Pub Date : 2022-09-01DOI: 10.1109/SYNASC57785.2022.00022
Vedad Hadžić, Roderick Bloem, Ankit Shukla, M. Seidl
Modern expansion-based solvers for quantified Boolean formulas (QBFs) are successful in many applications. However, no such solver supports the generation of proofs needed to independently validate the correctness of the solving result and for the extraction of winning strategies which encode concrete solutions to the application problems.In this paper, we present a complete tool chain for proof generation, result validation, and for universal winning strategy extraction in the context of expansion-based solving. In particular, we introduce a proof format for the ∀Exp-Res calculus on which expansion-based solving is founded, implement proof generation in a recent QBF solver, provide a checker for these proofs, and develop a new strategy extraction algorithm.
{"title":"FERPModels: A Certification Framework for Expansion-Based QBF Solving","authors":"Vedad Hadžić, Roderick Bloem, Ankit Shukla, M. Seidl","doi":"10.1109/SYNASC57785.2022.00022","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00022","url":null,"abstract":"Modern expansion-based solvers for quantified Boolean formulas (QBFs) are successful in many applications. However, no such solver supports the generation of proofs needed to independently validate the correctness of the solving result and for the extraction of winning strategies which encode concrete solutions to the application problems.In this paper, we present a complete tool chain for proof generation, result validation, and for universal winning strategy extraction in the context of expansion-based solving. In particular, we introduce a proof format for the ∀Exp-Res calculus on which expansion-based solving is founded, implement proof generation in a recent QBF solver, provide a checker for these proofs, and develop a new strategy extraction algorithm.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114560727","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}
Pub Date : 2022-09-01DOI: 10.1109/SYNASC57785.2022.00056
W. Roland, M. Kommenda, G. Berger‐Weber
Modeling and simulation is essential in polymer processing for predicting process characteristics and designing processing machines. Traditional models are based on analytical approaches. Over the last decades numerical simulation techniques have grown significantly with the rising computational power. With the ongoing digitalization the available data increased significantly and data-based modeling techniques have become popular also for production systems. Utilizing the available data powerful models, for instance, decision trees and artificial neural networks, can be trained. The prediction accuracy is strongly governed by the quality of the underlying training data. In this work, a hybrid approach is presented combining analytical, numerical and data-based approaches efficiently to overcome the limitations of the individual techniques. As a result, explicit symbolic regression models are obtained, which are optimized on the basis of a numerically derived dataset. The power of this approach is demonstrated by a selected use-case. These highly accurate models may be implemented into any further application.
{"title":"Application of Symbolic Regression in Polymer Processing","authors":"W. Roland, M. Kommenda, G. Berger‐Weber","doi":"10.1109/SYNASC57785.2022.00056","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00056","url":null,"abstract":"Modeling and simulation is essential in polymer processing for predicting process characteristics and designing processing machines. Traditional models are based on analytical approaches. Over the last decades numerical simulation techniques have grown significantly with the rising computational power. With the ongoing digitalization the available data increased significantly and data-based modeling techniques have become popular also for production systems. Utilizing the available data powerful models, for instance, decision trees and artificial neural networks, can be trained. The prediction accuracy is strongly governed by the quality of the underlying training data. In this work, a hybrid approach is presented combining analytical, numerical and data-based approaches efficiently to overcome the limitations of the individual techniques. As a result, explicit symbolic regression models are obtained, which are optimized on the basis of a numerically derived dataset. The power of this approach is demonstrated by a selected use-case. These highly accurate models may be implemented into any further application.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127995940","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}
Pub Date : 2022-09-01DOI: 10.1109/SYNASC57785.2022.00060
Marco Radovancovici, Darius Galis, Ciprian-Petrisor Pungila
In this paper we perform a practical analysis of techniques for minting genetic information, in particular genomic data, as non-fungible tokens (NFTs) in supporting blockchains, and perform a wide-range analysis of the best and most efficient tools to ensure such data’s privacy, non-repudiation and storage efficiency. We analyze the demands of the NFT-driven blockchain ecosystem today, and discuss how we can apply common approaches in storing and accessing genomic data, such as compression methods and encryption techniques for it, to the NFT world. We perform a practical experiment of our assessment, and compare the publicly available tools and libraries for achieving the aforementioned goal, in order to determine which one provides the best results in terms of speed and storage efficiency, and draw relevant conclusions as to which approach is more beneficial to NFT-driven ecosystems where genomic data could be safely preserved and actively traded.
{"title":"A Practical Analysis of Techniques for Minting Genetic Information as NFTs in Blockchain Technology","authors":"Marco Radovancovici, Darius Galis, Ciprian-Petrisor Pungila","doi":"10.1109/SYNASC57785.2022.00060","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00060","url":null,"abstract":"In this paper we perform a practical analysis of techniques for minting genetic information, in particular genomic data, as non-fungible tokens (NFTs) in supporting blockchains, and perform a wide-range analysis of the best and most efficient tools to ensure such data’s privacy, non-repudiation and storage efficiency. We analyze the demands of the NFT-driven blockchain ecosystem today, and discuss how we can apply common approaches in storing and accessing genomic data, such as compression methods and encryption techniques for it, to the NFT world. We perform a practical experiment of our assessment, and compare the publicly available tools and libraries for achieving the aforementioned goal, in order to determine which one provides the best results in terms of speed and storage efficiency, and draw relevant conclusions as to which approach is more beneficial to NFT-driven ecosystems where genomic data could be safely preserved and actively traded.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116560545","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}
Pub Date : 2022-09-01DOI: 10.1109/SYNASC57785.2022.00025
Shashank K. Mehta, M. Rajasree
The ${mathbb{Z}^n}$ lattice is the lattice generated by the set of all orthogonal unit integer vectors. Since it has an orthonormal basis, the shortest vector problem and the closest vector problem are easy to solve in this particular lattice. But, these problems are hard to solve when we consider a rotation of ${mathbb{Z}^n}$ lattice. In-fact, even though it is known that the ${mathbb{Z}^n}$-isomorphism problem is in NP ∩ Co-NP, we still don’t have an efficient algorithm to solve it. Motivated by the above, in this paper we investigate the properties of the bases of ${mathbb{Z}^n}$ lattice which are the sets of column/row vectors of unimodular matrices. We show that an integer primitive vector of norm strictly greater than 1 can be extended to a unimodular matrix U such that the remaining vectors have norm strictly smaller than the initial primitive vector. We also show a reduction from SVP in any lattice isomorphic to ${mathbb{Z}^n}$ to SVP in n − 1 dimensional sublattice of ${mathbb{Z}^n}$. We define two new classes of lattice bases and show certain results related to ${mathbb{Z}^n}$ bases. Finally, we study the relation between any solution to Successive Minima Problem and the set of Voronoi relevant vectors and present some bounds related to the compact bases of ${mathbb{Z}^n}$.
{"title":"On the bases of Zn lattice","authors":"Shashank K. Mehta, M. Rajasree","doi":"10.1109/SYNASC57785.2022.00025","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00025","url":null,"abstract":"The ${mathbb{Z}^n}$ lattice is the lattice generated by the set of all orthogonal unit integer vectors. Since it has an orthonormal basis, the shortest vector problem and the closest vector problem are easy to solve in this particular lattice. But, these problems are hard to solve when we consider a rotation of ${mathbb{Z}^n}$ lattice. In-fact, even though it is known that the ${mathbb{Z}^n}$-isomorphism problem is in NP ∩ Co-NP, we still don’t have an efficient algorithm to solve it. Motivated by the above, in this paper we investigate the properties of the bases of ${mathbb{Z}^n}$ lattice which are the sets of column/row vectors of unimodular matrices. We show that an integer primitive vector of norm strictly greater than 1 can be extended to a unimodular matrix U such that the remaining vectors have norm strictly smaller than the initial primitive vector. We also show a reduction from SVP in any lattice isomorphic to ${mathbb{Z}^n}$ to SVP in n − 1 dimensional sublattice of ${mathbb{Z}^n}$. We define two new classes of lattice bases and show certain results related to ${mathbb{Z}^n}$ bases. Finally, we study the relation between any solution to Successive Minima Problem and the set of Voronoi relevant vectors and present some bounds related to the compact bases of ${mathbb{Z}^n}$.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130280688","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}
Pub Date : 2022-09-01DOI: 10.1109/SYNASC57785.2022.00010
Camelia Chira
Network science is emerging as a vibrant research field with important applications in finance, biology, chemistry, physics, engineering and health. This short paper presents an overview of some challenging tasks related to the analysis of complex networks, including community detection, discovery of cycles and identification of important nodes. The solutions proposed for these important network analysis tasks engage Artificial Intelligence models and are briefly presented with an emphasis on their performance as well as the main related research questions. The analysis of financial networks is also discussed, showing the potential of using network science tools to discover financial cycles and paths.
{"title":"Complex Network Analysis using Artificial Intelligence Algorithms","authors":"Camelia Chira","doi":"10.1109/SYNASC57785.2022.00010","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00010","url":null,"abstract":"Network science is emerging as a vibrant research field with important applications in finance, biology, chemistry, physics, engineering and health. This short paper presents an overview of some challenging tasks related to the analysis of complex networks, including community detection, discovery of cycles and identification of important nodes. The solutions proposed for these important network analysis tasks engage Artificial Intelligence models and are briefly presented with an emphasis on their performance as well as the main related research questions. The analysis of financial networks is also discussed, showing the potential of using network science tools to discover financial cycles and paths.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128420529","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}
Pub Date : 2022-09-01DOI: 10.1109/SYNASC57785.2022.00049
Alexandru Ionascu, Sebastian-Aurelian Ștefănigă
In this paper we are discussing the possibility of a hybrid approach in renderable scenes. The main idea of the presented experiment is to render the human actors by using existing videos of the characters. The input video is first converted to a sprite dataset. The dataset is generated with supervised techniques but human intervention is also required. After that we extract body and pose parameters. Lastly, we render novel poses using a GAN-based approach similar to pix2pix.
{"title":"Semi-Supervised Pipeline for Human Sprites Neural Rendering","authors":"Alexandru Ionascu, Sebastian-Aurelian Ștefănigă","doi":"10.1109/SYNASC57785.2022.00049","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00049","url":null,"abstract":"In this paper we are discussing the possibility of a hybrid approach in renderable scenes. The main idea of the presented experiment is to render the human actors by using existing videos of the characters. The input video is first converted to a sprite dataset. The dataset is generated with supervised techniques but human intervention is also required. After that we extract body and pose parameters. Lastly, we render novel poses using a GAN-based approach similar to pix2pix.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130628736","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}
Pub Date : 2022-09-01DOI: 10.1109/SYNASC57785.2022.00065
George Popoiu
Even though machine learning methods are being used in practice for malware detection, there are still many hurdles to overcome. Nowadays, there are still some challenges remaining regarding machine learning for malware detection: having a false positive rate as low as possible, fast classification, low volatile and disk memory usage. Because of these constraints, security solutions often have to rely on simpler models rather than on more complex ones. This paper has the purpose of reducing the training phase false positive rate of SVM models in the context of malware detection by using reformulations of the SVM optimization problem. The results obtained show that the proposed linear SVM model can be a drop in replacement with better false positive rate than regular linear SVM models or the one side class perceptron [4].
{"title":"One side class SVM training methods for malware detection","authors":"George Popoiu","doi":"10.1109/SYNASC57785.2022.00065","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00065","url":null,"abstract":"Even though machine learning methods are being used in practice for malware detection, there are still many hurdles to overcome. Nowadays, there are still some challenges remaining regarding machine learning for malware detection: having a false positive rate as low as possible, fast classification, low volatile and disk memory usage. Because of these constraints, security solutions often have to rely on simpler models rather than on more complex ones. This paper has the purpose of reducing the training phase false positive rate of SVM models in the context of malware detection by using reformulations of the SVM optimization problem. The results obtained show that the proposed linear SVM model can be a drop in replacement with better false positive rate than regular linear SVM models or the one side class perceptron [4].","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125988048","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}
Pub Date : 2022-09-01DOI: 10.1109/SYNASC57785.2022.00038
Victor Bogdan, A. Brîndusescu
The constant increase of published papers and participating authors in conferences has risen the interest in studies related to conference and co-authorship analysis techniques. There are numerous different studies in the literature, which offer distinct perspectives regarding the author ranking, impact, collaborations and research interests through various analysis techniques. In our research, we are introducing a new collaborator group ranking analysis based on four different centrality measures, using published papers data from the SYNASC conference during the 2005-2021 period, with which we construct dynamic co-authorship networks. By using the proposed approach, we aim to study collaboration properties at micro-level, which could also determine the impact of the authors and collaborator groups on the conference itself. The results which we obtained are highlighting that some of the authors and collaborator groups excelled in multiple ranking types, by having a constant top ranking, thus also suggesting a significant overall impact, developed over time, on the conference.
{"title":"Authors and Collaborator Groups Ranking Analysis on SYNASC using Centrality Measures","authors":"Victor Bogdan, A. Brîndusescu","doi":"10.1109/SYNASC57785.2022.00038","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00038","url":null,"abstract":"The constant increase of published papers and participating authors in conferences has risen the interest in studies related to conference and co-authorship analysis techniques. There are numerous different studies in the literature, which offer distinct perspectives regarding the author ranking, impact, collaborations and research interests through various analysis techniques. In our research, we are introducing a new collaborator group ranking analysis based on four different centrality measures, using published papers data from the SYNASC conference during the 2005-2021 period, with which we construct dynamic co-authorship networks. By using the proposed approach, we aim to study collaboration properties at micro-level, which could also determine the impact of the authors and collaborator groups on the conference itself. The results which we obtained are highlighting that some of the authors and collaborator groups excelled in multiple ranking types, by having a constant top ranking, thus also suggesting a significant overall impact, developed over time, on the conference.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"887 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125372097","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}
As devices get ever so interconnected, the concept of Smart City is becoming less of a distant aspiration and more of a reality. One major discomfort which is persistent in traditional cities, from the perspective of a vehicle motorist, is the lack of guidance or coordination when in need of a parking space, be it in a shopping center, at the airport, at work or any other overcrowded point of interest. Motorists are frequently presented with little to no adequate information regarding the parking space availability within a parking lot, and in turn end up spending excessive time in hopes of finding an open spot. This work tackles the problem of improving operational efficiency regarding the widely known problem of finding a parking space, through automation and surveillance, in hopes of providing higher quality allocation and management services. We propose a smart parking system centered around environment monitoring with the help of motion sensors and live-feed cameras, as well as efficient parking space management through a reservation system. As a result, the entire process is automated and the end user is kept up to date, with regard to parking space availability, no longer needing to wander around in order to find a vacant spot. Given that the system does not require any human intervention, it efficiently manages the incoming traffic by ensuring all reservation expectations are met, and it does so effortlessly.
{"title":"Motorage - Computer vision-based self-sufficient smart parking system","authors":"Bogdan Budihală, Todor Ivascu, Sebastian-Aurelian Ștefănigă","doi":"10.1109/SYNASC57785.2022.00047","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00047","url":null,"abstract":"As devices get ever so interconnected, the concept of Smart City is becoming less of a distant aspiration and more of a reality. One major discomfort which is persistent in traditional cities, from the perspective of a vehicle motorist, is the lack of guidance or coordination when in need of a parking space, be it in a shopping center, at the airport, at work or any other overcrowded point of interest. Motorists are frequently presented with little to no adequate information regarding the parking space availability within a parking lot, and in turn end up spending excessive time in hopes of finding an open spot. This work tackles the problem of improving operational efficiency regarding the widely known problem of finding a parking space, through automation and surveillance, in hopes of providing higher quality allocation and management services. We propose a smart parking system centered around environment monitoring with the help of motion sensors and live-feed cameras, as well as efficient parking space management through a reservation system. As a result, the entire process is automated and the end user is kept up to date, with regard to parking space availability, no longer needing to wander around in order to find a vacant spot. Given that the system does not require any human intervention, it efficiently manages the incoming traffic by ensuring all reservation expectations are met, and it does so effortlessly.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115839364","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}
Pub Date : 2022-09-01DOI: 10.1109/SYNASC57785.2022.00052
Roxana Sipos-Lascu, L. Dioşan
Image processing applications include image classification, image segmentation, image synthesis and many others. Each such task depends on extracting an effective set of features to characterize the images, and texture analysis has proven to output some of the most valuable features. For this reason, image texture analysis has been an actively researched topic and numerous methods have been proposed, each of them having its advantages and limitations. In practical applications, it is impossible to ensure that all images have the same scale, rotation, viewpoint, etc., so texture analysis methods should ideally be invariant. This study inspects the most commonly used operators for extracting textural features, tests their accuracy in classifying the Kylberg texture dataset, and evaluates their invariant properties by the means of various synthetically transformed images. By conducting this analysis, we identified the shortcomings of the existing approaches, and will be able to address them in our future work by formulating some improvements to existing operators to increase their accuracy and to make them invariant to a larger set of transformations.
{"title":"An Evaluation of Image Texture Descriptors and their Invariant Properties","authors":"Roxana Sipos-Lascu, L. Dioşan","doi":"10.1109/SYNASC57785.2022.00052","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00052","url":null,"abstract":"Image processing applications include image classification, image segmentation, image synthesis and many others. Each such task depends on extracting an effective set of features to characterize the images, and texture analysis has proven to output some of the most valuable features. For this reason, image texture analysis has been an actively researched topic and numerous methods have been proposed, each of them having its advantages and limitations. In practical applications, it is impossible to ensure that all images have the same scale, rotation, viewpoint, etc., so texture analysis methods should ideally be invariant. This study inspects the most commonly used operators for extracting textural features, tests their accuracy in classifying the Kylberg texture dataset, and evaluates their invariant properties by the means of various synthetically transformed images. By conducting this analysis, we identified the shortcomings of the existing approaches, and will be able to address them in our future work by formulating some improvements to existing operators to increase their accuracy and to make them invariant to a larger set of transformations.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132877184","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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