Single Web Page Research Articles

Multidimensional data visualization and synchronization for revealing hidden pandemic information

Visualization is integral to uncovering hidden information in data and providing users with intuitive feedback for decision-making. Data visualization is crucial for transforming complex data into actionable insights across various domains. In recent years, coronavirus disease vaccines have become increasingly available to much of the population. However, the CDC (Centers for Disease Control and Prevention) often fails to consider multidimensional coronavirus pandemic data from a side-by-side perspective, limiting the ability of medical professionals and individuals to compare and interact with comprehensive data visualizations. Effectively displaying coronavirus and vaccination data collected from multiple sources is essential for interpreting pandemic transmission patterns and vaccine efficiency. This paper presents a new platform for innovative data visualizations that offers users intuitive feedback and a complete data story. We designed algorithms to seamlessly combine multiple parameters, synchronize attributes, and dynamically visualize data over time on a single webpage. Instead of integrating all attributes into a single plot, which can be overwhelming due to space limitations and make it difficult to extract crucial information from overcrowded display components, we developed algorithms to classify, enhance, and group all parameters based on their relationships and similarities. Furthermore, a side-by-side visualization method was created to dynamically link all parameters in multiple images for data exploration, trend comparison, hidden information detection, and correspondence analysis. Our platform provides real-time performance, enabling healthcare professionals to make informed decisions, communicate findings effectively, and uncover patterns that might not be apparent in raw data. The proposed multidimensional data visualization algorithms have broad applications in general data exploration and revealing hidden information.

Role of digital media in vaccination promotion. Second report on VaccinarSinToscana.org experience

Abstract Background Since March 27, 2017, the Department of Health Sciences, University of Florence, has been managing and editing an online portal of medical and scientific information, focused on preventive health care and vaccinations: “VaccinarSinToscana.org” (Get vaccinated in Tuscany). The aim of this study is to analyze website's consultation activity over the past four years by single users, visits and single webpage consultation. Methods VaccinarSinToscana website activity during the study period April 01, 2019-March 31, 2023 was investigated using Google Analytics. Microsoft Excel was used for data analysis. Results During the study period 365,070 new single users visited the portal and nearly 15% of them entered more than once for about 447,000 sessions. A total of 815,800 single web pages were visited individually, with an average of 2.23 web pages consulted per visit and an average session duration of 58 seconds. The largest increase in visits occurred in January 2021-June 2021 with the highest peak in March (155,737 single web pages). The most visited pages were about the national vaccination calendar, regional vaccine clinics, and the types of anti-COVID-19 vaccines available (particularly, the viral vector vaccines). Analysis of socio-demographic characteristics showed that the users most attracted to web consultation were female (57%) and the most ‘digital’ age group was 25-34 (24%). Other age groups account for 14% (18-24 years), 22% (35-44 years), 17% (45-54 years), 13% (55-64 years), and 11% (over 64 years). Mobile phones were the most used devices to access the portal (69%), personal computers for 28% and tablets for 3%. Conclusions The analysis of web pages consultation trend showed a relevant exponential increase of visits in the period January 2021-June 2021. The greatest interest of users, in this period marked by the early stages of the national SARS-CoV-2 vaccination campaign, was for searching news regarding anti-COVID-19 vaccination. Key messages • Institutional websites with evidence-based information could be useful tools for users to increase public confidence in vaccination. • The anti-COVID-19 vaccine campaign has increased website visit, demonstrating how this period has been characterized by curiosity, doubt, and citizens’ search for more information.

A practical multi-tab website fingerprinting attack

The rapid development of Internet of Things technology has allowed a massive number of devices to be connected, resulting in a lot of private data being transmitted over the network. To protect the security and privacy, anonymous communication technologies such as Tor are widely deployed. They use complex mechanisms such as encryption and multi-hop forwarding to hide the communication relationship and content. However, much existing work on the website fingerprinting attack has proven that there is still a risk of privacy disclosure. Website fingerprinting attacks can extract side channel information from encrypted traffic to form a fingerprint that identifies the victim’s destination website. But most work is conducted in the ideal environments, assuming the absence of background traffic, which raises doubts about the effectiveness in the real world. In this paper, we relax the strong assumptions of the attack model and propose a practical multi-tab website fingerprinting attack. We first constructed a CNN model to distinguish whether the unknown traffic is generated by accessing a single web page or multiple web pages. Then we design a split point recognition method based on the BalanceCascade algorithm to separate the overlapping traffic. Furthermore, we build recognition models based on ResNet and multi-head self-attention mechanism to identify the tail-missing and head-overlapping traffic sequences respectively. Extensive experiments are carried out in the real-world scenario to evaluate the proposed method. When the time interval is randomly selected within 5–15s, we achieve an accuracy of 77.34% in split point recognition. And the accuracy of identifying the two pages is 88.19% and 63.02% respectively.

Smart Governing System Based on Mesh Algorithm for Industrial Gas and Flame Detection with Alert System

The paper proposes a Wireless Sensor Network (WSN) solution for monitoring environmental parameters during industrial fires. The system includes sensors that collect real-time data on temperature ,humidity, and gas emissions. When the system detects a fire orgas out break ,it generates alerts and presents the data on a single webpage for easy visualization by first responders. The solution aims to provide near real time alerting, enabling prompt response to minimize the impact of industrial fires. The paper also presents experimental analysis of sensor node behavior during a fire in a test set-up, which demonstrated the effectiveness of the system in detecting fire outbreaks and generating alerts promptly. The proposed system has the potential to reduce the impact of industrial fires, including loss of human lives, vegetation, economic loss ,and environmental pollution.

Web browsing privacy in the deep learning era: Beyond VPNs and encryption

Web browsing privacy is a matter of paramount importance for the Internet users. While they try to protect themselves from being monitored by getting advantage of encryption or VPNs, users’ privacy is still unaccomplished, even taking into account the tangled web, with several domains visited at the same time in a single web page, or IP addresses of a cloud provider shared by several sites. In this work, we provide a novel approach to identify user web browsing that only takes into account the IP addresses that the user has connected to and without performing any DNS reverse resolutions. We use this sequence of addresses as an input of different state-of-the-art deep learning models, such as multi-layer perceptron and transformers, which are able to accurately identify which was the website actually visited among Alexa’s World Top 500 most visited domains. Moreover, we have also studied other factors, such as the dependence on the DNS server used to resolve the visited IP addresses, the accuracy for the top domains (e.g., Google, YouTube, Facebook, etc.), data augmentation by packet sampling simulation to improve our results, the impact on packet sampling and the fine-tuning and possible impact of model parameters or the scalability of our approach. We conclude that, using only a 10% of the packets, we can identify the visited website with an accuracy and F1 score between 94% and 95%.

An Automated Multi-Tab Website Fingerprinting Attack

In Website Fingerprinting (WF) attack, a local passive eavesdropper utilizes network flow information to identify which web pages a user is browsing. Previous researchers have demonstrated the feasibility and effectiveness of WF attacks under a strong Single Page Assumption: the network flow extracted by the adversary belongs to a single web page. In reality, the assumption may not hold because users tend to open multiple tabs simultaneously (or within a short period of time) so that their network traffic is mixed. In this article, we propose an automated multi-tab Website Fingerprinting attack that is able to accurately classify websites regardless of the number of simultaneously opened pages. Our design is powered by two innovative designs. First, we develop a split point classification method to dynamically identify the split point between the first page and its subsequent pages. As a result, the network traffic before the split point is solely generated for the first page. Then, we propose a new chunk-based WF classifier to infer the websites based on the initial chunk of clean traffic. For both classifiers, we apply automated feature selection to select a concise yet representative feature set. We implement a prototype of our design and perform extensive evaluations using SSH and Tor-based datasets to demonstrate the effectiveness of both our system components individually and the integrated system as a whole.

ABUSE RELATED POSTS REPORTER USING WEB CRAWLER IN PYTHON

This research paper shows one of the application areas where Web Scraping can be used to solve some social issues. According to [4], Web Scraping automates this process and helps you display all the data at the same place with ease and in a small amount of time. Main objective of Web Scraping is to extract information from one or many websites and process it into simple structures such as spreadsheets, database or CSV file. In our website, we have made use of this technology to solve a important social issue. Many cases of abuse have been increased with the use of Internet. Our website allows the authorized communities to search for such abuse related posts from different websites, while staying on a single webpage. The user will just have to enter the keyword, for e.g. – women abuse, and he will get the posts from several number of websites on a single webpage. The method explained in this paper is applied to a popular social platform – ‘Twitter’ as a use case. It can be applied to a number of websites. This will help the authorities to save time in searching for such crimes and give more time to solve these issues. KEYWORDS : Web Scraping, Django, HTML, CSS, Python, Selenium.

WashU Epigenome Browser update 2022.

WashU Epigenome Browser (https://epigenomegateway.wustl.edu/browser/) is a web-based genomic data exploration tool that provides visualization, integration, and analysis of epigenomic datasets. The newly renovated user interface and functions have enabled researchers to engage with the browser and genomic data more efficiently and effectively since 2018. Here, we introduce a new integrated panel design in the browser that allows users to interact with 1D (genomic features), 2D (such as Hi-C), 3D (genome structure), and 4D (time series) data in a single web page. The browser can display three-dimensional chromatin structures with the 3D viewer module. The 4D tracks, called ‘Dynamic’ tracks, animatedly display time-series data, allowing for a more striking visual impact to identify the gene or genomic region candidates as a function of time. Genomic data, such as annotation features, numerical values, and chromatin interaction data can all be viewed in the dynamic track mode. Imaging data from microscopy experiments can also be displayed in the browser. In addition to software development, we continue to service and expand the data hubs we host for large consortia including 4DN, Roadmap Epigenomics, TaRGET and ENCODE, among others. Our growing user/developer community developed additional track types as plugins, such as qBed and dynseq tracks, which extend the utility of the browser. The browser serves as a foundation for additional genomics platforms including the WashU Virus Genome Browser (for COVID-19 research) and the Comparative Genome Browser. The WashU Epigenome Browser can also be accessed freely through Amazon Web Services at https://epigenomegateway.org/.

An IoT-based Fire Safety Management System for Educational Buildings: A Case Study

Safety is a serious concern that should be addressed carefully in different locations including homes, workplaces and educational buildings. The risk of fire is the most significant threat in many educational facilities such as schools, universities, offices, etc. The main goal of this work is developing an effective system that allows early managing of fires to avoid material and human losses. With the advent of the Internet of Things (IoT), the implementation of such system became possible. A low-cost system incorporating IoT sensors is constructed in this study to collect data (heat, the number of people at the fire scene, ...) in real time. The system provides a control panel that displays readings from all sensors on a single web page. When the collected values exceed a particular threshold, the system sends a message to the building keeper’s phone, allowing him to notify the authorities or dispatch firemen in real time. One of the system’s most important characteristics is that it keeps track of how many people are at the fire scene, simplifying the evacuation process and allowing civil defense authorities to efficiently manage resources. The system has been successfully tested in a variety of circumstances in an educational building (Al-Faisaliah female campus, University of Jeddah, Saudi Arabia).

3dRS, a Web-Based Tool to Share Interactive Representations of 3D Biomolecular Structures and Molecular Dynamics Trajectories.

3D Representation Sharing (3dRS) is a web-based tool designed to share biomolecular structure representations, including 4D ensembles derived from Molecular Dynamics (MD) trajectories. The server offers a team working in different locations a single URL to share and discuss structural data in an interactive fashion, with the possibility to use it as a live figure for scientific papers. The web tool allows an easy upload of structures and trajectories in different formats. The 3D representation, powered by NGL viewer, offers an interactive display with smooth visualization in modern web browsers. Multiple structures can be loaded and superposed in the same scene. 1D sequences from the loaded structures are presented and linked to the 3D representation. Multiple, pre-defined 3D molecular representations are available. The powerful NGL selection syntax allows the definition of molecular regions that can be then displayed using different representations. Important descriptors such as distances or interactions can be easily added into the representation. Trajectory frames can be explored using a common video player control panel. Trajectories are efficiently stored and transferred to the NGL viewer thanks to an MDsrv-based data streaming. The server design offers all functionalities in one single web page, with a curated user experience, involving a minimum learning curve. Extended documentation is available, including a gallery with a collection of scenes. The server requires no registration and is available at https://mmb.irbbarcelona.org/3dRS.

Thomas H. Kunz

Thomas H. Kunz

A deep web data extraction model for web mining: a review

The World Wide Web has become a large pool of information. Extracting structured data from a published web pages has drawn attention in the last decade. The process of web data extraction (WDE) has many challenges, dueto variety of web data and the unstructured data from hypertext mark up language (HTML) files. The aim of this paper is to provide a comprehensive overview of current web data extraction techniques, in termsof extracted quality data. This paper focuses on study for data extraction using wrapper approaches and compares each other to identify the best approach to extract data from online sites. To observe the efficiency of the proposed model, we compare the performance of data extraction by single web page extraction with different models such as document object model (DOM), wrapper using hybrid dom and json (WHDJ), wrapper extraction of image using DOM and JSON (WEIDJ) and WEIDJ (no-rules). Finally, the experimentations proved that WEIDJ can extract data fastest and low time consuming compared to other proposed method.<br /><div> </div>

Page-Level Main Content Extraction From Heterogeneous Webpages

The main content of a webpage is often surrounded by other boilerplate elements related to the template, such as menus, advertisements, copyright notices, and comments. For crawlers and indexers, isolating the main content from the template and other noisy information is an essential task, because processing and storing noisy information produce a waste of resources such as bandwidth, storage space, and computing time. Besides, the detection and extraction of the main content is useful in different areas, such as data mining, web summarization, and content adaptation to low resolutions. This work introduces a new technique for main content extraction. In contrast to most techniques, this technique not only extracts text, but also other types of content, such as images, and animations. It is a Document Object Model-based page-level technique, thus it only needs to load one single webpage to extract the main content. As a consequence, it is efficient enough as to be used online (in real-time). We have empirically evaluated the technique using a suite of real heterogeneous benchmarks producing very good results compared with other well-known content extraction techniques.

An Interactive Web-Based Sexual Health Literacy Program for Safe Sex Practice for Female Chinese University Students: Multicenter Randomized Controlled Trial.

BackgroundSexual health concerns among young adults worldwide help to motivate preventative practices against sexually transmitted infections. To foster better sexual health, sexual health literacy must be enhanced. Little research has been conducted on the impact of gender power dynamics on sexual health, such as sexual coercion, even though the prevalence of sexual coercion remains high in China.ObjectiveThis study describes the development and systematic evaluation of a web-based sexual health literacy intervention called “Smart Girlfriend” for female Chinese university students.MethodsA multicenter randomized controlled trial was conducted with 781 female university students at 5 universities with dormitories in Hong Kong. Inclusion criteria were used to select unmarried, female, Chinese university students who were ≥18 years old and had not received a sexual health intervention in the past 12 months. Participants were randomly assigned to 2 groups: one group received an interactive web-based sexual health literacy intervention and the other group received a single webpage of online information about condom use. The intervention content was based on the Health Belief Model and the Continuum of Conflict and Control theory. The primary outcome was self-reported consistency of condom use with every partner at 3-month and 6-month follow-up assessments, analyzed using zero/one inflated beta (ZOIB) regression. The secondary outcome was an appraisal of the knowledge, attitudes, norms, and self-efficacy of condom use using the 25-item Multidimensional Condom Attitudes Scale (MCAS). The intention to treat was applied in analyses.ResultsOf 1503 individuals that were screened, 781 (52%) were randomized into 2 groups. The retention rates at the 3-month and 6-month follow-ups were 92% and 91%, respectively. Most participants were born locally (536/746, 72%), and 18% (134/746) self-reported as a sexual minority. ZOIB results regarding the consistency of condom use were not significant [model 1: odds ratio (OR) 2.25 with a 95% credible interval (CrI) of 0.84-6.36; model 2: OR 8.03 (95% CrI 0.22-330.31); model 3: OR 1.21 (95% CrI 0.78-1.86)]. Consistency in the intervention group was 5% higher (95% CI −1.90 to 11.63) than the control group at the 3-month follow-up, and 1% higher (95% CI −5.81 to 8·02) at the 6-month follow-up. MCAS scores at the 3-month follow-up were significantly higher in the intervention group (mean 122.51, SD 15.97) than the control group (mean 119.86, SD 15.85; P=.02).ConclusionsAn interactive web-based sexual health literacy program did not significantly increase the consistency of condom use compared to a single webpage of condom use information; however, it did temporarily improve knowledge, attitudes, norms, and self-efficacy regarding condom use. Future revisions of this intervention should be personalized and delivered with a proactive approach.Trial RegistrationClinicalTrials.gov NCT03695679; https://clinicaltrials.gov/ct2/show/NCT03695679

Comparative Contents Analysis of Selected Nigerian University Libraries’ Websites

The study underscored the need to periodically conduct contents analysis of Nigerian university library websites. This is because the content of websites is supposed to be dynamic and episodic analysis assists in overcoming their weaknesses and challenges which ensure improved quality that meets web ranking metrics. Therefore, employing qualitative research approach and case study methodology, 42 (25%) Nigerian university library websites were purposely and proportionately selected for the study using stratified sampling technique. Using a modified checklist of Mehta and Trivedi (2015) prepared from quality models and WCAG, data was collected directly from the websites. The data collected was analyzed using ATLAS for Window and Mac Qualitative Data Analysis Software. The result of the data analysis shows a significant number of the selected Nigerian university library websites are well structured and possessed basic information, there are still many features and information that are absent in many of the websites. Also many of the websites are small and limited in scope, with most consisting of a single webpage or two to four interlinked pages, presenting their messages up-front instead of encouraging navigation through the sites. The research recommends among others the need for Nigerian university libraries, while improving their websites, to significant make the website more develop and complex by introducing index page on their websites

Evaluation of plastic surgery resident aesthetic clinic websites

BackgroundThe purpose of the present study was to evaluate the website pages of integrated plastic and reconstructive surgery resident aesthetic clinics in the United States. MethodsWebsite pages dedicated to resident aesthetic clinics in all integrated plastic and reconstructive surgery residency programs (n = 79) were accessed between October and November 2019 and evaluated across several criteria, including: number of pages, procedures offered, faculty participation, respective prices, patient photographs, patient testimonials, contact information, and specific recruitment incentives. ResultsSeventy-nine integrated and 54 independent residency programs were identified, 31 of which had both an integrated and an independent residency program for a total of 102 distinct programs. Out of these, only 11 programs (10.8%) had a webpage dedicated to their resident aesthetic clinic (Figure 1). Twelve other programs (13.7%) that did not have a dedicated webpage mentioned a resident aesthetic clinic elsewhere on their residency program website. For each of the eleven programs with space for the resident aesthetic clinic, there was exactly one dedicated webpage. None of the programs with dedicated webpages included photographs of before and after cases, nor procedures performed, nor a listing of resident aesthetic clinic prices. Three of the dedicated webpages included information about faculty participation. Out of the 24 ASAPS endorsed aesthetic fellowship programs, 6 (25%) had a webpage dedicated to the fellow aesthetic clinic. Four of these webpages were comprised of a single webpage, while one program had 6 webpages and one program had 8 webpages. Only one program's webpage included before and after pictures. Four programs (16.7%) included information about faculty participation on the webpage. Five out of the 6 programs had a procedure list on the webpage. DiscussionResident aesthetic clinic websites are an important tool in recruiting patients as well as medical students. Addressing the lack and quality of such websites may improve recruitment of patients and students to plastic and reconstructive surgery residency programs. As a primary source of information for potential future residents and patients, plastic and reconstructive surgery programs need to maximize the content and utility of their websites.

The Experience of VaccinarSinToscana Website and the Role of New Media in Promoting Vaccination.

The Department of Health Sciences (University of Florence) developed a regional website “VaccinarSinToscana” in order to provide information on vaccines and communicate with the general population, as well as the healthcare community, at a regional and local level. The aim of this paper is to present the VaccinarSinToscana website framework and analyze the three-year activity of the website and the related social network account on Facebook in terms of dissemination and visibility. In the first three years since its launch, the VaccinarSinToscana portal has increased its visibility: the number of single users, visits and single web pages has grown exponentially. Our results also demonstrate how the Facebook account launch contributed enormously to the increase in the visibility of the website. The objective for the future of the VaccinarSinToscana portal is to grow further, in order to reach out to an even wider audience.

Using real-time online preprocessed mouse tracking for lower storage and transmission costs

Pageview is the most popular webpage analytic metric in all sectors including blogs, business, e-commerce, education, entertainment, research, social media, and technology. To perform deeper analysis, additional methods are required such as mouse tracking, which can help researchers understand online user behavior on a single webpage. However, the geometrical data generated by mouse tracking are extremely large, and qualify as big data. A single swipe on a webpage from left to right can generate a megabyte (MB) of data. Fortunately, the geometrical data of each x and y point of the mouse trail are not always needed. Sometimes, analysts only need the heat map of a certain area or perhaps just a summary of the number of activities that occurred on a webpage. Therefore, recording all geometrical data is sometimes unnecessary. This work introduces preprocessing during real-time and online mouse tracking sessions. The preprocessing that is introduced converts the geometrical data from each x and y point to a region-of-interest concentration, in other words only heat map areas that the analyzer is interested in. Ultimately, the approach used here is able to greatly reduce the storage and transmission cost of real-time online mouse tracking.

Automatically Discovering Relevant Images From Web Pages

Web pages contain irrelevant images along with relevant images. The classification of these images is an error-prone process due to the number of design variations of web pages. Using multiple web pages provides additional features that improve the performance of relevant image extraction. Traditional studies use the features extracted from a single web page. However, in this study, we enhance the performance of relevant image extraction by employing the features extracted from different web pages consisting of standard news, galleries, video pages, and link pages. The dataset obtained from these web pages contains 100 different web pages for each 200 online news websites from 58 different countries. For discovering relevant images, the most straightforward approach extracts the largest image on the web page. This approach achieves a 0.451 F-Measure score as a baseline. Then, we apply several machine learning methods using features in this dataset to find the most suitable machine learning method. The best f-Measure score is 0.822 using the AdaBoost classifier. Some of these features have been utilized in previous web data extraction studies. To the best of our knowledge, 15 new features are proposed for the first time in this study for discovering the relevant images. We compare the performance of the AdaBoost classifier on different feature sets. The proposed features improve the f-Measure by 35 percent. Besides, using only the cache feature, which is the most prominent feature, corresponds to 7 percent of this improvement.

Comparative contents analysis of selected Nigerian University libraries’ websites

The study underscored the need to periodically conduct contents analysis of Nigerian university library websites. This is because the content of websites is supposed to be dynamic and episodic analysis assists in overcoming their weaknesses and challenges which ensure improved quality that meets web ranking metrics. Therefore, employing qualitative research approach and case study methodology, 42 (25%) Nigerian university library websites were purposely and proportionately selected for the study using stratified sampling technique. Using a modified checklist of Mehta and Trivedi (2015) prepared from quality models and WCAG, data was collected directly from the websites. The data collected was analyzed using ATLAS for Window and Mac Qualitative Data Analysis Software. The result of the data analysis shows a significant number of the selected Nigerian university library websites are well structured and possessed basic information, there are still many features and information that are absent in many of the websites. Also many of the websites are small and limited in scope, with most consisting of a single webpage or two to four interlinked pages, presenting their messages up-front instead of encouraging navigation through the sites. The research recommends among others the need for Nigerian university libraries, while improving their websites, to significant make the website more develop and complex by introducing index page on their websites