基于PowerBuilder 10.5数据报表系统的设计与实现

在利用PowerBuilder10.5开发数据库应用程序时,大部分的数据报表都可以通过将数据窗口对象设计成Group样式来实现,它除了类似于Excel中的分类汇总功能外,还通过Trailer栏的计算域存放了各个统计项目的汇总值。主要介绍了Group样式明细数据报表的设计、Trailer栏计算域数据的动态获取、基于Trailer栏计算域数据汇总报表的动态生成以及打印模块的设计。     

Delphi中操作Excel的方法

本文介绍了在Delphi中操作Excel的主要原理、方法，实现了数据报表输出的Excel自动化，主要包括Excel对象的创建，Excel文件的导入与导出以及行列属性的设置等。     

PowerBuilder对复杂报表查询的一种方法

本文通过用ＰｏｗｅｒＢｕｉｌｄｅｒ开发工具对一类复杂的数据报表实现客户机／服务器模式的查询,以提高应用程序的报表查询速度,简化程序开发的工作量,并为其他类型复杂报表的查询提供一种参考模式。     

Windows平台应用程序权限能力控制研究

为限制应用程序的恶意行为,通过对Windows平台访问控制机制的研究以及对象管理器对资源管理方式的分析,提出在Windows7操作系统下应用程序权限能力控制方法。该方法使用权限控制和资源监控相结合的方式来实现,利用受限制的访问令牌和受限制的作业对象对应用程序进程权限进行限制,通过扩展对象管理器回调功能实现对应用程序访问资源的监控。最后通过实验验证了该方法的可行性和有效性,实现了对系统资源的细粒度监控。     

一种基于对象线索化RBAC的设计与实现

安全内核中通常是使用一个模块实现所有的安全策略，对应用程序的独立性不强，不利于安全策略的改变。信息安全工具包的安全内核采用了线索化的正交软件体系结构，对每个子系统使用对象技术实现，也对象化了访问安全工具包内部信息的应用程序，使安全内核独立于外部应用程序。安全内核内部对象也相互独立，便于功能扩展和维护。安全内核实施了麟作为对象的安全访问控制机制，保证了对象化应用程序的安全性，也保证了工具包内部信息的安全性。     

在Power Builder中利用BLOB数据类型实现Word文档的处理

在应用程序开发中多数时候只处理常规数据,在实际应用中为了实现系统的要求,开发者往往需要根据不同情况实现大数据的处理.本文阐述了在使用PowerBuilder开发应用程序中,如何利用OLE来实现word文档的处理,从而增强PowerBuilder开发过程中大数据类型的处理.     

Windows环境中实现持久对象的方法

持久对象是一种能够独立于创建对象的应用程序而存在，以实现在不同应用程序之间对其共享的对象。文本介绍在Ｗｉｎｄｏｗｓ环境中和用ＯＷＬ流式管理器实现这种持久对象的方法。ＯＷＬ流式管理器主要依赖了三个数据库：流式类注册数据库、读出和写入对象注册数据来实现持久对象读写管理和冲突处理。     

通过VB6.0数据报表对象创建多记录集报表

在VB6．0中通过一种变通的方法．实现在数据报表对象的细节区输出多个记录集中的记录。     

VB6.0数据报表对象技术及应用

本文介绍了使用VB6.0数据报表(DataReport)对象定制报表的实现方法.讨论了实现过程中若干值得注意的重要技术环节的解决方法,给出了设计实例,提出了高效开发打印报表的新途径.     

在Word中方便快捷地插入图标

因为我经常写一些有关软件应用的文章,为了使文章更加直观生动,在文章中插入软件或按钮的图标是个很好的办法。可是,在word中粘贴一幅画很容易,怎样才能插入需要的图标呢? 其实插入应用程序或windows图标很方便。在word中单击“插入” →“对象”,打开“对象”对话框,单击“数据包”确定(图     

用VC++语言创建WORD文档

简单文本格式无法生成复杂的表格和图形，因而采用WORD(或EXCEL)作为专用软件的数据报表文件是很有意义的，它可以按照用户要求将数据以图表的形式形象地表示出来。为了在VC 环境中创建和生成WORD文档，必须利用COM技术，把WORD程序看做COM服务器，把用户程序看做客户端，利用COM技术创建WORD对象，利用WORD对象调用VBScript函数(宏函数)来控制WORD文档的生成。通过研究证实该技术是可行的，并且对OFFICE软件都适用。     

基于词义类簇的文本聚类

文档表示是文本聚类的重要组成部分,该文旨在通过改进文档表示改进文本聚类。同义词和多义词现象是文档表示所面临的重要挑战。为此该文提出了词义类簇模型(Sense Cluster Model,SCM),在词义类簇空间上表示文档。SCM首先构造词义类簇空间,然后将文档表示在词义类簇空间上,获得每篇文档在每个词义类簇的概率。在词义类簇空间构造这一步骤中,首先利用词义归纳技术从文本中自动发现词义,接着采用词义聚类技术识别相同或者相似的词义从而获得词义类簇。词义类簇空间构造后,该文首先进行词义消歧,然后利用词义消歧的结果将文档表示在词义空间上。实验表明,SCM在标准测试集上的性能优于基线系统以及经典话题模型LDA。     

基于图的特征词权重算法及其在文档排序中的应用

信息检索的核心工作包括文档的分类和排序等操作,如何对文档中的特征词权重进行有效度量是其中的一项关键技术。利用词的共现等关系为每个文档建立文本图,基于邻接词间重要性相互影响的思路,结合文档中特征词的词频特性,迭代计算每个词的权重,进一步结合文本图的密度等全局特性,对信息检索的结果进行排序。实验证实,算法在标准数据集上具有良好的效果。     

LSISOM — A Latent Semantic Indexing Approach to Self-Organizing Maps of Document Collections

The Self Organizing Map (SOM) algorithm has been utilized, with much success, in a variety of applications for the automatic organization of full-text document collections. A great advantage of the SOM method is that document collections can be ordered in such a way so that documents with similar content are positioned at nearby locations of the 2-dimensional SOM lattice. The resulting ordered map thus presents a general view of the document collection which helps the exploration of information contained in the whole document space. The most notable example of such an application is the WEBSOM method where the document collection is ordered onto a map by utilizing word category histograms for representing the documents data vectors. In this paper, we introduce the LSISOM method which resembles WEBSOM in the sense that the document maps are generated from word category histograms rather than simple histograms of the words. However, a major difference between the two methods is that in WEBSOM the word category histograms are formed using statistical information of short word contexts whereas in LSISOM these histograms are obtained from the SOM clustering of the Latent Semantic Indexing representation of document terms.     

Document Image Retrieval through Word Shape Coding

This paper presents a document retrieval technique that is capable of searching document images without OCR (optical character recognition). The proposed technique retrieves document images by a new word shape coding scheme, which captures the document content through annotating each word image by a word shape code. In particular, we annotate word images by using a set of topological shape features including character ascenders/descenders, character holes, and character water reservoirs. With the annotated word shape codes, document images can be retrieved by either query keywords or a query document image. Experimental results show that the proposed document image retrieval technique is fast, efficient, and tolerant to various types of document degradation.     

融合单词贡献度与Word2Vec词向量的文档表示

针对现有文档向量表示方法受噪声词语影响和重要词语语义不完整的问题,通过融合单词贡献度与Word2Vec词向量提出一种新的文档表示方法.应用数据集训练Word2Vec模型,计算数据集中词语的贡献度,同时设置贡献度阈值,提取贡献度大于该阈值的单词构建单词集合.在此基础上,寻找文档与集合中共同存在的单词,获取其词向量并融合单...     

The NASA automated requirements measurement tool: a reconstruction

In the late 1990s the National Aeronautics and Space Administration (NASA) Software Assurance Technology Center (SATC) developed a tool to automatically analyze a requirements document and produce a detailed quality report. The report was based on statistical analysis of word frequencies at various structural levels of the document. The Automated Requirements Measurement (ARM) tool was further enhanced to include additional functionality such as custom definitions of quality indicators inputs for document analysis. By 2011 work on the ARM tool was discontinued. This paper describes the reverse-engineering and reproduction of the functionality of ARM. Recreating the functionality of this tool yielded valuable insight into certain quality metrics and provides a benchmark tool for future research. In addition to recreating and working with the ARM tool, this paper explores both existing and potential definitions of quality metrics in requirements specifications. Automated requirements analysis is a convergence of various fields of research, including text mining, quality analysis, and natural language processing. Informed by tangential areas of research in document understanding and data mining, recommendations are made for future areas of research and development in automated requirements analysis.     

Retrieval of machine-printed Latin documents through Word Shape Coding

This paper reports a document retrieval technique that retrieves machine-printed Latin-based document images through word shape coding. Adopting the idea of image annotation, a word shape coding scheme is proposed, which converts each word image into a word shape code by using a few shape features. The text contents of imaged documents are thus captured by a document vector constructed with the converted word shape code and word frequency information. Similarities between different document images are then gauged based on the constructed document vectors. We divide the retrieval process into two stages. Based on the observation that documents of the same language share a large number of high-frequency language-specific stop words, the first stage retrieves documents with the same underlying language as that of the query document. The second stage then re-ranks the documents retrieved in the first stage based on the topic similarity. Experiments show that document images of different languages and topics can be retrieved properly by using the proposed word shape coding scheme.     

DocuBurst: Visualizing Document Content using Language Structure

Textual data is at the forefront of information management problems today. One response has been the development of visualizations of text data. These visualizations, commonly based on simple attributes such as relative word frequency, have become increasingly popular tools. We extend this direction, presenting the first visualization of document content which combines word frequency with the human-created structure in lexical databases to create a visualization that also reflects semantic content. DocuBurst is a radial, space-filling layout of hyponymy (the IS-A relation), overlaid with occurrence counts of words in a document of interest to provide visual summaries at varying levels of granularity. Interactive document analysis is supported with geometric and semantic zoom, selectable focus on individual words, and linked access to source text.