An observation study was conducted with undergraduate students. The study includes a video analysis of coding activities, an analysis of the source code written during coding, and a structured interview with participants. This dataset contains video recordings of participants coding with the websites. It also contains the interview questions along with transcriptions. In addition, it contains the source code resulting from the participants.

Dataset Card for "code-search-net-javascript"

  Dataset Summary

This dataset is the JavaScript portion of the CodeSarchNet annotated with a summary column.The code-search-net dataset includes open source functions that include comments found at GitHub.The summary is a short description of what the function does.

  Languages

The dataset's comments are in English and the functions are coded in JavaScript

  Data Splits

Train, test, validation labels are… See the full description on the dataset page: https://huggingface.co/datasets/Nan-Do/code-search-net-javascript.

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.

Dive into Market Research Intellect's No Code Website Builder Tools Market Report, valued at USD 5.2 billion in 2024, and forecast to reach USD 12.3 billion by 2033, growing at a CAGR of 10.5% from 2026 to 2033.

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file

The global no-code website builder tools market is projected to reach $43.7 billion by 2033, exhibiting a CAGR of 12.4% during the forecast period. The increasing adoption of digital platforms by businesses and the growing demand for user-friendly website development tools are driving the market growth. The availability of a wide range of templates and drag-and-drop functionality makes no-code website builders accessible to individuals and businesses with limited technical expertise. Key trends shaping the market include the integration of artificial intelligence (AI) and machine learning (ML) to enhance the user experience, the rise of headless CMS platforms that enable greater flexibility and scalability, and the growing popularity of cloud-based no-code website builders that offer convenience and cost-effectiveness. The market is segmented into various types, applications, and regions, with North America holding a significant share due to the presence of leading technology companies and a large number of small and medium-sized businesses. Major players in the market include Wix, Bubble, Webflow, Squarespace, and WordPress, among others, who are focusing on expanding their offerings, forming strategic partnerships, and investing in research and development to gain a competitive edge. The no-code website builder tools market is experiencing exponential growth, with its value projected to reach over $17.6 billion by 2026. These tools empower non-technical individuals and businesses to create professional-looking websites without the need for programming knowledge.

This document is to provide you with the guidelines and processes required for committing web code in the hub environment.

Explore the historical Whois records related to sub-net-code-application.site (Domain). Get insights into ownership history and changes over time.

API URL: https://api.nlsc.gov.tw/other/ListLandSectionInput parameters: /County code/Township codeReturned results: XML format; including land section code, land code, and land nameAPI syntax example: https://api.nlsc.gov.tw/other/ListLandSection/B/B01

No Code Website Builder Tools Market size was valued at USD 1.97 Billion in 2023 and is estimated to reach USD 3.58 Billion by 2031, growing at a CAGR of 7.73 % from 2024 to 2031.

Global No Code Website Builder Tools Market Drivers

Growing Interest in Do-It-Yourself Website Creation: The demand for user-friendly, no-code platforms has increased as more companies and individuals want to build websites themselves rather than hiring professionals. These tools remove the technical obstacles typically connected with web building and enable consumers to create expert websites using drag-and-drop features and templates.

Economicalness: By removing the need to hire qualified developers, no-code website builders drastically lower the cost of web development, making it more accessible to startups, small enterprises, and individual entrepreneurs. A wide spectrum of consumers, including huge corporations and solopreneurs, find this cost appealing.

About This Dataset

The Roboflow Website Screenshots dataset is a synthetically generated dataset composed of screenshots from over 1000 of the world's top websites. They have been automatically annotated to label the following classes: :fa-spacer: * button - navigation links, tabs, etc. * heading - text that was enclosed in <h1> to <h6> tags. * link - inline, textual <a> tags. * label - text labeling form fields. * text - all other text. * image - <img>, <svg>, or <video> tags, and icons. * iframe - ads and 3rd party content.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/mOG3u3Z.png" alt="WIkipedia Screenshot">

Usage

Annotated screenshots are very useful in Robotic Process Automation. But they can be expensive to label. This dataset would cost over $4000 for humans to label on popular labeling services. We hope this dataset provides a good starting point for your project. Try it with a model from our model library.

Collecting Custom Data

Roboflow is happy to provide a custom screenshots dataset to meet your particular needs. We can crawl public or internal web applications. Just reach out and we'll be happy to provide a quote!

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Dataset Card for the Rosetta Code Dataset

  Dataset Summary

Rosetta Code is a programming chrestomathy site. The idea is to present solutions to the same task in as many different languages as possible, to demonstrate how languages are similar and different, and to aid a person with a grounding in one approach to a problem in learning another. Rosetta Code currently has 1,203 tasks, 389 draft tasks, and is aware of 883 languages, though we do not (and cannot) have… See the full description on the dataset page: https://huggingface.co/datasets/pharaouk/rosetta-code.

The global code editor market is experiencing robust growth, driven by the increasing demand for software development and the rising adoption of cloud-based solutions. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant growth is fueled by several key factors, including the expansion of the software development industry, the rising popularity of programming languages, and the increasing demand for efficient and user-friendly code editing tools. The shift towards cloud-based and web-based code editors is a prominent trend, offering developers enhanced collaboration capabilities and accessibility. While the on-premises deployment model still holds a significant market share, particularly among enterprise users prioritizing data security, the web-based segment is projected to witness faster growth due to its inherent flexibility and cost-effectiveness. The market is segmented by application (personal and enterprise) and deployment (on-premises and web-based). Enterprise applications dominate the market currently, but the personal use segment is showing significant growth potential, particularly amongst independent developers and hobbyists. Competitive pressures are high, with both established players like Microsoft and GitHub, and smaller, niche players offering specialized features and functionalities. The growth trajectory is expected to continue throughout the forecast period, driven by advancements in Artificial Intelligence (AI) integration within code editors and the increasing demand for low-code/no-code development platforms. However, factors such as the high initial investment costs for enterprise-grade solutions and the need for continuous skill development among developers could pose some restraints to market expansion. The geographical distribution reveals a significant concentration of market share in North America and Europe, reflecting the advanced technological landscape and substantial software development activities in these regions. However, developing economies in Asia Pacific and other regions are expected to contribute significantly to market growth in the coming years, fueled by increasing digitalization efforts and the growing adoption of technology across diverse sectors.

Site code key for M. Carr and T. Tinker Kelp Forest Resilience project.

Developers routinely integrate Stack Overflow code snippets into their codebases. However, the quality of snippets embedded in users’ answers remain elusive, and existing evaluations of code quality tend to be language or context-specific. Moreover, literature have found that contribution patterns vary depending on geographical locales, creating an unexplained rift between code quality, user location, and latent contextual regional factors.

The proposed study evaluates the quality of SQL, JavaScript, Python, Ruby, and Java snippets across reliability, readability, performance, and security dimensions, benchmarking findings across states in the USA and investigating how different diversity indicators correlate against code quality violations. The study culminates in a series of inductive content analyses that qualitatively supplement prior quality dimensions.

This replication package is provided for those interested in further examining our research methodology.

Phishing Websites Dataset

Overview

This dataset is designed to aid in the analysis and detection of phishing websites. It contains various features that help distinguish between legitimate and phishing websites based on their structural, security, and behavioral attributes.

Dataset Information

• Total Columns: 31 (30 Features + 1 Target)
• Target Variable: Result (Indicates whether a website is phishing or legitimate)

Features Description

URL-Based Features

• Prefix_Suffix – Checks if the URL contains a hyphen (-), which is commonly used in phishing domains.
• double_slash_redirecting – Detects if the URL redirects using //, which may indicate a phishing attempt.
• having_At_Symbol – Identifies the presence of @ in the URL, which can be used to deceive users.
• Shortining_Service – Indicates whether the URL uses a shortening service (e.g., bit.ly, tinyurl).
• URL_Length – Measures the length of the URL; phishing URLs tend to be longer.
• having_IP_Address – Checks if an IP address is used in place of a domain name, which is suspicious.

Domain-Based Features

• having_Sub_Domain – Evaluates the number of subdomains; phishing sites often have excessive subdomains.
• SSLfinal_State – Indicates whether the website has a valid SSL certificate (secure connection).
• Domain_registeration_length – Measures the duration of domain registration; phishing sites often have short lifespans.
• age_of_domain – The age of the domain in days; older domains are usually more trustworthy.
• DNSRecord – Checks if the domain has valid DNS records; phishing domains may lack these.

Webpage-Based Features

• Favicon – Determines if the website uses an external favicon (which can be a sign of phishing).
• port – Identifies if the site is using suspicious or non-standard ports.
• HTTPS_token – Checks if "HTTPS" is included in the URL but is used deceptively.
• Request_URL – Measures the percentage of external resources loaded from different domains.
• URL_of_Anchor – Analyzes anchor tags (<a> links) and their trustworthiness.
• Links_in_tags – Examines <meta>, <script>, and <link> tags for external links.
• SFH (Server Form Handler) – Determines if form actions are handled suspiciously.
• Submitting_to_email – Checks if forms submit data directly to an email instead of a web server.
• Abnormal_URL – Identifies if the website’s URL structure is inconsistent with common patterns.
• Redirect – Counts the number of redirects; phishing websites may have excessive redirects.

Behavior-Based Features

• on_mouseover – Checks if the website changes content when hovered over (used in deceptive techniques).
• RightClick – Detects if right-click functionality is disabled (phishing sites may disable it).
• popUpWindow – Identifies the presence of pop-ups, which can be used to trick users.
• Iframe – Checks if the website uses <iframe> tags, often used in phishing attacks.

Traffic & Search Engine Features

• web_traffic – Measures the website’s Alexa ranking; phishing sites tend to have low traffic.
• Page_Rank – Google PageRank score; phishing sites usually have a low PageRank.
• Google_Index – Checks if the website is indexed by Google (phishing sites may not be indexed).
• Links_pointing_to_page – Counts the number of backlinks pointing to the website.
• Statistical_report – Uses external sources to verify if the website has been reported for phishing.

Target Variable

• Result – The classification label (1: Legitimate, -1: Phishing)

Usage

This dataset is valuable for:
✅ Machine Learning Models – Developing classifiers for phishing detection.
✅ Cybersecurity Research – Understanding patterns in phishing attacks.
✅ Browser Security Extensions – Enhancing anti-phishing tools.

Overview The Vulnerability Fix Dataset is a collection of 35,000 code snippets containing both vulnerable and fixed versions of code. The dataset focuses on common software security vulnerabilities and their corresponding fixes, making it highly valuable for research in secure coding practices, automated vulnerability detection, and software security analysis. ** Dataset Structure** This dataset consists of three main columns:

vulnerability_type: The type of security vulnerability (e.g., SQL Injection, Cross-Site Scripting). vulnerable_code: The original code snippet containing the vulnerability. fixed_code: The secure version of the code with the vulnerability fixed. The dataset includes vulnerabilities across multiple programming languages, making it useful for machine learning, static analysis, and cybersecurity training.

Features of the Dataset The Vulnerability Fix Dataset contains the following key features:

vulnerability_type (String)

The category of the security vulnerability present in the code. Examples: SQL Injection Cross-Site Scripting (XSS) Buffer Overflow Command Injection Insecure Cryptographic Practices vulnerable_code (String)

The original code snippet that contains a security vulnerability. Written in various programming languages, including Java, Python, C, and JavaScript. Used for analyzing insecure coding patterns. fixed_code (String)

The corrected version of the vulnerable_code with security improvements. Demonstrates best practices in secure coding. Helps in training AI models for automatic vulnerability fixing. This dataset is structured to support research in automated vulnerability detection, static code analysis, and secure software development.

Create your own initiative by combining existing applications with a custom site. Use this initiative to form teams around a problem and invite your community to participate.

The no-code website builder market is experiencing robust growth, driven by the increasing demand for user-friendly website creation tools and the expanding digital presence of businesses and individuals. The market, estimated at $15 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% through 2033, reaching an estimated $45 billion. This growth is fueled by several key factors. Firstly, the ease of use offered by these platforms empowers non-technical users to build professional-looking websites without coding skills, significantly lowering the barrier to entry for online presence. Secondly, the diverse range of functionalities offered, from simple personal blogs to complex e-commerce stores, caters to a broad spectrum of users. Thirdly, the emergence of advanced features like AI-powered design assistance and integrated marketing tools further enhances the appeal and functionality of these platforms. Finally, the increasing adoption of mobile-first approaches and the growing need for cross-platform compatibility are driving innovation and demand within the market. Despite the rapid growth, market penetration remains relatively low, suggesting significant untapped potential. Competition among established players like Wix, Squarespace, and WordPress, alongside a wave of innovative newcomers, is intensifying. The market is segmented by website type (personal, business, community) and platform (mobile, desktop, application), reflecting the diverse needs of users. Geographic distribution shows strong growth across North America and Europe, while Asia-Pacific is emerging as a rapidly expanding market. While the ease of use is a major driver, some restraints include limitations on customization for complex websites and concerns regarding platform dependency and data security. Ongoing technological advancements, including AI-driven design and enhanced integration capabilities, will likely shape the future landscape of this dynamic market. The focus on user experience and innovative features will be crucial for players aiming for market leadership in the years to come.

The Department of Building and Safety is tasked with the enforcement of the City's building, electrical, mechanical and zoning regulations, which are elements of the Los Angeles Municipal Code. A Code Enforcement Case may originate from a customer service request to investigate a possible Code violation, from a planned and authorized inspection of a neighborhood for the purpose of reducing blight, or from a proactive inspection of a site due to an observed hazardous condition. A case is closed (indicated by a "C" status) when the site satisfies the requirements for Code compliance, or when no Code violation is found. A case remains open (indicated by an "O" status) until the site satisfies the requirements for Code compliance.