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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]

Abstract: Image acquisition has great influence on the performance of any computer vision application. Different methods can be utilized to acquire the digital image of a paper, whilst scanning scheme is among the most attractive methods. This attractiveness is because of the fewer types of potential deformations and the low cost of the scanning devices, e.g. flatbed scanners. However, paper is commonly placed imperfectly on the scanner. This slight rotation is not usually based on a pivot around the paper''s geometrical center (the well known regular rotation) but instead it is based on a pivot placed at the corner of the paper. Thus, the result is a digital image that is deformed with an “irregular rotation”. The characteristic of this deformation phenomenon is currently unknown to computer vision scientists. In this paper we provide an extensive investigation of this deformation. In addition, a new set of equations that sway and measure the transformation is proposed. Our investigation leads to the conclusion that the “irregular rotation” phenomenon produces a shear transformation. Furthermore, the experimental results confirm the theoretical findings. [Copyright &y& Elsevier]

Abstract: The need for applications that are developed especially for Computational Science and Engineering (CSE) has been growing rapidly in the recent years. These applications are often a prerequisite for research and have to be evolved and maintained for considerable periods of time. However, CSE researchers have traditionally put focus on achieving better computational performance and results rather than the software’s comprehensibility, maintainability and extensibility. This paper first presents two case studies on two different CSE research projects, where common and specific problems are identified. Second we propose solutions that intend to apply software engineering methodologies and tools to improve the CSE research software development. [ABSTRACT FROM AUTHOR]

Abstract: We present Open Review, a web-based platform aimed at stimulating executable papers by means of post-publication peer-review. Its goal is to bring computer science researchers to collaboratively build their work upon previous research results, in such a way that transparency, reproducibility and sustainability of research results are greatly improved. The main design goals of the platform are clarity, conciseness, and reproducibility. Its main features are to: (i) provide incentives for making research communities to participate, (ii) make papers executable by means of boards’ annotations, without necessarily involving the authors of an article, and (iii) give snapshots of the current research state on any given article. [Copyright &y& Elsevier]

Abstract: The Workshop for the Executable Paper Grand Challenge, taking place at the 2011 International Conference on Computational Science, is a platform for Grand Challenge contestants and finalists to present their concept of an Executable Paper, a method by which data intensive research is better represented in a scholarly journal article. The Executable Paper Grand Challenge is designed to enhance how scientific information is used and communicated in computer- and computational sciences. [Copyright &y& Elsevier]

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 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]

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]

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]

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]