预期效用的概念演化与计数属性——新儒学经济思想及因缘和合论之运用

消费是为满足人类欲望。如果不能满足欲望,就不能维持生命,发展生活。所以要满足欲望,是合理的企求。但欲望因人因时因地而异,具有种类和程度的不同。若就欲望全体来观察,可以发现欲望的种类是无限的,欲望也可以反复再现。通常满足丁某种欲望,可以渐次发生各色各样新的欲望。某特定欲望的强度可能是有限的,但将随着实现而使得其边际强度渐减。有些欲望不能延缓,必须当时即获得解决,例如食物欲望,这种欲望很明显就是生存欲望。有些欲望似乎可以延缓,例如读书欲望,是文化欲望,而且学无止境,学海无边。

在Visual Foxpro 6.0中使表单自动适应显示器分辨率属性的实用方法

如何在程序运行期间,动态改变表单的大小以适应显示器分辨率属性的变化,并且使表单上控件的大小和位置成比例地发生相应改变,以避免导致界面出现不均匀、扭曲现象.人们采用的一贯方法是在表单的Resize过程中,通过重定义表单的ScaleMode属性或根据控件的绝对位置与窗口大小相加减的办法,逐一改变表单中控件的Height、Width、Top、Left等属性来解决这个问题,但由于程序中各个表单所引用的控件数多少不一,类型相异,导致编程较为繁琐,代码极为臃肿,而且不可复用.

代码标识符属性特征向量相似度检测技术研究

每段程序代码中的标识符,包含了不同程序部件的名字属性信息,是程序代码中不可或缺的重要元素,为程序代码相似度检测提供了重要分析依据。该文通过提取程序代码中所有的标识符,并对其进行分类,获得程序代码的基础数据,将其中的属性信息映射到特征向量中,然后用改进的向量距离算法计算标识符相似度结果。实验证明,该文所述方法比传统的属性计数法能更有效快速地检测出标识符相似的代码。

基于属性识别方法的程序文本相似度检测方法

笔者主要利用属性计数法检测程序文本相似度的相似度。程序文本的属性包含多个方面,如程序的物理结构,包括程序文件大小、行数,等等。程序属性不易随程序表达形式改变而变化,即使改变也是少数的。属性计数法的这个特点,可以用来检测程序文本的相似度。

汇编语言程序相似性检测混合算法

根据汇编语言自身的特点,提出了结合属性计数和结构度量技术的相似性检测混合算法。在该方法中,将程序段的数目、子程序定义和调用的次数、循环指令loop出现的次数、转移指令出现的次数作为结构信息,73个使用频率较高的关键字作为属性信息。在从汇编语言程序中提取这些信息后,利用卡方检验来判断2个程序的相似性。实验结果表明,从混合算法得到的结果与人工检测的结果相一致,优于从属性计数和结构度量技术得到的结果。

代码相似性检测在程序设计教学中的应用

代码剽窃是程序设计课程中经常出现的一种作弊行为,检测剽窃的源代码、验证学生程序作业的原创性在教学中很重要。程序代码的相似度度量是剽窃检测的关键技术。本文首先对现有程序代码相似性检测技术进行研究,然后改进Halstead算法,提出了基于统计学方法程序代码相似性检测算法,最后对算法的有效性进行了实验分析。

程序代码相似度度量的研究与实现

针对程序代码相似度的度量问题,提出一种属性计数和结构度量相结合的方法,通过统计程序源代码的操作符和操作数个数,产生Halstead长度、Halstead词汇和Halstead容量3个程序的特征向量,利用向量夹角的余弦计算属性相似度,采用最长公共子序列算法获取结构相似度,从而衡量程序对间的相似程度。实验结果表明,该方法能够有效检测出学生作业中的相似程序代码。

基于多种技术的混合式程序代码抄袭检测方法

在分析现有程序代码抄袭检测系统的特点及局限性的基础上,提出一种综合文本分析、结构度量和属性计数技术的混合式程序抄袭检测方法。应用文档指纹技术和Winnowing算法计算程序的文本相似度;将程序代码表示成动态控制结构树(Dynamic Control Structure tree,DCS),运用Winnowing算法计算DCS树相似度,从而得到结构相似度;收集并统计程序中的每个变量信息,应用变量相似度算法分析变量信息节点获取变量相似度;分别赋予文本相似度、结构相似度和变量相似度一个权值,计算得到总体的代码相似度。实验结果表明,所提出的方法能够有效检测出各种抄袭行为。针对不同的抄袭门槛值,使用该方法的检测结果准确度和查全率高于JPLAG系统。特别对于结构简单的程序组,此方法和JPLAG系统检测结果的平均准确度分别为82.5%和69.5%,说明所提的方法更加有效。

程序代码相似度检测方法的设计与实现

程序代码的相似度是剽窃检测的关键技术。通过对现有程序代码相似度度量技术进行研究后,基于属性技术法、结构度量法提出了一种属性计数和结构度量相结合的方法。通过统计程序源代码的操作符和操作数个数以及程序逻辑结构从而产生出一个特征向量,利用向量夹角的余弦计算属性相似度。实验结果表明,该方法能够有效检测出作业中相似的程序代码。

Quality Assessment of Attribute Data in GIS Based on Simple Random Sampling

On the basis of the principles of simple random sampling, the statistical model of rate of disfigurement (RD) is put forward and described in detail. According to the definition of simple random sampling for the attribute data in GIS, the mean and variance of the RD are deduced as the characteristic value of the statistical model in order to explain the feasibility of the accuracy measurement of the attribute data in GIS by using the RD. Moreover, on the basis of the mean and variance of the RD, the quality assessment method for attribute data of vector maps during the data collecting is discussed. The RD spread graph is also drawn to see whether the quality of the attribute data is under control. The RD model can synthetically judge the quality of attribute data, which is different from other measurement coefficients that only discuss accuracy of classification.

程序代码相似度检测技术的研究与实现

针对传统相似度算法应用在程序设计课程作业检测中精度较低这一问题,通过研究最长公共子序列等算法,发现其优缺点,在分析的基础上,结合结构度量技术和属性技术两种技术,提出一种性能较好的程序相似度计算方法。方法首先对源程序进行初步处理,将程序中的注释语句和空格删除,再次确定常用元素及常用结构,然后利用Lex统计、抽取程序元素;利用开源代码ucc生成语法树,之后抽取相应的语法结构;最后生成特征向量,并计算代码相似度。实验结果表明该方法比最长公共子序列算法精度提高了10.6%。

程序代码相似度检测技术的研究与实现

针对传统相似度算法应用在程序设计课程作业检测中精度较低这一问题,通过研究最长公共子序列等算法,发现其优缺点,并提出了一种属性计数和结构度量技术相结合的程序相似度计算方法。该方法首先对源程序进行初步处理,将程序中的注释语句和空格删除,再确定常用元素及常用结构,然后利用Lex统计、抽取程序元素;利用开源代码ucc生成语法树,之后抽取相应的语法结构;最后生成特征向量,并计算代码相似度。实验结果表明该方法比最长公共子序列算法精度提高了10.6%。

Attribute Based DRM Scheme with Dynamic Usage Control in Cloud Computing

In order to achieve fine-grained access control in cloud computing,existing digital rights management(DRM) schemes adopt attribute-based encryption as the main encryption primitive.However,these schemes suffer from inefficiency and cannot support dynamic updating of usage rights stored in the cloud.In this paper,we propose a novel DRM scheme with secure key management and dynamic usage control in cloud computing.We present a secure key management mechanism based on attribute-based encryption and proxy re-encryption.Only the users whose attributes satisfy the access policy of the encrypted content and who have effective usage rights can be able to recover the content encryption key and further decrypt the content.The attribute based mechanism allows the content provider to selectively provide fine-grained access control of contents among a set of users,and also enables the license server to implement immediate attribute and user revocation.Moreover,our scheme supports privacy-preserving dynamic usage control based on additive homomorphic encryption,which allows the license server in the cloud to update the users' usage rights dynamically without disclosing the plaintext.Extensive analytical results indicate that our proposed scheme is secure and efficient.

Time-domain compressive dictionary of attributed scattering center model for sparse representation

Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction of the separable dictionary is a key issue for sparse representation technology. A compressive time-domain dictionary(TD) for ASC model is presented. Two-dimensional frequency domain responses of the ASC are produced and transformed into the time domain. Then these time domain responses are cutoff and stacked into vectors. These vectored time-domain responses are amalgamated to form the TD. Compared with the traditional frequency-domain dictionary(FD), the TD is a matrix that is quite spare and can markedly reduce the data size of the dictionary. Based on the basic TD construction method, we present four extended TD construction methods, which are available for different applications. In the experiments, the performance of the TD, including the basic model and the extended models, has been firstly analyzed in comparison with the FD. Secondly, an example of parameter estimation from SAR synthetic aperture radar(SAR) measurements of a target collected in an anechoic room is exhibited. Finally, a sparse image reconstruction example is from two apart apertures. Experimental results demonstrate the effectiveness and efficiency of the proposed TD.

Video learning based image classification method for object recognition

Automatic image classification is the first step toward semantic understanding of an object in the computer vision area.The key challenge of problem for accurate object recognition is the ability to extract the robust features from various viewpoint images and rapidly calculate similarity between features in the image database or video stream.In order to solve these problems,an effective and rapid image classification method was presented for the object recognition based on the video learning technique.The optical-flow and RANSAC algorithm were used to acquire scene images from each video sequence.After the selection of scene images,the local maximum points on comer of object around local area were found using the Harris comer detection algorithm and the several attributes from local block around each feature point were calculated by using scale invariant feature transform （SIFT） for extracting local descriptor.Finally,the extracted local descriptor was learned to the three-dimensional pyramid match kernel.Experimental results show that our method can extract features in various multi-viewpoint images from query video and calculate a similarity between a query image and images in the database.