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Using speaker diarization for streaming transcription with Amazon Transcribe and Amazon Transcribe Medical

Conversational audio data that requires transcription, such as phone calls, doctor visits, and online meetings, often has multiple speakers. In these use cases, it’s important to accurately label the speaker and associate them to the audio content delivered. For example, you can distinguish between a doctor’s questions and a patient’s responses in the transcription of […]

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Conversational audio data that requires transcription, such as phone calls, doctor visits, and online meetings, often has multiple speakers. In these use cases, it’s important to accurately label the speaker and associate them to the audio content delivered. For example, you can distinguish between a doctor’s questions and a patient’s responses in the transcription of a live medical consultation.

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications. With the launch of speaker diarization for streaming transcriptions, you can use Amazon Transcribe and Amazon Transcribe Medical to label the different speakers in real-time customer service calls, conference calls, live broadcasts, or clinical visits. Speaker diarziation or speaker labeling is critical to creating accurate transcription because of its ability to distinguish what each speaker said. This is typically represented by speaker A and speaker B. Speaker identification usually refers to when the speakers are specifically identified as Sally or Alfonso. With speaker diarization, you can request Amazon Transcribe and Amazon Transcribe Medical to accurately label up to five speakers in an audio stream. Although Amazon Transcribe can label more than five speakers in a stream, the accuracy of speaker diarization decreases if you exceed that number. In some cases, the different speakers may be on different channels (e.g. Call Center). In those cases you can use Amazon Transcribe Channel Identification to separate multiple channels from within a live audio stream to generate transcripts that label each audio channel

This post uses an example application to show you how to use the AWS SDK for Java to start a stream that enables you to stream your conversational audio from your microphone to Amazon Transcribe, and receive transcripts in real time with speaker labeling. The solution is a Java application that you can use to transcribe streaming audio from multiple speakers in real time. The application labels each speaker in the transcription results, which can be exported.

You can find the application in the GitHub repo. We include detailed steps to set up and run the application in this post.

Prerequisites

You need an AWS account to proceed with the solution. Additionally, the AmazonTranscribeFullAccess policy is attached to the AWS Identity and Access Management (IAM) role you use for this demo. To create an IAM role with the necessary permissions, complete the following steps:

  1. Sign in to the AWS Management Console and open the IAM console.
  2. On the navigation pane, under Access management, choose Roles.
  3. You can use an existing IAM role to create and run transcription jobs, or choose Create role.
  4. Under Common use cases, choose EC2. You can select any use case, but EC2 is one of the most straightforward ones.
  5. Choose Next: Permissions.
  6. For the policy name, enter AmazonTranscribeFullAccess.
  7. Choose Next: Tags.
  8. Choose Next: Review.
  9. For Role name, enter a role name.
  10. Remove the text under Role description.
  11. Choose Create role.
  12. Choose the role you created.
  13. Choose Trust relationships.
  14. Choose Edit trust relationship.
  15. Replace the trust policy text in your role with the following code:
{"Version": "2012-10-17", "Statement": [ {"Effect": "Allow", "Principal": {"Service": "transcribe.amazonaws.com" }, "Action": "sts:AssumeRole" } ]
} 

Solution overview

Amazon Transcribe streaming transcription enables you to send a live audio stream to Amazon Transcribe and receive a stream of text in real time. You can label different speakers in either HTTP/2 or Websocket streams. Speaker diarization works best for labeling between two and five speakers. Although Amazon Transcribe can label more than five speakers in a stream, the accuracy of speaker separation decreases if you exceed five speakers.

To start an HTTP/2 stream, we specify the ShowSpeakerLabel request parameter of the StartStreamTranscription operation in our demo solution. See the following code:

 private StartStreamTranscriptionRequest getRequest(Integer mediaSampleRateHertz) { return StartStreamTranscriptionRequest.builder() .languageCode(LanguageCode.EN_US.toString()) .mediaEncoding(MediaEncoding.PCM) .mediaSampleRateHertz(mediaSampleRateHertz) .showSpeakerLabel(true) .build(); }

Amazon Transcribe streaming returns a “result” object as part of the transcription response element that can be used to label the speakers in the transcript. To learn more about the parameters in this result object, see Response Syntax.

"TranscriptEvent": { "Transcript": { "Results": [ { "Alternatives": [ { "Items": [ { "Content": "string", "EndTime": number, "Speaker": "string", "StartTime": number, "Type": "string", "VocabularyFilterMatch": boolean } ], "Transcript": "string" } ], "EndTime": number, "IsPartial": boolean, "ResultId": "string", "StartTime": number } ] } }

Our solution demonstrates speaker diarization during transcription for real-time audio captured via the microphone. Amazon Transcribe breaks your incoming audio stream based on natural speech segments, such as a change in speaker or a pause in the audio. The transcription is returned progressively to your application, with each response containing more transcribed speech until the entire segment is transcribed. For more information, see Identifying Speakers.

Launching the application

Complete the following prerequisites to launch the Java application. If you already have JavaFX or Java and Maven installed, you can skip the first two sections (Installing JavaFX and Installing Maven). For all environment variables mentioned in the following steps, a good option is to add it to the ~/.bashrc file and apply these variables as required by typing “source ~/.bashrc” after you open a shell.

Installing JDK

As your first step, download and install Java SE. When the installation is complete, set the JAVA_HOME variable (see the following code). Make sure to select the path to the correct Java version and confirm the path is valid.

export JAVA_HOME=path-to-your-install-dir/jdk-14.0.2.jdk/Contents/Home

Installing JavaFX

For instructions on downloading and installing JavaFX, see Getting Started with JavaFX. Set up the environment variable as described in the instructions or by entering for following code (replace path/to with the directory where you installed JavaFX):

export PATH_TO_FX='path/to/javafx-sdk-14/lib'

Test your JavaFX installation as shown in the sample application on GitHub.

Installing Maven

Download the latest version of Apache Maven. For installation instructions, see Installing Apache Maven.

Installing the AWS CLI (Optional)

As an optional step, you can install the AWS Command Line Interface (AWS CLI). For instructions, see Installing, updating, and uninstalling the AWS CLI version 2. You can use the AWS CLI to validate and troubleshoot the solution as needed.

Setting up AWS access

Lastly, set up your access key and secret access key required for programmatic access to AWS. For instructions, see Programmatic access. Choose a Region closest to your location. For more information, see the Amazon Transcribe Streaming section in Service Endpoints.

When you know the Region and access keys, open a terminal window in your computer and assign them to environment variables for access within our solution:

  • export AWS_ACCESS_KEY_ID=<access-key>
  • export AWS_SECRET_ACCESS_KEY=<secret-access-key>
  • export AWS_REGION=<aws region>

Solution demonstration

The following video demonstrates how you can compile and run the Java application presented in this post. Use the following sections to walk through these steps yourself.

The quality of the transcription results depends on many factors. For example, the quality can be affected by artifacts such as background noise, speakers talking over each other, complex technical jargon, the volume disparity between speakers, and the audio recording devices you use. You can use a variety of capabilities provided by Amazon Transcribe to improve transcription quality. For example, you can use custom vocabularies to recognize out-of-lexicon terms. You can even use custom language models, which enables you to use your own data to build domain-specific models. For more information, see Improving Domain-Specific Transcription Accuracy with Custom Language Models.

Setting up the solution

To implement the solution, complete the following steps:

  1. Clone the solution’s GitHub repo in your local computer using the following command:
git clone https://github.com/aws-samples/aws-transcribe-speaker-identification-java

  1. Navigate to the main directory of the solution aws-transcribe-streaming-example-java with the following code:
cd aws-transcribe-streaming-example-java

  1. Compile the source code and build a package for running our solution:
    1. Enter mvn compile. If the compile is successful, you should a BUILD SUCCESS message. If there are errors in compilation, it’s most likely related to JavaFX path issues. Fix the issues based on the instructions in the Installing JavaFX section in this post.
    2. Enter mvn clean package. You should see a BUILD SUCCESS message if everything went well. This command compiles the source files and creates a packaged JAR file that we use to run our solution. If you’re repeating the build exercise, you don’t need to enter mvn compile every time.
  2. Run the solution by entering the following code:
--module-path $PATH_TO_FX --add-modules javafx.controls -jar target/aws-transcribe-sample-application-1.0-SNAPSHOT-jar-with-dependencies.jar

If you receive an error, it’s likely because you already had a version of Java or JavaFX and Maven installed and skipped the steps to install JDK and JavaFX in this post. In so, enter the following code:

java -jar target/aws-transcribe-sample-application-1.0-SNAPSHOT-jar-with-dependencies.jar

You should see a Java UI window open.

Running the demo solution

Follow the steps in this section to run the demo yourself. You need two to five speakers present to try out the speaker diarization functionality. This application requires that all speakers use the same audio input when speaking.

  1. Choose Start Microphone Transcription in the Java UI application.
  2. Use your computer’s microphone to stream audio of two or more people (not more than five) conversing.
  3. As of this writing, Amazon Transcribe speaker labeling supports real-time streams that are in US English

You should see the speaker designations and the corresponding transcript appearing in the In-Progress Transcriptions window as the conversation progresses. When the transcript is complete, it should appear in the Final Transcription window.

  1. Choose Save Full Transcript to store the transcript locally in your computer.

Conclusion

This post demonstrated how you can easily infuse your applications with real-time ASR capabilities using Amazon Transcribe streaming and showcased an important new feature that enables speaker diarization in real-time audio streams.

With Amazon Transcribe and Amazon Transcribe Medical, you can use speaker separation to generate real-time insights from your conversations such as in-clinic visits or customer service calls and send these to downstream applications for natural language processing, or you can send it to human loops for review using Amazon Augmented AI (Amazon A2I). For more information, see Improving speech-to-text transcripts from Amazon Transcribe using custom vocabularies and Amazon Augmented AI.


About the Authors

Prem Ranga is an Enterprise Solutions Architect based out of Houston, Texas. He is part of the Machine Learning Technical Field Community and loves working with customers on their ML and AI journey. Prem is passionate about robotics, is an Autonomous Vehicles researcher, and also built the Alexa-controlled Beer Pours in Houston and other locations.

Talia Chopra is a Technical Writer in AWS specializing in machine learning and artificial intelligence. She works with multiple teams in AWS to create technical documentation and tutorials for customers using Amazon SageMaker, MxNet, and AutoGluon. In her free time, she enjoys meditating, studying machine learning, and taking walks in nature.

Parsa Shahbodaghi is a Technical Writer in AWS specializing in machine learning and artificial intelligence. He writes the technical documentation for Amazon Transcribe and Amazon Transcribe Medical. In his free time, he enjoys meditating, listening to audiobooks, weightlifting, and watching stand-up comedy. He will never be a stand-up comedian, but at least his mom thinks he’s funny.

Mahendar Gajula is a Sr. Data Architect at AWS. He works with AWS customers in their journey to the cloud with a focus on data lake, data warehouse, and AI/ML projects. In his spare time, he enjoys playing tennis and spending time with his family.

Source: https://aws.amazon.com/blogs/machine-learning/using-speaker-diarization-for-streaming-transcription-with-amazon-transcribe-and-amazon-transcribe-medical/

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Are Chatbots Vulnerable? Best Practices to Ensure Chatbots Security

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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

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Best Technology Stacks For Mobile App Development

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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.

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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

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage. Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, […]

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Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage.

Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, which enables you to search and explore Hungarian cultural heritage, including 600,000 faces over 500,000 images. For example, you can find historical works by author Mór Jókai or photos on topics like weddings. The Arcanum team chose Amazon Rekognition to free valuable staff from time and cost-intensive manual labeling, and improved label accuracy to make 200,000 previously unsearchable images (approximately 40% of image inventory), available to users.

Amazon Rekognition makes it easy to add image and video analysis to your applications using highly scalable machine learning (ML) technology that requires no previous ML expertise to use. Amazon Rekognition also provides highly accurate facial recognition and facial search capabilities to detect, analyze, and compare faces.

Arcanum uses this facial recognition feature in their image database services to help you find particular people in Arcanum’s articles. This post discusses their challenges and why they chose Amazon Rekognition as their solution.

Automated image labeling challenges

Arcanum dedicated a team of three people to start tagging and labeling content for Hungaricana. The team quickly learned that they would need to invest more than 3 months of time-consuming and repetitive human labor to provide accurate search capabilities to their customers. Considering the size of the team and scope of the existing project, Arcanum needed a better solution that would automate image and object labelling at scale.

Automated image labeling solutions

To speed up and automate image labeling, Arcanum turned to Amazon Rekognition to enable users to search photos by keywords (for example, type of historic event, place name, or a person relevant to Hungarian history).

For the Hungaricana project, preprocessing all the images was challenging. Arcanum ran a TensorFlow face search across all 28 million pages on a machine with 8 GPUs in their own offices to extract only faces from images.

The following screenshot shows what an extract looks like (image provided by Arcanum Database Ltd).

The images containing only faces are sent to Amazon Rekognition, invoking the IndexFaces operation to add a face to the collection. For each face that is detected in the specified face collection, Amazon Rekognition extracts facial features into a feature vector and stores it in an Amazon Aurora database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.

The image preprocessing helped create a very efficient and cost-effective way to index faces. The following diagram summarizes the preprocessing workflow.

As for the web application, the workflow starts with a Hungaricana user making a face search request. The following diagram illustrates the application workflow.

The workflow includes the following steps:

  1. The user requests a facial match by uploading the image. The web request is automatically distributed by the Elastic Load Balancer to the webserver fleet.
  2. Amazon Elastic Compute Cloud (Amazon EC2) powers application servers that handle the user request.
  3. The uploaded image is stored in Amazon Simple Storage Service (Amazon S3).
  4. Amazon Rekognition indexes the face and runs SearchFaces to look for a face similar to the new face ID.
  5. The output of the search face by image operation is stored in Amazon ElastiCache, a fully managed in-memory data store.
  6. The metadata of the indexed faces are stored in an Aurora relational database built for the cloud.
  7. The resulting face thumbnails are served to the customer via the fast content-delivery network (CDN) service Amazon CloudFront.

Experimenting and live testing Hungaricana

During our test of Hungaricana, the application performed extremely well. The searches not only correctly identified people, but also provided links to all publications and sources in Arcanum’s privately owned database where found faces are present. For example, the following screenshot shows the result of the famous composer and pianist Franz Liszt.

The application provided 42 pages of 6×4 results. The results are capped to 1,000. The 100% scores are the confidence scores returned by Amazon Rekognition and are rounded up to whole numbers.

The application of Hungaricana has always promptly, and with a high degree of certainty, presented results and links to all corresponding publications.

Business results

By introducing Amazon Rekognition into their workflow, Arcanum enabled a better customer experience, including building family trees, searching for historical figures, and researching historical places and events.

The concept of face searching using artificial intelligence certainly isn’t new. But Hungaricana uses it in a very creative, unique way.

Amazon Rekognition allowed Arcanum to realize three distinct advantages:

  • Time savings – The time to market speed increased dramatically. Now, instead of spending several months of intense manual labor to label all the images, the company can do this job in a few days. Before, basic labeling on 150,000 images took months for three people to complete.
  • Cost savings – Arcanum saved around $15,000 on the Hungaricana project. Before using Amazon Rekognition, there was no automation, so a human workforce had to scan all the images. Now, employees can shift their focus to other high-value tasks.
  • Improved accuracy – Users now have a much better experience regarding hit rates. Since Arcanum started using Amazon Rekognition, the number of hits has doubled. Before, out of 500,000 images, about 200,000 weren’t searchable. But with Amazon Rekognition, search is now possible for all 500,000 images.

 “Amazon Rekognition made Hungarian culture, history, and heritage more accessible to the world,” says Előd Biszak, Arcanum CEO. “It has made research a lot easier for customers building family trees, searching for historical figures, and researching historical places and events. We cannot wait to see what the future of artificial intelligence has to offer to enrich our content further.”

Conclusion

In this post, you learned how to add highly scalable face and image analysis to an enterprise-level image gallery to improve label accuracy, reduce costs, and save time.

You can test Amazon Rekognition features such as facial analysis, face comparison, or celebrity recognition on images specific to your use case on the Amazon Rekognition console.

For video presentations and tutorials, see Getting Started with Amazon Rekognition. For more information about Amazon Rekognition, see Amazon Rekognition Documentation.


About the Authors

Siniša Mikašinović is a Senior Solutions Architect at AWS Luxembourg, covering Central and Eastern Europe—a region full of opportunities, talented and innovative developers, ISVs, and startups. He helps customers adopt AWS services as well as acquire new skills, learn best practices, and succeed globally with the power of AWS. His areas of expertise are Game Tech and Microsoft on AWS. Siniša is a PowerShell enthusiast, a gamer, and a father of a small and very loud boy. He flies under the flags of Croatia and Serbia.

Cameron Peron is Senior Marketing Manager for AWS Amazon Rekognition and the AWS AI/ML community. He evangelizes how AI/ML innovation solves complex challenges facing community, enterprise, and startups alike. Out of the office, he enjoys staying active with kettlebell-sport, spending time with his family and friends, and is an avid fan of Euro-league basketball.

Source: https://aws.amazon.com/blogs/machine-learning/arcanum-makes-hungarian-heritage-accessible-with-amazon-rekognition/

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