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Detecting phishing web pages is a challenging task. The existing detection method for phishing web page based on DOM (Document Object Model) is mainly aiming at obtaining structural characteristics but ignores the overall representation of web pages and the semantic information that HTML tags may have. This paper regards DOMs as a natural language with Doc2Vec model and learns the structural semantics automatically to detect phishing web pages. Firstly, the DOM structure of the obtained web page is parsed to construct the DOM tree, then the Doc2Vec model is used to vectorize the DOM tree, and to measure the semantic similarity in web pages by the distance between different DOM vectors. Finally, the hierarchical clustering method is used to implement clustering of web pages. Experiments show that the method proposed in the paper achieves higher recall and precision for phishing classification, compared to DOM-based structural clustering method and TF-IDF-based semantic clustering method. The result shows that using Paragraph Vector is effective on DOM in a linguistic approach.

Precise web page classification can be achieved by evaluating features of web pages, and the structural features of web pages are effective complements to their textual features. Various classifiers have different characteristics, and multiple classifiers can be combined to allow classifiers to complement one another. In this study, a web page classification method based on heterogeneous features and a combination of multiple classifiers is proposed. Different from computing the frequency of HTML tags, we exploit the tree-like structure of HTML tags to characterize the structural features of a web page. Heterogeneous textual features and the proposed tree-like structural features are converted into vectors and fused. Confidence is proposed here as a criterion to compare the classification results of different classifiers by calculating the classification accuracy of a set of samples. Multiple classifiers are combined based on confidence with different decision strategies, such as voting, confidence comparison, and direct output, to give the final classification results. Experimental results demonstrate that on the Amazon dataset, 7-web-genres dataset, and DMOZ dataset, the accuracies are increased to 94.2%, 95.4%, and 95.7%, respectively. The fusion of the textual features with the proposed structural features is a comprehensive approach, and the accuracy is higher than that when using only textual features. At the same time, the accuracy of the web page classification is improved by combining multiple classifiers, and is higher than those of the related web page classification algorithms.

Inform plays a very important role in life and nowadays, the world largely depends on the World Wide Web to obtain any information. Web comprises of a lot of websites of every discipline, whereas websites consists of web pages which are interlinked with each other with the help of hyperlinks. The success of a website largely depends on the design aspects of the web pages. Researchers have done a lot of work to appraise the web pages quantitatively. Keeping in mind the importance of the design aspects of a web page, this paper aims at the design of an automated evaluation tool which evaluate the aspects for any web page. The tool takes the HTML code of the web page as input, and then it extracts and checks the HTML tags for the uniformity. The tool comprises of normalized modules which quantify the measures of design aspects. For realization, the tool has been applied on four web pages of distinct sites and design aspects have been reported for comparison. The tool will have various advantages for web developers who can predict the design quality of web pages and enhance it before and after implementation of website without user interaction.

The explosive growth of the amount of information on Internet has made Web page classification essential for Web information management, retrieval, and integration, Web page indexing, topic-specific Web crawling, topic-specific information extraction models, advertisement removal, filtering out unwanted, futile, or harmful contents, and parental control systems. Owing to the recent staggering growth of performance and memory space in computing machines, along with specialization of machine learning models for text and image classification, many researchers have begun to target the Web page classification problem. Yet, automatic Web page classification remains at its early stages because of its complexity, diversity of Web pages’ contents (images of different sizes, text, hyperlinks, etc.), and its computational cost. This paper not only surveys the proposed methodologies in the literature, but also traces their evolution and portrays different perspectives toward this problem. Our study investigates the following: (a) metadata and contextual information surrounding the terms are mostly ignored in textual content classification, (b) the structure and distribution of text in HTML tags and hyperlinks are understudied in textual content classification, (c) measuring the effectives of features in distinguishing among Web page classes or measuring the contribution of each feature in the classification accuracy is a prominent research gap, (d) image classification methods rely heavily on computationally intensive and problem-specific analyses for feature extraction, (e) semi-supervised learning is understudied, despite its importance in Web page classification because of the massive amount of unlabeled Web pages and the high cost of labeling, (f) deep learning, convolutional and recurrent networks, and reinforcement learning remain underexplored but intriguing for Web page classification, and last but not least (g) developing a detailed testbed along with evaluation metrics and establishing standard benchmarks remain a gap in assessing Web page classifiers.

This study developed a web-based text editor to eliminate the incompatibility of computer keyboard with the three major indigenous languages in Nigeria. It also aims to reduce the time taken to produce characters with diacritical marks. The editors produced valid Unicode characters and require pressing less buttons to generating all the symbols of the alphabets for the three major indigenous languages in Nigeria. Client-side technologies were used to develop these applications. Three web pages, designated for Yorùbá, Igbo and Hausa language were generated with HTML. CSS was used to define the look and feel of the HTML elements on each page. Regular Expressions implemented in JavaScript functions were used to convert selected ASCII characters into desired Unicode characters. The editors are available at http://www.gazaliwakil.com.ng. The editors work well on latest version of browsers like (Google Chrome, Mozilla Firefox, Opera, and Internet Explorer). They are very light, consume minimal server resources and can work offline. The system was launched Fifty-one (51) times to extract data comprising the Loading, Scripting, Rendering, Painting, System, and Idle time. The obtained result showed that on the average, it takes about 13.77ms to load the HTML DOM elements, 42.83ms to load the javaScript, 13.10ms and 1.73ms for rendering and painting the page by CSS. Additional time taken are 43.91ms and 3,045.10ms for the system and idle time respectively. A total time of 3,160.43ms (3.16s) is required when any of the editors is launched before the page can accept inputs from the users. It also takes the editors 2.66ms to add diacritical marks on a letter. This would, in effect, not reduce the typing speed of users.

Web browsing has never been easy for blind people, primarily due to the serial press-and-listen interaction mode of screen readers -- their "go-to'' assistive technology. Even simple navigational browsing actions on a page require a multitude of shortcuts. Auto-suggesting the next browsing action has the potential to assist blind users in swiftly completing various tasks with minimal effort. Extant auto-suggest feature in web pages is limited to filling form fields; in this paper, we generalize it to any web screen-reading browsing action, e.g., navigation, selection, etc. Towards that, we introduce SuggestOmatic, a personalized and scalable unsupervised approach for predicting the most likely next browsing action of the user, and proactively suggesting it to the user so that the user can avoid pressing a lot of shortcuts to complete that action. SuggestOmatic rests on two key ideas. First, it exploits the user's Action History to identify and suggest a small set of browsing actions that will, with high likelihood, contain an action which the user will want to do next, and the chosen action is executed automatically. Second, the Action History is represented as an abstract temporal sequence of operations over semantic web entities called Logical Segments - a collection of related HTML elements, e.g., widgets, search results, menus, forms, etc.; this semantics-based abstract representation of browsing actions in the Action History makes SuggestOmatic scalable across websites, i.e., actions recorded in one website can be used to make suggestions for other similar websites. We also describe an interface that uses an off-the-shelf physical Dial as an input device that enables SuggestOmatic to work with any screen reader. The results of a user study with 12 blind participants indicate that SuggestOmatic can significantly reduce the browsing task times by as much as 29% when compared with a hand-crafted macro-based web automation solution.

Hidden web contains huge amount of high quality data which are not indexed to search engines. Hidden web refers to web pages which are generated dynamically by embedding backend data matching the search keywords, in server-side templates. They are created for human consumption and makes automated processing cumbersome since structured data is embedded within unstructured HTML tags. In order to enable machine processing, structured data must be detected, extracted and annotated. Many heuristic based approaches DeLa [1], MSAA [2] are available in the literature to perform automatic annotation. Most of these techniques fail if data values didn't contain labels present as part of the attribute value itself or if it is not available explicitly as part of the form interface or query response pages. The proposed technique addresses this issue by collecting domain keywords from multiple websites belonging to the business domain of interest and then, it captures the pattern in the form of semantic rules. Experimental results show that single heuristics is not sufficient to label all the data value groups. The annotators are applied one after the other according to their capability of assigning the most appropriate label. Experiments show that this technique has improved the precision and recall values compared to the existing annotation techniques. Keywords: Annotation, Semantic labeling, Deep web, Surface web

Navigating back-and-forth between segments in webpages is well-known to be an arduous endeavor for blind screen-reader users, due to the serial nature of content navigation coupled with the inconsistent usage of accessibility enhancing features such as WAI-ARIA landmarks and skip navigation links by web developers. Without these supporting features, navigating modern webpages that typically contain thousands of HTML elements in their DOMs, is both tedious and cumbersome for blind screen-reader users. Existing approaches to improve non-visual navigation efficiency typically propose ‘one-size-fits-all’ solutions that do not accommodate the personal needs and preferences of screen-reader users. To fill this void, in this paper, we present sTag, a browser extension embodying a semi-automatic method that enables users to easily create their own Table Of Contents (TOC) for any webpage by simply ‘tagging’ their preferred ‘semantically-meaningful’ segments (e.g., search results, filter options, forms, menus, etc.) while navigating the webpage. This way, all subsequent accesses to these segments can be made via the generated TOC that is made instantly accessible via a special shortcut or a repurposed mouse/touchpad action. As tags in sTag are attached to the abstract semantic segments instead of actual DOM nodes in the webpage, sTag can automatically generate equivalent TOCs for other similar webpages, without requiring the users to duplicate their tagging efforts from scratch in these webpages. An evaluation with 15 blind screen-reader users revealed that sTag significantly reduced the content-navigation time and effort compared to those with a state-of-the-art solution.

Since 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.

Making electronic gadgets that meet today's consumer standards has become a difficult task. Electronic gadgets are expected to have displays with visually appealing interfaces and, at the same time, be physically and screen-interactive, making the development process of these devices time-consuming and challenging. To address this problem, we have created Appliancizer, an online synthesis tool that can automatically generate sophisticated electronic devices from web pages. Appliancizer takes advantage of the similarities between software and physical interfaces to reduce development steps and allow the rapid development of electronic devices. By matching the interface of hardware components with the interface of graphical HTML elements found on web pages, our tool allows a designer to transform HTML elements from a digital to a tangible interface without changing the application source code. Finally, a modular design enables our tool to automatically combine the circuit design and low-level hardware code of selected hardware components into a complete design. Attendees can interact with our online tool and produce manufacturable PCBs from web pages.

This paper proposes an automatic scheme of web data extraction for live streaming platforms. Based on the similar desired blocks on web pages and corresponding HTML tag structures, the proposed scheme can automatically identify the desired blocks as well as desired content without any visual identification or computational training. The proposed scheme is a general data mining solution for diverse live streaming platforms. Furthermore, it adapts to a certain level of revision on web pages by means of updating its data-extracting parameters.

The article presents an analysis of neural networks that can be used to generating web page program code from input image. As part of the research of the possibilities of modification of algorithms for generating code, the method of generating code simultaneous using the generative adversarial and recurrent neural networks based on the input image is proposed. Methods for creating source code using generative adversarial network (GAN) and recurrent neural networks (RNN) are analyzed. Using domain-specific language (DSL) for tree generation code method, it was possible to use GAN and RNN together, achieving on the average 10% productivity increase. Using GRID’s mesh, the generated code is adaptable. To implement the proposed method, software with a graphical user interface is created. Classes of GAN and RNN were considered, among which the feasibility of using a neural network with long-term short-term memory (LSTM) for analysis and forecasting of tags, blocks and HTML elements of the language in the input image was selected and substantiated. The stages of the research are presented. For the experiment datasets made on the basis of existing sites from open sources for 2018–2019 are used. The estimation of the effectiveness of the proposed method is described.

To interact with webpages, people who are blind use special-purpose assistive technology, namely screen readers that enable them to serially navigate and listen to the content using keyboard shortcuts. Although screen readers support a multitude of shortcuts for navigating over a variety of HTML tags, it has been observed that blind users typically rely on only a fraction of these shortcuts according to their personal preferences and knowledge. Thus, a mismatch between a user's repertoire of shortcuts and a webpage markup can significantly increase browsing effort even for simple everyday web tasks. Also, inconsistent usage of ARIA coupled with the increased adoption of styling and semantic HTML tags (e.g., , ) for which there is limited screen-reader support, further make interaction arduous and frustrating for blind users. To address these issues, in this work, we explore personalized annotation of webpages that enables blind users to efficiently navigate webpages using their preferred shortcuts. Specifically, our approach automatically injects personalized 'annotation' nodes into the existing HTML DOM such that blind users can quickly access certain semantically-meaningful segments (e.g., menu, search results, filter options, calendar widget, etc.) on the page, using their preferred screen-reader shortcuts. Using real shortcut profiles collected from 5 blind screen-reader users doing representative web tasks, we observed that with personalized annotation, the interaction effort can be potentially reduced by as much as 48 (average) shortcut presses.

According to the website quality of the organization dimension and website usability, the consistency of the layout of the web page is how the general layout elements format is common to all pages. Consistency can make it easier for users in terms of navigation. In this study, researchers used a case study on the East Kalimantan High Prosecutors’ website (http://www.kejati-kaltim.go.id/). The inequality location of the layout makes inconsistency. In this research, web page layout structure data was collected using a Chrome Extension called HTML Tree Generator by adding additional functions according to semantic elements, so that automatically downloads HTML elements in the Document Object Model (DOM). Then, the general layout structure generator only took the semantic elements from the most common layout as its input. Matching Algorithm used to solve the problem of inconsistency in general layout structure, by looking for differences of structure using set difference. Combination functions are used to determine the comparison of each page. Set difference produce “false” if there are one or more differences. Research on the consistency of web page layout structure from case studies yields a consistency score of 92.1%. This score indicates an inconsistency in the web page layout. However, after fixed inconsistent pages, the recalculation score can produce a consistent web page layout.

“Web development encompasses many stages, which involve designing the website done by making a wireframe first, after which the code for the frontend and backend is appropriately written over many iterations until the developers arrive at a fully functional and satisfying website for their need. We see how every website starts with the basic idea and structure of the website that can be described using a wireframe. It shows the basic elements and gives developers an idea about the structure of the website. It is given to the developers to create the boilerplate code that places all the elements into place accordingly. The task of converting the wireframe to HTML code is tedious and time-consuming. At present, the boilerplate code is mostly written manually. Currently, the users have to write the HTML code for structuring the elements on the webpage, which leads to a lot of redundant work and users investing their precious time into it. Generally, where the structure of web pages is the same, users would tend to maintain a copy of the pre-existing boilerplate code for reusability. In most cases, the boilerplate code differs for web pages despite the code for the HTML elements being the same. In such situations, the work becomes redundant. However, this process can be automated to ease the world of the web developer community. To make this process effortless, we propose a machine learning model. It will be trained to identify specific symbols and shapes as elements, recognize texts from the wireframe. The model will take in wireframe images as input through the web application. The idea is to process input, recognize each element in the wireframe using the Open Source Computer vision library (OpenCV). Identified elements will each have a corresponding code in the backend. Once the elements on the wireframe are identified, the corresponding code is written into an HTML file. The user gets the HTML file as an output. The creation of the boilerplate code for a website becomes much less time-consuming, which gives developers the freedom to test out many different designs and layouts before opting for the one that best suits their needs.”

HTML tags are typically discarded in free text Named Entity Recognition from Web pages. We investigate whether these discarded tags might be used to improve NER performance. We compare Text+Tags sentences with their Text-Only equivalents, over five datasets, two free text segmentation granularities and two NER models. We find an increased F1 performance for Text+Tags of between 0.9% and 13.2% over all datasets, variants and models. This performance increase, over datasets of varying entity types, HTML density and construction quality, indicates our method is flexible and adaptable. These findings imply that a similar technique might be of use in other Web-aware NLP tasks, including the enrichment of deep language models.

Web scraping is a process of extracting valuable and interesting text information from web pages. Most of the current studies targeting this task are mostly about automated web data extraction. In the extraction process, these studies first create a DOM tree and then access the necessary data through this tree. The construction process of this tree increases the time cost depending on the data structure of the DOM Tree. In the current web scraping literature, it is observed that time efficiency is ignored. This study proposes a novel approach, namely UzunExt, which extracts content quickly using the string methods and additional information without creating a DOM Tree. The string methods consist of the following consecutive steps: searching for a given pattern, then calculating the number of closing HTML elements for this pattern, and finally extracting content for the pattern. In the crawling process, our approach collects the additional information, including the starting position for enhancing the searching process, the number of inner tag for improving the extraction process, and tag repetition for terminating the extraction process. The string methods of this novel approach are about 60 times faster than extracting with the DOM-based method. Moreover, using these additional information improves extraction time by 2.35 times compared to using only the string methods. Furthermore, this approach can easily be adapted to other DOM-based studies/parsers in this task to enhance their time efficiencies. © 2013 IEEE.

The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, to information retrieval or natural language processing. Existing approaches are lacking as they rely on large amounts of hand-crafted features for classification. This results in models that are tailored to a specific distribution of web pages, e.g. from a certain time frame, but lack in generalization power. We propose a neural sequence labeling model that does not rely on any hand-crafted features but takes only the HTML tags and words that appear in a web page as input. This allows us to present a browser extension which highlights the content of arbitrary web pages directly within the browser using our model. In addition, we create a new, more current dataset to show that our model is able to adapt to changes in the structure of web pages and outperform the state-of-the-art model., Comment: WWW20 Demo paper

A markup language (like HTML) is better suited for building a view. It is declarative and easy to read. As we build web pages in an Angular application, views are built with HTML. Data and logic stay in the TypeScript classes. Traditionally, the JavaScript API (and JQuery) allows you to query and select an HTML element. The JavaScript code might read the value from the selected element or set a value to it. This becomes error prone and convoluted as the application grows in size and complexity.

Extracting web content is to obtain the required data embedded in web pages, usually including structured records, such as product information, and text content, such as news. Web pages use a large number of HTML tags to organize and to present various information. Both knowing little about the structures of web pages and mixing kinds of information in web pages are making the extraction process very challenging to guarantee extraction performance and extraction adaptability. This study proposes a unified web content extraction framework that can be applied in various web environments to extract both structured records and text content. First, we construct a characteristic container to hold kinds of characteristics related with extraction objectives, including visual text information, content semantics(instead of HTML tag semantics), web page structures, etc. Second, the above characteristics are integrated into an extraction framework for extraction decisions on different web sites. Especially, we put forward different strategies, path aggregation for extracting text content and HMM model for structured records, to locate the extraction area by exploiting both those extraction characteristics. Comparative experiments on multiple web sites with popular extraction methods, including CETR, CETD and CNBE, show that our proposed extraction method can provide better extraction precision and extraction adaptability.

The aim of this study is to propose an information extraction system, called BigGrams, which is able to retrieve relevant and structural information (relevant phrases, keywords) from semi-structural web pages, i.e. HTML documents. For this purpose, a novel semi-supervised wrappers induction algorithm has been developed and embedded in the BigGrams system. The wrappers induction algorithm utilizes a formal concept analysis to induce information extraction patterns. Also, in this article, the author (1) presents the impact of the configuration of the information extraction system components on information extraction results and (2) tests the boosting mode of this system. Based on empirical research, the author established that the proposed taxonomy of seeds and the HTML tags level analysis, with appropriate pre-processing, improve information extraction results. Also, the boosting mode works well when certain requirements are met, i.e. when well-diversified input data are ensured.

The selection of a suitable document representation approach plays a crucial role in the performance of a document clustering task. Being able to pick out representative words within a document can lead to substantial improvements in document clustering. In the case of web documents, the HTML markup that defines the layout of the content provides additional structural information that can be further exploited to identify representative words. In this paper we introduce a fuzzy term weighing approach that makes the most of the HTML structure for document clustering. We set forth and build on the hypothesis that a good representation can take advantage of how humans skim through documents to extract the most representative words. The authors of web pages make use of HTML tags to convey the most important message of a web page through page elements that attract the readers' attention, such as page titles or emphasized elements. We define a set of criteria to exploit the information provided by these page elements, and introduce a fuzzy combination of these criteria that we evaluate within the context of a web page clustering task. Our proposed approach, called Abstract Fuzzy Combination of Criteria (AFCC), can adapt to datasets whose features are distributed differently, achieving good results compared to other similar fuzzy logic based approaches and TF-IDF across different datasets., This is the accepted version of an article accepted for publication in IEEE Transactions on Fuzzy Systems

To automatically extract data records from Web pages, the data record extraction algorithm is required to be robust and efficient. However, most of existing algorithms are not robust enough to cope with rich information or noisy data. In this paper, we propose a novel suffix tree-based extraction method (STEM) for this challenging task. First, we extract a sequence of identifiers from the tag paths of Web pages. Then, a suffix tree is built on top of this sequence and four refining filters are proposed to screen out data regions that might not contain data records. To evaluate model performance, we define an evaluation metric called pattern similarity and perform rigorous experiments on five real data sets. The promising experimental results have demonstrated that the proposed STEM is superior to the state-of-the-art algorithms like MDR, TPC and CTVS with respect to precision, recall and pattern similarity. Moreover, the time complexity of STEM is linear to the total number of HTML tags contained in Web pages, which indicates the potential applicability of STEM in a wide range of Web-scale data record extraction applications.

In recent years, data on the Internet has grown exponentially, attaining enormous dimensions. This situation makes it difficult to obtain useful information from such data. Web mining is the process of using data mining techniques such as association rules, classification, clustering, and statistics to discover and extract information from Web documents. Optimization algorithms play an important role in such techniques. In this work, the parliamentary optimization algorithm (POA), which is one of the latest social-based metaheuristic algorithms, has been adopted for Web page classification. Two different data sets (Course and Student) were selected for experimental evaluation, and HTML tags were used as features. The data sets were tested using different classification algorithms implemented in WEKA, and the results were compared with those of the POA. The POA was found to yield promising results compared to the other algorithms. This study is the first to propose the POA for effective Web page classification.

With the number of display technologies, cameras, operating systems and software solutions, one of the only technologies that is compatible across this diversity is the web browser. We propose to show that the technologies now present in web browsers allow an indepent management of the color information on large variety of devices. For this purpose we introduce the basic concepts of color management and then we show how to implement them with WebAssembly and WebGL by introducing the concept of WebCMM. A WebCMM adapted for the color management of HTML elements in 2D, 3D but also for virtual environments. Finally, we present how we can implement this WebCMM for a real case of color workflow implemented in a demonstrator web page.

Tento článek popisuje proces sbírání a tvorby anglického datasetu pro trénování algoritmů pro automatickou extrakci informací z diskuzních fór. Dataset se skládá z ručně označených příspěvků skládajících se z jména uživatele, data publikování, textu příspěvku a citací/reakcí. Dataset obsahuje 79 rozdílných webových serverů s minimálně 500 stránek z každého. Každá webová stránka se skládá z struktury HTML tagů. Také jsou popsány prvotní experimenty se základními architekturami neuronových sítí a SVM. This paper describes the process of collecting, maintaining and exploiting an English dataset of web discussions. The dataset consists of many web discussions with hand-annotated posts in the context of a tree structure of a web page. Each post consists of username, date, text, and citations used by its author. The dataset contains 79 different websites with at least 500 pages from each. Each web page consists of a tree structure of HTML tags with texts taken from selected web pages. In the paper, we also describe algorithms trained on the dataset. The algorithms employ basic architectures (such as a bag of words with an SVM classifier and an LSTM network) to set a baseline for the dataset.

Keyphrase extraction is an important part of natural language processing (NLP) research, although little research is done in the domain of web pages. The World Wide Web contains billions of pages that are potentially interesting for various NLP tasks, yet it remains largely untouched in scientific research. Current research is often only applied to clean corpora such as abstracts and articles from academic journals or sets of scraped texts from a single domain. However, textual data from web pages differ from normal text documents, as it is structured using HTML elements and often consists of many small fragments. These elements are furthermore used in a highly inconsistent manner and are likely to contain noise. We evaluated the keyphrases extracted by several state-of-the-art extraction methods and found that they did not transfer well to web pages. We therefore propose WebEmbedRank, an adaptation of a recently proposed extraction method that can make use of structural information in web pages in a robust manner. We compared this novel method to other baselines and state-of-the-art methods using a manually annotated dataset and found that WebEmbedRank achieved significant improvements over existing extraction methods on web pages.

Web developers face the stringent task of designing informative web pages while keeping the page-load time low. This task has become increasingly challenging as most web contents are now generated by processing ever-growing amount of user data stored in back-end databases. It is difficult for developers to understand the cost of generating every web-page element, not to mention explore and pick the web design with the best trade-off between performance and functionality. In this paper, we present Panorama, a view-centric and database-aware development environment for web developers. Using database-aware program analysis and novel IDE design, Panorama provides developers with intuitive information about the cost and the performance-enhancing opportunities behind every HTML element, as well as suggesting various global code refactorings that enable developers to easily explore a wide spectrum of performance and functionality trade-offs.

Webpage retrieval is the process of obtaining and presenting more related webpage from the largest collection of webpage resources according to the user’s need. The tremendous growth in information resources on the Internet makes the information retrieval process a tedious and difficult task for users. Due to information overloading, there is a need for better techniques to retrieve most relevant information from web. This paper presents the webpage retrieval system by using the PSO algorithm. In presented system, to extract the text from web documents, all html tags are removed. After that stop words and special characters are removed from extracted text for recovering only meaningful contents. The TF-IDF concept is used for feature selection. Now PSO optimization technique is used for identifying and refining the features set, these selected features are stored in a database which is used for webpage retrieval process. On the other side of the system, in search technique firstly the user query sequences are transformed into multiple query strings and using these query strings the search is performed over the database. The performance of the search system is computed in terms of accuracy and error rate. The results are also compared with the traditional search model which demonstrate the proposed technique is superior then the traditional search system.

In this paper, HTML tags and attributes are used to determine different structural position of text in a web page. Tags- attributes based models are used to assign a weight to a text that exist in different structural position of web page. Genetic algorithms (GAs), harmony search (HS), and particle swarm optimization (PSO) algorithms are used to select the informative terms using a novel tags-attributes and term frequency weighting scheme. These informative terms with heuristic weight give emphasis to important terms, qualifying how well they semantically explain a webpage and distinguish them from each other. The proposed approach is developed by customizing Terrier and tested over the Clueweb09B, WT10g, .GOV2 and uncontrolled data collections. The performance of the proposed approach is found to be encouraging against five baseline ranking models. The percentage gain of approach achieved is 75-90%, 70-83% and 43-60% in P@5, P@10 and MAP, respectively.

Extracting the user reviews in websites such as forums, blogs, newspapers, commerce, trips, etc. is crucial for text processing applications (e.g. sentiment analysis, trend detection/monitoring and recommendation systems) which are needed to deal with structured data. Traditional algorithms have three processes consisting of Document Object Model (DOM) tree creation, extraction of features obtained from this tree and machine learning. However, these algorithms increase time complexity of extraction process. This study proposes a novel algorithm that involves two complementary stages. The first stage determines which HTML tags correspond to review layout for a web domain by using the DOM tree as well as its features and decision tree learning. The second stage extracts review layout for web pages in a web domain using the found tags obtained from the first stage. This stage is more time-efficient, being approximately 21 times faster compared to the first stage. Moreover, it achieves a relatively high accuracy of 96.67% in our experiments of review block extraction.

Responsive Snippets is the accompanying software of my recent work on responsive text summarization, an approach to web design aimed at allowing desktop web pages to be read in response to the size of the device a user is browsing with. Responsive Snippets allow designers to create HTML summaries quickly and effortlessly, by simply using CSS selectors and media queries. Responsive Snippets can be especially useful for news and blog sites, as a means to quickly allow users get a glimpse of the main body content, although they can be applied to any kind of HTML elements having text. The software is publicly available, so that others can build upon this work.

Content extraction is the process that aims to separate the main content of web pages from the bulk of template and decorative components. We present a method of doing this which achieves competitive performance on the Cleaneval dataset and sets a new state-of-the-art with an F1 score of 0.96 on the Dragnet dataset. We accomplish this by modeling the task as a classification problem over HTML tags using features based on information from the DOM tree. Not only do we obtain a performance increase over current methods, but we do so with minimal feature engineering and without the extensive preprocessing steps of other methods.

As the Web is a large collection of data growing daily, an automatic Web page classification mechanism is needed to effectively reach to useful information. Majority of the Web pages are in the form of HTML documents, therefore the aim of this study is to explore the effect of HTML tags on classification process, and try to determine the most valuable HTML tags for feature extraction of the classification task. To achieve this goal, we employ 13 different datasets, and use 5 popular classifiers that are SVM, naïve bayes (NB), kNN, C4.5, and OneR. The statistical analysis shows that, the features extracted by using solely the anchor, or tags can be used as an alternative to the features extracted from the whole Web page. SVM is the best among the classifiers used in this study. Using the HTML tags for feature extraction improves classification accuracy.

Growing popularity and diversity of genomic data demand portable and versatile genome browsers. Here, we present an open source programming library called GIVE that facilitates the creation of personalized genome browsers without requiring a system administrator. By inserting HTML tags, one can add to a personal webpage interactive visualization of multiple types of genomics data, including genome annotation, “linear” quantitative data, and genome interaction data. GIVE includes a graphical interface called HUG (HTML Universal Generator) that automatically generates HTML code for displaying user chosen data, which can be copy-pasted into user’s personal website or saved and shared with collaborators. GIVE is available at: https://www.givengine.org/. Electronic supplementary material The online version of this article (10.1186/s13059-018-1465-6) contains supplementary material, which is available to authorized users.

Internationalization enables companies to reach a global audience by adapting their websites to locale specific language and content. However, such translations can often introduce Internationalization Presentation Failures (IPFs) — distortions in the intended appearance of a website. It is challenging for developers to design websites that can inherently adapt to varying lengths of text from different languages. Debugging and repairing IPFs is complicated by the large number of HTML elements and CSS properties that define a web page's appearance. Tool support is also limited as existing techniques can only detect IPFs, with the repair remaining a labor intensive manual task. To address this problem, we propose a search-based technique for automatically repairing IPFs in web applications. Our empirical evaluation showed that our approach was able to successfully resolve 98% of the reported IPFs for 23 real-world web pages. In a user study, participants rated the visual quality of our fixes significantly higher than the unfixed versions.

Skeleton is an intuitive framework for lightweight projects. It is extremely lightweight with a handful of HTML elements and was developed with a mobile-first philosophy. In this chapter, you will learn how to install Skeleton. You will also learn about its grid system and attributes, and then you’ll build a landing web page with Skeleton.

Background: Identification of differentially expressed genes from gene expression profiling studies is a necessary and important step in the analysis of microarray and RNA-seq data. Such data is deposited into the Gene Expression Omnibus (GEO). However, currently, to extract and analyze differentially expressed genes from GEO requires significant computational expertise. Methodology: GEO2Enrichr has a front-end browser extension and back-end server application. The back-end is written in Python and uses the Flask framework for the web application server. The server exposes three API endpoints to: (1) query GEO; (2) identify the differentially expressed genes; and (3) query Enrichr. The API endpoints are chained into a useful data pipeline. The front-end is a Google Chrome Extension written in JavaScript and CSS and programmatically inserts HTML elements into the native GEO interface. The Chrome web browser loads the extension whenever the user navigates to an NCBI GEO webpage. GEO2Enrichr unobtrusively inserts itself onto the page as a single button and checkboxes for easy sample selection. The button opens a modal box that allows users to edit their selected data, choose settings for differential expression identification, insert metadata about the experiments, download their resultant gene lists, and submit their gene lists to Enrichr for further analysis. Differential expression analysis has five options: Characteristic Direction1, T-test, limma, SAM, and fold-change. Other options are available to control for the size of the resultant differentially expressed gene lists by setting p-value and FDR cutoffs. Results: GEO2Enrichr is a Google Chrome Extension and Python-based API that adds functionality to GEO by allowing users to pipe Simple Omnibus Format in Text (SOFT) files to a differential expression analysis tool and then pipe the differentially expressed genes for analysis to Enrichr2, a popular gene list enrichment analysis web application. GEO2Enrichr addresses a need for improved functionality of GEO by embedding new features into the existing GEO pages, allowing researchers to easily select samples and process them for differential expression by various methods and perform enrichment analyses. GEO2Enrichr is free and available for installation at the Chrome Web Store. Conclusions: GEO2Enrichr can facilitate the more broad reusability of the GEO resource by lowering the point of entry to biologists without computational expertise. The systematic use of GEO2Enrichr can generate a new useful searchable resource. Overall, GEO2Enrichr can lead to improved extraction of knowledge from data3. [1] http://maayanlab.net/CD [2] http://amp.pharm.mssm.edu/Enrichr [3] http://bd2k.nih.gov Citation Format: Gregory W. Gundersen, Matthew R. Jones, Avi Ma'ayan. GEO2Enrichr: A Google Chrome extension to extract gene sets from the Gene Expression Omnibus and analyze these lists for common biological functions. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-28.

A Web database typically responds to a query with a Web page, which encodes the query results into semi-structured data objects using HTML tags. We call such data objects Web data records or data records. Mining Web data records is very important for many applications, e.g., meta search, comparative shopping, etc. This paper proposes a new effective approach called AutoRM, which mines data records from single Web page automatically. AutoRM involves three major steps: (1) constructing the DOM tree of the given Web page; (2) mining all sets of adjacent similar C-Records (Candidate data Records) from the constructed DOM tree; (3) mining actual data records from C-Records. In many Web pages, similar data records are distributed in bigger and adjacent similar objects. Existing approaches typically identify such objects as data records. Conversely, AutoRM views such objects as C-Records, and mines actual data records from them. One key issue for mining similar data records is the boundary detection of each data record. Existing approaches typically make some brittle assumptions for handling this issue. By making more robust assumptions, AutoRM tends to detect data record boundaries more accurately. Experimental results show that AutoRM is highly effective, and outperforms state-of-the-art approaches.

Phishing is an internet attack that aims to get users sensitive information by fraud websites. Website phishing is one of the major attacks by which most of internet users are being fooled by the phisher. The best way to protect from phishing is to recognize a phish. Phishing emails usually appear to come from well-known organization and ask your personal information such as credit card number, security number, account number or passwords. What actually attacker does? The attacker creates the no of replicas of authenticate sites, and users are forced to direct to that websites by attracting them with offers. As standard mentioned in W3C (World Wide Web Consortium), I am proposing a system which can easily recognize the difference between authenticate site and phishing site. There are certain standards which are given by W3C (World Wide Web Consortium), based on these standards I am choosing some features which can easily describe the difference between legit site and phish site. To protect you from phishing, I am proposing a model to determine the fraud sites. To determine the phishing attack, URL features and HTML features of web page are considered. Clustering algorithm such as K-Means clustering is applied on the database and prediction techniques such as Naive Bayes Classifier is applied. By applying this, probability of the web site as valid Phish or Invalid Phish. To check the validity of URL,if still we are not able decide the validity of web page then Naive Bayes Classifier is applied . also training model is applied for the extraction of HTML tag features of site and probability. Index Terms Anti Phishing Technique, Bayesian Approach, Data Mining, Database Clustering, and Phishing Attack.

A web page is a digital document created using Hyper Text Markup Language (HTML). A Web page is suitable for World Wide Web (WWW), which runs on a web browser and hence can be accessed across the globe. Graphical User Interface (GUI) is an interface, which provides ways for humans to interact with the system using HTML controls. In software design, look and feel of GUI comprises design aspects of the web page, the layout of the web page, HTML controls used on the web page, etc. Usually creation of a web page begins by drawing GUI design on a paper first. Web designers then design the web pages using web development tool on the paper. This paper proposes a novel approach to create HTML page automatically from the GUI design drawn on the paper. It considers a GUI design having various HTML controls such as radio button, checkbox, textbox, command button and label. A scanned image of GUI design is provided as an input to the system. Then, system segments the various HTML controls. The segmented HTML controls are then identified. Identified HTML controls are stored in XML database. Actually, XML database contains the name and position of the component on the GUI design. Finally, this XML file is parsed to generate HTML page.

Web pages not only contain main content, but also other elements such as navigation panels, advertisements and links to related documents. To ensure the high quality of web page, a good boilerplate removal algorithm is needed to extract only the relevant cfrom web page. Main textual contents are just included in HTML source code which makes up the files. The goal of content extraction or boilerplate detection is to separate the main content from navigation chrome, advertising blocks, and copyright n otices in web pages. The system removes boilerplate and extracts main content. In this system, there are two phases: Feature Extraction phase and Clustering phase. The system classifies the noise or content from HTML web page. Content Extraction algorithmdescribes to get high performance without parsing DOM trees. After observation the HTML tags, one line may not contain a piece of complete information and long texts are distributed in close lines, this system usesLine-Block concept to determine the distance of any two neighbor lines with text and Feature Extraction such as text-to-tag ratio (TTR), anchor text-to-text ratio (ATTR) and new content feature as Title Keywords Density (TKD) classifies noise or content. After extracting the features, the system uses these features as parameters in threshold method to classify the block are content or non- content.

Change in content of web documents is a constant process and this rate of change is different for different pages. This change must be updated at the search engine database else a user gets a superseded image of the web documents. Many methods for change detection have been developed that use tree based comparisons to decide whether two versions of a web document are same or not. But these methods are prone to high complexity and ambiguity. Also frequent crawler revisits results in increased pressure on Internet traffic and bandwidth usage. In this paper network efficient web page change detection technique for migrating crawlers is being proposed that effectively detects structural and content changes by comparing proposed tag and text code for each of the html tags contained in the web page. The proposed method performs well and is able to detect changes even at minute level while keeping the network load low.

The study of the similarity between web applications has extended alongside with the informational explosion resulted from the fast communication means through Internet. The copyright of web applications is difficult to be appreciated in this domain and this is the reason for the development of novel web technologies and mechanisms of measuring the similarity between two webpages. In this paper, we will present a modality of measurement of the similarity degree between two webpages regarding the HTML tag-based webpages. The degree of similarity will be determined approximately, being dependent of the webpages used from the both websites and the tags set used in the comparison of the webpages. The selection of webpages in order to determine the degree of similarity between two webpages will be made using genetic algorithms. In the final part of the paper there are presented the results obtained with the implementation of the algorithm presented in the paper.

Growing popularity and diversity of genomic data demands portable and versatile genome browsers. Here, we present an open source programming library, called GIVE that facilitates creation of personalized genome browsers without requiring a system administrator. By inserting HTML tags, one can add to a personal webpage interactive visualization of multiple types of genomics data, including genome annotation, “linear” quantitative data (wiggle), and genome interaction data. GIVE includes a graphical interface called HUG (HTML Universal Generator) that automatically generates HTML code for displaying user chosen data, which can be copy-pasted into user’s personal website or saved and shared with collaborators. The simplicity of use was enabled by encapsulation of novel data communication and visualization technologies, including new data structures, a memory management method, and a double layer display method. GIVE is available at: http://www.givengine.org/.

On the Internet, various communication channels are readily available for companies, communities, and individuals. As companies have increasingly made use of social websites and instant messaging technologies for marketing and public relations, additional effort has been put in place to create interactive web contents to attract online audience. It is easy for anyone to simply copy and modify existing web contents for their own use. A common approach to protecting the copyright of a companys web content is digital watermarking with copyright information. The watermarking technique is a good way to achieve the goal of copyright protection. This paper presents a web page copyright protection method by integrating Cartesian product combination, CSS and HTML tag capitalization method, HTML attribute combination method, HTML attribute quotation mark method, and CSS attribute value embedding method. The experimental results demonstrate that the proposed method success achieved the goal of copyright protection for web pages.

A screen reader's sequential press-and-listen interface makes for an unsatisfactory and often times painful web-browsing experience for blind people. To help alleviate this situation, we introduce Web Screen Reading Automation Assistant (SRAA) for automating users' screen-reading actions (e.g., finding price of an item) on demand, thereby letting them focus on what they want to do rather than on how to get it done. The key idea is to elevate the interaction from operating on (syntactic) HTML elements, as is done now, to operating on web entities (which are semantically meaningful collections of related HTML elements, e.g., search results, menus, widgets, etc.). SRAA realizes this idea of semantic abstraction by constructing a Web Entity Model (WEM), which is a collection of web entities of the underlying webpage, using an extensive generic library of custom-designed descriptions of commonly occurring web entities across websites. The WEM brings blind users closer to how sighted people perceive and operate on web entities, and together with a natural-language user interface, SRAA relieves users from having to press numerous shortcuts to operate on low-level HTML elements - the principal source of tedium and frustration. This paper describes the design and implementation of SRAA. Evaluation with 18 blind subjects demonstrates its usability and effectiveness.

3D objects are now being embedded within HTML pages without the need for additional software, such as browser plug-ins. However, 2D and 3D web content are still typically treated as separate entities with limited interaction. Our research presents a working prototype implementation of a dual-mode user interface for interactive 3D environments. The developed interface allows the user to instantly switch between a traditional hypertext interface and an immersive 3D environment that incorporates 2D HTML elements. The results from an initial user study show that 2D and dual-mode interfaces allow for quicker retrieval of information than 3D websites alone and result in higher user satisfaction.

HTML (hypertext markup language) is a markup language for creating web documents (web pages). The main function of a browser is to receive the web page from the web server and display it. The browser applies all fonts, styles, and layouts specified by the HTML tags and CSS property values. This content can be further manipulated by using JavaScript, the programming language of the browser.

Web page clustering is a focal task in Web Mining to organize the content of websites, understanding their structure and discovering interactions among web pages. It is a tricky task since web pages have multiple dimension based on textual, hyperlink and HTML formatting (i.e. HTML tags and visual) properties. Existing algorithms use this information almost independently, mainly because it is difficult to combine them. This paper makes a contribution on clustering of web pages in a website by taking into account a distributional representation that combines all these features into a single vector space. The approach first crawls the website by using web pages’ HTML formatting and web lists in order to identify and represent the hyperlink structure by means of an adapted skip-gram model. Then, this hyperlink structure and the textual information are fused into a single vector space representation. The obtained representation is used to cluster websites using simultaneously their hyperlink structure and textual information. Experiments on real websites show that the proposed method improves clustering results.