Connect with us

AI

Build alerting and human review for images using Amazon Rekognition and Amazon A2I

The volume of user-generated content (UGC) and third-party content has been increasing substantially in sectors like social media, ecommerce, online advertising, and photo sharing. However, such content needs to be reviewed to ensure that end-users aren’t exposed to inappropriate or offensive material, such as nudity, violence, adult products, or disturbing images. Today, some companies simply […]

Published

on

The volume of user-generated content (UGC) and third-party content has been increasing substantially in sectors like social media, ecommerce, online advertising, and photo sharing. However, such content needs to be reviewed to ensure that end-users aren’t exposed to inappropriate or offensive material, such as nudity, violence, adult products, or disturbing images. Today, some companies simply react to user complaints to take down offensive images, ads, or videos, whereas many employ teams of human moderators to review small samples of content. However, human moderators alone can’t scale to meet these needs, leading to a poor user experience or even a loss of brand reputation.

With Amazon Rekognition, you can automate or streamline your image and video analysis workflows using machine learning (ML). Amazon Rekognition provides an image moderation API that can detect unsafe or inappropriate content containing nudity, suggestiveness, violence, and more. You get a hierarchical taxonomy of labels that you can use to define your business rules, without needing any ML experience. Each detection by Amazon Rekognition comes with a confidence score between 0–100, which provides a measure of how confident the ML model is in its prediction.

Content moderation still requires human reviewers to audit results and judge nuanced situations where AI may not be certain in its prediction. Combining machine predictions with human judgment and managing the infrastructure needed to set up such workflows is hard, expensive, and time-consuming to do at scale. This is why we built Amazon Augmented AI (Amazon A2I), which lets you implement a human review of ML predictions and is directly integrated with Amazon Rekognition. Amazon A2I allows you to use in-house, private, or third-party vendor workforces with a web interface that has instructions and tools they need to complete their review tasks.

You can easily set up the criteria that triggers a human review of a machine prediction; for example, you can send an image for further human review if Amazon Rekognition’s confidence score is between 50–90. Amazon Rekognition handles the bulk of the work and makes sure that every image gets scanned, and Amazon A2I helps send the remaining content for further review to best utilize human judgment. Together, this helps ensure that you get full moderation coverage while maintaining very high accuracy, at a fraction of the cost to review each image manually.

In this post, we show you how to use Amazon Rekognition image moderation APIs to automatically detect explicit adult, suggestive, violent, and disturbing content in an image and use Amazon A2I to onboard human workforces, set up human review thresholds of the images, and define human review tasks. When these conditions are met, images are sent to human reviewers for further review, which is performed according to the instructions in the human review task definition.

Prerequisites

This post requires you to complete the following prerequisites:

  • Create an AWS Identity and Access Management (IAM) role. To create a human review workflow, you need to provide an IAM role that grants Amazon A2I permission to access Amazon Simple Storage Service (Amazon S3) for reading objects to render in a human task UI and writing the results of the human review. This role also needs an attached trust policy to give Amazon SageMaker permission to assume the role. This allows Amazon A2I to perform actions in accordance with permissions that you attach to the role. For example policies that you can modify and attach to the role you use to create a flow definition, see Add Permissions to the IAM Role Used to Create a Flow Definition.
  • Configure permission to invoke the Amazon Rekognition DetectModerationLabels You need to attach the AmazonRekognitionFullAccess policy to the AWS Lambda function that calls the Amazon Rekognition detect_moderation_labels API.
  • Provide Amazon S3 Access, Put, and Get permission to Lambda if you wish to have Lambda use Amazon S3 to access images for analysis.
  • Give the Lambda function AmazonSageMakerFullAccess access to the Amazon A2I services for the human review.

Creating a private work team

A work team is a group of people that you select to review your documents. You can create a work team from a workforce, which is made up of Amazon Mechanical Turk workers, vendor-managed workers, or your own private workers that you invite to work on your tasks. Whichever workforce type you choose, Amazon A2I takes care of sending tasks to workers. For this post, you create a work team using a private workforce and add yourself to the team to preview the Amazon A2I workflow.

To create your private work team, complete the following steps:

  1. Navigate to the Labeling workforces page on the Amazon SageMaker console.
  2. On the Private tab, choose Create private team.
  3. For Team name, enter an appropriate team name.
  4. For Add workers, you can choose to add workers to your workforce by importing workers from an existing user group in AWS Cognito or by inviting new workers by email.

For this post, we suggest adding workers by email. If you create a workforce using an existing AWS Cognito user group, be sure that you can access an email in that workforce to complete this use case.

  1. Choose Create private team.
  2. On the Private tab, choose the work team you just created to view your work team ARN.
  3. Record the ARN to use when you create a flow definition in the next section.

After you create the private team, you get an email invitation. The following screenshot shows an example email.

  1. Choose the link to log in and change your password.

You’re now registered as a verified worker for this team. The following screenshot shows the updated information on the Private tab.

Your one-person team is now ready, and you can create a human review workflow.

Creating a human review workflow

In this step, you create a human review workflow, where you specify your work team, identify where you want output data to be stored in Amazon S3, and create instructions to help workers complete your document review task.

To create a human review workflow, complete the following:

  1. In the Augmented AI section on the Amazon SageMaker console, navigate to the Human review workflows
  2. Choose Create human review workflow.

On this page, you configure your workflow.

  1. Enter a name for your workflow.
  2. Choose an S3 bucket where you want Amazon A2I to store the output of the human review.
  3. Choose an IAM role for the workflow.

You can create a new role automatically with Amazon S3 access and an Amazon SageMaker execution policy attached, or you can choose a role that already has these permissions attached.

  1. In the Task type section, select Rekognition – Image moderation.
  2. In the Amazon Rekognition-Image Moderation – Conditions for invoking human review section, you can specify conditions that trigger a human review.

For example, if the confidence of the output label produced by Amazon Rekognition is between the range provided (70–100, for this use case), the document is sent to the portal for human review. You can also select different confidence thresholds for each image moderation output label through Amazon A2I APIs.

  1. In the Worker task template creation section, if you already have an A2I worker task template, you can choose Use your own template. Otherwise, select Create from a default template and enter a name and task description. For this use case, you can use the default worker instructions provided.
  2. In the Workers section, select Private.
  3. For Private teams, choose the private work team you created earlier.
  4. Choose Create.

You’re redirected to the Human review workflows page, where you can see the name and ARN of the human review workflow you just created.

  1. Record the ARN to use in the next section.

Configuring Lambda to run Amazon Rekognition

In this step, you create a Lambda function to call the Amazon Rekognition API detect_moderation_labels. You use the HumanLoopConfig parameter of detect_moderation_labels to integrate an Amazon A2I human review workflow into your Amazon Rekognition image moderation job.

  1. On the Lambda console, create a new function called A2IRegok.
  2. For Runtime, choose Python 3.7.
  3. Under Permission, choose Use an existing role.
  4. Choose the role you created.
  5. In the Function code section, remove the function code and replace it with the following code.
    1. Inside the Lambda function, import two libraries: uuid and boto3.
    2. Modify the function code as follows:
      1. Replace the FlowDefinationArn in line 12 with one you saved in the last step.
      2. On line 13, provide a unique name to the HumanLoopName or use uuid to generate a unique ID.
      3. You use the detect_moderation_labels API operation to analyze the picture (JPG, PNG). To use the picture from the Amazon S3 bucket, specify the bucket name and key of the file inside the API call as shown in lines 7 and 8.
 1 import boto3 2 import uuid 3 4 def lambda_handler(event, context): 5 if event: 6 7 bucket_name = "a2idemorekog". # Add your sourcebucketname 8 src_filename = "1.png". # Add the src filename 9 rekognition = boto3.client('rekognition') 10 human_loop_unique_id = str(uuid.uuid4()) + '1' 11 humanLoopConfig = { 12 'FlowDefinitionArn':"arn:aws:sagemaker:us-east-1:123456789123:flow-definition/a2i-rekognition-wf", 13 'HumanLoopName':human_loop_unique_id 14 } 15 16 response = rekognition.detect_moderation_labels( 17 Image = { 18 "S3Object": { 19 "Bucket": bucket_name, 20 "Name": src_filename, 21 } 22 }, 23 HumanLoopConfig = humanLoopConfig 24 )

Calling Amazon Rekognition using Lambda

To configure and run a serverless function, complete the following steps:

  1. On the Lambda console, choose your function.
  2. Choose Configure test events from the drop-down menu.

The editor appears to enter an event to test your function.

  1. On the Configure test event page, select Create new test event.
  2. For Event template, choose hello-world.
  3. For Event name, enter a name; for example, DemoEvent.
  4. You can change the values in the sample JSON. For this use case, no change is needed.

For more information, see Run a Serverless “Hello, World!” and Create a Lambda function with the console.

  1. Choose Create.
  2. To run the function, choose Test.

When the test is complete, you can view the results on the console:

  • Execution result – Verifies that the test succeeded
  • Summary – Shows the key information reported in the log output
  • Log output – Shows the logs the Lambda function generated

The response to this call contains the inference from Amazon Rekognition and the evaluated activation conditions that may or may not have led to a human loop creation. If a human loop is created, the output contains HumanLoopArn. You can track its status using the Amazon A2I API operation DescribeHumanLoop.

Completing a human review of your image

To complete a human review of your image, complete the following steps:

  1. Open the URL in the email you received.

You see a list of reviews you are assigned to.

  1. Choose the image you want to review.
  2. Choose Start working.

After you start working, you must complete the task within 60 minutes.

  1. Choose an appropriate category for the image.

Before choosing Submit, if you go to the Human review workflow page on the Amazon SageMaker console and choose the human review workflow you created, you can see a Human loops summary section for that workflow.

  1. In your worker portal, when you’re done working, choose Submit.

After you complete your job, the status of the human loop workflow is updated.

If you navigate back to the Human review workflow page, you can see the human loop you just completed has the status Completed.

Processing the output

The output data from your review is located in Bucket when you configured your human review workflow on the Amazon A2I console. The path to the data uses the following pattern: YYYY/MM/DD/hh/mm/ss.

The output file (output.json) is structured as follows:

{ "awsManagedHumanLoopRequestSource": "AWS/Rekognition/DetectModerationLabels/Image/V3", "flowDefinitionArn": "arn:aws:sagemaker:us-east-1:111122223333:flow-definition/a2i-rekog-blog", "humanAnswers": [ { "answerContent": { "AWS/Rekognition/DetectModerationLabels/Image/V3": { "moderationLabels": [ { "name": "Weapon Violence", "parentName": "Violence" }, { "name": "Violence", "parentName": "" } ] } }, "submissionTime": "2020-05-27T15:44:39.726Z", "workerId": "000cd1c234b5fcc7", "workerMetadata": { "identityData": { "identityProviderType": "Cognito", "issuer": "https://cognito-idp.us-east-1.amazonaws.com/us-east-1_00aa00a", "sub": "b000a000-0b00-0ae0-bf00-0000f0bfd00d" } } } ], "humanLoopName": "389fd1a7-c658-4020-8f73-e9afcbfa8fd31", "inputContent": { "aiServiceRequest": { "humanLoopConfig": { "flowDefinitionArn": "arn:aws:sagemaker:us-east-1:111122223333:flow-definition/a2i-rekog-blog", "humanLoopName": "389fd1a7-c658-4020-8f73-e9afcbfa8fd31" }, "image": { "s3Object": { "bucket": "AWSDOC-EXAMPLE-BUCKET", "name": "1.png" } } }, "aiServiceResponse": { "moderationLabels": [ { "confidence": 80.41172, "name": "Weapon Violence", "parentName": "Violence" }, { "confidence": 80.41172, "name": "Violence", "parentName": "" } ], "moderationModelVersion": "3.0" }, "humanTaskActivationConditionResults": { "Conditions": [ { "And": [ { "ConditionParameters": { "ConfidenceLessThan": 100, "ModerationLabelName": "*" }, "ConditionType": "ModerationLabelConfidenceCheck", "EvaluationResult": true }, { "ConditionParameters": { "ConfidenceGreaterThan": 60, "ModerationLabelName": "*" }, "ConditionType": "ModerationLabelConfidenceCheck", "EvaluationResult": true } ], "EvaluationResult": true } ] }, "selectedAiServiceResponse": { "moderationLabels": [ { "confidence": 80.4117202758789, "name": "Weapon Violence", "parentName": "Violence" }, { "confidence": 80.4117202758789, "name": "Violence", "parentName": "" } ], "moderationModelVersion": "3.0" } }
}

In this JSON object, you have all the input and output content in one place so that you can parse one file to get the following:

  • humanAnswers – Contains answerContent, which lists the labels chosen by the human reviewer, and workerMetadata, which contains information that you can use to track private workers
  • inputContent – Contains information about the input data object that was reviewed, the label category options available to workers, and the responses workers submitted

For more information about the location and format of your output data, see Monitor and Manage Your Human Loop.

Conclusion

This post has merely scratched the surface of what Amazon A2I can do. Amazon A2I is available in 12 Regions. For more information, see Region Table. To learn more about the Amazon Rekognition DetectModerationLabels API integration with Amazon A2I, see Use Amazon Augmented AI with Amazon Rekognition.

For video presentations, sample Jupyter notebooks, or more information about use cases like document processing, object detection, sentiment analysis, text translation, and others, see Amazon Augmented AI Resources.


About the Author

Suresh Patnam is a Solutions Architect at AWS. He helps customers innovate on the AWS platform by building highly available, scalable, and secure architectures on Big Data and AI/ML. In his spare time, Suresh enjoys playing tennis and spending time with his family.

Source: https://aws.amazon.com/blogs/machine-learning/build-alerting-and-human-review-for-images-using-amazon-rekognition-and-amazon-a2i/

AI

Things to Know about Free Form Templates

A single file that includes numerous supporting files is commonly known as a form template. Some files will define or show the controls to appear on the free form templates or design. The collections of these supporting files or templates are also called form files. While designing free form templates, users should be able to […]

The post Things to Know about Free Form Templates appeared first on 1redDrop.

Published

on

A single file that includes numerous supporting files is commonly known as a form template. Some files will define or show the controls to appear on the free form templates or design. The collections of these supporting files or templates are also called form files. While designing free form templates, users should be able to view and also work with the form files. 

It will create a new free form template by copying and storing those files within a folder. A form template (.XSN) file designing or creation of a single file will include various supporting files. Users may fill out the online form by accessing the .XML form file, which is a form template.

Designing Free Form Templates

There are numerous processes that define free form template design, and are as follows:

  • Designing the form’s appearance – the instructional text, labels, and controls
  • Controls will assist with user interaction behavior on the form template. You can design a specific section to appear or disappear when the user chooses a particular option
  • Whether the form template may include some additional views. For a permit application form design, for example, you have to provide different views for each person. One view especially for the electrical contractor, next for the receiving agent, and finally, the investigator. He or she will deny or approve the permit application
  • Next, you need to know how & where to store the form data. Designing free from templates will allow users to submit their data within the database either online or direct access. If not, they can also store the same in any specific shared folder
  • It is essential to design the other elements, colors, and fonts within the form template
  • Users must be able to personalize the form. Allowing users to include various rows within the optional section, repeating section, or a repeating table
  • Users should receive a notification when they forget to input a mandatory field or make mistakes within the form
  • After completing the free form templates design, you can publish the same online using a .XSN file format

Club Signup Form

A simple registration form can help your Club Signup Form creation process go smoother. This signup form could be an ideal solution for a new club membership registration for any organization or club.

Application Form

Application form templates are much easier to use & set-up to streamline your application process. You can customize this online form and utilize the same for numerous applications. Make use of this application form as a job application form, volunteer applications, contest entries, or high school scholarship applications. It is an ideal solution for scholarship programs, nonprofit organizations, business owners, and many such users and use cases.

Scheduling Form

Scheduling form templates are handy and can be used for numerous appointment booking requirements. A scheduling form is also utilized for various appointment scheduling or online reservations and booking purposes. Regardless of your business requirement, it is easy to customize the form template.

Concept Testing Survey

While testing a new design or concept, it is essential to gather the responses quickly. Freeform templates for a concept testing survey make it much easier to gather product feedback and reach the target audience. It is essential to conduct market research while planning to release a new product. A mobile-friendly form will allow you to utilize the survey questions for collecting the product’s consumer input quickly.

Credit Card Order Form

It is not always a complex process to provide an online credit card payment form for the customers. This form template will allow you to access numerous services or products for collecting card payment information. You can utilize this yet-another endless and simple payment form.

Employment Application Form

The employment application form for recruitment will assist the HR team to gather the required information from candidates. During the interview or application process, you can easily remove any expensive follow-ups. Some of the fields are contact information, employment history, useful information, etc. as well as an outline of the job description, consent for background checks, military service record, anticipated start date, any special skills, and many more. It is optional to enable notifications for the form owners to receive an alert or email when a new employment application is submitted.

Source: https://1reddrop.com/2020/10/24/things-to-know-about-free-form-templates/?utm_source=rss&utm_medium=rss&utm_campaign=things-to-know-about-free-form-templates

Continue Reading

AI

Are Chatbots Vulnerable? Best Practices to Ensure Chatbots Security

Published

on

Rebecca James
credit IT Security Guru

A simple answer is a Yes! Chatbots are vulnerable. Some specific threats and vulnerabilities risk chatbots security and prove them a wrong choice for usage. With the advancement in technology, hackers can now easily target the hidden infrastructure of a chatbot.

The chatbot’s framework has an opportunity for the attackers ready to inject the malicious codes or commands that might unlock the secured data of the customers and your business. However, the extent of the attack’s complexity and success might depend on the messaging platform’s security.

Are you thinking about how chatbots are being exposed to attacks? Well! Hackers are now highly advanced. They attack the chatbots in two ways, i.e., either by social engineering attack or by technical attacks.

  • An evil bot can impersonate a legal user by using backup data of the possibly targeted victims by social engineering attack. All such data is collected from various sources like the dark web and social media platforms. Sometimes they use both sources to gain access to some other user’s data by a bot providing such services.
  • The second attack is technical. Here also attackers can turn themself into evil bots who exchange messages with the other bots. The purpose is to look for some vulnerabilities in the target’s profile that can be later exploited. It can eventually lead to the compromise of the entire framework that protects the data and can ultimately lead to data theft.

To ensure chatbots security, the bot creators must ensure that all the security processes are in place and are responsible for restoring the architecture. The data flow via the chatbot system should also be encrypted both in transit and rest.

To further aid you in chatbot security, this article discusses five best practices to ensure chatbots security. So, let’s read on.

The following mentioned below are some of the best practices to ensure the security of chatbots.

It’s always feared that data in transit can be spoofed or tampered with the sophistication of cybercriminals’ technology and smartness. It’s essential to implement end-to-end encryption to ensure that your entire conversation remains secured. It means that by encryption, you can prevent any third person other than the sender and the receiver from peeping into your messages.

Encryption importance can’t be neglected in the cyber world, and undoubtedly the chatbot designers are adapting this method to make sure that their chatbot security is right on the point. For more robust encryption, consider using business VPNs that encrypt your internet traffic and messages. With a VPN, you can also prevent the threats and vulnerabilities associated with chatbots.

1. 8 Proven Ways to Use Chatbots for Marketing (with Real Examples)

2. How to Use Texthero to Prepare a Text-based Dataset for Your NLP Project

3. 5 Top Tips For Human-Centred Chatbot Design

4. Chatbot Conference Online

Moreover, it’s a crucial feature of other chat services like WhatsApp and other giant tech developers. They are anxious to guarantee security via encryption even when there’s strict surveillance by the government. Such encryption is to fulfill the legal principles of the GDPR that says that companies should adopt measures to encrypt the users’ data.

User identity authentication is a process that verifies if the user is having secure and valid credentials like the username and password. The login credentials are exchanged for having a secure authentication token used during the complete user session. If you haven’t, then you should try out this method for boosting user security.

Authentication timeouts are another way to ensure your chatbots security. This method is more common in banks as the token can be used for the predetermined time.

Moreover, two-factor authentication is yet another method to prove user identity. Users are asked to verify identity either by a text message or email, depending on the way they’ve chosen. It also helps in the authorization process as it permits access to the right person and ensures that information isn’t mishandled or breached.

The self-destructive message features open another way for enhancing chatbot security. This option comes in handy when the user provides their personally identifiable information. Such information can pose a serious threat to user privacy and should be destroyed or deleted within a set period. This method is handier when you’re associated with backing or any other financial chatbots.

By using secure protocols, you can also ensure chatbots security. Every security system, by default, has the HTTPS protocol installed in it. If you aren’t an IT specialist, you can also identify it when you view the search bar’s URL. As long as your data is being transferred via HTTPS protocol and encrypted connections, TLS and SSL, your data is secured from vulnerabilities and different types of cyber-attacks.

Thus, make sure to use secure protocols for enhanced security. Remember that when Chatbots are new, the coding and system used to protect it is the same as the existing HIMs. They interconnect with their security systems and have more than one encryption layer to protect their users’ security.

Do you know what the most significant security vulnerability that’s challenging to combat is? Wondering? Well! It’s none other than human error. User behavior must be resolved using commercial applications because they might continue to believe that the systems are flawed.

No doubt that an unprecedented number of users label the significance of digital security, but still, humans are the most vulnerable in the system. Chatbot security continues to be a real big problem until the problem of user errors comes to an end. And this needs education on various forms of digital technology, including chatbots.

Here the customers aren’t the ones who are to be blamed. Like customers, employees can make a mistake, and they do make most of the time. To prevent this, the chatbot developers should form a defined strategy, including the IT experts, and train them on the system’s safe use. Doing so enhances the team’s skillset and allows them to engage with the chatbot system confidently.

However, clients can’t be educated like the employees. But at least you can provide them a detailed road map of securely interacting with the system. It might involve other professionals who can successfully engage customers and educate them on the right way to interact with the chatbots.

Several emerging technologies are keen to play a vital role in protecting the chatbots against threats and vulnerabilities in the upcoming time, among all the most potent method behavior analytics and Artificial Intelligence developments.

  • User Behavioral Analytics: It’s a process that uses applications to study the patterns of user behavior. It enables them to implement complex algorithms and statistical analysis to detect any abnormal behavior that possibly represents a security threat. Analytical tools are quite common and powerful; thus, this methodology can become a fundamental component of the chatbot system.
  • Developments in AI: Artificial technology is a two-end sword that offers benefits and threats simultaneously. But, as AI is predicted to fulfill its potential, it will provide an extra security level to the systems. It is mainly because of its ability to wipe a large amount of data for abnormalities that recognizes security breaches and threats.

The Bottom Line

Security concerns have always been there with new technologies and bring new threats and vulnerabilities with them. Although chatbots are an emerging technology, the security practices that stand behind them are present for a long time and are effective. Chatbots are the innovative development of the current era, and emerging technologies like AI will transform the way businesses might interact with the customers and ensure their security.

Source: https://chatbotslife.com/are-chatbots-vulnerable-best-practices-to-ensure-chatbots-security-d301b9f6ce17?source=rss—-a49517e4c30b—4

Continue Reading

AI

Best Technology Stacks For Mobile App Development

Published

on

What’s the Best Tech Stack for Mobile App Development? Read To Know

Which is the Best Tech Stack for Mobile Application Development? Kotlin, React Native, Ionic, Xamarin, Objective-C, Swift, JAVA… Which One?

Image Source: Google

Technology Stack for smartphones is like what blood is for the human body. Without a technology stack, it is hard even to imagine smartphones. Having a smartphone in uncountable hands is rising exponentially. For tech pundits, this is one unmissable aspect of our digital experience wherein tech stack is as critical as ROI.

The riveting experience for a successful mobile app predominantly depends on technology stacks.

The unbiased selection of mobile apps development language facilitates developers to build smooth, functional, efficient apps. They help businesses tone down the costs, focus on revenue-generation opportunities. Most importantly, it provides customers with jaw-dropping amazement, giving a reason to have it installed on the indispensable gadget in present times.

In today’s time, when there are over 5 million apps globally, and by all conscience, these are whopping no.s and going to push the smartphone industry further. But now you could see mobile app development every ‘nook and corner.’ But the fact is not who provides what but understanding the behavioural pattern of users.

So the pertinent question is, which is the ideal tech stack to use for mobile app development?

In native mobile app development, all toolkits, mobile apps development language, and the SDK are supported and provided by operating system vendors. Native app development thus allows developers to build apps compatible with specific OS environments; it is suitable for device-specific hardware and software. Hence it renders optimized performance using the latest technology. However, since Android & iOS imparts — — a unique platform for development, businesses have to develop multiple mobile apps for each platform.

1. Waz

2. Pokemon Go

3. Lyft

1.Java: The popularity of JAVA still makes it one of the official programming languages for android app development until the introduction of Kotlin. Java itself is at the core of the Android OS. Many of us even see the logo of Java when the device reboots. However, contradictions with Oracle (which owns the license to Java) made Google shift to open-source Java SDK for versions starting from Android 7.0 Nougat

2.Kotlin: According to Google I/O conference in 2019- Kotlin is the officially supported language for Android app development. It is entirely based on Java but has a few additions which make it simpler and easier to work.

1. 8 Proven Ways to Use Chatbots for Marketing (with Real Examples)

2. How to Use Texthero to Prepare a Text-based Dataset for Your NLP Project

3. 5 Top Tips For Human-Centred Chatbot Design

4. Chatbot Conference Online

It’s my gut feeling like other developers to say that Kotlin is simply better. It has a leaner, more straightforward and concise code than open-cell Java, and several other advantages about handling null-pointer exceptions and more productive coding.

HERE’S A Programming Illustration Defining the CONCISENESS OF KOTLIN CODE

public class Address {

private String street;

private int streetNumber;

private String postCode;

private String city;

private Country country;

public Address(String street, int streetNumber, String postCode, String city, Country country) {

this.street = street;

this.streetNumber = streetNumber;

this.postCode = postCode;

this.city = city;

this.country = country;

}

@Override

public boolean equals(Object o) {

if (this == o) return true;

if (o == null || getClass() != o.getClass()) return false;

Address address = (Address) o;

if (streetNumber != address.streetNumber) return false;

if (!street.equals(address.street)) return false;

if (!postCode.equals(address.postCode)) return false;

if (!city.equals(address.city)) return false;

return country == address.country;

}

@Override

public int hashCode() {

int result = street.hashCode();

result = 31 * result + streetNumber;

result = 31 * result + postCode.hashCode();

result = 31 * result + city.hashCode();

result = 31 * result + (country != null ? country.hashCode() : 0);

return result;

}

@Override

public String toString() {

return “Address{“ +

“street=’” + street + ‘\’’ +

“, streetNumber=” + streetNumber +

“, postCode=’” + postCode + ‘\’’ +

“, city=’” + city + ‘\’’ +

“, country=” + country +

‘}’;

}

public String getStreet() {

return street;

}

public void setStreet(String street) {

this.street = street;

}

public int getStreetNumber() {

return streetNumber;

}

public void setStreetNumber(int streetNumber) {

this.streetNumber = streetNumber;

}

public String getPostCode() {

return postCode;

}

public void setPostCode(String postCode) {

this.postCode = postCode;

}

public String getCity() {

return city;

}

public void setCity(String city) {

this.city = city;

}

public Country getCountry() {

return country;

}

public void setCountry(Country country) {

this.country = country;

}

}

class Address(street:String, streetNumber:Int, postCode:String, city:String, country:Country) {

var street: String

var streetNumber:Int = 0

var postCode:String

var city: String

var country:Country

init{

this.street = street

this.streetNumber = streetNumber

this.postCode = postCode

this.city = city

this.country = country

}

public override fun equals(o:Any):Boolean {

if (this === o) return true

if (o == null || javaClass != o.javaClass) return false

Val address = o as Address

if (streetNumber != address.streetNumber) return false

if (street != address.street) return false

if (postCode != address.postCode) return false

if (city != address.city) return false

return country === address.country

}

public override fun hashCode():Int {

val result = street.hashCode()

result = 31 * result + streetNumber

result = 31 * result + postCode.hashCode()

result = 31 * result + city.hashCode()

result = 31 * result + (if (country != null) country.hashCode() else 0)

return result

}

public override fun toString():String {

return (“Address{“ +

“street=’” + street + ‘\’’.toString() +

“, streetNumber=” + streetNumber +

“, postCode=’” + postCode + ‘\’’.toString() +

“, city=’” + city + ‘\’’.toString() +

“, country=” + country +

‘}’.toString())

}

}

I’d say KOTLIN IS THE BEST FIND FOR ANDROID APP DEVELOPMENT.Google has dug deeper with some plans ahead since announcing it as an official language. Moreover, it signals Google’s first steps in moving away from the Java ecosystem, which is imminent, considering its recent adventures with Flutter and the upcoming Fuchsia OS.

Objective C is the same for iOS what Java is for Android. Objective-C, a superset of the C programming language( with objective -oriented capabilities and dynamic run time) initially used to build the core of iOS operating system across the Apple devices. However, Apple soon started using swift, which diminishes the importance of Objective -C in comparison to previous compilations.

Apple introduced Swift as an alternative to Objective-C in late 2015, and it has since been continued to be the primary language for iOS app development.Swift is more functional than Objective-C, less prone to errors, dynamic libraries help reduce the size and app without ever compromising performance.

Now, you would remember the comparison we’ve done with Java and kotlin. In iOS, objective-C is much older than swift with much more complicated syntax. Giving cringeworthy feel to beginners to get started with Objective-C.

Image Source: Google

THIS IS WHAT YOU DO WHEN INITIALIZING AN ARRAY IN OBJECTIVE-C:

NSMutableArray * array =[[NSMutableArray alloc] init];

NOW LOOK AT HOW THE SAME THING IS DONE IN SWIFT:

var array =[Int]()

SWIFT IS MUCH MORE ` WHAT WE’VE COVERED HERE.

In cross-platform app development, developers build a single mobile app that can be used on multiple OS platforms. It is made possible by creating an app with a shared common codebase, adapted to various platforms.

Image Source: Google

Popular Cross-platform apps:

  1. Instagram
  2. Skype
  3. LinkedIN

React Native is a mobile app development framework based on JavaScript. It is used and supported by one of the biggest social media platforms- Facebook. In cross-platform apps built using React Native, the application logic is coded in JavaScript, whereas its UI is entirely native. This blog about building a React Native app is worth reading if you want to know why its stakes are higher.

Xamarin is a Microsoft-supported cross-platform mobile app development tool that uses the C# programming language. Using Xamarin, developers can build mobile apps for multiple platforms, sharing over 90% of the same code.

TypeScript is a superset of JavaScript, and is a statically-typed programming language supported by Microsoft. TypeScript can be used along with the React Native framework to make full use of its error detection features when writing code for react components.

In Hybrid mobile app development, developers build web apps using HTML, CSS & JavaScript and then wrap the code in a native shell. It allows the app to be deployed as a regular app, with functionality at a level between a fully native app and a website rendered(web browser).

Image Source: Google
  1. Untappd
  2. Amazon App Store
  3. Evernote

Apache Cordova is an open-source hybrid mobile app development framework that uses JavaScript for logic operations and while HTML5 & CSS3 for rendering. PhoneGap is a commercialized, free, and open-source distribution of Apache Cordova owned by Adobe. The PhoneGap platform was developed to deliver non-proprietary, free, and open-source app development solutions powered by the web.

Ionic is a hybrid app development framework based on AngularJS. Similar to other hybrid platforms, it uses HTML, CSS & JavaScript to build mobile apps. Ionic is primarily focused on the front-end UI experience and integrates well with frameworks such as Angular, Vue, and ReactJS.

To summarize, there are 3 types of mobile apps- Native mobile apps, Cross-platform mobile apps, and Hybrid mobile apps; each offers unique technologies, frameworks, and tools of their own. I have enlisted here the best mobile app technology stacks you could use for mobile app development.

The technologies, tools, and frameworks mentioned here are used in some of the most successful apps. With support from an expert, a well-established mobile app development company, that may give much-needed impetus in the dynamic mobile app development world.

Source: https://chatbotslife.com/best-technology-stacks-for-mobile-app-development-6fed70b62778?source=rss—-a49517e4c30b—4

Continue Reading
AI9 hours ago

Things to Know about Free Form Templates

AI21 hours ago

Are Chatbots Vulnerable? Best Practices to Ensure Chatbots Security

AI21 hours ago

Best Technology Stacks For Mobile App Development

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI2 days ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

Trending