Android maintained its position as the leading mobile operating system worldwide in the first quarter of 2025 with a market share of about ***** percent. Android's closest rival, Apple's iOS, had a market share of approximately ***** percent during the same period. The leading mobile operating systems Both unveiled in 2007, Google’s Android and Apple’s iOS have evolved through incremental updates introducing new features and capabilities. The latest version of iOS, iOS 18, was released in September 2024, while the most recent Android iteration, Android 15, was made available in September 2023. A key difference between the two systems concerns hardware - iOS is only available on Apple devices, whereas Android ships with devices from a range of manufacturers such as Samsung, Google and OnePlus. In addition, Apple has had far greater success in bringing its users up to date. As of February 2024, ** percent of iOS users had iOS 17 installed, while in the same month only ** percent of Android users ran the latest version. The rise of the smartphone From around 2010, the touchscreen smartphone revolution had a major impact on sales of basic feature phones, as the sales of smartphones increased from *** million units in 2008 to **** billion units in 2023. In 2020, smartphone sales decreased to **** billion units due to the coronavirus (COVID-19) pandemic. Apple, Samsung, and lately also Xiaomi, were the big winners in this shift towards smartphones, with BlackBerry and Nokia among those unable to capitalize.

Context Imagine you have to process bug reports about an application your company is developing, which is available for both Android and iOS. Could you find a way to automatically classify them so you can send them to the right support team?

Content The dataset contains data from two StackExchange forums: Android Enthusiasts and Ask Differently (Apple). I pre-processed both datasets from the raw XML files retrieved from Internet Archive in order to only contain useful information for building Machine Learning classifiers. In the case of the Apple forum, I narrowed down to the subset of questions that have one of the following tags: "iOS", "iPhone", "iPad".

Think of this as a fun way to learn to build ML classifiers! The training, validation and test sets are all available, but in order to build robust models please try to use the test set as little as possible (only as a last validation for your models).

Acknowledgements The image was retrieved from unsplash and made by @thenewmalcolm. Link to image here.

The data was made available for free under a CC-BY-SA 4.0 license by StackExchange and hosted by Internet Archive. Find it here.

License

CC-BY-SA

Original Data Source: Question Classification: Android or iOS?

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

This is a public release of Beiwe-generated data. The Beiwe Research Platform collects high-density data from a variety of smartphone sensors such as GPS, WiFi, Bluetooth, gyroscope, and accelerometer in addition to metadata from active surveys. A description of passive and active data streams, and a documentation concerning the use of Beiwe can be found here. This data was collected from an internal test study and is made available solely for educational purposes. It contains no identifying information; subject locations are de-identified using the noise GPS feature of Beiwe.

As part of the internal test study, data from 6 participants were collected from the start of March 21, 2022 to the end of March 28, 2022. The local time zone of this study is Eastern Standard Time. Each participant was notified to complete a survey at 9am EST on Monday, Thursday, and Saturday of the study week. An additional survey was administered on Tuesday at 5:15pm EST. For each survey, subjects were asked to respond to the prompt "How much time (in hours) do you think you spent at home?".

This is our initial public release of Beiwe-generated data. The Beiwe Research Platform collects high-density data from a variety of smartphone sensors including GPS, WiFi, Bluetooth, and accelerometer. To learn more about Beiwe, check out the Onnela Lab page, the paper introducing the platform, or the Beiwe wiki.

Examples of how to use the data and more information will be updated on the Github repo: https://github.com/mkiang/beiwe_data_sample

Report issues via the Github repo.

Replay-Mobile is a dataset for face recognition and presentation attack detection (anti-spoofing). The dataset consists of 1190 video clips of photo and video presentation attacks (spoofing attacks) to 40 clients, under different lighting conditions. These videos were recorded with an iPad Mini2 (running iOS) and a LG-G4 smartphone (running Android).

Database Description

All videos have been captured using the front-camera of the mobile device (tablet or phone). The front-camera produces colour videos with a resolution of 720 pixels (width) by 1280 pixels (height) and saved in ".mov" file-format. The frame rate is about 25 Hz. Real-accesses have been performed by the genuine user (presenting one's true face to the device). Attack-accesses have been performed by displaying a photo or a video recording of the attacked client, for at least 10 seconds.

Real client accesses have been recorded under five different lighting conditions (controlled, adverse, direct, lateral and diffuse). In addition, to produce the attacks, high-resolution photos and videos from each client were taken under conditions similar to those in their authentication sessions (lighton, lightoff).

The 1190 real-accesses and attacks videos were then grouped in the following way:

Training set: contains 120 real-accesses and 192 attacks under different lighting conditions;

Development set: contains 160 real-accesses and 256 attacks under different lighting conditions;

Test set: contains 110 real-accesses and 192 attacks under different lighting conditions;

Enrollment set: contains 160 real-accesses under different lighting conditions, to be used exclusively for studying the baseline performance of face recognition systems. (This set is again partitioned into 'Training', 'Development' and 'Test' sets.)

Attacks

For photos attacks a Nikon coolix P520 camera, which records 18Mpixel photographs, has been used. Video attacks were captured using the back-camera of a smartphone LG-G4, which records 1080p FHD video clips using its 16 Mpixel camera.

Attacks have been performed in two ways:

A matte-screen was used to perform the attacks (i.e., to display the digital photo or video of the attacked identity). For all such (matte-screen) attacks, a stand was used to hold capturing devices.

Print attacks. For "fixed" attacks, both capturing devices were supported on a stand (as for matte-screen attacks). For "hand" attacks, the spoofer held the capturing device in his/her own hands while the spoof-resource (printed photo) was stationary.

In total, 16 attack videos were registered for each client, 8 for each of the attacking modes described above.

4 x mobile attacks using an Philips 227ELH screen (with resolution 1920x1080 pixels)

4 x tablet attacks using an Philips 227ELH screen (with resolution 1920x1080 pixels)

2 x mobile attacks using hard-copy print attacks fixed (produced on a Konica Minolta ineo+ 224e color laser printer) occupying the whole available printing surface on A4 paper

2 x mobile attacks using hard-copy print attacks fixed (produced on a Konica Minolta ineo+ 224e color laser printer) occupying the whole available printing surface on A4 paper

2 x mobile attacks using hard-copy print attacks hand (produced on a Konica Minolta ineo+ 224e color laser printer) occupying the whole available printing surface on A4 paper

2 x mobile attacks using hard-copy print attacks hand (produced on a Konica Minolta ineo+ 224e color laser printer) occupying the whole available printing surface on A4 paper

Reference

If you use this database, please cite the following publication:

Artur Costa-Pazo, Sushil Bhattacharjee, Esteban Vazquez-Fernandez and Sébastien Marcel,"The REPLAY-MOBILE Face Presentation-Attack Database", IEEE BIOSIG 2016. 10.1109/BIOSIG.2016.7736936 http://publications.idiap.ch/index.php/publications/show/3477

Top Flutter Packages Dataset

Flutter is an open source framework by Google for building beautiful, natively compiled, multi-platform applications from a single codebase. It is gaining quite a bit of popularity because of ability to code in a single language and have it running on Android/iOS and web as well. This dataset contains a snapshot of Top 5000+ flutter/dart packages hosted on Flutter package repository The dataset was scraped in August-2024. We aim to use this dataset to… See the full description on the dataset page: https://huggingface.co/datasets/deepklarity/top-flutter-packages.

This dataset contains a collection of user reviews and ratings for the Livin' by Mandiri mobile application. Livin' by Mandiri is a digital financial service platform developed by Bank Mandiri, one of Indonesia's largest banks, offering features like payments, money transfers, and financial management on mobile devices for both Android and iOS users. The data was collected by scraping reviews from the Google Play Store, providing insights into user feedback and app performance.

Columns

The dataset is provided in a CSV file and includes the following columns: * date: The date when the user review was submitted, in datetime format. * review: The textual content of the user's review. * rating: The user's rating, on a scale of 1 to 5. * thumbs_up: The total number of 'thumbs up' or likes given by other users to that particular review. * version: The version of the app when the user submitted the review.

Distribution

The dataset is structured as a CSV file. It contains approximately 155,192 records, representing reviews submitted between 30 September 2021 and 24 December 2022.

Review counts show significant peaks at certain times: * 12/29/2021 - 01/07/2022: 16,439 reviews * 02/21/2022 - 03/02/2022: 11,191 reviews * 05/22/2022 - 05/31/2022: 10,247 reviews * 07/06/2022 - 07/15/2022: 10,477 reviews * 07/15/2022 - 07/24/2022: 11,011 reviews

Rating Distribution: * 4.92 - 5.00 (5-star equivalent): 86,215 reviews * 1.00 - 1.08 (1-star equivalent): 39,183 reviews * 3.96 - 4.04 (4-star equivalent): 10,951 reviews * 3.00 - 3.08 (3-star equivalent): 9,464 reviews * 1.96 - 2.04 (2-star equivalent): 9,379 reviews

Thumbs Up Distribution: * 0.00 - 47.94: 154,810 reviews (majority of reviews received low 'thumbs up' counts) * Higher counts are present but significantly less frequent, with a maximum of 2,397 thumbs up for a single review.

App Version Distribution: * 1.0.2: 28% of reviews * [null]: 24% of reviews (indicating no version information available for these reviews) * Other: 48% of reviews across various versions.

Usage

This dataset is ideal for: * Exploratory Data Analysis (EDA) to understand trends in user feedback. * Sentiment Analysis to gauge overall user satisfaction and identify emotional tones in reviews. * App performance monitoring and identifying areas for improvement based on user comments and ratings. * Market research into digital banking service perception in Indonesia. * Academic research on financial technology adoption and mobile app user behaviour.

Coverage

• Geographic Scope: The reviews are primarily from users in Indonesia, given that Bank Mandiri is a major Indonesian bank.
• Time Range: The data spans from 30 September 2021 to 24 December 2022.
• Demographic Scope: The dataset reflects feedback from users of the Livin' by Mandiri mobile app on Android and iOS devices.

License

CC-BY-NC

Who Can Use It

This dataset is beneficial for: * Data Analysts and Scientists: For performing EDA, sentiment analysis, and building predictive models related to user satisfaction. * App Developers and Product Managers: To understand user pain points, identify popular features, and guide future app updates. * Researchers: Studying digital finance, user experience, and mobile app ecosystems in emerging markets like Indonesia. * Business Intelligence Professionals: To inform strategic decisions based on customer feedback and market sentiment.

Dataset Name Suggestions

• Livin' by Mandiri App Reviews
• Indonesian Mobile Banking User Feedback
• Bank Mandiri Digital App Ratings
• Fintech App Reviews Indonesia
• Livin' App User Sentiment Data

Attributes

Original Data Source: Livin' by Mandiri App Reviews

Differences between operating systems (Android, iOS, Mac OS, and Windows; Study 2).

This dataset encompasses 2.5 billion annual data points on location visits, app usage, and mobile web clickstream activities. Collected from over 100,000 triple-opt-in first-party U.S. Daily Active Users (DAU), it offers a robust foundation for understanding consumer behaviors.

At its core, this dataset contains unstructured event-level data, capturing both brick-and-mortar and app + web visits and interactions. The data is collected from both iOS and Android smartphones, providing an in-depth analysis and interpretation of validated consumer behaviors.

One of the key strengths of this dataset, is its utilization of OmniTraffic technology, which seamlessly integrates location, app, and web behaviors from individual consumers. By meticulously tracking the "who, what, where and when" of both online and offline visits, it provides comprehensive insights into consumer journeys.

Moreover, this dataset goes beyond mere observation by incorporating validated behaviors to uncover the underlying motivations driving consumer decisions. This deeper understanding of "the why" behind behaviors sets it apart, offering invaluable insights into consumer preferences and trends.

• 4+ year history
• iOS & Android

The primary use cases and verticals of our Behavioral Data Product are diverse and varied. Some key applications include:

• Data Acquisition and Modeling: Our data helps businesses acquire valuable insights into consumer behavior and enables modeling for various research objectives.

• Shopper Data Analysis: By understanding purchase behavior and patterns, businesses can optimize their strategies, improve targeting, and enhance customer experiences.

• Media Consumption Insights: Our data provides a deep understanding of viewer behavior and patterns across popular platforms like YouTube, Amazon Prime, Netflix, and Disney+, enabling effective media planning and content optimization.

• App Performance Optimization: Analyzing app behavior allows businesses to monitor usage patterns, track key performance indicators (KPIs), and optimize app experiences to drive user engagement and retention.

• Location-Based Targeting: With our detailed location data, businesses can map out consumer visits to physical venues and combine them with web and app behavior to create predictive ad targeting strategies.

• Audience Creation for Ad Placement: Our data enables the creation of highly targeted audiences for ad campaigns, ensuring better reach and engagement with relevant consumer segments.

The Behavioral Data Product complements our comprehensive suite of data solutions in the broader context of our data offering. It provides granular and event-level insights into consumer behaviors, which can be combined with other data sets such as survey responses, demographics, or custom profiling questions to offer a holistic understanding of consumer preferences, motivations, and actions.

MFour's Behavioral Data empowers businesses with unparalleled consumer insights, allowing them to make data-driven decisions, uncover new opportunities, and stay ahead in today's dynamic market landscape.

This data release includes two Wikipedia datasets related to the readership of the project as it relates to the early COVID-19 pandemic period. The first dataset is COVID-19 article page views by country, the second dataset is one hop navigation where one of the two pages are COVID-19 related. The data covers roughly the first six months of the pandemic, more specifically from January 1st 2020 to June 30th 2020. For more background on the pandemic in those months, see English Wikipedia's Timeline of the COVID-19 pandemic.Wikipedia articles are considered COVID-19 related according the methodology described here, the list of COVID-19 articles used for the released datasets is available in covid_articles.tsv. For simplicity and transparency, the same list of articles from 20 April 2020 was used for the entire dataset though in practice new COVID-19-relevant articles were constantly being created as the pandemic evolved.Privacy considerationsWhile this data is considered valuable for the insight that it can provide about information-seeking behaviors around the pandemic in its early months across diverse geographies, care must be taken to not inadvertently reveal information about the behavior of individual Wikipedia readers. We put in place a number of filters to release as much data as we can while minimizing the risk to readers.The Wikimedia foundation started to release most viewed articles by country from Jan 2021. At the beginning of the COVID-19 an exemption was made to store reader data about the pandemic with additional privacy protections:- exclude the page views from users engaged in an edit session- exclude reader data from specific countries (with a few exceptions)- the aggregated statistics are based on 50% of reader sessions that involve a pageview to a COVID-19-related article (see covid_pages.tsv). As a control, a 1% random sample of reader sessions that have no pageviews to COVID-19-related articles was kept. In aggregate, we make sure this 1% non-COVID-19 sample and 50% COVID-19 sample represents less than 10% of pageviews for a country for that day. The randomization and filters occurs on a daily cadence with all timestamps in UTC.- exclude power users - i.e. userhashes with greater than 500 pageviews in a day. This doubles as another form of likely bot removal, protects very heavy users of the project, and also in theory would help reduce the chance of a single user heavily skewing the data.- exclude readership from users of the iOS and Android Wikipedia apps. In effect, the view counts in this dataset represent comparable trends rather than the total amount of traffic from a given country. For more background on readership data per country data, and the COVID-19 privacy protections in particular, see this phabricator.To further minimize privacy risks, a k-anonymity threshold of 100 was applied to the aggregated counts. For example, a page needs to be viewed at least 100 times in a given country and week in order to be included in the dataset. In addition, the view counts are floored to a multiple of 100.DatasetsThe datasets published in this release are derived from a reader session dataset generated by the code in this notebook with the filtering described above. The raw reader session data itself will not be publicly available due to privacy considerations. The datasets described below are similar to the pageviews and clickstream data that the Wikimedia foundation publishes already, with the addition of the country specific counts.COVID-19 pageviewsThe file covid_pageviews.tsv contains:- pageview counts for COVID-19 related pages, aggregated by week and country- k-anonymity threshold of 100- example: In the 13th week of 2020 (23 March - 29 March 2020), the page 'Pandémie_de_Covid-19_en_Italie' on French Wikipedia was visited 11700 times from readers in Belgium- as a control bucket, we include pageview counts to all pages aggregated by week and country. Due to privacy considerations during the collection of the data, the control bucket was sampled at ~1% of all view traffic. The view counts for the control title are thus proportional to the total number of pageviews to all pages.The file is ~8 MB and contains ~134000 data points across the 27 weeks, 108 countries, and 168 projects.Covid reader session bigramsThe file covid_session_bigrams.tsv contains:- number of occurrences of visits to pages A -> B, where either A or B is a COVID-19 related article. Note that the bigrams are tuples (from, to) of articles viewed in succession, the underlying mechanism can be clicking on a link in an article, but it may also have been a new search or reading both articles based on links from third source articles. In contrast, the clickstream data is based on referral information only- aggregated by month and country- k-anonymity threshold of 100- example: In March of 2020, there were a 1000 occurences of readers accessing the page es.wikipedia/SARS-CoV-2 followed by es.wikipedia/Orthocoronavirinae from ChileThe file is ~10 MB and contains ~90000 bigrams across the 6 months, 96 countries, and 56 projects.ContactPlease reach out to research-feedback@wikimedia.org for any questions.

Note: This dataset has been archived and is no longer being updated. COVID Alert CT is Connecticut's voluntary, anonymous, exposure-notification smartphone app. If downloaded, the app will alert users if they have come into close contact with somebody who tests positive for COVID-19. This dataset includes the cumulative and weekly activations for COVID Alert CT for iOS and Android smartphones. The _location of app users is not tracked--the app uses Bluetooth technology to detect when another person with the same app comes within 6 feet. The phones exchange a secure code with the each other to record that they were near. The number of codes issued and claimed is also included in this dataset. Data presented are based on a weekly reporting period (Sunday - Saturday). All data are preliminary and are subject to change. Additional information on COVID-19 Contact Tracing can be found here: https://portal.ct.gov/coronavirus/covidalertCT/homepage

DoctorP (doctorp.org) is a multifunctional platform for plant disease detection, designed for use with agricultural and ornamental crops. The platform provides various interfaces, including mobile applications for iOS and Android, a Telegram bot, and an API for seamless integration with external services. Users and services can upload photos of diseased plants to receive predictions and treatment recommendations.

DoctorP supports an extensive range of disease classification models. This dataset features a reduced-scale (128x128) collection of real-life images, comprising over 4,000 samples across 68 classes of plant diseases, pests, and their effects.

Researchers are encouraged to utilize this dataset for scientific tasks, with proper citation of the corresponding research:

Uzhinskiy, A. Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain. Biology 2025, 14, 99. https://doi.org/10.3390/biology14010099

Uzhinskiy, A.; Ososkov, G.; Goncharov, P.; Nechaevskiy, A.; Smetanin, A. Oneshot Learning with Triplet Loss for Vegetation Classification Tasks. Comput. Opt. 2021, 45, 608–614

For suggestions on improving the app, reach out to info@doctorp.org

Context

This is the dataset that I used in my iOS and Android plant disease detection app, PlantifyDr. You can check out my full open-source project here: https://github.com/lavaman131/PlantifyDr

Content

The dataset contains over 125,000 jpg images of 10 different plant types: Apple, Bell pepper, Cherry, Citrus, Corn, Grape, Peach, Potato, Strawberry, and Tomato. The total number of plant diseases is 37. Augmentations have already been applied to the data, but feel free to add your own augmentations if you like.

Acknowledgements

Special thanks to: https://data.mendeley.com/datasets/tywbtsjrjv/1 https://www.kaggle.com/vipoooool/new-plant-diseases-dataset https://github.com/pratikkayal/PlantDoc-Dataset https://data.mendeley.com/datasets/3f83gxmv57/2

for the data.

Inspiration

The Food and Agriculture Organization of the United Nations (FAO) estimates that annually between 20 to 40 percent of global crop production is lost. Each year, plant diseases cost the global economy around $220 billion. I hoped to use deep learning to solve this problem and be able to better educate farmers and the public with the necessary knowledge to treat their plants.

What’s Included

Introducing the Portuguese Shopping List Image Dataset - a diverse and comprehensive collection of handwritten text images carefully curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Portuguese language.

Containing more than 2000 images, this Portuguese OCR dataset offers a wide distribution of different types of shopping list images. Within this dataset, you'll discover a variety of handwritten text, including sentences, and individual item name words, quantity, comments, etc on shopping lists. The images in this dataset showcase distinct handwriting styles, fonts, font sizes, and writing variations.

To ensure diversity and robustness in training your OCR model, we allow limited (less than three) unique images in a single handwriting. This ensures we have diverse types of handwriting to train your OCR model on. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible Portuguese text.

The images have been captured under varying lighting conditions, including day and night, as well as different capture angles and backgrounds. This diversity helps build a balanced OCR dataset, featuring images in both portrait and landscape modes.

All these shopping lists were written and images were captured by native Portuguese people to ensure text quality, prevent toxic content, and exclude PII text. We utilized the latest iOS and Android mobile devices with cameras above 5MP to maintain image quality. Images in this training dataset are available in both JPEG and HEIC formats.

In addition to the image data, you will receive structured metadata in CSV format. For each image, this metadata includes information on image orientation, country, language, and device details. Each image is correctly named to correspond with the metadata.

This metadata serves as a valuable resource for understanding and characterizing the data, aiding informed decision-making in the development of Portuguese text recognition models.

We are committed to continually expanding this dataset by adding more images with the help of our native Portuguese crowd community.

If you require a customized OCR dataset containing shopping list images tailored to your specific guidelines or device distribution, please don't hesitate to contact us. We have the capability to curate specialized data to meet your unique requirements.

Additionally, we can annotate or label the images with bounding boxes or transcribe the text in the images to align with your project's specific needs using our crowd community.

This image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage this shopping list image OCR dataset to enhance the training and performance of text recognition, text detection, and optical character recognition models for the Portuguese language. Your journey to improved language understanding and processing begins here.

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The presence of business in the digital space is a must now. Indeed, there’s hardly any company, be it a small startup or an international corporation, that wouldn’t be available online. For this, the company may use one of two options — to develop an app or a website, or both.

In the case of a limited budget, business owners often have to make a choice. Thus, considering that mobile traffic bypassed the desktop’s in 2016 and continues to grow, it becomes obvious that the business should become accessible and convenient for smartphone users. But what is better a responsive website or a mobile application?

Entrepreneurs often turn to development companies to ask this question. Lacking sufficient knowledge, they hope to get answers to their questions from people with experience in this field. So, we decided to compile a guide that will give you clear and understandable information.

Mobile app

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Let's look at the stats. It will help you understand why a mobile app may be the obvious choice for your client.

In 2019, smartphone users installed about 204 billion(!) applications on their devices. On average, this is more than 26 applications per inhabitant of the planet Earth. And if this is not enough evidence, here’s one more point. The expected revenue of mobile applications will be $189 billion in 2020.

It sounds impressive, but this does not mean that a mobile application is something indispensable for every business. Not at all. Let's go through the pros and cons of a mobile application and try to understand when it is needed.

Pros

• A new level of interaction. Mobile applications are a more convenient method of interaction. They load and process content faster. One more useful feature is notifications. Perhaps, applications are the best way to inform users about new updates, promotions, and other news (who will read long letters in the mail?).
• Personalized targeting. Mobile applications are ideal for products or services that need to be used on an ongoing basis. The options like creating accounts, entering profile information, etc., make applications more personalized than websites. All this allows the business to target their audience more accurately without wasting money.
• Offline usage. That’s another major advantage. Applications can provide users with access to content without an internet connection.

Cons

• Development costs. In order to reach the maximum audience with a mobile app, it is necessary to cover two main operating systems — iOS and Android. Development for each OS can be too expensive for small business owners and they will have to make difficult choices. The way out of this situation is cross-platform development. Why? Because there’s no need to guess which platform targets prefer using — iOS or Android. Instead, you create just one app that runs seamlessly on both platforms.

• Maintenance. The application is a technical product that needs constant support. Upgrades should be carried out in a timely manner. Often, users need to personally update applications by downloading a new version, which is annoying. Regular bug-fixing for various devices (smartphones, tablets) and different operating systems might be a real problem. Plus, any update should be confirmed by the store where the application is placed.

• Suitable for businesses that provide interactive and personalized content (refers to all lifestyle and healthcare solutions), require regular app usage (for instance, to-do lists), rely on visual interaction and so on. For games, like Angry Birds, creating an app is also a wise choice.

Website

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In order to be convenient for users of mobile devices, a website should be responsive. We want to make an emphasis on this since it is critically important. Most of the traffic on the Internet comes from mobile devices, so your website should be adaptable, or in other words, mobile-friendly. If a mobile user needs to zoom in all the necessary elements and text to see something, they will immediately quit your website.

On the other hand, a responsive website has the following benefits.

Pros

• Maintenance. Maintaining a website is less costly. When compared to applications where the user mu...

What’s Included

Introducing the Finnish Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Finnish language.

Containing a total of 2000 images, this Finnish OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible Finnish text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native Finnish people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of Finnish text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native Finnish crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the Finnish language. Your journey to enhanced language understanding and processing starts here.

SmartParking is a trial designed to help ease traffic congestion and lower travel times by using real-time bay sensor data and the ParkCBR app to show drivers where they are more likely to find available car parking in the Manuka shopping precinct. Android users can download the ParkCBR from GooglePlay Store and iOS users from the AppStore. The Lots dataset shows the locations and describes each lot.

Loudoun Express Request (LEx) is a citizen request system for members of the public to submit requests for service and report concerns to the county government via the internet and a mobile application. Our goal is to increase the efficiency, security, and accountability in responding to citizen concerns and questions. LEx is available as a mobile app for iOS and Android users!

Click Here : Bitdefender Login

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The Importance of Your Bitdefender Account Before diving into the step-by-step guidance, it’s important to understand why your Bitdefender account plays a pivotal role in your digital protection. Bitdefender Central is the central management hub that allows users to install security applications, 📞📲🤙 ☎ Call (+1→315→805→0009)👈 manage their devices, monitor real-time threats, update subscriptions, and access support. It’s the gateway to a secure, well-monitored digital environment.

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Devices and Platforms Supported by Bitdefender Central Bitdefender Central is compatible with a variety of platforms, including Windows, macOS, Android, and iOS. Whether you're using a desktop computer or a mobile device, you can access your account and manage your digital security from anywhere. 📞📲🤙 ☎ Call (+1→315→805→0009)👈 The ability to log in through both web browsers and mobile apps gives users the flexibility to stay protected on the go.

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Preparation Before Logging In When approaching the question “How Do I 'Login Bitdefender Account'? A Simple Guide”, it's crucial to ensure you're properly prepared for the process. Having the right information on hand will make the login experience smoother and more efficient. Here’s what you should have ready

The email address associated with your Bitdefender account

The correct password for your account

Access to your email or mobile device for verification if multi-factor authentication is enabled

A secure internet connection to prevent interruptions during login

Preparation minimizes the risk of login errors and ensures that you can access.

Step-by-Step Guide to Logging into Bitdefender Account via Web Browser To begin managing your digital security, open your preferred web browser. Type in the Bitdefender Central web address in the address bar. This action will redirect you to the official login page. Enter your registered email address and password in the designated fields. If you've enabled two-factor authentication, you will be prompted to enter a verification code sent to your mobile device or email.

After successfully entering the required information, click on the login button to access your Bitdefender Central dashboard. If this is your first time logging in on a new device or browser, you may be asked to verify your identity further for security purposes.

Once logged in, you will see an overview of your protected devices, active subscriptions, recent alerts, and available downloads. This central hub allows you to navigate through your security services with ease.

How to Use the Bitdefender Central App for Login For users who prefer mobile access, the Bitdefender Central app provides all the essential features in a streamlined format. Begin by downloading the app from the official app store on your Android or iOS device. Open the app and enter the same email and password associated with your Bitdefender account. Just like the web version, you may be prompted to enter a two-step verification code.

Once logged in, the mobile app allows you to monitor threats, manage devices, renew subscriptions, and contact support directly. The mobile interface is designed to be user-friendly and offers most of the functionalities found in the desktop dashboard.

What to Do If You Forget Your Password One common issue users face is forgetting their account password. If you're wondering, “How Do I 'Login Bitdefender Account'? A Simple Guide”, this section is particularly useful. On the login page, look for the “Forgot Password” option. Click on it, and you will be prompted to enter the email address associated with your account. After submitting your email, Bitdefender will send you a password reset link.

Open the email, click on the provided link, and follow the instructions to create a new password. Make sure your new password is strong and unique, combining upper and lowercase letters, numbers, and special characters. After resetting your password, return to the login page and use your updated credentials to access your account.

Common Login Issues and How to Fix Them If you’re still unable to log in, several issues could be responsible. Understanding these potential obstacles can help you resolve them quickly

Incorrect Email or Password: Double-check your spelling, and make sure there are no extra spaces.

Account Not Verified: Make sure you completed the email verification when you first signed up.

Two-Factor Authentication Failure: Ensure you have access to the correct device or method used for verification.

Browser Compatibility: Use updated browsers like Chrome, Firefox, Safari, or Edge for optimal performance.

Network Issues: A weak or unstable internet connection can prevent successful login.

Addressing these issues will increase your chances of logging in smoothly and without frustration.

Security Measures to Protect Your Account Securing your Bitdefender account should be a top priority. Enable two-factor authentication to add an extra layer of security. Avoid using easily guessed passwords or reusing credentials from other platforms. Always log out of your account when using public or shared computers.

Regularly update your password and monitor your login history within Bitdefender Central to detect any unusual activity. These proactive steps ensure that only you have access to your account and sensitive data.

Managing Your Subscriptions After Login Once you’ve successfully logged in, managing your subscription becomes effortless. The Bitdefender Central dashboard displays your active plans, renewal dates, and device coverage. You can add new devices, remove outdated ones, or renew your license directly from this platform.

For users with multi-device or family plans, Bitdefender Central allows easy sharing of security across different users by sending invite links or installation files.

This centralization makes it easy to stay on top of your cybersecurity needs without switching between different applications or platforms.

Additional features available post-login include VPN activation, identity theft protection, ransomware remediation tools, and web protection toggles. These options can be enabled or configured directly from the Central platform.

How to Contact Support Through Your Account If you encounter technical or account-related issues after logging in, Bitdefender Central offers built-in customer support options. You can access live chat, email support, or phone support directly from the dashboard. Each method connects you with knowledgeable representatives who can help resolve concerns, update account information, or guide you through more complex procedures.

The support section also includes a knowledge base filled with how-to guides, video tutorials, and FAQs. Many users find solutions through these self-help resources without needing to wait for an agent.

Importance of Logging In Regularly Regularly accessing your Bitdefender account ensures that you remain up to date on your system’s security status. The dashboard notifies you of expired subscriptions, potential threats, and system performance issues. Frequent logins also allow you to download the latest security patches and updates, keeping your protection at its peak.

Additionally, regular login habits reinforce account security, as any unauthorized attempts will be more noticeable to the user.

Final Thoughts on Bitdefender Login Successfully navigating the Bitdefender Central login process is the foundation of a secure digital experience. Whether you're a first-time user or a seasoned subscriber, knowing how to log in, manage