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Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity , clarity , novelty , and significance , based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions. • Define four criteria for quality evaluation of scientific papers: integrity, clarity, novelty, and significance. • Propose a framework for quality evaluation of scientific papers based on coarse features and knowledge entity network. • An effective algorithm for measuring the novelty and significance of scientific papers based on knowledge entity networks. • Create and release a rigorous dataset, which could serve as the gold standard for quality evaluation of scientific papers. • Conduct extensive experiments to validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

This paper presents results of topic modeling and network models of topics using the ICCS corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of ICCS, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the authors’ keywords. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization(NMF) topic modeling algorithm to discover topics and labeled and grouped results hierarchically. We used Gephi to study static networks of topics, and an R library called DyA to analyze the dynamic networks of topics. [ABSTRACT FROM AUTHOR]

Background: The National Cancer Institute Informatics Technology for Cancer Research (ITCR) program provides a series of funding mechanisms to create an ecosystem of open-source software (OSS) that serves the needs of cancer research. As the ITCR ecosystem substantially grows, it faces the challenge of the long-term sustainability of the software being developed by ITCR grantees. To address this challenge, the ITCR sustainability and industry partnership working group (SIP-WG) was convened in 2019.Objective: The charter of the SIP-WG is to investigate options to enhance the long-term sustainability of the OSS being developed by ITCR, in part by developing a collection of business model archetypes that can serve as sustainability plans for ITCR OSS development initiatives. The working group assembled models from the ITCR program, from other studies, and from the engagement of its extensive network of relationships with other organizations (eg, Chan Zuckerberg Initiative, Open Source Initiative, and Software Sustainability Institute) in support of this objective.Methods: This paper reviews the existing sustainability models and describes 10 OSS use cases disseminated by the SIP-WG and others, including 3D Slicer, Bioconductor, Cytoscape, Globus, i2b2 (Informatics for Integrating Biology and the Bedside) and tranSMART, Insight Toolkit, Linux, Observational Health Data Sciences and Informatics tools, R, and REDCap (Research Electronic Data Capture), in 10 sustainability aspects: governance, documentation, code quality, support, ecosystem collaboration, security, legal, finance, marketing, and dependency hygiene.Results: Information available to the public reveals that all 10 OSS have effective governance, comprehensive documentation, high code quality, reliable dependency hygiene, strong user and developer support, and active marketing. These OSS include a variety of licensing models (eg, general public license version 2, general public license version 3, Berkeley Software Distribution, and Apache 3) and financial models (eg, federal research funding, industry and membership support, and commercial support). However, detailed information on ecosystem collaboration and security is not publicly provided by most OSS.Conclusions: We recommend 6 essential attributes for research software: alignment with unmet scientific needs, a dedicated development team, a vibrant user community, a feasible licensing model, a sustainable financial model, and effective product management. We also stress important actions to be considered in future ITCR activities that involve the discussion of the sustainability and licensing models for ITCR OSS, the establishment of a central library, the allocation of consulting resources to code quality control, ecosystem collaboration, security, and dependency hygiene. [ABSTRACT FROM AUTHOR]

Scholarly interest in Green IT has been rapidly growing over the past 15 years in a wide variety of disciplines including computer science, information systems, sustainability, production management and engineering. This is not surprising given the growing environmental impacts of information and communication technologies. This article presents a bibliometric analysis of academic literature across 1862 sources to improve our understanding of the evolution and current state of this multidisciplinary field. The corpus includes 4067 published journal and conference papers identified using the Scopus database. A descriptive performance analysis of the corpus using Bibliometrix software first allows us to identify the most influential works, authors, sources, institutions, and countries in the field. Examination of its conceptual structure reveals that most work has focused on the design and use phases of ICTs and neglected the manufacturing and end-of-life phases. Further insights are provided and opportunities for future research are identified. [ABSTRACT FROM AUTHOR]

With the rapid development of computer science, technologies such as virtual reality and semantic feature fusion have been greatly developed in recent years. [n order to construct a digital media intelligent art creation model, this paper first designs a digital media intelligent creation framework based on virtual reality and maintains the original pattern and color distribution of natural images as accurately as possible. This method first performs automatic color segmentation and clustering on the image to extract key lines; then, re-colors the different segmented regions; and finally completes the detail enhancement. For the optimization of color matching, the algorithm integrates the contrast of the color block, and uses the differential evolution strategy to optimize the color configuration scheme. In addition, this paper also proposes a generative adversarial network-based on semantic feature fusion for digital media intelligent art creation. The network introduces multi-scale feature fusion by embedding the residual block feature pyramid structure and directly generates the final fine image by adaptively fusing these features. Only one discriminator can generate a realistic image of 256px x 256px. The experimental results have shown that this paper makes full ilse of virtual reality and seniantic feature fusion technology, and obtains better results and better performance of intelligent art creation results on digital media than existing methods. [ABSTRACT FROM AUTHOR]

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

One of the elementary operations in computing systems is multiplication. Therefore, high-speed and low-power multipliers design is mandatory for efficient computing systems. In designing low-energy dissipation circuits, reversible logic is more efficient than irreversible logic circuits but at the cost of higher complexity. This paper introduces an efficient signed/unsigned 4 × 4 reversible Vedic multiplier with minimum quantum cost. The Vedic multiplier is considered fast as it generates all partial product and their sum in one step. This paper proposes two reversible Vedic multipliers with optimized quantum cost and garbage output. First, the unsigned Vedic multiplier is designed based on the Urdhava Tiryakbhyam (UT) Sutra. This multiplier consists of bitwise multiplication and adder compressors. Compared with Vedic multipliers in the literature, the proposed design has a quantum cost of 111 with a reduction of 94% compared to the previous design. It has a garbage output of 30 with optimization of the best-compared design. Second, the proposed unsigned multiplier is expanded to allow the multiplication of signed numbers as well as unsigned numbers. Two signed Vedic multipliers are presented with the aim of obtaining more optimization in performance parameters. DesignI has separate binary two's complement (B2C) and MUX circuits, while DesignII combines binary two's complement and MUX circuits in one circuit. DesignI shows the lowest quantum cost, 231, regarding state-of-the-art. DesignII has a quantum cost of 199, reducing to 86.14% of DesignI. The functionality of the proposed multiplier is simulated and verified using XILINX ISE 14.2. [ABSTRACT FROM AUTHOR]

The global spread of the COVID-19 pandemic posed unprecedented challenges to higher education institutions worldwide. As governments announced lockdown measures that placed restrictions and mandatory closures on establishments that traditionally required gatherings, universities worldwide cancelled face-to-face classes and adapted their mode of offering to online teaching and learning. Using an interpretive study based on a single case, this paper presents the author's most profound experiences with online teaching and learning of the Data Structures and Algorithms course at the University of Namibia (UNAM). Throughout the teaching of this course, the author kept an up-to-date reflective practice journal, and required her students to engage in reflective practice. The author also had an opportunity to engage and discuss her experiences with other lecturers in the School of Computing. The paper therefore presents a narrative of reflections on the meaning of teaching and learning as adapted to emergency online teaching during the COVID-19 pandemic. Findings suggest that reflective practice during emergency online teaching presents an opportunity for educators to experiment with new delivery approaches, as well as to monitor how their interventions can impact the achievement of learning outcomes. [ABSTRACT FROM AUTHOR]

Text generation tools, often presented as a form of generative artificial intelligence, have the potential to pose a threat to the integrity of the educational system. They can be misused to afford students marks and qualifications that they do not deserve. The emergence of recent tools, such as ChatGPT, appear to have left the educational community unprepared, despite the fact that the computer science community has been working to develop and improve such tools for years. This paper provides an introduction to text generation tools intended for a non-specialist audience, discussing the types of assessments that students can outsource, showing the type of prompts that can be used to generate text, and illustrating one possible watermarking technique that may allow generated text to be detected. A small-scale study into watermarking suggests that this technique is feasible and show technical promise but should not be relied on as a solution to widespread use of artificial intelligence based tools by students. Alternative solutions are needed, including encouraging the educational community to work with artificial intelligence rather than against it. As such, the paper concludes by discussing seven potential areas for further exploration. [ABSTRACT FROM AUTHOR]

Graph mining is one of the significant tasks in the field of computer science. Most of the applications generate a vast amount of data which is represented with the help of a graph. Due to this graph representation, these applications have become complex and increased in size. Finding relevant information from that graph becomes a complicated task. For this community detection algorithms play a vital role in graph partitioning to retrieve relevant information. Finding communities in a graph reduces the complexity of the graph due to related data comes closer to forming a community. Many algorithms have been introduced in the last decade; the Clique percolation method (CPM) is the benchmark algorithm for finding an overlapping community. But in this method, some nodes remain unclassified, nodes that are not part of the clique. Paper proposed the clique-based Louvain algorithm(CBLA), which can classify the non-classified node (NCN) obtained after finding cliques in one of the communities by applying the Louvain algorithm. Louvain algorithm is used to classify the non-overlapped community, but with the help of cliques, it will also detect the overlapped nodes. This paper compared the proposed algorithm with four other benchmark algorithms. The proposed algorithm gives equal or enhanced performance among all compared algorithms. [ABSTRACT FROM AUTHOR]

The volume of scientific documents and articles has increased dramatically in the last decade. It makes too difficult to find the relevant documents based on the user’s query. Information retrieval systems help researchers to find relevant scientific articles. One of the capabilities that help researchers to find the relevant papers is the feature of finding related scientific articles to an article. In other words, this feature allows the researcher to view other related articles by selecting one article. The purpose of this research is to present a method based on the analysis of bibliographic references to identify related scientific articles in digital libraries. The statistical population of this research is the articles published in the last 5 years in Persian and English publications indexed in the ISC in the field of computer science. The proposed method is able to find the articles that are most similar to the given article by analyzing the references of the articles and ranking them based on their similarity. To do that, after extracting the title and obtaining the similarity between the existing references among the articles, those articles that have the most similarity are identified and sorted based on their similarity. The proposed method has been compared with other methods, and the obtained results on both Persian and English data are promising. [ABSTRACT FROM AUTHOR]

Graph theory is a powerful and essential tool for applied scientists and engineers in analyzing and designing algorithms for several problems. Graph theory has a vital role in complex systems, especially in computer sciences. Applications of graph theory can be found in many scientific disciplines such as operational research, engineering, life sciences, management sciences, coding, and computer science. In the literature, there are two algorithms for constructing decompositions for the circulant graphs C 2 r , r with 2 r vertices and r degree. For r , m ⩾ 2 , a circulant-balanced complete multipartite graph C mr , (m - 1) r is a generalization to C 2 r , r. Here, the major contributions are the generalization of the algorithms used for constructing the decompositions for the circulant graphs C 2 r , r to generalized algorithms that can be used for the decomposition of C mr , (m - 1) r that have mr vertices and (m - 1) r degree. These generalized algorithms manage us to decompose the circulant graphs C mr , (m - 1) r by different graph classes. The paper's novelty is demonstrated by the fact that it is the first to suggest general approaches for decomposing circulant-balanced complete multipartite graphs. The constructed results in the present paper are suitable for generating different codes and also have great importance in the design of experiments. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most user authentication mechanisms of cloud systems depend on the credentials approach in which a user submits his/her identity through a username and password. Unfortunately, this approach has many security problems because personal data can be stolen or recognized by hackers. This paper aims to present a cloud-based biometric authentication model (CBioAM) for improving and securing cloud services. The research study presents the verification and identification processes of the proposed cloud-based biometric authentication system (CBioAS), where the biometric samples of users are saved in database servers and the authentication process is implemented without loss of the users' information. The paper presents the performance evaluation of the proposed model in terms of three main characteristics including accuracy, sensitivity, and specificity. The research study introduces a novel algorithm called "Bio_Authen_as_a_Service" for implementing and evaluating the proposed model. The proposed system performs the biometric authentication process securely and preserves the privacy of user information. The experimental result was highly promising for securing cloud services using the proposed model. The experiments showed encouraging results with a performance average of 93.94%, an accuracy average of 96.15%, a sensitivity average of 87.69%, and a specificity average of 97.99%. [ABSTRACT FROM AUTHOR]

In this section of Resonance, we invite readers to pose questions likely to be raised in a classroom situation. We may suggest strategies for dealing with them, or invite responses, or both. "Classroom" is equally a forum for raising broader issues and sharing personal experiences and viewpoints on matters related to teaching and learning science. We present a three-part paper consisting of a survey of some of the extensively used techniques in the dynamic graph model that have appeared recently in the computer science community. In this third part of the three-part survey paper, we present a set of techniques to prove lower bounds for dynamic graph problems. In the first part1 of the survey, we presented the motivation for research in dynamic graph algorithms and an introduction to dynamic graphs. In the second part2 of the survey, we presented a set of techniques to prove upper bounds for dynamic graph problems. A dynamic graph is a graph that has a fixed vertex set and an evolving edge set. The edge set keeps changing due to the insertion of a new edge or the deletion of an existing edge. Research in dynamic graphs has succeeded in obtaining efficient upper bounds for an extensive collection of problems. However, there are problems of stubborn nature. Such complex problems lead to the investigation of lower bounds. [ABSTRACT FROM AUTHOR]

This paper presents the novel task of bluespotted ribbontail (BR) ray (Taeniura lymma) recognition using deep learning based computer vision methods to enable the identification of specific individuals of this species. Mapping the specific individuals in relation to location and time will allow marine researchers to understand their movement patterns, habitat choice, life span, size of the population and more – data which could allow monitoring and establishing a tailor-made conservation plan for this species. Our work is pioneer on this recognition problem. We give a detailed description of the three basic steps of detection, feature extraction and recognition in this vision problem and perform experiments to explore the system configuration and what improves the performance. A feature extraction enhancement as well as a crucial effect of a split into different main poses are demonstrated. Though the precision results achieved in this paper are still moderate and should be further improved, they are nevertheless promising and reasonable for practical use if the six best matches are chosen. For this scenario, almost 85% precision for upper-pose model, and almost 80% precision for left- and right-pose models, are achieved demonstrating the feasibility of the pipeline suggested as well as opportunities for improvement. • We present a pioneer novel application of deep learning based computer vision methods for bluespotted ribbontail (BR) ray recognition, to facilitate the ecological study of this species. • We explored what improves the performance. • An effective feature extraction enhancement technique is applied in our framework. • The crucial effect of a split into different main poses in order to achieve BR ray recognition is demonstrated. [ABSTRACT FROM AUTHOR]

Clustering with outliers is one of the most fundamental problems in Computer Science. Given a set X of n points and two numbers k,m, the clustering with outliers aims to exclude m points from X and partition the remaining points into k clusters that minimizes a certain cost function. In this paper, we give a general approach for solving clustering with outliers, which results in a fixed-parameter tractable (FPT) algorithm in k and m--i.e., an algorithm with running time of the form f(k,m) · nO(1) for some function f--that almost matches the approximation ratio for its outlier-free counterpart. As a corollary, we obtain FPT approximation algorithms with optimal approximation ratios for k-Median and k-Means with outliers in general and Euclidean metrics. We also exhibit more applications of our approach to other variants of the problem that impose additional constraints on the clustering, such as fairness or matroid constraints. [ABSTRACT FROM AUTHOR]

The multiple-attribute decision-making (MADM) problem is resolved through the q -rung complex diophantine neutrosophic normal set (q -rung CDNNS). An important way to express uncertain information is using q -rung orthopair fuzzy sets (q -ROFs). Yager introduced q -ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q -rung CDNN weighted averaging (q -rung CDNNWA), q -rung CDNN weighted geometric (q -rung CDNNWG), q -rung generalized CDNN weighted averaging (q -rung GCDNNWA) and q -rung generalized CDNN weighted geometric (q -rung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology. Four factors are to evaluate when determining a sensor robotics system's quality: resolution, sensitivity, error, and environment. It is possible to compare expert opinions with the criteria and determine the best alternative. Therefore, the value of q significantly impacts the model's results. To prove that the models considered are valid and useful, we will compare the current and proposed models. Thus, q has a significant impact on the results of the model. [ABSTRACT FROM AUTHOR]

Worldwide, scientists conduct research finding answers to questions, and that to do this they have to use, measure, and analyze data. Nowadays, more than never, computer programs are intensive used by researchers from various domains in order to ahieve their goals. There are different software helping scholars to get research results. R is currently one of the top programming languages preferred for accomplishing data science. Descending from the S programming language, R is an independent, open-source, and free software environment, which can be used for statistical analysis, visualization and reporting. In present, the R environment has become one of the most used statistical analysis tools, being used in university and academic research environments, but also in the business environment. More and more companies are using R as a data analysis tool. R is also supported by the academic community. The world's major universities support R. R is being used in the disciplines of finance, banking, insurance, economics, stock market, marketing, computer science, and many other disciplines and fields. Specialists in these fields need R knowledge and skills to analyze data. This article promotes learning of R programming language by students, researchers, and teachers from economic field. Learning R programming language is the acquisition of information, knowledge, and skills of R software. It is an ongoing process that takes place throughout whole professional life without an end date. Learning a programming language like R is the same as learning a spoken language? What is the difference between R and RStudio? How can we gain R skills? These are the questions we propose to answer through this research paper. The goal is helping beginners to enter into the R system. Results consist in providing information and sources to help economist and other people interested in starting learning R programming language. [ABSTRACT FROM AUTHOR]

With the growth of the discipline of digital communication, the topic has acquiredmore attention in the cybersecuritymedium. The Intrusion Detection (ID) system monitors network traffic to detect malicious activities. The paper introduces a novel Feature Selection (FS) approach for ID. Reptile Search Algorithm (RSA)--is a new optimization algorithm; in this method, each agent searches a new region according to the position of the host, which makes the algorithm suffers from getting stuck in local optima and a slow convergence rate. To overcome these problems, this study introduces an improved RSA approach by integrating Cauchy Mutation (CM) into the RSA's structure. Thus, the CM can effectively expand search space and enhance the performance of the RSA. The developed RSA-CM is assessed on five publicly available ID datasets: KDD-CUP99, NSL-KDD, UNSW-NB15, CIC-IDS2017, and CIC-IDS2018 and two engineering problems. The RSA-CM is compared with the original RSA, and three other state-of-the-art FS methods, namely particle swarm optimization, grey wolf optimization, and multi-verse optimizer, and quantitatively is evaluated using fitness value, the number of selected optimum features, accuracy, precision, recall, and F1- score evaluationmeasures. The results reveal that the developed RSA-CMgot better results than the other competitive methods applied for FS on the ID datasets and the examined engineering problems. Moreover, the Friedman test results confirm that RSA-CMhas a significant superiority compared to other methods as an FS method for ID. [ABSTRACT FROM AUTHOR]

The healthcare IoT system is considered to be a significant and modern medical system. There is broad consensus that these systems will play a vital role in the achievement of economic growth in numerous growth countries. Among the major challenges preventing the fast and widespread adoption of such systems is the failure to maintain the data privacy of patients and the integrity of remote clinical diagnostics. Recently, the author proposed an end-to-end authentication scheme for healthcare IoT systems (E2EA), to provide a mutual authentication with a high data rate between the communication nodes of the healthcare IoT systems. Although the E2EA authentication scheme supports numerous attractive security services to resist various types of attack, there is an ambiguous view of the impact of the desynchronization attack on the E2EA authentication scheme. In general, the performance of the authentication scheme is considered a critical issue when evaluating the applicability of such schemes, along with the security services that can be achieved. Therefore, this paper discusses how the E2EA authentication scheme can resist the desynchronization attack through all possible attack scenarios. Additionally, the effect of the desynchronization attack on the E2EA scheme performance is analyzed in terms of its computation and communication costs, based on a comparison with the recently related authentication schemes that can prevent such attack. Moreover, this research paper finds that the E2EA authentication scheme can not only prevent the desynchronization attack, but also offers a low cost in terms of computations and communications, and can maintain consistency and synchronization between the communication nodes of the healthcare IoT systems during the next authentication sessions. [ABSTRACT FROM AUTHOR]

The Journal of Computer Science & Technology (JCS&T) has achieved the third quartile (Q3) ranking in the Computer Science subject area according to Scimago Journal & Country Rank (SJR). The latest issue of the journal features five papers covering various topics such as high-performance computing, visualization, machine learning, software engineering, and e-government. The papers were authored by 21 individuals from nine different universities in Argentina, Ecuador, Jordan, Spain, and Uruguay. Additionally, the issue includes an overview of a Ph.D. thesis. The editor expresses gratitude to the editorial staff, authors, reviewers, and collaborating universities for their contributions to this edition. [Extracted from the article]

Automatic classification of the plants is growing area of association with computer science and Botany, it has attracted many researchers to subsidize plant classification using image processing and machine learning techniques. Plants can be classified using number of traits such as leaf color, flowers, leaves, roots, leaf shape, leaf size etc. highly depends upon feature selection methods. However extraction of features from selected trait is most significant state in classification. State-of-the-art classification can be achieved by using leaf characteristics such as leaf venation patterns, leaf spectral signatures, leaf color, leaf shape, etc. This paper describes multimodal plant classification system using leaf venation patterns and its spectral signatures as a significant features. This paper shows that the feature fusion can be used to achieve efficient plant identification. The accuracy of identification for leaf spectral data, leaf venation features and HOG features is validated, it signifies that feature fusion technique performs better than that of non-imaging spectral signatures features only, with recognition result of 98.03% GAR and 93.51% GAR, respectively. [ABSTRACT FROM AUTHOR]

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent’s model. To do so, the agent provides an explanation that can be used to update the model of human such that the agent’s plan is feasible or optimal to the human user. Existing approaches to solve this problem have been based on automated planning methods and have been limited to classical planning problems only. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. In particular, we propose a logic-based framework for explanation generation, where given a knowledge base KBa (of an agent) and a knowledge base KBh (of a human user), each encoding their knowledge of a planning problem, and that KBa entails a query q (e.g., that a proposed plan of the agent is valid), the goal is to identify an explanation є ⊆ KBa such that when it is used to update KBh, then the updated KBh also entails q. More specifically, we make the following contributions in this paper: (1) We formally define the notion of logic-based explanations in the context of model reconciliation problems; (2) We introduce a number of cost functions that can be used to reflect preferences between explanations; (3) We present algorithms to compute explanations for both classical planning and hybrid systems planning problems; and (4) We empirically evaluate their performance on such problems. Our empirical results demonstrate that, on classical planning problems, our approach is faster than the state of the art when the explanations are long or when the size of the knowledge base is small (e.g., the plans to be explained are short). They also demonstrate that our approach is efficient for hybrid systems planning problems. Finally, we evaluate the real-world efficacy of explanations generated by our algorithms through a controlled human user study, where we develop a proof-of-concept visualization system and use it as a medium for explanation communication. [ABSTRACT FROM AUTHOR]

The technological progress in computer science (particularly, in machine learning) has contributed to the improvement of medical services, both in detecting and treating diseases. The large volumes of data, that are overwhelming for human experts (doctors, nurses), can easily be managed by automated systems, as long as we have the computational resources. Obviously, human experts are still essential in the process - we think of the use of computer science in medicine as a collaboration between medical staff and artificial intelligence. The usual types of data that can be processed by automated systems are text, sound, and image types. In this paper, we approach the diagnosis subject and focus on data consisting of sound. We created a heart murmur detection system - it analyzes recordings and tells the user whether the sound samples indicate a heart murmur or not, based on a trained machine learning model. One of the main advantages of our system is the fact that we ran a large number of experiments, with different configurations of denoising techniques and features taken into consideration. We were able to draw some interesting conclusions, for example, we found out which features are the most important for the classification and which features are not worth computing. Also, our work denotes a thorough understanding of sound processing. [ABSTRACT FROM AUTHOR]

In a world whose attention to environmental and health problems is very high, the issue of properly managing nuclear waste is of a primary importance. Information and Communication Technologies have the due to support the definition of the next-generation plants for temporary storage of such wasting materials. This paper investigates on the adoption of one of the most cutting-edge techniques in computer science and engineering, i.e. Digital Twins, with the combination of other modern methods and technologies as Internet of Things, model-based and data-driven approaches. The result is the definition of a methodology able to support the construction of risk-aware facilities for storing nuclear waste. [ABSTRACT FROM AUTHOR]

This article discusses the correlation between first-year students' prior knowledge of programming and their academic achievements. Many students who enroll in the Faculty of Computer Science and Information Technology at Riga Technical University drop out during their first year. Therefore, it is crucial to support first-year students in acquiring programming knowledge successfully. The Faculty of Computer Science and Information Technology provides an additional course called "Algorithmization Practice" for students without prior programming experience. The authors of this paper conducted research to investigate whether this course helps bridge the gap between students with and without prior programming knowledge and whether it improves the academic performance of students without prior knowledge in their first year. The collected data was summarized using descriptive statistics, and the final scores of students from different groups were compared to identify any differences between those who attended the "Algorithmization Practice" course and those who did not. Based on a sample of 1744 introductory programming course students, our analysis indicates that students who attended the additional course demonstrated better academic performance. [ABSTRACT FROM AUTHOR]

Copyright of Revista Virtual Universidad Católica del Norte is the property of Revista Virtual Universidad Catolica del Norte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid rise in generative AI chatbots, such as ChatGPT and SQL Query Builder, there has been an increased interest in how these programs play a role in academia, specifically in higher education. The goal of this research paper is to gain an understanding on how Computer Science (CS) and Information Systems (IS) professors view these new technologies, and how they perceive they will affect academia moving forward. A survey was conducted, whereby we wanted to gain an insight into how the sudden rise of AI chatbots, specifically ChatGPT and SQL Query Builder, were playing a part in academia, and where professors believe it will lead. Our findings are discussed, and the data collected from the responses is also displayed. Finally, we conclude with a discussion on what we believe should be future areas of research with regards to this topic, based on other questions that came about as a result of our research. [ABSTRACT FROM AUTHOR]

Computer science, engineering and technologies are witnessing a vital role in providing challenging demands of users. Artificial intelligence, machine learning and robotic process automation strive to improve the intelligent behavior of computers. Fast human like responses of text chatbot can perform better if and only if it is optimized. Hyper parameter optimization methods are popular for successfully boosting up the overall performance of model. In this paper we focus on creating chatbot using random forest and optimizing its performance by hyper parameter tuning halving grid search. We propose chatbot model 1 without optimization, chatbot model 2 with optimization and chatbot model3 with optimization and best values of key performance indicators. Computations are performed before optimization and after optimization for measurement factors including accuracy, precision, recall and f1-scores. Three different models proposed, and performance are compared for each model with respect to precision, recall, f1-scores and accuracy. [ABSTRACT FROM AUTHOR]

Based on the analysis of data we observe that the share of single-authored papers was significantly high in theoretical computer science, while collaborative efforts dominate computer science system research like PL, AI, ML, etc. Collaborative authorship is higher in journals over conferences. Further, values of collaborative indicators are also high for journals except for the Machine Learning (ML) subfield. In addition, the author distribution patterns are different for conferences and journals. The findings also exhibited diversity in authorship trends across sub-fields of CS research. Our results show collaboration trends in conferences and journals of major CS subfields. Such collaborative patterns benefit the funding agency, policymakers, scientific community, and researchers to plan and execute their research. [ABSTRACT FROM AUTHOR]

The generation of SAT instances is an important issue in computer science, and it is useful for researchers to verify the effectiveness of SAT solvers. Addressing this issue could inspire researchers to propose new search strategies. SAT problems exist in various real-world applications, some of which have more than one solution. However, although several algorithms for generating random SAT instances have been proposed, few can be used to generate hard instances that have multiple predefined solutions. In this paper, we propose the KHidden-M algorithm to generate SAT instances with multiple predefined solutions that could be hard to solve by the local search strategy when the number of predefined solutions is small enough and the Hamming distance between them is not less than half of the solution length. Specifically, first, we generate an SAT instance that is satisfied by all of the predefined solutions. Next, if the generated SAT instance does not satisfy the hardness condition, then a strategy will be conducted to adjust clauses through multiple iterations to improve the hardness of the whole instance. We propose three strategies to generate the SAT instance in the first part. The first strategy is called the random strategy, which randomly generates clauses that are satisfied by all of the predefined solutions. The other two strategies are called the estimating strategy and greedy strategy, and using them, we attempt to generate an instance that directly satisfies or is closer to the hardness condition for the local search strategy. We employ two SAT solvers (i.e., WalkSAT and Kissat) to investigate the hardness of the SAT instances generated by our algorithm in the experiments. The experimental results show the effectiveness of the random, estimating and greedy strategies. Compared to the state-of-the-art algorithm for generating SAT instances with predefined solutions, namely, M-hidden, our algorithm could be more effective in generating hard SAT instances. [ABSTRACT FROM AUTHOR]

The research domains and solutions of information technology have been rapidly changed as information technology continuously and deeply deployed into our society. On the other hand, it can be considered that there are some common knowledge creation processes in information technology researches. In this paper, we propose a common research process on creating new researches which were extracted by retrospectively reengineering knowledge creation model of our Information Technology research papers that were conducted with my co-authors. The research creation model is applied and evaluated by our publications. [ABSTRACT FROM AUTHOR]

The study discussed in this article attempts to trace the research trends in information and communication technology (ICT) in top 10 countries during 1999-2018, as reflected by scholarly publications index in the Scopus database. During the mentioned time period, total 18,382 research papers were published. Of these, maximum 1622 (8.82%) records were published in 2017. The annual growth (- 11.07 to 51.30) and the compound annual growth (- 0.01 to 0.23) have shown a fluctuating trend. However, a considerable number of records (7345) were published in the field of computer science while 52.35% of the records (9623) were journal article-type publications. 'Information and communication technology' was the most significant keyword used in 4997 records while the average (145.3) publications were contributed by top 10 organizations in which Japan National Institute of Information and Communications Technology was the most productive organization with 256 records. The maximum contributing country was the United States, standing for 22% of the total publications produced. Toyoshima, M. is the most prolific author with 53 publications on a particular topic, that is, ICT. The overall relative growth has shown a decreasing trend (0.92 to 0.09) while during the period of study, the doubling time has shown an increasing trend (0.75 to 7.54). Out of the total 18,382 records, total 209,919 citations were recorded in which the maximum citations (20,548) were found in 2010. Out of the total 55,913 research papers, maximum (5058) records were contributed by two authors while the least number of publications (2186) came from four authors. The mean value of the degree of collaborations was 0.92 and the overall degree of collaboration has exhibited an increasing trend during the period of study. The average author's collaboration coefficient and modified collaboration coefficient was recorded to be 0.69 while collaborative index was recorded at 4.21. However, Lotka's law of authors' productivity shows, the authors observed are somehow unlike from the numbers of authors expected during the period of study. [ABSTRACT FROM AUTHOR]

In this paper the challenge of promoting computational thinking for all by contextualized computing education is addressed. The two phases learning session we designed was implemented in the AlgoRythmics environment which includes ten algorithmic dance choreographies (and attached interactive computer animations). In addition to previous studies that had focused only on supporting students in assimilating the strategy of the studied algorithms, this study examined whether they are able to build on this knowledge by extracting from visualizations some algorithm efficiency related concepts too. To this end, the learning environment was complemented/supplemented with targeted questioning (with and without teacher guidance). Participants (first year undergraduate students) were grouped based on the number of years they had learned programming in high school (0, 1/2 or 4 years). We performed two analyses: (1) group-0 vs. group-1/2 and group-4 (N=181; self-paced setting: algorithm visualization complemented with teacher prepared questions); (2) group-0 (one-group pretest-posttest design, N=46; supplementary Socratic questioning). Research results revealed that the AlgoRythmics environment, if complemented/supplemented with tutorial questionasking, could be an effective instrument in introducing students with no prior knowledge in computing (group-0), even with deeper Computer Science concepts such as algorithm efficiency. In this paper the challenge of promoting computational thinking for all by contextualized computing education is addressed. The two phases learning session we designed was implemented in the AlgoRythmics environment which includes ten algorithmic dance choreographies (and attached interactive computer animations). In addition to previous studies that had focused only on supporting students in assimilating the strategy of the studied algorithms, this study examined whether they are able to build on this knowledge by extracting from visualizations some algorithm efficiency related concepts too. To this end, the learning environment was complemented/supplemented with targeted questioning (with and without teacher guidance). Participants (first year undergraduate students) were grouped based on the number of years they had learned programming in high school (0, 1/2 or 4 years). We performed two analyses: (1) group-0 vs. group-1/2 and group-4 (N=181; self-paced setting: algorithm visualization complemented with teacher prepared questions); (2) group-0 (one-group pretest-posttest design, N=46; supplementary Socratic questioning). Research results revealed that the AlgoRythmics environment, if complemented/supplemented with tutorial questionasking, could be an effective instrument in introducing students with no prior knowledge in computing (group-0), even with deeper Computer Science concepts such as algorithm efficiency. [ABSTRACT FROM AUTHOR]

In recent years, blockchain technology has been applied in the educational domain because of its salient advantages, i.e., transparency, decentralization, and immutability. Available systems typically use public blockchain networks such as Ethereum and Bitcoin to store learning results. However, the cost of writing data on these networks is significant, making educational institutions limit data sent to the target network, typically containing only hash codes of the issued certificates. In this paper, we present a system based on a private blockchain network for lifelong learning data authentication and management named B4E (Blockchain For Education). B4E stores not only certificates but also learners' training data such as transcripts and educational programs in order to create a complete record of the lifelong education of each user and verify certificates that they have obtained. As a result, B4E can address two types of fake certificates, i.e., certificates printed by unlawful organizations and certificates issued by educational institutions for learners who have not met the training requirements. In addition, B4E is designed to allow all participants to easily deploy software packages to manage, share, and check stored information without depending on a single point of access. As such, the system enhances the transparency and reliability of the stored data. Our experiments show that B4E meets expectations for deployment in reality. [ABSTRACT FROM AUTHOR]

Students are using file sharing sites to breach academic integrity in light of the Covid-19 pandemic. This paper analyses the use of one such site, Chegg, which offers "homework help" and other academic services to students. Chegg is often presented as a file sharing site in the academic literature, but that is just one of many ways in which it can be used. As this paper demonstrates, Chegg can and is used for contract cheating This is despite the apparent existence of an Honour Code on Chegg which asks students not to breach academic integrity. With pandemic led safety considerations leading to increased online teaching and assessment, the paper analyses data relating to how Chegg is used by students in five STEM subjects, namely Computer Science, Mechanical Engineering, Electrical Engineering, Physics and Chemistry. The results show that students are using Chegg to request exam style questions. They demonstrate that contract cheating requests can be put live and answered within the short duration of an examination. The number of student requests posted for these five subjects increased by 196.25% comparing the time period April 2019 to August 2019 with the period April 2020 to August 2020. This increase corresponds with the time when many courses moved to be delivered and assessed online. The growing number of requests indicates that students are using Chegg for assessment and exam help frequently and in a way that is not considered permissible by universities. The paper concludes by recommending that academic institutions put interventions in place to minimise the risk to educational standards posed by sites such as Chegg, particularly since increased online teaching and assessment may continue after the pandemic. [ABSTRACT FROM AUTHOR]

Today's production methods are challenged by changing paradigms from a static constraint mass production and mass customization to continuing personalization and regionalization of product, production and markets. It is yet unclear what future production systems will be the most appropriate, how they will look like and how the current, uncertain trends in manufacturing can be tackled most effectively. In this paper, first results from a comprehensive questionnaire on »The Intelligent Production of the Future«, performed by a consortium of researchers from the Institute for Computer Science and Control (SZTAKI) and the Fraunhofer-Institute for Manufacturing Engineering and Automation IPA are outlined. During this survey, stakeholders and peers from more than 70 companies and research institutions in Germany, Austria and Hungary have been asked to share their impressions, describe their opinions and rate central statements on future production systems, methodologies and trends. The paper outlines the most important topics of future production systems and lessons learned during the evaluation of the survey. [ABSTRACT FROM AUTHOR]

It is our pleasure to welcome you to this AIS Transactions on Human Computer Interaction special issue on artificial intelligence (AI) fairness, trust, and ethics. This special issue received research papers that unpacked the potential, challenges, impacts, and theoretical implications of AI. This special issue contains four papers that integrate research across diverse fields of study, such as social science, computer science, engineering, design, values, and other diverse topics related to AI fairness, trust, and ethics broadly conceptualized. This issue contains three of the four papers (along with a regular paper of the journal). The fourth or last paper of this special issue is forthcoming in March 2021. We hope that you enjoy these papers and, like us, look forward to similar research published in AIS Transactions on Human Computer Interaction. [ABSTRACT FROM AUTHOR]

Learning Management System (LMS) has been a widely-used learning media so a study is required to know the trend of its development. The present study aimed to analyze the types of documents, languages, contributing countries, top affiliates, sponsorship funding, top productive authors, research citations, subject areas, top source titles, trend mapping visualization, and top-cited 100 publications, and review some publications on LMS research during 1991-2021 using bibliometric analysis. The metadata were obtained by Scopus database and analyzed by VOSViewer within 2.689 documents. The bibliometric analysis results showed that LMS research had conference papers as the most widely published document type and English was the most commonly used language. The country with the most publications was the United States of America. National Natural Science Foundation of China became the top funding sponsor. The top affiliate was Bina Nusantara University. The most productive authors were Sabine Graf. Top cited author achieved by Fred D. Davis, and the top subject areas were Computer Science. Then, Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence and Lecture Notes In Bioinformatics became the title of the top source. Trends of LMS research in 1991-2021 were: 1) related to E-learning; 2) implementation of learning activities and student-teacher cases; 3) technology integration in learning; 4) distance learning; 5) technology education; 6) online learning environment; and 7) interactive learning environment. [ABSTRACT FROM AUTHOR]

Analyzing human behaviors using computer-based approaches is a new dimension of digital humanity. It integrates computing with healthcare, psychology, and social media. Like other healthcare and medical-related problems, anxiety and depressive disorders are common in Pakistan. In the existing era, this disease is not only affecting people of all ages but the impact of depression is also witnessed on social media communications. On social media, anyone could not guess about the mental conditions of a user through his/ her tweets or comments. Especially, the prediction of depressed unknown users through his behavior on social media is a hard task. Computer science and the medical domain can eliminate such healthcare-related issues. In this paper, a system has been proposed to detect and analyze the behavior of a user on social media. The proposed system comprises of an algorithm that performs sentimental analysis of users’ tweets and comments and a web-based platform to share best practices and success stories of users who have recovered from the disease. The system has been tested on real-time data obtained from tweeter using Application Programming Interface (API). Top trends have been focused to obtain the data and the proposed system successfully detected the users under depression. After working on the huge dataset, the proposed system will be a good contribution to humanity. [ABSTRACT FROM AUTHOR]

Internet has been source of knowledge for decades. The pool of information cannot be sustained in absence of the network of networks. Internet has many useful applications in commercial, social and educational areas. In today's scenario, e-learning is also one of the useful applications in the world of Internet. The medium of e-learning has achieved advancement in various fields such as adaptive e-learning systems. The branch of computer science with psycholinguistics has done tremendous job in providing technical solutions to learners. However, learning disorders on the platform of e-learning still require lots of research. Therefore, this paper provides a personalized assessment model for alphabet learning with learning objects for children's who face dyslexia. The cognitive inclination of dyslexic learner has been determined using assessment model. This paper studies the cognitive potential of dyslexic learner and has built a personalized e-learning platform to alleviate their alphabetical problems. [ABSTRACT FROM AUTHOR]

This paper aims at identifying the key role of "Smart Energy and Power Systems Modelling", within the context of Energy Informatics. The main objective is to describe how the specific subject of "Smart Energy and Power Systems Modelling" can give a key contribution within the novel domain of Energy Informatics, by successfully linking and integrating the different disciplines involved. First the paper will present how and where the specific subject of "Smart Energy and Power Systems Modelling" can position itself within the broad Energy Informatics domain. Afterwards the paper will explain how Cyber-Physical Systems (CPS) and Information and Communication Technologies (IoT), coupled with "Smart Energy and Power Systems Modelling", can enhance the Energy Informatics domain. In addition, the main challenges and opportunities for Energy Informatics specialists will be outlined, with regard to the interdisciplinary approach that characterises this field. [ABSTRACT FROM AUTHOR]

To date, all stakeholders are working intensively on policy design for artificial intelligence. All initiatives center around the requirement that AI algorithms should be fair. But what exactly does it mean? And how can algorithmic fairness be translated to legal and policy terms? These are the main questions that this paper aims to explore. Each discipline approaches those questions differently. While computer scientists may favor one notion of fairness over others across the board, this paper argues in favor of a case-by-case analysis and application of the relevant fairness notion. The paper discusses the legal limitations of the computer science (CS) notions of fairness and suggests a typology of matching each CS notions to its corresponding legal mechanism. The paper concludes that fairness is contextual. The fact that each notion, or group of notions, correspond with a different legal mechanism, makes them suitable for a certain policy domain more than others. Thus, throughout the paper, examples for possible applicability of the CS notions to some policy domains will be introduced. In addition, the paper will highlight for both developers and policymakers the practical steps that need to be taken in order to better address algorithmic fairness. In some instances, notions of fairness that seem, on their face, unproductive from a technical perspective, could in fact be quite helpful from a legal perspective. In other instances, desirable notions in the eye of computer scientists could be challenging to implement in the legal regime, due to the need to determine complex moral and legal questions. Thus, as the article emphasizes, a one-size-fits-all solution is not applicable for algorithmic fairness. Rather, an approach that demonstrates a deep understanding of the specific context that a certain algorithm is operating in can guarantee a fairer outcome. [ABSTRACT FROM AUTHOR]

Automatic grading is not a new approach but the need to adapt the latest technology to automatic grading has become very important. As the technology has rapidly became more powerful on scoring exams and essays, especially from the 1990s onwards, partially or wholly automated grading systems using computational methods have evolved and have become a major area of research. In particular, the demand of scoring of natural language responses has created a need for tools that can be applied to automatically grade these responses. In this paper, we focus on the concept of automatic grading of short answer questions such as are typical in the UK GCSE system, and providing useful feedback on their answers to students. We present experimental results on a dataset provided from the introductory computer science class in the University of North Texas. We first apply standard data mining techniques to the corpus of student answers for the purpose of measuring similarity between the student answers and the model answer. This is based on the number of common words. We then evaluate the relation between these similarities and marks awarded by scorers. We consider an approach that groups student answers into clusters. Each cluster would be awarded the same mark, and the same feedback given to each answer in a cluster. In this manner, we demonstrate that clusters indicate the groups of students who are awarded the same or the similar scores. Words in each cluster are compared to show that clusters are constructed based on how many and which words of the model answer have been used. The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer. We argue that computational methods be used to enhance the reliability of human scoring, and not replace it. Humans are required to calibrate the system, and to deal with situations that are challenging. Computational methods can provide insight into which student answers will be found challenging and thus be a place human judgement is required. [ABSTRACT FROM AUTHOR]

Situational cognition can help students to construct their knowledge to a great extent. In order to solve the problem of lack of situational cognition in College English teaching, this paper studies the validity of students' situational cognition in College English teaching based on the method of image block gain optimization. First, this paper analyses the general situation of situational cognition capacity in College English teaching in China at present, and puts forward the function of device image in constructing situational cognitive competence in teaching. Then, it divides device image into blocks under pseudo-haze conditions, and proposes the optimization method of block gain. Finally, on the basis of block gain, it makes an empirical test of situational cognitive competence in College English teaching. The empirical results show that image block gain optimization can effectively improve the construction of situational cognition capacity in College English teaching. With the help of this study, some new and useful ideas can be traced for the development of computer science and college English teaching, and also stimulate the further improvement of College English education in China. [ABSTRACT FROM AUTHOR]

As an international scientific journal, Studies in Informatics and Control (SIC) covers the field of Information Technology (IT) and topics related to research areas, as well as important applications in IT, with particular emphasis on Advanced Automatic Control, Modeling and Optimization. SIC has greatly contributed to the areas where it involves since its first online publication way back in 1992. This paper sets out to analyze the structure and the underlying trend of the journal by making use of bibliometric methods. Firstly, the classical indicators are provided to illustrate the performance of the journal. This current study performs an in-depth analysis of the most productive and the most influential authors, institutions, and countries/regions, as well as the most cited research works published in SIC. Secondly, the visualization tools VoS viewer and CiteSpace are used to create scientific maps that may explain the structure of the journal in an intuitionistic way. In the science mapping, the co-citation maps, and the co-authorship networks of various items (such as authors, institutions, and countries/regions) are conducted. Also, the bursts detection of these items are derived. The co-occurrence of keywords and their bursts detection and timeline review are shown, respectively. Finally, some conclusions are given. This paper provides a comprehensive and visual understanding of this well-regarded scientific journal. [ABSTRACT FROM AUTHOR]

International Journal of Computers Communications & Control (IJCCC) is an international journal in the fields of automation control systems and computer science. According to Web of Science (WoS), the first document of IJCCC was published in 2006. In this paper, we study the research trends of publications in IJCCC by performing bibliometric analysis from 2006 to 2019. 982 publications are selected from WoS after data preprocessing by VOS viewer and CiteSpace. Firstly, fundamental information of publications is explored including the type, the annual trend and the most cited publications in IJCCC. Secondly, characteristics of countries/regions, institutions and authors are presented in terms of evaluation indicators. Next, landscape analysis is conducted to show the development of IJCCC at level of countries/regions, institutions, authors and references, such as co-authorship analysis, bibliographic coupling analysis, co-citation and burst detection analysis, cooccurrence and timeline view analysis. Based on which, discussions about current challenges and possible research trends of IJCCC are provided. Finally, some main findings are summarized. This paper offers a valuable reference for scholars to understand the research trends of IJCCC and grasp hot topics related to relative fields. [ABSTRACT FROM AUTHOR]

The text is a foreword for a special issue of the Journal of Information Science & Engineering on Combinatorial Mathematics and Computational Theory. It explains that these fields have a significant impact on computer science, mathematics, and science in general. Combinatorial Mathematics involves the study of discrete objects and has applications in various areas, while Computational Theory focuses on studying algorithms and their efficiency. The text also mentions a symposium that was held to promote academic exchanges and advance research in these fields. The special issue includes four papers on topics such as opinion optimization, local diagnosis, enumerating furthest pairs, and determining the 2-tuple total domination number of a graph. The authors express gratitude to the reviewers and authors for their contributions to the special issue. [Extracted from the article]