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This dataset offers an extensive assortment of food reviews, designed to support investigations and examinations for restaurant recommendations within the realms of user preference patterns, natural language processing (NLP), and machine learning. Developed for educational and research objectives, this dataset presents a varied array of restaurant reviews spanning diverse cuisine categories.\(\)

Description of dataset: restaurant_reviews.json

City Latitude Longitude Name Postal Code Review Count Stars - review score from 1-5 stars State/Province Review details

Potential use cases: Developing NLP models for user preference matching Building predictive models to forecast cuisine popularity trends Analyzing regional culinary preferences disparities and opportunities for people looking to enter the domain

Undress AI apps, powered by advanced AI and deep learning, have sparked both curiosity and controversy. These tools use generative algorithms to digitally alter images, but their ethical implications and potential for misuse cannot be ignored.

In 2025, the landscape of such apps continues to evolve, with some gaining popularity for their capabilities. Here’s a quick look at the top 7 Undress AI apps making waves this year

1. Undress.app Why I Recommend It: Undress.app stands out as one of the best undress AI apps available today. With its user-friendly interface and advanced technology, it allows users to generate unclothed images quickly and safely. The app prioritizes user privacy, ensuring that no data is saved or shared, making it a trustworthy choice for those interested in exploring AI-generated content.

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Key Features: User-Friendly Interface: The app is designed to be intuitive, making it easy for anyone to navigate.

Multiple Generation Modes: Users can choose from various modes such as Lingerie, Bikini, and NSFW to customize their experience.

High-Quality Results: The AI processes images to deliver high-quality, unblurred results, even for free trial accounts.

Privacy and Security: The app does not save any user data, ensuring complete confidentiality.

My Experience: Using Undress.app was a seamless experience. The sign-up process was quick, and I appreciated the variety of modes available. The results were impressive, showcasing the app's advanced AI capabilities. Overall, it was a satisfying experience that I would recommend to others.

Pros: Free Credits: New users receive free credits upon signing up, allowing them to try the app without any financial commitment.

Versatile Usage: The app works with both male and female photos, as well as anime images, providing a wide range of options.

Cons: Sign-Up Required: Users must create an account to access the app, which may deter some potential users.

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1. Undressai.tools Why I Recommend It Undressai.tools combines powerful AI algorithms with a seamless user experience, making it an excellent choice for both casual users and professionals. The app prioritizes user privacy by automatically deleting generated images within 48 hours.

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Key Features Stable Diffusion Technology: Produces high-quality, coherent outputs with minimal artifacts.

Generative Adversarial Networks (GANs): Utilizes two neural networks to create highly realistic images of nudity.

Image Synthesis: Generates realistic skin textures that replace removed clothing for lifelike results.

User-Friendly Interface: Allows users to easily upload images and modify them with just a few clicks.

My Experience Using Undressai.tools was a delightful experience. The interface was intuitive, allowing me to upload images effortlessly. I appreciated the ability to outline areas for modification, which resulted in impressive and realistic outputs. The app's speed and efficiency made the process enjoyable, and I was amazed by the quality of the generated images.

Pros High-quality image generation with realistic results.

Strong emphasis on user privacy and data security.

Cons Some users may find the results vary based on the quality of the uploaded images.

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1. Nudify.online Why I Recommend It Nudify.online stands out due to its commitment to user satisfaction and the quality of its generated images. The application is designed for entertainment purposes, ensuring a safe and enjoyable experience for users over the age of 18.

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Key Features High Accuracy: The AI Nudifier boasts the highest accuracy in generating realistic nudified images.

User-Friendly Interface: The platform is easy to navigate, allowing users to generate images in just a few clicks.

Privacy Assurance: Users are reminded to respect the privacy of others and are solely responsible for the images they create.

No Deepfake Content: The application strictly prohibits the creation of deepfake content, ensuring ethical use of the technology.

My Experience Using Nudify.online was a seamless experience. The application is straightforward, and I was able to generate high-quality nudified images quickly. The results were impressive, showcasing the power of AI technology. I appreciated the emphasis on user responsibility and privacy, which made me feel secure while using the app.

Pros Highly realistic image generation. Easy to use with a simple login process.

Cons Limited to users aged 18 and above, which may restrict access for younger audiences.

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1. Candy.ai Candy.ai stands out as one of the best undress AI apps available today. It offers users a unique and immersive experience, allowing them to create and interact with their ideal AI girlfriend. The platform combines advanced deep-learning technology with a user-friendly interface, making it easy to explore various fantasies and desires.

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Why I Recommend It Candy.ai is highly recommended for those seeking a personalized and intimate experience. The app allows users to design their AI girlfriend according to their preferences, ensuring a tailored interaction that feels genuine and engaging.

Key Features Customizable AI Girlfriend: Users can choose body type, personality, and clothing, creating a truly unique companion.

Interactive Chat: The AI girlfriend engages in meaningful conversations, responding quickly and intuitively to user prompts.

Photo Requests: Users can request photos or selfies of their AI girlfriend in various outfits, enhancing the immersive experience.

Privacy and Security: Candy.ai prioritizes user privacy, ensuring that all interactions remain confidential and secure.

My Experience Using Candy.ai has been an enjoyable journey. The ability to customize my AI girlfriend made the experience feel personal and engaging. I appreciated how quickly she responded to my messages, making our interactions feel natural. The option to request photos added an exciting layer to our relationship, allowing me to explore my fantasies in a safe environment.

Pros Highly customizable experience tailored to individual preferences.

Strong emphasis on user privacy and data security.

Cons Some users may find the AI's responses occasionally lack depth.

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1. UndressHer.app Why I Recommend It This app combines creativity with advanced AI technology, making it easy for anyone to design their perfect AI girlfriend. The variety of customization options ensures that every user can create a unique character that resonates with their preferences.

Key Features Extensive Customization: Choose from over 200 unique options to design your AI girlfriend.

Flexible Pricing: Various token bundles are available, including a free option for casual users.

High-Quality Images: Premium and Ultimate plans offer images without watermarks and in the highest quality.

User-Friendly Interface: Simple navigation makes it easy to create and modify your AI girlfriend.

My Experience Using UndressHer.app has been a delightful experience. The customization options are extensive, allowing me to create a character that truly reflects my preferences. The app is intuitive, making it easy to navigate through the various features. I particularly enjoyed the ability to undress my AI girlfriend, which added an exciting layer to the design process. Overall, it was a fun and engaging experience.

Pros Offers a free option for users to try before committing to paid plans.

High-quality AI-generated images with no watermarks in premium plans.

Cons Some users may find the token system a bit limiting for extensive use.

1. Undress.vip Why I Recommend It Undress.vip offers a unique blend of entertainment and technology, making it a top choice for users interested in AI-driven experiences. Its ability to generate realistic images while maintaining user privacy is a significant advantage.

Key Features Realistic Image Generation: The app uses advanced algorithms to create lifelike images.

User-Friendly Interface: Easy navigation ensures a seamless experience for all users.

Privacy Protection: User data is kept secure, allowing for worry-free usage.

Regular Updates: The app frequently updates its features to enhance user experience.

My Experience Using Undress.vip has been a delightful experience. The app is intuitive, and I was able to generate images quickly without any technical difficulties. The quality of the images exceeded my expectations, and I appreciated the emphasis on privacy. Overall, it was a fun and engaging way to explore AI technology.

Pros High-Quality Outputs: The images produced are remarkably realistic.

Engaging User Experience: The app is entertaining and easy to use.

Cons Limited Free Features: Some advanced features require a subscription.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

We find evidence to support common behaviour in smartphone usage based on analysis of application (app) switching. This is an overlooked aspect of smartphone usage that gives additional insight beyond screen time and the particular apps that are accessed. Using a dataset of usage behaviour from 53 participants over a 6-week period, we find strong similarity in the structure of networks built from app switching, despite diversity in the apps used, and the volume of app switching. App switch networks exhibit small-world, broad-scale network features, with a rapid popularity decay, suggesting that preferential attachment may drive next-app decision making.

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

The most successful music streaming service in the United States was Apple Music as of September, with the most up to date information showing that 49.5 million users accessed the platform each month. Spotify closely followed, with a similarly impressive 47.7 million monthly users.

What is a music streaming service?

Music streaming services provide their users with a database compiled of songs, playlists, albums and videos, where content can be accessed online, downloaded, shared, bookmarked and organized.

The music streaming business is huge, and has sometimes been lauded as the savior of the music industry. The biggest two services are in constant competition for the monopoly of the market. Apple Music was launched in 2015, whereas Spotify has been around since 2008. Other popular streaming services include Deezer, SoundCloud and iHeartRadio.

Do artists make a lot of money from streaming services? 

In short, unfortunately not. Both Apple Music and Spotify have been frequently criticized for the tiny royalty payments they offer artists. Particularly for emerging talent, streaming services are far from a lucrative source of income. Bigger, established stars like Taylor Swift are more likely to regularly make a good amount of money this way. But either way, a track needs to go viral or be streamed several million times before it earns any real cash.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

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Dive into a world of flavors and innovation with our meticulously curated BBC Food Recipes dataset. This dataset provides detailed information on an extensive array of recipes featured on BBC Food, making it a treasure trove for data scientists, food bloggers, and developers building recipe apps.

What’s Included?

• Recipe Titles: Names of the dishes, making it easy to identify and categorize.
• Ingredients: Comprehensive ingredient lists for accurate analysis and integration.
• Preparation Methods: Step-by-step cooking instructions for a complete culinary guide.
• Cooking Time: Details on preparation and cooking durations for each recipe.
• Dietary Information: Insights into vegan, vegetarian, gluten-free, and other dietary preferences.

Why Use This Dataset?

With thousands of recipes spanning various cuisines and dietary requirements, this dataset is perfect for:

1. Creating Recommendation Engines: Power your apps with personalized recipe suggestions.
2. Analyzing Food Trends: Discover emerging culinary patterns and ingredient popularity.
3. Exploring the World of Cooking: Unearth new techniques and flavor combinations for your projects.

For additional culinary insights and data resources, explore the Food and Beverage Data offered by Crawl Feeds. It's an exceptional tool for enhancing recipe applications, trend analysis, and food research.

Enhance your projects with structured, high-quality recipe data from one of the most trusted sources in the culinary world. Start leveraging the BBC Food Recipes dataset to cook up innovative ideas and insights today.

The dataset contains all publicly accessible green spaces in the city of Heidelberg including an attribute table with three main indicators on the use and perception of urban green spaces (Popularity_Indicator, Aesthetics_Indicator and Animals_Indicator) derived from social media. In addition to these three main values, the attribute table contains a further 18 statistical values, which were calculated by intersecting the green areas with classified social media data and are documented in the metadata description. The green space polygons were generated using an automatic approach described in Ludwig et al. (2021) is described in more detail. The green spaces and indicator values ​​are part of the central database (Cakir et al., 2021) for the evaluation of green spaces in Heidelberg according to criteria or suitability for specific activities using the meinGrün app (app.meingruen.org). The popularity of urban green spaces in Heidelberg was measured by the density of location-related social media posts. The processing of the data for green spaces is presented and described in a notebook (pub.zih.tu-dresden.de/~s7398234/vis/zielgeometrien-intersect_v6.html) The aesthetics indicator describes the aesthetic value of urban green spaces in Heidelberg and was based on conceptualized and measured the density of aesthetics-related social media posts. For the identification of the social media posts related to the aesthetic value of urban green spaces, a novel methodology based on unsupervised text classification and targeted filtering of social media posts was developed and in Gugulica & Burghardt, 2021 - work in progress - is described in more detail. The animals indicator shows the presence of wild animals in urban green spaces in Heidelberg. The quantification of the wildlife indicator is based on the underlying assumption that densities of social media posts related to wildlife and wildlife photography potentially reflect demand for wildlife viewing and indicate hotspots for that activity. In order to identify the relevant social media posts for the calculation of the indicator, the above methodology, based on unsupervised text classification and targeted filtering of social media posts and described in more detail in Gugulica & Burghardt, 2021 - in progress - was used , used. Location-aware social media data from Instagram, Flickr, and Twitter (including photos annotated with text and text messages) were used to quantify the popularity, aesthetics, and wildlife indicators of urban green spaces in Heidelberg. The data was identified using the embedded location information and a custom bounding box, and retrieved and collected via the API provided by each of the platforms. Only publicly available social media posts published between January 1, 2015 and October 31, 2020 were considered and saved as a CSV file along with meta information such as user ID, coordinates, captions, recording and uploading date saved. Duplicates were removed and after overlaying the dataset with the target polygons, the final dataset for Heidelberg comprised 308,496 posts (28,886 tweets, 245,992 Instagram posts, and 33,618 Flickr posts). The choice of platforms was mainly determined by the popularity of the social media channels and the specificity of the respective content. In order to cover a broader range of users, the three data sources were combined, which led to more robust results due to the increased data width. References: Cakir, S., Schorcht, M., Stanley, C., Theodor, R., Ludwig, C., Gugulica, M., Dunkel, A., & Hecht, R. (2021). Urban green spaces and indicators: Heidelberg (2021 version) [Data set]. Leibniz Institute of Ecological Urban and Regional Development, Weberplatz 1, 01217 Dresden, Germany. https://doi.org/10.26084/IOERFDZ-DATA-DE-2021-2 Ludwig, C.; Hecht, R.; Lautenbach, S.; Schorcht, M.; Zipf, A. (2021): Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions. In: ISPRS International Journal of Geo-Information 10 (2021) 4, p.251 https://doi.org/10.3390/ijgi10040251

Mobile Application Coverage: The 30% Curse and Ways Forward## Purpose In this artifact, we provide the information about our benchmarks used for manual and tool exploration. We include coverage results achieved by tools and human analysts as well as plots of the coverage progression over time for analysts. We further provide manual analysis results for our case study, more specifically extracted reasons for unreachability for the case study apps and extracted code-level properties, which constitute a ground truth for future work in coverage explainability. Finally, we identify a list of beyond-GUI exploration tools and categorize them for future work to take inspiration from. We are claiming available and reusable badges; the artifact is fully aligned with the results described in our paper and comprehensively documented.## ProvenanceThe paper preprint is available here: https://people.ece.ubc.ca/mjulia/publications/Mobile_Application_Coverage_ICSE2025.pdf## Data The artifact submission is organized into five parts:- 'BenchInfo' excel sheet describing our experiment dataset- 'Coverage' folder containing coverage results for tools and analysts (RQ1) - 'Reasons' excel sheet describing our manually extracted reasons for unreachability (RQ2)- 'ActivationProperties' excel sheet describing our manually extracted code properties of unreached activities (RQ3)- 'ActivationProperties-Graph' pdf which presents combinations of the extracted code properties in a graph format.- 'BeyondGUI' folder containing information about identified techniques which go beyond GUI exploration.The artifact requires about 15MB of storage.### Dataset: 'BenchInfo.xlsx'This file list the full application dataset used for experiments into three tabs: 'BenchNotGP' (apps from AndroTest dataset which are not on Google Play), 'BenchGP' (apps from AndroTest which are also on Google Play) and 'TopGP' (top ranked free apps from Google Play). Each tab contains the following information:- Application Name- Package Name- Version Used (Latest)- Original Version- # Activities- Minimum SDK- Target SDK- # Permissions (in Manifest)- List of Permissions (in Manifest)- # Features (in Manifest)- List of Features (in Manifest)The 'TopGP' sheet also includes Google-Play-specific information, namely:- Category (one of 32 app categories)- Downloads- Popularity RankThe 'BenchGP' and 'BenchNotGP' sheets also include the original version (included in the AndroTest benchmark) and the source (one of F-Droid, Github or Google Code Archives).### RQ1: 'Coverage'The 'Coverage' folder includes coverage results for tools and analysts, and is structured as follows:- 'CoverageResults.xlsx": An excel sheet containing the coverage results achieved by each human analysts and tool. - The first tab described the results over all apps for analysts combined, tools combined, and analysts + tools, which map to Table II in the paper. - Each of the following 42 tab, one per app in TopGP, marks the activities reached by Analyst 1, Analyst 2, Tool 1 (ape) and Tool 2 (fastbot), with an 'x' in the corresponding column to indicate that the activity was reached by the given agent.- 'Plots': A folder containing plots of the progressive coverage over time of analysts, split into one folder for 'Analyst1' and one for 'Analyst2'. - Each of the analysts' folder includes a subfolder per benchmark ('BenchNotGP', 'BenchGP' and 'TopGP'), containing as many png files as applications in the benchmark (respectively 47, 14 and 42 image files) named 'ANALYST_[X]_[APP_PACKAGE_NAME]'.png.### RQ2: 'Reasons.xslx'This file contains the extracted reasons for unreachability for the 11 apps manually analyzed. - The 'Summary' tab provides an overview of unreached activities per reasons over all apps and per app, which corresponds to Table III in the paper. - The following 11 tabs, each corresponding to and named after a single application, describe the reasons associated with each activity of that application. Each column corresponds to a single reason and 'x' indicates that the activity is unreached due to the reason in that column. The top row sums up the total number of activities unreached due to a given reason in each column.- The activities at the bottom which are greyed out correspond to activities that were reached during exploration, and are thus excluded from the reason extraction.### RQ3: 'ActivationProperties.xslx'This file contains the full list of activation properties extracted for each of the 185 activities analyzed for RQ2.The first half of the columns (columns C-M) correspond to the reasons (excluding Transitive, Inconclusive and No Caller) and the second half (columns N-AD) correspond to properties described in Figure 5 in the paper, namely:- Exported- Activation Location: - Code: GUI/lifecycle, Other Android or App-specific - Manifest- Activation Guards: - Enforcement: In Code or In Resources - Restriction: Mandatory or Discretionary- Data: - Type: Parameters, Execution Dependencies - Format: Primitive, Strings, ObjectsThe rows are grouped by applications, and each row correspond to an activity of that application. 'x' in a given column indicates the presence of the property in that column within the analyzed path to the activity. The third and fourth rows sums up the numbers and percentages for each property, as reported in Figure 5.### RQ3: 'ActivationProperties-Graph.pdf'This file shows combinations of the individual properties listed in 'ActivationProperties.xlsx' in a graph format, extending the combinations described in Table IV with data (types and format) and reasons for unreachability.### BeyondGUIThis folder includes:- 'ToolInfo.xlsx': an excel sheet listing the identified 22 beyond-GUI papers, the date of publication, availability, invasiveness (Source code, Bytecode, framework, OS) and their targeting strategy (None, Manual or Automated).- ToolClassification.pdf': a pdf file describing our paper selection methodology as well as a classication of the techniques in terms of Invocation Strategy, Navigation Strategy, Value Generation Strategy, and Value Generation Types. We fully introduced these categories in the pdf file.## Requirements & technology skills assumed by the reviewer evaluating the artifactThe artifact entirely consists of Excel sheets which can be opened with common Excel visualization software, i.e., Microsoft Excel, coverage plots as PNG files and PDF files. It requires about 15MB of storage in total.No other specific technology skills are required of the reviewer evaluating the artifact.

The dataset contains all publicly accessible green spaces in the city of Dresden including an attribute table with three main indicators on the use and perception of urban green spaces (Popularity_Indicator, Aesthetics_Indicator and Animals_Indicator) derived from social media. In addition to these three main values, the attribute table contains a further 18 statistical values, which were calculated by intersecting the green areas with classified social media data and are documented in the metadata description. The green space polygons were generated using an automatic approach described in Ludwig et al. (2021) is described in more detail. The green spaces and indicator values ​​are part of the central database (Cakir et al., 2021) for the evaluation of green spaces in Dresden according to criteria or suitability for certain activities using the meinGrün app (app.meingruen.org). The popularity of urban green spaces in Dresden was measured by the density of location-related social media posts. The processing of the data for green spaces is presented and described in a notebook (pub.zih.tu-dresden.de/~s7398234/vis/zielgeometrien-intersect_v6.html). The aesthetics indicator describes the aesthetic value of urban green spaces in Dresden and was conceptualized and measured based on the density of aesthetics-related social media posts. For the identification of the social media posts related to the aesthetic value of urban green spaces, a novel methodology based on unsupervised text classification and targeted filtering of social media posts was developed and in Gugulica & Burghardt, 2021 - work in progress - is described in more detail. The animals indicator shows the presence of wild animals in urban green spaces in Dresden. The quantification of the wildlife indicator is based on the underlying assumption that densities of social media posts related to wildlife and wildlife photography potentially reflect demand for wildlife viewing and indicate hotspots for that activity. In order to identify the relevant social media posts for the calculation of the indicator, the above methodology, based on unsupervised text classification and targeted filtering of social media posts and described in more detail in Gugulica & Burghardt, 2021 - in progress - was used , used. For quantifying the popularity, aesthetics and wildlife indicators of urban green spaces in Dresden, location-aware social media data from Instagram, Flickr and Twitter (including photos annotated with text and text messages) was used. The data was identified using the embedded location information and a custom bounding box, and retrieved and collected via the API provided by each of the platforms. Only publicly available social media posts published between January 1, 2015 and October 31, 2020 were considered and saved as a CSV file along with meta information such as user ID, coordinates, captions, recording and uploading date saved. Duplicates were removed and after overlaying the dataset with the target polygons, the final dataset for Dresden included 782,310 social media posts (59,101 tweets, 664,925 Instagram posts, and 58,284 Flickr posts). The choice of platforms was mainly determined by the popularity of the social media channels and the specificity of the respective content. In order to cover a broader range of users, the three data sources were combined, which led to more robust results due to the increased data width. References: Cakir, S., Schorcht, M., Stanley, C., Rieche, T., Ludwig, C., Gugulica, M., Dunkel, A., Hecht, R. (2021). Urban green spaces and indicators: Dresden (2021 version) [Data set]. Leibniz Institute of Ecological Urban and Regional Development, Weberplatz 1, 01217 Dresden, Germany. https://doi.org/10.26084/IOERFDZ-DATA-DE-2021-1 Ludwig, C.; Hecht, R.; Lautenbach, S.; Schorcht, M.; Zipf, A. (2021): Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions. In: ISPRS International Journal of Geo-Information 10 (2021) 4, p.251 https://doi.org/10.3390/ijgi10040251

In June 2025, Roblox was the most-downloaded gaming app in the Google Play Store worldwide. The creative gaming platform generated more than 21.28 million downloads from Android users. Block Blast! was the second-most popular gaming app title with approximately 19.9 million downloads from global users.

Popular DBMS, including MySQL, Postgres, MSSQL, Redis, Mongo, Oracle, ElasticSearch, Memcashed and database managers like phpMyAdmin.

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As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

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SkyBot

This is the dataset powering http://skybot.cam, an app that captures planes flying over top of my house.

https://i.imgur.com/DhxlR8J.png" alt="Skycam Tweet">

Upon the project gaining popularity on Hacker News from the above tweet, I thought I'd share the dataset and an example model to make it easier for others to build a plane spotting app, too.

About this Project

I built a system to take photos of all of the airplanes that fly over my house. Most of these planes are passing by at more than 30,000 feet! It uses ADS-B to track where the aircraft are relative to the camera, points the camera in the right direction and snaps a photo. I then run a few serverless functions that are running to detect where the aircraft is in the image and make a thumbnail. Much of the services are hosted on Azure. There's more details on the overall project here! http://skybot.cam/about. The project is open source as a part of my work from IQT as well.

https://i.imgur.com/Lrv71Aq.png" alt="Skybot Infrastructure">

About the Dataset

The dataset is of airfract that was captured as they flew overhead. It includes a mix of large and small passenger jets and an assortment of business jets. There are also a images with buildings and contrails, where there is not aircraft present.

Use Cases

This dataset should allow for a plane dectector model to be built like for plane spotting and plane detection.

About Me

I'm Luke Berndt, I work on Azure products at Microsoft. You can learn more about me here: http://lukeberndt.com/