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Phishing is one amongst the main issues visaged by cyber-world and ends up in monetary losses for each industries and people. Detection of phishing attack with high accuracy has forever been a difficult issue. At present, visual similarities-based techniques square measure terribly helpful for police work phishing websites expeditiously. Phishing web site appearance terribly similar in look to its corresponding legitimate web site to deceive users into basic cognitive process that they are browsing the right web site. Visual similarity primarily based phishing detection techniques utilize the feature set like text content, text format, HTML tags, Cascading sheet (CSS), image, then forth, to form the choice. These approaches compare the suspicious web site with the corresponding legitimate web site by victimisation numerous options and if the similarity is larger than the predefined threshold price then it is declared phishing [2].

People can write to the government about what they want through social media in the freedom of information era. Especially in the Covid-19 pandemic, the public's desire for government attention is getting higher. The purpose of this activity is to collect community needs at the beginning of the pandemic. The period of information retrieval is from March 30 to April 5, 2020. The activity uses the crawling method, which is gathering information with browser tools. The implementation stage starts from taking information with selenium, identifying posts and comments with Html tags, calculating the emergence of issues with N-gram NLP, and analyzing sentiment with Naive Bayes and Support-Vector Machine. The results showed that the community did not have panic to need foods with a total reaction of 172. The community was more worried about the spread of outbreaks from outside NTB with a reaction of 1,421.

The Internet is transforming into the main source of information. Standards are being developed so that people with different types of disabilities are not to feel excluded. Tools based on certain standards automate tasks, but they are also important to work correctly. The study consisted of checking the pages with selected tools discussed in the literature dealing with website accessibility. The validators are identically configured each time the website verifies compliance with the WCAG 2.1 standard. The number of errors detected per page varies significantly when using individual tools. The error rate calculated for the number of all HTML elements of the page ranges between less than 1% and 80%. It is not possible to unambiguously select the worst service on the basis of analyzing all results obtained, it can be a different service for each tool. The tools automate the activities of verifying availability. Each of the validators tested found errors, some of the same. The best solution is not to rely on just one tool, because the results obtained can relate to completely different elements of the page. It all depends on the care of the creators in the preparation of the tool and their care for compliance with standards.

Tag helpers are C# classes that transform HTML elements in a view or page. Common uses for tag helpers include generating URLs for forms using the application’s routing configuration, ensuring that elements of a specific type are styled consistently, and replacing custom shorthand elements with commonly used fragments of content. In this chapter, I describe how tag helpers work and how custom tag helpers are created and applied. In Chapter 26, I describe the built-in tag helpers, and in Chapter 27, I use tag helpers to explain how HTML forms are created. Table 25-1 puts tag helpers in context.

Purpose – to study website’s work and content of the travel agency on the example of the "Laspi" travel agency, identify its technical properties and offer methods to increase the web-resource leading position in the Yandex and Google search engines by performing SEO-analysis. Design/Method/Research approach. Internet resources SEO-analysis. Findings.The travel product promotion directly depends on the travel market participants' advertising tools' effectiveness, mainly travel agents. It is determined that one of the new technologies that increase the advertising effectiveness, in particular via the travel agencies’ web resources, is SEO-technology. The authors Identified technical shortcomings of its operation, mainly related to search queries statistics, the subject site visits, the semantic core operation, the site improvement, the site increasing citation, and the number of persistent references in the network. It is proved that updating site development, changing its environment, analyzing user behavior, namely the Og Properties micro markup, updating HTML tags, analytical programs placing, iframe objects selection, and other activities, increase the content uniqueness. As a result, search engines scanned the site, and the search results took first place for the positions essential for the web resource. Originality/Value. The leading positions increasing mechanism application, website operation optimization allow search engines to bring it to the TOP of the most popular travel sites. Theoretical implications. To optimize the web resource operation, a mechanism for improving its leading position is proposed that includes three steps: the general website characteristics of marketing, SEO-analysis, recommendations provision. Practical implications. The research is practical in improving the site’s technical operation and increasing its leading position in Yandex and Google search engines. Research limitations/Future research. Further research aims at the site further analysis after making the proposed changes to its operation. Paper type – empirical.

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

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.

The article of record as published may be found at http://dx.doi.org/10.1109/SPW53761.2021.00048 2021 IEEE Symposium on Security and Privacy Workshops Modern web applications rely heavily on dynamic content, i.e., page updates made by the browser using an XMLHttpRequest and more recently the JavaScript Fetch API. These requests are often made on behalf of user actions, such as typing on the keyboard or pointing at an HTML element. As a result, the timings of the user's actions are strongly correlated with the timings of packets that carry these events. In this work, we examine several dynamic web applications and the ability to measure human behavior in encrypted network traffic by using deep temporal features. Our approach relies on the ability to accurately detect a subset of packets that correspond to user actions. Leveraging recent work in keystroke dynamics, we show that user identification can be performed with modest accuracy utilizing the packet timings induced by a user typing into a search engine. While this tool could be used by forensic investigators to perform target identification among encrypted network traffic, it also raises a privacy concern in which an on-path remote adversary able to detect these packets may infer user behaviors. Work done while a student at the Naval Postgraduate School

The World Wide Web (WWW) is the largest available repository of information. This huge amount of information put forward the challenges of retrieval of trustworthy information from WWW. It defies researchers with new issues of diversity and complexity while retrieving the web information. Information retrieval from the web demands approaches that span beyond conventional information retrieval. Heterogeneity, complexity and the huge volume of web information requires a unique approach to retrieve information. Besides, end-users introduce some difficulties in the retrieval process. Sometimes queries submitted by the user are subtle and ambiguous. The primary concern in information retrieval is the issue of predicting the relevance of documents. In this article, a new approach is proposed that rationally separates web document into five layers, namely, title, header, hyperlink, meta tag and body layer. The proposed method effectively combines the textual information and structural evidence of web document for retrieving information from Web. In the proposed layered vector space model, each layer has an allocated priority which is used to compute weight factor for these layers. The proposed method deduces equation that effectively combines priority of the layer and length of the layer to calculate the weight of the layer.

Much recent work focuses on leveraging semantic lexicons like WordNet to enhance word representation learning (WRL) and achieves promising performance on many NLP tasks. However, most existing methods might have limitations because they require high-quality, manually created, semantic lexicons or linguistic structures. In this paper, we propose to leverage semantic knowledge automatically mined from web structured data to enhance WRL. We first construct a semantic similarity graph, which is referred as semantic knowledge, based on a large collection of semantic lists extracted from the web using several pre-defined HTML tag patterns. Then we introduce an efficient joint word representation learning model to capture semantics from both semantic knowledge and text corpora. Compared with recent work on improving WRL with semantic resources, our approach is more general, and can be easily scaled with no additional effort. Extensive experimental results show that our approach outperforms the state-of-the-art methods on word similarity, word sense disambiguation, text classification and textual similarity tasks.

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.

In modern web applications, the elaborate GUI of the front end is often developed with large-scale JavaScript frameworks. In such systems, the behavior of one HTML element is usually defined by code in different JavaScript functions scattered all over the source files, and a piece of code may also have influence on other functions and variables all over the source files. Therefore, the data and control flow of the front end may be hard to follow. We propose an automated approach to integrate the overall data and control flow of the front end into an overview model, which then provides us with an excellent starting point to application of formal methods to prove the correctness of the front end. Our approach is hence helpful for comprehension of the front end code and validation of its correctness.

Inscriptis provides a library, command line client and Web service for converting HTML to plain text. Its development has been triggered by the need to obtain accurate text representations for knowledge extraction tasks that preserve the spatial alignment of text without drawing upon heavyweight, browser-based solutions such as Selenium. In contrast to related software packages, Inscriptis (i) provides a layout-aware conversion of HTML that more closely resembles the rendering obtained from standard Web browsers; and (ii) supports annotation rules, i.e., user-provided mappings that allow for annotating the extracted text based on structural and semantic information encoded in HTML tags and attributes. These unique features ensure that downstream knowledge extraction components can operate on accurate text representations, and may even use information on the semantics and structure of the original HTML document., Comment: Preprint of the published version, which includes all improvements made during the review process

This paper explores the concept of web animations with the motive to generate exportable video files using a browser. We explain the process underlying animations such as tweening and controlling individual states of an object's animation cycle. We can composite many animations to follow a single timeline and capture individual frames at periodic intervals. Such frames, when stitched together, form a video. We will also explain the integration of an audio layer than runs in synchronization with the animations for better perception.

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.

We present preliminary results of a project investigating the design development of popular websites between 1996 and 2020 via HTML analysis and basic computer vision methods. We acquired a corpus of website screenshots of the current top 47 popular websites. We crawled a snapshot of every month of these websites via the wayback machine of the Internet Archive platform since the time snapshots are stored to gather 7,953 screenshots and HTML pages. We report upon quantitative analysis results concerning HTML elements, color distributions and visual complexity throughout the years.

The aim of this research is to develop a more accurate method to detect unreliable news articles without considering the article content. Fake news articles are defined here as those articles which consist entirely of intentionally fabricated unreliable news. The approach taken in this work to detect fake news articles was to consider the type and frequency of HTML tags used in articles. By comparing the counts for HTML tags used in reliable and unreliable online articles, it was found that there are distinct differences between the HTML tags used in the two types of article sources (unreliable or reliable). Two datasets were used with different labelling of ground truth. The first dataset used, NELA 2017 (News Landscape 2017), comprises 136,000 news articles, obtained from 92 different news sources, dated between April 2017 and October 2017. The sources of the articles in NELA 2017 were categorized as either reliable or unreliable, using a media bias fact-checker resource and this was used to label the articles as either reliable or unreliable. The FakeNewsNet dataset is comprised of over 15000 news articles and tweets obtained from a fact-checking website, Gossip Cop, and has preassigned ground truth labels (fake or real). After analysis of NELA 2017, it was found that unreliable articles have 166 tags that were never used by the reliable articles and that there are 8 HTML tags that are used only in the reliable articles. Based on these findings, classification algorithms were employed on the extracted HTML tags. Experimental results show that the KNN classifier (k-nearest neighbors) and the CART classifier (classification and regression tree) give the best performance, having accuracies of around 97% when 10-fold cross-validation was implemented on the NELA 2017 dataset. Accuracies of around 72% were found when the same techniques were applied to the FakeNewsNet dataset. Using the NELA dataset, a comparison was also carried out between this new approach and two other approaches to detect fake news articles – one that uses content analysis and a second that combines content analysis and HTML tags. It was found that the new approach has similar, or often better, accuracy than other methods. This research offers a promising approach to detect unreliable news articles without having to consider the content words of the articles.

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.

In recent era data available on the internet is playing a vital role. According to research, most precious and valuable data is present in the deep web so interest in techniques to efficiently site invisible web is increasing. The challenges to extract the deep web are requirement of huge volume of resources, dynamic nature of the deep web, coverage of a wider area of deep web and higher efficiency of collected results from deep web with accuracy. Along with all the above challenges, user demand of privacy and identity is to be maintained. In this paper we proposed a smart crawler that efficiently searches the deep web and avoids visiting irrelevant pages. A smart crawler starts crawling from the center page of seed URL and goes on crawling till the last link available. The crawler has an ability to separate active and inactive links based on requests to sever of hyperlink. The crawler also contains text-based site classifier using neural network and natural language processing as Term Frequency Inverse Document Frequency and Bag of Words with supervised machine learning techniques as logistic regression, support vector machine and naive bayes. Also HTML tags are extracted from hyperlinks along with data which plays a huge role in data analysis and all this is separately saved in a centralized database. Our experimental results with efficient link reaping rate and classification show higher accuracy compared to different crawlers.

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.

Text classification (TC) is the task of automatically assigning documents to a fixed number of categories. TC is an important component in many text applications. Many of these applications perform preprocessing. There are different types of text preprocessing, e.g., conversion of uppercase letters into lowercase letters, HTML tag removal, stopword removal, punctuation mark removal, lemmatization, correction of common misspelled words, and reduction of replicated characters. We hypothesize that the application of different combinations of preprocessing methods can improve TC results. Therefore, we performed an extensive and systematic set of TC experiments (and this is our main research contribution) to explore the impact of all possible combinations of five/six basic preprocessing methods on four benchmark text corpora (and not samples of them) using three ML methods and training and test sets. The general conclusion (at least for the datasets verified) is that it is always advisable to perform an extensive and systematic variety of preprocessing methods combined with TC experiments because it contributes to improve TC accuracy. For all the tested datasets, there was always at least one combination of basic preprocessing methods that could be recommended to significantly improve the TC using a BOW representation. For three datasets, stopword removal was the only single preprocessing method that enabled a significant improvement compared to the baseline result using a bag of 1,000-word unigrams. For some of the datasets, there was minimal improvement when we removed HTML tags, performed spelling correction or removed punctuation marks, and reduced replicated characters. However, for the fourth dataset, the stopword removal was not beneficial. Instead, the conversion of uppercase letters into lowercase letters was the only single preprocessing method that demonstrated a significant improvement compared to the baseline result. The best result for this dataset was obtained when we performed spelling correction and conversion into lowercase letters. In general, for all the datasets processed, there was always at least one combination of basic preprocessing methods that could be recommended to improve the accuracy results when using a bag-of-words representation.

In-text compression algorithms, a set of symbols as alphabets are defined. These alphabets are the basic building blocks of the language. Traditionally 8-bit ASCII characters are chosen as alphabets. In this paper, an approach that chooses larger symbols as alphabets is proposed. For example, in the case of the English language, a set of words and special characters are chosen as alphabets. In the case of HTML documents, HTML tags are included along with alphabets from the English language described before. Better compression results can be achieved since words have larger byte size than individual ASCII characters but still get mapped to similar-sized encodings. Compressing the data to be transmitted across networks implies less usage of network bandwidth, faster transmission of data, sending several data files over a single transfer and faster reading and writing of data. Thus, the transmission of a compressed file is cost-effective and also saves time in communication. The results prove that the proposed approach gives better file size compression concerning the compression ratio when compared with state-of-the-art algorithms.

Introduction: Smartphone and mobile apps have become an indispensable companion in our modern-day living and working environment. In the medical field, web browsers and medical apps have transformed the medical practice as the most current knowledge, best practices, medical references, and calculators are available instantaneously at our fingertips. However, these functionality and usage are limited by the creativity and imagination of the app developers. To overcome these limitations, doctors should think out-of-the-box and educate themselves to be appdevelopers to create functional and useful apps in their field. Our aim was to create medical apps from ground-up independently without formal software development training. Material and method: Hybrid app technology is a popular method for creating apps for newbies due to the familiarity with web browsers. We explore the Ionic© Framework which is a free and open-source front-end with good resources and documentation. This programming technology assumes that everyone has experience with web browsers and the modern browsers essentially run on three technology stacks which are Hypertext Markup Language (HTML), JavaScript and Cascading Style Sheets (CSS). The HTML is a standard markup language for web browser while JavaScript is a scripting language for running commands and functions. The CSS is used for describing how HTML elements should be displayed. Results: We successfully published three apps on the Apple AppStore and Google Play, namely, OrthoBoard©, OpList and OOLH. Based on our experience of designing, coding, testing and deploying the apps, it is not impossible for medical doctors to achieve this goal and utilizing the app-development skills as an armamentarium for teaching, learning, and medical practice tools. Conclusions: App development is an essential skill especially for medical educators to leverage the technological advancement in their teaching and learning as well as medical practice in tandem with our nations’ aspiration towards Education 4.0.

“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

Reverse image search is a content-based image retrieval (CBIR) query technique that takes a sample image as an input, and search is performed based on it. Reverse image search is characterized by a lack of search terms. Using reverse image search, one can find the original source of images, find plagiarized photos, detect fake accounts on social media, etc. Reverse image search works by uploading an image by the user, and searching of images is carried out by using the corresponding meta tags, HTML tags or color distributions of the image. The search engine currently functions by using this information and not the context of the image, thus resulting in inaccurate outcomes. Labeling the image using keywords relevant to the image is a very generic explanation to an image and can lead to ambiguous interpretations. This can be overcome by giving a more specific description to an image, thereby preventing misinterpretations and providing more specific results. The proposed system aims at optimizing the search results by depicting more clearly the relationships between the objects in an image with the help of image captioning.

Any HTML element can have a background. Backgrounds can be images, solid colors, or even gradients.

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.

A user query facet is a collection of items that summarizes the content covered by a query. In general, the most significant information of a user query is present in the top retrieved document that are in the form of lists. In this work, we propose a framework Automatic Extraction of Facets for User Queries [AEFUQ] that extract the user query facets automatically by grouping the list based on three categories namely HTML tags, free text patterns and repeat regions. Grouping of the list is based on domain sites present in the list. We observe that some of the lists are not relevant for extracting the facets. In order to prune these lists, the importance of each item in the lists that are present in the group G is evaluated and Cosine Similarity (CS) between two items is calculated. Further, based on CS score obtained, High Quality Clustering (HQC) algorithm is proposed to cluster the items that has the most number of point in each iteration to obtain more number of facets. Finally, the top most items from each cluster are provided as the best facets for the user query. Experiments are conducted on User Q and Random Q dataset. It is observed that the proposed method AEFUQ outperforms by providing a large number of useful query facets compared to QDMiner method [1].

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.

Extracting content from online news websites, typically known as web scraping, is challenging due to the variety of presentation layout employed by those websites. To facilitate data collection from these websites, this paper introduces a method to correctly identify a Region of Interest (ROI) of a news article by using web scraping techniques. A ROI should consists of title, content and main picture. A search algorithm based on the Steepest Ascent Hill Climbing (SAHC) was employed by define a heuristic function from common features in the HTML tag of a news article. There were five features that are used in the heuristic function, namely heading 1 (h1), image (img), line break (br), paragraph (p), and the length of textual content. The proposed SAHC achieved a success rate of 88,9% for searching a ROI in a news article. We also identified several limitations of our algorithm, especially when a feature did not exist or when multiple similar features did exist.

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.

Information retrieval is the process of obtaining and presenting more related information from the largest collection of information 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 information 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. 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 information retrieval process. In other hand input query is converted into more than one similar semantic query strings. These query strings are compared with the obtained feature sets in the database by using the cosine similarity function. The most similar text is retrieved as an outcome of the information retrieval system.

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.