

Statistical information theory is a method for quantifying the amount of stochastic uncertainty in a system. This theory originated in communication theory. The application of information theoretic approaches has been extended to different fields. This paper aims to perform a bibliometric analysis of information theoretic publications listed on the Scopus database. The data of 3701 documents were extracted from the Scopus database. The software used for analysis includes Harzing's Publish or Perish and VOSviewer. Results including publication growth, subject areas, geographical contributions, country co-authorship, most cited publications, keyword co-occurrence analysis, and citation metrics are presented in this paper. Publication growth has been steady since 2003. The United States has the highest number of publications and received more than half of the total citations from all 3701 publications. Most of the publications are in computer science, engineering, and mathematics. The United States, the United Kingdom, and China have the highest collaboration across countries. The focus on information theoretic is slowly shifting from mathematical models to technology-driven applications such as machine learning and robotics. This study highlights the trends and developments of information theoretic publications, which helps researchers to understand the state of the art of information theoretic approaches for future contributions in this research domain. [ABSTRACT FROM AUTHOR]

This research discusses the use of Augmented Reality, Virtual Reality and Mixed Reality technology applications in the learning process of relevant content to the Computer Science area. This systematic review aims to identify applications that use technologies to represent virtual environments and support the teaching and learning of Computer Science subjects. A protocol was elaborated and executed, resulting in the final selection of 14 papers from four databases, published from 2010 to 2018. The examined papers presented information that categorized technology applications in terms of tools used. Contents addressed to the identification of applied instructional strategies and techniques, and the recognition of effects on the learning process. As a result, we found virtual environments that show potential to teaching basic content in courses related to Computer Science. In addition, the application of virtual environments in this educational scenario has provided positive effects on the learning process, such as increased interactivity, easier content absorption, increased motivation and interest in the subjects, providing greater understanding and improving efficiency in content transmission. [ABSTRACT FROM AUTHOR]

Evidence from the Linnean Society’s ledger of papers received in 1858 points to 1 July as the date of submission of the Darwin–Wallace papers for the famous meeting of the same evening. It has been widely assumed, and repeatedly stated, that the submission was on 30 June. but this article shows that this seems to be a wrong assumption. Acceptance of this later submission date also provides an explanation of the ‘oddities’ in the letter of transmittal. In turn, this reading implies further details about the separate roles of Hooker and Lyell in the crucial 13 days before the meeting. [ABSTRACT FROM AUTHOR]

The study analyses research output of Anna University (Tamil Nadu) using Scopus database during the period 1988 to 2017 (30 years). Further predominance was given for Type of Source, Chronological growth, Subject wise contribution, Document type preferred, Collaborated Country, Collaborated Institutions, Contributed Journal, Highly contributed author, Keywords Preferred and Language of the paper. A total of 23,883 records have been identified and the same has been analyzed using bibliometric study. There has been steady growth of publications and 50% of articles appeared during the last five years between 2013 and 2017. The publications were doubled once in seven years. The average publication per year works out to 795 papers and nearly 78% of contributions were from Engineering; Computer Science; Materials Science, Physics and Astronomy. Around 17 Countries collaborated and contributed to more than 101 papers. The top three journals which have more publications were, “International Journal of Applied Engineering Research”; “European Journal of Scientific Research” and “Journal of Crystal Growth”. [ABSTRACT FROM AUTHOR]

The paper examines mobile technology research covering 29792 global publications as indexed in Scopus database during 2007-16. The mobile technology registered 5.05% annual growth and averaged citation impact of 4.31 CPP. The top 10 most productive countries accounted for 73.54% global publications share, and country-wise they contributed 3.14% to 18.08%, global publication share. The USA is world leader in mobile technology research with 18.08% share, closely followed by China (17.19%). The remaining top countries contributed global publications share from 3.14% to 6.69%. The international collaborative publications share of top 10 most productive countries varied from 10.58% (India) to 40.70% (U.K.) in mobile technology research. Computer Science, among subjects, contributed the largest publication share (64.97%), followed by Engineering (37.41%), Social Sciences (10.75%) etc. The top 20 most productive organizations and authors contributed 9.15% and 1.15% global publication share respectively and 10.42% and 2.54% global citation share respectively during 2007-16. The top 20 journals contributed 5.66% share of global journal output. [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]

Researchers have proposed different obfuscation transformations supported by numerous smartphone protection tools (obfuscators and deobfuscators). However, there is a need for a comprehensive study to empirically characterize these tools that belong to different categories of transformations. We propose a property-based framework to systematically classify twenty cutting-edge tools according to their features, analysis type, programming language support, licensing, applied obfuscation transformations, and general technical drawbacks. Our analysis predominantly reveals that very few tools work at the dynamic level, and most tools (which are static-based) work for Java or Java-based ecosystems (e.g., Android). The findings also show that the widespread adoption of renaming transformations is followed by formatting and code injection. In addition, this paper pinpoints the technical shortcomings of each tool; some of these drawbacks are common in static-based analyzers (e.g., resource consumption), and other drawbacks have negative effects on the experiment conducted by students (e.g., a third-party library involved). According to these critical limitations, we provide some timely recommendations for further research. This study can assist not only Android developers and researchers to improve the overall health of their apps but also the managers of computer science and cybersecurity academic programs to embed suitable obfuscation tools in their curricula. [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 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]

In this paper, we present and discuss a promising research avenue, that is the use of graph-based models and software for prosopographical data sets. Our case study will be constituted by Romans 1by1 (http://romans1by1.com/), a digital-born prosopography focusing on people attested in classical era inscriptions; it presently hosts approximately 18,000 open access persons files. The project aimed at employing new techniques and methodologies that come from other fields (i.e. computer science), in order to approach the study of ancient population in an innovative way, to ease the research, and to create an open-access tool, available for the academic community. In the scope of this paper, we use Romans1by1 as an example to explore the perspectives of ingesting the information from a prosopographical relational database into a graph database. Graph based modelling was employed to reveal data and details on the lives of the 'ordinary' people who lived in the Roman Empire. [ABSTRACT FROM AUTHOR]

This paper investigates the evolving landscape of blockchain technology in renewable energy. The study, based on a Scopus database search on 21 February 2024, reveals a growing trend in scholarly output, predominantly in engineering, energy, and computer science. The diverse range of source types and global contributions, led by China, reflects the interdisciplinary nature of this field. This comprehensive review delves into 33 research papers, examining the integration of blockchain in renewable energy systems, encompassing decentralized power dispatching, certificate trading, alternative energy selection, and management in applications like intelligent transportation systems and microgrids. The papers employ theoretical concepts such as decentralized power dispatching models and permissioned blockchains, utilizing methodologies involving advanced algorithms, consensus mechanisms, and smart contracts to enhance efficiency, security, and transparency. The findings suggest that blockchain integration can reduce costs, increase renewable source utilization, and optimize energy management. Despite these advantages, challenges including uncertainties, privacy concerns, scalability issues, and energy consumption are identified, alongside legal and regulatory compliance and market acceptance hurdles. Overcoming resistance to change and building trust in blockchain-based systems are crucial for successful adoption, emphasizing the need for collaborative efforts among industry stakeholders, regulators, and technology developers to unlock the full potential of blockchains in renewable energy integration. [ABSTRACT FROM AUTHOR]

This document is a special section of the SIAM Journal on Computing, focusing on the Fifty-Ninth Annual IEEE Symposium on Foundations of Computer Science (FOCS 2018). It includes ten fully refereed papers that were presented at the symposium in Paris in October 2018. The papers cover a wide range of topics, including complexity theory, graph algorithms, approximation algorithms, cryptography, quantum computing, pseudorandom generators, matrix multiplication, and graph sparsification. The papers underwent an extensive refereeing process, and the authors and anonymous referees are thanked for their efforts. The special section was prepared with the help of the guest editors and the SIAM Publications Manager. [Extracted from the article]

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]

Baba is You is a 2019 indie puzzle game whose levels are two-dimensional grids of objects. This paper demonstrates how a universal Turing machine could be created as a custom level in the game if a specific in-game limit were to be removed. A player could perform any computation by playing this level, allowing infinitely many new and meaningful puzzles to be created. Furthermore, the construction is such that the level is won if and only if the Turing machine halts, proving perfect play of the game to be unattainable by any computer. The machine is found to be exponentially time-complex, and the paper concludes with a discussion of the practical feasibility of in-game computation. [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]

The purpose of this paper is to commemorate the late scholar Loet Leydesdorff for his great academic contribution on the basis of data from Web of Science. In the span of more than 40 years, he had 526 publications, with the years 2004-2021 being the most productive (394 publications). His international collaborations spread widely across 36 countries, with Germany, the USA, the UK, China, Russia, and South Korea being the most significant. His most frequent collaboration partners included Lutz Bornmann (Germany), Staša Milojević (USA), Caroline Wagner (USA), Henry Etzkowitz (USA), Jonathan Adams (UK), Ronald Rousseau (Belgium), and Ping Zhou (China). With a broad and deep knowledge background, Leydesdorff's research extended across multiple disciplines and fields, but he was most active in library and information science and computer science. He made profound contributions to the study of bibliometrics, innovation systems (the Triple Helix model), and communications. Leydesdorff had a remarkable and extensive citation impact, with citations in 221 WoS subject categories from 120 countries. His publications in 1996, 1998, 2000, 2005, 2006, and 2012 are highly cited, and those on university-industry-government relations (the Triple Helix model) are the most cited. [ABSTRACT FROM AUTHOR]

Purpose: The research on social media-based academic communication has made great progress with the development of the mobile Internet era, and while a large number of research results have emerged, clarifying the topology of the knowledge label network (KLN) in this field and showing the development of its knowledge labels and related concepts is one of the issues that must be faced. This study aims to discuss the aforementioned issue. Design/methodology/approach: From a bibliometric perspective, 5,217 research papers in this field from CNKI from 2011 to 2021 are selected, and the title and abstract of each paper are subjected to subword processing and topic model analysis, and the extended labels are obtained by taking the merged set with the original keywords, so as to construct a conceptually expanded KLN. At the same time, appropriate time window slicing is performed to observe the temporal evolution of the network topology. Specifically, the basic network topological parameters and the complex modal structure are analyzed empirically to explore the evolution pattern and inner mechanism of the KLN in this domain. In addition, the ARIMA time series prediction model is used to further predict and compare the changing trend of network structure among different disciplines, so as to compare the differences among different disciplines. Findings: The results show that the degree sequence distribution of the KLN is power-law distributed during the growth process, and it performs better in the mature stage of network development, and the network shows more stable scale-free characteristics. At the same time, the network has the characteristics of "short path and high clustering" throughout the time series, which is a typical small-world network. The KLN consists of a small number of hub nodes occupying the core position of the network, while a large number of label nodes are distributed at the periphery of the network and formed around these hub nodes, and its knowledge expansion pattern has a certain retrospective nature. More knowledge label nodes expand from the center to the periphery and have a gradual and stable trend. In addition, there are certain differences between different disciplines, and the research direction or topic of library and information science (LIS) is more refined and deeper than that of journalism and media and computer science. The LIS discipline has shown better development momentum in this field. Originality/value: KLN is constructed by using extended labels and empirically analyzed by using network frontier conceptual motifs, which reflects the innovation of the study to a certain extent. In future research, the influence of larger-scale network motifs on the structural features and evolutionary mechanisms of KLNs will be further explored. [ABSTRACT FROM AUTHOR]

Computational thinking, defined as a way of thinking that can be applied to various fields that require problem-solving skills, has become prevalent in education. Students, i.e., future specialists, have to be prepared for complex thinking competence, necessary for solving business and societal problems, for which a combination of mathematical thinking and computational thinking is essential. The preliminary premise is that there is a correlation between ability in specific mathematical and computational fields. Therefore, this paper aims to highlight the significance of investigating the relations between those fields from linguistic point of view. In order to better understand the relationship between abilities in specific mathematical and computational fields, this paper presents an analysis of a new approach, namely, developing hypotheses for exploring the relationship between metalanguages of different fields of Mathematics and Computer Science. Additionally, the paper describes the first stage of a study on a doctoral level in an attempt to suggest possible statistical analyses suitable for testing hypotheses based on meta-analysis of the current literature. [ABSTRACT FROM AUTHOR]

Parallel computing has become a crucial topic in the concern of computer science and also it is revealed to be critical when researching in high performance. The evolution of computer architectures towards an improved number of nodes, where parallelism could be the approach to option for speeding up an algorithm within the last few decades. The combination of processing units build a model of computation (circuits) has gained an essential place in the area of high performance computing (HPC) due to its configuration and considerable processing supremacy that is parallel, series, etc.... In this paper, we study the idea of parallel computing, and its programming models and also explore some theoretical and technical concepts which can be often needed to understand the Interconnection network. In particular, we show how this technology is new in assisting the field of computational physics, especially when the issue is data parallel. In this paper firstly we convert the graphical model of perfect difference network into circuit diagram then convert circuit diagram into switching function, then simplify it and redraw the equivalent network. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to aid authors in choosing alternative keywords that help their papers gain visibility. These alternative keywords must have a certain level of popularity in the scientific community and, simultaneously, be keywords with fewer competitors. The competitors are derived from other papers containing the same keywords. Having fewer competitors would allow an author's paper to have a higher consult frequency. In order to recommend keywords, we must first determine an attention–survival score. The attention score is obtained using the popularity of a keyword. The survival score is derived from the number of manuscripts using the same keyword. With these two scores, we created a new algorithm that finds alternative keywords with a high attention–survival score. We used ontologies to ensure that alternative keywords proposed by our method are semantically related to the original authors' keywords that they wish to refine. The hierarchical structure in an ontology supports the relationship between the alternative and input keywords. To test the sensibility of the ontology, we used two sources: WordNet and the Computer Science Ontology (CSO). Finally, we launched a survey for the human validation of our algorithm using keywords from Web of Science papers and three ontologies: WordNet, CSO, and DBpedia. We obtained good results from all our tests. [ABSTRACT FROM AUTHOR]

Data are becoming increasingly important in science and society, and thus data literacy is a vital asset to students as they prepare for careers in and outside science, technology, engineering, and mathematics and go on to lead productive lives. In this paper, we discuss why the strongest learning experiences surrounding data literacy may arise when students are given opportunities to work with authentic data from scientific research. First, we explore the overlap between the fields of quantitative reasoning, data science, and data literacy, specifically focusing on how data literacy results from practicing quantitative reasoning and data science in the context of authentic data. Next, we identify and describe features that influence the complexity of authentic data sets (selection, curation, scope, size, and messiness) and implications for data-literacy instruction. Finally, we discuss areas for future research with the aim of identifying the impact that authentic data may have on student learning. These include defining desired learning outcomes surrounding data use in the classroom and identification of teaching best practices when using data in the classroom to develop students' data-literacy abilities.

"Building Computational Thinkers," a three-year research study, explored how educators and designers can most effectively support the development of computational thinking capacity, and how these learning experiences could be customized to meet the needs of different learners. This research study focused on three specific exhibit design approaches that conveyed problem decomposition content in "The Science Behind Pixar" ("Pixar"), a 13,000 square foot traveling exhibition about the computer science, mathematics, and science behind Pixar's innovative films. Phase One investigated how novice learners could be supported to interact with exhibits and understand problem solving strategies that tackle complex, creative challenges in computer programming. Phase Two investigated the affordances of these exhibits to build capacity, feelings of efficacy, and interest in problem decomposition content in middle and high school youth. The findings in this paper describe the types of scaffolds that can be used to support computational thinking in novice youth, as well as how a combination of exhibit approaches were found to increase youth perceptions, understanding, and beliefs of computer programming. It will also discuss how two exhibit approaches worked particularly well for engaging girls in problem decomposition content.

Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management Systems store a large amount of data that could help to generate predictive models to early identification of students in online and blended learning. The contribution of this paper is twofold: First, a new adaptive predictive model is presented based only on students' grades specifically trained for each course. A deep analysis is performed in the whole institution to evaluate its performance accuracy. Second, an early warning system is developed, focusing on dashboards visualization for stakeholders (i.e., students and teachers) and an early feedback prediction system to intervene in the case of at-risk identification. The early warning system has been evaluated in a case study on a first-year undergraduate course in computer science. We show the accuracy of the correct identification of at-risk students, the students' appraisal, and the most common factors that lead to at-risk level.

Most educational institutions adopted the hybrid teaching mode through learning management systems. The logging data/clickstream could describe learners' online behavior. Many researchers have used them to predict students' performance, which has led to a diverse set of findings, but how to use insights from captured data to enhance learning engagement is an open question. Furthermore, identifying students at risk of failure is only the first step in truly addressing this issue. It is important to create actionable predictive model in the real-world contexts to design interventions. In this paper, we first extracted features from students' learning activities and study habits to predict students' performance in the Kung Fu style competency education. Then, we proposed a TrAdaBoost-based transfer learning model, which was pretrained using the data of the former course iteration and applied to the current course iteration. Our results showed that the generalization ability of the prediction model across the teaching iterations is high, and the model can achieve relatively high precision even when the new data are not sufficient to train a model alone. This work helped in timely intervention toward the at-risk students. In addition, two intervention experiments with split-test were conducted separately in Fall 2017 and Summer 2018. The statistical tests showed that both behavior-based reminding intervention and error-related recommending intervention that based on early prediction played a positive role in improving the blended learning engagement.

The data-driven approach is popular to automate learning of fuzzy rules and tuning membership function parameters in fuzzy inference systems (FIS) development. However, researchers highlight different challenges and issues of this FIS development because of its complexity. This paper evaluates the current state of the art of FIS development complexity issues in Computer Science, Software Engineering and Information Systems, specifically: 1)What complexity issues exist in the context of developing FIS? 2) Is it possible to systematize existing solutions of identified complexity issues? We have conducted a hybrid systematic literature review combined with a systematic mapping study that includes keyword map to address these questions. This review has identified the main FIS development complexity issues that practitioners should consider when developing FIS. The paper also proposes a framework of complexity issues and their possible solutions in FIS development. [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]

The challenges of Reproducibility and Replicability (R & R) in computer science experiments have become a focus of attention in the last decade, as efforts to adhere to good research practices have increased. However, experiments using Deep Learning (DL) remain difficult to reproduce due to the complexity of the techniques used. Challenges such as estimating poverty indicators (e.g., wealth index levels) from remote sensing imagery, requiring the use of huge volumes of data across different geographic locations, would be impossible without the use of DL technology. To test the reproducibility of DL experiments, we report a review of the reproducibility of three DL experiments which analyze visual indicators from satellite and street imagery. For each experiment, we identify the challenges found in the data sets, methods and workflows used. As a result of this assessment we propose a checklist incorporating relevant FAIR principles to screen an experiment for its reproducibility. Based on the lessons learned from this study, we recommend a set of actions aimed to improve the reproducibility of such experiments and reduce the likelihood of wasted effort. We believe that the target audience is broad, from researchers seeking to reproduce an experiment, authors reporting an experiment, or reviewers seeking to assess the work of others. Plain Language Summary: This paper aims to help researchers understand the challenges of reproducing Deep Learning (DL) publications, mitigate reproducibility gaps, and make their own work more reproducible. We build on the work of others and add recommendations organized by (a) the quality of the data set (and associated metadata), (b) the DL methodology, (c) the implementation methodology, and the infrastructure used. To our knowledge, this is the first initiative of its kind to address the problem of reproducibility in remote sensing imagery and DL problems for real‐world tasks. We hope this paper lowers the barrier to entry for the DL community to improve research. Following the lifecycle mantra: reproduce!, then replicate! With the goal of improving reproducibility! Key Points: We discuss the reproducibility challenges faced in research by Deep Learning approaches using Big DataWe provide advice for pre‐screening papers (before experiments) to avoid poorly invested effortWe present a recipe with a set of mitigation strategies to address common errors users (researchers, authors, reviewers) may encounter [ABSTRACT FROM AUTHOR]

The paper presents a bibliometric analysis from 2014 to 2020 of the emerging and engaging field of quantum computing called Quantum Machine Learning (QML). The study discusses the analysis results from the comprehensive high indexed databases worldwide such as Institute of Electrical and Electronics Engineers (IEEE), Scopus, Web of Science (WOS), Google Scholar and the Association for Computing Machinery (ACM). Tools like iMapbuilder, IBM and SPSS Statistics are used to provide meaningful insights and flawless representations of the extracted data. There has been little research to provide a macroscopic overview of renowned authors, subject areas, funding agencies and patent applications related to Quantum Clustering (QC). The result and analysis of this study show an interesting fact, most researchers are now aware of quantum technology from the past few years. The purpose of bibliometric and patentometric analysis papers is to figure out the importance and utility of the QC research area. Most of the countries are taking an initiative to seek attention towards QC but the analysis shows that China and the US are leading. The survey revealed that the maximum numbers of publications of QC are from Physics and Astronomy followed by Computer Science. [ABSTRACT FROM AUTHOR]

This issue of the Communications Standards Supplement includes updated versions of selected papers from the seventh International Telecommunications Union (ITU) Kaleidoscope Academic Conference. The conference took place in Barcelona, Spain, 9-11 December 2015, as part of the celebration of the ITU's 150th anniversary. The topic of the conference was "Trust in the Information Society", and the event was technically co-sponsored by the Institute of Electrical and Electronics Engineers (IEEE), the IEEE Communication Society, and the Institute of Electronics, Information and Communication Engineers of Japan (IEICE). The conference host was the Universitat Autonoma de Barcelona, Spain. [ABSTRACT FROM AUTHOR]

Security aspects play an important role in almost all of the application scenarios given the vulnerabilities inherent in wireless ad hoc networking from the very fact that radio communication takes place (e.g. in tactical applications) to routing, man-in-the-middle and elaborate data injection attacks. Security has become a primary concern in order to provide protected communication between mobile nodes in a hostile environment. It is vital to protect the network from different kinds of security threats. This paper proposes a security solution for manets using a pre-existing routing protocol, ad hoc ondemand vector routing (aodv). Here the improvement is done by the inclusion of fuzzy logic approach. The fuzzy logic will analyse the network and perform the rerouting based on fuzzy decision. It will perform the routing over intrusion safe path. [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 emergence of new technologies such as AI, machine learning, and so on drives the discussion over human consciousness among researchers from various disciplines, particularly those from philosophy of mind and computer science. This is true since certain academic theories argue that new technologies like those are much more efficient at simulating human tasks. This allows the discussion concerning human consciousness to acquire attention among researchers interested in learning more about human consciousness. As a result, this study provides a bibliometric analysis of human consciousness research from 2000 to 2022 to provide researchers and academics worldwide with an overview of the outcomes and trends in human consciousness research. A textual query on two databases; Scopus (1413 articles), and PubMed (258 papers) using the term "human consciousness" was conducted on 16 August 2022 by collecting 1671 academic works from 2000 to 2022 relating to human consciousness research for in-depth study. Bibliometric analyses were performed using Rstudio software version 4.2.0 and biblioshiniy for bibliometrix to show the current of human consciousness research. This bibliometric study examined the yearly scientific publishing growth, the most productive authors, most often term that has been utilised, most renowned journal name, and which nation has the maximum cooperation with other country. According to the findings of the study, there is a significant inconsistency in worldwide patterns in yearly scientific output, with the number of publications growing and declining. Among all nations, United States of America (USA) published the most papers around human consciousness study. According to the research, it has shown that the most relevant contributors is Jun'ichi Takeno (Scopus), and Brian L Edlow (PubMed). Moreover, the most famous journal in human consciousness research includes Journal of Consciousness Studies (Scopus), and Frontiers in Psychology (PubMed). The significance of this study may aid new researchers in this area by offering information on important publications and authors to refer while undertaking research on this issue. Furthermore, this research helps other researchers understand current trends in this field of study. As an outcome, the objective for conducting this study is to give bibliometric analysis and to fulfill research gaps in bibliometric studies of human consciousness by giving information in the form of an overview, and suggestions. [ABSTRACT FROM AUTHOR]

In our study, we examine the impact of citation network structures on the ability to discern valuable research topics in Computer Science literature. We use the bibliographic information available in the DBLP database to extract candidate phrases from scientific paper abstracts. Following that, we construct citation networks based on direct citation, co-citation and bibliographic coupling relationships between the papers. The candidate research topics, in the form of keyphrases and n-grammes, are subsequently ranked and filtered by a graph-text ranking algorithm. This selection of the highest ranked potential topics is further evaluated by domain experts and through the Wikipedia knowledge base. The results obtained from these citation networks are complementary, returning valid but non-overlapping output phrases between some pairs of networks. In particular, bibliographic coupling appears to capture more unique information than either direct citation or co-citation. These findings point towards the possible added value in combining bibliographic coupling analysis with other structures. At the same time, combining direct citation and co-citation is put into question. We expect our findings to be utilised in method design for research topic identification. [ABSTRACT FROM AUTHOR]

Recent advances in arts education policy, as outlined in the latest National Core Arts Standards, advocate for bringing digital media into the arts education classroom. The promise of such Science, Technology, Engineering, Arts, and Mathematics (STEAM)-based approaches is that, by coupling Science, Technology, Engineering, and Mathematics (STEM) and the arts, new understandings and artifacts emerge that transcend either discipline. Evidence of this can be seen through fundamental shifts in both fields; in the arts, artists are expanding the creative potential for design through computational flexibility, which affords artists the ability to exceed the limitations of their tools. The infusion of the arts into STEM has shown to be equally transformative, with the emergence of tools and communities that not only engender new content understandings but also invite participation from populations historically underrepresented in STEM fields. Drawing on over a decade of research at the intersection of the arts, creativity, and new technologies from the Creativity Labs at Indiana University, this article theorizes the learning that takes place at effective couplings of STEAM to assist today's educators in realizing the potential for transformative experiences for learners of all levels. This article provides a synthesis of this past work across two compelling cases of STEAM-based tools, materials, and activities (i.e., the media-rich programming environment Scratch as well as the work the LilyPad Arduino used to create electronic textiles), incorporating findings from more than 50 peer-reviewed papers and books, and conceptually outlines an approach to "gathering STEAM" in arts education classrooms today. Implications are explored for policy makers in teacher education to think about preservice curriculum and field experiences; policy makers in arts education to think about tools needed in classrooms today; as well as how art education can play a critical role in STEM disciplines and offer solutions to address STEM pipeline challenges. Such efforts extend current and prior discussions in the arts education landscape about the use of new technologies, and draw our attention to how new technologies can be leveraged for artistic expression.

Many interventions have been proposed to improve the retention and graduation rates of engineering students. One such intervention is to use study groups for first-year college students; such groups provide a structured environment in which the students can learn course material from each other outside of class and can provide the students with a sense of community. In this paper, we report on the impacts fostered by study groups in first-year mathematics courses on the odds of retaining and graduating engineering students. Students who participated in the study groups are compared to students of similar academic preparation who did not participate in such groups. It is found that student participation in study groups is significantly associated with the higher odds of being retained in engineering studies through the first 3 years of college. The results reported here are not as certain for the effect of study group participation on 5-year graduation odds for engineering students and some possible reasons for this are discussed.