A metamodeling approach for pattern specification and management

The formal specification of design patterns is central to pattern research and is the foundation of solving various pattern-related problems.In this paper,we propose a metamodeling approach for pattern specification,in which a pattern is modeled as a meta-level class and its participants are meta-level references.Instead of defining a new metamodel,we reuse the Unified Modeling Language(UML)metamodel and incorporate the concepts of Variable and Set into our approach,which are unavailable in the UML but essential for pattern specification.Our approach provides straightforward solutions for pattern-related problems,such as pattern instantiation,evolution,and implementation.By integrating the solutions into a single framework,we can construct a pattern management system,in which patterns can be instantiated,evolved,and implemented in a correct and manageable way.     

基于Snort系统的网络入侵检测模型的研究

网络入侵检测是网络安全领域研究的热点问题,通过应用分组交换检测机制和Markov链,提出基于Snort系统的IDS模型,设计Snort系统规则库和检测引擎。分析结果表明,改进模型和方法可以提高网络入侵检测中海量数据的检测准确率和效率。     

基于主曲线的微阵列数据分类

提出了一种基于主曲线(principal curves)的微阵列数据分类方法(PC)。主曲线是第一主成分的非线性推广,它是数据集合的“骨架”,数据集合是主曲线的“云”。基于主曲线的微阵列数据分类方法,首先利用专门设计的算法在训练数据集上计算出每类样本的主曲线,然后根据测试样本与各类样本主曲线距离的期望方差来确定测试样本所属的类别。实验结果表明,该分类方法在进行小样本微阵列数据分类时性能优于现有的方法。     

基于ROC曲线寻优的支持向量机性能研究

支持向量机在小样本模式识别领域具有优势,但其性能评估及核参数、正则化参数的选择尚未有标准算法.将受试者操作特性曲线(Receiver Operating Characteristic,ROC)引入支持向量机分类性能分析和建模参数优化问题.在核参数及正则化参数所构成的二维空间中,调整模型参数阈值描绘ROC曲线,通过比较不同分类器ROC曲线下面积实现模型的性能分析,研究了基于ROC曲线最佳工作点的模型优化问题.工程实例表明,ROC曲线下面积有效地量化了模型的识别性能,并给出了一定寻优范围内的模型参数最优点,可以在SVM模型参数优化问题中推广应用.     

《GIS软件工程》实验模式研究

GIS软件工程实验是《GIS软件工程》教学过程中的重要环节,分析《GIS软件工程》的特点,结合在教学实验过程中具体的问题,提出基于柔性案例的GIS软件工程实验模式,并从柔性案例的选择、柔点的选择设置方法、实验过程等方面,分别展开描述,对实验实施中的优缺点进行总结。     

KR字符串匹配算法的研究与实现

字符串匹配是判断模式串(短串)是否是文本串(长串)的子串。KR算法是一种随机串匹配算法,详细介绍KR串匹配算法的算法描述及代码实现过程,并对该算法进行测试,讨论该算法的实现效率。     

基于神经网络的数字识别

本文介绍了神经网络、数字识别的相关内容及其概念,在此基础上重点研究了基于神经网络的数字识别系统。本文对基于神经网络的数字识别系统包含的功能,以及每个功能模块所使用的技术进行了阐述。最后对基于神经网络的数字识别系统所具有的优点进行了分析和讨论。     

一种基于XML的可复用Web图表框架

软件复用是提高软件开发效率及产品质量的一条行之有效的途径。本文采用工厂方法和迭代器设计模式设计了一种基于XML和JFreeChart的可复用Web图表框架。该框架使用JFreeChart图表引擎动态生成图表,以XML格式传递图表数据,并且通过充分利用XML的可扩展性和对复杂数据的描述能力,封装了图表生成的细节,为类型不同、条目数量变化的图表数据提供了统一的接口。因此,该框架可大大增强软件的可复用性和可维护性,降低图表开发的难度,并提高开发效率。     

基于模式搜索的连续函数寻优

模式搜索方法是求解最优化问题的一种直接搜索方法,它只根据函数值信息来进行求优。但模式搜索对起始点非常敏感,对好的起始点,模式搜索可以搜索到全局最优点,但对某些起始点,模式搜索只能搜索到局部极值点。本文提出基于模式搜索的一个新方法,实验证明,它能搜索到全局最优。     

Who is LB1? Discriminant analysis for the classification of specimens

Many problems in paleontology reduce to finding those features that best discriminate among a set of classes. A clear example is the classification of new specimens. However, these classifications are generally challenging because the number of discriminant features and the number of samples are limited. This has been the fate of LB1, a new specimen found in the Liang Bua Cave of Flores. Several authors have attributed LB1 to a new species of Homo, H. floresiensis. According to this hypothesis, LB1 is either a member of the early Homo group or a descendent of an ancestor of the Asian H. erectus. Detractors have put forward an alternate hypothesis, which stipulates that LB1 is in fact a microcephalic modern human. In this paper, we show how we can employ a new Bayes optimal discriminant feature extraction technique to help resolve this type of issues. In this process, we present three types of experiments. First, we use this Bayes optimal discriminant technique to develop a model of morphological (shape) evolution from Australopiths to H. sapiens. LB1 fits perfectly in this model as a member of the early Homo group. Second, we build a classifier based on the available cranial and mandibular data appropriately normalized for size and volume. Again, LB1 is most similar to early Homo. Third, we build a brain endocast classifier to show that LB1 is not within the normal range of variation in H. sapiens. These results combined support the hypothesis of a very early shared ancestor for LB1 and H. erectus, and illustrate how discriminant analysis approaches can be successfully used to help classify newly discovered specimens.