Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Reference Exascale Architecture (Extended Version)
Get PDFWhile political commitments for building exascale systems have been made, turning these systems into platforms for a wide range of exascale applications faces several technical, organisational and skills-related challenges. The key technical challenges are related to the availability of data. While the first exascale machines are likely to be built within a single site, the input data is in many cases impossible to store within a single site. Alongside handling of extreme-large amount of data, the exascale system has to process data from different sources, support accelerated computing, handle high volume of requests per day, minimize the size of data flows, and be extensible in terms of continuously increasing data as well as an increase in parallel requests being sent. These technical challenges are addressed by the general reference exascale architecture. It is divided into three main blocks: virtualization layer, distributed virtual file system, and manager of computing resources. Its main property is modularity which is achieved by containerization at two levels: 1) application containers - containerization of scientific workflows, 2) micro-infrastructure - containerization of extreme-large data service-oriented infrastructure. The paper also presents an instantiation of the reference architecture - the architecture of the PROCESS project (PROviding Computing solutions for ExaScale ChallengeS) and discusses its relation to the reference exascale architecture. The PROCESS architecture has been used as an exascale platform within various exascale pilot applications. This paper also presents performance modelling of exascale platform with its validation
Processing Radio Astronomical Data Using the PROCESS Software Ecosystem
Get PDFIn this paper we discuss our efforts in "unlocking" the Long Term Archive (LTA) of the LOFAR radio telescope using the software ecosystem developed in the PROCESS project. The LTA is a large (>50 PB) archive that expands with about 7 PB per year by the ingestion of new observations. It consists of coarsely calibrated "visibilities", i.e. correlations between signals from LOFAR stations. Converting these observations into sky maps (images), which are needed for astronomy research, can be challenging due to the data sizes of the observations and the complexity and compute requirements of the software involved. Using the PROCESS software environment and testbed, we enable a simple point-and-click-reduction of LOFAR observations into sky maps for users of this archive. This work was performed as part of the PROCESS project which aims to provide generalizable open source solutions for user friendly exascale data processing
Automating Test Case Identification in Java Open Source Projects on GitHub
Get PDFSoftware testing is one of the very important Quality Assurance (QA) components. A lot of researchers deal with the testing process in terms of tester motivation and how tests should or should not be written. However, it is not known from the recommendations how the tests are written in real projects. In this paper, the following was investigated: (i) the denotation of the word "test" in different natural languages; (ii) whether the number of occurrences of the word "test" correlates with the number of test cases; and (iii) what testing frameworks are mostly used. The analysis was performed on 38 GitHub open source repositories thoroughly selected from the set of 4.3 M GitHub projects. We analyzed 20 340 test cases in 803 classes manually and 170 k classes using an automated approach. The results show that: (i) there exists a weak correlation (r = 0.655) between the number of occurrences of the word "test" and the number of test cases in a class; (ii) the proposed algorithm using static file analysis correctly detected 97 % of test cases; (iii) 15 % of the analyzed classes used ttmain() function whose represent regular Java programs that test the production code without using any third-party framework. The identification of such tests is very complex due to implementation diversity. The results may be leveraged to more quickly identify and locate test cases in a repository, to understand practices in customized testing solutions, and to mine tests to improve program comprehension in the future
Big Data Analytics for Energy Consumption Prediction in Smart Grid Using Genetic Algorithm and Long Short Term Memory
Get PDFSmart Grids (SG) have smart meters and advance metering infrasturutre (AMI) which generates huge data. This data can be used for predicting energy consumption using big data analytics. A very limited work has been carried out in the literature which shows the utilization of big data in energy consumption prediction. In this paper, the proposed method is based on Genetic Algorithm - Long Short Term Memory (GA-LSTM). LSTM memorises values over an arbitrary interval that manages time series data very effictively while GA is an evolutionary process that is used for optimization. GA combines with LSTM to process hyperparameters such as hidden layers, epochs, data intervals, batchsize and activation functions. Hence, GA creates a new vector for optimum solution that provides minimum error. These methods provide the best performance when compared with existing benchmarks. Moreover, GA-LSTM is used in a multi-threaded environment which increases the speed of convergence. Here, the multi-core platform is operated for solving one-dimensional GA-based inverse scattering problems. The result shows that GA-LSTM provides better convergence as compared to random approach techniques. For validating the results, Pennsylvania-New Jersey-Maryland Interconnection (PJM) energy consumption data has been used while adopting different performance evaluation metrics
Clustering and Bootstrapping Based Framework for News Knowledge Base Completion
Get PDFExtracting the facts, namely entities and relations, from unstructured sources is an essential step in any knowledge base construction. At the same time, it is also necessary to ensure the completeness of the knowledge base by incrementally extracting the new facts from various sources. To date, the knowledge base completion is studied as a problem of knowledge refinement where the missing facts are inferred by reasoning about the information already present in the knowledge base. However, facts missed while extracting the information from multilingual sources are ignored. Hence, this work proposed a generic framework for knowledge base completion to enrich a knowledge base of crime-related facts extracted from online news articles in the English language, with the facts extracted from low resourced Indian language Hindi news articles. Using the framework, information from any low-resourced language news articles can be extracted without using language-specific tools like POS tags and using an appropriate machine translation tool. To achieve this, a clustering algorithm is proposed, which explores the redundancy among the bilingual collection of news articles by representing the clusters with knowledge base facts unlike the existing Bag of Words representation. From each cluster, the facts extracted from English language articles are bootstrapped to extract the facts from comparable Hindi language articles. This way of bootstrapping within the cluster helps to identify the sentences from a low-resourced language that are enriched with new information related to the facts extracted from a high-resourced language like English. The empirical result shows that the proposed clustering algorithm produced more accurate and high-quality clusters for monolingual and cross-lingual facts, respectively. Experiments also proved that the proposed framework achieves a high recall rate in extracting the new facts from Hindi news articles
Current Trends in Software Engineering Bachelor Theses
Get PDFThis article presents short analysis and observations on current trends and directions in conducting engineering theses in the field of computer science. This report is based on collected bachelor theses in AGH Computer Science Department for academic year 2020/2021 as well as the conducted competition for the best engineering theses held during XXII KKIO 2021 Software Engineering Conference. The awarded works are briefly presented as an illustration to the drawn conclusions
Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation
Get PDFGiven various features of a software project, it may face different administrative challenges requiring right decisions by software project managers. A major challenge is to estimate software development cost for which different methods have been proposed by many researchers. According to the literature, the capability of a proposed model or method is demonstrated in a specific set of software projects. Hence, the aim of this study is to present a model to take advantage of the capabilities of various software development cost estimation models and methods simultaneously. For this purpose, a new model called "open hybrid model" was proposed based on the firefly algorithm. The proposed model includes an extensible bank of estimation methods. The model also includes an extensible bank of rules to describe the relation between existing methods. Considering project conditions, the proposed model tries to find the best rule for combining estimation methods in the methods bank. Three datasets of real projects were used to evaluate the precision of the proposed model, and the results were compared with those of other 11 methods. The results were compared based on performance parmeters widely used to show the accuracy and stability of estimation models. According to the results, the open hybrid model was able to select the most appropriate methods present in the methods bank
Isolated Word Recognition by Recursive HMM Parameter Estimation Algorithm
Get PDFAutomatic speech recognition (ASR) technologies enable humans to communicate with computers. Isolated word recognition (IWR) is an important part of many known ASR systems. Minimizing the word error rate in cases of incremental learning is a unique challenge for developing an on-line ASR system. This paper focuses on on-line IWR using a recursive hidden Markov model (HMM) multivariate parameter estimation algorithm. The maximum likelihood method was used to estimate the unknown parameters of the model, and an algorithm for the adapted recursive EM algorithm for HMMs parameter estimation was derived. The resulting recursive EM algorithm is unique among its counterparts because of state transition probabilities calculation. It obtains more accurate parameter estimates compared to other algorithms of this type. In our experiment, the algorithm was implemented and adapted to several datasets for IWR. Thus, the recognition rate and algorithm convergence results are discussed in this work
Moving Target Detection Based on an Adaptive Low-Rank Sparse Decomposition
Get PDFFor the exact detection of moving targets in video processing, an adaptive low-rank sparse decomposition algorithm is proposed in this paper. In the paper's algorithm, the background model and the solved frame vector are first used to construct an augmented matrix, then robust principal component analysis (RPCA) is used to perform a low-rank sparse decomposition on the enhanced augmented matrix. The separated low-rank part and sparse noise correspond to the background and motion foreground of the video frame, respectively, the incremental singular value decomposition method and the current background vector are used to update the background model. The experimental results show that the algorithm can deal with complex scenes such as light changes and background motion better, and the algorithm's delay and memory consumption can be reduced effectively