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Amazon Rekognition adds support for six new content moderation categories

Amazon Rekognition content moderation is a deep learning-based service that can detect inappropriate, unwanted, or offensive images and videos, making it easier to find and remove such content at scale. Amazon Rekognition provides a detailed taxonomy of moderation categories, such as Explicit Nudity, Suggestive, Violence, and Visually Disturbing. You can now detect six new categories: […]

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Amazon Rekognition content moderation is a deep learning-based service that can detect inappropriate, unwanted, or offensive images and videos, making it easier to find and remove such content at scale. Amazon Rekognition provides a detailed taxonomy of moderation categories, such as Explicit Nudity, Suggestive, Violence, and Visually Disturbing.

You can now detect six new categories: Drugs, Tobacco, Alcohol, Gambling, Rude Gestures, and Hate Symbols. In addition, you get improved detection rates for already supported categories.

In this post, we learn about the details of the content moderation service, how to use the APIs, and how you can perform comprehensive moderation using AWS machine learning (ML) services. Lastly, we see how customers in social media, broadcast media, advertising, and ecommerce create better user experiences, provide brand safety assurances to advertisers, and comply with local and global regulations.

Challenges with content moderation

The daily volume of user-generated content (UGC) and third-party content has been increasing substantially in industries like social media, ecommerce, online advertising, and photo sharing. You may want to review this content to ensure that your end-users aren’t exposed to potentially inappropriate or offensive material, such as nudity, violence, drug use, adult products, or disturbing images. In addition, broadcast and video-on-demand (VOD) media companies may be required to ensure that the content they create or license carries appropriate ratings as per compliance guidelines for various geographies or target audiences.

Many companies employ teams of human moderators to review content, while others simply react to user complaints to take down offensive images, ads, or videos. However, human moderators alone can’t scale to meet these needs at sufficient quality or speed, which leads to poor user experience, prohibitive costs to achieve scale, or even loss of brand reputation.

Amazon Rekognition content moderation enables you to streamline or automate your image and video moderation workflows using ML. You can use fully managed image and video moderation APIs to proactively detect inappropriate, unwanted, or offensive content containing nudity, suggestiveness, violence, and other such categories. Amazon Rekognition returns a hierarchical taxonomy of moderation-related labels that make it easy to define granular business rules as per your own standards and practices, user safety, or compliance guidelines—without requiring any ML experience. You can then use machine predictions to automate certain moderation tasks completely or significantly reduce the review workload of trained human moderators, so they can focus on higher-value work.

In addition, Amazon Rekognition allows you to quickly review millions of images or thousands of videos using ML, and flag only a small subset of assets for further action. This makes sure that you get comprehensive but cost-effective moderation coverage for all your content as your business scales, and your moderators can reduce the burden of looking at large volumes of disturbing content.

Granular moderation using a hierarchical taxonomy

Different use cases need different business rules for content review. For example, you may want to just flag content with blood, or detect violence with weapons in addition to blood. Content moderation solutions that only provide broad categorizations like violence don’t provide you with enough information to create granular rules. To address this, Amazon Rekognition designed a hierarchical taxonomy with 4 top-level moderation categories (Explicit Nudity, Suggestive, Violence, and Visually Disturbing) and 18 subcategories, which allow you to build nuanced rules for different scenarios.

We have now added 6 new top-level categories (Drugs, Hate Symbols, Tobacco, Alcohol, Gambling, and Rude Gestures), and 17 new subcategories to provide enhanced coverage for a variety of use cases in domains such as social media, photo sharing, broadcast media, gaming, marketing, and ecommerce. The full taxonomy is provided in the following table.

Top-level Category Second-level Category
Explicit Nudity Nudity
Graphic Male Nudity
Graphic Female Nudity
Sexual Activity
Illustrated Explicit Nudity
Adult Toys
Suggestive Female Swimwear Or Underwear
Male Swimwear Or Underwear
Partial Nudity
Barechested Male
Revealing Clothes
Sexual Situations
Violence Graphic Violence Or Gore
Physical Violence
Weapon Violence
Weapons
Self Injury
Visually Disturbing Emaciated Bodies
Corpses
Hanging
Air Crash
Explosions and Blasts
Rude Gestures Middle Finger
Drugs Drug Products
Drug Use
Pills
Drug Paraphernalia
Tobacco Tobacco Products
Smoking
Alcohol Drinking
Alcoholic Beverages
Gambling Gambling
Hate Symbols Nazi Party
White Supremacy
Extremist

How it works

For analyzing images, you can use the DetectModerationLabels API to pass in the Amazon Simple Storage Service (Amazon S3) location of your stored images, or even use raw image bytes in the request itself. You can also specify a minimum prediction confidence. Amazon Rekognition automatically filters out results that have confidence scores below this threshold.

The following code is an image request:

{ "Image": { "S3Object": { "Bucket": "bucket", "Name": "input.jpg" } }, "MinConfidence": 60
}

You get back a JSON response with detected labels, the prediction confidence, and information about the taxonomy in the form of a ParentName field:

{ "ModerationLabels": [ { "Confidence": 99.24723052978516, "ParentName": "", "Name": "Explicit Nudity" }, { "Confidence": 99.24723052978516, "ParentName": "Explicit Nudity", "Name": "Sexual Activity" },
]
}

For more information and a code sample, see Content Moderation documentation. To experiment with your own images, you can use the Amazon Rekognition console.

In the following screenshot, one of our new categories (Smoking) was detected (image sourced from Pexels.com).

For analyzing videos, Amazon Rekognition provides a set of asynchronous APIs. To start detecting moderation categories on your video that is stored in Amazon S3, you can call StartContentModeration. Amazon Rekognition publishes the completion status of the video analysis to an Amazon Simple Notification Service (Amazon SNS) topic. If the video analysis is successful, you call GetContentModeration to get the analysis results. For more information about starting video analysis and getting the results, see Calling Amazon Rekognition Video Operations. For each detected moderation label, you also get its timestamp. For more information and a code sample, see Detecting Inappropriate Stored Videos.

For nuanced situations or scenarios where Amazon Rekognition returns low-confidence predictions, content moderation workflows still require human reviewers to audit results and make final judgements. You can use Amazon Augmented AI (Amazon A2I) to easily implement a human review and improve the confidence of predictions. Amazon A2I is directly integrated with Amazon Rekognition moderation APIs. Amazon A2I allows you to use in-house, private, or even third-party vendor workforces with a user-defined web interface that has instructions and tools to carry out review tasks. For more information about using Amazon A2I with Amazon Rekognition, see Build alerting and human review for images using Amazon Rekognition and Amazon A2I.

Audio, text, and customized moderation

You can use Amazon Rekognition text detection for images and videos to read text, and then check it against your own list of prohibited words or phrases. To detect profanities or hate speech in videos, you can use Amazon Transcribe to convert speech to text, and then check it against a similar list. If you want to further analyze text using natural language processing (NLP), you can use Amazon Comprehend.

If you have very specific or fast-changing moderation needs and access to your own training data, Amazon Rekognition offers Custom Labels to easily train and deploy your own moderation models with a few clicks or API calls. For example, if your ecommerce platform needs to take action on a new product carrying an offensive or politically sensitive message, or your broadcast network needs to detect and blur the logo of a specific brand for legal reasons, you can quickly create and operationalize new models with custom labels to address these scenarios.

Use cases

In this section, we discuss three potential use cases for expanded content moderation labels, depending on your industry.

Social media and photo-sharing platforms

Social media and photo-sharing platforms work with very large amounts of user-generated photos and videos daily. To make sure that uploaded content doesn’t violate community guidelines and societal standards, you can use Amazon Rekognition to flag and remove such content at scale even with small teams of human moderators. Detailed moderation labels also allow for creating a more granular set of user filters. For example, you might find images containing drinking or alcoholic beverages to be acceptable in a liquor ad, but want to avoid ones showing drug products and drug use under any circumstances.

Broadcast and VOD media companies

As a broadcast or VOD media company, you may have to ensure that you comply with the regulations of the markets and geographies in which you operate. For example, content that shows smoking needs to carry an onscreen health advisory warning in countries like India. Furthermore, brands and advertisers want to prevent unsuitable associations when placing their ads in a video. For example, a toy brand for children may not want their ad to appear next to content showing consumption of alcoholic beverages. Media companies can now use the comprehensive set of categories available in Amazon Rekognition to flag the portions of a movie or TV show that require further action from editors or ad traffic teams. This saves valuable time, improves brand safety for advertisers, and helps prevent costly compliance fines from regulators.

Ecommerce and online classified platforms

Ecommerce and online classified platforms that allow third-party or user product listings want to promptly detect and delist illegal, offensive, or controversial products such as items displaying hate symbols, adult products, or weapons. The new moderation categories in Amazon Rekognition help streamline this process significantly by flagging potentially problematic listings for further review or action.

Customer stories

We now look at some examples of how customers are deriving value from using Amazon Rekognition content moderation:

SmugMug operates two very large online photo platforms, SmugMug and Flickr, enabling more than 100M members to safely store, search, share, and sell tens of billions of photos. Flickr is the world’s largest photographer-focused community, empowering photographers around the world to find their inspiration, connect with each other, and share their passion with the world.

As a large, global platform, unwanted content is extremely risky to the health of our community and can alienate photographers. We use Amazon Rekognition’s content moderation feature to find and properly flag unwanted content, enabling a safe and welcoming experience for our community. At Flickr’s huge scale, doing this without Amazon Rekognition is nearly impossible. Now, thanks to content moderation with Amazon Rekognition, our platform can automatically discover and highlight amazing photography that more closely matches our members’ expectations, enabling our mission to inspire, connect, and share.”

– Don MacAskill, Co-founder, CEO & Chief Geek

Mobisocial is a leading mobile software company, focused on building social networking and gaming apps. The company develops Omlet Arcade, a global community where tens of millions of mobile gaming live-streamers and esports players gather to share gameplay and meet new friends.

“To ensure that our gaming community is a safe environment to socialize and share entertaining content, we used machine learning to identify content that doesn’t comply with our community standards. We created a workflow, leveraging Amazon Rekognition, to flag uploaded image and video content that contains non-compliant content. Amazon Rekognition’s content moderation API helps us achieve the accuracy and scale to manage a community of millions of gaming creators worldwide. Since implementing Amazon Rekognition, we’ve reduced the amount of content manually reviewed by our operations team by 95%, while freeing up engineering resources to focus on our core business. We’re looking forward to the latest Rekognition content moderation model update, which will improve accuracy and add new classes for moderation.”

-Zehong, Senior Architect at Mobisocial

Conclusion

In this post, we learned about the six new categories of inappropriate or offensive content now available in the Amazon Rekognition hierarchical taxonomy for content moderation, which contains 10 top-level categories and 35 subcategories overall. We also saw how Amazon Rekognition moderation APIs work, and how customers in different domains are using them to streamline their review workflows.

For more information about the latest version of content moderation APIs, see Content Moderation. You can also try out your own images on the Amazon Rekognition console. If you want to test visual and audio moderation with your own videos, check out the Media Insights Engine (MIE)—a serverless framework to easily generate insights and develop applications for your video, audio, text, and image resources, using AWS ML and media services. You can easily spin up your own MIE instance using the provided AWS CloudFormation template, and then use the sample application.


About the Author

Venkatesh Bagaria is a Principal Product Manager for Amazon Rekognition. He focuses on building powerful but easy-to-use deep learning-based image and video analysis services for AWS customers. In his spare time, you’ll find him watching way too many stand-up comedy specials and movies, cooking spicy Indian food, and pretending that he can play the guitar.

Source: https://aws.amazon.com/blogs/machine-learning/amazon-rekognition-adds-support-for-six-new-content-moderation-categories/

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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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