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In their paper 'RIS-Assisted Secure UAV Communications with Resource Allocation and Cooperative Jamming', Guo et al. combine an RIS with unmanned aerial vehicles (UAVs) to improve the physical layer security of a wireless network. Current 5G wireless systems depend heavily on the quality of the wireless propagation environment, which had conventionally been modelled as an exogenous entity that can only be adapted and not controlled. This new vision of wireless network design is enabled by an emerging technology of reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs). [Extracted from the article]

An automatic computer vision algorithm that detects individual paper fibres from an image, assesses the possibility of grasping the detected fibres with microgrippers and detects the suitable grasping points is presented. The goal of the algorithm is to enable automatic fibre manipulation for mechanical characterisation, which has traditionally been slow manual work. The algorithm classifies the objects in images based on their morphology, and detects the proper grasp points from the individual fibres by applying given geometrical constraints. The authors test the ability of the algorithm to detect the individual fibres with 35 images containing more than 500 fibres in total, and also compare the graspability analysis and the calculated grasp points with the results of an experienced human operator with 15 images that contain a total of almost 200 fibres. The detection results are outstanding, with fewer than 1% of fibres missed. The graspability analysis gives sensitivity of 0.83 and specificity of 0.92, and the average distance between the grasp points of the human and the algorithm is 220 µm. Also, the choices made by the algorithm are much more consistent than the human choices. [ABSTRACT FROM AUTHOR]

Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph‐based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real‐world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation‐based methods were applied to the entities and relationships to generate their low‐dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation‐based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques have made a breakthrough in many different research fields, such as computer vision, natural language processing, network management and mobile communication. One of the primary goals in 5G network pursues high efficiency, higher spectrum and energy efficiency, higher system capacity and data rates, and lower latency with cost reduction. The papers on the topic of "AI-enabled intelligent networks for 5G and beyond" contain novel network architectures and mechanisms by using AI/ML/DL approaches for 5G/B5G networks. [Extracted from the article]

An editorial is presented on deep learning and big data analytics for processing and analyzing data in 5G and 6G applications. Topics include research activities and areas such as bioinformatics, beyond 5G and 6G communications, healthcare, internet of things (IoT), manufacturing business, and social networks; and providing services with massive connectivity, ultra-low latency, extremely high security, extremely low energy consumption, and ultra-high data-rate.

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]

Summary: Art painting is a very important major in China's education system. Under the background of the new era, the country vigorously promotes the modernization of education and the development of intelligence. Many subjects of teaching have been more mature and carried out intelligent teaching based on virtual reality technology. But throughout the art painting profession, the research on this aspect is almost blank. The main reason is that there are deep academic barriers between art painting and computer science. Therefore, this paper puts forward the application research of multi‐source information art painting fusion interactive 3D dynamic scene virtual reality technology. This paper deeply analyzes the needs of art painting and its teaching reform and believes that the existing virtual reality technology cannot adapt to the teaching environment of art painting. In view of the possible inaccurate calculation and other problems, this paper puts forward the optimization scheme. The core of the scheme is to use the information integration advantages of multi‐source information fusion to provide more comprehensive and complete object information for 3D modeling of virtual reality technology, so as to improve the imaging quality of virtual reality technology. In order to meet the requirement of high precision, the calculation method is optimized, the calculation steps are simplified, and the sensor selection is optimized again. This paper introduces the design principle and operation steps of the optimization method in detail. In order to further verify the actual effect of this method, comparative analysis method is used to test the comprehensive performance. In order to ensure the test quality, the median filtering algorithm and fuzzy algorithm are compared. Through the analysis and comparison of the data, the optimization method in this paper greatly improves the accuracy of calculation and the comprehensive performance is improved. [ABSTRACT FROM AUTHOR]

Three‐dimensional reconstruction of medical tomographic images is a technological way to provides a 3D model of biological tissue. As it not only provides a 3D model, but also shows the spatial location of this model, it received widespread attention. By human‐computer interaction, doctors can easily conduct disease analysis and surgical planning. This paper introduces a novel three‐dimensional reconstruction technology of magnetic resonance images, which deals with the brightness, contrast adjustment, and three‐dimensional reconstruction of Dicom images. This paper uses the Visualization Toolkit VTK to implement Dicom 3D image reconstruction. VTK has a large number of callable functions built in and encapsulates some common visualization algorithms. When visualizing medical data, VTK can directly call the related functions. VTK three‐dimensional reconstruction can help people intuitively see the contents of a 3D image. Displaying two‐ dimensional images is easy, but displaying 3D images is not. Previously, because of the limited technical means, even if the data of the 3D image is obtained, it can only be displayed as a slice of the 2D image, which is not intuitive. With the development of computer science and technology, the use of computer graphics technology to model and render 3D objects in real‐time has become the mainstream method of object display, while the past two‐dimensional display method has been gradually phased out. To describe the process of 3D modeling more accurately and intuitively, this paper focuses on a reconstruction technique, polygon drawing. Polygon drawing uses segmentation technology to perform contour recognition, extraction, and other operations on a series of two‐dimensional images, and ultimately restores the three‐dimensional model of the detected object and displays it as a surface. First, the original data is a discrete three‐dimensional spatial rule data field. The images generated by CT and MRI of tomography scanners belong to this data type, that is, the DICOM type image used in this paper. Second, the boundary between the content part and the background part in the three‐dimensional image is found in the unit of volume, and triangles are extracted to fit the boundary. Then, all the voxels are traversed to find out which triangles are finally assembled to form a triangular mesh of the surface of the real point in the image. Finally, a triangle mesh model is built to render the model, and the rendered model is put into the window to interact with users. [ABSTRACT FROM AUTHOR]

Security is an afterthought process for the development of software in earlier days but now the time has been changed. Now, security is on top priority and involved from the beginning of software development. Security requirements are the prime concern for the development and quality of any software product. The specification and verification of security requirements need a lot of attention from the computer science community in the process of the software development life cycle. Formal Methods are a widely used and well‐recognized approach for the specification and verification of any safety‐critical system. Formal methods play an important role in the requirement phase to the design phase of software development. In this study, we summarized the outcomes of related papers to find out the current state of the art in the proposed area. In this manuscript, three research questions are frame and we try to find the answer to these research questions to the best of our effort and knowledge. The objective of this research paper is to find out the gap analysis, state of art, and trends in the proposed area. The academician needs to pursue more effort toward the formal specification of security requirements, providing a deeper understanding to help security experts in the development of systems. [ABSTRACT FROM AUTHOR]

Artificial neural networks are an integral part of the curriculum in many undergraduate computer science programs. This paper introduces NNeduca, a simulation software system designed to teach undergraduate students the fundamental concepts of artificial neural networks. The system is written in Java and aims to provide an intuitive user interface, support for research and learning activities, and the ability to expand. This tool allows students to design and train a neural network with any architecture they desire and visualize some artificial neural network theoretical concepts such as definitions, topologies, training methods, and structure. An overview of educational software systems to aid in learning the fundamental concepts of neural networks was created based on a systematic literature review. A comparison of existing simulation systems and the developed NNeduca tool was performed in this paper. The evaluation was carried out using a newly developed model for assessing the quality of selected systems using established criteria formed as a result of a thorough examination of relevant methods of evaluating educational software. The usability evaluation results show that the NNeduca tool received the highest ratings, indicating that it can significantly improve the teaching process in artificial neural network courses. [ABSTRACT FROM AUTHOR]

Purpose: To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. Methods: We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. Findings A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty‐one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN‐Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting‐edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. Conclusions: Although AI has been paid more and more attention to the field of nursing, there is still a lack of high‐yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi‐disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions. [ABSTRACT FROM AUTHOR]

This paper considers relatively long‐term possibilities for the future relationship between humans and superintelligent robots (SRs). The great technological developments in fields such as artificial intelligence (AI), robotics and computer science have made it quite likely that we will see the advent of SRs towards the end of this century (or somewhat later). If SRs have a higher moral and legal status than typical adult human beings based on their greater psychological capacities, then they should also be entitled to human rights. However, even though SRs might be entitled to stronger moral and legal rights for this reason, it might nonetheless be necessary to limit their (otherwise justified) claims to avoid causing human beings to become extinct or endangered. The paper provides an argument in support of SRs' claims to human rights but also warns about the socio‐political, moral and legal implications of taking such a step. [ABSTRACT FROM AUTHOR]

The launch of a Computer Science curriculum specification in upper secondary schools in Ireland in 2018 was a landmark and a historic development in Irish education. Addressing the historical policy decisions adopted towards establishing the specification, this paper presents an analysis of developments from the 1970s as revealed in key policies and other documents. Positioning the policy change within the context of influences, context of policy text production and within the context of practices, the paper presents an overview of the Irish Computer Science specification. Alongside the background to the evolution in computer science education over the period, the article takes into account the national and cultural contexts and narrates the journey travelled to arrive at this pivotal position. Developments in a global context are presented in comparison with curricula in other countries and some similarities and differences identified. The article uncovers consequences of the specification for the national curriculum, key skills integration and computer science teacher preparation. The article provides an important analysis of the policy trajectory of computer science in Ireland based on a visibly relevant corpus of documents tracing the different stages of this policy and comparing it to similar experiences implemented in other European countries. [ABSTRACT FROM AUTHOR]

This document is an editorial summarizing the Applied Geometric Algebras in Computer Science and Engineering (AGACSE) conference held in Brno, Czech Republic in September 2021. The conference aimed to promote the use of geometric algebra in fields such as image processing, robotics, and quantum computing. The conference proceedings were published in the journal Mathematical Methods in the Applied Sciences. The editorial provides a list of accepted papers, covering topics such as applied geometry, technological applications, algebra, and quantum phenomena. One specific paper explores the use of geometric algebra in teaching rotations through neural networks. The document is a compilation of research papers showcasing the applications of geometric algebra in various fields, including robotics, control systems, image processing, cryptography, and physics. Each paper presents a specific problem or application and proposes a unique approach or solution using geometric algebra. The authors compare their methods with existing techniques and provide mathematical analysis to support their claims. Overall, the papers demonstrate the versatility and effectiveness of geometric algebra in different domains. [Extracted from the article]

This document is a summary of a special issue of the journal Concurrency and Computation: Practice and Experience, which contains eight selected papers from the 14th International Conference on Parallel Processing and Applied Mathematics (PPAM 2022). The conference focused on topics related to high-performance computing (HPC) and included discussions on emerging technologies, algorithms, and software tools. The selected papers cover a range of subjects, including the convergence of iterative methods for solving nonlinear equations, testing interval arithmetic libraries, automatic performance tuning for symmetric eigenvalue problems, GPU computing, simulation of molecular magnetism, load balancing in distributed memory systems, predicting execution time of computational workloads, and resource selection for cloud-based applications. The papers provide valuable insights and advancements in the field of HPC. [Extracted from the article]

Motivation: Medical image analysis involves a series of tasks used to assist physicians in qualitative and quantitative analyses of lesions or anatomical structures which can significantly improve the accuracy and reliability of medical diagnoses and prognoses. Traditionally, these tedious tasks were finished by experienced physicians or medical physicists and were marred with two major problems, low efficiency and bias. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are still scarce. We hope that this review article could serve as the stepping stone for related research in the future. Significance: We found that although reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field still find it hard to understand and deploy in clinical settings. One possible cause is a lack of well‐organized review articles intended for readers without professional computer science backgrounds. Rather than to provide a comprehensive list of all reinforcement learning models applied in medical image analysis, the aim of this review is to help the readers formulate and solve their medical image analysis research through the lens of reinforcement learning. Approach & Results: We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers were carefully reviewed and categorized according to the type of image analysis task. In this article, we first reviewed the basic concepts and popular models of reinforcement learning. Then, we explored the applications of reinforcement learning models in medical image analysis. Finally, we concluded the article by discussing the reviewed reinforcement learning approaches' limitations and possible future improvements. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are employed to solve complex and large‐scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics. [ABSTRACT FROM AUTHOR]

This paper proposes a data‐driven and deep learning based model for the prediction of the buckling load of square and rectangular hollow sections (SHS and RHS) based on Finite Element Method (FEM) results of the local buckling effects. The data was elaborated during the EU‐funded research project – HOLLOSSTAB (2016‐2019), where a new design method – termed the "Generalized Slenderness‐based Resistance Method" (GSRM) ‐ was developed, aiming to simplify through a unified analytical approach time consuming computational analyses for the prediction of the local resistance of SHS and RHS. This method further generalizes the concepts of established design methods such as the Direct Strength Method. This paper investigates an alternative approach to the development of design rules for this same mechanical problem: the application of deep learning models for the prediction of the buckling load through simple input parameters, such as geometric quantities, material values and applied load to circumvent the application of the analytical formulae. Thereby, a Deep Neural Network (DNN) model is created and calibrated for the prediction of the buckling loads of SHS and RHS based on the training data given by the extensive FEM parametric study. A statistical evaluation in accordance with EN1990 elaborates a partial safety factor for the proposed DNN. The predictions from the DNN are then compared to the numerical results from FEM analyses and to the predictions of the GSRM and EC3. Since the numerical results were originally validated against large scale experimental tests, this newly developed DNN makes use of a very large set of very representative (numerical) tests. A further comparison assesses the computed partial safety factor and the related quantities of interest for the DNN to the respective counterparts of the GSRM method. This work represents one of the first steps for the development of design methods in the field structural design adopting the most recent advances in computer sciences, and future enhancements of the currently developed models are discussed and proposed as a conclusion. [ABSTRACT FROM AUTHOR]

This study employs Google trends and data from the web of science to evaluate the popularity, growth, and impacts of visual analytics (VA) as a research field and data science technique. The paper undertakes quantitative analyses and visualization of the temporal trends, from VA's emergence in 2000 to the end of 2019. The trend analysis helps to forecast future growth in the research and practice of VA. The study highlights four outcomes. First, there is a robust direct relationship among the variables, including VA's growth on the Google trends, the scientific literature production (SLP), and usage of the published documents. Second, the SLP's growth pattern highlights VA's popularity as an emerging field with an overall annual increase of 17.4%. The high citation counts of the published scholarship indicate a significant impact and a continuous growth of the VA field. Third, VA contributes to diverse disciplines other than computer science and information systems, from business and economics to engineering, healthcare, biomedical and chemical sciences, and arts and humanities. VA helps researchers and practitioners in multidisciplinary fields analyze multidimensional data, enhance data visualization, knowledge discovery, generating insights, and make informed decisions. On the reverse, other disciplines contribute to propelling VA's popularity through research productivity, usage, and citation impacts. Finally, a trend analysis predicts sustained future growth of VA technology in research and practice to dissect and sensemaking of the increasingly massive and complex data structures, which is now the norm in many fields. [ABSTRACT FROM AUTHOR]

The rapid deployment of emergency remote teaching in the pandemic presents sweeping societal‐level information systems phenomena worthy of scholarly inquiry. This paper reports findings from teacher interviews conducted with K‐12 public school teachers, exploring how digital access and use gaps in communities reflect wider digital and social inequalities as schools fulfilled emergency remote teaching mandates, becoming swept up into e‐learning technology expansion trends propelled by mandates, and unfettered corporate edtech. Results show persistence of home and school level digital affordance gaps as hindrances to pandemic pedagogy. We build upon theory of the digital divide, and crisis and critical informatics literature considering how critical approaches to the study of socio‐technical systems research can inform these understandings, providing insights into how localized digital inequities contribute to broader digital inequality and social inequality, in the educative processes expected of public education in democratic societies. Our work gives voice to one highly pressured and conflicted stakeholder in these dynamics—K‐12 public school teachers—and demonstrates some of the ways in which digital inequity gaps may play a further magnifying role of societal division through expanding edtech deployment in K‐12 grades, if current edtech trends hold. [ABSTRACT FROM AUTHOR]

Huge collections of published research documents are available in various repositories in Indian universities and research organizations. The efficient retrieval, estimation of research trends, and identification of research gaps with the global trend in different research areas, can be a great guiding tool for regulating research in the appropriate direction. This research attempts to analyse the trend of research activities carried out by Indian researchers in the discipline of Computer Science from 2010 to 19 and forecast the upcoming research trend for the years 2020–23. A repository of the abstracts based on domains given in the Computer Science Ontology (CSO) published by the Indian researchers is developed from the Scopus database. Document Index Graph document representation model is used to store the repository, shared phrases across the documents are extracted and phrase‐based similarity is computed. Combining the Single‐term and Phrase‐based similarity, the hybrid similarity is generated and similar documents are clustered using the DBSCAN clustering technique. Topics are identified for each cluster using the Latent Dirichlet Allocation algorithm and are automatically labelled using CSO. For each topic, the trend analysis and forecasting have been done using the Auto‐Regressive Integrated Moving Average. For the assessment of the forecasting performance, the dataset from 2010 to 17 is used as a training dataset and 2018–19 as a testing dataset. The average forecasting for the year 2018 for all CSO domains belongs to the Good forecasting category with Mean Absolute Percentage Error (MAPE) 18.34, and 2019 shows reasonable forecasting with MAPE 30.20 as per the MAPE interpretation given by Lewis. For each topic the average forecasting for years 2018–19 shows either Highly accurate, Good or Reasonable forecasting. As a result, the top four domains for the years 2020–23 are also identified which can help initial researchers in the identification of a relevant topic for research and exploration. [ABSTRACT FROM AUTHOR]

In this paper, we discuss a generalization of Vieta theorem (Vieta's formulas) to the case of Clifford geometric algebras. We compare the generalized Vieta formulas with the ordinary Vieta formulas for characteristic polynomial containing eigenvalues. We discuss Gelfand–Retakh noncommutative Vieta theorem and use it for the case of geometric algebras of small dimensions. We introduce the notion of a simple basis‐free formula for a determinant in geometric algebra and prove that a formula of this type exists in the case of arbitrary dimension. Using this notion, we present and prove generalized Vieta theorem in geometric algebra of arbitrary dimension. The results can be used in symbolic computation and various applications of geometric algebras in computer science, computer graphics, computer vision, physics, and engineering. [ABSTRACT FROM AUTHOR]

Orthogonal array and a large set of orthogonal arrays are important research objects in combinatorial design theory, and they are widely applied to statistics, computer science, coding theory, and cryptography. In this paper, some new series of large sets of orthogonal arrays are given by direct construction, juxtaposition construction, Hadamard construction, finite field construction, and difference matrix construction. Subsequently, many new infinite classes of orthogonal arrays are obtained by using these large sets of orthogonal arrays and Kronecker product. [ABSTRACT FROM AUTHOR]

This paper studies the structure of semantic theories over modular computational systems and applies the algebraic Theory of Institutions to provide a logical representation of such theories. A modular semantic theory is here defined by a cluster of semantic theories, each for a single program's module, and by a set of relations connecting models of different semantic theories. A semantic theory of a single module is provided in terms of the set of ∑‐models mapped from the category Th of ∑‐theories and generating a hierarchy of structures from an abstract model to a concrete model of data. The collection of abstract models representing different modules of a program is formalised as the category of institutions INS, where theory morphisms express refinements, integrations, and compositions between couples of modules. Finally, it is required that a morphism in INS at any level occurs iff the same morphism occurs at the lower level alongside the Th hierarchy. [ABSTRACT FROM AUTHOR]

Education 4.0 is a recently introduced concept focused on innovation, novelty, use of technology and connections with employment and industry. In particular, in engineering disciplines like computer science (CS) it is essential that educators keep up to date with industry developments. Indeed, how CS educators effectively design and implement innovative teaching and learning deserves more systematic attention. This study aims to catalogue and synthesise learning design approaches to teaching and learning within CS: (1) Which innovative pedagogic approaches are used in teaching of CS? (2) Which approaches align with Education 4.0? (3) What skills and competences do educators require to align CS teaching with Education 4.0? Our systematic literature review (SLR) included CS papers published between 2016 and 2020. Two hundred and thirty‐one studies were identified of which 66 were included in the final phase, which were coded by a multidisciplinary team. The findings indicated that many CS educators included Education 4.0 learning design elements. We found a clear distinctive three‐cluster solution: (1) EDU4 light, (2) project‐based/hands‐on learning and (3) full EDU4 (refer to Reference [7] conceptualisation, while Education 4.0 refers to our own definition [Reference 71]. These findings suggest three broad flavours when designing innovative CS practices, which might help educators align their practice. [ABSTRACT FROM AUTHOR]

Remote aerial sensing provides a non‐invasive, large geographical‐scale technology for avian monitoring, but the manual processing of images limits its development and applications. Artificial Intelligence (AI) methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, has several challenges: (1) imbalanced (i.e., long‐tailed) data distribution, (2) annotation uncertainty in categorization, and (3) dataset discrepancies across different study sites. Here we use aerial imagery data of waterbirds around Cape Cod and Lake Michigan in the United States to examine how these challenges limit avian recognition performance. We review existing solutions and demonstrate as use cases how methods like Label Distribution Aware Marginal Loss with Deferred Re‐Weighting, hierarchical classification, and FixMatch address the three challenges. We also present a new approach to tackle the annotation uncertainty challenge using a Soft‐fine Pseudo‐Label methodology. Finally, we aim with this paper to increase awareness in the ecological remote sensing community of these challenges and bridge the gap between ecological applications and state‐of‐the‐art computer science, thereby opening new doors to future research. [ABSTRACT FROM AUTHOR]

Background and Context: Most studies about programming misconceptions are conducted at the undergraduate and graduate levels. Since the age level for starting learning programming is getting lower, there is a need for determining programming misconceptions for younger learners. Objective: Our goal is to determine programming misconceptions and their time‐resistance for programming novices at the K‐12 level using Python as a text‐based programming language. Method: We conducted research in the school settings during informatics classes among 98 fifth‐grade students in three elementary schools. We analysed the data collected from two Python tests students took immediately after programming lectures and 5 months later. Results and Conclusions: We detected seven misconceptions: (a) Assigning the expression instead of the calculated value, (b) When reassigning the variable value, assigning the sum value to the variable, (c) Using the symbolic name of the variable instead of its value, (d) Using the first (or previously) assigned variable value, (e) Datatype misconception, (f) Sequencing misconception, and (g) Swapping variable values. By shifting focus on variables and spending more time introducing variables and sequencing during teaching, the appearance of known misconceptions is minimized compared to previous findings. Lay Description: What is already known about this topic: Programming misconceptions are known issues since the beginning of learning programming in the early 1980sMost programming misconceptions are time resistant.Most studies about misconceptions are conducted at the undergraduate and graduate levels. What this paper adds: The goal of the study is to determine programming misconceptions for programming novices at the K‐12 level.Three more misconceptions about basic programming concepts are revealed compared to the previous findings.By shifting focus on variables and spending more time introducing variables and sequencing during teaching, the appearance of known misconceptions is minimized. Implications for practice and/or policy: Being aware of misconceptions is an important aspect of a teacher's pedagogical knowledge to foster students learning.Aiming to prevent the known misconceptions, teachers can minimize it by interventions during teaching.The educators should emphasize the underlying conceptual programming concepts rather than syntactic ones related to the programming language. [ABSTRACT FROM AUTHOR]

Scientific statistical working in a team is a special challenge for students in high schools, especially with a civic statistical problem. Ideally, they are following the PPDAC cycle: they formulate a problem together, plan an investigation, collect the data, use software to analyze the data, and formulate results in a seminar paper. This article is written jointly by a student and a computer science teacher involved in the project, offering insights from both perspectives. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including the latest developments on distributed control; network linear equations; and online distributed optimal adaptive algorithm.

Summary: Automated measurement programs are an efficient way of collecting, processing, and visualizing measures in large software development companies. The number of measurements in these programs is usually large, which is caused by a diversity of the needs of the stakeholders. In this paper, we present the application of the self‐healing concepts to assure the availability of measurements to the stakeholders without the need for effort‐intensive and costly manual interventions of the operators. We study the measurement infrastructure at one of the development units of a large infrastructure provider. In this paper, we present how the Monitor, Analyze, Plane, and Execute with Knowledge model was instantiated in a simplistic manner to reduce the need for manual intervention in the operation of the measurement systems. Based on the experiences from the 2 cases studied in this paper, we show how an evolution toward self‐healing measurement systems is done both with a dedicated failure taxonomy and with an effective straightforward handling of the most common errors in the execution. The mechanisms studied and presented in this paper show that self‐healing provides significant improvements to the operation of the measurement program and reduces the need for daily oversight by an operator for the measurement systems. [ABSTRACT FROM AUTHOR]

A coreset of an input set is its small summarization, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem on the original (full) set, for a given family of problems (models/classifiers/loss functions). Coresets have been suggested for many fundamental problems, for example, in machine/deep learning, computer vision, databases, and theoretical computer science. This introductory paper was written following requests regarding the many inconsistent coreset definitions, lack of source code, the required deep theoretical background from different fields, and the dense papers that make it hard for beginners to apply and develop coresets. The article provides folklore, classic, and simple results including step‐by‐step proofs and figures, for the simplest (accurate) coresets. Nevertheless, we did not find most of their constructions in the literature. Moreover, we expect that putting them together in a retrospective context would help the reader to grasp current results that usually generalize these fundamental observations. Experts might appreciate the unified notation and comparison table for existing results. Open source code is provided for all presented algorithms, to demonstrate their usage, and to support the readers who are more familiar with programming than mathematics. This article is categorized under:Algorithmic Development > Structure DiscoveryFundamental Concepts of Data and Knowledge > Big Data MiningTechnologies > Machine Learning [ABSTRACT FROM AUTHOR]

Affect can significantly influence education/learning. Thus, understanding a learner's affect throughout the learning process is crucial for understanding motivation. In conventional education/learning research, learner motivation can be known through postevent self-reported questionnaires. With the advance of affective computing technology, researchers are able to objectively identify and measure a learner's affective status during the entire learning process in a real-time manner, and then they are able to understand the interrelationship between emotion, motivation and learning performance. There are over 100 papers in the ScienceDirect database with the keywords 'affective computing in education' or 'affective computing in learning,' which reveals that this emerging technology has been applied to education/learning. This study intends to categorize and summarize those measurements so as to realize their applicability, feasibility and trends. Finally, some challenges and suggestions are then raised for helping educational researchers when applying affective computing technology. [ABSTRACT FROM AUTHOR]

One of the main challenges in computer science and image processing is 2D human pose estimation. Specifically, occlusion and in particular occlusion of human joints caused by camera angle are of paramount importance. In this paper, a new highly accurate network was proposed that can estimate 2D human poses in video images using deep learning. We employ the Single Shot MultiBox Detector network to detect the centre position of each human within a video frame and then use the stacked hourglass network to estimate the 2D human pose. We approximate the human motion as a linear motion between different frames in a certain period; and optimize the human centres based on the local outlier factor and Kalman filters. The same method is applied to optimize the human pose estimations in video, which can address the inaccurate prediction caused by human joints occlusion. The proposed adaptive network is tested using the two well‐known benchmarks for human pose estimation (MPII and Joint‐annotated Human Motion Data Base datasets), and we also generate some 2D human pose estimating qualitative results of single and multiple people in Internet videos. The experimental results show that the proposed network has strong practicability and can achieve high accuracy on adaptive estimating the 2D human pose in video. [ABSTRACT FROM AUTHOR]

This paper studies how relationships form in heterogeneous information networks (HINs). The objective is not only to predict relationships in a given HIN more accurately but also to discover the interdependency between different type of relationships. A new relationship prediction method MULRP based on multilabel learning (MLL in brief) is proposed. In MULRP, the types of relationship between two nodes are represented by the meta‐paths between nodes and each type of relationship is given a label. Under the framework of MLL, any potential relationships including the target relationship can be predicted. Moreover, the method can output the reasonable dependency scores between relationships. Thus, more viable paths will be provided to facilitate the formation of new relationships. The proposed method is evaluated on two real datasets: The DBLP Computer Science Bibliography(abbr. DBLP) network and Twitter network. The experimental results show that by using heterogeneous information in a supervised MLL setting, MULRP achieves better performance in comparison to several baseline binary classification methods and a state‐of‐art relationship prediction method. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the need for new approaches to research regarding coding to support students in developing practices in computational thinking, such as abstraction and decomposition, in multidisciplinary contexts. We explore students' activities with a tool integrating constructionist textual programming activity with game‐based learning and specifically game modding. In this context, we designed a programmable 'design‐to‐play' game developed with the computational environment MaLT2. MaLT2 offers the affordances of textual programming, dynamic manipulation, and 3D navigation for the design of 3D animated models aiming to give children access to, otherwise, complex, computational and mathematical ideas. To develop an understanding of children's learning activity regarding computational practices, we organised an empirical study with middle‐school students, who played a game called 'Code‐the‐Mime'. It is a charades‐based game in which the players manipulate, programme, and modify a digital human model to describe a word to their teammates. The preliminary findings indicate that the affordances of MaLT2 in conjunction with the game context enabled students to express and develop key computational practices, including decomposition, pattern recognition, analysis and abstraction, in a meaningful and multidisciplinary context. Practitioner notesWhat is already known about this topic Computational Thinking is considered a key 21st‐century skill in preparing the young to become digital citizens. It involves concepts and practices that can be used to solve problems computationally across multiple fields. However, there is still limited knowledge of how students develop computational practices, such as abstraction, pattern recognition, decomposition, and how they may express and apply them in diverse contexts. Students' engagement with computational practices is unlikely to be supported either by closed, simplified coding tasks or higher‐level advanced programming exercises. There is a need to clarify the manifestation of these practices and how they can be realised and expressed and used by learners in meaningful and transdisciplinary contexts.What this paper adds It suggests the design of constructionist computational games that integrate design and programming into the gameplay, aiming to engage students with computational practices in a multidisciplinary, authentic context. It provides an example of a 'design‐to‐play' charades‐like game, developed in a 3D modelling programming environment, that embeds real‐life representations into computational design, to enable 'syntonic learning' of computational practices. Furthermore, it analyses student learning activity to elaborate on arguments and issues related to this approach.Implications for practice and/or policy There is added value in disconnecting computational thinking from positivist diagnostic approaches related to respective concepts and studying it in ways more related to realistic problem‐solving situations and multidisciplinary contexts. The study contributes to the scientific clarification of computational practices concerning how they are being realised and expressed by the students in different contexts through an original example of educational practice. The discussed approach and tools can contribute to the design and development of innovative digital media, embedding affordances for concepts and practices while maintaining relevance and interest for their users. [ABSTRACT FROM AUTHOR]

Research relating to the development of computational thinking (CT) at primary school level is still in its infancy despite indications that it is most effective when introduced in primary/early secondary education. Teachers are pivotal to ensuring children develop CT, so it is essential they are effectively prepared, starting at preservice level, to incorporate CT into pedagogical practices. Grounded in constructionist principles and adopting the stance that CT be developed as part of subject areas other than computer science, this qualitative study presents findings focused on the understandings of CT of 51 preservice teachers who engaged in a digital learning specialism. Carried out over a 2‐year period, the research investigated preservice teachers' understandings of CT and how the design of the specialism helped to develop their understanding. Findings highlight deep understandings of CT among the preservice teachers who were able to make connections between constructionism and the development of CT. They also demonstrated a high level of pedagogical knowledge, indicating they understood why they designed particular challenges for children as part of their classroom experience. Findings are relevant for the design of teacher preparation programmes illustrating how CT can be effectively embedded to combine theory and practice. Practitioner notesWhat is already known about this topic Existing research base in relation to preservice teacher education and computational thinking is limited.When computational thinking is introduced as part of initial teacher education, preservice teachers: develop more accurate and increased understandings of computational thinkingdemonstrate more positive attitudes towards the implementation of computational thinking in the classroom.Duration of module seems to be a significant factor. A 1‐week module might be enough to develop preservice teachers' surface‐level understanding of computational thinking, but it would not provide them with enough knowledge to embed computational thinking in meaningful ways in the classroom (Yadav et al., 2014).What this paper adds Adopting the stance that computational thinking should be developed as part of subject areas other than computer science.This paper presents and discusses findings from the implementation of a model for preservice teacher education grounded in constructionism, that supports preservice primary teachers in building an understanding of CT as well as the pedagogical knowledge necessary for embedding CT into their classroom practice.Implications for practice and/or policy The insights gained from this study are particularly relevant for the design of teacher preparation programmes indicating how CT can be effectively embedded to combine theory and practice.This will ensure that CT concepts are not developed in a decontexualised manner but are embedded within the prescribed curriculum in a relevant and meaningful manner. [ABSTRACT FROM AUTHOR]

Background: Plagiarism is a very serious offence in academic institutions. Yet there is some reluctance to address plagiarism by educators as its enforcement can require a significant time commitment if not handled wisely. Handling plagiarism at scale has the potential to exacerbate this problem. Objectives: This article explores the challenges educators face when it comes to enforcing plagiarism from within the course environment, presents a solution that addresses these challenges, and provides guidelines for preventing and detecting plagiarism in our at‐scale courses. Methods: A workflow is shared which was created from our experience of handling plagiarism within our large online computer science graduate level program. We present an empirical study of this workflow that shows the overall prevalence of misconduct. Results and Conclusions: Results demonstrate this workflow being effectively and efficiently applied to address plagiarism across four courses and three semesters by a single individual working half time. We have observed that warnings on low‐stakes assignments can be very effective at deterring future misconduct. We have also observed that cases come to a quicker resolution when students readily acknowledge their own misconduct after being promptly notified. Takeaways from the study: Plagiarism should not only be managed through institution wide policies, but also through effective strategies implemented in the course environment. This is particularly important when handling plagiarism at scale. Addressing plagiarism starts in the course and therefore effective strategies for handling it there are needed. Lay Description: What is currently known about the subject matter?The area of plagiarism in at scale academic programs as a whole is relatively young.Many questions have been raised about the prevalence of cheating in remote classes.The past several years has seen the growth of at‐scale online programs.Little evidence exists that shows cheating is more common in online courses than face‐to‐face. What this paper adds to this?We present some of the challenges we encounter in handling plagiarism in our at scale courses.We present a workflow for handling plagiarism derived from our experience in our courses.We address both code and essay plagiarism as our program is in computer science.We present results over the last three semesters of addressing plagiarism through this workflow. The implications of study findingsWe show that prompt warnings on low‐stakes assignments lead to a reduced rate of plagiarism.We show that cases can be handled efficiently through appropriate intervention strategies.We show that cheating can be significantly reduced through careful course design.We provide generalizable guidelines to address plagiarism in at‐scale programs. [ABSTRACT FROM AUTHOR]

In the last 20 years, the South African higher education has changed significantly, influenced by global trends national development goals and pressure from local educational imperatives, in the context of a digitally networked world. Shifts in technology enhanced pedagogical practices and in discourses around information and communication technologies (ICTs) have had varying degrees of influence in higher education. This paper takes a rearview of a 20-year journey of technology enhanced learning in South African higher education. An analysis of literature view is presented chronologically in four phases: phase 1 (1996-2000), phase 2 (2001-05), phase 3 (2006-10) and phase 4 (2011-16). In phase 1 technology was used predominantly for drill and practice, computer-aided instruction, with growing consciousness of the digital divide. In phase 2 institutions primarily focused on building ICT infrastructure, democratizing information, policy development and research; they sought to compare the effectiveness of teaching with or without technology. During phase 3 institutions began to include ICTs in their strategic directions, digital divide debates focused on epistemological access, and they also began to conduct research with a pedagogical agenda. In phase 4 mobile learning and social media came to the fore. The research agenda shifted from whether students would use technology to how to exploit what students already use to transform teaching and learning practices. The paper concludes that South Africa's higher education institutions have moved from being solely responsible for both their own relatively poor ICT infrastructure and education provision to cloud-based ICT infrastructure with 'unlimited' educational resources that are freely, openly and easily available within and beyond the institution. Although mobile and social media are more evident now than ever before, teaching and learning practice in South African higher education remains largely unchanged. [ABSTRACT FROM AUTHOR]