Efficient Flexible M-Tree Bulk Loading Using FastMap and Space-Filling Curves

Many database applications currently deal with objects in a metric space. Examples of such objects include unstructured multimedia objects and points of interest (POIs) in a road network. The M-tree is a dynamic index structure that facilitates an efficient search for objects in a metric space. Studies have been conducted on the bulk loading of large datasets in an M-tree. However, because previous algorithms involve excessive distance computations and disk accesses, they perform poorly in terms of their index construction and search capability. This study proposes two efficient M-tree bulk loading algorithms. Our algorithms minimize the number of distance computations and disk accesses using FastMap and a space-filling curve, thereby significantly improving the index construction and search performance. Our second algorithm is an extension of the first, and it incorporates a partitioning clustering technique and flexible node architecture to further improve the search performance. Through the use of various synthetic and real-world datasets, the experimental results demonstrated that our algorithms improved the index construction performance by up to three orders of magnitude and the search performance by up to 20.3 times over the previous algorithm.     

Distance and similarity measures of Pythagorean fuzzy sets based on the Hausdorff metric with application to fuzzy TOPSIS

Pythagorean fuzzy sets (PFSs) were proposed by Yager in 2013 to treat imprecise and vague information in daily life more rigorously and efficiently with higher precision than intuitionistic fuzzy sets. In this paper, we construct new distance and similarity measures of PFSs based on the Hausdorff metric. We first develop a method to calculate a distance between PFSs based on the Hasudorff metric, along with proving several properties and theorems. We then consider a generalization of other distance measures, such as the Hamming distance, the Euclidean distance, and their normalized versions. On the basis of the proposed distances for PFSs, we give new similarity measures to compute the similarity degree of PFSs. Some examples related to pattern recognition and linguistic variables are used to validate the proposed distance and similarity measures. Finally, we apply the proposed methods to multicriteria decision-making by constructing a Pythagorean fuzzy Technique for Order Preference by Similarity to an Ideal Solution and then present a practical example to address an important issue related to social sector. Numerical results indicate that the proposed methods are reasonable and applicable and also that they are well suited in pattern recognition, linguistic variables, and multicriteria decision-making with PFSs.     

在线SDN控制器性能监控器设计与研究

随着网络规模和网络数据流量的变大,软件定义网络控制器性能问题可能成为网络瓶颈,为了在动态负载情况下仍然可以获取控制器准确的性能状态,设计了一个在线的性能监控器框架,并定义了控制器时延性能衡量指标Lpdex.在一款广泛使用的FloodLight控制器中实现了性能监控器的简单原型,经过实验评估,验证了控制器性能监控器的可行性和Lpdex可在线准确地反映控制器在不同负载情况下时延性能的变化.     

A distance-type-insensitive clustering approach

In this paper, we offer a method aiming to minimize the role of distance metric used in clustering. It is well known that distance metrics used in clustering algorithms heavily influence the end results and also make the algorithms sensitive to imbalanced attribute/feature scales. To solve these problems, a new clustering algorithm using a per-attribute/feature ranking operating mechanism is proposed in this paper. Ranking is a rarely used discrete, nonlinear operator by other clustering algorithms. However, it also has unique advantages over the dominantly used continuous operators. The proposed algorithm is based on the ranks of the data samples in terms of their spatial separation and is able to provide a more objective clustering result compared with the alternative approaches. Numerical examples on benchmark datasets prove the validity and effectiveness of the proposed concept and principles.     

一种适用于混合型分类数据的聚类算法

传统的K-modes算法采用简单的属性匹配方式计算同一属性下不同属性值的距离，并且计算样本距离时令所有属性权重相等。在此基础上，综合考虑有序型分类数据中属性值的顺序关系、无序型分类数据中不同属性值之间的相似性以及各属性之间的关系等，提出一种更加适用于混合型分类数据的改进聚类算法，该算法对无序型分类数据和有序型分类数据采用不同的距离度量，并且用平均熵赋予相应的权重。实验结果表明，改进算法在人工数据集和真实数据集上均有比K-modes算法及其改进算法更好的聚类效果。     

聚类混合型数据的密度峰值改进算法

聚类混合型数据，通常是依据样本属性类别的不同分别进行评价。但这种将样本属性划分到不同子空间中分别度量的方式，割裂了样本属性原有的统一性；导致对样本个体的相似性评价产生了非一致的度量偏差。针对这一问题，提出以二进制编码样本属性，再由海明差异对属性编码施行统一度量的新的聚类算法。新算法通过在统一的框架内对混合型数据实施相似性度量，避免了对样本属性的切割，在此基础上又根据不同属性的性质赋予其不同的权重，并以此评价样本个体之间的相似程度。实验结果表明，新算法能够有效地聚类混合型数据；与已有的其他聚类算法相比较，表现出更好的聚类准确率及稳定性。     

以任意高维纠缠态为量子信道的受控隐形传态

目的是利用高维量子纠缠态为量子信道，讨论未知单粒子态的受控隐形传输问题。以三维量子纠缠态为信道，提出一个二维任意单粒子态的受控隐形传输协议。提出了以任意[d]-维量子纠缠态为量子信道，[t]-维任意单粒子态的隐形传输协议[(t相似文献   

结合时间戳的指纹密钥数据加解密传输方案

目的 对于生物密钥而言，生物特征数据的安全与生物密钥的管理存储都很关键。为了构造能够应用在通信数据传输场景的生物密钥，同时保证生物特征本身的模糊性与密码学的精确性处于一种相对平衡状态，提出一种基于时间戳与指纹密钥的数据加解密传输方案。方法 利用发送方指纹特征点之间的相对信息，与保密随机矩阵生成发送方指纹密钥；借助通信双方的预先设定数与时间戳，生成接收方恢复指纹密钥时所需的辅助信息；利用发送方指纹密钥加密数据，实现密文数据的传输。结果 本文方法在仿真通信双方数据加解密的实现中，测试再生指纹密钥的识别率（GAR）与误识率（FAR）。通过实验数据分析，表明了本文提出的指纹密钥生成方法的可用性，以及指纹密钥作为数字身份所具备的可认证性，其中真实发送方的再生指纹密钥识别率可高达99.8%，并且本方案还可用于即时通信、对称加密等多种场景当中。结论 本文方法利用时间戳确定了通信事件的唯一性与不可否认性，同时实现了指纹密钥恢复时的"一次一密"。此外，方案通过保密随机矩阵实现了发送方指纹密钥的可撤销，极大程度保障了指纹数据的安全性。     

连续偏序集上的p.q.度量与格上的拟一致结构

利用拟一致分子格、拓扑分子格的分明化定理,比较自然地引入了连续偏序集上的一种p.q.度量,证明了其与完全分配格上的拟一致结构的相互确定关系,并证明了其序列式性质与可数可乘性。这一种度量是一般拓扑学中的伪拟度量的自然而颇具序特色的推广。