As of December 2024, there were in total around **** million websites registered in China. This represent an increase from around **** million by the end of 2023.

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

This graph depicts the estimated number of visitors to the Christie's website and app from 2017 to 2019. In 2019, the number of online visitors amounted to approximately **** million, up from approximately ** million the previous year.

This dataset provides comprehensive contact information extracted from websites in real-time. It includes emails, phone numbers, and social media profiles, and other contact methods found across website pages. The data is extracted through intelligent parsing of website content, meta information, and structured data. Users can leverage this dataset for lead generation, sales prospecting, business development, and contact database building. The API enables efficient extraction of contact details from any website, helping businesses streamline their outreach and contact discovery processes. The dataset is delivered in a JSON format via REST API.

Between 2019 and 2020, the number of shoppers who visited fast-fashion retailers' websites generally increased on a total basis. In the first quarter of 2020, which coincides with the start of the coronavirus pandemic, visitor numbers experienced a noticeable drop, but in the following months numbers picked up again. Mobile website usage was slightly more popular with shoppers. Most recently, fast fashion retailer websites attracted *** million shoppers on mobile web.

Websites in the energy, utilities, and construction sector averaged the largest amount of visits per online session worldwide. In the fourth quarter of 2022, desktop users in that segment visited around seven pages per online session. Travel and hospitality ranked second, with an average of almost six pages visited. In terms of mobile users, travel and hospitality registered the highest number of page views, followed by retail.

Weather Channel had 285.6 million average visitors to its website in the 12 months running to May 2024, making it the leading global news brand worldwide in this respect. Following in second place was the New York Times with 113 million web visitors.

A complete list of live websites using the react-number-format technology, compiled through global website indexing conducted by WebTechSurvey.

Go to http://on.ny.gov/1J8tPSN on the New York Lottery website for past Mega Millions results and payouts.

OpenWeb Ninja's Company Website Contacts Scraper API extracts/scrapes B2B Contact Data such as B2B Email Data, Phone Number Data, and Social Contacts from a website domain in real-time.

The API pulls public data from a company website domain & related sources on the web and returns email addresses, phone numbers, Facebook URL, TikTok URL, Instagram URL, LinkedIn URL, Twitter URL, Youtube Channel URL, GitHub URL, and Pinterest URL, when available.

OpenWeb Ninja's Company Website Contacts Scraper API's B2B Contact Data, Phone Number Data, and Social Contacts Data is typically used for: - B2B Contact Enrichment - B2B Email Marketing - B2B Lead Generation - Ads Targeting - Marketing/Sales Data Enrichment

OpenWeb Ninja's Company Website Contacts Scraper API Stats & Capabilities: - 1000+ Emails and Phone Numbers per company website domain are supported - 8 Social networks covered: Facebook, TikTok, Instagram, LinkedIn, Twitter, Youtube, GitHub, and Pinterest. - Scrapes all website pages, quickly. - Support for getting website domain by company name

Go to http://on.ny.gov/1Cx6zvs or http://on.ny.gov/1KYjE6X on the New York Lottery website for past Daily Numbers/Win-4 results and payouts.

In the end of 2023, China's internet had amassed almost *** billion unique webpages, slightly over a third of which were registered in the country's capital Beijing. Guangdong and Zhejiang were other provinces reporting the highest number of registered webpages in China.

Sign Up for a free trial: https://rampedup.io/sign-up-%2F-log-in - 7 Days and 50 Credits to test our quality and accuracy.

These are the fields available within the RampedUp Global dataset.

CONTACT DATA: Personal Email Address - We manage over 115 million personal email addresses Professional Email - We manage over 200 million professional email addresses Home Address - We manage over 20 million home addresses Mobile Phones - 65 million direct lines to decision makers Social Profiles - Individual Facebook, Twitter, and LinkedIn Local Address - We manage 65M locations for local office mailers, event-based marketing or face-to-face sales calls.

JOB DATA: Job Title - Standardized titles for ease of use and selection Company Name - The Contact's current employer Job Function - The Company Department associated with the job role Title Level - The Level in the Company associated with the job role Job Start Date - Identify people new to their role as a potential buyer

EMPLOYER DATA: Websites - Company Website, Root Domain, or Full Domain Addresses - Standardized Address, City, Region, Postal Code, and Country Phone - E164 phone with country code Social Profiles - LinkedIn, CrunchBase, Facebook, and Twitter

FIRMOGRAPHIC DATA: Industry - 420 classifications for categorizing the company’s main field of business Sector - 20 classifications for categorizing company industries 4 Digit SIC Code - 239 classifications and their definitions 6 Digit NAICS - 452 classifications and their definitions Revenue - Estimated revenue and bands from 1M to over 1B Employee Size - Exact employee count and bands Email Open Scores - Aggregated data at the domain level showing relationships between email opens and corporate domains. IP Address -Company level IP Addresses associated to Domains from a DNS lookup

CONSUMER DATA: Education - Alma Mater, Degree, Graduation Date Skills - Accumulated Skills associated with work experience
Interests - Known interests of contact Connections - Number of social connections. Followers - Number of social followers

Download our data dictionary: https://rampedup.io/our-data

As many general retailers or mass distribution channels experienced an exponential growth during the months of the COVID-19 induced lockdown in France, the source wanted to measure the total number of backlinks on the different retailers websites. Thus, Carrefour.fr was the leading general retailer with the most backlinks amounting to 8,300 on their website. A strategy of acquiring backlinks which therefore seems to be paying off for the major retailer, which drew around eight percent of its overall traffic through this means.

🇺🇦 우크라이나 English The set contains location, postal address, communication numbers, official website and email addresses

Data set of phone numbers for state-of-the-art businesses, municipalities and county authorities. It is intended to be used together with the data set of the units of public administration. This dataset is part of several data sets about public enterprises. The data sets are referred to as the agency base and were previously on Norge.no. They contain an overview of public enterprises, i.e. government agencies and enterprises’ central, regional and local units, county municipalities and municipalities. Data sets are not updated. The data sets contain information about the name of the enterprise, visiting address, postal address, telephone number, e-mail address, web address (URL), map coordinates (position), coverage (which municipalities the business covers), organisation number, overarching activity, type of organisation, type of affiliation (the way in which an enterprise is linked to the executive government) and quality assessments of the website. Look up on the keyword/tag agency base to see the other datasets. The establishment base is closed and is no longer maintained by the Directorate of Digitalisation (formerly Difi). The datasets were last updated in January 2012. Note that this does not mean that all data was updated in January 2012, but that the last changes were made at that time. Reference to the source When using this dataset, we ask that the source be referred to as follows (cf the NLOD license): The service is based on open data sets from the Directorate of Digitalisation and is subject to the Norwegian License for Public Data (NLOD). The data was last updated in 2012 and is no longer maintained by the Directorate of Digitalisation.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "South Carolina SHPO" data publication.

According to a report published by DataReportal, as of December 2020, Huay.com had the highest number of visited pages per one time of around 25.88 pages. In that same period, Huay.com had a monthly traffic of approximately 32.3 million visits in Thailand.

The set contains the directory of the Department of municipal resources - postal and e-mail address, page address in the social network, data about the head, work schedule