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This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network.

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.

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.

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.

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.

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.

(Series Information) European Papers - A Journal on Law and Integration, 2023 8(2), 647-663 | Article | (Table of Contents) I. Introduction. – II. Consumer law enforcement through computational investigations. – II.1. The CPC Regulation. – II.2. DSA. – III. Computational Measurements of Influencer Activity: a case study for digital enforcement. – IV. Consumer forensics: a new field for digital detectives. – IV.1. Consolidating data collection and analysis methods for consumer law. – IV.2. Classifying new forms of consumer harms. – IV.3. Developing new approaches to consumer protection. – IV.4. Exploring the legal and ethical implications of consumer forensics. – V. Conclusion. | (Abstract) Effective enforcement on digital markets is one of the most essential considerations for contemporary consumer law and policy. On digital markets, traders engage in very sophisticated commercial practices that are opaque to the average consumer. Online information is necessary for the monitoring of how companies engage in legal compliance, and where public authorities should intervene. This puts a lot of pressure on public administration to develop investigation and enforcement approaches that match the different consumer harms and needs arising out of digital markets. As it is virtually impossible to police technology practices without understanding the technologies and business models behind them, public authorities need to arm themselves with the means necessary to detect digital violations. This Article focuses on the digital enforcement of consumer protection law in the European Union and proposes a new field of research focused on the investigation and enforcement of consumer violations on digital markets in the form of “consumer forensics”. In the author’s opinion, consumer forensics is the answer to the question of how consumer enforcement regulation can become future-proof. As digitalization is rapidly affecting the way in which consumers are protected on the Internet, both the substantive and procedural dimensions of regulatory effectiveness will be impacted by evidence gathering to understand and further prove the existence of new online harms. To show the potential of this topic, the Article will offer some in-depth insights from a very specific topic of administrative scrutiny, namely measuring influencer marketing activities that are relevant for consumer protection.

Abstract 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.

As the world grapples with the pressing challenge of environmental sustainability, the need for innovative solutions to combat air pollution has become paramount. Air pollution is a complex issue that necessitates real-time monitoring of pollution sources for effective mitigation. This paper explores the potential of swarm algorithms applied as a novel and efficient approach to address this critical environmental concern. Swarm algorithms offer a promising framework for coordinating fleets of drones to collaboratively monitor and analyze air pollution sources. The unique capabilities of drones, including their agility, accessibility, and versatility, make them ideal candidates for aerial data collection. When harnessed in a swarm, these drones can create a dynamic and adaptable network that provides a more comprehensive and fine-grained understanding of air pollution dynamics. This paper delves into the conceptual foundations of using swarm algorithms in drone-based air pollution monitoring.

Software engineering (SE) and computer science (CS) programs in universities worldwide are marked by a gender gap, which subsequently translates into a gender gap at the industry level. However, there are positive activities that can help attract more women to these male-dominant professions. This study maps the literature related to the achievement of gender balance in SE and CS university-level education and identifies future research directions. More specifically, this article reports on a systematic mapping study of female-inclusive SE and CS tertiary education programs. The authors collected 882 publications between 2015 and 2022 from five databases (ACM, IEEE, Scopus, Web of Science, and Science Direct), selecting 143 peer-reviewed papers for further analysis. The results showed that the main academic contributors were researchers from the USA. The majority of the publications contained observations and explanations regarding the gender gap in computing education. However, an important part of the literature considered proposals and practical activities for achieving gender balance in SE and CS programs. Finally, the authors classified the literature related to female-inclusive SE and CS tertiary education programs, identified the main research focuses and regional distribution, and considered ideas for future research.

The application of computer science in management and economics is a rapidly growing field that combines the analytical and technological capabilities of computer science with the strategic and operational needs of management and economics. The main aim of this research paper is to analyze the main academic contributors, sources, and international collaborations from 2014 to 2022 in computer science in the areas of management and economics, as well as to analyze the main subtopics developed over time. Bibliometric techniques were used to carry out the literature review, which allows an objective analysis of the academic literature through quantitative indicators. The results reveal a significant shift towards data-driven decision making in management, with artificial intelligence and machine learning improving predictive analytics, operational efficiency, and economic forecasting and modeling, highlighting the essential role of digital transformation in these disciplines, with significant implications for researchers, practitioners and decision-makers. It concludes that all stakeholders should work to develop a more informed and innovative approach to maximize the exploitation of the potential offered by computational sciences in different contexts. This includes the integration of advanced computational tools to improve decision making and operational efficiency, or the exploitation of computational models for more effective forecasting and policy decision making, as well as the continuous analysis of emerging areas in this field, being aware of the ethical, privacy and security challenges presented by these technologies, in order to ensure a responsible and equitable application.

Summary: Automatic identification system (AIS) is a vessel-based system for the automatic broadcast and reception of vessel information, and it also supports data for trajectory prediction. Since the vessel’s sailing route is flexible and changeable and the AIS broadcast is unconfirmed, the trajectory varies greatly and the original AIS data contains some noisy trajectory, which leads to low prediction accuracy and stability. Therefore, to solve the above problem, this paper proposes a trajectory prediction method based on bidirectional gate recurrent unit (BiGRU) and trajectory direction vector (TDV) with attention mechanism. This paper firstly proposes a TDV to associate latitude and longitude with the course and speed. Then the paper proposes an attention mechanism to self-adaptively update weight to the TDV in different stages to eliminate unreasonable predicted trajectory points. Finally, this paper combines the TDV attention mechanism and the BiGRU network to train a vessel trajectory prediction model.

This paper aims to study the blockchain concept domain in the computer science field due to bibliometric study. Authors employed bibliometric and network analysis techniques to analyze existing literature. In total, 719 articles in the period of 2019 to August 2023 from the Web of Science (WOS) database were analyzed after applying search string, and criteria for inclusion and exclusion. Initial data screening involved the extraction of fundamental information, followed by data analysis based on co-occurrence, bibliographic coupling, and citation using special program software VOSviewer and R program. research areas "compute science" and "engineering". In addition to that, VOSviewer and R-based tools illustrate the application of text mining involves utilizing computational techniques to extract, analyze, and represent the key concepts and relationships within the field of blockchain technology. Data analysis primarily involved co-occurrence analysis, bibliographic coupling, co-authorship examination, citation analysis, and co-citation analysis. In the context of a blockchain concept thematic analysis, was applied clustering by coupling. Furthermore, it was conducted the thematic analysis to scrutinize the content of prior studies in the computer science field using clustering by coupling. Ranking of the authors, organizations, and countries was applied according to total link strength metric which was used to quantify the overall strength of connections between nodes within a network. Besides, citation analysis has also been conducted to assess the articles' ranking, considering both worldwide and localized citations. Bibliometric results indicate blockchain concepts within such thematic frameworks as access control scheme, identity management system, supply chain management, artificial intelligence integration, blockchain technology applications, and blockchain smart contract.

The paper presents a new algorithm for reflection symmetry detection, which is specialized to detect maximal symmetric patterns in an Earth observation (EO) dataset. First, we stress the particularities that make symmetry detection in EO data different from detection in other geometric sets. The EO data acquisition cannot provide exact pairs of symmetric elements and, therefore, the approximate symmetry must be addressed, which is accomplished by voxelization. Besides this, the EO data symmetric patterns in the top view usually contain the most useful information for further processing and, thus, it suffices to detect symmetries with vertical symmetry planes. The algorithm first extracts the so-called interesting voxels and then finds symmetric pairs of line segments, separately for each horizontal voxel slice. The results with the same symmetry plane are then merged, first in individual slices and then through all the slices. The detected maximal symmetric patterns represent the so-called partial symmetries, which can be further processed to identify global and local symmetries. LiDAR datasets of six urban and natural attractions in Slovenia of different scales and in different voxel resolutions were analyzed in this paper, demonstrating high detection speed and quality of solutions.

BIM technology is a breakthrough in the construction industry whose application is of great value, and the engineering design, engineering budget and cost control combined with BIM technology have been developed rapidly. Therefore, in this paper, BIM technology is adopted to optimise the construction mode, and the genetic algorithm is selected to solve the scheduling model of construction. By applying an interdisciplinary research method combined with computer science and technology, an intelligent optimisation model of construction can provide a new research perspective and technical method for the development of the construction industry.

Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to truncate data dimensions when dealing with data with multiple dimensions. This is not necessarily because of its merit in terms of mathematical algorithm, but partly because it is easy to conduct with commonly accessible statistical software. On the other hand, dimension reduction might not be the best analysis when modeling data with no more than 20 dimensions. Using state-of-the-art techniques, researchers in various research disciplines (e.g., computer vision) classified data with more than hundreds of dimensions with neural networks and revealed the inner structure of the data. Therefore, it might be more sophisticated to process human perception data directly with neural networks. In this paper, we introduce the multi-dimensional data analysis platform (MuDAP), a powerful toolbox for data analysis in cognitive science. It utilizes artificial intelligence as well as network analysis, an analysis method that takes advantage of data symmetry. With the graphic user interface, a researcher, with or without previous experience, could analyze multiple dimensional data with great ease.

In this paper, we address the problem of designing new formats of computer science orientation activities to be offered during high school students internships in Computer Science Bachelor degrees. In order to cover a wide range of computer science topics as well to deal with soft skills and gender gap issues, we propose a teamwork format, called smart rogaining, that offer engaging introductory activities to prospective students in a series of checkpoints dislocated along the different stages of a rogaine. The format is supported by a smart mobile and web application. Our proposal is aimed at stimulating the interest of participants in different areas of computer science and at improving digital and soft skills of participants and, as a side effect, of staff members (instructors and university students). In the paper, we introduce the proposed format and discuss our experience in the editions organized at the University of Genoa before the COVID-19 pandemic (2019 and 2020 waves).

Computer science is regarded one of the subjects best suited for teaching with ICT-supported CLIL, as the English language is the lingua franca of computing, and the computer science classroom is an ideal place to apply digital technologies. This paper aims to explore the role of content and language integrated learning (CLIL) in the teaching and learning of computer science in English – focusing especially on using information and communication technology (ICT). For this purpose, a systematic literature review based on the PRISMA protocol was carried out. A total of 31 documents published in the period 2011–2021 on CLIL in computer science and ICT in the CLIL environment were analysed. In this article, first, the advantages of using CLIL in computer science will be discussed, followed by illustrating the benefits and challenges of applying ICT in CLIL lessons. Finally, trends in research on technology enhanced CLIL will be presented, outlining existing findings as well as identifying research gaps in the field. The most important shortcomings and difficulties outlined in the related literature are the limited research on both CLIL and ICT supported CLIL in the subject of computer science, the inconsistent terminology for using ICT in CLIL, and the need in CLIL teacher training courses for the effective use of digital technologies. Therefore, the findings of this paper identify a variety of future research agendas in order to increase the effective, ICT-supported CLIL in the subject of computer science.

Summary: In forensic investigations, forensic intelligence is required for illicit drug profiling in order to allow police officers and law enforcements to recognize crime developments and adjust their actions. In the present paper, we propose a novel framework for Digital Forensic Drug Intelligence (DFDI) by fusing digital forensic and drug profiling data through intelligent cycles, where a targeted and iterative collection of evidence from diverse sources is a core step in the process of drug profiling. Drug profiling data combined with digital data from seized devices collected, examined, and analyzed will allow authorities to generate valuable information about illicit drug trafficking routes and manufacturing. Such data can be stored in seized illicit drug databases to build in an intelligent way, all findings, hypotheses and recommendations, allowing law enforcement to make decisions. Our framework will potentially provide a better understanding of profiling, trafficking and distribution of illicit drugs.

Summary: Severe weather conditions pose a significant challenge for computer vision algorithms in autonomous driving applications, particularly regarding robustness. Image rain-removal algorithms have emerged as a potential solution by leveraging the power of neural networks to restore rain-free backgrounds in images. However, existing research overlooks the vulnerability concerns in neural networks, which exposes a potential threat to the intelligent perception of autonomous vehicles in rainy conditions. This paper proposes a universal rain-removal attack (URA) that exploits the vulnerability of image rain-removal algorithms. By generating a non-additive spatial perturbation, URA significantly diminishes scene restoration similarity and image quality. The imperceptible and generic perturbation employed by URA makes it a crucial tool for vulnerability detection in image rain-removal algorithms and a potential real-world AI attack method. Experimental results demonstrate that URA can reduce scene repair capability by 39.5% and image generation quality by 26.4%, effectively targeting state-of-the-art rain-removal algorithms.

This paper attempts to link selected sādhanā protocols to software engineering and explains their effect on making the sādhanā more effective. Similarly, after having tested the described techniques, it suggests a number of positive results in applying these concepts and protocols to sādhanā. The application of sankalpa to these new areas helps to optimize the time management without losing the depth of the sādhanā.

This article analyzes the peculiarities of teaching English to students of faculties specializing in the study of computer science and Internet technologies. The distinctive features of the vocabular strata which are characteristic of the English language in this professional field are revealed. The study of professional vocabulary layers such as terms, idioms, collocations, abbreviations, set-expressions, jargon and slang is an important aspect of mastering English language. Proficiency and free orientation in these areas of the language are necessary for cross-cultural communication with colleagues from the scientific community, writing qualification papers, scientific articles and presentations as well as participation in international conferences. This will allow graduates of faculties related to computational mathematics and cybernetics to easily join their professional community, discuss problems, receive new information and grow in professional and scientific fields. The authors propose the principles of teaching construction and methodology, including the compilation of textbooks and materials for interactive learning platforms to let the students of the relevant faculties master this lexical base. Examples of tasks and exercises aimed at studying the necessary lexical volume as well as the keys to them are given. Tasks are based on lists of lexical units with explanations and fragments of materials from computer journals, conferences, articles, books and Internet resources containing these units to illustrate their use in speech. The authors successfully apply this methodology in the framework of teaching English to students at the intermediate, upper intermediate and advanced levels. This technique will allow training highly qualified specialists and scientific personnel in the targeted field on the basis of the Faculty of Computational Mathematics and Cybernetics of the Lomonosov Moscow State University and the corresponding faculties of other Higher Educational Institutions.

In this paper, we describe two related scripting methods of cartographic data processing and visualization that provide 2D and 3D mapping of Japan with different algorithm complexity. The first algorithm utilizes Generic Mapping Toolset (GMT), which is known as an advanced console-based program for spatial data processing. The modules of GMT combine the functionality of scripting with the aspects of geoinformatics, which is especially effective for the rapid analysis of large geospatial datasets, multi-format data processing, and mapping in 2D and 3D modes. The second algorithm presents the use of the R programming language for cartographic visualization and spatial analysis. This R method utilizes the packages ‘tmap’, ‘raster’, ‘maps’, and ‘mapdata’ to model the morphometric elements of the Japanese archipelago, such as slope, aspect, hillshade and elevation. The general purpose graphical package ‘ggplot2’ of R was used for mapping the prefectures of Japan. The two scripting approaches demonstrated an established correspondence between the programming languages and cartography determined with the use of scripts for data processing. They outperform several well-known and state-of-the-art GIS methods for mapping due to their high automation of data processing. Cartography has largely reflected recent advances in data science, the rapid development of scripting languages, and transfer in the approaches of data processing. This extends to the shift from the traditional GIS to programming languages. As a response to these new challenges, we demonstrated in this paper the advantages of using scripts in mapping, which consist of repeatability and the flexible applicability of scripts in similar works.

Characterization of intertextual references among authors is fundamental for the study of Latin literature. In this paper, we describe a large-scale intertextuality dataset compiled from three modern commentaries on Valerius Flaccus’ epic poem Argonautica. The dataset includes 945 references to earlier and contemporary Roman authors, as well as associated metadata required for use of multiple intertext search tools. To illustrate the dataset’s reuse potential, we perform a new benchmark analysis of Fīlum, a sequence alignment tool for intertextuality detection.

This paper explores a series of thoughts about the meaning of intelligence in neuroscience and computer science. This work aims to present an understandable definition that fits our contemporary artificial intelligence background. The research methodology of this essay lies in existing theories of artificial intelligence, focused on computer science and neuroscience. I analyze the relationship between intelligence and neuroscience and Hawkin’s Thousand Brains Theory, an approach to show what it is an intelligent agent according to neuroscience. Here, the main result relies on the verification that intelligence is only possible in the neocortex. According to this result, the study performs a second critical analysis aiming to demonstrate why there is no artificial intelligence today.

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.

With sensors becoming increasingly ubiquitous, there is tremendous potential for innovative Internet of Things (IoT) applications across a wide variety of domains, including healthcare, agriculture, entertainment, environmental monitoring, and transportation. The rapid growth of IoT applications has increased the demand for experienced professionals with strong IoT hands-on skills. However, undergraduate students in STEM education still lack experience in how to use IoT technologies to develop such innovative applications. This is in part because the current computing curricula do not adequately cover the fundamental concepts of IoT. This paper presents a case study from integrating innovative IoT technologies into the Computer Science (CS) curriculum at Prairie View A&M University (PVAMU). This paper presents a set of IoT learning modules that can be easily integrated into existing courses of CS curriculum to engage students in smart-IoT. The modules developed have been used to introduce a new project-based course in the CS department at PVAMU that focuses on intelligent IoT technologies. Findings from external evaluation of the curricular change are also presented. These note positive impacts on student interest in and learning about IoT across multiple courses and semesters.

Literature suggests that the lack of female involvement in computer science (CS) is detrimental to the field. The disconnect between females and CS is not due to their ability to succeed in the su ject, but rather the perceptions and stigmas surrounding it. This paper will focus on female interest in CS at a medium sized suburban middle school in Western Pennsylvania. The two guiding points for the paper include 1) the idea that female role models are thought to be crucial in the advancement of closing the gender division and 2) Harvey Mudd College’s approach to female CS learning. The measure of each subject’s CS interest was collected through pre and post-surveys during workshops led by the researcher. The paper ultimately argues that yes, the girls at the specified middle school are more interested in computer science after engaging in creative problem solving led by a female role model.

In this paper we investigate groups which admit the existence of weighted consistent approximations for pairwise comparisons matrices. These approximations are defined by extending the classical matrix projection for R_{+} to abstract weighted projections on the non-linear sets of transitive group-valued matrices. It is of interest that all of them are represented by general explicit formulae dependent on an abstract logarithmic function. This general approach is applied to the groups Z^{∗}_{p} and F^{∗}_{2m} which are of fundamental importance in in cryptography. Finally, we use our unified mathematical model of pairwise comparisons for continuous one-parameter unitary groups, which play a fundamental role in physics.

Computer science concepts have an important part in other subjects and thinking computationally is being recognized as an important skill for everyone, which leads to the increasing interest in developing computational thinking (CT) as early as at the comprehensive school level. Therefore, research is needed to have a common understanding of CT skills and develop a model to describe the dimensions of CT. Through a systematic literature review, using the EBSCO Discovery Service and the ACM Digital Library search, this paper presents an overview of the dimensions of CT defined in scientific papers. A model for developing CT skills in three stages is proposed: i) defining the problem, ii) solving the problem, and iii) analyzing the solution. Those three stages consist of ten CT skills: problem formulation, abstraction, problem reformulation, decomposition, data collection and analysis, algorithmic design, parallelization and iteration, automation, generalization, and evaluation.

The volume and diversity of scientific literature are escalating every day and millions of new scientific articles are published every year. Researchers work and publish in their respective fields of interest. A major portion of the scientific community publishing in the same field of interest forms a trend in the field which could be deemed as relatively more popular than other trends. A pioneering researcher picks a field of interest by depending upon its popularity. This may have a positive impact on the acceptance of a study or high count of citations in future. This study identifies how significant it is to follow a research trend and the impact of the field of study (FoS) trend on research paper citations. For this purpose, we have chosen the field of Computer Science and Microsoft Academic Graph dataset from the 2007-2015 time period. In the dataset, every paper has a list of fields of study. The FoS provided in MAG is systematized hierarchically into 4 levels; level-0 - level-3. In this study, we apply the clustering technique to the FoS and citations pattern separately. Likewise, we also analyze how papers following a FoS trend, gain citations over the time. We also introduce a novel method Field of Study Multigraph (FoM) using graph centrality measures degree, betweenness and closeness to analyze the FoS trend, citation trend, and the relation between research areas in scientific articles from the domain of Computer Science. The experimental results show that the FoS has a certain impact on citation count. Furthermore, the results depicts that if papers belong to the same FoS, then there are 66% of the chances of having a similar citation pattern and that they have the same citation trend as they also achieved a high correlation value. This proves that a FoS has a certain impact on the citation count of a paper and researchers should contemplate the FoS trend before selecting a particular research area.

This paper presents results of bibliometric and text-based analysis of Computer Science research output produced by academia from the Republic of Moldova during the 2013-2018 years. The authors have collected data for research output indexed in three databases: Web of Science (WoS), Scopus, and National Bibliometric Instrument (NBI). Data from these databases were collected to obtain important indicators such as total number of research papers and citations, citation impact, h-index, collaboration patterns, top institutions, authors and publication sources. A detailed comparative analysis was performed based on these data. The authors also have performed a text-based analysis on keywords of papers indexed to identify thematic trends for research in Computer Science field. The analytical results present a detailed and useful picture of the status and the competence in Computer Science research in the Republic of Moldova.

Summary: Materials science literature has grown exponentially in recent years making it difficult for individuals to master all of this information. This constrains the formulation of new hypotheses that scientists can come up with. In this work, we explore whether materials science knowledge can be automatically inferred from textual information contained in journal papers. Using a data set of 0.5 million polymer papers, we show, using natural language processing methods that vector representations trained for every word in our corpus can indeed capture this knowledge in a completely unsupervised manner. We perform time-based studies through which we track popularity of various polymers for different applications and predict new polymers for novel applications based solely on the domain knowledge contained in our data set. Using co-relations detected automatically from literature in this manner thus, opens up a new paradigm for materials discovery.

Summary: Social media and online navigation bring us enjoyable experiences in accessing information, and simultaneously create information cocoons (ICs) in which we are unconsciously trapped with limited and biased information. We provide a formal definition of IC in the scenario of online navigation. Subsequently, by analyzing real recommendation networks extracted from Science, PNAS, and Amazon websites, and testing mainstream algorithms in disparate recommender systems, we demonstrate that similarity-based recommendation techniques result in ICs, which suppress the system navigability by hundreds of times. We further propose a flexible recommendation strategy that addresses the IC-induced problem and improves retrieval accuracy in navigation, which are demonstrated by simulations on real data and online experiments on the largest video website in China. This paper quantifies the challenge of ICs in recommender systems and presents a viable solution, which offer insights into the industrial design of algorithms, future scientific studies, as well as policy making.

The paper maps quantum computing research on various publication and citation indicators, using data from Scopus database covering 10-year period 2007-16. Quantum computing research cumulated 4703 publications in 10 years, registered a slow 3.39% growth per annum, and averaged 14.30 citations per paper during the period. Top 10 countries dominate the field with 93.15% global publications share. The USA accounted for the highest 29.98% during the period. Australia tops in relative citation index (2.0). International collaboration has been a major driver of research in the subject; 14.10% to 62.64% of national level output of top 10 countries appeared as international collaborative publications. Computer Science is one of the most popular areas of research in quantum computing research. The study identifies top 30 most productive organizations and authors, top 20 journals reporting quantum computing research, and 124 highly cited papers with 100+ citations per paper.

This study aims to evaluate the impact of using augmented reality, gamification, and serious games in computer science education. The study presents the development process of an educational mobile application, describes an experiment that was conducted and involved 117 higher education students, and analyzes the results of a 49-item paper-based questionnaire. In total, 8 research questions were explored. The results of the study revealed that several educational benefits can be yielded when integrating such applications in teaching and learning activities and actively involving students in the design and development process. In particular, the application was assessed as an effective learning tool that could enrich and improve the educational process and create interactive, inclusive, and student-centered learning environments. Its use led mostly to positive effects and experiences while maintaining the negative ones to a minimum and most students expressed positive emotions. Students were able to learn in a more enjoyable and interesting manner, and their motivation, engagement, self-efficacy, and immersion were greatly increased. Students’ innate need for autonomy, competence, and relatedness was satisfactorily met and both their intrinsic and extrinsic learning motivations were triggered. They felt a sense of belonging and cultivated their social skills. The potential of the application to improve students’ knowledge acquisition and academic achievements was also observed. The application also enabled students to improve their computational thinking and critical thinking skills. Therefore, the potential of combining augmented reality, gamification, and serious games to enhance students’ cognitive and social–emotional development was highlighted.

The characterisation of geometric shapes produces their concise description and is, therefore, important for subsequent analyses, for example in Computer Vision, Machine Learning, or shape matching. A new method for extracting characterisation vectors of 2D geometric shapes is proposed in this paper. The shape of interest, embedded into a raster space, is swept several times by sweep-lines having different slopes. The interior shape’s points, being in the middle of its boundary and laying on the actual sweep-line, are identified at each stage of the sweeping process. The midpoints are then connected iteratively into chains. The chains are filtered, vectorised, and normalised. The obtained polylines from the vectorisation step are used to design the shape’s characterisation vector for further application-specific analyses. The proposed method was verified on numerous shapes, where single- and multi-threaded implementations were compared. Finally, characterisation vectors, among which some were rotated and scaled, were determined for these shapes. The proposed method demonstrated a good rotation- and scaling-invariant identification of equal shapes.

For research domains such as life sciences, which pursue fundamental scientific understanding and applications intended for immediate use, academic entrepreneurship has played a pivotal role in commercialization. This paper presents an evaluation method of researchers related to user-inspired fundamental research, using global databases of startup finances and academic research papers of ''startup readiness.'' Case studies of startups related to biopharmaceutical research topics suggest that the biopharmaceutical field has rich opportunities stemming from scientific research, commercialization, and entrepreneurship. This evaluation method sorts specific industry segments by which financing activities are active, and by which related growing research topics attract increased academic attention. We constructed networks of author citation and co-authorship from paper citation networks related to research topics in industry segments in the biopharmaceutical domain. Results obtained across all research topics we surveyed demonstrated that authors in the top 10% of degree centrality ranking in both networks are far more likely to be startup participants than other authors. Our computational approach might provide convenient, dynamic, global, and real-time understanding of the “startup readiness” of researchers working with research topics for which academic attention is emerging in actively financed biopharmaceutical fields.

This paper intends to present the work done during the Summer School Tran(s)missions that took place in synchronous and asynchronous form from 10 to 19 September 2021 at the Department of Humanities, University of Roma Tre. The course, presented in collaboration with the Virtual Humanities Lab of Brown University, was attended by 13 students, including researchers, artists, curators, library science specialists, engineers, and technical archivists conducting interdisciplinary studies in the field of Italian Studies and visual culture, opening up their own perspectives to investigation the relationship between word and image, visual culture, media studies, material culture studies, film studies, digital storytelling, creative writing, digital and public humanities, visual arts, art history, history, and computer science.

Eye gaze tracking has been used to study the influence of visual stimuli on consumer behavior and attentional processes. Eye gaze tracking techniques have made substantial contributions in advertisement design, human computer interaction, virtual reality and disease diagnosis. Eye gaze estimation is considered critical for prediction of human attention, and hence indispensable for better understanding human activities. In this paper, Latent Semantic Analysis is used to develop an information model for identifying emerging research trends within eye gaze estimation techniques. An exhaustive collection of 423 titles and abstracts of research papers published during 2005–2018 were used. Five major research areas and ten research trends were classified based upon this study.

This paper presents software fault detection, which is dependent upon the effectiveness of the testing and debugging team. A more skilled testing team can achieve higher rates of debugging success, and thereby removing a larger fraction of faults identified without introducing additional faults. A complex software is often subject to two or more stages of testing that exhibits distinct rates of fault discovery. This paper proposes a two-stage Enhanced neighborhood-based particle swarm optimization (NPSO) technique with the assimilation of the three conventional non homogeneous Poisson process (NHPP) based growth models of software reliability by introducing an additional fault introduction parameter. The proposed neuro and swarm recurrent neural network model is compared with similar models, to demonstrate that in some cases the additional fault introduction parameter is appropriate. Both the theoretical and predictive measures of goodness of fit are used for demonstration using data sets through NPSO.

An experimental study was conducted to predict the student's awareness of Information and Communication Technology (ICT) and Mobile Technology (MT) in Indian and Hungarian university's students. A primary dataset was gathered from two popular universities located in India and Hungary in the academic year 2017–2018. This paper focuses on the prediction of two major parameters from dataset such as usability and educational benefits using four machine learning classifiers multilayer perceptron (ANN), Support vector machine (SVM), K-nearest neighbor (KNN) and Discriminant (DISC). The multi-classification problem was solved with test, train and validated datasets using machine learning classifiers. One hand, feature aggregation with the train-test-validation technique improved the ANN's prediction accuracy of educational benefits for both countries. Another hand, ANN's accuracy decreases significantly in the prediction of usability. Further, SVM and ANN outperformed the KNN and the DISC in the prediction of awareness level towards ICT and MT in India and Hungary. Also, this paper reveals that the future awareness level for the educational benefits will be Very High or Moderate in both countries. Also, the awareness level is predicted as High and Moderate for usability parameter in both countries. Further, ANN and SVM accuracy and prediction time is compared with T-test at 0.05 significance level which distinguished CPU training time is taken by ANN and SVM using K-fold and Hold out method. Also, K-fold enhanced the significant prediction accuracy of SVM and ANN. the authors also used a STAC web platform to compare the accuracy datasets using T-test and ANOVA test at 0.05 significant level and we found ANN and SVM classifier has no significant difference in prediction accuracy in each dataset. Also, the authors recommend presented predictive models to be deployed as a real-time module of the institute's website for the real-time prediction of ICT & MT awareness level.

The purpose of this paper is to give some insights into the immense role of Frege’s first order logic (FOL) in the development of computer science. We argue that the FOL is fundamental in computer science, and that some computer science subfields could not have existed without their theoretical foundations built on this form of logic. Among these subfields, one can mention the Type Theory, Databases, Descriptive Complexity, Artificial Intelligence, Logic Programming, and Automated Theorem Proving. To illustrate our point, an in-depth attention will in particular be given to the foundational development of the most popular logic programming language, PROLOG, and the Automated Theorem Proving (ATP) systems. Importantly, when studying the interactions between logic and computer science in the literature, we can observe a significant gap in the provision of the appropriate abstraction level. Specifically, we often encounter two different levels of abstraction. The first of these is relatively high even when describing technical notions in computer science, which obviously produces a lack of precision. The second adopts a technical-oriented approach which easily makes the topic and discussion unintuitive or inaccessible to the non-specialist. The paper attempts to remedy these problems by adopting a balanced approach that provides a moderate level of abstraction that targets a deeper understanding of the topic without imposing a very technical presentation on the reader

We have engaged in a significant restructuring of our computer science curriculum. This paper describes the process that we followed and illustrates the generalizable approach through a case study. We also demonstrate that the revision had several positive outcomes that went beyond our expectations. The case study describes a computer science computer program revision that sets goals to ensure the program's long-term viability, content alignment with labor market expectations, reasonable alignment with accreditation standards, and student success, diversity, and retention. The study demonstrated an increased ability for students to personalize their educational experience, leading to a clearly identifiable program and an improved value proposition. It also documents how the process resulted in creating a one-credit orientation seminar that contributes to increased student retention and enhances diversity in the major.

In this paper, a network consisting of an object localization module and a discriminative feature extraction module is designed for fine-grained image retrieval (FGIR). In order to reduce the interference of complex backgrounds, the object localization module is introduced into the network before feature extraction. By selecting the convolutional feature descriptors, the main object is separated from the background, and thus, most of the interference is filtered out. Further, in order to improve the overall performance of the network, a discriminative filter bank is introduced into the network as the local feature detector. Hence, the local discriminative features can be extracted directly from the original feature map. The experimental results based on the CUB-200-2011 and Cars-196 datasets demonstrate that the proposed method can improve the performance of FGIR.

Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task. Therefore, the early and steadily growing interest of computer science educators in the automated assessment of programming exercises is not surprising. The automated assessment of programming assignments has been an active area of research for over a century, and interest in it continues to grow as it adapts to new developments in computer science and the resulting changes in educational requirements. It is therefore of paramount importance to understand the work that has been performed, who has performed it, its evolution over time, the relationships between publications, its hot topics, and open problems, among others. This paper presents a bibliometric study of the field, with a particular focus on the issue of automatic feedback generation, using literature data from the Web of Science Core Collection. It includes a descriptive analysis using various bibliometric measures and data visualizations on authors, affiliations, citations, and topics. In addition, we performed a complementary analysis focusing only on the subset of publications on the specific topic of automatic feedback generation. The results are highlighted and discussed.

This paper aims to find the actuarial tables that best represent the occurrences of mortality and disability in the Brazilian Armed Forces, thus providing a better dimensioning of the costs of military pensions to be paid by the pension system. To achieve this goal, an optimization software was developed that tests 53 actuarial tables for the death of valid military personnel, 21 boards for entry into the disability of assets, and 21 boards for mortality of invalids. The software performs 199 distinct adherence tests for each table analyzed through linear aggravations and de-escalations in the probabilities of death and disability. The statistical–mathematical method used was the chi-square adherence test in which the selected table is the one with the null hypothesis “observed data” equal to the “expected data” with the highest degree of accuracy. It is expected to bring a significant contribution to society, as a model of greater accuracy reduces the risk of a large difference between the projected cost and the cost observed on the date of the year, thus contributing to the maintenance of public governance. Additionally, the unprecedented and dual nature of the methodology presented here stands out. As a practical contribution, we emphasize that the results presented streamline the calculation of actuarial projections, reducing by more than 90% the processing times of calculations referring to actuarial projections of retirees from the armed forces. As a limitation of the study, we emphasize that, although possibly replicable, the database was restricted only to the Brazilian Armed Forces.

Summary: One of the top priorities in any laboratory is archiving experimental data in the most secure, efficient, and errorless way. It is especially important to those in chemical and biological research, for it is more likely to damage experiment records. In addition, the transmission of experiment results from paper to electronic devices is time-consuming and redundant. Therefore, we introduce an open-source no-code electronic laboratory notebook, Elegancy, a cloud-based/standalone web service distributed as a Docker image. Elegancy fits all laboratories but is specially equipped with several features benefitting biochemical laboratories. It can be accessed via various web browsers, allowing researchers to upload photos or audio recordings directly from their mobile devices. Elegancy also contains a meeting arrangement module, audit/revision control, and laboratory supply management system. We believe Elegancy could help the scientific research community gather evidence, share information, reorganize knowledge, and digitize laboratory works with greater ease and security.

Basic Digital Education (BDE) is already planned to be integrated with the forthcoming curriculum for Austrian primary schools (6–10 years) as it was already implemented for lower secondary schools (10–14 years) in 2018. BDE includes the most essential and novel developments of Computational Thinking (CT), which are fundamentally responsible for nurturing students' problem-solving skills. Thus, evaluating teaching materials, scaffolding guidelines, and assessments is becoming increasingly important for the successful implementation of CT in Austrian classrooms. This study is a part of a longitudinal multi-cycle educational design research project aiming to explore how to foster CT and to raise the awareness, importance, and confidence of teachers and students in applying CT for everyday uses. Our paper focuses on a sub-study in which teaching units for grade 3 and 4 students (8–10 years) were designed by combining an Open Educational Resource (OER) textbook and Physical Computing with the micro:bit device. The designed learning environment consists of three units and was implemented in two classes over 3 weeks. The two classes were further split into two groups each, to ensure better support during implementation. The class teachers received upfront teacher training and conducted pre- and post-test assessments with the students. The resulting data was then analyzed to gain insights into the effects on CT skills of the young learners. Results showed that combining block-based programming and physical computing devices could become a promising approach to promote computational thinking skills in lower school grades. Furthermore, the observed direction of the designed units supports low-barrier access to increase the desired uses of CT in classrooms.

The paper is devoted to the pilot implementation of CPD programs for universities academic staff advanced training in the International Computer Science Continuous Professional Development Centre of Tomsk State University. The Centre was created within the framework of the project “Human Resources for the Digital Economy” of the national program “Digital Economy of the Russian Federation”. The article presents the structure of CPD programs by tracks: research and development, advanced educational technologies and academic programs of a new generation in the IT field. The authors discuss the results of online training of more than 1000 academic stuff members from more than 100 universities for 4 months in the second half of 2020.