Abstract Two vertices $u$ and $v$ in a graph $G=(V,E)$ are in the same orbit if there exists an automorphism $phi $ of $G$ such that $phi (u)=v$. The orbit number of a graph $G$, denoted by $Orb(G)$, is the smallest number of orbits, which form a partition of $V(G)$, in $G$. All vertex-transitive graphs $G$ are with $Orb(G)=1$. Since the $n$-dimensional hypercube, denoted by $Q_{n}$, is vertex-transitive, it follows that $Orb(Q_{n})=1$ for $ngeq 1$. Pai, Chang, and Yang proved that the $n$-dimensional folded crossed cube, denoted by $FCQ_{n}$, is vertex-transitive if and only if $nin {1,2,4}$, namely $Orb(FCQ_{1})=Orb(FCQ_{2})=Orb(FCQ_{4})=1$. In this paper, we prove that $Orb(FCQ_{n})=2^{lceil frac{n}{2}rceil -2}$ if $ngeq 6$ is even and $Orb(FCQ_{n}) = 2^{lceil frac{n}{2}rceil -1}$ if $ngeq 3$ is odd.
{"title":"The Orbits of Folded Crossed Cubes","authors":"Jia-Jie Liu","doi":"10.1093/comjnl/bxad096","DOIUrl":"https://doi.org/10.1093/comjnl/bxad096","url":null,"abstract":"Abstract Two vertices $u$ and $v$ in a graph $G=(V,E)$ are in the same orbit if there exists an automorphism $phi $ of $G$ such that $phi (u)=v$. The orbit number of a graph $G$, denoted by $Orb(G)$, is the smallest number of orbits, which form a partition of $V(G)$, in $G$. All vertex-transitive graphs $G$ are with $Orb(G)=1$. Since the $n$-dimensional hypercube, denoted by $Q_{n}$, is vertex-transitive, it follows that $Orb(Q_{n})=1$ for $ngeq 1$. Pai, Chang, and Yang proved that the $n$-dimensional folded crossed cube, denoted by $FCQ_{n}$, is vertex-transitive if and only if $nin {1,2,4}$, namely $Orb(FCQ_{1})=Orb(FCQ_{2})=Orb(FCQ_{4})=1$. In this paper, we prove that $Orb(FCQ_{n})=2^{lceil frac{n}{2}rceil -2}$ if $ngeq 6$ is even and $Orb(FCQ_{n}) = 2^{lceil frac{n}{2}rceil -1}$ if $ngeq 3$ is odd.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136213845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The extensive potential of Internet of Things (IoT) technology has enabled the widespread real-time perception and analysis of health conditions. Furthermore, the integration of IoT in the healthcare industry has resulted in the development of intelligent applications, including smartphone-based healthcare, wellness-aware recommendations and smart medical systems. Building upon these technological advancements, this research puts forth an enhanced framework designed for the real-time monitoring, detection and prediction of health vulnerabilities arising from air pollution. Specifically, a four-layered model is presented to categorize health-impacting particles associated with air pollution into distinct classes based on probabilistic parameters of Health Adversity (HA). Subsequently, the HA parameters are extracted and temporally analyzed using FogBus, a fog computing platform, to identify vulnerabilities in individual health. To facilitate accurate prediction, an assessment of the Air Impact on Health is conducted using a Differential Evolution-Recurrent Neural Network. Moreover, the temporal analysis of health vulnerability employs the Self-Organized Mapping technique for visualization. The proposed model’s validity is evaluated using a challenging dataset comprising nearly 60 212 data instances obtained from the online University of California, Irvine repository. Performance enhancement is assessed by comparing the proposed model with state-of-the-art decision-making techniques, considering statistical parameters such as temporal effectiveness, coefficient of determination, accuracy, specificity, sensitivity, reliability and stability.
{"title":"An Intelligent Air Monitoring System For Pollution Prediction: A Predictive Healthcare Perspective","authors":"Veerawali Behal, Ramandeep Singh","doi":"10.1093/comjnl/bxad099","DOIUrl":"https://doi.org/10.1093/comjnl/bxad099","url":null,"abstract":"Abstract The extensive potential of Internet of Things (IoT) technology has enabled the widespread real-time perception and analysis of health conditions. Furthermore, the integration of IoT in the healthcare industry has resulted in the development of intelligent applications, including smartphone-based healthcare, wellness-aware recommendations and smart medical systems. Building upon these technological advancements, this research puts forth an enhanced framework designed for the real-time monitoring, detection and prediction of health vulnerabilities arising from air pollution. Specifically, a four-layered model is presented to categorize health-impacting particles associated with air pollution into distinct classes based on probabilistic parameters of Health Adversity (HA). Subsequently, the HA parameters are extracted and temporally analyzed using FogBus, a fog computing platform, to identify vulnerabilities in individual health. To facilitate accurate prediction, an assessment of the Air Impact on Health is conducted using a Differential Evolution-Recurrent Neural Network. Moreover, the temporal analysis of health vulnerability employs the Self-Organized Mapping technique for visualization. The proposed model’s validity is evaluated using a challenging dataset comprising nearly 60 212 data instances obtained from the online University of California, Irvine repository. Performance enhancement is assessed by comparing the proposed model with state-of-the-art decision-making techniques, considering statistical parameters such as temporal effectiveness, coefficient of determination, accuracy, specificity, sensitivity, reliability and stability.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135251174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Identifying crucial nodes in complex networks is paid more attention in recent years. Some classical methods, such as degree centrality, betweenness centrality and closeness centrality, have their advantages and disadvantages. Recently, the gravity model is applied to describe the relationship of nodes in a complex network. However, the interaction force in gravity model follows the square law of distance, which is inconsistent with the actual situation. Most people are generally affected by those who are surrounding them, which means that local influence should be emphasized. To address this issue, we propose an indexing method called localized decreasing gravity centrality by maximizing the local influence of a node. In the proposed measure, the mass and radius of gravity model are redefined, which can represent the spreading ability of the node. In addition, a decreasing weight is added to strengthen the local influence of a node. To evaluate the performance of the proposed method, we utilize four different types of networks, including interaction networks, economic networks, collaboration networks and animal social networks. Also, two different infectious disease models, susceptible-infectious-recovered (SIR) and susceptible-exposed-low risk-high risk-recovered (SELHR), are utilized to examine the spreading ability of influential nodes.
{"title":"Identifying and Ranking Influential Spreaders in Complex Networks by Localized Decreasing Gravity Model","authors":"Nan Xiang, Xiao Tang, Huiling Liu, Xiaoxia Ma","doi":"10.1093/comjnl/bxad097","DOIUrl":"https://doi.org/10.1093/comjnl/bxad097","url":null,"abstract":"Abstract Identifying crucial nodes in complex networks is paid more attention in recent years. Some classical methods, such as degree centrality, betweenness centrality and closeness centrality, have their advantages and disadvantages. Recently, the gravity model is applied to describe the relationship of nodes in a complex network. However, the interaction force in gravity model follows the square law of distance, which is inconsistent with the actual situation. Most people are generally affected by those who are surrounding them, which means that local influence should be emphasized. To address this issue, we propose an indexing method called localized decreasing gravity centrality by maximizing the local influence of a node. In the proposed measure, the mass and radius of gravity model are redefined, which can represent the spreading ability of the node. In addition, a decreasing weight is added to strengthen the local influence of a node. To evaluate the performance of the proposed method, we utilize four different types of networks, including interaction networks, economic networks, collaboration networks and animal social networks. Also, two different infectious disease models, susceptible-infectious-recovered (SIR) and susceptible-exposed-low risk-high risk-recovered (SELHR), are utilized to examine the spreading ability of influential nodes.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135482953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We consider two finite and disjoint sets of homogeneous robots deployed at the nodes of an infinite grid graph. The grid graph also comprises two finite and disjoint sets of prefixed meeting nodes located over the nodes of the grid. The objective of our study is to design a distributed algorithm that gathers all the robots belonging to the first team at one of the meeting nodes belonging to the first type, and all the robots in the second team must gather at one of the meeting nodes belonging to the second type. The robots can distinguish between the two types of meeting nodes. However, a robot cannot identify its team members. This paper assumes the strongest adversarial model, namely the asynchronous scheduler. We have characterized all the initial configurations for which the gathering problem is unsolvable. For the remaining initial configurations, the paper proposes a distributed gathering algorithm. Assuming the robots are capable of global-weak multiplicity detection, the proposed algorithm solves the problem within a finite time period. The algorithm runs in $Theta (dn)$ moves and $O(dn)$ epochs, where $d$ is the diameter of the minimum enclosing rectangle of all the robots and meeting nodes in the initial configuration, and $n$ is the total number of robots in the system.
{"title":"Gathering Over Heterogeneous Meeting Nodes","authors":"Abhinav Chakraborty, Subhash Bhagat, Krishnendu Mukhopadhyaya","doi":"10.1093/comjnl/bxad101","DOIUrl":"https://doi.org/10.1093/comjnl/bxad101","url":null,"abstract":"Abstract We consider two finite and disjoint sets of homogeneous robots deployed at the nodes of an infinite grid graph. The grid graph also comprises two finite and disjoint sets of prefixed meeting nodes located over the nodes of the grid. The objective of our study is to design a distributed algorithm that gathers all the robots belonging to the first team at one of the meeting nodes belonging to the first type, and all the robots in the second team must gather at one of the meeting nodes belonging to the second type. The robots can distinguish between the two types of meeting nodes. However, a robot cannot identify its team members. This paper assumes the strongest adversarial model, namely the asynchronous scheduler. We have characterized all the initial configurations for which the gathering problem is unsolvable. For the remaining initial configurations, the paper proposes a distributed gathering algorithm. Assuming the robots are capable of global-weak multiplicity detection, the proposed algorithm solves the problem within a finite time period. The algorithm runs in $Theta (dn)$ moves and $O(dn)$ epochs, where $d$ is the diameter of the minimum enclosing rectangle of all the robots and meeting nodes in the initial configuration, and $n$ is the total number of robots in the system.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135546176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Recent studies show that neural networks are vulnerable to backdoor attacks, in which compromised networks behave normally for clean inputs but make mistakes when a pre-defined trigger appears. Although prior studies have designed various invisible triggers to avoid causing visual anomalies, they cannot evade some trigger detectors. In this paper, we consider the stealthiness of backdoor attacks from input space and feature representation space. We propose a novel backdoor attack named sparse backdoor attack, and investigate the minimum required trigger to induce the well-trained networks to make incorrect results. A U-net-based generator is employed to create triggers for each clean image. Considering the stealthiness of the trigger, we restrict the elements of the trigger between −1 and 1. In the aspect of the feature representation domain, we adopt an entanglement cost function to minimize the gap between feature representations of benign and malicious inputs. The inseparability of benign and malicious feature representations contributes to the stealthiness of our attack against various model diagnosis-based defences. We validate the effectiveness and generalization of our method by conducting extensive experiments on multiple datasets and networks.
{"title":"Sparse Backdoor Attack Against Neural Networks","authors":"Nan Zhong, Zhenxing Qian, Xinpeng Zhang","doi":"10.1093/comjnl/bxad100","DOIUrl":"https://doi.org/10.1093/comjnl/bxad100","url":null,"abstract":"Abstract Recent studies show that neural networks are vulnerable to backdoor attacks, in which compromised networks behave normally for clean inputs but make mistakes when a pre-defined trigger appears. Although prior studies have designed various invisible triggers to avoid causing visual anomalies, they cannot evade some trigger detectors. In this paper, we consider the stealthiness of backdoor attacks from input space and feature representation space. We propose a novel backdoor attack named sparse backdoor attack, and investigate the minimum required trigger to induce the well-trained networks to make incorrect results. A U-net-based generator is employed to create triggers for each clean image. Considering the stealthiness of the trigger, we restrict the elements of the trigger between −1 and 1. In the aspect of the feature representation domain, we adopt an entanglement cost function to minimize the gap between feature representations of benign and malicious inputs. The inseparability of benign and malicious feature representations contributes to the stealthiness of our attack against various model diagnosis-based defences. We validate the effectiveness and generalization of our method by conducting extensive experiments on multiple datasets and networks.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135482952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiang Zhang, Junjiang He, Tao Li, Xiaolong Lan, Wenbo Fang, Yihong Li
Abstract The problem of data imbalance is common in reality, which greatly affects the performance of classifiers. Most of the solutions are to balance the data set by generating new minority class samples, which are faced with the problems of selecting the appropriate area for generating samples, fuzzy classification boundary and uneven distribution of samples. To solve these problems, we propose a novel oversampling algorithm named space partitioning adaptive weighted synthetic minority oversampling technique (SPAW-SMOTE). We first divide the data space into boundary space and non-boundary space based on spatial partitioning techniques. The number of samples to be generated is assigned to different spaces by the designed adaptive weighting algorithm, which is used to solve the problems of uneven distribution of samples and easy to blur the classification boundary. Finally, we also endeavor to develop a new generation algorithm to reduce the probability of overlapping samples generated when synthesizing new samples and to ensure the diversity of new samples. Experimental results on 18 real-world data sets show that the average performance (G-mean, F1-measure and Area Under Curve) of SPAW-SMOTE is significantly better than other existing oversampling techniques.
{"title":"SPAW-SMOTE: Space Partitioning Adaptive Weighted Synthetic Minority Oversampling Technique For Imbalanced Data Set Learning","authors":"Qiang Zhang, Junjiang He, Tao Li, Xiaolong Lan, Wenbo Fang, Yihong Li","doi":"10.1093/comjnl/bxad098","DOIUrl":"https://doi.org/10.1093/comjnl/bxad098","url":null,"abstract":"Abstract The problem of data imbalance is common in reality, which greatly affects the performance of classifiers. Most of the solutions are to balance the data set by generating new minority class samples, which are faced with the problems of selecting the appropriate area for generating samples, fuzzy classification boundary and uneven distribution of samples. To solve these problems, we propose a novel oversampling algorithm named space partitioning adaptive weighted synthetic minority oversampling technique (SPAW-SMOTE). We first divide the data space into boundary space and non-boundary space based on spatial partitioning techniques. The number of samples to be generated is assigned to different spaces by the designed adaptive weighting algorithm, which is used to solve the problems of uneven distribution of samples and easy to blur the classification boundary. Finally, we also endeavor to develop a new generation algorithm to reduce the probability of overlapping samples generated when synthesizing new samples and to ensure the diversity of new samples. Experimental results on 18 real-world data sets show that the average performance (G-mean, F1-measure and Area Under Curve) of SPAW-SMOTE is significantly better than other existing oversampling techniques.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135484277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ali Marandi, Pedro Geraldo M R Alves, Diego F Aranha, Rune Hylsberg Jacobsen
Abstract Privacy-preserving smart meter data collection and analysis are critical for optimizing smart grid environments without compromising privacy. Using homomorphic encryption techniques, smart meters can encrypt collected data to ensure confidentiality, and other untrusted nodes can further compute over the encrypted data without having to recover the underlying plaintext. As an illustrative example, this approach can be useful to compute the monthly electricity consumption without violating consumer privacy by collecting fine-granular data through small increments of time. Toward that end, we propose an architecture for privacy-preserving smart meter data collection, aggregation and analysis based on lattice-based homomorphic encryption. Furthermore, we compare the proposed method with the Paillier and Boneh–Goh–Nissim (BGN) cryptosystems, which are popular alternatives for homomorphic encryption in smart grids. We consider different services with different requirements in terms of multiplicative depth, e.g. billing, variance and nonlinear support vector machine classification. Accordingly, we measure and show the practical overhead of using the proposed homomorphic encryption method in terms of communication traffic (ciphertext size) and latency. Our results show that lattice-based homomorphic encryption is more efficient than Paillier and BGN for both multiplication and addition operations while offering more flexibility in terms of the computation that can be evaluated homomorphically.
{"title":"Lattice-Based Homomorphic Encryption For Privacy-Preserving Smart Meter Data Analytics","authors":"Ali Marandi, Pedro Geraldo M R Alves, Diego F Aranha, Rune Hylsberg Jacobsen","doi":"10.1093/comjnl/bxad093","DOIUrl":"https://doi.org/10.1093/comjnl/bxad093","url":null,"abstract":"Abstract Privacy-preserving smart meter data collection and analysis are critical for optimizing smart grid environments without compromising privacy. Using homomorphic encryption techniques, smart meters can encrypt collected data to ensure confidentiality, and other untrusted nodes can further compute over the encrypted data without having to recover the underlying plaintext. As an illustrative example, this approach can be useful to compute the monthly electricity consumption without violating consumer privacy by collecting fine-granular data through small increments of time. Toward that end, we propose an architecture for privacy-preserving smart meter data collection, aggregation and analysis based on lattice-based homomorphic encryption. Furthermore, we compare the proposed method with the Paillier and Boneh–Goh–Nissim (BGN) cryptosystems, which are popular alternatives for homomorphic encryption in smart grids. We consider different services with different requirements in terms of multiplicative depth, e.g. billing, variance and nonlinear support vector machine classification. Accordingly, we measure and show the practical overhead of using the proposed homomorphic encryption method in terms of communication traffic (ciphertext size) and latency. Our results show that lattice-based homomorphic encryption is more efficient than Paillier and BGN for both multiplication and addition operations while offering more flexibility in terms of the computation that can be evaluated homomorphically.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135580002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiale Huang, Jigang Wu, Long Chen, Yalan Wu, Yidong Li
Abstract Ridesharing is an effective approach to alleviate traffic congestion. In most existing works, drivers and passengers are assigned prices without considering the constraints of desired benefits. This paper investigates ridesharing by formulating a matching and pricing problem to maximize the total payoff of drivers, with the constraints of desired benefit and quality of experience. An efficient algorithm is proposed to solve the formulated problem based on coalitional double auction. Secondary pricing based strategy and sacrificed minimum bid based strategy are proposed to support the algorithm. This paper also proves that the proposed algorithm can achieve a Nash-stable coalition partition in finite steps, and the proposed two strategies guarantee truthfulness, individually rational and budget balance. Extensive simulation results on the real-world dataset of taxi trajectory in Beijing city show that the proposed algorithm outperforms the existing ones, in terms of average total payoff of drivers while meeting the benefits of passengers.
{"title":"Coalitional Double Auction For Ridesharing With Desired Benefit And QoE Constraints","authors":"Jiale Huang, Jigang Wu, Long Chen, Yalan Wu, Yidong Li","doi":"10.1093/comjnl/bxad092","DOIUrl":"https://doi.org/10.1093/comjnl/bxad092","url":null,"abstract":"Abstract Ridesharing is an effective approach to alleviate traffic congestion. In most existing works, drivers and passengers are assigned prices without considering the constraints of desired benefits. This paper investigates ridesharing by formulating a matching and pricing problem to maximize the total payoff of drivers, with the constraints of desired benefit and quality of experience. An efficient algorithm is proposed to solve the formulated problem based on coalitional double auction. Secondary pricing based strategy and sacrificed minimum bid based strategy are proposed to support the algorithm. This paper also proves that the proposed algorithm can achieve a Nash-stable coalition partition in finite steps, and the proposed two strategies guarantee truthfulness, individually rational and budget balance. Extensive simulation results on the real-world dataset of taxi trajectory in Beijing city show that the proposed algorithm outperforms the existing ones, in terms of average total payoff of drivers while meeting the benefits of passengers.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract As a structural topological index, the number of subtrees has great significance for the analysis and design of hybrid locally reliable networks. In this paper, with generating function and introducing a novel two-forest dual transformation technique, we solve the subtree enumerating problems of two representatives of the self-similar networks, such as the hierarchical lattice and $(u,v)$-flower networks. Moreover, by means of the circle weight transfer technique, two linear time algorithms of computing the subtree generation functions of these two families of networks are also proposed. The subtree density of two special cases for these self-similar networks is briefly discussed as an application.
{"title":"Enumeration Of Subtrees Of Two Families Of Self-Similar Networks Based On Novel Two-Forest Dual Transformations","authors":"Daoqiang Sun, Hongbo Liu, Yu Yang, Long Li, Heng Zhang, Asfand Fahad","doi":"10.1093/comjnl/bxad090","DOIUrl":"https://doi.org/10.1093/comjnl/bxad090","url":null,"abstract":"Abstract As a structural topological index, the number of subtrees has great significance for the analysis and design of hybrid locally reliable networks. In this paper, with generating function and introducing a novel two-forest dual transformation technique, we solve the subtree enumerating problems of two representatives of the self-similar networks, such as the hierarchical lattice and $(u,v)$-flower networks. Moreover, by means of the circle weight transfer technique, two linear time algorithms of computing the subtree generation functions of these two families of networks are also proposed. The subtree density of two special cases for these self-similar networks is briefly discussed as an application.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Random numbers are very important for the security of computer system. However, generating qualified random numbers is difficult because we cannot always successfully introduce dedicated random number hardware into computer system. Although most operating systems provide random number generation capabilities, the effective entropy supply is still dependent on the hardware platform including memory and clocks etc. However, obtaining hardware events such as clocks requires system privileges, which is not conducive for entropy estimation at the application layer. In contrast, data related to the sensor hardware can be extracted directly at the application layer. These sensor data contain some randomness and may be used as a noise source. In this way, applications can use these sensors to implement their own proprietary random number generators. Before taking these sensors as the noise source, it is necessary to fully evaluate their entropy supply capability. In this paper, 300 Android smartphones and 30 iOS smartphones are selected as samples and their sensor entropy supply capabilities are comprehensively evaluated. Based on the entropy evaluation results, we give some suggestions on how to generate random numbers using these sensor data. We first design a framework for evaluating the entropy supply capability for smartphone sensors, based on the min-entropy estimation method proposed in NIST SP 800-90B. According to this framework, we simulate stationary and mobile working states for each smartphone, and collect sufficient sensor data as the min-entropy estimation dataset. The min-entropy estimation results show that in the stationary working state, each ACCELEROMETER sensor data collection can obtain at least 1.5 bits of entropy in Android, while each GYROSCOPE sensor data collection can obtain at least 20 bits of entropy in iOS. In the mobile working state, each ACCELEROMETER sensor data collection can obtain at least 1.9 bits of entropy, while each GYROSCOPE sensor data acquisition in iOS system can obtain at least 27 bits of entropy. This means that we can still get a stable entropy output from the sensor even when the smartphone is in stationary working state. Statistical analysis of the data using cross correlation methods suggests it is hard for an attacker to guess or predict the random numbers generated by a smartphone through another smartphone put in the similar external environment.
{"title":"An Evaluation On The Entropy Supplying Capability Of Smartphone Sensors","authors":"Dinghua Zhang, Shihao Wu, Yang Li, Quan Pan","doi":"10.1093/comjnl/bxad081","DOIUrl":"https://doi.org/10.1093/comjnl/bxad081","url":null,"abstract":"Abstract Random numbers are very important for the security of computer system. However, generating qualified random numbers is difficult because we cannot always successfully introduce dedicated random number hardware into computer system. Although most operating systems provide random number generation capabilities, the effective entropy supply is still dependent on the hardware platform including memory and clocks etc. However, obtaining hardware events such as clocks requires system privileges, which is not conducive for entropy estimation at the application layer. In contrast, data related to the sensor hardware can be extracted directly at the application layer. These sensor data contain some randomness and may be used as a noise source. In this way, applications can use these sensors to implement their own proprietary random number generators. Before taking these sensors as the noise source, it is necessary to fully evaluate their entropy supply capability. In this paper, 300 Android smartphones and 30 iOS smartphones are selected as samples and their sensor entropy supply capabilities are comprehensively evaluated. Based on the entropy evaluation results, we give some suggestions on how to generate random numbers using these sensor data. We first design a framework for evaluating the entropy supply capability for smartphone sensors, based on the min-entropy estimation method proposed in NIST SP 800-90B. According to this framework, we simulate stationary and mobile working states for each smartphone, and collect sufficient sensor data as the min-entropy estimation dataset. The min-entropy estimation results show that in the stationary working state, each ACCELEROMETER sensor data collection can obtain at least 1.5 bits of entropy in Android, while each GYROSCOPE sensor data collection can obtain at least 20 bits of entropy in iOS. In the mobile working state, each ACCELEROMETER sensor data collection can obtain at least 1.9 bits of entropy, while each GYROSCOPE sensor data acquisition in iOS system can obtain at least 27 bits of entropy. This means that we can still get a stable entropy output from the sensor even when the smartphone is in stationary working state. Statistical analysis of the data using cross correlation methods suggests it is hard for an attacker to guess or predict the random numbers generated by a smartphone through another smartphone put in the similar external environment.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136375560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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