基于标题机器学习的网页分割方法

针对已有网页分割方法都基于文档对象模型实现且实现难度较高的问题,提出了一种采用字符串数据模型实现网页分割的新方法。该方法通过机器学习获取网页标题的特征,利用标题实现网页分割。首先,利用网页行块分布函数和网页标题标签学习得到网页标题特征;然后,基于标题将网页分割成内容块;最后,利用块深度对内容块进行合并,完成网页分割。理论分析与实验结果表明,该方法中的算法具有O(n)的时间复杂度和空间复杂度,该方法对于高校门户、博客日志和资源网站等类型的网页具有较好的分割效果,并且可以用于网页信息管理的多种应用中,具有良好的应用前景。     

基于文本和视觉特征融合的Web动画素材标注

为了提高对Web动画素材的组织、管理,该文提出了基于文本特征和视觉特征融合的Web动画素材标注算法。首先利用自动提取的Web动画素材上下文信息,结合Web动画素材名称、页面主题、URL以及ALT等属性组成特征集,提取出文本关键字;然后利用视觉与标注字之间的相关性,对自动提取的标注字进行过滤,实现Web动画素材的自动标注。实验表明该文提出的基于文本特征和视觉特征融合的Web动画素材标注算法可有效地应用于Web动画素材自动标注。     

基于视觉特征的网页信息抽取方法研究

面对大规模异构网页,基于视觉特征的网页信息抽取方法普遍存在通用性较差、抽取效率较低的问题。针对通用性较差的问题,该文提出了基于视觉特征的使用有监督机器学习的网页信息抽取框架WEMLVF。该框架具有良好的通用性,通过对论坛网站和新闻评论网站的信息抽取实验,验证了该框架的有效性。然后,针对视觉特征提取时间代价过高导致信息抽取效率较低的问题,该文使用WEMLVF,分别提出基于XPath和基于经典包装器归纳算法SoftMealy的自动生成信息抽取模板的方法。这两种方法使用视觉特征自动生成信息抽取模板,但模板的表达并不包含视觉特征,使得在使用模板进行信息抽取的过程中无需提取网页的视觉特征,从而既充分利用了视觉特征在信息抽取中的作用,又显著提升了信息抽取的效率,实验结果验证了这一结论。     

Extracting multiple news attributes based on visual features

The problem of automatically extracting multiple news attributes from news pages is studied in this paper. Most previous work on web news article extraction focuses only on content. To meet a growing demand for web data integration applications, more useful news attributes, such as title, publication date, author, etc., need to be extracted from news pages and stored in a structured way for further processing. An automatic unified approach to extract such attributes based on their visual features, including independent and dependent visual features, is proposed. Unlike conventional methods, such as extracting attributes separately or generating template-dependent wrappers, the basic idea of this approach is twofold. First, candidates for each news attribute are extracted from the page based on their independent visual features. Second, the true value of each attribute is identified from the candidates based on dependent visual features such as the layout relationships among the attributes. Extensive experiments with a large number of news pages show that the proposed approach is highly effective and efficient.     

Web page recommendation via twofold clustering: considering user behavior and topic relation

Web page recommendations have attracted increasing attention in recent years. Web page recommendation has different characteristics compared to the classical recommenders. For example, the recommender cannot simply use the user-item utility prediction method as e-commerce recommendation, which would face the repeated item cold-start problem. Recent researches generally classify the web page articles before recommending. But classification often requires manual labors, and the size of each category may be too large. Some studies propose to utilize clustering method to preprocess the web page corpus and achieve good results. But there are many differences between different clustering methods. For instance, some clustering methods are of high time complexity; in addition, some clustering methods rely on initial parameters by iterative computing whose results probably aren’t stable. In order to solve the above issues, we propose a web page recommendation based on twofold clustering by considering both effectiveness and efficiency, and take the popularity and freshness factors into account. In our proposed clustering, we combined the strong points of density-based clustering and the k-means clustering. The core idea is that we used the density-based clustering in sample data to get the number of clusters and the initial center of each cluster. The experimental results show that our method performs better diversity and accuracy compared to the state-of-the-art approaches.

     

Exploiting link structure for web page genre identification

As the World Wide Web develops at an unprecedented pace, identifying web page genre has recently attracted increasing attention because of its importance in web search. A common approach for identifying genre is to use textual features that can be extracted directly from a web page, that is, On-Page features. The extracted features are subsequently inputted into a machine learning algorithm that will perform classification. However, these approaches may be ineffective when the web page contains limited textual information (e.g., the page is full of images). In this study, we address genre identification of web pages under the aforementioned situation. We propose a framework that uses On-Page features while simultaneously considering information in neighboring pages, that is, the pages that are connected to the original page by backward and forward links. We first introduce a graph-based model called GenreSim, which selects an appropriate set of neighboring pages. We then construct a multiple classifier combination module that utilizes information from the selected neighboring pages and On-Page features to improve performance in genre identification. Experiments are conducted on well-known corpora, and favorable results indicate that our proposed framework is effective, particularly in identifying web pages with limited textual information.     

一种互联网新闻网页的采集分析方法

设计了一种采集分析互联网新闻网页的方法。该方法根据给定的新闻网站的入口地址在网络上找出所有的相关链接;区分这些链接所指向的页面特征,过滤掉相关性不大的内容,提取所有新闻网页的链接;进而进行多层次链接分析,根据新闻的图片、标题字体属性及日期,采用NewsPageRank算法计算每个新闻链接的权重。测试结果表明该方法对Internet上的新闻站点普遍具有较好的分析效果,性能可以满足实用要求。     

基于多特性融合的代词消解方法研究

互联网已成为一个海量的开放式知识库,其中包含着许多有价值的信息,而网页是互联网信息承载的载体,将 信息结构化成为知识库构建的基础。网页信息不仅包含许多指代词,还含有自身的标题。指代词消解是信息结构化的前提, 综合网页信息具有的一般性和特殊性的特点,本文提出基于多特性融合的代词消解方法研究,能更好地适应网页信息代词的 消解,提高网页信息代词消解的准确率。     

个性化网页编辑器的可视化设计与实现

通用的网页编辑工具忽视了不同用户在数据需求方面的个性差异，降低了网页编辑工具的使用效率，本文提出了一种基于可视化文档和数据库技术的个性化网页编辑器的新思路，通过对网页编辑器个性化特征的分析，介绍了这一编辑器的结构和功能。并给出利用Borland C Builder的VCL组件实现文档可视化设计的方法。     

基于多示例学习的中文Web目录页面推荐

多示例学习为中文Web挖掘提供了一种新的思路.提出中文Web目录页面推荐这种特殊的Web挖掘任务,并且将其转化为多示例学习问题来解决.在真实世界数据集上的实验结果显示,该方法能够有效地解决该问题.     

基于表格语义的Web信息抽取方法的研究

在Web页面常用到表格这种元素。本文提出一种根据表格语义来进行信息抽取方法。首先提出了一种短语语义相似度的度量方法,然后利用短语语义的相似度确定表格标题行(列),并对表格行(列)与抽取字段的对应关系进行计算,最后计算表格的整体语义,度量该表格与所要抽取的内容有多大相关度。     

A novel feature selection framework for automatic web page classification

The number of Internet users and the number of web pages being added to WWW increase dramatically every day.It is therefore required to automatically and e?ciently classify web pages into web directories.This helps the search engines to provide users with relevant and quick retrieval results.As web pages are represented by thousands of features,feature selection helps the web page classifiers to resolve this large scale dimensionality problem.This paper proposes a new feature selection method using Ward s minimum variance measure.This measure is first used to identify clusters of redundant features in a web page.In each cluster,the best representative features are retained and the others are eliminated.Removing such redundant features helps in minimizing the resource utilization during classification.The proposed method of feature selection is compared with other common feature selection methods.Experiments done on a benchmark data set,namely WebKB show that the proposed method performs better than most of the other feature selection methods in terms of reducing the number of features and the classifier modeling time.     

基于SolidWorks 的可视化定制研究

针对可视化定制系统中如何实时自动装配和交互浏览的问题,提出一种基于 SolidWorks 二次开发自动装配的方法。用户在网页上确定定制方案后,服务器端和与服务器有 固定联系的一台主机远程通讯,对SolidWorks 二次开发后的自动装配程序安装在此主机上,服 务器端远程启动主机上的自动装配程序,并接收装配完整的eDrawings 文件,将三维模型反馈 到网页上显示,用户可以在网页上实现虚拟交互浏览。该方法可以实现三维模型在多选择方案 下的实时自动组装和网页上的虚拟交互。     

多场景文本的细粒度命名实体识别

命名实体识别一直是数据挖掘领域的经典问题之一,尤其随着网络数据的剧增,如果能对多来源的文本数据进行多领域、细粒度的命名实体识别,显然能够为很多的数据挖掘应用提供支持。该文提出一种多领域、细粒度的命名实体识别方法,利用网络词典回标文本数据获得了大量的粗糙训练文本。为防止训练文本中的噪声干扰命名实体识别的结果,该算法将命名实体识别的过程划分为两个阶段,第一个阶段先获得命名实体的领域标签,之后利用命名实体的上下文确定命名实体的细粒度标签。实验结果显示,该文提出的方法使F₁值在全领域上平均值达到了80%左右。     

网页设计中的视觉传达

在网页的设计中,增强网页信息传达的有效性有很多途径,本论文从视觉传达和网页设计的基本理论入手,结合网页设计中视觉感受的心理学特征,总结出了网页设计中视觉设计的一般原则,并探讨了如何在网页设计运用视觉传达的方法,以取得更好的信息传达效果。     

Web News Extraction via Tag Path Feature Fusion Using DS Theory

Contents, layout styles, and parse structures of web news pages differ greatly from one page to another. In addition, the layout style and the parse structure of a web news page may change from time to time. For these reasons, how to design features with excellent extraction performances for massive and heterogeneous web news pages is a challenging issue. Our extensive case studies indicate that there is potential relevancy between web content layouts and their tag paths. Inspired by the observation, we design a series of tag path extraction features to extract web news. Because each feature has its own strength, we fuse all those features with the DS (Dempster-Shafer) evidence theory, and then design a content extraction method CEDS. Experimental results on both CleanEval datasets and web news pages selected randomly from well-known websites show that the F ₁-score with CEDS is 8.08% and 3.08% higher than existing popular content extraction methods CETR and CEPR-TPR respectively.     

一种基于集成学习的钓鱼网站检测方法

针对钓鱼攻击者常用的伪造HTTPS网站以及其他混淆技术,借鉴了目前主流基于机器学习以及规则匹配的检测钓鱼网站的方法RMLR和PhishDef,增加对网页文本关键字和网页子链接等信息进行特征提取的过程,提出了Nmap-RF分类方法。Nmap-RF是基于规则匹配和随机森林方法的集成钓鱼网站检测方法。根据网页协议对网站进行预过滤,若判定其为钓鱼网站则省略后续特征提取步骤。否则以文本关键字置信度,网页子链接置信度,钓鱼类词汇相似度以及网页PageRank作为关键特征,以常见URL、Whois、DNS信息和网页标签信息作为辅助特征,经过随机森林分类模型判断后给出最终的分类结果。实验证明,Nmap-RF集成方法可以在平均9~10 μs的时间内对钓鱼网页进行检测,且可以过滤掉98.4%的不合法页面,平均总精度可达99.6%。     

基于动态行为分析的网页木马检测方法

网页木马是一种在网页中插入攻击脚本,利用浏览器及其插件中的漏洞,使受害者的系统静默地下载并安装恶意程序的攻击形式.本文结合动态程序分析和机器学习方法,提出了基于动态行为分析的网页木马检测方法.首先,针对网页木马攻击中的着陆页上的攻击脚本获取行为,监控动态执行函数执行,包括动态生成函数执行、脚本插入、页面插入和URL跳转,并根据一套规则提取这些行为,此外提取与其相关的字符串操作记录作为特征.其次,针对利用堆恶意操作注入shellcode的行为,提出堆危险指标作为特征.最后从Alexa和VirusShare收集了500个网页样本作为数据集,用机器学习方法训练分类模型.实验结果表明：与现有方法相比,文中方法具有准确率高（96.94%）、能有效对抗代码混淆的干扰（较低的误报率6.1%和漏报率1.3%）等优点.     

PCA-based web page watermarking

The tamper-proof of web pages is of great importance. Some watermarking schemes have been reported to solve this problem. However, both these watermarking schemes and the traditional hash methods have a problem of increasing file size. In this paper, we propose a novel watermarking scheme for the tamper-proof of web pages, which is free of this embarrassment. For a web page, the proposed scheme generates watermarks based on the principal component analysis (PCA) technique. The watermarks are then embedded into the web page through the upper and lower cases of letters in HTML tags. When a watermarked web page is tampered, the extracted watermarks can detect the modifications to the web page, thus we can keep the tampered one from being published. Extensive experiments are performed on the proposed scheme and the results show that the proposed scheme can be a feasible and efficient tool for the tamper-proof of web pages.     

The Engagement of Website Initial Aesthetic Impressions: An Experimental Investigation

ABSTRACT

This study investigates how website design features, web page order and visual complexity, influence users’ initial website aesthetic impressions and how such impressions subsequently enhance engagement and intention to use the website. A laboratory experiment was conducted to test the hypotheses using different levels of web page order (high vs. low), visual complexity (high vs. low), and exposure time (one-second vs. no-time-constraint). Overall, the results from structural equation modeling (SEM) analysis suggest that web page order significantly influences visual appeal, engagement, and intention. In addition, the results of multigroup SEM analysis reveal that users evaluate website design very quickly (within 1 s), and that these evaluations remain remarkably consistent over time.