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Optimizing the cost of training AWS DeepRacer reinforcement learning models

AWS DeepRacer is a cloud-based 3D racing simulator, an autonomous 1/18th scale race car driven by reinforcement learning, and a global racing league. Reinforcement learning (RL), an advanced machine learning (ML) technique, enables models to learn complex behaviors without labeled training data and make short-term decisions while optimizing for longer-term goals. But as we humans can attest, learning something […]

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AWS DeepRacer is a cloud-based 3D racing simulator, an autonomous 1/18th scale race car driven by reinforcement learning, and a global racing league. Reinforcement learning (RL), an advanced machine learning (ML) technique, enables models to learn complex behaviors without labeled training data and make short-term decisions while optimizing for longer-term goals. But as we humans can attest, learning something well takes time—and time is money. You can build and train a simple “all-wheels-on-track” model in the AWS DeepRacer console in just a couple of hours. However, if you’re building complex models involving multiple parameters, a reward function using trigonometry, or generally diving deep into RL, there are steps you can take to optimize the cost of training.

As a Senior Solutions Architect and an AWS DeepRacer PitCrew member, you ultimately rack up a lot of training time. Recently we shared tips for keeping it frugal with Blaine Sundrud, host of DeepRacer TV News. This post discusses that advice in more detail. To see the interview, check out the August 2020 Qualifiers edition of DRTV.

Also, look out for the cost-optimization article coming soon to the AWS DeepRacer Developer Guide for step-by-step procedures on these topics.

The AWS DeepRacer console provides you with many tools to help you get the most of training and evaluating your RL models. After you build a model based on a reward function, which is the incentive plan you create for the agent, your AWS DeepRacer vehicle, you need to train it. This means you enable the agent to explore various actions in its environment, which, for your vehicle is its track. There it attempts to take actions that result in rewards. Over time it learns the behaviors that will lead to a maximum reward—training time that takes machine time and costs money. My goal is to share how avoiding overtraining, validating your model, analyzing logs, using transfer learning, and creating a budget can help keep the focus on fun, not cost.

Overview

In this post, we walk you through some strategies for training better performing and more cost-effective AWS DeepRacer models:

Avoid overtraining

When training an RL model, more isn’t always better. Training longer than necessary can lead to overfitting, which means a model doesn’t adapt, or generalize well, from the environment it’s trained in to a novel environment, real or online. For AWS DeepRacer, a model that is overfit may perform well on a virtual track, but conditions like gravity, shadows on the track, the friction of the wheels on the track, wear in the gears, degradation of the battery, and even smudges on the camera lens can lead to the car running slowly or veering off a replica of that track in the real world. When training and racing exclusively in the AWS DeepRacer console, a model overfitted to an oval track will not do as well on a track with s-curves. In practical terms, you can think of an email spam filter that has been overtrained on messages about window replacements, credit card programs, and rich relatives in foreign lands. It might do an excellent job detecting spam related to those topics, but a terrible job finding spam related to scam insurance plans, gutters, home food delivery, and more original get-rich-quick schemes. To learn more about overfitting, watch AWS DeepRacer League – Overfitting.

We now know overtraining that leads to overfitting isn’t a good thing, but one of the first lessons an ML practitioner learns is that undertraining isn’t good either. So how much training is enough? The key is to stop training at the point when performance begins to degrade. With AWS DeepRacer, the Training Reward graph shows the cumulative reward received per training episode. You can expect this graph to be volatile initially, but over time the graph should trend upwards and to the right, and, as your model starts converging, the average should flatten out. As you watch the reward graph, also keep an eye on the agent’s driving behavior during training. You should stop training when the percentage of the track the car completes is no longer improving. In the following image, you can see a sample reward graph with the “best model” indicated. When the model’s track completion progress per episode continuously reaches 100% and the reward levels out, more training will lead to overfitting, a poorly generalized model, and wasted training time.

When to stop training

Validate your model

A reward function describes the immediate feedback, as a reward or penalty score, your model receives when your AWS DeepRacer vehicle moves from one position on the track to a new one. The function’s purpose is to encourage the vehicle to make moves along the track that reach a destination quickly, without incident or accident. A desirable move earns a higher score for the action, or target state, and an illegal or wasteful move earns a lower score. It may seem simple, but it’s easy to overlook errors in your code or find that your reward function unintentionally incentivizes undesirable moves. Validating your reward function both in theory and practice helps you avoid wasting time and money training a model that doesn’t do what you want it to do.

The validate function is similar to a Python lint tool. Choosing Validate checks the syntax of the reward function, and if successful, results in a “passed validation” message.

After checking the code, validate the performance of your reward function early and often. When first experimenting with a new reward function, train for a short period of time, such as 15 minutes, and observe the results to determine whether or not the reward function is performing as expected. Look at the reward results and percentage of track completion on the reward graph to see that they’re increasing (see the following example graph). If it looks like a well performing model, you can clone that model and train for additional time or start over with the same reward function. If the reward doesn’t improve, you can investigate and make adjustments without wasting training time and putting a dent in your pocketbook.

Analyze logs to improve efficiency

Focusing on the training graph alone does not give you a complete picture. Fortunately, AWS DeepRacer produces logs of actions taken during training. Log analysis involves a detailed look at the outputs produced by the AWS DeepRacer training job. Log analysis might involve an aggregation of the model’s performance at various locations on the track or at different speeds. Analysis often includes various kinds of visualization, such as plotting the agent’s behavior on the track, the reward values at various times or locations, or even plotting the racing line around the track to make sure you’re not oversteering and that your agent is taking the most efficient path. You can also include Python print() statements in your reward function to output interim results to the logs for each iteration of the reward function.

Without studying the logs, you’re likely only making guesses about where to improve. It’s better to rely on data to make these adjustments. You usually get a better model sooner by studying the logs and tweaking the reward function. When you get a decent model, try conducting log analysis before investing in further training time.

The following graph is an example of plotting the racing line around a track.

For more information about log analysis, see Using Jupyter Notebook for analysing DeepRacer’s logs.

Try transfer learning

In ML, as in life, there is no point in reinventing the wheel. Transfer learning involves relying on knowledge gained while solving one problem and applying it to a different, but related, problem. The shape of the AWS DeepRacer Convolutional Neural Network (CNN) is determined by the number of inputs (such as the cameras or LIDAR) and the outputs (such as the action space). A new model has weights set to random values, and a certain amount of training is required to converge to get a working model.

Instead of starting with random weights, you can copy an existing trained model. In the AWS DeepRacer environment, this is called cloning. Cloning works by making a deep copy of the neural network—the AWS DeepRacer CNN—including all the nodes and their weights. This can save training time and money.

The learning rate is one of the hyperparameters that controls the RL training. During each update, a portion of the new weight for each node results from the gradient-descent (or ascent) contribution, and the rest comes from the existing node weight. The learning rate controls how much a gradient-descent (or ascent) update contributes to the network weights. If you are interested in learning more about gradient descent, check out this post on optimizing deep learning.

You can use a higher learning rate to include more gradient-descent contributions for faster training, but the expected reward may not converge if the learning rate is too large. Try setting the learning rate reasonably high for the initial training. When it’s complete, clone and train the network for additional time with a reduced learning rate. This can save a significant amount of training time by allowing you to train quickly at first and then explore more slowly when you’re nearing an optimal solution.

Developers often ask why they can’t modify the action space during or after cloning. It’s because cloning a model results in a duplicate of the original network, and both the inputs and the action space are fixed. If you increase the action space, the behavior of a network with additional output nodes that had no connections to the other layers and no weights is unpredictable, and could lead to a lot more training or even a model that can’t converge at all. CNNs with node weights equal to zero are unpredictable. The nodes might even be deactivated (recall that 0 times anything is 0). Likewise, pruning one or more nodes from the output layer also drives unknown outcomes. Both situations require additional training to ensure the model works as expected, and there is no guarantee it will ever converge. Radically changing the reward function may result in a cloned model that doesn’t converge quickly or at all, which is a waste of time and money.

To try transfer learning following steps in the AWS DeepRacer Developer Guide, see Clone a Trained Model to Start a New Training Pass.

Create a budget

So far, we’ve looked at things you can do within the RL training process to save money. Aside from those I’ve discussed in the AWS DeepRacer console, there is another tool in AWS Management console that can help you keep your spend where you want it—AWS Budgets. You can set monthly, quarterly, and annual budgets for cost, usage, reservations, and savings plans.

On the Cost Management page, choose Budgets and create a budget for AWS DeepRacer.

To set a budget, sign in to the console and navigate to AWS Budgets. Then select a period, effective dates, and a budget amount. Next, configure an alert so that you receive an email notification when usage exceeds a stated percentage of that budget.

You can also configure an Amazon Simple Notification Service (Amazon SNS) topic to have chatbot alerts sent to Amazon Chime or Slack.

Clean up when done

When you’re done training, evaluating, and racing, it’s good practice to shut down unneeded resources and perform cleanup actions. Storage costs are minimal, but delete any models or log files that aren’t needed. If you used Amazon SageMaker or AWS RoboMaker, save and stop your notebooks and if they are no longer needed, delete them. Make sure you end any running training jobs in both services.

Conclusion

In this post, we covered several tips for optimizing spend for AWS DeepRacer, which you can apply to many other ML projects. Try any or all of these tips to minimize your expenses while having fun learning ML, by getting started in the AWS DeepRacer Console today!


About the Authors

 Tim O’Brien brings over 30 years of experience in information technology, security, and accounting to his customers. Tim has worked as a Senior Solutions Architect at AWS since 2018 and is focused on Machine Learning and Artificial Intelligence.
Previously, as a CTO and VP of Engineering, he led product design and technical delivery for three startups. Tim has served numerous businesses in the Pacific Northwest conducting security related activities, including data center reviews, lottery security reviews, and disaster planning.

A wordsmith, futurist, and relatively fresh recruit to the position of technical writer – AI/ML at AWS, Heather Johnston-Robinson is excited to leverage her background as a maker and educator to help people of all ages and backgrounds find and foster their spark of ingenuity with AWS DeepRacer. She recently migrated from adventures in the maker world with Foxbot Industries, Makerologist, MyOpen3D, and LEGO robotics to take on her current role at AWS.

Source: https://aws.amazon.com/blogs/machine-learning/optimizing-the-cost-of-training-aws-deepracer-reinforcement-learning-models/

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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

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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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