浅谈校园网络安全防范

随着国家校园网工程的逐步推进,越来越多的学校已经逐步建立并完善了自己的校园网络,并应用于教学和管理中,校园网络安全问题也日益突显,因此,如何加强和维护校园网络安全是摆在校园网络管理人员面前的重大课题.本文从校园网络安全概述、现状及主要防范措施几个方面来阐述校园网络安全防范.     

无线传感器网络操作系统TinyOS综述

TinyOS是无线传感器网络专用的操作系统。本文首先分析TinyOS操作系统的关键技术——组件化结构、调度机制、通讯模型及能量管理方式,然后将TinyOS与另外两个操作系统μCOS-II、μT-Kernel在内核大小、时间可确定性、低功耗等方面进行简要对比,最后总结TinyOS操作系统的缺点和存在的问题,为学习使用TinyOS提供指导。     

基于TRIZ理论的叶片仿生设计与有限元力学分析

改善风力机叶片气动性能,提高叶片刚度、稳定性,已成为大型风力机叶片设计迫切需要解决的问题。生物体独特的外部形状、功能、结构、材料的完美统一,高超的环境适应能力,也为风力机仿生设计提供了不竭灵感。仿生过程是一个分析综合创新的过程,运用TRIZ产品进化理论对风力机叶片技术所处发展阶段进行研究,有助于更清楚地了解风力机叶片的发展趋势和创新的必要性,使研究工作更具系统性和规律性。应用TRIZ产品进化理论,对风力机叶片技术的相关专利数据进行统计并绘制曲线图,通过与标准曲线的区段比较找出风力机叶片所处的发展阶段和接下来的发展趋势与对策。将鱼鳍作为仿生对象进行建模与力学分析,得到仿生模型的力学指标。     

A simple linear time certifying LBFS-based algorithm for recognizing trivially perfect graphs and their complements

We introduce a simple, linear time algorithm for recognizing trivially perfect (TP) graphs. It improves upon the algorithm of Yan et al. [J.-H. Yan, J.-J. Chen, G.J. Chang, Quasi-threshold graphs, Discrete Appl. Math. 69 (3) (1996) 247–255] in that it is certifying, producing a P₄ or a C₄ when the graph is not TP. In addition, our algorithm can be easily modified to recognize the complement of TP graphs (co-TP) in linear time as well. It is based on lexicographic BFS, and in particular the technique of partition refinement, which has been used in the recognition of many other graph classes [D.G. Corneil, Lexicographic breadth first search—a survey, in: WG 2004, in: Lecture Notes in Comput. Sci., vol. 3353, Springer, 2004, pp. 1–19].     

Cancellable biometrics and annotations on BioHash

Lately, the once powerful one-factor authentication which is based solely on either password, token or biometric approach, appears to be insufficient in addressing the challenges of identity frauds. For example, the sole biometric approach suffers from the privacy invasion and non-revocable issues. Passwords and tokens are easily forgotten and lost. To address these issues, the notion of cancellable biometrics was introduced to denote biometric templates that can be cancelled and replaced with the inclusion of another independent authentication factor. BioHash is a form of cancellable biometrics which mixes a set of user-specific random vectors with biometric features. In verification setting, BioHash is able to deliver extremely low error rates as compared to the sole biometric approach when a genuine token is used. However, this raises the possibility of two identity theft scenarios: (i) stolen-biometrics, in which an impostor possesses intercepted biometric data of sufficient high quality to be considered genuine and (ii) stolen-token, in which an impostor has access to the genuine token and used by the impostor to claim as the genuine user. We found that the recognition rate for the latter case is poorer. In this paper, the quantised random projection ensemble based on the Johnson–Lindenstrauss Lemma is used to establish the mathematical foundation of BioHash. Based on this model, we elucidate the characteristics of BioHash in pattern recognition as well as security view points and propose new methods to rectify the stolen-token problem.     

多工作模式产品配色意象的灰色评价方法

针对产品开发过程中的色彩规划问题,考虑多工作模式产品的配色特征,构建一种多工作模式产品的配色意象评价模型.基于灰色关联分析法建立产品方案色彩与意象之间的关联模型,运用灰色聚类法判断产品配色方案的意象值;将产品各工作模式下的配色意象同时作为评价属性并赋予相应的权值,进行综合评价.文中方法兼顾了多工作模式产品色彩面积的可变性和消费者主观意象的不确定性,将多工作模式产品的配色意象评价方法置于灵活、直观的框架中.最后以工程机械产品为应用实例,证明该方法较单属性评价方法更为贴近消费者的主观意象.     

一种山区图像边缘信息的分水岭变换提取方法

为了能够抑制引起过分割的根本原因-噪声和细密纹理,提出了一种改进的提取山区图的山脊线、山谷线的边缘信息的分水岭算法,该算法着重于解决传统分水岭的过分割问题;该算法首先对梯度图进行一种改进的多尺度梯度算子处理,以减小图像噪声对分水岭的过度分割的影响;然后利用基于标记符控制的最小强制技术去除由噪声和不必要的图像细节形成的谷底,使修正后的梯度图像只对应包含区域极大值的标记图像,改进后的方法能准确地提取出所需要的关键边缘信息.     

Pattern Classification With Class Probability Output Network

The output of a classifier is usually determined by the value of a discriminant function and a decision is made based on this output which does not necessarily represent the posterior probability for the soft decision of classification. In this context, it is desirable that the output of a classifier be calibrated in such a way to give the meaning of the posterior probability of class membership. This paper presents a new method of postprocessing for the probabilistic scaling of classifier's output. For this purpose, the output of a classifier is analyzed and the distribution of the output is described by the beta distribution parameters. For more accurate approximation of class output distribution, the beta distribution parameters as well as the kernel parameters describing the discriminant function are adjusted in such a way to improve the uniformity of beta cumulative distribution function (CDF) values for the given class output samples. As a result, the classifier with the proposed scaling method referred to as the class probability output network (CPON) can provide accurate posterior probabilities for the soft decision of classification. To show the effectiveness of the proposed method, the simulation for pattern classification using the support vector machine (SVM) classifiers is performed for the University of California at Irvine (UCI) data sets. The simulation results using the SVM classifiers with the proposed CPON demonstrated a statistically meaningful performance improvement over the SVM and SVM-related classifiers, and also other probabilistic scaling methods.