CloudWeb: A Cloud Based Webpage Transforming for Mobile Devices

<p>Web data extraction is the process of extracting user required information from web page. The information consists of semi-structured data not in structured format. The extraction data involves the web documents in html format. Nowadays, most people uses web data extractors because the extraction involve large information which makes the process of manual information extraction takes time and complicated. We present in this paper WEIDJ approach to extract images from the web, whose goal is to harvest images as object from template-based html pages. The WEIDJ (Web Extraction Image using DOM (Document Object Model) and JSON (JavaScript Object Notation)) applies DOM theory in order to build the structure and JSON as environment of programming. The extraction process leverages both the input of web address and the structure of extraction. Then, WEIDJ splits DOM tree into small subtrees and applies searching algorithm by visual blocks for each web page to find images. Our approach focus on three level of extraction; single web page, multiple web page and the whole web page. Extensive experiments on several biodiversity web pages has been done to show the comparison time performance between image extraction using DOM, JSON and WEIDJ for single web page. The experimental results advocate via our model, WEIDJ image extraction can be done fast and effectively.</p>

CHAPTER 1 - Cross-Site Scripting

Mobile devices have made the world a small place. They are also being used widely, to access the Web. However, because most available web pages are designed for desktop PC in mind, it is inconvenient to browse these large web pages on a mobile device with a small screen. With the enormous increase in mobile users since last decade, to such an extent that it even exceeds the number of desktop users in many developing countries, it becomes crucial to design systems for Web Manipulation, there by providing easier and greater accessibility and device independence. In this paper, we propose a new browsing technique to facilitate efficient web access and provide a better user experience on a device with rather small screen as compared to the usual desktop. It also proposes a novel technique to handle multimedia content, especially the images and video on a web page. In order to solve the problem of incomplete tags in HTML, an equivalent XML is generated for the desired webpage. For efficient browsing, the web page is represented as a multi level hierarchy, with the index of major sections on the first level and the sections indexed again in to sub-sections on the second level and so on, until we reach sub-sections that can be represented as sub-pages that fit in the small screen. We also propose an efficient algorithm for navigating through the sub-pages, thereby providing better user experience. The proposed technique provides better and efficient web browsing on mobile small displays up to user satisfaction.

As the amount of web pages increase, identifying and retrieving distinct contents from the web has increasingly become more and more difficult. The traditional approach for extracting data from web page documents is to analyze the DOM (Document Object Model) structure of a HTML page and find a common pattern. However, the number of possible DOM layout patterns is virtually infinite, which means that there is no common pattern that can be used for all kinds of web pages. In this paper, we focus on the pages that are linked to a search engine and aim to analyze the features of relevant and meaningful contents instead of a common pattern. Three features of relevant snippets are introduced. They are: quantity of text, correlation between snippet and query that is inputted into a search engine, and HTML structure. Nine parameters are used to describe the three features. Also, a SVM learning experiment is conducted to verify the effectiveness of the three features. The results show that the HTML structure feature is the most effective feature which can determine whether a snippet is relevant or not.

SummarySince it is common for the users of a web page to access it through a wide variety of devices—including desktops, laptops, tablets and phones—web developers rely on responsive web design (RWD) principles and frameworks to create sites that are useful on all devices. A correctly implemented responsive web page adjusts its layout according to the viewport width of the device in use, thereby ensuring that its design suitably features the content. Since the use of complex RWD frameworks often leads to web pages with hard‐to‐detect responsive layout failures (RLFs), developers employ testing tools that generate reports of potential RLFs. Since testing tools for responsive web pages, like ReDeCheck, analyse a web page representation called the Document Object Model (DOM), they may inadvertently flag concerns that are not human visible, thereby requiring developers to manually confirm and classify each potential RLF as a true positive (TP), false positive (FP), or non‐observable issue (NOI)—a process that is time consuming and error prone. The conference version of this paper presented Viser, a tool that automatically classified three types of RLFs reported by ReDeCheck. Since Viser was not designed to automatically confirm and classify two types of RLFs that ReDeCheck's DOM‐based analysis could surface, this paper introduces Verve, a tool that automatically classifies all RLF types reported by ReDeCheck. Along with manipulating the opacity of HTML elements in a web page, as does Viser, the Verve tool also uses histogram‐based image comparison to classify RLFs in web pages. Incorporating both the 25 web pages used in prior experiments and 20 new pages not previously considered, this paper's empirical study reveals that Verve's classification of all five types of RLFs frequently agrees with classifications produced manually by humans. The experiments also reveal that Verve took on average about 4 s to classify any of the RLFs among the 469 reported by ReDeCheck. Since this paper demonstrates that classifying an RLF as a TP, FP, or NOI with Verve, a publicly available tool, is less subjective and error prone than the same manual process done by a human web developer, we argue that it is well‐suited for supporting the testing of complex responsive web pages.

Web-Adapted Supervised Segmentation to Improve a New Tactile Vision Sensory Substitution (TVSS) Technology

Cellular phones are widely used to access the Web. However, most available Web pages are designed for desktop PCs, and it is inconvenient to browse these large Web pages on a cellular phone with a small screen and poor interfaces. Users who browse a Web page on a cellular phone have to scroll through the whole page to find the desired content, and must then search and scroll within that content in detail to get useful information. This paper describes the design and implementation of a novel Web browsing system for cellular phones. This system includes a Web page overview to reduce scrolling operations when finding objective content within the page. Furthermore, it adaptively presents content according to its characteristics to reduce burdensome operations when searching within content.

This work aims to provide a page segmentation algorithm which uses both visual and content information to extract the semantic structure of a web page. The visual information is utilized using the VIPS algorithm and the content information using a pre-trained Naive Bayes classifier. The output of the algorithm is a semantic structure tree whose leaves represent segments having unique topic. However contents of the leaf segments may possibly be physically distributed in the web page. This structure can be useful in many web applications like information retrieval, information extraction and automatic web page adaptation. This algorithm is expected to outperform other existing page segmentation algorithms since it utilizes both content and visual information.

Internet access using mobile handheld devices has surpassed that of fixed Internet, but most web pages are designed for desktop browsers. Mobile devices have resource limitations. Consequently, web pages designed for desktop browsing cannot be well displayed on mobile devices. Thus, dynamically transforming web contents designed for desktops into contents which can be displayed in a mobile device is an important task. Various approaches of content adaptation have been proposed to achieve this goal, but no single content adaptation strategy has been found to be effective. On the other hand, e-learning contents have grown manifold over years. In this paper, we provide a framework for an e-learning content adaptation system that can be used with desktops and mobile devices seamlessly. We combine multiple content adaptation strategies to make e-learning contents appropriate for mobile devices. Among the various content adaptation strategies, the segmentation algorithm plays the most important role. Our experiments demonstrate that the proposed scheme can be successfully utilized for content adaptation in mobile and handheld devices.

Web pages consist of different visual segments, serving different purposes. Typical structural segments are header, right or left columns and main content. Segments can also have nested structure which means some segments may include other segments. Understanding these segments is important in properly displaying web pages for small screen devices and in alternative forms such as audio for screen reader users. There exist different techniques in identifying visual segments in a web page. One successful approach is Vision Based Segmentation Algorithm (VIPS Algorithm) which uses both the underlying source code and also the visual rendering of a web page. However, there are some limitations of this approach and this paper explains how we have extended and improved VIPS and built it in Java. We have also conducted some online user evaluations to investigate how people perceive the success of the segmentation approach and in which granularity they prefer to see a web page segmented. This paper presents the preliminary results which show that, people perceive segmentation with higher granularity as better segmentation regardless of the web page complexity.

My PhD research aims to develop novel solutions to the challenge of identifying web page structure through the visual analysis of web pages as images. The intention is to then combine this back end design with various front end applications in order to provide improved web experiences for users with assistive needs (e.g. assisting visually impaired users by supporting more selective screenreader output, or improving experiences of users with cognitive deficits by allowing reduction of clutter or zooming in on selected web page content). I propose to build a comprehensive computer vision-based system to analyse the semantic structure of web pages based purely on an image of the rendered page, which will produce a rich representation of the page as a tree of regions labelled according to their semantic role. Most research into web page segmentation has focused on the use of the structure of the DOM tree and visual features derived from properties specified in the DOM tree. I argue, however, that the image of the rendered page may be a better representation to use, since it is created by the page designer to convey the structure of the page to the user, while the source code and DOM tree are simply intended to cause the browser's rendering engine to produce the correct appearance, and treat many types of content as black boxes. Additionally, my proposed system uses exactly the information seen by a user regardless of implementation method; this gives advantages in implementation-independence and versatility.

Today, the average consumer is comfortable browsing a web site using a desktop, laptop or kiosk computer. However, this is changing a rapid rate as more and more consumers access the same web content using a small screen device such as a mobile phone or personal digital assistant (PDA). Regardless of the consumers’ expertise with the mobile device, the experience is becoming frustrating as they try to navigate their way through web content that has been designed for a large screen; a design that results in cumbersome layout, slow-loading images and ‘off-screen’ navigation points when viewed on a small screen device. The result is dissatisfaction on part of the consumer, and possible loss of revenue for an e-business enterprise. The main objective of this paper is to highlight several techniques and technologies that are currently available that can assist in displaying ‘user-friendly’ web pages regardless of the screen size of the device.

Page segmentation is often one of the initial steps when performing data mining on a web page. In the past years, several methods of page segmentation have been developed that are based on visual perception of the web page. In this paper, we propose a generic method for improving efficiency of virtually all vision-based segmentation algorithms. Our method called cluster-based page segmentation takes the widely spread concept of web templates and utilises it for improving the efficiency of vision-based page segmentation by clustering web pages and performing the segmentation on the clusters instead of each page in the cluster. To prove the efficiency of our algorithm, we offer experimental results gathered using three different vision-based segmentation algorithms.

Text block segmentation plays an important role in image search systems.Web pages are segmented into blocks for getting text around the image, which will later be used in retrieval process based on user query keywords.This paper presents semantic based text block segmentation for image retrieval system.Text block segmentation is performed for image indexing using semantic relevance between text blocks.Before segmentation process, web page is broken down in DOM (document object model) tree structure, where tags are used as nodes in the tree.Relatedness between text blocks are computed based on semantic relevance between terms.Semantic relevance in this paper is computed based on the hierarchical structure of the words and their hierarchical information is obtained from Wordnet dictionary.Image search by semantic relevance hence improves the accuracy of the search process Index Terms-DOM tree, semantic relevance, text block segmentation.

Data on the web page can be available in various formats, such as table. With the growing of web pages, the need to extract data from tables is increasing. Results of the extraction can be used for integration with other web tables or stored in a database. This study discusses the extraction of data from a table on a web page using a Document Object Model (DOM) tree. The initial step of this extraction process is to transform the HTML document into a DOM tree. Then, by applying search methods Depth First Search (DFS), part of the data in the table is extracted and stored in a CSV file. An engine has been developed using Visual Basic. The results show that the engine can automatically extract data from the table that has the following characteristics: the number of rows and columns are not limited, able to handle all of the table orientation layout, and able to handle tables that are merged cells.