List Of URLs Research Articles

Trust evaluation of health websites by eliminating phishing websites and using similarity techniques

SummaryEvery user uses a search engine to find health information from websites. Content‐rich health websites are considered in our research as wrong information in these websites can threaten life. Search engines give a list of URLs related to their search keyword. Generally, the user follows the top websites displayed by the search engine. Newly constructed websites do not have ratings, hit counts, and reviews. The search engine does not display newly constructed websites in their top rank. In such a case, the newly constructed website with the same content as the website displayed at the top of the search engine loses the user's trust. Another problem is; the phishing website URLs are also displayed by the Google Search engine, which appear similar to the genuine websites. To solve the problem and enhance the trust of health websites which is not at the top of the search engine among users, we have proposed an approach that extracts all URLs based on the keyword. It identifies all legitimate URLs using a Machine Learning classifier. Address bar features, Domain name features, HTML, and JavaScript features were identified for the dataset of getting legitimate URLs. Three classifiers (Decision Tree, Random Forest, and Support Vector Machine) were trained and evaluated. Decision Tree has the highest training accuracy, 94.125, testing accuracy, 92.75, and precision score of 96.97. The cross‐validation score of all three models is almost 93. Therefore, Decision tree is used to identify legitimate websites. After getting the list of legitimate URLs, all the content of the legitimate website is extracted. A Semantic Similarity between top‐rank legitimate website content and legitimate websites is found using Natural language processing techniques. Then the websites are ranked based on similarity and the value of the trust is assigned from highly trustable to less trustable. We have compared and correlated our results with the Web of Trust, a reputation tool for trust analysis, and have achieved a positive correlation. Thus, our approach removes phishing websites and enhances the trust in other websites that are not at the top of the search engine.

Mapping Real-World Use of the Onion Router

Since its inception, The Onion Router (TOR) has been discussed as an anonymizing tool used for nefarious purposes. Past scholarship has focused on publicly available lists of onion URLs containing illicit or illegal content. The current study is an attempt to move past these surface-level explanations and into a discussion of actual use data; a multi-tiered system to identify real-world TOR traffic was developed for the task. The researcher configured and deployed a fully functioning TOR “exit” node for public use. A Wireshark instance was placed between the node and the “naked” internet to collect usage data (destination URLs, length of visit, etc.), but not to deanonymize or otherwise unmask TOR users. For 6 months, the node ran and collected data 24 hr per day, which produced a data set of over 4.5 terabytes. Using Python, the researcher developed a custom tool to filter the URLs into human-readable form and to produce descriptive data. All URLs were coded and categorized into a variety of classifications, including e-commerce, banking, social networking, pornography, and cryptocurrency. Findings reveal that most TOR usage is rather benign, with users spending much more time on social networking and e-commerce sites than on those with illegal drug or pornographic content. Likewise, visits to legal sites vastly outnumber visits to illegal ones. Although most URLs collected were for English-language websites, there were a sizable amount for Russian and Chinese sites, which may demonstrate the utilization of TOR in countries where internet access is censored or monitored by government actors. Akin to other new technologies which have earned bad reputations, such as file-sharing program BitTorrent and intellectual property theft or cryptocurrency Bitcoin and online drug sales, this study demonstrates that TOR is utilized by offenders and non-offenders alike.

KARAJ: An Efficient Adaptive Multi-Processor Tool to Streamline Genomic and Transcriptomic Sequence Data Acquisition.

Here we developed KARAJ, a fast and flexible Linux command-line tool to automate the end-to-end process of querying and downloading a wide range of genomic and transcriptomic sequence data types. The input to KARAJ is a list of PMCIDs or publication URLs or various types of accession numbers to automate four tasks as follows; firstly, it provides a summary list of accessible datasets generated by or used in these scientific articles, enabling users to select appropriate datasets; secondly, KARAJ calculates the size of files that users want to download and confirms the availability of adequate space on the local disk; thirdly, it generates a metadata table containing sample information and the experimental design of the corresponding study; and lastly, it enables users to download supplementary data tables attached to publications. Further, KARAJ provides a parallel downloading framework powered by Aspera connect which reduces the downloading time significantly.

Advancing Online Teaching: Creating Equity-Based Digital Learning Environments by Kevin Kelly and Todd Zakrajsek

Reviewed by: Advancing Online Teaching: Creating Equity-Based Digital Learning Environments by Kevin Kelly and Todd Zakrajsek E. Nicole Meyer Kelly, Kevin, and Todd Zakrajsek. Advancing Online Teaching: Creating Equity-Based Digital Learning Environments. Stylus, 2021. ISBN 978-1-62036-722-3. Pp. xv + 248. While the move to online teaching caused by the Covid-19 pandemic was sudden (March 2020), the push to online teaching will continue to increase. For many French instructors, the notions of diversity, equity, and inclusion are on our minds due to the global reach of French, and our continuing efforts to include other French-speaking cultures into our teaching. This volume successfully incorporates inclusion, learning equity, and universal design for learning (UDL) into course design, writing course outcomes, course structure(s), activities and assessment. Thus, Kelly and Zakrajsek’s work helps French teachers to create an “instructor presence,” address different modes of learning, augment multiple paths for student success, and create the human connection our students crave. Many will appreciate the evidence-based ways to make adjustments to online courses, concrete teaching tips, and the differentiation between assessing teaching and student learning. The authors convincingly argue that to “advance online learning,” we must “teach in an increasingly inclusive, equitable, and compassionate way” (3). Divided into three parts, the first section concisely defines underlying concepts such as UDL, fostering human connectedness, and equity. It also addresses (re)writing SMART (Specific, Measurable, Achievable, Realistic, Time-Related) learning outcomes for equitable, inclusive online courses, and suggests concrete ways to establish a transparent organizational framework. To optimize engagement with course content, emerging technologies prove crucial to many. Best practices include aligning activities and assessment strategies with desired outcomes. The second section of the volume provides useful ways of promoting respectful, engaging discussions and other meaningful, often interactive, online activities. French teachers all invest immense time building authentic and engaging learning situations. Learning to manage student and instructor expectations through workload management strategies while assuring truly diverse, equitable and inclusive learning environments is essential to our well-being. The final part of the volume aids us to make effective adjustments to our online courses. A growth-minded approach to teaching online underlies the authors’ suggestions. Useful recommendations for authentic assessment of both our courses and of student learning translate well to our discipline. The complete bibliography, lists of URLs and applicable technologies are helpful. The strengths of this work are multiple: its usefulness for experienced and novice online instructors, its solid theoretical grounding, as well as a truly inclusive approach that includes Quality Matters (QM) considerations and goes beyond. Together, these features can remind instructors of essential factors for all students, including the building of a respectful equity-minded learning community. Suggestions of engaging, inclusive thinking, and diverse discussion and other activities prove applicable for language and more advanced French students. For teachers seeking primarily French-speaking examples or French-focused tools to create safe classroom environments [End Page 211] welcoming LGBTQIA+ students, this volume will prove less useful. Rethinking the French Classroom: New Approaches to Teaching Contemporary French and Francophone Women (Routledge 2019) and Teaching Diversity and Inclusion: Examples from a French-Speaking Classroom. (Routledge 2021) better serve that purpose. While not targeting those specific lenses, this current volume should prove useful to French teachers as they design and enact their online courses. [End Page 212] E. Nicole Meyer Augusta University (GA) Copyright © 2022 American Association of Teachers of French

Psychonauts’ psychedelics: A systematic, multilingual, web-crawling exercise

Psychedelics alter the perception of reality through agonist or partial agonist interaction with the 2A serotoninergic receptor. They are classified as phenethylamines, tryptamines and lysergamides. These classes, according to the United Nations Office on Drugs and Crime (UNODC) and European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), account for an important percentage of the new psychoactive substances (NPS) current scenario.The paper aimed at: a) identifying and categorising psychedelic molecules from a list of psychonaut websites and NPS online resources; and b) comparing the NPSfinderⓇ results with those from the European and United Nations databases. A crawling software (i.e. ‘NPSfinderⓇ’) was created to automatically scan, 24/7, a list of URLs and to extract a range of information (chemical/street names, chemical formulae, etc.) to facilitate NPS identification. Data collected were manually analysed and compared with the EMCDDA and UNODC databases.The overall number of psychedelic NPS detected by NPSfinderⓇ (November 2017-February 2020) was 1344, almost ten-times higher than that reported by the UNODC and EMCDDA combined. Of these, 994 previously unknown molecules were identified as (potential) novel psychedelics, suggesting a strong discrepancy between online and real-world NPS scenarios. The results show the interest of psychonauts, and maybe of the much larger community of ‘recreational’ drug users, towards psychedelics. Moreover, examining online scenario may help in assessing the availability in the real world of psychedelic NPS; understanding drug trends; and in possibly predicting future drug scenarios

The Advantage of the Right in Social Media News Sharing

We analyze social media activity during one of the largest protest mobilizations in U.S. history to examine ideological asymmetries in the posting of news content. We extract the list of URLs shared on social media during the mobilization period and we characterize those web sites in terms of their audience reach and the ideological composition of that audience. We also analyze the reliability of the sites in terms of the credibility and transparency of the information they publish. We show that there is no evidence of unreliable sources having any prominent visibility during the protest period, but we do identify asymmetries in the ideological slant of the sources shared, with a clear bias towards right-leaning domains. These results support the “conservative advantage” thesis, which points to the structural conditions that allow better message production from the right. Our results suggest that online networks are contested spaces where the activism of progressive movements coexists with the narratives of mainstream media, which gain visibility under the same stream of information (or hashtags) but whose reporting is not necessarily aligned with the activists’ goals.

Exploring the removal of online child sexual abuse material in the UK: Processes and practice

This paper explores the processes involved in the removal of online child sexual abuse material. It specifically focuses on the work of the Internet Watch Foundation (IWF) in the UK who are partially responsible for the removal of this content. The empirical work was carried out between May 2017 and September 2017 and explored whether the IWFs processes at removing online child sexual abuse material were both useful and effective to the police and the wider community. The research applied a mixed methods approach: Semi-structured interviews with employees (N = 10) and key stakeholders (N = 9), seeking to explore the strengths and challenges of both the task and the IWFs remit. Both employees and stakeholders saw the value in the original and innovative ‘space’ and ‘approach’ the IWF took in removing child sexual abuse material. This included the evolving nature of their tools, from the original URL list filter, to the more adaptable image hashing process. However, challenges around transparency, visibility and partnership were also raised. With online child sexual abuse ever evolving, it is important to consider novel ways in which intervention and prevention of victimization and offending can take place. Where a multi-disciplinary approach is needed in supporting victims, this research provides an insight into how one such organisation uses tools and techniques, different to traditional statutory services or law enforcement responses.

PPSB: An Open and Flexible Platform for Privacy-Preserving Safe Browsing

Safe Browsing (SB) is an important security feature in modern web browsers to help detect new unsafe websites. Although useful, recent studies have pointed out that the widely adopted SB services, such as Google Safe Browsing and Microsoft SmartScreen, can raise privacy concerns since users’ browsing history might be subject to unauthorized leakage to service providers. In this paper, we present a Privacy-Preserving Safe Browsing (PPSB) platform. It bridges the browser that uses the service and the third-party blacklist providers who provide unsafe URLs, with the guaranteed privacy of users and blacklist providers. Particularly, in PPSB, the actual URL to be checked, as well as its associated hashes or hash prefixes, never leave the browser in cleartext. This protects the user’s browsing history from being directly leaked or indirectly inferred. Moreover, these lists of unsafe URLs, the most valuable asset for the blacklist providers, are always encrypted and kept private within our platform. Extensive evaluations using real datasets (with over 1 million unsafe URLs) demonstrate that our prototype can function as intended without sacrificing normal user experience, and block unsafe URLs at the millisecond level. All resources, including Chrome extension, Docker image, and source code, are available for public use.

An effective approach to enhancing a focused crawler using Google

In this paper, we share our experience in augmenting a focused crawler of our vertical search engine designed to work with academic slides. The goal of the focused crawler was to collect Microsoft PowerPoint files from academic institutions. A previous approach based on a general web crawler can fail to collect a sufficient number of files mainly because of the robots exclusion protocol and missing hyperlinks. As a remedy to these problems, we propose a combinatory approach in which the indexing information maintained by a general web search engine such as Google is utilized for target URL list generation through our query generator, further then complemented by our URL extractor and file downloader. Because Google has already crawled billions of web pages, it will be more cost-efficient and potentially effective to systematically retrieve the desired information from Google than to redo crawling from scratch by ourselves. Our focused crawler, which we call SlideCrawler, has been used for our vertical search engine CourseShare since the fall of 2011. The capability of SlideCrawler was verified for the top-500 world wide universities. SlideCrawler collected about one million files from the top-500 universities. Further, the study results show that SlideCrawler outperforms Nutch, collecting 3.7 times more slide files.

A novel MEDLINE topic indexing method using image presentation

MEDLINE is one of the largest databases of biomedical literatures. The search results from MEDLINE for medical terms are in the form of lists of articles with PubMed IDs. To further explore and select articles that may help identify potentially interesting interactions between terms, users need to navigate through the lists of URLs to retrieve and read actual articles to find relevancies among these terms. Such work becomes extremely time consuming and unbearably tedious when each query returns tens of thousands results with an uncertain recall rate. To overcome this problem, we develop a topic-specific image indexing and presentation method for discovering interactions or relatedness of medical terms from MEDLINE, based on which a prototype tool is implemented to help discover interactions between terms of types of diseases. The merits of the method is illustrated by search examples using the tool and MEDLINE abstract dataset.

Personal Web Library: organizing and visualizing Web browsing history

PurposeModern Web browsers all provide a history function that allows users to see a list of URLs they have visited in chronological order. The history log contains rich information but is seldom used because of the tedious nature of scrolling through long lists. This paper aims to propose a new way to improve users’ Web browsing experience by analyzing, clustering and visualizing their browsing history.Design/methodology/approachThe authors developed a system called Personal Web Library to help users develop awareness of and understand their Web browsing patterns, identify their topics of interest and retrieve previously visited Web pages more easily.FindingsUser testing showed that this system is usable and attractive. It found that users can easily see patterns and trends at different time granularities, recall pages from the past and understand the local context of a browsing session. Its flexibility provides users with much more information than the traditional history function in modern Web browsers. Participants in the study gained an improved awareness of their Web browsing patterns. Participants mentioned that they were willing to improve their time management after viewing their browsing patterns.Practical implicationsAs more and more daily activities rely on the internet and Web browsers, browsing data captures a large part of users’ lives. Providing users with interactive visualizations of their browsing history can facilitate personal information management, time management and other meta-level activities.Originality/valueThis paper aims to help users gain insights into and improve their Web browsing experience, the authors hope that the work they conducted can spur more research contributions in this underdeveloped yet important area.

Document Clustering Approach for Meta Search Engine

The size of WWW is growing exponentially with ever change in technology. This results in huge amount of information with long list of URLs. Manually it is not possible to visit each page individually. So, if the page ranking algorithms are used properly then user search space can be restricted up to some pages of searched results. But available literatures show that no single search system can provide qualitative results from all the domains. This paper provides solution to this problem by introducing a new meta search engine that determine the relevancy of query corresponding to web page and cluster the results accordingly. The proposed approach reduces the user efforts, improves the quality of results and performance of the meta search engine.

The Research of a Spider Based on Crawling Algorithm

This paper conducts a deep research on data mining in three areas including work flow, key technologies and software algorithm of the spider. The paper analyzes the work flow and key technologies of the spider facing URL in details. It also brings forward the mind that adopting several queues to manage the URL list, in order to download HTML, files in high speed we sort the URLs by document correlativity. The aim of this paper is to design a well-adjusted and perfectly functional software model of the spider. Sun JDK+Borland Jbuilder+SQL Server+IIS+Bot package is used as the software development environment support.

The Data Extraction Using Distributed Crawler Inside Multi-Agent System

The paper discusses the use of web crawler technology. We created an application based on standard web crawler. Our application is determined for data extraction. Primarily, the application was designed to extract data using keywords from a social network Twitter. First, we created a standard crawler, which went through a predefined list of URLs and gradually download page content of each of the URLs. Page content was then parsed and important text and metadata were stored in a database. Recently, the application was modified in to the form of the multi-agent system. The system was developed in the C# language, which is used to create web applications and sites etc. Obtained data was evaluated graphically. The system was created within Indect project at the VSB-Technical University of Ostrava.

SHS-HTTPS enforcer

Web servers provide immunity against Man In The Middle (MITM) attacks and eavesdropping by using HTTP Strict Transport Security (HSTS) to force user agents to communicate only over HTTPS connections. However, the initial connection request from a user is made over an insecure HTTP connection. This issue was addressed by user agents; Google Chrome and Firefox, implicitly, by including a static list of URLs to be accessed only over secure HTTPS connections. Since, these user agents maintain their lists independently, the URLs used by one user agent are invisible to another. A user is prone to MITM attacks, especially in public hotspot environments, when accessing a URL present in the list of secure URLs of one browser but not in another, since the initial handshake from that user agent is insecure. Attacks can be initiated by modifying the outgoing HTTP packets and also the HTTPS response packets from the webserver. This motivated us to propose a solution independent of user agents, by merging the static URL lists of different user agents and enforcing HTTPS for all those URLs. In this paper, we propose a solution, SHSHTTPS Enforcer that introduces a local daemon to enforce URL redirection before the request flows out of the client for the URLs in a list compiled from multiple sources. The proposed solution has been demonstrated through a prototype implementation of the Squid Proxy server as our local daemon. The experiment was conducted by providing a URL, which was not present in one browser's list but was present in another browser's list. It was evident that SHS-HTTPS Enforcer enforced HTTPS successfully and MITM attacks were prevented.

Machine Learning Based Architecture for Rule Establishment of Web Proxy Server

In present scenario Internet has become an integral part of every ones life, as many services like mail, news, chat are available and huge amounts of information on almost any subject is available. However, in most cases the bandwidth to connect to the Internet is limited. It needs to be used efficiently and more importantly productively. Generally, bandwidth is distributed among groups of users based on some policy constraints. However, it turns out that the users do not always use the entire allocated bandwidth at all times. Also, some times they need more bandwidth than the bandwidth allocated to them. Ideally, productive usage should be preferred over unproductive usage when bandwidth is scarce. But when it is abundant then any kind of use can be permitted provided it is in consonance with policy. The bandwidth usage patterns of users vary with time of the day, time of the year and requirements. So there is a need for dynamic allocation of bandwidth that satisfies the requirements of the users, manages variable usage and is consistent with administrative usage policy. Internet usage is varied and in the context of an institution or organization an administrator would like to maximize productive usage. There is, therefore, a need to implement control access policies, which prevents unproductive use but at the same time does not, to the extent possible, impose censorship. Squid proxy server is a full-featured web proxy, which increases the efficiency of the Internet link by providing caching and proxy services. Squid provides many mechanisms to set access control policies. However, deciding which policies to implement requires experimentation and usage statistics that must be processed to obtain useful data. The proposed architecture elaborated in this paper is based on machine learning to determine policies depending on the content of current URLs being visited. The main component in this architecture is the Squid traffic Analyzer, which classifies the traffic and generates URL lists. These URL lists are used in formulating access policies. The concept of delay priority will also be introduced which gives more options to system administrators in setting policies for bandwidth management. As Squid allows HTTP tunneling, it forms a loophole for strict policy management. In this paper the proxy tunneling in Squid has also been considered and some possible solutions to this problem will also be suggested.

Investigate the Performance of Document Clustering Approach Based on Association Rules Mining

The challenges of the standard clustering methods and the weaknesses of Apriori algorithm in frequent termset clustering formulate the goal of our research. Based on Association Rules mining, an efficient approach for Web Document Clustering (ARWDC) has been devised. An efficient Multi-Tire Hashing Frequent Termsets algorithm (MTHFT) has been used to improve the efficiency of mining association rules by targeting improvement in mining of frequent termset. Then, the documents are initially partitioned based on association rules. Since a document usually contains more than one frequent termset, the same document may appear in multiple initial partitions, i.e., initial partitions are overlapping. After making partitions disjoint, the documents are grouped within the partition using descriptive keywords, the resultant clusters are obtained effectively. In this paper, we have presented an extensive analysis of the ARWDC approach for different sizes of Reuter's datasets. Furthermore the performance of our approach is evaluated with the help of evaluation measures such as, Precision, Recall and F-measure compared to the existing clustering algorithms like Bisecting K-means and FIHC. The experimental results show that the efficiency, scalability and accuracy of the ARWDC approach has been improved significantly for Reuters datasets. The internet has become the largest data repository, facing the problem of information overload. The existence of an abundance of information, in combination with the dynamic and heterogeneous nature of the Web, makes information retrieval a tedious process for the average user. Search engines, Meta-Search engines and Web Directories have been developed in order to help the users quickly and easily satisfy their information need. The Search engine performs exact matching between the query terms and the keywords that characterize each web page and presents the results to the user. These results are long lists of URLs, which are very hard to search. Furthermore, users without domain expertise are not familiar with the appropriate terminology thus not submitting the right query terms, leading to the retrieval of more irrelevant pages. This has led to the need for the development of new techniques to assist users effectively navigate, trace and organize the available web documents, with the ultimate goal of finding those best matching their needs. Document Clustering is one of the techniques that can play an important role towards the achievement of this objective. Document clustering has become an increasingly important task in analyzing huge numbers of documents distributed among various sites. Furthermore organizing them into different groups called as clusters, where the documents in each cluster share some common properties according to defined similarity measure. The fast and high-quality document clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Document clustering has been studied intensively because of its wide applicability in areas such as Web Mining, Search Engines, Information Retrieval, and Topological Analysis. Document Clustering is different than document classification. In document classification, the classes (and their properties) are known a priori, and documents are assigned to these classes; whereas, in document clustering, the number, properties, or membership (composition) of classes is not known in advance. Thus, classification is an example of supervised machine learning and clustering that of

Towards improving the online shopping experience: A client-based platform for post-processing Web search results

The quality of results to Web search queries is substantially limited because of the cost and short processing times allowed at search engine's data center to retrieve relevant pages, augment ads, and present them to the end-user. We tackle such an issue by proposing a radically different system, where the search engine replies to a query with a large list of relevant URLs. Our client-side platform then proceeds to download the target HTML files only, parse them, understand their content, and present a summary to the user. Different types of summaries can be created by using plug-ins attached to the base platform. Each plug-in provides the required functionality according to the type of summary desired by the user. With this novel mechanism, our system offers increased computational power for post-processing search results and consequently improves and personalizes the user's search experience while maintaining constant workload at the search engine. We present prototype implementations of the proposed search assistant and an associated shopping plug-in capable of detecting whether a Web-page encapsulates a direct commercial offering. We also review measurements related to projected system performance and demonstrate its applicability to different scenarios.

Latency Reduction in Mobile Environment

This paper reports an integrated caching and pre-fetching technique to reduce latency in a mobile environment. The proposed model consist of a bandwidth monitoring agent to find out current bandwidth usage, a prediction module to predict the number and the list of urls to be pre-fetched and a pre-fetch module to pre-fetch the web pages and store them in a pre-fetch area. A hash table is used for storing and processing the URLs. Simulation result shows that the browser implemented in a mobile environment maintains almost constant web traffic even if pre-fetching is done and latency is reduced up to 40-70%.

Smart Approach to Reduce the Web Crawling Traffic of Existing System using HTML based Update File at Web Server

Web crawler is used for downloading information from web. Web pages are changed without any notice. Web crawler frequently revisits websites to check updates. It is expected that 40% of present internet traffic is because of web crawling. In this paper we propose a file which maintains the list of updated URLs of web pages of web site. Format of file is based on HTML. Crawler will only visit the UPDATE File, and need not have to revisit the full website to know the updates. This scheme can easily implement on today’s system with little modification on web application and web crawler. In simulator we test proposed method; using a website of 13 pages for experiment. Experiment results shows that this scheme is very promising.