Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always ...Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always effective and efficient in representing bivariate graph-based data.This study proposes a novel node-link visual model–visual entropy(Vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges simultaneously.We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams.In the first experiment,we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy.Three static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,Saturation-Transparency,and Numerical values.We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks.The participants achieved higher accuracy of their responses using Vizent and Numerical values;however,both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks.Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy.The performance of the Vizent graph was then compared to the Numerical values visualization.The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented,while no significant difference in accuracy was found.The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.展开更多
科学分析城市建设用地扩张的时空特征有助于实现城市的可持续发展,为了更全面地探索建设用地的发展规律以及建设用地扩张与道路发展相关性的分布特点,以福州市土地利用/覆盖变化数据和开源地图(open street map, OSM)路网数据为基础,利...科学分析城市建设用地扩张的时空特征有助于实现城市的可持续发展,为了更全面地探索建设用地的发展规律以及建设用地扩张与道路发展相关性的分布特点,以福州市土地利用/覆盖变化数据和开源地图(open street map, OSM)路网数据为基础,利用多环缓冲区、等角扇形、经纬网络划分研究单元,计算建设用地整体和局部的分形纬数、紧凑度、密度、扩张强度、扩张速度等扩张指数以及道路线密度、道路加权核密度,利用相关分析和双变量空间自相关分析,探讨2000—2020年福州市建设用地扩张的时空规律及其与道路网络分布的空间相关性。结果表明:福州市建设用地面积的增长速度呈现先升高后回落的趋势,扩张的主要区域为距市中心3~17 km区域且具有向外扩张的趋势;东南方向扩张强度最大,东北和西南部扩张速度较快,西部、北部扩张强度与扩张速度均较低;2015、2020年建设用地与道路网络的分布呈显著的空间相关性,全局莫兰指数(Moran’I)分别为0.829和0.835,高-高聚类集中在城市中心,低-低聚类集中在城市西部以及边缘地区,市中心以及东南沿海地区为建设用地与道路网络发展的主要地区,且二者发展较为协调。展开更多
针对无线传感器网络密钥管理中连通性、效能及安全性不足问题,基于阶层式无线传感器网络与二元对称多项式提出ESKDM(Efficient and Scalability Key Distribution Mechanism)方案。该方案通过运用多项式的特性,使传感器节点之间只需透...针对无线传感器网络密钥管理中连通性、效能及安全性不足问题,基于阶层式无线传感器网络与二元对称多项式提出ESKDM(Efficient and Scalability Key Distribution Mechanism)方案。该方案通过运用多项式的特性,使传感器节点之间只需透过ID的信息交换即可建立密钥。相较于同类方案,ESKDM方案大幅度的降低传感器节点的通信成本,并完全抵抗节点捕获攻击。无论网络节点的大小,一般节点所需要储存的秘密信息都为固定的信息,能够支持大型的WSN。也提出在新增节点的同时,不需要在线的基站存在,更能适用于实际应用。展开更多
密钥预分配方案关乎无线传感器网络节点间协同的安全问题.现有的密钥预分配方案大多存在连通率低,抗捕获性差,灵活性差等缺点.该文在分析现有密钥预分配方案的基础之上,对TD(k,n)模型作出了适当的改进,设计出一种均匀分布TD(k,n)模型,引...密钥预分配方案关乎无线传感器网络节点间协同的安全问题.现有的密钥预分配方案大多存在连通率低,抗捕获性差,灵活性差等缺点.该文在分析现有密钥预分配方案的基础之上,对TD(k,n)模型作出了适当的改进,设计出一种均匀分布TD(k,n)模型,引入Blundo二元对称多项式加密方法,并结合提出的均匀分配TD(k,n)模型,进一步提出了一种改进的分组密钥预分配方案IGDKPS(improved key-predistribution scheme based on group deployment).理论分析和仿真结果表明:IGDKPS方案在安全连通率、抗捕获性、灵活性等方面均有良好表现.展开更多
Bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is le...Bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. Bayesian network （BN） is a well-known graphical model which is used in BOA. Learning a propel model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.展开更多
基金the Ministry of National Education,Turkey for financially supporting the first author’s PhD study at Newcastle University,UK.
文摘Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always effective and efficient in representing bivariate graph-based data.This study proposes a novel node-link visual model–visual entropy(Vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges simultaneously.We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams.In the first experiment,we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy.Three static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,Saturation-Transparency,and Numerical values.We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks.The participants achieved higher accuracy of their responses using Vizent and Numerical values;however,both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks.Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy.The performance of the Vizent graph was then compared to the Numerical values visualization.The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented,while no significant difference in accuracy was found.The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.
文摘科学分析城市建设用地扩张的时空特征有助于实现城市的可持续发展,为了更全面地探索建设用地的发展规律以及建设用地扩张与道路发展相关性的分布特点,以福州市土地利用/覆盖变化数据和开源地图(open street map, OSM)路网数据为基础,利用多环缓冲区、等角扇形、经纬网络划分研究单元,计算建设用地整体和局部的分形纬数、紧凑度、密度、扩张强度、扩张速度等扩张指数以及道路线密度、道路加权核密度,利用相关分析和双变量空间自相关分析,探讨2000—2020年福州市建设用地扩张的时空规律及其与道路网络分布的空间相关性。结果表明:福州市建设用地面积的增长速度呈现先升高后回落的趋势,扩张的主要区域为距市中心3~17 km区域且具有向外扩张的趋势;东南方向扩张强度最大,东北和西南部扩张速度较快,西部、北部扩张强度与扩张速度均较低;2015、2020年建设用地与道路网络的分布呈显著的空间相关性,全局莫兰指数(Moran’I)分别为0.829和0.835,高-高聚类集中在城市中心,低-低聚类集中在城市西部以及边缘地区,市中心以及东南沿海地区为建设用地与道路网络发展的主要地区,且二者发展较为协调。
文摘针对无线传感器网络密钥管理中连通性、效能及安全性不足问题,基于阶层式无线传感器网络与二元对称多项式提出ESKDM(Efficient and Scalability Key Distribution Mechanism)方案。该方案通过运用多项式的特性,使传感器节点之间只需透过ID的信息交换即可建立密钥。相较于同类方案,ESKDM方案大幅度的降低传感器节点的通信成本,并完全抵抗节点捕获攻击。无论网络节点的大小,一般节点所需要储存的秘密信息都为固定的信息,能够支持大型的WSN。也提出在新增节点的同时,不需要在线的基站存在,更能适用于实际应用。
文摘密钥预分配方案关乎无线传感器网络节点间协同的安全问题.现有的密钥预分配方案大多存在连通率低,抗捕获性差,灵活性差等缺点.该文在分析现有密钥预分配方案的基础之上,对TD(k,n)模型作出了适当的改进,设计出一种均匀分布TD(k,n)模型,引入Blundo二元对称多项式加密方法,并结合提出的均匀分配TD(k,n)模型,进一步提出了一种改进的分组密钥预分配方案IGDKPS(improved key-predistribution scheme based on group deployment).理论分析和仿真结果表明:IGDKPS方案在安全连通率、抗捕获性、灵活性等方面均有良好表现.
文摘Bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. Bayesian network （BN） is a well-known graphical model which is used in BOA. Learning a propel model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.
