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Android Developers: 3 latest new features in Android

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Many new updates happened for Android developers lately after Google I/O. Initially there was no restriction on some features but now they have updated them with some restrictions.

We have covered new features and the old features as well with new restrictions.

Here are the old features with new restrictions:

• Background Execution Limits

Whenever an app runs in the background, it consumes some of the device’s limited resources, like RAM. This can result in an impaired user experience, especially if the user is using a resource-intensive app, such as playing a game or watching a video.
To lower the chance of these problems, Android O places limitations on what apps can do while users aren’t directly interacting with them. Apps are restricted in two ways:

Background Service Limitations: When an app’s service is running in the background might consume device resources which may lead to bad user experience, to avoid these type of issues Android system applies a number of limitations on background services, this does not apply to foreground services, which are more noticeable to the user.
Broadcast Limitations: Apps targeted Android O can not use their manifest to register for implicit broadcasts. They can still register for these broadcasts at runtime, and they can use the manifest to register for explicit broadcasts targeted specifically at their app.

Note: The restrictions are applied by default applied to apps which are targeting Android O and in terms of other applications users can enable these restrictions from the Settings screen even if the app has not targeted Android O.

• Android Background Location Limits

Considering battery usage and user experience , background apps which are using Android locations APIs to fetch the user’s location will receive location updates less frequently when the app is being used in a device running Android O, developers who are using Fused Location Provider (FLP), Geofencing, GNSS Measurements, Location Manager, Wi-Fi Manager will get affected by this change.

• Notifications

  1. Notification Badges

    Notification Badges are the new way of notifying users regarding the new notifications arrived for a particular app, this will display badges on app icons in supported launchers which show notifications associated with one or more notification channels in an app, which the user has not yet dismissed or acted on.

  2. Notification Channels

    Using Notification channels developers can group their application’s notifications by category so that the user can apply few characteristics basing on the notification category. When you target Android O, you must implement one or more notification channels to display notifications to your users. If you don’t target Android O, your apps behave the same as they do on Android 7.0 when running on Android O devices.

Google says that the following characteristics can be applied to notification channels and that when the user assigns one of these, it will be applied channel- wide and they are as follows

  • Importance
  • Sound
  • Lights
  • Vibration
  • Show on lock screen
  • Override do not disturb

Here are some new features:

• New in UI and Styling

There are bunch of new features of UI and Styling are introduced in Android O and are as follows

1. Fonts

Android introduced fonts in XML through which we can use custom fonts as resources, You can add your custom font file in res/font/ folder to bundle fonts as resources and can access as a normal resource file and Android Support Library 26 introduce support for APIs to request fonts from a provider application instead of bundling files into your project which helps in reducing your application size
To use these font features on devices running Android API version 14 and higher, a developer needs to use the Support Library 26.

2. Auto Sizing Textviews

By using Support Library 26 Beta developers can now instruct to their app’s Textview to automatically increase or decrease the size to fit perfectly within the boundaries of the Textview.

3. Adaptive Icons

Adaptive icons can display app’s launcher icons in a variety of shapes across different devices for instance in Google Nexus the launcher icon might be in circular and in some Samsung device it might be squircle. Google says that with Android O, each device can provide a mask for the icon, which the OS can use to render all icons with the same shape. This will likely be embraced by OEMs(Original Equipment Manufacturer) who would like to have some unique looking home screens.

4. Autofill Framework

This framework will help the user by pre-filling the user information and user can save time as Filling out forms is a time-consuming and error-prone task. Users can easily get frustrated with apps that require these type of tasks. The Autofill Framework improves the user experience by providing the following benefits:

Less time spent in filling fields Autofill saves users from re-typing information.
Minimize user input errors Typing is prone to errors, especially on mobile devices. Removing the necessity of typing information also removes the errors that come with it.

• Picture in Picture Mode

In Android 7.0, Android TV users can now watch a video in a pinned window in a corner of the screen when navigating within or between apps whereas it was not available to other devices whereas from Android O Picture in Picture is available to all the devices, not just the Android TV.

• Kotlin For Android

Java is the mostly used programming language for the development of Android, When you run a Java application, the app is compiled into a set of instructions called Bytecode and runs in a virtual machine. Many alternative Languages has been introduced to also run on the JVM through which the resulting app looks the same for the JVM
JetBrains, known for IntelliJ IDEA (Android Studio is based on IntelliJ), has introduced the Kotlin language.Kotlin is a statically-typed programming language that runs on the JVM. It can also be compiled to JavaScript source code.

Why Kotlin For Android?

  • Interoperability with Java
  • Intuitive and easy to read
  • Good Android Studio Support
  • Safe to avoid entire classes of errors such as null pointer exceptions.
  • Less to write compared to Java
  • Safe to avoid entire classes of errors such as null pointer exceptions.
  • Versatile for building server-side applications, Android apps or frontend code running in the browser.

Stay tuned for more new updates on Android.

Check out these articles to catch the latest trends in mobile apps:

  1. 7 Important Points To Consider Before Developing A Mobile App
  2. The Clash of Clans: Kotlin Vs. Flutter
  3. Google for India September event 2019 key highlights
  4. Learn Ionic Framework From Scratch in Less Than 15 Minutes!
  5. AI in Mobile Development
  6. 10 Reasons to Learn Swift Programming Language
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When Data Meets the Heart: A Tale of Sentiments and Science

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Do you think technology will advance to a point where people rely on it for deeper emotional connections, perhaps even finding companionship? Just like in the movie Her, where a man falls for an AI, we all thought it was science fiction. But it seems we’re closer to that reality than we might have imagined. Now, it’s not just about crunching numbers. Technology is evolving every day, becoming so advanced that it’s learning to interpret human emotions and reactions. This is the core of sentiment analysis, where data meets emotions, and technology helps us make sense of human feelings in ways that were once only imaginable.

Is Data Science the Key to Unlocking Sentiment Analysis?

Sentiment analysis is more than just gauging emotions in text; it’s a powerful application of data science that transforms chaotic data into actionable insights. Data science deciphers human feelings hidden in reviews, tweets, and comments, enabling AI to capture not just whether sentiments are positive or negative but also the nuances of emotional expression. With the ongoing evolution in data science, sentiment analysis is moving beyond basic detection to uncover deeper emotional insights, allowing businesses to truly understand their customer’s sentiments. This capability empowers organizations to anticipate customer behavior and make informed decisions in a data-driven world.

According to Forbes, 80% of the world’s data is unstructured, like blog posts, reviews, and customer feedback. Sentiment analysis helps companies make sense of this unorganized heap using data analytics, turning it into actionable insights. Tools like Python libraries for sentiment analysis and AI models help refine this process further, offering businesses more profound insights into customer behavior.

How Does Sentiment Analysis Work?

Imagine you’ve just posted a review online: “This phone has a great camera, but the battery life is terrible.” While a human can quickly spot that you love the camera but hate the battery, AI needs to go a step further by:

  1. Text Preprocessing: Breaking the sentence down into words (tokens), removing stop words (like “the” and “has”), and normalizing the text.
  2. Natural Language Processing (NLP): This is where the AI engine learns the context of each word. It identifies if the sentiment is positive (great camera) or negative (terrible battery life).
  1. Machine Learning Models: These models classify the sentiment of the text. With more data science applications, the models become better at predicting human emotions.

Why Does Sentiment Analysis Matter?

In a world where emotions drive decisions, sentiment analysis helps businesses, governments, and even individuals make better decisions. Whether it’s reading reviews, understanding customer feedback, or gauging public opinion on social media, sentiment analysis tells us how people feel.

Beyond the Text: How Data Science Decodes Emotional Intelligence

What if Data science could detect more than just positive or negative feelings? What if it could understand sarcasm, context, and complex emotions like nostalgia or regret? The future of sentiment analysis is heading towards these intricate feelings, making it possible to “read between the lines”. With advancements in data science and machine learning, sentiment analysis is set to dive deeper into human emotions, potentially offering an unprecedented understanding of how we feel.

Real-World Applications

  • Customer Service: Have you ever left a review or complaint on a company’s Twitter? Chances are AI detected your dissatisfaction before a human even read it.
  • Healthcare: Doctors and mental health professionals are using sentiment analysis to detect early signs of depression or anxiety based on patient communication.
  • Politics: Predicting election outcomes? Analyzing public sentiment towards political candidates can be more accurate than traditional polls.

The Road Ahead: Can Data Science Fully Understand Us?

While today’s data science techniques are great at reading general sentiment from text, we’re not yet at the stage where machines can truly “understand” emotions. However, advancements in data science continue to refine how we interpret human sentiment. Techniques like sentiment mining and sentiment classifier are being used to recognize the subtle emotional cues. Perhaps one day, the power of data science will allow us to decode human emotions with such precision that it fundamentally changes the way we interact with data, bringing emotional insights into our daily lives.

Feeling curious? Explore how Mantra Labs is shaping the future with cutting-edge data science techniques and solutions that can read the world’s emotions—literally.

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