A statistical model of cluster stability

In the current paper we present a method for assessing cluster stability. This method, combined with a clustering algorithm, yields an estimate of the data partition, namely, the number of clusters. We adopt the cluster stability standpoint where clusters are imagined as islands of “high” density in a sea of “low” density. Explicitly, a cluster is associated with its high density core. Our approach offers to evaluate the goodness of a cluster by the similarity amongst the entire cluster and its core. We propose to measure this resemblance by two-sample tests or by probability distances between appropriate probability distributions. The distances are calculated on clustered samples drawn from the source population according to two different distributions. The first law is the underlying set distribution. The second law is constructed so that it represents the clusters’ cores. Here, a variant of the k-nearest neighbor density estimation is applied, so that items belonging to cores have a much higher chance to be selected. As the sample distribution is unknown a distribution-free two-sample test is required to examine the mentioned correspondence. For constructing such a test, we use distance functions built on negative definite kernels. In practice, outliers in the samples and limitations of the clustering algorithm heavily contribute to the noise level. As a result of this shortcoming the distance values have to be determined for many pairs of samples and therefore an empirical distance's distribution is obtained. The distribution is dependent on the examined number of clusters. To prevent this property for biasing the results we normalize the distances. It is conjectured that the true number of clusters yields the most concentrated normalized distribution. To measure the concentration we use the sample mean and the sample 25th percentile. The paper exhibits the good performance of the proposed method on synthetic and real-world data.     

An Improved (and Practical) Parameterized Algorithm for the Individual Haplotyping Problem MFR with Mate-Pairs

The Individual Haplotyping MFR problem is a computational problem that, given a set of DNA sequence fragment data of an individual, induces the corresponding haplotypes by dropping the minimum number of fragments. Bafna, Istrail, Lancia, and Rizzi proposed an algorithm of time O(2^2k m ² n+2^3k m ³) for the problem, where m is the number of fragments, n is the number of SNP sites, and k is the maximum number of holes in a fragment. When there are mate-pairs in the input data, the parameter k can be as large as 100, which would make the Bafna-Istrail-Lancia-Rizzi algorithm impracticable. The current paper introduces a new algorithm PM-MFR of running time , where k ₁ is the maximum number of SNP sites that a fragment covers (k ₁ is smaller than n), and k ₂ is the maximum number of fragments that cover a SNP site (k ₂ is usually about 10). Since the time complexity of the algorithm PM-MFR is not directly related to the parameter k, the algorithm solves the Individual Haplotyping MFR problem with mate-pairs more efficiently and is more practical in real biological applications. This research was supported in part by the National Natural Science Foundation of China under Grant Nos. 60433020 and 60773111, the Program for New Century Excellent Talents in University No. NCET-05-0683, the Program for Changjiang Scholars and Innovative Research Team in University No. IRT0661, and the Scientific Research Fund of Hunan Provincial Education Department under Grant No. 06C526.     

基于并行点火PCNN模型的图像分割新方法

提出一种并行点火脉冲耦合神经网络(Parallelized firing pulse coupled neural networks, PFPCNN)模型的图像分割方法. 首先用改进的Unit-linking PCNN (ULPCNN)模型对图像进行增强, 便于后续的图像分割. 然后采用PFPCNN新模型对增强后的图像进行分割, 最后用最大香农熵方法判定最佳分割结果. 各种复杂场景下的仿真实验及定量评价表明, 本文提出的图像分割方法, 其效果明显优于常规的PCNN分割方法.     

网络蠕虫的分类及其传播模型研究

近几年,蠕虫频繁爆发,已成为互联网安全的主要威胁。为了清楚地理解蠕虫所造成的威胁,很有必要对蠕虫进行分类,以便进行深入的研究。该文对目前蠕虫传播模型进行了深入地研究,主要包括随机网络和无尺度网络上的模型。指出在构建无尺度网络时现有算法的一个共同缺陷——没有考虑链路的成本。最后预测了蠕虫的发展趋势,并提出了一些蠕虫防御的措施。     

基于LabVIEW的远程测控系统视频采集的设计与实现

本文提出了一种在虚拟仪器开发平台LabVIEW上,利用ActiveX技术和普通USB摄像头实现视频采集的新方法;设计了远程系统的总体结构;通过LabVIEW与ActiveX的协同编程,实现了视频预览、录像、拍照及实验数据和视频数据的实时传输等功能;通过一个分压特性测量实验阐明了该视频系统在远程测控中的具体应用。     

基于区域GAC模型的二值化水平集图像分割算法

针对测地线主动轮廓(GAC)模型进行了改进,提出了一种基于区域的GAC模型.通过构造基于区域统计信息的符号压力函数取代边界停止函数,有效解决了弱边界目标或离散状边界目标的分割问题.该模型采用二值化水平集方法实现,避免了传统实现方法水平集函数需要重新初始化为符号距离函数,从而导致稳定性差、计算量大、实现复杂等缺点.对不同类型图像的试验结果表明:该算法迭代收敛速度比GAC模型传统实现方法明显加快,且可有效防止边界泄漏,分割效果优于传统GAC模型与C-V模型.     

高维数据环境下网络异常检测的改进否定选择算法

人工免疫中的否定选择算法目前已成功地应用于异常检测的低维数据集上,但在高维数据集上的效果不大理想。为了改善算法的性能,分析了现有否定选择算法在网络异常检测中的不足,对带变长检测器的否定选择算法进行了修改,提出了一种改进的否定选择算法,新算法中通过移动检测器控制检测器覆盖情况。仿真实验结果表明了改进算法在网络异常检测中高维数据集上的有效性,可以取得较高的检测率和较低的误报率。     

Mobile service oriented architectures for NN-queries

The ever-increasing number of mobile device users has also raised the users’ expectation of mobile services accordingly. This phenomenon has given pressures to the mobile service providers to improve their services in order to stay competitive in the market. The service oriented approach is seen to be a promising scheme for mobile services. This paper presents mobile service oriented architectures for Nearest-Neighbor (NN) queries that are classified into five categories, namely (i) intermittent query mobile services, (ii) continuous query mobile services, (iii) context-aware mobile services, (iv) continuous moving object query mobile services, and (v) data broadcast mobile services. These services incorporate query, location and context-aware services, ontological context model, and broadcast. The proposed architectures are concerned with mobile services for clients on the move requesting services based on their current location, which is arguably the most important feature in a wireless environment. Furthermore, we also discuss the Quality-of-Service (QoS) requirement for mobile services in which request latency time is one of the most important parameters to consider. Some analytical models for query latency measurement are presented and the results are compared with the simulation experiments.     

TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data

Statistical analysis of air mass back trajectories combined with long-term ambient air pollution measurements are useful tools for source identification. Using these methods, the geographic information system (GIS) based software, TrajStat, was developed to view, query, and cluster the trajectories and compute the potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses when measurement data are included.     

Self-adaptive utility-based web session management

In the Internet, where millions of users are a click away from your site, being able to dynamically classify the workload in real time, and predict its short term behavior, is crucial for proper self-management and business efficiency. As workloads vary significantly according to current time of day, season, promotions and linking, it becomes impractical for some ecommerce sites to keep over-dimensioned infrastructures to accommodate the whole load. When server resources are exceeded, session-based admission control systems allow maintaining a high throughput in terms of properly finished sessions and QoS for a limited number of sessions; however, by denying access to excess users, the website looses potential customers.In the present study we describe the architecture of AUGURES, a system that learns to predict Web user’s intentions for visiting the site as well its resource usage. Predictions are made from information known at the time of their first request and later from navigational clicks. For this purpose we use machine learning techniques and Markov-chain models. The system uses these predictions to automatically shape QoS for the most profitable sessions, predict short-term resource needs, and dynamically provision servers according to the expected revenue and the cost to serve it. We test the AUGURES prototype on access logs from a high-traffic, online travel agency, obtaining promising results.