Millions Of Websites Research Articles

CarbonTag: A Browser-Based Method for Approximating Energy Consumption of Online Ads

Energy is today the most critical environmental challenge. The amount of carbon emissions contributing to climate change is significantly influenced by both the production and consumption of energy. Measuring and reducing the energy consumption of services is a crucial step toward reducing adverse environmental effects caused by carbon emissions. Millions of websites rely on online advertisements to generate revenue, with most websites earning most or all of their revenues from ads. As a result, hundreds of billions of online ads are delivered daily to internet users to be rendered in their browsers. Both the delivery and rendering of each ad consume energy. This study investigates how much energy online ads use in the rendering process and offers a way for predicting it as part of rendering the ad. To the best of the authors' knowledge, this is the first study to calculate the energy usage of single advertisements in the rendering process. Our research further introduces different levels of consumption by which online ads can be classified based on energy efficiency. This classification will allow advertisers to add energy efficiency metrics and optimize campaigns towards consuming less possible.

The Co-evolution of the WordPress Platform and Its Plugins

One can extend the features of a software system by installing a set of additional components called plugins. WordPress, as a typical example of such plugin-based software ecosystems, is used by millions of websites and has a large number (i.e., 54,777) of available plugins. These plugin-based software ecosystems are different from traditional ecosystems (e.g., NPM dependencies) in the sense that there is high coupling between a platform and its plugins compared to traditional ecosystems for which components might not necessarily depend on each other (e.g., NPM libraries do not depend on a specific version of NPM or a specific version of a client software system). The high coupling between a plugin and its platform and other plugins causes incompatibility issues that occur during the co-evolution of a plugin and its platform as well as other plugins. In fact, incompatibility issues represent a major challenge when upgrading WordPress or its plugins. According to our study of the top 500 most-released WordPress plugins, we observe that incompatibility issues represent the third major cause for bad releases, which are rapidly (within the next 24 hours) fixed via urgent releases. Thirty-two percent of these incompatibilities are between a plugin and WordPress while 19% are between peer plugins. In this article, we study how plugins co-evolve with the underlying platform as well as other plugins, in an effort to understand the practices that are related support such co-evolution and reduce incompatibility issues. In particular, we investigate how plugins support the latest available versions of WordPress, as well as how plugins are related to each other, and how they co-evolve. We observe that a plugin’s support of new versions of WordPress with a large amount of code change is risky, as the releases that declare such support have a higher chance to be followed by an urgent release compared to ordinary releases. Although plugins support the latest WordPress version, plugin developers omit important changes such as deleting the use of removed WordPress APIs, which are removed a median of 873 days after the APIs have been removed from the source code of WordPress. Plugins introduce new releases that are made according to a median of five other plugins, which we refer to as peer-triggered releases. A median of 20% of the peer-triggered releases are urgent releases that fix problems in their previous releases. The most common goal of peer-triggered releases is the fixing of incompatibility issues that a plugin detects as late as after a median of 36 days since the last release of another plugin. Our work sheds light on the co-evolution of WordPress plugins with their platform as well as peer plugins in an effort to uncover the practices of plugin evolution, so WordPress can accordingly design approaches to avoid incompatibility issues.

A web content mining application for detecting relevant pages using Jaccard similarity

<span lang="EN-US">The tremendous growth in the availability of enormous text data from a variety of sources creates a slew of concerns and obstacles to discovering meaningful information. This advancement of technology in the digital realm has resulted in the dispersion of texts over millions of web sites. Unstructured texts are densely packed with textual information. The discovery of valuable and intriguing relationships in unstructured texts demands more computer processing. So, text mining has developed into an attractive area of study for obtaining organized and useful data. One of the purposes of this research is to discuss text pre-processing of automobile marketing domains in order to create a structured database. Regular expressions were used to extract data from unstructured vehicle advertisements, resulting in a well-organized database. We manually develop unique rule-based ways of extracting structured data from unstructured web pages. As a result of the information retrieved from these advertisements, a systematic search for certain noteworthy qualities is performed. There are numerous approaches for query recommendation, and it is vital to understand which one should be employed. Additionally, this research attempts to determine the optimal value similarity for query suggestions based on user-supplied parameters by comparing MySQL pattern matching and Jaccard similarity.</span>

Graph neural networks and cross-protocol analysis for detecting malicious IP addresses.

An internet protocol (IP) address is the foundation of the Internet, allowing connectivity between people, servers, Internet of Things, and services across the globe. Knowing what is connecting to what and where connections are initiated is crucial to accurately assess a company’s or individual’s security posture. IP reputation assessment can be quite complex because of the numerous services that may be hosted on that IP address. For example, an IP might be serving millions of websites from millions of different companies like web hosting companies often do, or it could be a large email system sending and receiving emails for millions of independent entities. The heterogeneous nature of an IP address typically makes it challenging to interpret the security risk. To make matters worse, adversaries understand this complexity and leverage the ambiguous nature of the IP reputation to exploit further unsuspecting Internet users or devices connected to the Internet. In addition, traditional techniques like dirty-listing cannot react quickly enough to changes in the security climate, nor can they scale large enough to detect new exploits that may be created and disappear in minutes. In this paper, we introduce the use of cross-protocol analysis and graph neural networks (GNNs) in semi-supervised learning to address the speed and scalability of assessing IP reputation. In the cross-protocol supervised approach, we combine features from the web, email, and domain name system (DNS) protocols to identify ones which are the most useful in discriminating suspicious and benign IPs. In our second experiment, we leverage the most discriminant features and incorporate them into the graph as nodes’ features. We use GNNs to pass messages from node to node, propagating the signal to the neighbors while also gaining the benefit of having the originating nodes being influenced by neighboring nodes. Thanks to the relational graph structure we can use only a small portion of labeled data and train the algorithm in a semi-supervised approach. Our dataset represents real-world data that is sparse and only contain a small percentage of IPs with verified clean or suspicious labels but are connected. The experimental results demonstrate that the system can achieve 85.28% accuracy in detecting malicious IP addresses at scale with only 5% of labeled data.

Representing Biases, Inequalities and Silences in National Web Archives: Social, Material and Technical Dimensions

Contemporaneous collecting of the publicly available web has provided researchers with an invaluable source with which to interpret various aspects of the recent past. With millions of websites gathered, stored and made accessible in national web archives over the past 25 years, this paper argues for the need to reflect upon, and respond to, the biases, inequalities and silences that exist in these vast repositories. This article presents a research agenda for web archivists and web historians to together think broadly about the social, material and technical dimensions that shape what is included in web archives, and what is excluded. A key challenge impacting this effort is that various complexities and contingencies of archival formation are obscured. These include wider social inequalities, the entanglement of human and machine decision-making in the archiving process, changing dynamics of power over information online and the environmental impact of technical systems. Accounting for these social, material and technical factors that shape the formation of web archives provides opportunities to develop and use archives in ways that better acknowledge both the strengths and limitations of national web archives as a proxy for the web’s past.

Violence in the Age of Technology: Theological Reflections on Cyber Crime

It is generally believed that our societies are becoming more and more violent day by day. This is indeed not entirely true in that there are certain violent deeds that have subsided while other have been on the rise. This can be observed in the current age of information technology more so fashioned over the internet and related telecommunication services and gadgets. This paper debunks violence in terms of cybercrime from a theological reflection. With the escalated expansion of globalization and advancements in information technology, digitalization is an invaluable tool that is transforming the private, public, and professional lives of people; both Christians and non-Christians. Virtually, people depend on the internet and digital devices from communication to business transactions and so on. There are millions of websites and blogs offering various services and information to the population enabling people to acquire the readily available massive information. As much as we celebrate and enjoy the technology, it equally has liabilities due to cyber violence perpetrated by fraudsters, conmen, hackers, and other cyber criminals. Concerns such as impersonation, forgery, falsification, fraudulent representation, and presentation of facts as well as counterfeiting are common parlance, but the implication is adverse. Most internet users are vulnerable to traps set by conmen. The crimes are real as elucidated by the two personal examples given in this paper. People and governments are losing millions of monies daily through advance free fraud. It is prudent for Christians not to wait until one becomes a victim in order to acknowledge the risks and become vibrant participants in the mitigation of digital crimes

WEB PAGE CLASSIFICATION WITH DEEP LEARNING METHODS

Today, millions of websites on the Internet are widely used to access information. For effective use of web pages with increasing numbers every day, they need to be well classified. In this study, binary and multi-class classification models have been created which can classify web pages with high accuracy. In our experiments, URLs and categories of English web pages in the Open Directory Project (ODP) were used. Training dataset was created by pulling web page texts from URL information. To our knowledge, this is the first comprehensive web page classification dataset for Turkish. In this study, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning methods which are effective in text classification are used. Word embedding was used instead of n-gram approaches commonly used for feature extraction in text classification studies. In this study, hyper-parameter optimization was performed for deep learning models. Binary and multi-class classification models were created with the best parameters. Binary classification models were compared with the results of another study, and multi-class classification models were compared with each other. The performances of all models were examined by considering their training time and f1 scores.

HANDLING VULNERABLE SCRIPT CODE IN WEB ENGINEERING

Abstract- Network protection in our everyday lives is becoming increasingly critical today. Since we cannot live without the Internet, it is important to have a good and security environment for networking. Cross site scripting (XSS) does, however, attack millions of websites. We can use XSS to insert malicious scripting code into apps and then return it to the customer side. If users are using the web browser to visit the injecting place of the malicious script code, it is directly run on the customer machine. The main words of XSS are commonly found in the JavaScript browser or on the server component to filter malicious code. However, it is difficult to collect all keywords in the detecting-list in order to prevent XSS attack. However, it is possible to create various forms of malicious scripting. It is also worthwhile for more people to work on XSS and find more ways to prevent XSS attacks.

Children\u2019s ability to edit their memories when learning about the environment from credible and noncredible websites

One of the many sources of information easily available to children is the internet and the millions of websites providing accurate, and sometimes inaccurate, information. In the current investigation, we examined children’s ability to use credibility information about websites when learning about environmental sustainability. In two studies, children studied two different websites and were tested on what they had learned a week later using a multiple-choice test containing both website items and new distracters. Children were given either no information about the websites or were told that one of the websites (the noncredible website) contained errors and they should not use any information from that website to answer the test. In both studies, children aged 7- to 9-years reported information from the noncredible website even when instructed not to, whereas the 10- to 12-year-olds used the credibility warning to ‘edit out’ information that they had learned from the noncredible website. In Study 2, there was an indication that the older children spontaneously assessed the credibility of the website if credibility markers were made explicit. A plausible explanation is that, although children remembered information from the websites, they needed explicit instruction to bind the website content with the relevant source (the individual websites). The results have implications for children’s learning in an open-access, digital age where information comes from many sources, credible and noncredible. Education in credibility evaluation may enable children to be critical consumers of information thereby resisting misinformation provided through public sources.

Malware Inputs Detection Approach (Tool) based on Machine Learning [MIDT-SVM

The internet plays a significant part in our daily lives. It has become an integral part of everyone's daily routine or way of life. The Dark Web is a virtually unidentifiable hidden layer of the Web that is frequently used to preserve and retrieve private data. However, a number of cases have been recorded in which this network has been used to covertly perform illicit and unlawful operations. Internet is the open platform for user Input. Can be supplied either in Formatted or in unformatted form. Web attacker always tries to get unauthorized access by several hit and trail methods, but it also needs knowledge for database design fingerprints and signatures. Now a day’s millions of websites are getting sick by these undesired flaws. Several Security algorithms and techniques are designed for detecting, protecting and blocking malicious activities on web applications. Some web attack requires third party software to gain unauthorized access, but other gains benefit from designing loop holes remains on web applications. The most popular attacks works on database footprints are phishing attack, SQL-Injection attack, spamming, Password sniffing etc.In the propose work web attack detection technique has been proposed which detects multiple web attacks based on their signature and patterns by matching dataset strings. The technique uses SVM (Support Vector Machine) for classification of Dataset classes. The designed system is light-weight, support multiple platform environments, easy to update, if new attacks are found and able to work on different programming languages. The System also shows recommended list of malicious data and authentic data to designer for proper precautionary designing. The accuracy of system for Different cases and it is found to be more than 97%, which is best among available system, when compared using different parameters evaluation.

The Unseen Web, an Enormous Mass of Internet

Deep web is the part of the internet; it holds 99% of the internet rest of the internet is known assurface web and it is access able by standard search engines, Deep web is famous by different namesand can be called as invisible and hidden web but it can’t be accessed by the standard exploratoryengines this is due to the un indexed contents by common exploratory engines. Deep web content isinvisible under HTML form. Deep web provides anonymous internet platform for hackers, governmentagencies and others. Due to this no one can trace the location of the user and don’t have anykind of information about the web surfing of the user. Dark web is the part of Deep web, the reasonbehind it commonly used for the black market i.e. Drug sale, ammunition sale, and many otherillegal activities. It is the consist of millions of websites some of which are very informative. Whileon the other hand some of the portion is legal to visit and rest is illegal. These websites can beaccessed by using the popular tools, i.e. TOR, I2P which provide anonymity. About 2 million peopleuse the TOR browser to browse deep and dark web.

Image Map: Alternative for Password Based Authentication

In the world today there are millions of websites and other web applications which operate with customizations to the user using the user account registration. This naturally requires a user name or a unique ID like email address and a password. This kind of authentication is being employed from days past to invention of authentication systems. But today with many such websites it has become a herculean task to remember the passwords of each and every sites. There are solutions like writing down or noting on any electronic device but these has equal physical chances of being revealed. So there is a need for a brand new authentication techniques which are better than existing ones and are easy to implement. Image map is one such an alternative that can replace traditional systems. This technique makes use of images taken from users during registration and certain number of points on one exclusive image from taken set of images.

Using the Semantic Web as a Source of Training Data

Deep neural networks are increasingly used for tasks such as entity resolution, sentiment analysis, and information extraction. As the methods are rather training data hungry, it is necessary to use large training sets in order to enable the methods to play their strengths. Millions of websites have started to annotate structured data within HTML pages using the schema.org vocabulary. Popular types of entities that are annotated are products, reviews, events, people, hotels, and other local businesses [12]. These semantic annotations are used by all major search engines to display rich snippets in search results. This is also the main driver behind the wide-scale adoption of the annotation techniques. This article explores the potential of using semantic annotations from large numbers of websites as training data for supervised entity resolution, sentiment analysis, and information extraction methods. After giving an overview of the types of structured data that are available on the Semantic Web, we focus on the task of product matching in e‑commerce and explain how semantic annotations can be used to gather a large training dataset for product matching. The dataset consists of more than 20 million pairs of offers referring to the same products. The offers were extracted from 43 thousand e‑shops, that provide schema.org annotations including some form of product identifiers, such as manufacturer part numbers (MPNs), global trade item numbers (GTINs), or stock keeping units (SKUs). The dataset, which we offer for public download, is orders of magnitude larger than the Walmart-Amazon [7], Amazon-Google [10], and Abt-Buy [10] datasets that are widely used to evaluate product matching methods. We verify the utility of the dataset as training data by using it to replicate the recent result of Mudgal et al. [15] stating that embeddings and RNNs outperform traditional symbolic matching methods on tasks involving less structured data. After the case study on product data matching, we focus on sentiment analysis and information extraction and discuss how semantic annotations from the Web can be used as training data within both tasks.

The Papal Election in the Philippines: Negotiating Religious Authority in Newspapers

The Roman Catholic Church has always been present in news media worldwide. In 2013 the papal election caused special attention from the media because of the resignation by Benedict xvi. Heated discussions not only on the possible successor, but also on the necessary obligations of the Pope took place in the press discourse. Is there a changing understanding of authority, as the press coverage as well as millions of websites, Twitter contributions etc. suggested? The influence of new media on religious authority structures is assessed quite differently so far. However, what exactly is understood as religious authority? The paper presents exemplarily the press coverage in the Philippines and discusses the construction of religious authority in the press discourse during the papal election. Applying an analysis model, it thereby analyzes the different construction processes within a specific media genre and asks how and what kind of authority was ascribed to.

Health information quality on the internet in gynecological oncology: a multilingual evaluation.

Oncological internet information quality is considered variable, but no comprehensive analysis of gynecological malig- nancies exists. The present authors' objectives were to compare the quality of common malignancy websites and to assess for language or disease differences; and secondly, to perform a quality comparison between medical and layperson terminology. World Health Organization (WHO) Health on the Net (HON) principles may be applied to websites using an automated toolbar function. Using a search engine (www.Google.com) 8,400 websites were assessed using keywords 'endometrial, 'uterine', 'cervical', 'ovarian', 'vaginal', 'vulvar', plus 'cancer', in English, French, German, and Spanish; repeated for alternate terms e.g. 'cervix', 'womb'. Searches for "vaginal' 'uterine', 'cervical', and 'endometrial' each returned millions of websites. The total percentage of all assessed HON-accredited sites was notably low across all search terms (median 15%; range 3-19%). Significant differences by malignancy type (p < 0.0001), language (p < 0.0001), and tertiles (thirds) of the first 150 websites returned (p < 0.0001). French language had most accredited websites. Using alternate terms demonstrated significant differences (p < 0.001) in accredited websites for most gynecological cancers. Internet data on gynecological malignancies is overwhelming. Further, a lack of validation of the majority of gynecological oncologic sites should be appreciated with discrepancies in quality and number of websites across diseases, languages, and also between medical and layperson terms. Physicians should encourage and more importantly their professional bodies should participate in the development of informative, ethical, and reliable health websites on the internet and direct patients to them.

Social4all: Definition of specific adaptations in Web applications to improve accessibility

Web accessibility guidelines help developers to create websites which can more easily be used by people with different limitations. The principles and techniques of accessibility focus on the suitable use of standard Web components, alternative methods to present information, and alternatives to facilitate user interaction. Currently, the biggest part in creating accessible websites is played by Web developers, because they manage the page code. Unfortunately, there are millions of websites which do not follow accessibility guidelines, as this usually requires great effort and knowledge of accessibility issues. This research aims to create a platform, based on a novel approach, which allows a set of accessibility problems to be solved without modifying the original page code. The proposed platform is able to analyse websites and detect many accessibility problems automatically; after this, a guided assistant is used to offer adequate solutions to each detected problem. The assistant tries to abstract references to Web implementation issues and to explain every accessibility problem in an understandable way for non-technical people. This new approach could be useful to improve the level of accessibility of many websites for people besides Web developers.

Arbitrary Category Classification of Websites Based on Image Content

This paper presents a comprehensive methodology for general large-scale image-based classification tasks. It addresses the Big Data challenge in arbitrary image classification and more specifically, filtering of millions of websites with abstract target classes and high levels of label noise. Our approach uses local image features and their color descriptors to build image representations with the help of a modified k-NN algorithm. Image representations are refined into image and website class predictions by a two-stage classifier method suitable for a very large-scale real dataset. A modification of an Extreme Learning Machine is found to be a suitable classifier technique. The methodology is robust to noise and can learn abstract target categories; website classification accuracy surpasses 97% for the most important categories considered in this study.

‘Heartbleed’ flaw leaves millions of websites, email servers and other services vulnerable to attack

A flaw discovered in versions of OpenSSL dating back two years means that attackers are capable of stealing credentials, including private keys, from websites, email servers and other online systems. In what's being touted as the biggest security issue in decades, some specialists are advising people to change all their online passwords.

The Website Interface Design and Investigation of Interactive Availability

At present, the website construction has become one of the necessary works of any industry. However, among hundreds of millions of websites, interface design is very convenient for people to use, and a small number of fast. The reason, including web interface design, web technology adoption, etc. will influence people to visit a website. Among these reasons, the interactive design of website interfaces and become the primary problem of experience that people visit the website. To address this problem, the current website interface and interactive design were discussed in this article, and try to propose improved method of most common small and medium-sized websites.

Bogus arguments for unproven treatments

Bogus arguments for unproven treatments