Using X Social Networks (formerly Twitter) and web news mining to predict the measles outbreak

Measles,an infectious disease caused by the measles virus,remains a significant public health concern worldwide due to its highly contagious nature and potential for severe complications[1].In addition to symptoms such as high fever,cough,Koplik spots,and rash,measles can lead to serious complications including pneumonia and myocarditis,particularly in vulnerable populations such as young children[1,2].

Algorithms of mining data records from website automatically

In order to improve the accuracy and integrality of mining data records from the web, the concepts of isomorphic page and directory page and three algorithms are proposed. An isomorphic web page is a set of web pages that have uniform structure, only differing in main information. A web page which contains many links that link to isomorphic web pages is called a directory page. Algorithm 1 can find directory web pages in a web using adjacent links similar analysis method. It first sorts the link, and then counts the links in each directory. If the count is greater than a given valve then finds the similar sub-page links in the directory and gives the results. A function for an isomorphic web page judgment is also proposed. Algorithm 2 can mine data records from an isomorphic page using a noise information filter. It is based on the fact that the noise information is the same in two isomorphic pages, only the main information is different. Algorithm 3 can mine data records from an entire website using the technology of spider. The experiment shows that the proposed algorithms can mine data records more intactly than the existing algorithms. Mining data records from isomorphic pages is an efficient method.

多维深度导向的Java Web模糊测试方法

随着Java Web的广泛应用,其安全问题日益突出。模糊测试作为一种有效的漏洞挖掘方法,目前已经被用于Java漏洞的检测。然而,由于Java Web应用代码规模庞大、业务逻辑复杂,现有的漏洞挖掘工具在模糊测试中存在随机性高、代码检测深度低的问题,导致漏洞挖掘的准确率较低。因此,文章提出基于多维深度导向的Java Web模糊测试方法。该方法使用Jimple作为待测Java Web应用字节码的三地址码中间表示,并生成代码对应的函数间调用图和函数内控制流图,在此基础上分析每个基本块的多维深度。同时,根据多维深度和模糊测试执行时间优化模糊测试指导策略,设计相应的输入结构解析策略、能量分配策略和变异算法调度策略,提升模糊测试的准确性。实验结果表明,相较于现有的模糊测试工具Peach和Kelinci,该方法能够在性能消耗较低的情况下取得更好的漏洞检测效果。

基于Website信息发布平台上的二次开发

介绍Website信息发布平台的基本功能 ,并且将利用该平台建立Web站点与传统方法建立Web站点相比较 ;

一种灵活高效的增量式Web平行语料抽取方法

从Web中抽取平行语料对于机器翻译和其他多语语言处理任务来说非常重要,由此提出了一种从Web中灵活高效地增量抽取平行语料的方法,通过持续地对Common Crawl的Web抓取存档进行下载、扫描和分析统计,增量更新域名下的语言文本长度统计数据。对于任意给定的感兴趣目标语言对,抽取方法基于域名下的语言文本长度统计数据确定抓取网站入口,并根据目标语言进行定向抓取,忽略多语域名和目标语言外的链接。此外还提出了一种在多语域名内基于语义相似性进行全局对齐的新的句子对齐方法。实验表明,增量抽取能够持续不断地获得新的平行语料,根据指定的语言对进行抽取,可以灵活地获得感兴趣的目标语言对平行语料;新的对齐方法在对齐效率上明显优于全局方法,且能完成局部方法无法完成的对齐;在6个语言方向中,抽取到的平行语料在4个中低资源语言方向的质量优于现有Web开源平行语料,在2个高资源语言方向的质量接近现有最好的Web开源平行语料。

A Classification Algorithm to Improve the Design of Websites

In very short time today web has become an enormously important tool for communicating ideas, conducting business and entertainment. At the time of navigation, web users leave various records of their action. This vast amount of data can be a useful source of knowledge for predicting user behavior. A refined method is required to carry out this task. Web usages mining (WUM) is the tool designed to do this task. WUM system is used to extract the knowledge based on user behavior during the web navigation. The extracted knowledge can be used for predicting the users’ future request when user is browsing the web. In this paper we advanced the online recommender system by using a Longest Common Subsequence (LCS) classification algorithm to classify users’ navigation pattern. Classification using the proposed method can improve the accuracy of recommendation and also proposed an algorithm that uses LCS method to know the user behavior for improvement of design of a website.

图形图像处理软件在Web前端设计中的有效应用

在5G网络时代背景下,网站成为人们获取信息、进行交流的重要平台,人们在使用网站的过程中,不仅重视其使用质量还关注网站的界面美观度,为了满足人们对网站的审美要求,需要将图形图像处理技术应用到Web前端设计中,为用户提供优质的视觉体验。该文章简单介绍图形图像处理软件和Web前端开发技术,分析图形图像处理软件在Web前端设计中的应用价值,从页面布局设计制作方法、Logo的设计制作方法、视频设计制作方法和图像融合处理方法等方面探讨图形图像处理软件在Web前端设计中的有效应用,旨在为相关研究提供参考。

Learning Association Rules and Tracking the Changing Concepts on Webpages:An Effective Pornographic Websites Filtering Approach

We applied the decision tree algorithm to learn association rules between webpage’s category(pornographic or normal) and the critical features.Based on these rules, we proposed an efficient method of filtering pornographic webpages with the following major advantages: 1) a weighted window-based technique was proposed to estimate for the condition of concept drift for the keywords found recently in pornographic webpages; 2) checking only contexts of webpages without scanning pictures; 3) an incremental learning mechanism was designed to incrementally update the pornographic keyword database.

An Efficient Mechanism for Product Data Extraction from E-Commerce Websites

A large amount of data is present on the web which can be used for useful purposes like a product recommendation,price comparison and demand forecasting for a particular product.Websites are designed for human understanding and not for machines.Therefore,to make data machine-readable,it requires techniques to grab data from web pages.Researchers have addressed the problem using two approaches,i.e.,knowledge engineering and machine learning.State of the art knowledge engineering approaches use the structure of documents,visual cues,clustering of attributes of data records and text processing techniques to identify data records on a web page.Machine learning approaches use annotated pages to learn rules.These rules are used to extract data from unseen web pages.The structure of web documents is continuously evolving.Therefore,new techniques are needed to handle the emerging requirements of web data extraction.In this paper,we have presented a novel,simple and efficient technique to extract data from web pages using visual styles and structure of documents.The proposed technique detects Rich Data Region(RDR)using query and correlative words of the query.RDR is then divided into data records using style similarity.Noisy elements are removed using a Common Tag Sequence(CTS)and formatting entropy.The system is implemented using JAVA and runs on the dataset of real-world working websites.The effectiveness of results is evaluated using precision,recall,and F-measure and compared with five existing systems.A comparison of the proposed technique to existing systems has shown encouraging results.

Empirical Investigation of Website Design Affecting E-Loyalty

Given the rapid growth of e-commerce and the importance of understanding online customer behavior,it is necessary to develop a universal model as a means to measure the relevant construct.Website design plays an important role in e-commerce because it directly affects online customers during the purchase process.Online customers will continue to be loyal because of good website design.However,the effect of website design has not been clearly defined and a suitable framework for evaluating the status of website design is lacking.The purpose of this study is to develop a comprehensive framework that can guide successful web design and examine the impact of web design on e-loyalty.207 Taobao customers from China completed an online survey.All scales were analyzed by reliability,construct validity,and convergent validity.Multiple regression analysis is used to test research hypotheses.Findings indicated that the factor of website design should be revised and classified into visual design and information&navigation design.The website design had a significantly positive effect on e-loyalty.The relative order of importance of these predictors was information&navigation design and visual design.

Chameleon聚类算法在Web开源情报主题挖掘中的应用研究

信息时代的开源情报传播速度快、体量大、时效性强,大量数据难以用人工进行分析,为了解决对海量数据分析的效率,研究设计了Web开源情报信息处理方法。该方法首先利用网络爬虫通过URL爬取目标情报,之后用DOM树对网页内容进行整理,采用TextRank算法提取关键词并使用Chameleon聚类算法构建主题挖掘模型,该模型用于情报主题生成,自动进行情报主题分析。性能测试表明,基于Chameleon聚类算法的Web开源情报信息处理方法能够对开源情报进行有效分析。

煤矿安全Web数据采集技术研究及应用

针对矿井安全分析所需的事故、处罚等不易获取的数据,选择互联网公开的Web数据作为数据源,在分析总结Web查询结果页面具有的视觉特征基础上,提出了一种基于视觉与DOM树的Web数据抽取方法(VDLE)。首先,引入视觉块重心偏移量定位数据区域,然后利用谱聚类算法定位数据区域内结构相似的节点簇,结合文本组织多样性对数据记录进行定位。实验结果表明,VDLE的抽取结果查准率为99%,比D-EEM提高8.51%,比VIDE查准率提高4.32%;VDLE的抽取结果查全率为98.75%,较D-EEM查全率提高13.33%,较ViDE查全率提高8.17%。在此基础上,研发了煤矿安全Web数据采集系统,现场实验结果表明,该系统采集的事故信息弥补完善了矿井安全信息储备,为矿井安全分析奠定了数据基础。

用WEBSITE构建Web服务器

WebSite1.1e作为一种Web服务器,具有简单易用、帮助文档完全、功能完善、对系统要求不高及完全免费的优点,是普通用户实现这些梦想的最好选择。WebSite是O'Reilly & Associates公司推出的Web产品,可以基于不同的操作系统。对一般用户而言,Win95使用最为广泛,本文主要介绍基于Win95的WebSite1.1e的安装及配置。

基于Web前端开发技术的黄河文化传播网站构建

黄河文化是中华文明的根与魂,展现着中华儿女的风骨与精神,是维系全国人民的精神纽带。目前我国各界对黄河文化的挖掘力度不足,致使黄河文化缺乏系统整合,文化传播效果有限。文章借助软件“HBuilder X”,基于Web前端开发技术,利用HTML、CSS制作网页,构建黄河文化传播网站,对黄河文化进行分层研究、系统整合,为黄河文化的传播提供了新的途径和平台。

基于Web挖掘技术的图书馆服务推荐算法分析

为实现精确化、实时化的图书馆图书信息推荐,保证图书馆的服务质量和效率,文中应用Web挖掘技术,设计了一种新型、先进的图书馆服务推荐算法。首先,系统论述了Web挖掘技术的概念、分类及流程。其次,从离线部分、在线部分两个方面入手,实现了图书馆服务推荐系统的框架设计。最后,在参照图书馆服务推荐系统框架的基础上,从建立推荐池、产生初步推荐集、融合离线部分产生个性化推荐集等方面入手,实现了对图书馆服务推荐算法的设计,为相关工作提供了有效的参考。

基于Web-Log Mining的Web文档聚类

速度和效果是聚类算法面临的两大问题.DBSCAN(density based spatial clustering of applications with noise)是典型的基于密度的一种聚类方法,对于大型数据库的聚类实验显示了它在速度上的优越性.提出了一种基于密度的递归聚类算法(recursive density based clustering algorithm,简称RDBC),此算法可以智能地、动态地修改其密度参数.RDBC是基于DBSCAN的一种改进算法,其运算复杂度和DBSCAN相同.通过在Web文档上的聚类实验,结果表明,RDBC不但保留了DBSCAN高速度的优点,而且聚类效果大大优于DBSCAN.

基于Web-Log Mining的N元预测模型

随着Web上用户访问信息的不断增加,特别是Web服务器可提供大量的日志文件,使得有可能对这些大数据集进行知识挖掘,例如,对用户未来的访问进行预测.提出了一种利用服务器日志文件,运用N元(N-gram)预测模型对用户未来可能进行的Web访问请求进行预测.这种模型会选择性地对用户可预测的请求进行预测,从而大大提高了预测精度.实验证明,在自然语言中普遍适用的N元预测模型同样适用于网页预测.同时,采用了一种有效的简化手段,大大压缩了模型的大小,使得5元模型和传统的2元模型大小基本相同,而预测精度提高了1倍.该结果可以广泛地运用到Web上,包括网页的预发送、预取、推荐以及Web上的caching机制.试验是建立在真实的Web日志上的,该算法无论在预测精度上还是在可适用度上都优于以往的算法.

基于Web Mining的智能化、个性化的远程教育模型研究

该文提出了一个新的基于Web Mining的远程教育模型，它能够充分利用站点上积累下来的信息，更好地用于远程教学。

基于Web-LogMining寻找目标网页最优期望定位

为了优化网站的访问效能,实现网站实际结构与用户的使用行为相吻合,该文主要应用Web挖掘技术,以网站的服务器Web日志作为数据源,使用算法FEL和算法CRLL从用户访问事务序列中寻找目标网页的期望定位,并以最少“后退”次数为原则生成推荐链接列表。网站设计者可根据该列表,修改网页之间的链接关系,达到减少对目标网页搜索时间的目的。