Abstract In this paper, we report on PIN-code typing behaviour on touchscreen devices of 112 subjects. Detailed statistical analysis revealed that the major di erence between subjects is in inter-key latency. Key-press duration variations are insignificant compared to inter-key latency variations. Subjects were grouped into meaningful clusters using clustering. The resulting clusters were of slow, medium, and fast typists. The dataset was split randomly into two equal size subsets. The first subset was used to train different synthetic data generators, while the second subset was used to evaluate an authentication attack using the generated synthetic data. The evaluation showed that slow typists were the hardest to attack. Both the dataset and the software are publicly available at https://github.com/margitantal68/sapipin_paper.
{"title":"SapiPin: Observations on PIN-code typing dynamics","authors":"M. Antal, Krisztián Búza","doi":"10.2478/ausi-2023-0002","DOIUrl":"https://doi.org/10.2478/ausi-2023-0002","url":null,"abstract":"Abstract In this paper, we report on PIN-code typing behaviour on touchscreen devices of 112 subjects. Detailed statistical analysis revealed that the major di erence between subjects is in inter-key latency. Key-press duration variations are insignificant compared to inter-key latency variations. Subjects were grouped into meaningful clusters using clustering. The resulting clusters were of slow, medium, and fast typists. The dataset was split randomly into two equal size subsets. The first subset was used to train different synthetic data generators, while the second subset was used to evaluate an authentication attack using the generated synthetic data. The evaluation showed that slow typists were the hardest to attack. Both the dataset and the software are publicly available at https://github.com/margitantal68/sapipin_paper.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"25 1","pages":"10 - 24"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89564511","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 E-super arithmetic graceful labelling of a graph G is a bijection f from the union of the vertex set and edge set to the set of positive integers (1, 2, 3, … |V(G) ∪ E(G)|) such that the edges have the labels from the set {1, 2, 3, …, |E(G)|} and the induced mapping f* given by f* (uv) = f(u) + f(v) − f(uv) for uv ∈ E(G) has the range {|V(G) ∪ E(G)| + 1, |V(G) ∪ E(G)| + 2, …, |V(G)| + 2|E(G)|} In this paper we prove that Hi(m, m) and Hi(1) (m, m) and chain of even cycles C4,n, C6,n are E-super arithmetic graceful.
文摘E-super算术优雅的标签图G是一个双射f将从欧盟的顶点集和边集的正整数集(1、2、3、…| V (G)∪E (G) |),边缘有标签从集合{1,2,3,…,| E (G) |}和诱导映射f *的* (uv) = f (u) + (V)−f为紫外线(uv)∈E (G)范围{| V (G)∪E (G) | + 1, | V (G)∪E (G) | + 2,…,V (G) | | + 2 | E (G) |}在本文中,我们证明嗨(m m)和嗨(1)(m m)甚至链周期C4, n,C6 n是e -超级算术优美的。
{"title":"E-super arithmetic graceful labelling of Hi(m, m), Hi(1) (m, m) and chain of even cycles","authors":"S. Anubala, V. Ramachandran","doi":"10.2478/ausi-2023-0007","DOIUrl":"https://doi.org/10.2478/ausi-2023-0007","url":null,"abstract":"Abstract E-super arithmetic graceful labelling of a graph G is a bijection f from the union of the vertex set and edge set to the set of positive integers (1, 2, 3, … |V(G) ∪ E(G)|) such that the edges have the labels from the set {1, 2, 3, …, |E(G)|} and the induced mapping f* given by f* (uv) = f(u) + f(v) − f(uv) for uv ∈ E(G) has the range {|V(G) ∪ E(G)| + 1, |V(G) ∪ E(G)| + 2, …, |V(G)| + 2|E(G)|} In this paper we prove that Hi(m, m) and Hi(1) (m, m) and chain of even cycles C4,n, C6,n are E-super arithmetic graceful.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":" 35","pages":"81 - 90"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72378685","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}
H. Ramane, B. Parvathalu, K. Ashoka, Daneshwari Patil
Abstract The energy E(G) of a graph G is the sum of the absolute values of eigenvalues of G and the Seidel energy ES(G) is the sum of the absolute values of eigenvalues of the Seidel matrix S of G. In this paper, some relations between the energy and Seidel energy of a graph in terms of different graph parameters are presented. Also, the inertia relations between the graph eigenvalue and Seidel eigenvalue of a graph are given. The results in this paper generalize some of the existing results.
{"title":"Some relations between energy and Seidel energy of a graph","authors":"H. Ramane, B. Parvathalu, K. Ashoka, Daneshwari Patil","doi":"10.2478/ausi-2023-0005","DOIUrl":"https://doi.org/10.2478/ausi-2023-0005","url":null,"abstract":"Abstract The energy E(G) of a graph G is the sum of the absolute values of eigenvalues of G and the Seidel energy ES(G) is the sum of the absolute values of eigenvalues of the Seidel matrix S of G. In this paper, some relations between the energy and Seidel energy of a graph in terms of different graph parameters are presented. Also, the inertia relations between the graph eigenvalue and Seidel eigenvalue of a graph are given. The results in this paper generalize some of the existing results.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"249 1","pages":"46 - 59"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82904895","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}
Mahnaz Taleb Sereshki, M. M. Zanjireh, Mahdi Bahaghighat
Abstract Text mining is an intriguing area of research, considering there is an abundance of text across the Internet and in social medias. Nevertheless outliers pose a challenge for textual data processing. The ability to identify this sort of irrelevant input is consequently crucial in developing high-performance models. In this paper, a novel unsupervised method for identifying outliers in text data is proposed. In order to spot outliers, we concentrate on the degree of similarity between any two documents and the density of related documents that might support integrated clustering throughout processing. To compare the e ectiveness of our proposed approach with alternative classification techniques, we performed a number of experiments on a real dataset. Experimental findings demonstrate that the suggested model can obtain accuracy greater than 98% and performs better than the other existing algorithms.
{"title":"Textual outlier detection with an unsupervised method using text similarity and density peak","authors":"Mahnaz Taleb Sereshki, M. M. Zanjireh, Mahdi Bahaghighat","doi":"10.2478/ausi-2023-0008","DOIUrl":"https://doi.org/10.2478/ausi-2023-0008","url":null,"abstract":"Abstract Text mining is an intriguing area of research, considering there is an abundance of text across the Internet and in social medias. Nevertheless outliers pose a challenge for textual data processing. The ability to identify this sort of irrelevant input is consequently crucial in developing high-performance models. In this paper, a novel unsupervised method for identifying outliers in text data is proposed. In order to spot outliers, we concentrate on the degree of similarity between any two documents and the density of related documents that might support integrated clustering throughout processing. To compare the e ectiveness of our proposed approach with alternative classification techniques, we performed a number of experiments on a real dataset. Experimental findings demonstrate that the suggested model can obtain accuracy greater than 98% and performs better than the other existing algorithms.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"16 1","pages":"91 - 110"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89096221","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 A graph G is said to be a semi-splitting block graph if there exists a graph H such that SB(H) ≌ G. This paper establishes a characterisation of semi-splitting block graphs based on the partition of the vertex set of G. The vertex (edge) connectivity and p-connectedness (p-edge connectedness) of SB(G) are examined. For all integers a, b with 1 < a < b, the existence of the graph G for which κ (G) = a, κ (SB(G)) = b and λ (G) = a, λ (SB(G)) = b are proved independently. The characterization of graphs with κ(SB(G)) = κ (G) and a necessary condition for graphs with κ (SB(G)) = λ (SB(G)) are achieved.
摘要:如果存在一个图H满足SB(H)≌G，则称图G为半分裂块图。本文基于G的顶点集的划分，建立了半分裂块图的一个刻画，研究了SB(G)的顶点(边)连通性和p边连通性。对于所有1 < a < b的整数a, b，证明了k (G) = a, k (SB(G)) = b和λ (G) = a， λ (SB(G)) = b的图G的存在性。得到了κ(SB(G)) = κ(G)的图的表征和κ(SB(G)) = λ (SB(G))的图的必要条件。
{"title":"On connectivity of the semi-splitting block graph of a graph","authors":"Nivedha Baskar, T. A. Mangam, M. Acharya","doi":"10.2478/ausi-2023-0012","DOIUrl":"https://doi.org/10.2478/ausi-2023-0012","url":null,"abstract":"Abstract A graph G is said to be a semi-splitting block graph if there exists a graph H such that SB(H) ≌ G. This paper establishes a characterisation of semi-splitting block graphs based on the partition of the vertex set of G. The vertex (edge) connectivity and p-connectedness (p-edge connectedness) of SB(G) are examined. For all integers a, b with 1 < a < b, the existence of the graph G for which κ (G) = a, κ (SB(G)) = b and λ (G) = a, λ (SB(G)) = b are proved independently. The characterization of graphs with κ(SB(G)) = κ (G) and a necessary condition for graphs with κ (SB(G)) = λ (SB(G)) are achieved.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"15 11 1","pages":"170 - 180"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86664904","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 rupture degree is one the most important vulnerability parameter in networks which are modelled by graphs. Let G(V (G),E (G)) be a simple undirected graph. The rupture degree is defined by r(G) = max{w(G–S )–|S |–m(G–S ):S ⊂ V (G) and w(G–S )>1} where m(G–S ) is the order of a largest connected component in G–S and w(G–S ) is the number of components of G–S, respectively. In this paper, we consider the vertex contraction method based on the network agglomeration operation for each vertex of G. Then, we have presented two graph vulnerability parameters called by agglomeration rupture degree and average lower agglomeration rupture degree. Furthermore, the exact values of them for some graph families are given. Finally, we proposed a polynomial time heuristic algorithm to obtain the values of agglomeration rupture degree and average
{"title":"On agglomeration-based rupture degree in networks and a heuristic algorithm","authors":"Muammer Ağtaş, T. Turacı","doi":"10.2478/ausi-2023-0010","DOIUrl":"https://doi.org/10.2478/ausi-2023-0010","url":null,"abstract":"Abstract The rupture degree is one the most important vulnerability parameter in networks which are modelled by graphs. Let G(V (G),E (G)) be a simple undirected graph. The rupture degree is defined by r(G) = max{w(G–S )–|S |–m(G–S ):S ⊂ V (G) and w(G–S )>1} where m(G–S ) is the order of a largest connected component in G–S and w(G–S ) is the number of components of G–S, respectively. In this paper, we consider the vertex contraction method based on the network agglomeration operation for each vertex of G. Then, we have presented two graph vulnerability parameters called by agglomeration rupture degree and average lower agglomeration rupture degree. Furthermore, the exact values of them for some graph families are given. Finally, we proposed a polynomial time heuristic algorithm to obtain the values of agglomeration rupture degree and average","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"338 1","pages":"124 - 145"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72432632","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 Interstitial Lung Diseases (ILDs) represent a heterogeneous group of several rare diseases that are di cult to predict, diagnose and monitor. There are no predictive biomarkers for ILDs, clinical signs are similar to the ones for other lung diseases, the radiological features are not easy to recognize, and require manual radiologist review. Data-driven support for ILD prediction, diagnosis and disease-course monitoring are great unmet need. Numerous image processing techniques and computer-aided diagnostic and decision-making support methods have been developed over the recent years. The current review focuses on such solutions, discussing advancements on the fields of Quantitative CT, Complex Networks, and Convolutional Neural Networks.
{"title":"Enhanced imagistic methodologies augmenting radiological image processing in interstitial lung diseases","authors":"József Palatka, L. Kovács, László Szilágyi","doi":"10.2478/ausi-2023-0011","DOIUrl":"https://doi.org/10.2478/ausi-2023-0011","url":null,"abstract":"Abstract Interstitial Lung Diseases (ILDs) represent a heterogeneous group of several rare diseases that are di cult to predict, diagnose and monitor. There are no predictive biomarkers for ILDs, clinical signs are similar to the ones for other lung diseases, the radiological features are not easy to recognize, and require manual radiologist review. Data-driven support for ILD prediction, diagnosis and disease-course monitoring are great unmet need. Numerous image processing techniques and computer-aided diagnostic and decision-making support methods have been developed over the recent years. The current review focuses on such solutions, discussing advancements on the fields of Quantitative CT, Complex Networks, and Convolutional Neural Networks.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"48 1","pages":"146 - 169"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83766526","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 an isolate-free graph 𝒵 = (V𝒵, E𝒵), a set C of vertices is termed as a connected certified dominating set of 𝒵 if, |N𝒵(u) ∩ (V𝒵C)| = 0 or |N𝒵(u) ∩ (V𝒵C)| ≥ 2 ∀u ∈C, and the subgraph 𝒵[C] induced by C is connected. The cardinality of the minimal connected certified dominating set of graph 𝒵 is called the connected certified domination number of 𝒵 denoted by γcerc (Z). In graph 𝒵, if the deletion of any arbitrary edge changes the connected certified domination number, then we call it a connected certified domination edge critical. If the deletion of any random edge does not a ect the connected certified domination number, then we refer to it as a connected certified domination edge stable graph. In this paper, we investigate those graphs which are connected certified domination edge critical and stable upon edge removal. We then study some properties of connected certified domination edge critical and stable graphs.
抽象的isolate-free图𝒵= (V𝒵E𝒵),一组连接的顶点称为一个C认证控制套𝒵如果| N𝒵(u)∩(V𝒵 C) | = 0或| N𝒵(u)∩(V𝒵 C) |≥2∀u∈C,和子图𝒵[C] C引起的连接。图𝒵的最小连通认证控制集的cardinality称为𝒵的连通认证控制数，用γcerc (Z)表示。在图𝒵中，如果任意边的删除改变了连通认证控制数，则称其为连通认证控制边临界。如果任意一条随机边的删除不影响连通认证统治数，则称其为连通认证统治边稳定图。在本文中，我们研究了那些被证明是连通的控制边在去边后是临界且稳定的图。在此基础上，研究了连通认证控制边临界稳定图的一些性质。
{"title":"Connected certified domination edge critical and stable graphs","authors":"Azham Ilyass Lone, V. Goswami","doi":"10.2478/ausi-2023-0003","DOIUrl":"https://doi.org/10.2478/ausi-2023-0003","url":null,"abstract":"Abstract In an isolate-free graph 𝒵 = (V𝒵, E𝒵), a set C of vertices is termed as a connected certified dominating set of 𝒵 if, |N𝒵(u) ∩ (V𝒵C)| = 0 or |N𝒵(u) ∩ (V𝒵C)| ≥ 2 ∀u ∈C, and the subgraph 𝒵[C] induced by C is connected. The cardinality of the minimal connected certified dominating set of graph 𝒵 is called the connected certified domination number of 𝒵 denoted by γcerc (Z). In graph 𝒵, if the deletion of any arbitrary edge changes the connected certified domination number, then we call it a connected certified domination edge critical. If the deletion of any random edge does not a ect the connected certified domination number, then we refer to it as a connected certified domination edge stable graph. In this paper, we investigate those graphs which are connected certified domination edge critical and stable upon edge removal. We then study some properties of connected certified domination edge critical and stable graphs.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"23 1","pages":"25 - 37"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81887030","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 connected graph with n vertices, m edges. The distance signless Laplacian matrix DQ(G) is defined as DQ(G) = Diag(Tr(G)) + D(G), where Diag(Tr(G)) is the diagonal matrix of vertex transmissions and D(G) is the distance matrix of G. The distance signless Laplacian eigenvalues of G are the eigenvalues of DQ(G) and are denoted by δ1Q(G), δ2Q(G), ..., δnQ(G). δ1Q is called the distance signless Laplacian spectral radius of DQ(G). In this paper, we obtain upper and lower bounds for SDQ (G) in terms of the Wiener index, the transmission degree and the order of the graph.
{"title":"On the spread of the distance signless Laplacian matrix of a graph","authors":"S. Pirzada, Mohd Abrar, Ul Haq","doi":"10.2478/ausi-2023-0004","DOIUrl":"https://doi.org/10.2478/ausi-2023-0004","url":null,"abstract":"Abstract Let G be a connected graph with n vertices, m edges. The distance signless Laplacian matrix DQ(G) is defined as DQ(G) = Diag(Tr(G)) + D(G), where Diag(Tr(G)) is the diagonal matrix of vertex transmissions and D(G) is the distance matrix of G. The distance signless Laplacian eigenvalues of G are the eigenvalues of DQ(G) and are denoted by δ1Q(G), δ2Q(G), ..., δnQ(G). δ1Q is called the distance signless Laplacian spectral radius of DQ(G). In this paper, we obtain upper and lower bounds for SDQ (G) in terms of the Wiener index, the transmission degree and the order of the graph.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"41 1","pages":"38 - 45"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86456725","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 connected graph with vertex set V(G)and edge set E(G). The eccentric connectivity index of G is defined as ∑ν∈V(G)ec(ν) deg(ν) sumlimits_{nuin{rm{V}}left({rm{G}}right)}{{rm{ec}}left(nuright),{rm{deg}}left(nuright)} where ec(v) the eccentricity of a vertex v and deg(v)is its degree and denoted by ɛc(G). In this paper, we investigate the eccentric connectivity index of the transformation graph Gxy+.
{"title":"Eccentric connectivity index in transformation graph Gxy+","authors":"A. Aytaç, Belgin Vatansever","doi":"10.2478/ausi-2023-0009","DOIUrl":"https://doi.org/10.2478/ausi-2023-0009","url":null,"abstract":"Abstract Let G be a connected graph with vertex set V(G)and edge set E(G). The eccentric connectivity index of G is defined as ∑ν∈V(G)ec(ν) deg(ν) sumlimits_{nuin{rm{V}}left({rm{G}}right)}{{rm{ec}}left(nuright),{rm{deg}}left(nuright)} where ec(v) the eccentricity of a vertex v and deg(v)is its degree and denoted by ɛc(G). In this paper, we investigate the eccentric connectivity index of the transformation graph Gxy+.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"52 1","pages":"111 - 123"},"PeriodicalIF":0.3,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82168614","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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