Abstract Music information retrieval has lately become an important field of information retrieval, because by profound analysis of music pieces important information can be collected: genre labels, mood prediction, artist identification, just to name a few. The lack of large-scale music datasets containing audio features and metadata has lead to the construction and publication of the Million Song Dataset (MSD) and its satellite datasets. Nonetheless, mainly because of licensing limitations, no freely available lyrics datasets have been published for research. In this paper we describe the construction of an English lyrics dataset based on the Last.fm Dataset, connected to LyricWiki’s database and MusicBrainz’s encyclopedia. To avoid copyright issues, only the URLs to the lyrics are stored in the database. In order to demonstrate the eligibility of the compiled dataset, in the second part of the paper we present genre classification experiments with lyrics-based features, including bagof-n-grams, as well as higher-level features such as rhyme-based and statistical text features. We obtained results similar to the experimental outcomes presented in other works, showing that more sophisticated textual features can improve genre classification performance, and indicating the superiority of the binary weighting scheme compared to tf–idf.
{"title":"Connecting the Last.fm Dataset to LyricWiki and MusicBrainz. Lyrics-based experiments in genre classification","authors":"Z. Bodó, Eszter Szilágyi","doi":"10.2478/ausi-2018-0009","DOIUrl":"https://doi.org/10.2478/ausi-2018-0009","url":null,"abstract":"Abstract Music information retrieval has lately become an important field of information retrieval, because by profound analysis of music pieces important information can be collected: genre labels, mood prediction, artist identification, just to name a few. The lack of large-scale music datasets containing audio features and metadata has lead to the construction and publication of the Million Song Dataset (MSD) and its satellite datasets. Nonetheless, mainly because of licensing limitations, no freely available lyrics datasets have been published for research. In this paper we describe the construction of an English lyrics dataset based on the Last.fm Dataset, connected to LyricWiki’s database and MusicBrainz’s encyclopedia. To avoid copyright issues, only the URLs to the lyrics are stored in the database. In order to demonstrate the eligibility of the compiled dataset, in the second part of the paper we present genre classification experiments with lyrics-based features, including bagof-n-grams, as well as higher-level features such as rhyme-based and statistical text features. We obtained results similar to the experimental outcomes presented in other works, showing that more sophisticated textual features can improve genre classification performance, and indicating the superiority of the binary weighting scheme compared to tf–idf.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"1 1","pages":"158 - 182"},"PeriodicalIF":0.3,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87719868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We consider the concept of statistical complexity to write the quasiperiodical damped systems applying the snapshot attractors. This allows us to understand the behaviour of these dynamical systems by the probability distribution of the time series making a difference between the regular, random and structural complexity on finite measurements. We interpreted the statistical complexity on snapshot attractor and determined it on the quasiperiodical forced pendulum.
{"title":"Statistical complexity of the quasiperiodical damped systems","authors":"Á. Fülöp","doi":"10.2478/ausi-2018-0012","DOIUrl":"https://doi.org/10.2478/ausi-2018-0012","url":null,"abstract":"Abstract We consider the concept of statistical complexity to write the quasiperiodical damped systems applying the snapshot attractors. This allows us to understand the behaviour of these dynamical systems by the probability distribution of the time series making a difference between the regular, random and structural complexity on finite measurements. We interpreted the statistical complexity on snapshot attractor and determined it on the quasiperiodical forced pendulum.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"27 1","pages":"241 - 256"},"PeriodicalIF":0.3,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75557057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Let G be a simple connected graph. The reciprocal transmission Tr′G(ν) of a vertex ν is defined as TrG′(ν)=∑u∈V(G)1dG(u,ν), u≠ν. $${rm{Tr}}_{rm{G}}^prime ({rm{nu }}) = sumlimits_{{rm{u}} in {rm{V}}(G)} {{1 over {{{rm{d}}_{rm{G}}}(u,{rm{nu }})}}{rm{u}} ne {rm{nu }}.} $$ The reciprocal distance signless Laplacian (briefly RDSL) matrix of a connected graph G is defined as RQ(G)= diag(Tr′ (G)) + RD(G), where RD(G) is the Harary matrix (reciprocal distance matrix) of G and diag(Tr′ (G)) is the diagonal matrix of the vertex reciprocal transmissions in G. In this paper, we investigate the RDSL spectrum of some classes of graphs that are arisen from graph operations such as cartesian product, extended double cover product and InduBala product. We introduce minimum covering reciprocal distance signless Laplacian matrix (or briey MCRDSL matrix) of G as the square matrix of order n, RQC(G) := (qi;j), qij={1+Tr′(νi)ifi=jandνi∈CTr′(νi)ifi=jandνi∉C1d(νi,νj)otherwise $${{rm{q}}_{{rm{ij}}}} = left{ {matrix{ {1 + {rm{Tr}}prime ({{rm{nu }}_{rm{i}}})} & {{rm{if}}} & {{rm{i = j}}} & {{rm{and}}} & {{{rm{nu }}_{rm{i}}} in {rm{C}}} cr {{rm{Tr}}prime ({{rm{nu }}_{rm{i}}})} & {{rm{if}}} & {{rm{i = j}}} & {{rm{and}}} & {{{rm{nu }}_{rm{i}}} notin {rm{C}}} cr {{1 over {{rm{d(}}{{rm{nu }}_{rm{i}}},{{rm{nu }}_{rm{j}}})}}} & {{rm{otherwise}}} & {} & {} & {} cr } } right.$$ where C is a minimum vertex cover set of G. MCRDSL energy of a graph G is defined as sum of eigenvalues of RQC. Extremal graphs with respect to MCRDSL energy of graph are characterized. We also obtain some bounds on MCRDSL energy of a graph and MCRDSL spectral radius of 𝒢, which is the largest eigenvalue of the matrix RQC (G) of graphs.
{"title":"Minimum covering reciprocal distance signless Laplacian energy of graphs","authors":"A. Alhevaz, M. Baghipur, E. Hashemi, Y. Alizadeh","doi":"10.2478/ausi-2018-0011","DOIUrl":"https://doi.org/10.2478/ausi-2018-0011","url":null,"abstract":"Abstract Let G be a simple connected graph. The reciprocal transmission Tr′G(ν) of a vertex ν is defined as TrG′(ν)=∑u∈V(G)1dG(u,ν), u≠ν. $${rm{Tr}}_{rm{G}}^prime ({rm{nu }}) = sumlimits_{{rm{u}} in {rm{V}}(G)} {{1 over {{{rm{d}}_{rm{G}}}(u,{rm{nu }})}}{rm{u}} ne {rm{nu }}.} $$ The reciprocal distance signless Laplacian (briefly RDSL) matrix of a connected graph G is defined as RQ(G)= diag(Tr′ (G)) + RD(G), where RD(G) is the Harary matrix (reciprocal distance matrix) of G and diag(Tr′ (G)) is the diagonal matrix of the vertex reciprocal transmissions in G. In this paper, we investigate the RDSL spectrum of some classes of graphs that are arisen from graph operations such as cartesian product, extended double cover product and InduBala product. We introduce minimum covering reciprocal distance signless Laplacian matrix (or briey MCRDSL matrix) of G as the square matrix of order n, RQC(G) := (qi;j), qij={1+Tr′(νi)ifi=jandνi∈CTr′(νi)ifi=jandνi∉C1d(νi,νj)otherwise $${{rm{q}}_{{rm{ij}}}} = left{ {matrix{ {1 + {rm{Tr}}prime ({{rm{nu }}_{rm{i}}})} & {{rm{if}}} & {{rm{i = j}}} & {{rm{and}}} & {{{rm{nu }}_{rm{i}}} in {rm{C}}} cr {{rm{Tr}}prime ({{rm{nu }}_{rm{i}}})} & {{rm{if}}} & {{rm{i = j}}} & {{rm{and}}} & {{{rm{nu }}_{rm{i}}} notin {rm{C}}} cr {{1 over {{rm{d(}}{{rm{nu }}_{rm{i}}},{{rm{nu }}_{rm{j}}})}}} & {{rm{otherwise}}} & {} & {} & {} cr } } right.$$ where C is a minimum vertex cover set of G. MCRDSL energy of a graph G is defined as sum of eigenvalues of RQC. Extremal graphs with respect to MCRDSL energy of graph are characterized. We also obtain some bounds on MCRDSL energy of a graph and MCRDSL spectral radius of 𝒢, which is the largest eigenvalue of the matrix RQC (G) of graphs.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"40 12","pages":"218 - 240"},"PeriodicalIF":0.3,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72469105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Problem generators are practical solutions for generating a set of inputs to specific problems. These inputs are widely used for testing, comparing and optimizing placement algorithms. The problem generator presented in this paper fills the gap in the area of 2D Cutting & Packing as the sum of the area of the small objects is equal to the area of the Large Object and has at least one perfect solution. In this paper, the already proposed Upper Deck algorithm is revisited and used to test the proposed generator outputs. This algorithm bypasses the dead area problem that occurs in most of all well-known strategies of the 2D Single Knapsack Problem where we have a single large rectangle to cover with small, heterogeneous rectangle shapes, whom total area exceeds the large object’s area. The idea of placing the small shapes in a free corner simplifies and speeds the placement algorithm as only the available angles are checked for possible placements, and collision detection only requires the checking of corners and edges of the placed shape. Since the proposed generator output has at least one exact solution, a series of optimization performed on the algorithm is also presented.
{"title":"Exact fit problem generator for cutting and packing, revisiting of the upper deck placement algorithm","authors":"Levente Filep, L. Illyés","doi":"10.2478/ausi-2018-0005","DOIUrl":"https://doi.org/10.2478/ausi-2018-0005","url":null,"abstract":"Abstract Problem generators are practical solutions for generating a set of inputs to specific problems. These inputs are widely used for testing, comparing and optimizing placement algorithms. The problem generator presented in this paper fills the gap in the area of 2D Cutting & Packing as the sum of the area of the small objects is equal to the area of the Large Object and has at least one perfect solution. In this paper, the already proposed Upper Deck algorithm is revisited and used to test the proposed generator outputs. This algorithm bypasses the dead area problem that occurs in most of all well-known strategies of the 2D Single Knapsack Problem where we have a single large rectangle to cover with small, heterogeneous rectangle shapes, whom total area exceeds the large object’s area. The idea of placing the small shapes in a free corner simplifies and speeds the placement algorithm as only the available angles are checked for possible placements, and collision detection only requires the checking of corners and edges of the placed shape. Since the proposed generator output has at least one exact solution, a series of optimization performed on the algorithm is also presented.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"90 1","pages":"73 - 85"},"PeriodicalIF":0.3,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86153996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Sentiment classification is the process of exploring sentiments, emotions, ideas and thoughts in the sentences which are expressed by the people. Sentiment classification allows us to judge the sentiments and feelings of the peoples by analyzing their reviews, social media comments etc. about all the aspects. Machine learning techniques and Lexicon based techniques are being mostly used in sentiment classification to predict sentiments from customers reviews and comments. Machine learning techniques includes several learning algorithms to judge the sentiments i.e Navie bayes, support vector machines etc whereas Lexicon Based techniques includes SentiWordnet, Wordnet etc. The main target of this survey is to give nearly full image of sentiment classification techniques. Survey paper provides the comprehensive overview of recent and past research on sentiment classification and provides excellent research queries and approaches for future aspects
{"title":"A survey on sentiment classification algorithms, challenges and applications","authors":"Muhammad Rizwan Rashid Rana, Asif Nawaz, J. Iqbal","doi":"10.2478/ausi-2018-0004","DOIUrl":"https://doi.org/10.2478/ausi-2018-0004","url":null,"abstract":"Abstract Sentiment classification is the process of exploring sentiments, emotions, ideas and thoughts in the sentences which are expressed by the people. Sentiment classification allows us to judge the sentiments and feelings of the peoples by analyzing their reviews, social media comments etc. about all the aspects. Machine learning techniques and Lexicon based techniques are being mostly used in sentiment classification to predict sentiments from customers reviews and comments. Machine learning techniques includes several learning algorithms to judge the sentiments i.e Navie bayes, support vector machines etc whereas Lexicon Based techniques includes SentiWordnet, Wordnet etc. The main target of this survey is to give nearly full image of sentiment classification techniques. Survey paper provides the comprehensive overview of recent and past research on sentiment classification and provides excellent research queries and approaches for future aspects","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"218 1","pages":"58 - 72"},"PeriodicalIF":0.3,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73806815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Szilágyi, David Iclanzan, Zoltán Kapás, Z. Szabó, Ágnes Győrfi, László Lefkovits
Abstract Several hundreds of thousand humans are diagnosed with brain cancer every year, and the majority dies within the next two years. The chances of survival could be easiest improved by early diagnosis. This is why there is a strong need for reliable algorithms that can detect the presence of gliomas in their early stage. While an automatic tumor detection algorithm can support a mass screening system, the precise segmentation of the tumor can assist medical staff at therapy planning and patient monitoring. This paper presents a random forest based procedure trained to segment gliomas in multispectral volumetric MRI records. Beside the four observed features, the proposed solution uses 100 further features extracted via morphological operations and Gabor wavelet filtering. A neighborhood-based post-processing was designed to regularize and improve the output of the classifier. The proposed algorithm was trained and tested separately with the 54 low-grade and 220 high-grade tumor volumes of the MICCAI BRATS 2016 training database. For both data sets, the achieved accuracy is characterized by an overall mean Dice score > 83%, sensitivity > 85%, and specificity > 98%. The proposed method is likely to detect all gliomas larger than 10 mL.
{"title":"Low and high grade glioma segmentation in multispectral brain MRI data","authors":"L. Szilágyi, David Iclanzan, Zoltán Kapás, Z. Szabó, Ágnes Győrfi, László Lefkovits","doi":"10.2478/ausi-2018-0007","DOIUrl":"https://doi.org/10.2478/ausi-2018-0007","url":null,"abstract":"Abstract Several hundreds of thousand humans are diagnosed with brain cancer every year, and the majority dies within the next two years. The chances of survival could be easiest improved by early diagnosis. This is why there is a strong need for reliable algorithms that can detect the presence of gliomas in their early stage. While an automatic tumor detection algorithm can support a mass screening system, the precise segmentation of the tumor can assist medical staff at therapy planning and patient monitoring. This paper presents a random forest based procedure trained to segment gliomas in multispectral volumetric MRI records. Beside the four observed features, the proposed solution uses 100 further features extracted via morphological operations and Gabor wavelet filtering. A neighborhood-based post-processing was designed to regularize and improve the output of the classifier. The proposed algorithm was trained and tested separately with the 54 low-grade and 220 high-grade tumor volumes of the MICCAI BRATS 2016 training database. For both data sets, the achieved accuracy is characterized by an overall mean Dice score > 83%, sensitivity > 85%, and specificity > 98%. The proposed method is likely to detect all gliomas larger than 10 mL.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"23 1","pages":"110 - 132"},"PeriodicalIF":0.3,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85677132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Product Derivation represents one of the main challenges that Software Product Line (SPL) faces. Deriving individual products from shared software assets is a time-consuming and an expensive activity. In this paper, we (1) present an MDE approach for engineering SPL and (2) propose to leverage model-to-model transformations (MMT) and model-to-text (MTT) transformations for supporting both domain engineering and application engineering processes. In this work, we use ATL as a model-to-model transformation language and Acceleo as a model-to-text transformation language.The proposed approach is discussed with e-Health product line applications.
{"title":"On the use of model transformation for the automation of product derivation process in SPL","authors":"Nesrine Lahiani, Djamal Bennouar","doi":"10.2478/ausi-2018-0003","DOIUrl":"https://doi.org/10.2478/ausi-2018-0003","url":null,"abstract":"Abstract Product Derivation represents one of the main challenges that Software Product Line (SPL) faces. Deriving individual products from shared software assets is a time-consuming and an expensive activity. In this paper, we (1) present an MDE approach for engineering SPL and (2) propose to leverage model-to-model transformations (MMT) and model-to-text (MTT) transformations for supporting both domain engineering and application engineering processes. In this work, we use ATL as a model-to-model transformation language and Acceleo as a model-to-text transformation language.The proposed approach is discussed with e-Health product line applications.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"18 1","pages":"43 - 57"},"PeriodicalIF":0.3,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82284750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this paper we present the latest developments in SMT-RAT, a tool for the automated check of quantifier-free real and integer arithmetic formulas for satisfiability. As a distinguishing feature, SMT-RAT provides a set of solving modules and supports their strategic combination. We describe our CArL library for arithmetic computations, the available modules implemented on top of CArL, and how modules can be combined to satisfiability-modulo-theories (SMT) solvers. Besides the traditional SMT approach, some new modules support also the recently proposed and highly promising model-constructing satisfiability calculus approach.
{"title":"Modular strategic SMT solving with SMT-RAT","authors":"Gereon Kremer, E. Ábrahám","doi":"10.2478/ausi-2018-0001","DOIUrl":"https://doi.org/10.2478/ausi-2018-0001","url":null,"abstract":"Abstract In this paper we present the latest developments in SMT-RAT, a tool for the automated check of quantifier-free real and integer arithmetic formulas for satisfiability. As a distinguishing feature, SMT-RAT provides a set of solving modules and supports their strategic combination. We describe our CArL library for arithmetic computations, the available modules implemented on top of CArL, and how modules can be combined to satisfiability-modulo-theories (SMT) solvers. Besides the traditional SMT approach, some new modules support also the recently proposed and highly promising model-constructing satisfiability calculus approach.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"31 1","pages":"25 - 5"},"PeriodicalIF":0.3,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77559893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Following up on our previous study on applying hierarchical clustering algorithms to high energy particle physics, this paper explores the possibilities to use deep learning to generate models capable of processing the clusterization themselves. The technique chosen for training is reinforcement learning, that allows the system to evolve based on interactions between the model and the underlying graph. The result is a model, that by learning on a modest dataset of 10, 000 nodes during 70 epochs can reach 83, 77% precision for hierarchical and 86, 33% for high energy jet physics datasets in predicting the appropriate clusters.
{"title":"Hierarchical clustering with deep Q-learning","authors":"Richard Forster, A. Fulop","doi":"10.2478/ausi-2018-0006","DOIUrl":"https://doi.org/10.2478/ausi-2018-0006","url":null,"abstract":"Abstract Following up on our previous study on applying hierarchical clustering algorithms to high energy particle physics, this paper explores the possibilities to use deep learning to generate models capable of processing the clusterization themselves. The technique chosen for training is reinforcement learning, that allows the system to evolve based on interactions between the model and the underlying graph. The result is a model, that by learning on a modest dataset of 10, 000 nodes during 70 epochs can reach 83, 77% precision for hierarchical and 86, 33% for high energy jet physics datasets in predicting the appropriate clusters.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"24 1","pages":"109 - 86"},"PeriodicalIF":0.3,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73841365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The reconstruction and analyze of measured data play important role in the research of high energy particle physics. This leads to new results in both experimental and theoretical physics. This requires algorithm improvements and high computer capacity. Clustering algorithm makes it possible to get to know the jet structure more accurately. More granular parallelization of the kt cluster algorithms was explored by combining it with the hierarchical clustering methods used in network evaluations. The kt method allows to know the development of particles due to the collision of high-energy nucleus-nucleus. The hierarchical clustering algorithms works on graphs, so the particle information used by the standard kt algorithm was first transformed into an appropriate graph, representing the network of particles. Testing was done using data samples from the Alice offine library, which contains the required modules to simulate the ALICE detector that is a dedicated Pb-Pb detector. The proposed algorithm was compared to the FastJet toolkit's standard longitudinal invariant kt implementation. Parallelizing the standard non-optimized version of this algorithm utilizing the available CPU architecture proved to be 1:6 times faster, than the standard implementation, while the proposed solution in this paper was able to achieve a 12 times faster computing performance, also being scalable enough to efficiently run on GPUs.
{"title":"Hierarchical kt jet clustering for parallel architectures","authors":"Richard Forster, Á. Fülöp","doi":"10.1515/ausi-2017-0012","DOIUrl":"https://doi.org/10.1515/ausi-2017-0012","url":null,"abstract":"Abstract The reconstruction and analyze of measured data play important role in the research of high energy particle physics. This leads to new results in both experimental and theoretical physics. This requires algorithm improvements and high computer capacity. Clustering algorithm makes it possible to get to know the jet structure more accurately. More granular parallelization of the kt cluster algorithms was explored by combining it with the hierarchical clustering methods used in network evaluations. The kt method allows to know the development of particles due to the collision of high-energy nucleus-nucleus. The hierarchical clustering algorithms works on graphs, so the particle information used by the standard kt algorithm was first transformed into an appropriate graph, representing the network of particles. Testing was done using data samples from the Alice offine library, which contains the required modules to simulate the ALICE detector that is a dedicated Pb-Pb detector. The proposed algorithm was compared to the FastJet toolkit's standard longitudinal invariant kt implementation. Parallelizing the standard non-optimized version of this algorithm utilizing the available CPU architecture proved to be 1:6 times faster, than the standard implementation, while the proposed solution in this paper was able to achieve a 12 times faster computing performance, also being scalable enough to efficiently run on GPUs.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"8 1","pages":"195 - 213"},"PeriodicalIF":0.3,"publicationDate":"2017-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75889962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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