Pub Date : 2017-09-01DOI: 10.1109/CSITECHNOL.2017.8312155
J. Mothe, K. Mkhitaryan, M. Haroutunian
Real world complex networks may contain hidden structures called communities or groups. They are composed of nodes being tightly connected within those groups and weakly connected between them. Detecting communities has numerous applications in different sciences such as biology, social network analysis, economics and computer science. Since there is no universally accepted definition of community, it is a complicated task to distinguish community detection algorithms as each of them use a different approach, resulting in different outcomes. Thus large number of articles are devoted to investigating community detection algorithms, implementation on both real world and artificial data sets and development of evaluation measures. In this article several state of the art algorithms and evaluation measures are studied which are used in clustering and community detection literature. The main focus of this article is to survey recent work and evaluate community detection algorithms using stochastic block model.
{"title":"Community detection: Comparison of state of the art algorithms","authors":"J. Mothe, K. Mkhitaryan, M. Haroutunian","doi":"10.1109/CSITECHNOL.2017.8312155","DOIUrl":"https://doi.org/10.1109/CSITECHNOL.2017.8312155","url":null,"abstract":"Real world complex networks may contain hidden structures called communities or groups. They are composed of nodes being tightly connected within those groups and weakly connected between them. Detecting communities has numerous applications in different sciences such as biology, social network analysis, economics and computer science. Since there is no universally accepted definition of community, it is a complicated task to distinguish community detection algorithms as each of them use a different approach, resulting in different outcomes. Thus large number of articles are devoted to investigating community detection algorithms, implementation on both real world and artificial data sets and development of evaluation measures. In this article several state of the art algorithms and evaluation measures are studied which are used in clustering and community detection literature. The main focus of this article is to survey recent work and evaluate community detection algorithms using stochastic block model.","PeriodicalId":332371,"journal":{"name":"2017 Computer Science and Information Technologies (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132480712","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 : 2017-09-01DOI: 10.1109/CSITECHNOL.2017.8312144
Suren Martirosyan
To overcome the issues of safety, reliability and efficiency in nanoscale designs, advanced methods of fault detection and diagnosis were developed. Multi-level model based methods of fault detection were proposed using a hierarchy of detection and diagnosis methods and dynamic models, since previous approaches do not give a deeper insight and mainly limit or trend checking of some measurable output variables, which usually makes it impossible to do fault diagnosis. The new developed methods generate several symptoms indicating the difference between nominal and faulty statuses. Based on different symptoms, fault diagnosis procedures follow, determining the fault by applying the developed classification scheme. In this paper, the validity of a memory scrambling aware multi-level fault diagnosis flow is shown by experiments and different case scenarios.
{"title":"Application of memory scrambling aware multi-level diagnosis flow","authors":"Suren Martirosyan","doi":"10.1109/CSITECHNOL.2017.8312144","DOIUrl":"https://doi.org/10.1109/CSITECHNOL.2017.8312144","url":null,"abstract":"To overcome the issues of safety, reliability and efficiency in nanoscale designs, advanced methods of fault detection and diagnosis were developed. Multi-level model based methods of fault detection were proposed using a hierarchy of detection and diagnosis methods and dynamic models, since previous approaches do not give a deeper insight and mainly limit or trend checking of some measurable output variables, which usually makes it impossible to do fault diagnosis. The new developed methods generate several symptoms indicating the difference between nominal and faulty statuses. Based on different symptoms, fault diagnosis procedures follow, determining the fault by applying the developed classification scheme. In this paper, the validity of a memory scrambling aware multi-level fault diagnosis flow is shown by experiments and different case scenarios.","PeriodicalId":332371,"journal":{"name":"2017 Computer Science and Information Technologies (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125294700","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 : 2017-06-23DOI: 10.1109/csitechnol.2017.8312134
A. Shahverdian
The paper studies the higher-order absolute differences taken from progressive terms of time-homogeneous binary Markov chains. Two theorems presented are the limiting theorems for these differences, when their order k converges to infinity. Theorems 1 and 2 assert that there exist some infinite subsets E of natural series such that kth order differences of every such chain converge to the equi-distributed random binary process as k growth to infinity remaining on E. The chains are classified into two types, and E depends only on the type of the given chain. Two kinds of discrete capacities for subsets of natural series are defined, and in their terms such sets E are described.
{"title":"Full randomness in the higher difference structure of two-state Markov chains","authors":"A. Shahverdian","doi":"10.1109/csitechnol.2017.8312134","DOIUrl":"https://doi.org/10.1109/csitechnol.2017.8312134","url":null,"abstract":"The paper studies the higher-order absolute differences taken from progressive terms of time-homogeneous binary Markov chains. Two theorems presented are the limiting theorems for these differences, when their order k converges to infinity. Theorems 1 and 2 assert that there exist some infinite subsets E of natural series such that kth order differences of every such chain converge to the equi-distributed random binary process as k growth to infinity remaining on E. The chains are classified into two types, and E depends only on the type of the given chain. Two kinds of discrete capacities for subsets of natural series are defined, and in their terms such sets E are described.","PeriodicalId":332371,"journal":{"name":"2017 Computer Science and Information Technologies (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114095464","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 : 2017-06-01DOI: 10.1109/CSITECHNOL.2017.8312150
S. Darbinyan, I. Karapetyan
A cycle in a balanced bipartite digraph is called a pre-Hamiltonian if it contains all the vertices of the balanced bipartite digraph except two. In this paper we give a sufficient condition for the existence of pre-Hamiltonian cycle in a strongly connected balanced bipartite digraph.
{"title":"A sufficient condition for pre-Hamiltonian cycles in bipartite digraphs","authors":"S. Darbinyan, I. Karapetyan","doi":"10.1109/CSITECHNOL.2017.8312150","DOIUrl":"https://doi.org/10.1109/CSITECHNOL.2017.8312150","url":null,"abstract":"A cycle in a balanced bipartite digraph is called a pre-Hamiltonian if it contains all the vertices of the balanced bipartite digraph except two. In this paper we give a sufficient condition for the existence of pre-Hamiltonian cycle in a strongly connected balanced bipartite digraph.","PeriodicalId":332371,"journal":{"name":"2017 Computer Science and Information Technologies (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123224096","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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