Computer Science Research Research Articles

Visual Analytics Review: An Early and Continuing Success of Convergent Research With Impact

The growth of the field of visual analytics reflects a sustained, long-term commitment to applying convergent research principles to computer science research. Charting the future research directions that the field of visual analytics will evolve toward requires an understanding of the field's rich, productive history of collaboration and a discussion of current challenges.

A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)?

Teachers, schools, districts, states, and technology developers endeavor to personalize learning experiences for students, but definitions of personalized learning (PL) vary and designs often span multiple components. Variability in definition and implementation complicate the study of PL and the ways that designs can leverage student characteristics to reliably achieve targeted learning outcomes. We document the diversity of definitions of PL that guide implementation in educational settings and review relevant educational theories that could inform design and implementation. We then report on a systematic review of empirical studies of personalized learning using PRISMA guidelines. We identified 376 unique studies that investigated one or more PL design features and appraised this corpus to determine (1) who studies personalized learning; (2) with whom, and in what contexts; and (3) with focus on what learner characteristics, instructional design approaches, and learning outcomes. Results suggest that PL research is led by researchers in education, computer science, engineering, and other disciplines, and that the focus of their PL designs differs by the learner characteristics and targeted outcomes they prioritize. We further observed that research tends to proceed without a priori theoretical conceptualization, but also that designs often implicitly align to assumptions posed by extant theories of learning. We propose that a theoretically guided approach to the design and study of PL can organize efforts to evaluate the practice, and forming an explicit theory of change can improve the likelihood that efforts to personalize learning achieve their aims. We propose a theory-guided method for the design of PL and recommend research methods that can parse the effects obtained by individual design features within the “many-to-many-to-many” designs that characterize PL in practice.

Patches as an expressive medium for exploratory multi‐agent modelling

AbstractThis paper outlines the potential gains for Constructionist research and praxis in modelling that might be obtained by recognising the power of the Patch—a humble computational being in the NetLogo modelling environment that has been overshadowed by its more popular fellow agent, the Turtle. To contextualise this opportunity, I describe how Constructionist modelling has thrived by promoting forms of learning that rely on learners’ identifying with agents. I argue that patches are a neglected agent type in this multi‐agent modelling tradition, and that the possibilities for learners to adopt the patch perspective in support of exploratory forms of modelling and aesthetic expression have been under‐researched. Nevertheless, I show there are a variety of powerful ways for learners––both individually and in groups––to identify with patches. I describe ongoing research showing how taking an aesthetic approach to patches has the potential to support individuals and groups in powerful forms of learning with and about multi‐agent modelling. Practitioner notesWhat is already known about this topic Turtles (movable agents in Logo and Constructionist environments descended from Logo) can be ‘transitional objects’ that provide learners a way to make powerful ideas their own. These agents can be powerful ‘objects‐to‐think‐with’ in large part because they encourage learners to identify with them in a form of learning known as ‘syntonic learning’. Expressive activities that draw on learners’ aesthetic interests can support their learning with and about computational representations. Multi‐agent modelling is a powerful extension of Logo‐based learning environments that provides access to powerful ideas about complex systems and their emergent properties. In the multi‐agent setting, individual learners and/or groups of learners can identify syntonically with agents to provide entry points for reasoning about complexity. What this paper adds Patches (non‐movable agents in the NetLogo modelling environment) are under‐represented in the research on multi‐agent modelling, and the potential for learners to adopt the patches’ perspective has been neglected. An aesthetically driven approach to patches can ground students’ understanding of their expressive value. Participatory activities in which learners play the role of patches (called ‘Stadium Card’ activities) can ground the patch perspective, so that learners can achieve a form of syntonicity and/or collectively adopt the perspective of patches in the aggregate. Participatory activities that blend intrinsic and extrinsic perspectives on the patch grid can further enhance learners’ facility with programming for patches and their understanding of patches’ collective expressive power. Implications for practice and/or policy Balancing the focus between turtles and patches can enrich the modelling toolbox of learners new to agent‐based modelling. Patches do capture important aspects of individual and collective experience, and so can be good objects‐to‐think‐with, especially when conceptualising phenomena at a larger scale. The expressive potential of the patch grid is an important topic for computer science as well (eg, through 2D cellular automata). This is a rich context for learning in itself, which can be made accessible to groups of learners through physical or virtual participatory role‐play. Moreover, physical or virtual grids of people‐patches may exhibit novel aggregate computational properties that could in turn become interesting areas for research in computer science.

“Let’s Talk about Gender” – Development of a Card Deck on (Gender) Sensitivity in HCI Research and Practice Based on a Contrasting Literature Review

Abstract Funding bodies in Western societies increasingly require researchers to address gender in their proposals – though often exclusively framed around binary notions. With oppressive power structures being prevalent and persuasive, these seep into current practices of Computer Science and Human-Computer Interaction (HCI) research. However, current curricula rarely provide actors in this space with grounded guidance on gender issues tied to their inquiries. Hence, developing an increased awareness of our societal responsibility towards equity can be challenging without an appropriate starting point. Drawing on a close reading of select literature discussing gender sensitivity in HCI research, we derived practical guidance in the form of recommendations for the design, proposal, conduct and presentation of research. Based on these recommendations, we then present the design of a card deck and initial tests thereof at ditact women’s IT summer university. Our analysis offers a starting point for HCI students and interested researchers to explore questions and issues around gender and to identify how gender relates to their research. This sensitisation may aid them in further reflecting on how they might better account for gendered implications of their work.

Machine Learning and Deep Learning Approaches to Analyze and Detect COVID-19: A Review.

COVID-19 also referred to as Corona Virus disease is a communicable disease that is caused by a coronavirus. Significant number of people who are tainted with this infection will have to brave and encounter moderate to severe respiratory sickness. Aged persons, sick, convalescing people and all those having underlying health complications like diabetes, chronic breathing diseases and cardiovascular diseases are bound to contract this sickness if not taken proper care of. At the current scenario, there are neither definite treatments nor inoculations against COVID-19. Nevertheless, there are numerous continuing clinical trials assessing the impending treatments and vaccines. Sensing the threatening impacts of Covid-19, researchers of computer science have started using various techniques and approaches of Machine Learning and Deep Learning to detect the presence of the disease using X-rays and CT images. The biggest stumbling block here is that there are only a few datasets available. There is also less number of experts for marking the information explicit to this new strain of infection in people. Artificial Intelligence centred tools can be designed and developed quickly for adapting the existing AI models and for leveraging the ability to modify and associating them with the preliminary clinical understanding to address the new group of COVID-19 and the novel challenges associated with it. In this paper, we look into a few techniques of Machine Learning and Deep Learning that have been employed to analyse Corona Virus Data.

#Election2020: the first public Twitter dataset on the 2020 US Presidential election

Credible evidence-based political discourse is a critical pillar of democracy and is at the core of guaranteeing free and fair elections. The study of online chatter is paramount, especially in the wake of important voting events like the recent November 3, 2020 U.S. Presidential election and the inauguration on January 21, 2021. Limited access to social media data is often the primary obstacle that limits our abilities to study and understand online political discourse. To mitigate this impediment and empower the Computational Social Science research community, we are publicly releasing a massive-scale, longitudinal dataset of U.S. politics- and election-related tweets. This multilingual dataset encompasses over 1.2 billion tweets and tracks all salient U.S. political trends, actors, and events from 2019 to the time of this writing. It predates and spans the entire period of the Republican and Democratic primaries, with real-time tracking of all presidential contenders on both sides of the aisle. The dataset also focuses on presidential and vice-presidential candidates, the presidential elections and the transition from the Trump administration to the Biden administration. Our dataset release is curated, documented, and will continue to track relevant events. We hope that the academic community, computational journalists, and research practitioners alike will all take advantage of our dataset to study relevant scientific and social issues, including problems like misinformation, information manipulation, conspiracies, and the distortion of online political discourse that has been prevalent in the context of recent election events in the United States. Our dataset is available at: https://github.com/echen102/us-pres-elections-2020.

Technical Vocabulary in Computer Science Research Articles: A Corpus-based Study

Technical Vocabulary in Computer Science Research Articles: A Corpus-based Study

Research on Application of Ultrasound Medical Imaging Technology in Big Data Mining of Regional Medical Imaging

Ultrasound medical imaging technology is one of the main methods of medical non-invasive diagnosis, and it is the focus of research in the medical field at home and abroad. Medical images have a large amount of data and contain a wealth of image feature information and rules, which need to be studied and understood. Therefore, the research of data mining technique for reading medical images has become a very important field in the interdisciplinary research of medical and computer science. The high resolution of medical images, the mass of data, and the complexity of image feature expressions make the research of data mining technology in medical images of great academic value and broad application prospects. At present, research on data mining for medical images has just started, and there are still many problems in the direct application of existing data mining methods. Researching and exploring the theoretical and practical problems of medical image data mining, such as data mining methods and algorithms suitable for medical image, which has significant and crucial value, and it is of great importance to help physicians in clinical diagnosis of medical images. This article introduces the background, definition and basic process of data mining technology, the characteristics of medical imaging data and the key techniques of medical image data mining. In view of the data mining research of human abdominal medical images is a completely new field, human abdominal imaging is the most complicated part of medical images. Solving the problem of abdominal imaging is of great value to the entire medical image. For regional medical image big data mining, we can use ultrasound images of the human abdomen. The clustering feature extraction algorithm and its implementation based on the approximate density structure of medical images proposed in this article, and innovative research results such as classification rule mining methods, are used to mine medical image data research, automatic diagnosis of clinical medical images, and early diagnosis of clinical medicine are of great significance.

TELEMATIC SYNERGY OF MECHATRONIC SYSTEMS IN TRANSPORT APPLICATIONS

Problem. The results of fundamental and applied research of computer science and software engineering in industry and transport are considered. Informational socialization is offered to provide knowledge and skills to persons wishing to work in the IT industry. A kind of sensory cognitive representation of the spatial-temporal orientation of transport applications and synergy (performance assessment) of development, updating of applications, support of the semantic site of the transport-road portal of automobile transfer. Goal. Increasing the competitiveness of transport and road organizations by increasing the efficiency of virtual management of the car transfer, taking into account the virtual logistics and IT industry. Methodology. The idea and basic tasks of synergetic integration of the following technologies: WEB, Internet in general, distribution of computer resources of the client and server part of the Industry 4.0 Results and originality. The most useful in the synergy of WEB implementation is the use as a feature of a dynamic site aggregator WEB portal in defined as the location of vehicles and visualization of the relevant transport situation. Practical value.It is obtaining additional computer resources in the conditions of application of WEB, which is offered for implementation, does not require significant capital expenditures. Costs associated only with the assistance of this new work of existing staff - WEB users of these solutions. The implementation of these scientific and practical results shows that the greatest benefit from using the cloud infrastructure instead of supporting its own IT infrastructure is not even technological, but financials.

A Branch-and-Bound Algorithm for Building Optimal Data Gathering Tree in Wireless Sensor Networks

In this paper, we consider the maximum lifetime data gathering tree (MLDT) problem in sensor networks. A data gathering tree is a spanning tree rooted at a specified sink so that every node can send its messages to the sink along the tree. The lifetime of a tree is defined as the minimum lifetime among nodes where each node’s lifetime is determined by its initial energy and transmission load. The MLDT problem is NP-hard, and the state-of-the-art solution formulates a decision version of the problem as an integer linear program (ILP) and then solves it by conducting binary search over all possible lifetimes. In this paper, we first give an ILP for the optimization problem rather than its decision version, and then show that using ILP solvers to solve these programs could be highly inefficient. We then propose a branch-and-bound algorithm that incorporates two novel features. First, the bounding method takes into account integer flows, and contains a new set of constraints. Second, a special set of edges are deleted to reduce the number of subproblems generated by the branching process. Numerical simulations on randomly generated networks show that the proposed algorithm outperforms existing algorithms in terms of the number of solved problem instances in a fixed amount of time. Summary of Contribution: We study the maximum lifetime data gathering tree (MLDT) problem in the context of wireless sensor network. MLDT is a fundamental problem in both computer science and operations research. Since sensor nodes are often resource limited, the data gathering tree must be carefully constructed to prolong the network lifetime. In this paper, we first give an integer linear program for the optimization problem rather than its decision version, and then show that using ILP solvers to solve these programs could be highly inefficient. We then propose a branch and bound algorithm that incorporates two novel features.

Subgraph Matching on Multiplex Networks

An active area of research in computational science is the design of algorithms for solving the subgraph matching problem to find copies of a given template graph in a larger world graph. Prior works have largely addressed single-channel networks using a variety of approaches. We present a suite of filtering methods for subgraph isomorphisms for multiplex networks (with different types of edges between nodes and more than one edge within each channel type). We aim to understand the entire solution space rather than focusing on finding one isomorphism. Results are shown on several classes of datasets: (a) Sudoku puzzles mapped to the subgraph isomorphism problem, (b) Erdös-Rényi multigraphs, (c) real-world datasets from Twitter and transportation networks, (d) synthetic data created for the DARPA MAA program.

Research and trends in computer science and educational technology during 2016–2020: Results of a content analysis

The general purpose of this study is to conduct a content study on ‘computer’ and ‘educational technologies’ research and trends between 2016 and 2020. The topics were evaluated according to years, universities of the authors, citations, keywords, document type, source, sponsors and publication languages. The articles examined in the research include keywords related to ‘computer’ and ‘instructional technologies’ between 2016 and 2020; 1,798 articles obtained by scanning the Scopus database according to the title, keywords and summary of the articles were examined. When the results of the study were examined, it was concluded that English, which is the universal language, is very common; the researches are mostly published as ‘conference papers’ and the most used keyword in the study is ‘Computer Science’. In addition, it has been concluded that computer science is the basis of educational technologies in recent years. Similar content analysis studies may be recommended for other software used in computer training.
 
 Keywords: Computer, educational technology, computer science, content analysis, trends.

Machine learning in emotional intelligence studies: a survey

ABSTRACT Research has proven that having high level of emotional intelligence (EI) can reduce the chance of getting mental illness. EI, and its component, can be improved with training, but currently the process is less flexible and very time-consuming. Machine learning (ML), on the other hand, can analyse huge amount of data to discover useful trends and patterns in shortest time possible. Despite the benefits, ML usage in EI training is scarce. In this paper, we studied 92 journal articles to discover the trend of the ML utilisation in the study of EI and its components. This survey aims to pave way for future studies that could lead to implementation of ML in EI training, and to rope in researchers in psychology and computer science to find possibilities of having a generic ML algorithm for every EI’s components. Our findings show an increasing trend to apply ML on EI components, and Support Vector Machine and Neural Network are the two most popular ML algorithms used in those researches. We also found that social skill and empathy are the least exposed EI components to ML. Finally, we provide recommendations for future research direction of ML in EI domain, and EI in ML.

A Scientometric Exploration of Crowdsourcing: Research Clusters and Applications

Crowdsourcing is a multidisciplinary research area that represents a rapidly expanding field where new applications are constantly emerging. Research in this area has investigated its use for citizen science in data gathering for research and crowdsourcing for industrial innovation. Previous studies have reviewed and categorized crowdsourcing research using qualitative methods. This has led to the limited coverage of the entire field, using smaller discrete parts of the literature and mostly reviewing the industrial aspects of crowdsourcing. This study uses a scientometric analysis of 7059 publications over the period 2006–2019 to map crowdsourcing research to identify clusters and applications. Our results are the first in the literature to map crowdsourcing research holistically. In this article, we classify its usage in the three domains of innovation, engineering, and science, where 11 categories and 26 subcategories are further developed. The results of this article reveal that the most active scientific clusters where crowdsourcing is used are environmental sciences and ecology. For the engineering domain, it is computer science, telecommunication, and operations research. In innovation, idea crowdsourcing, crowdfunding, and crowd creation are the most frequent areas. The findings of this study map crowdsourcing usage across different fields and illustrate emerging crowdsourcing applications.

Motivation and Self-Regulated Learning in Computer Science: Lessons Learned From a Multiyear Program of Classroom Research

Contribution: This article presents a synthesis of the findings and implications from the IC2Think program of research in undergraduate computer science (CS) courses examining student motivation and self-regulated learning (SRL). These studies illuminate both the difficulty and potential for motivating CS students, as well as the uniqueness of CS as a context for studying undergraduate motivation. Background: Computing disciplines are increasingly important in preparing the future workforce. It is imperative that CS educators understand how to motivate students and enhance student outcomes. Synthesizing findings across multiple studies allows for the emergence of new insights into student motivation and SRL. Research Questions: Which aspects of students' motivation and SRL are predictive of achievement and retention in CS and how can findings inform CS education? Methodology: The primary methodology is a comprehensive review of seven years of research on undergraduate CS education. Studies use a variety of analysis techniques, examine a range of constructs, and include multiple introductory and advanced CS courses. Studies of relationships between variables and change over time were conducted. Findings: The present synthesis of studies on motivation and SRL highlights the complex, counter-intuitive, and positive aspects of student motivation in CS.

Robust Optimization in Uncertain Capacitated Arc Routing Problems: Progresses and Perspectives [Review Article

The capacitated arc routing problem is an important NP-hard problem with numerous real-world applications. The capacitated arc routing problem with uncertainties refers to those instances where there are uncertainties in decision variables, objective functions and/or constraints. The capacitated arc routing problem with uncertainties captures real-world situations much better than a static capacitated arc routing problem because few real-world problems are static and certain. Uncertainties in the capacitated arc routing problem pose new research challenges. Algorithms that work well for a static and certain capacitated arc routing problem may not work on the version with uncer-tainties. There have been increasing progresses in studying the capacitated arc routing problem with uncertainties during the past two decades. However, the papers on the capacitated arc rout-ing problem with uncertainties have been scattered around in different journals and conferences in artificial intelligence, computer science, and operational research. Different defini-tions and formulations of capacitated arc routing problem with uncertainties are used by different papers, making comparisons difficult. In order to better understand the state-of-the-art in solv-ing the capacitated arc routing problem with uncertainties, this paper presents a comprehensive review of the problem and its key research issues. Not only has the paper summarized the progresses so far, key research issues are identified, including scalability of the algorithms, performance measures, common benchmarks, etc. Future research direc-tions are also identified at the end of this review.

Adaptive Fusion of Multi-Scale YOLO for Pedestrian Detection

Although pedestrian detection technology is constantly improving, pedestrian detection remains challenging because of the uncertainty and diversity of pedestrians in different scales and of occluded pedestrian modes. This study followed the common framework of single-shot object detection and proposed a divide-and-rule method to solve the aforementioned problems. The proposed model introduced a segmentation function that can split pedestrians who do not overlap in one image into two subimages. By using a network architecture, multiresolution adaptive fusion was performed on the output of all images and subimages to generate the final detection result. This study conducted an extensive evaluation of several challenging pedestrian detection data sets and finally proved the effectiveness of the proposed model. In particular, the proposed model achieved the most advanced performance on data sets from Visual Object Classes 2012 (VOC 2012), the French Institute for Research in Computer Science and Automation, and the Swiss Federal Institute of Technology in Zurich and obtained the most competitive results in a triple-width VOC 2012 experiment carefully designed by the present study.

New Frontiers of Robo-Advising: Consumption, Saving, Debt Management, and Taxes

Traditional forms of robo-advice were targeted to help individuals make portfolio allocation decisions. Based on the balance-sheet view of households, the scope for robo-advising has been expanding to many other personal-finance choices, such as households' saving and consumption decisions, debt management, mortgage uptake, tax management, and lending. This chapter reviews existing research on these new functions of robo-advising with a special emphasis on the questions that are still open for researchers across several disciplines. We also discuss the attempts to optimize jointly all personal-finance decisions, which we label ``holistic robo-advisors.'' We conclude by assessing fruitful avenues for research and practice in finance, computer science, marketing, decision science, information systems, law, and sociology.

A linear algorithm for a minimax location of a path-shaped facility with a specific length in a weighted tree network

This paper considers the problem of locating a path-shaped facility of a specific size on a weighted tree network. The criterion for optimality used in this paper is the minimax criterion in which the weighted distance to the farthest vertex from the facility is minimised. The minimax criterion gives acceptable results from the point of view of the fast response for the clients who are located far away from the facility. This location problem usually has applications in computer science, information science, and operations research. An O(n) time algorithm is proposed for finding the optimal location of a path-shaped facility of a bounded length on a weighted tree network, where n is the number of vertices in the tree.

AUTOMATION OF QURANIC READINGS GATHERING PROCESS

Quran has been the focus of many computer science and computational linguistics researchers. However, insufficient contribution is directed towards Quranic readings. This field focuses on the different ways in which Quranic words can be recited. Quranic readings gathering is a recitation process that is well-known by the specialists in this field. In this process, all the possible ways of reading a verse are covered in a compact and well-structured manner. To the best of the author’s knowledge, no computational automation has been cited for this process in literature. In this paper, this process was algorithmically formulated and a developed software system was presented to automate this process. This system took Quranic verse texts as input and generated a text file containing the detailed gathering of the readings of this verse as done by a specialised reciter. The system was applied to the verses of Surah Al-Baqarah and the output text was carefully validated against authentic books in this field. This system can be the basis of software systems for teaching and vocal construction of Quranic readings gathering. It would be useful for students who are interested in Quranic readings.