Matteo Camilli, C. Bellettini, L. Capra, Mattia Monga
Time Basic Petri nets are an expressive extension of Petri nets, suitable to model real-time systems. This paper introduces a coverability analysis technique to cope with structurally unbounded Time Basic Petri net models exhibiting non-urgent behavior: i.e., models in which transitions may choose to do not fire and let time pass, even if this could lead to transition disabling. The approach we present exploits the identification of anonymous temporal information, that is the possibility of erasing timestamps associated with specific tokens without compromising the correctness of model's temporal evolution. In particular, we extend the classical Karp-Miller coverability algorithm in two ways: first, we adapt the acceleration function to deal with symbolic states and to identify unboundedness due to time anonymous tokens, second, we employ an aggressive pruning strategy to remove included/covered portions of the reachability tree during exploration.
{"title":"Coverability Analysis of Time Basic Petri Nets with Non-Urgent Behavior","authors":"Matteo Camilli, C. Bellettini, L. Capra, Mattia Monga","doi":"10.1109/SYNASC.2016.036","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.036","url":null,"abstract":"Time Basic Petri nets are an expressive extension of Petri nets, suitable to model real-time systems. This paper introduces a coverability analysis technique to cope with structurally unbounded Time Basic Petri net models exhibiting non-urgent behavior: i.e., models in which transitions may choose to do not fire and let time pass, even if this could lead to transition disabling. The approach we present exploits the identification of anonymous temporal information, that is the possibility of erasing timestamps associated with specific tokens without compromising the correctness of model's temporal evolution. In particular, we extend the classical Karp-Miller coverability algorithm in two ways: first, we adapt the acceleration function to deal with symbolic states and to identify unboundedness due to time anonymous tokens, second, we employ an aggressive pruning strategy to remove included/covered portions of the reachability tree during exploration.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130578353","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}
Partial words have been studied by Blanchet-Sadri et al., but bi-ideals or reccurrent words have been studied for centuries by many researchers. This paper gives a solution for some problems for partial reccurrent words. This paper gives an algorithm for a given finitely generated bi-ideal, how to construct a new basis of ultimately finitely generated bi-ideal, which generates the same given bi-ideal. The paper states that it is always possible to find a basis for a given finitely generated bi-ideal. The main results of this paper are presented in third section. At first, we show that if two irreduciable bi-ideals are different, they will differ in infinitely many places. This led to the statement that it is possible to fill the finite number of holes for a given finitely generated bi-ideal, but a counterexample shows that it is not possible for infinitely many holes.
{"title":"Partial Finitely Generated Bi-Ideals","authors":"Raivis Bēts, J. Buls","doi":"10.1109/SYNASC.2016.065","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.065","url":null,"abstract":"Partial words have been studied by Blanchet-Sadri et al., but bi-ideals or reccurrent words have been studied for centuries by many researchers. This paper gives a solution for some problems for partial reccurrent words. This paper gives an algorithm for a given finitely generated bi-ideal, how to construct a new basis of ultimately finitely generated bi-ideal, which generates the same given bi-ideal. The paper states that it is always possible to find a basis for a given finitely generated bi-ideal. The main results of this paper are presented in third section. At first, we show that if two irreduciable bi-ideals are different, they will differ in infinitely many places. This led to the statement that it is possible to fill the finite number of holes for a given finitely generated bi-ideal, but a counterexample shows that it is not possible for infinitely many holes.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132009252","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}
Nowadays, the attacks are no longer performed from a single computer but from thousands, sometimes millions of systems that are located all over the globe and are grouped in a network called botnet. The most widely used technique to control a botnet is to try to connect to many domain names, generated according to an algorithm called domain generating algorithm (DGA). In this paper we present different algorithms that can determine if a computer is part of a botnet by looking at its network traffic. Since in some cases the network traffic is impossible to be shared due to privacy reasons we also analyze the case where just limited information can be provided (such as a netflow log). The algorithms presented here were obtained after reverse engineering and analyzing the DGA of 18 different botnets including some that were taken down (such as Cryptolocker) and ones that are still alive and thriving (such as PushDo, Tinba, Nivdort, DirtyLocker, Dobot, Patriot, Ramdo, Virut, Ramnit and many more).
{"title":"Identifying DGA-Based Botnets Using Network Anomaly Detection","authors":"Dragos Gavrilut, George Popoiu, Razvan Benchea","doi":"10.1109/SYNASC.2016.053","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.053","url":null,"abstract":"Nowadays, the attacks are no longer performed from a single computer but from thousands, sometimes millions of systems that are located all over the globe and are grouped in a network called botnet. The most widely used technique to control a botnet is to try to connect to many domain names, generated according to an algorithm called domain generating algorithm (DGA). In this paper we present different algorithms that can determine if a computer is part of a botnet by looking at its network traffic. Since in some cases the network traffic is impossible to be shared due to privacy reasons we also analyze the case where just limited information can be provided (such as a netflow log). The algorithms presented here were obtained after reverse engineering and analyzing the DGA of 18 different botnets including some that were taken down (such as Cryptolocker) and ones that are still alive and thriving (such as PushDo, Tinba, Nivdort, DirtyLocker, Dobot, Patriot, Ramdo, Virut, Ramnit and many more).","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123282863","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}
The Schulz-type methods for computing generalizedmatrix inverses are a family of iterative methods that are popular for their high order of convergence (≥ 2). We propose two new scaled acceleration techniques for such type of iterative methods for real matrices (based on Frobenius norm minimization) andlay out efficient algorithms to implement these techniques. Testresults show one of our techniques to be most effective for densematrices but also works for sparse cases as well.
{"title":"Convergence Acceleration of Iterative Methods for Inverting Real Matrices Using Frobenius Norm Minimization","authors":"Ajinkya Borle, S. Lomonaco","doi":"10.1109/SYNASC.2016.031","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.031","url":null,"abstract":"The Schulz-type methods for computing generalizedmatrix inverses are a family of iterative methods that are popular for their high order of convergence (≥ 2). We propose two new scaled acceleration techniques for such type of iterative methods for real matrices (based on Frobenius norm minimization) andlay out efficient algorithms to implement these techniques. Testresults show one of our techniques to be most effective for densematrices but also works for sparse cases as well.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133147384","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}
Topic Modeling is a type of statistical model that tries to determine the topics present in a corpus of documents. The accuracy measures applied to clustering algorithm can also be used to assess the accuracy of topic modeling algorithms because determining topics for documents is similar with clustering them. This paper presents an experimental validation regarding the accuracy of Latent Dirichlet Allocation in comparison with Non-Negative Matrix Factorization and K-Means. The experiments use different weighting schemas when constructing the document-term matrix to determine if the accuracy of the algorithm improves. Two well known, already labeled text corpora are used for testing. The Purity and Adjusted Rand Index are used to evaluate the accuracy. Also, a time performance comparison regarding the run-time of these algorithms is presented.
{"title":"Comparing Different Term Weighting Schemas for Topic Modeling","authors":"Ciprian-Octavian Truică, F. Rădulescu, A. Boicea","doi":"10.1109/SYNASC.2016.055","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.055","url":null,"abstract":"Topic Modeling is a type of statistical model that tries to determine the topics present in a corpus of documents. The accuracy measures applied to clustering algorithm can also be used to assess the accuracy of topic modeling algorithms because determining topics for documents is similar with clustering them. This paper presents an experimental validation regarding the accuracy of Latent Dirichlet Allocation in comparison with Non-Negative Matrix Factorization and K-Means. The experiments use different weighting schemas when constructing the document-term matrix to determine if the accuracy of the algorithm improves. Two well known, already labeled text corpora are used for testing. The Purity and Adjusted Rand Index are used to evaluate the accuracy. Also, a time performance comparison regarding the run-time of these algorithms is presented.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115198333","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}
For a biological system modeled by a continuous dynamical system defined by rational functions with two parameters, we propose a numerical method to compute the fold and Hopf bifurcation boundaries of the system restricted in a finite region in the parametric space under certain assumptions. The bifurcation boundaries divide their complement in the region into connected subsets, called cells, such that above each of them the number of equilibria is constant and the stability of each equilibrium remains unchanged. The boundaries are generated by first tracing the fold and Hopf bifurcation curves in a higher dimensional space and then projecting them onto the parameter plane. One advantage of this method is that it can exploit global information of real varieties and generate complete boundaries based on homotopy continuation methods and critical point techniques. The bistability properties of several biological systems are successfully analyzed by our method.
{"title":"A Numerical Method for Analyzing the Stability of Bi-Parametric Biological Systems","authors":"Changbo Chen, Wenyuan Wu","doi":"10.1109/SYNASC.2016.026","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.026","url":null,"abstract":"For a biological system modeled by a continuous dynamical system defined by rational functions with two parameters, we propose a numerical method to compute the fold and Hopf bifurcation boundaries of the system restricted in a finite region in the parametric space under certain assumptions. The bifurcation boundaries divide their complement in the region into connected subsets, called cells, such that above each of them the number of equilibria is constant and the stability of each equilibrium remains unchanged. The boundaries are generated by first tracing the fold and Hopf bifurcation curves in a higher dimensional space and then projecting them onto the parameter plane. One advantage of this method is that it can exploit global information of real varieties and generate complete boundaries based on homotopy continuation methods and critical point techniques. The bistability properties of several biological systems are successfully analyzed by our method.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"14 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114111381","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}
There is currently a large amount of histopathological images due to the intensive prevention screening programs worldwide. This fact overloads the pathologists' tasks. Hence, there is a connected high need for a quantitative image-based evaluation of digital pathology slides. The current work extracts 76 numerical features from 357 histopathological images and focuses on the selection of the most valuable features that conducts to a smaller data set on which a SVM classifier achieves a better prediction. The gain in accuracy is of over 4% more than in the situation when the entire data set was used. The paper also indicates a subset of the attributes that proved to be the most informative with respect to 4 feature selection approaches.
{"title":"In Search of the Optimal Set of Indicators when Classifying Histopathological Images","authors":"C. Stoean","doi":"10.1109/SYNASC.2016.074","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.074","url":null,"abstract":"There is currently a large amount of histopathological images due to the intensive prevention screening programs worldwide. This fact overloads the pathologists' tasks. Hence, there is a connected high need for a quantitative image-based evaluation of digital pathology slides. The current work extracts 76 numerical features from 357 histopathological images and focuses on the selection of the most valuable features that conducts to a smaller data set on which a SVM classifier achieves a better prediction. The gain in accuracy is of over 4% more than in the situation when the entire data set was used. The paper also indicates a subset of the attributes that proved to be the most informative with respect to 4 feature selection approaches.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126172062","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}
We consider the computational problem to learn models from data that are possibly contaminated with outliers. We design and analyze algorithms for robust location and robust linear regression. Such algorithms are essential for solving central problems of robust statistics and outlier detection. We show that our algorithms, which are based on a novel extension of the Median-of-Means method by employing the discrete geometric median, are accurate, efficient and robust against many outliers in the data. The discrete geometric median has many desirable characteristics such as it works for general metric spaces and preserves combinatorial and statistical properties. Furthermore, there is an exact and efficient algorithm to compute it, and an even faster approximation algorithm. We present theoretical and experimental results. In particular, we emphasize the generality of Median-of-Means and its ability to speedup and parallelize algorithms which additionally are accurate and robust against many outliers in the data.
{"title":"Efficient and Robust Median-of-Means Algorithms for Location and Regression","authors":"Alexander Kogler, Patrick Traxler","doi":"10.1109/SYNASC.2016.041","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.041","url":null,"abstract":"We consider the computational problem to learn models from data that are possibly contaminated with outliers. We design and analyze algorithms for robust location and robust linear regression. Such algorithms are essential for solving central problems of robust statistics and outlier detection. We show that our algorithms, which are based on a novel extension of the Median-of-Means method by employing the discrete geometric median, are accurate, efficient and robust against many outliers in the data. The discrete geometric median has many desirable characteristics such as it works for general metric spaces and preserves combinatorial and statistical properties. Furthermore, there is an exact and efficient algorithm to compute it, and an even faster approximation algorithm. We present theoretical and experimental results. In particular, we emphasize the generality of Median-of-Means and its ability to speedup and parallelize algorithms which additionally are accurate and robust against many outliers in the data.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126465012","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}
Filling the gap between natural language expressions and ontology concepts or properties is the new trend in Semantic Web. Ontology lexicalization introduces a new layer of lexical information for ontology properties and concepts. We propose a method based on unsupervised learning for the extraction of the potential lexical expressions of DBpedia propertiesfrom Wikipedia text corpus. It is a resource-driven approach that comprises three main steps. The first step consists of the extraction of DBpedia triples for the aimed property followed by the extraction of Wikipedia articles describing the resources from these triples. In the second step, sentences mostly related to the property are extracted from the articles and they are analyzed with a Semantic Role Labeler resulting in a set of SRL annotated trees. In the last step, clusters of expressions are built using spectral clustering based on the distances between the SRL trees. The clusters with the least variance are considered to be relevant for the lexical expressions of the property.
{"title":"Towards Lexicalization of DBpedia Ontology with Unsupervised Learning and Semantic Role Labeling","authors":"A. Marginean, Kando Eniko","doi":"10.1109/SYNASC.2016.048","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.048","url":null,"abstract":"Filling the gap between natural language expressions and ontology concepts or properties is the new trend in Semantic Web. Ontology lexicalization introduces a new layer of lexical information for ontology properties and concepts. We propose a method based on unsupervised learning for the extraction of the potential lexical expressions of DBpedia propertiesfrom Wikipedia text corpus. It is a resource-driven approach that comprises three main steps. The first step consists of the extraction of DBpedia triples for the aimed property followed by the extraction of Wikipedia articles describing the resources from these triples. In the second step, sentences mostly related to the property are extracted from the articles and they are analyzed with a Semantic Role Labeler resulting in a set of SRL annotated trees. In the last step, clusters of expressions are built using spectral clustering based on the distances between the SRL trees. The clusters with the least variance are considered to be relevant for the lexical expressions of the property.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116985009","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}
In the paper we give a characterization of identifiability for regularizations with gauges of compact convexes. This extends the classic identifiability results from the standard l1-regularization framework in compressive sensing. We show that the standard dual certificate techniques can no longer work by themselves ouside the polytope case. We then apply the general characterization to the caseof block-sparse regularizations and obtain an identification algorithm based on a combination of the standard duality and a convex-projection technique.
{"title":"Identifiability for Gauge Regularizations and Algorithms for Block-Sparse Synthesis in Compressive Sensing","authors":"F. Turcu, C. Dossal, Marc Nicodeme","doi":"10.1109/SYNASC.2016.029","DOIUrl":"https://doi.org/10.1109/SYNASC.2016.029","url":null,"abstract":"In the paper we give a characterization of identifiability for regularizations with gauges of compact convexes. This extends the classic identifiability results from the standard l1-regularization framework in compressive sensing. We show that the standard dual certificate techniques can no longer work by themselves ouside the polytope case. We then apply the general characterization to the caseof block-sparse regularizations and obtain an identification algorithm based on a combination of the standard duality and a convex-projection technique.","PeriodicalId":268635,"journal":{"name":"2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127534190","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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