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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]

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]

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]

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 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 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]

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]

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]

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]

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]

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]

Background: In Spring 2020, the COVID‐19 pandemic sent universities into emergency remote education. The pandemic has been disruptive but offers the opportunity to learn about ways to support students in other situations where abrupt changes to teaching and learning are necessary. Purpose/Hypothesis: We described the responses of engineering and computer science students to a series of prompts about their experiences with remote learning. Design/Method: Data about students' remote learning experiences were collected from undergraduate engineering and computer science students at four different universities through an end‐of‐semester survey. Descriptive statistics were calculated, and qualitative responses were analyzed using qualitative content analysis through the lenses of master narrative theory and sociocultural theory. Results: Student responses revealed how their individual circumstances combined to reduce motivation, create home environments detrimental to completing schoolwork, and increase stress. Many students described the negative impacts of remote learning, but some students found positive aspects of the situation. The majority of students did not indicate a change in their desire or plans to pursue engineering or computer science majors. Conclusions: There was wide variation in how students experienced the disruption to university learning during Spring 2020. Implications of this paper can help not only in cases where emergency remote learning is needed in the future but also as universities seek to return to "normal" operations in 2022 and beyond. [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]

Computational thinking (CT), which is a cognitive skill used to solve problems with computational solutions, has drawn increasing attention among researchers and practitioners due to the growing recognition of CT competence as a 21st century skill. Collaboration is commonly integrated into CT education to facilitate novice learning, but there is inadequate knowledge regarding the influences of collaboration in CT education. This meta‐analysis examined the overall effects on the cognitive, social and affective competencies of collaborative versus individual problem solving in CT through programming. We identified 33 publications involving 4717 learners, which allowed for 220 effect size comparisons. We found a medium effect size (Hedges' g = 0.562; p < 0.001) in favour of collaborative problem solving on cognitive learning outcomes and a small effect size (Hedges' g = 0.316; p < 0.01) on affective learning outcomes using a random‐effects model. Categorical moderator analysis revealed the moderating effects of educational level, programming environment, study duration, grouping method and group size. The competency model that was generated from the synthesized literature on collaborative computational problem solving yielded insights into the learning design and assessment of relevant activities. Practitioner notesWhat is already known about the topic?Although professional programming usually requires collaboration to accomplish common goals in the work environment, programming is predominantly taught on an individual basis in educational settings.Some intervention studies have reported inconsistent learning outcomes (positive and negative) from integrating collaborative problem solving (CPS) into programming education.There is a need for further scrutiny and exploration of the role of collaboration in the process of learning to program.What does this paper add?Our meta‐analysis showed that CPS is an effective pedagogical tool for developing students' cognitive, affective and social competencies.The moderator analysis revealed that CPS in programming has cognitive benefits irrespective of grouping method, group size and educational levels; however, there is a need for caution in regard to designing collaborative problem‐solving activities for different programming environments and durations.The model generated from the literature synthesis can yield insights into better assessing and designing collaborative problem‐solving activities.Implications for practice and/or policy?Integrating CPS into programming education can prepare students with 21st century skills.Social and affective competencies are crucial to students' learning capacity, which is an issue that needs to be better assessed.Future studies could look more rigorously into the effects of CPS in visual programming and robotics programming among younger age groups, such as primary or middle school students. [ABSTRACT FROM AUTHOR]

Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed‐effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often‐used technique to evaluate high dimensional integrals, to search for D‐efficient designs for estimating parameters in nonlinear mixed‐effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature‐inspired metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

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]

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]

Automation in structural health monitoring (SHM) has greatly benefited from computer science's recent advances. Unlike images, the existing datasets for other types of input, such as vibration‐based damage data, are relatively smaller, less diverse, and highly imbalanced. Therefore, the reliability of data‐driven models developed for safety‐critical assessments can be questionable. This paper proposes a dual Bayesian inference where damage predictions are accompanied by measuring the model's confidence in predictions. First, it is shown how dual classification is integrated with Bayesian inference. Later, we introduce a surrogate deep learning module to transform the raw uncertainty output into an easily interpretable prediction uncertainty index (PUI). The PUI metric can be used to alarm a decision‐maker of the potential mistakes. The proposed dual Bayesian models are investigated on a 2D structure with seven different sensor layouts. Our approach yields increased robustness for different metrics compared with the benchmark. In addition to the performance boost, PUI information paves the way for a risk‐informed implementation of deep learning models in vibration‐based damage diagnosis. [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]

Summary: Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper, we review techniques exploiting the graph structure for exact inference, borrowed from optimisation and computer science. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated. The so‐called treewidth of the graph characterises this algorithmic complexity: low‐treewidth graphs can be processed efficiently. The first point that we illustrate is therefore the idea that for inference in graphical models, the number of variables is not the limiting factor, and it is worth checking the width of several tree decompositions of the graph before resorting to the approximate method. We show how algorithms providing an upper bound of the treewidth can be exploited to derive a 'good' elimination order enabling to realise exact inference. The second point is that when the treewidth is too large, algorithms for approximate inference linked to the principle of variable elimination, such as loopy belief propagation and variational approaches, can lead to accurate results while being much less time consuming than Monte‐Carlo approaches. We illustrate the techniques reviewed in this article on benchmarks of inference problems in genetic linkage analysis and computer vision, as well as on hidden variables restoration in coupled Hidden Markov Models. Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper we review techniques exploiting the graph structure for exact inference, borrowed from optimisation and computer science. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated. The so‐called treewidth of the graph characterises this algorithmic complexity: low‐treewidth graphs can be processed efficiently. The first point that we illustrate is therefore the idea that for inference in graphical models, the number of variables is not the limiting factor, and it is worth checking the width of several tree decompositions of the graph before resorting to approximate methods. We show how algorithms providing an upper bound of the treewidth can be exploited to derive a 'good' elimination order enabling to realise exact inference. The second point is that when the treewidth is too large, algorithms for approximate inference linked to the principle of variable elimination, such as loopy belief propagation and variational approaches, can lead to accurate results while being much less time consuming than Monte‐Carlo approaches. We illustrate the techniques reviewed in this article on benchmarks of inference problems in genetic linkage analysis and computer vision, as well as on hidden variables restoration in coupled Hidden Markov Models. [ABSTRACT FROM AUTHOR]

The development of system‐of‐systems (SoS) requires a continuous interplay between design decisions on the SoS level and those on the level of its constituent systems (CS), which often preexist and need to be adapted as the SoS evolves. This involves not only preparing the CS to participate in a particular SoS, but also designing the CS architecture to make it easily adaptable to a future SoS context. The problem is in part addressed in an emerging SoS framework in the manufacturing domain called Industry 4.0. It focuses on connected and digitalized production with the ambition of increasing flexibility and efficiency. This paper investigates how Industry 4.0 standards can be used in an SoS context to make CS more flexible and adaptive, and evaluates their usefulness outside manufacturing. The study is based on a case from the construction domain, for which a generic SoS architecture is developed. Several extensions and adaptations of Industry 4.0 are suggested, including specifications of ontologies for missions and workflows. [ABSTRACT FROM AUTHOR]

Biodiversity informatics is the application of informatics techniques to ecology and biodiversity sciences. The premise is utilizing natural history collections/data, such as specimens and biodiversity observations, with information and communication technology. During the past two decades, biodiversity informatics has improved dramatically and has been applied increasingly in ecology science. In this paper, I review biodiversity informatics for ecology with a particular focus on the treatment of data. First, I discuss the traditional perspective of data collection and its usage in the ecological community. Then, I provide an overview of the trajectory of changing perspectives of data treatment relative to developing biodiversity informatics (i.e., the infiltration of the data reuse and sharing concepts). Specifically, I discuss the significance of data reuse, which offers numerous benefits for research, and data sharing with their supporting mechanisms and case studies. Finally, I discuss potential future developments in biodiversity informatics relative to the field of ecology. [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]

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]

One of the challenges facing education systems in general and the South African education system in particular is how to understand ways that teachers change from nonusers of technologies to becoming transformative teachers with technology. Despite numerous initiatives, not limited to training, workshops and so forth, to bring about sustained and wide-spread teacher change, transmission/delivery-based pedagogies and chalk-and-talk methods continue to dominate. While policy directives and professional development programmes aim to effect change in teachers' practice, they tend to fail to create sustainable change in teachers' practice of using emerging technologies (ETs). This paper reports on a study that sought to understand how teachers change their pedagogy of teaching with ETs. Using a Design-Based Research approach, the paper reports on the teachers' pedagogical change framework (Teaching Change Frame -TCF) as a diagnostic tool for locating and mapping how teachers' change. The TCF maps teachers' existing pedagogies and ET uses, and designs a pathway of a change process to effect the desired change. The TCF was tested and refined using data from 325 teachers drawn from rural, resource-constrained schools, urban, well-resourced schools and from preservice teaching students in a decontextualized environment. Following three iterations it was found that teachers' use of ETs in regulated, restrictive ways correlate with transmission pedagogies, unregulated, dispersed ways correlate with transformative pedagogies. The use of TCF not only located teaching pedagogies but also provide different pathways to ensure sustainable change. Findings emphasize the need for teachers to encourage learners to build/create/construct with ETs and for increased interaction in fostering nonregulated dispersed use of ETs. [ABSTRACT FROM AUTHOR]

Nursing Sciences are at a moment of paradigmatic transition. The aim of this paper is to reflect on the new epistemological paradigms of nursing science from a critical approach. In this paper, we identified and analysed some new research lines and trends which anticipate the reorganization of nursing sciences and the paradigms emerging from nursing care: biotechnology-centred knowledge; the interface between nursing knowledge and new information technologies; body care centred knowledge; the human body as a cyborg body; and the rediscovery of an aesthetic knowledge in nursing care. [ABSTRACT FROM AUTHOR]

Thanks to the recent developments in the computer science, simulations are becoming an increasingly widespread approach that can help the designers in the development of new products. In the specific field of gearboxes, simulations are used mainly for structural evaluations. However, while for the structural design beside the simulations, many analytical methods and international standard are available; for the prediction of the power losses and the efficiency of gears, neither accurate analytical methods nor automated simulation tools are available. The authors work on this topic since years and have developed new methodologies based on computational fluid dynamics. With respect to general purpose commercial software, these techniques allow a significant reduction of the computational effort and have the capability to take into account particular physical phenomena that occurs in gears, such as cavitation, and for which no information are available in literature. The purpose of this paper is to introduce a new automated mesh-partitioning strategy implemented to extend the applicability of the previously developed computational effort reduction method to complex gearboxes getting over the geometrical limitations adopted in the past. To show the capabilities of this new strategy, we simulated a planetary gearbox that represents at the same time one of the most complicated kinematic arrangements of gears and the configuration for which the numerical fluid dynamics simulation can give the major contribution both with planar simplified models as well as with complete 3D models. [ABSTRACT FROM AUTHOR]

A widespread assumption in the contemporary discussion of probabilistic models of cognition, often attributed to the Bayesian program, is that inference is optimal when the observer's priors match the true priors in the world-the actual 'statistics of the environment.' But in fact the idea of a 'true' prior plays no role in traditional Bayesian philosophy, which regards probability as a quantification of belief, not an objective characteristic of the world. In this paper I discuss the significance of the traditional Bayesian epistemic view of probability and its mismatch with the more objectivist assumptions about probability that are widely held in contemporary cognitive science. I then introduce a novel mathematical framework, the observer lattice, that aims to clarify this issue while avoiding philosophically tendentious assumptions. The mathematical argument shows that even if we assume that 'ground truth' probabilities actually do exist, there is no objective way to tell what they are. Different observers, conditioning on different information, will inevitably have different probability estimates, and there is no general procedure to determine which one is right. The argument sheds light on the use of probabilistic models in cognitive science, and in particular on what exactly it means for the mind to be 'tuned' to its environment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a high-speed multiview face localization and tracking method with a minimum bounding box using template matching with genetic algorithm (GA). In this method, a head is treated as a cylinder, and the multiview face can be represented using the development of lateral surface (two-dimensional (2D) model). The face can be localized by a template, which is generated from the model and corresponds to the target face direction. Processing is very fast because the parameters for both template generation and affine transformation are simultaneously optimized by GA. In the experiment, challenging 60-video sequences are created in a situation where subjects drastically move their faces in a room, using a standard computer and web camera. Then, the proposed method is applied to the sequences, and the performance is investigated. As a result, the proposed method achieves fast and accurate multiview face localization and tracking. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

When producing a description of a target referent in a visual context, speakers need to choose a set of properties that distinguish it from its distractors. Computational models of language production/generation usually model this as a search process and predict that the time taken will increase both with the number of distractors in a scene and with the number of properties required to distinguish the target. These predictions are reminiscent of classic findings in visual search; however, unlike models of reference production, visual search models also predict that search can become very efficient under certain conditions, something that reference production models do not consider. This paper investigates the predictions of these models empirically. In two experiments, we show that the time taken to plan a referring expression-as reflected by speech onset latencies-is influenced by distractor set size and by the number of properties required, but this crucially depends on the discriminability of the properties under consideration. We discuss the implications for current models of reference production and recent work on the role of salience in visual search. [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]

This paper explores a 'pedagogy first' approach to technology enhanced learning developed by Sheffield Hallam University (SHU) as a method to encourage use of, and experimentation with, technology within teaching practice and to promote the mainstreaming of innovative practice. Through a consultative approach where all staff members were invited to contribute, SHU has created a Teaching Approaches Menu that reflects practice at the institution and can be used to explore teaching practice and appropriate supporting technology either by individuals or as part of a facilitated discussion. Background to the project is provided, along with the design philosophy and approach, including a brief review of other frameworks. The Teaching Approaches Menu is introduced, its development outlined, and some initial feedback is presented. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to detect free-surfaces on particle-based volume representation. In contrast to most particle-based free-surface detection methods, which perform the surface identification based on physical and geometrical properties derived from the underlying fluid flow simulation, the proposed approach only demands the spatial location of the particles to properly recognize surface particles, avoiding even the use of kernels. Boundary particles are identified through a Hidden Point Removal (HPR) operator used for visibility test. Our method is very simple, fast, easy to implement and robust to changes in the distribution of particles, even when facing large deformation of the free-surface. A set of comparisons against state-of-the-art boundary detection methods show the effectiveness of our approach. The good performance of our method is also attested in the context of fluid flow simulation involving free-surface, mainly when using level-sets for rendering purposes. [ABSTRACT FROM AUTHOR]

Feature curves on surface meshes are usually defined solely based on local shape properties such as dihedral angles and principal curvatures. From the application perspective, however, the meaningfulness of a network of feature curves also depends on a global scale parameter that takes the distance between feature curves into account, i.e., on a coarse scale, nearby feature curves should be merged or suppressed if the surface region between them is not representable at the given scale/resolution. In this paper, we propose a computational approach to the intuitive notion of scale conforming feature curve networks where the density of feature curves on the surface adapts to a global scale parameter. We present a constrained global optimization algorithm that computes scale conforming feature curve networks by eliminating curve segments that represent surface features, which are not compatible to the prescribed scale. To demonstrate the usefulness of our approach we apply isotropic and anisotropic remeshing schemes that take our feature curve networks as input. For a number of example meshes, we thus generate high quality shape approximations at various levels of detail. [ABSTRACT FROM AUTHOR]

A well-known conjecture in topological graph theory says that the genus distribution of every graph is logconcave. In this paper, the genus distribution of the circular ladder CLn is re-derived, using overlap matrices and Chebyshev polynomials, which facilitates proof that this genus distribution is log-concave. [ABSTRACT FROM AUTHOR]

The complexity of a quantum state may be closely related to the usefulness of the state for quantum computation. We discuss this link using the tree size of a multiqubit state, a complexity measure that has two noticeable (and, so far, unique) features: it is in principle computable, and non-trivial lower bounds can be obtained, hence identifying truly complex states. In this paper, we first review the definition of tree size, together with known results on the most complex three and four qubits states. Moving to the multiqubit case, we revisit a mathematical theorem for proving a lower bound on tree size that scales superpolynomially in the number of qubits. Next, states with superpolynomial tree size, the Immanant states, the Deutsch-Jozsa states, the Shor's states and the subgroup states, are described. We showed that the universal resource state for measurement based quantum computation, the 2D cluster state, has superpolynomial tree size. Moreover, we showed how the complexity of subgroup states and the 2D cluster state can be verified efficiently. The question of how tree size is related to the speed up achieved in quantum computation is also addressed. We show that superpolynomial tree size of the resource state is essential for measurement based quantum computation. The necessary role of large tree size in the circuit model of quantum computation is still a conjecture; and we prove a weaker version of the conjecture. [ABSTRACT FROM AUTHOR]

The General Medical Council (GMC) requires UK-based doctors to have 'appropriate IT skills, which include word processing and data collection'.3 However, we feel that in this age of increasing technological developments, the GMC defined standard of sufficient IT skills may soon be rendered obsolete. In this paper, we argue for the incorporation of computer science skills into the medical curriculum. [Extracted from the article]

In recent years, several publications in computer science journals have proposed new heuristic methods for parsimony analysis. This contribution discusses those papers, including methods highly praised by their authors, such as Hydra, Sampars and GA + PR + LS. Trees of comparable or better scores can be obtained using the program TNT, but from one to three orders of magnitude faster. In some cases, the search methods are very similar to others long in use in phylogenetics, but the enormous speed differences seem to correspond more to poor implementations than to actual differences in the methods themselves. [ABSTRACT FROM AUTHOR]

I was very honoured to receive the SSC 2013 Gold Medal, which caused me to reflect on different aspects of my research career. In this paper, I provide a personal reflection on those projects of mine, which could be regarded as 'interdisciplinary.' I present informal summaries of my experiences with these projects, and consider possible lessons to be learned. The Canadian Journal of Statistics 42: 509-524; 2014 © 2014 Statistical Society of Canada [ABSTRACT FROM AUTHOR]