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Especially on the Internet, popular topics in computer sciences which are artificial intelligence, big data, business analytics, data mining, data science, deep learning, and machine learning have been compared or classified using confusing Venn diagrams without any scientific proof. Relationships among the topics have been visualized in this study with the help of Venn diagrams to add scientificity to visualizations. Therefore, this study aims to determine the interactions among the seven popular topics in computer sciences. Five books for each topic (35 books) were included in the analysis. To illustrate the interactions among these topics, the Latent Dirichlet Allocation (LDA) analysis, a topic modeling analysis method, was applied. Further, the pairwise correlation was applied to determine the relationships among the chosen topics. The LDA analysis produced expected results in differentiating the topics, and pairwise correlation results revealed that all the topics are related to each other and that it is challenging to differentiate between them. [ABSTRACT FROM AUTHOR]

Noise and missing data are intrinsic characteristics of real-world data, leading to uncertainty that negatively affects the quality of knowledge extracted from the data. The burden imposed by missing data is often severe in sensors that collect data from the physical world, where large gaps of missing data may occur when the system is temporarily off or disconnected. How can we reconstruct missing data for these periods? We introduce an accurate and efficient algorithm for missing data reconstruction (imputation), that is specifically designed to recover off-period segments of missing data. This algorithm, Ghost, searches the sequential dataset to find data segments that have a prior and posterior segment that matches those of the missing data. If there is a similar segment that also satisfies the constraint – such as location or time of day – then it is substituted for the missing data. A baseline approach results in quadratic computational complexity, therefore we introduce a caching approach that reduces the search space and improves the computational complexity to linear in the common case. Experimental evaluations on five real-world datasets show that our algorithm significantly outperforms four state-of-the-art algorithms with an average of 18 percent higher F-score. [ABSTRACT FROM AUTHOR]

The research focus of an institution indicates the academic strength of the institution. This academic strength goes a long way to determine the quality of academic product expected from such an institution. To this effect, this study aims at analyzing the research publication strength of 70 Nigerian universities that are visible in Scopus database for Computer Science from inception to 22 December 2017. They include 35 federal, 27 state and 8 private universities. This study is divided into two phases; the first phase consists of six major steps in a waterfall model and the second is carried out by adopting a knowledge discovery model using a clustering algorithm for pattern discovery in an unstructured data model. The data source for the analysis is the Scopus indexing database recommended by the Times Higher Education for international ranking of higher institutions. From the first phase, the predominant outlet for each institution was discovered alongside the percentage of publications in the dominant outlet and the number of publications by the institution within the database over the period of the analysis. In the second phase, the predominant areas of research of each institution within the field of Computer Science was determined. In particular, it was observed that such areas as deep learning and data science are under researched. The findings also revealed networking as the most published area in Nigerian universities, and a number of Nigerian computer science scholars engage in application research which link Computer Science with other disciplines with Nigeria and Africa as case studies. The findings from this research can support postgraduate students' decision in making the right choice of institution based on their research interest. It will also serve as an eye-opener for industries in promoting a result-oriented university-industry partnership. [ABSTRACT FROM AUTHOR]

Decomposition of a matrix into low-rank matrices is a powerful tool for scientific computing and data analysis. The purpose is to obtain a low-rank matrix by decomposition of the original matrix into a product of smaller and lower-rank matrices or by randomly projecting the matrix down to a lower-dimensional space. Such decomposition requires less storage and computational burden. The focus of this paper is on randomized methods which try as much as possible to preserve the original matrix properties by applying the subspace sampling. In many applications, randomized algorithms in terms of accuracy, stability and speed are much better than the classical decomposition algorithms. In this study, we propose a sparse orthogonal transformation matrix to reduce the dimension of the data. The results show that compared with the most accurate methods, the transformation speed is much faster and can save a lot of memory in the case of huge matrices. [ABSTRACT FROM AUTHOR]

Distributed vertex-centric model has been recently proposed for large-scale graph processing. Due to the simple but efficient programming abstraction, similar graph computing frameworks based on GPUs are gaining more and more attention. However, prior works of GPU-based graph processing suffer from load imbalance and irregular memory access because of the inherent characteristics of graph applications. In this paper, we propose a generalized graph computing framework for GPUs to simplify existing models but with higher performance. In particular, two novel algorithmic optimizations, lightweight approximate sorting and data layout transformation, are proposed to tackle the performance issues of current systems. With extensive experimental evaluation under a wide range of real world and synthetic workloads, we show that our system can achieve 1.6$\times$ to 4.5 $\times$ speedups over the state-of-the-art. [ABSTRACT FROM PUBLISHER]

To adequately address climate change, we need novel data-science methods that account for the spatiotemporal and physical nature of climate phenomena. Only then will we be able to move from statistical analysis to scientific insights. [ABSTRACT FROM PUBLISHER]

In recent years, intense usage of computing has been the main strategy of investigations in several scientific research projects. The progress in computing technology has opened unprecedented opportunities for systematic collection of experimental data and the associated analysis that were considered impossible only few years ago. This paper focuses on the strategies in use: it reviews the various components that are necessary for an effective solution that ensures the storage, the long term preservation, and the worldwide distribution of large quantities of data that are necessary in a large scientific research project. The paper also mentions several examples of data management solutions used in High Energy Physics for the CERN Large Hadron Collider (LHC) experiments in Geneva, Switzerland which generate more than 30,000 terabytes of data every year that need to be preserved, analyzed, and made available to a community of several tenth of thousands scientists worldwide. [ABSTRACT FROM AUTHOR]

Understanding the difference between data objects is a major problem especially in a scientific collaboration which allows scientists to collectively reuse data, modify and adapt scripts developed by their peers to process data while publishing the results to a centralized data store. Although data provenance has been significantly studied to address the origins of a data item, it does not however address changes made to the source code. Systems often appear as a large number of modules each containing hundreds of lines of code. It is, in general, not obvious which parts of source code contributed to the change in data object. The paper introduces the Class-Based Object Versioning framework, which overcomes some of the shortcomings of popular versioning systems (e.g. CVS, SVN) in maintaining data and code provenance information in scientific computing environments. The framework automatically identifies and captures useful fine-grained changes in the data and code of scripts that perform scientific experiments so that important information about intermediate stages (i.e. unrecorded changes in experiment parameters and procedures) can be identified and analyzed. The benefits of such a system include querying specific methods and code attributes for data items of interest, finding missing gaps of data lineage and implicit storage of intermediate data. [ABSTRACT FROM AUTHOR]

Abstract: Discrete transforms are of primary importance and fundamental kernels in many computationally intensive scientific applications. In this paper, we investigate the performance of two such algorithms; Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT), on the Sony–Toshiba–IBM Cell Broadband Engine (Cell/B.E.), a heterogeneous multicore chip architected for intensive gaming applications and high performance computing. We design an efficient parallel implementation of Fast Fourier Transform (FFT) to fully exploit the architectural features of the Cell/B.E. Our FFT algorithm uses an iterative out-of-place approach and for 1K to 16K complex input samples outperforms all other parallel implementations of FFT on the Cell/B.E. including FFTW. Our FFT implementation obtains a single-precision performance of 18.6 GFLOP/s on the Cell/B.E., outperforming Intel Duo Core (Woodcrest) for inputs of greater than 2K samples. We also optimize Discrete Wavelet Transform (DWT) in the context of JPEG2000 for the Cell/B.E. DWT has an abundant parallelism, however, due to the low temporal locality of the algorithm, memory bandwidth becomes a significant bottleneck in achieving high performance. We introduce a novel data decomposition scheme to achieve highly efficient DMA data transfer and vectorization with low programming complexity. Also, we merge the multiple steps in the algorithm to reduce the bandwidth requirement. This leads to a significant enhancement in the scalability of the implementation. Our optimized implementation of DWT demonstrates 34 and 56 times speedup using one Cell/B.E. chip to the baseline code for the lossless and lossy transforms, respectively. We also provide the performance comparison with the AMD Barcelona (Quad-core Opteron) processor, and the Cell/B.E. excels the AMD Barcelona processor. This highlights the advantage of the Cell/B.E. over general purpose multicore processors in processing regular and bandwidth intensive scientific applications. [Copyright &y& Elsevier]

About 160 years ago, the concept of entropy was introduced in thermodynamics by Rudolf Clausius. Since then, it has been continually extended, interpreted, and applied by researchers in many scientific fields, such as general physics, information theory, chaos theory, data mining, and mathematical linguistics. This paper presents The Entropy Universe, which aims to review the many variants of entropies applied to time-series. The purpose is to answer research questions such as: How did each entropy emerge? What is the mathematical definition of each variant of entropy? How are entropies related to each other? What are the most applied scientific fields for each entropy? We describe in-depth the relationship between the most applied entropies in time-series for different scientific fields, establishing bases for researchers to properly choose the variant of entropy most suitable for their data. The number of citations over the past sixteen years of each paper proposing a new entropy was also accessed. The Shannon/differential, the Tsallis, the sample, the permutation, and the approximate entropies were the most cited ones. Based on the ten research areas with the most significant number of records obtained in the Web of Science and Scopus, the areas in which the entropies are more applied are computer science, physics, mathematics, and engineering. The universe of entropies is growing each day, either due to the introducing new variants either due to novel applications. Knowing each entropy's strengths and of limitations is essential to ensure the proper improvement of this research field. [ABSTRACT FROM AUTHOR]

The Longest Common Subsequence (LCSS) is considered as a classic problem in computer science. In most studies related to time series data mining, LCSS had been mentioned as the best and the most usable similarity measurement method. The results of time series data mining under LCSS strongly depend on the similarity threshold, because the similarity measurement approach in LCSS is a zero–one approach. Since there is no knowledge about the data, and it is very difficult to determine the right amount of similarity threshold, using LCSS can actually lead to poor results. In this research, a new similarity measurement method named Developed Longest Common Subsequence (DLCSS) has been suggested for time series data mining based on LCSS. In DLCSS, by defining two similarity thresholds and determining their values, LCSS' shortcoming was eliminated. The performance of DLCSS was compared with performance of LCSS and Dynamic Time Warping (DTW) using 1-Nearest neighbor and k-medoids clustering techniques. This evaluation was carried out on 63 time series datasets of UCR collection. Using these results, it could be claimed that the 1-NN accuracy and clustering accuracy under DLCSS is better than that of under LCSS and DTW with at least 99.5% and 99% confidence, respectively. Also, DLCSS has better effect in correctly predicting the number of clusters compared to LCSS and DTW. In addition, the effect of DLCSS in determining the better cluster representatives is greater than that of under LCSS and DTW with at least 99.95% confidence. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel machine-learning approach that analyzes student assessment scores across a teaching period to predict their final exam performance. One challenge for many universities around the world is identifying the students who are at risk of failing a subject sufficiently early enough to provide proactive interventions that aim to minimize the risk of failure due to several reasons such as the volume of (big) data. We propose a data-driven strategy using machine learning, an application of artificial intelligence that has become popular for extracting knowledge from data by combining strategies and processes from statistics and computer science. By being able to predict what a student's exam performance is ahead of time, interventions can occur, and students can be provided with extra support from their teachers to aid them in achieving the best result possible. In this research, we collected data from a popular information-technology subject at an Australian university and applied a machine-learning algorithm to the data to predict a few hundred students' exam scores. We also developed a framework of learningsupport activities that would be of most benefit to at-risk students to achieve maximum impact before their exam would be conducted. We discovered through our approach that we can accurately predict the bottom 20–30 percent of students at risk, enabling a large cohort of students to be helped through our intervention framework, which we believe can have a positive impact on their future results. [End Page 636] [ABSTRACT FROM AUTHOR]