Make Children’s Artwork look like Eric Carle Illustrations

v_hungry_caterpillar
The Very Hungry Caterpillar, by Eric Carle

Famous author and artist Eric Carle turns 91 today. I remember loving his books when I was a kid, especially The Very Hungry Caterpillar and Brown Bear, Brown Bear, What Do You See?. Each book features his distinctive art style. The images are collages composed of tissue paper and acrylic paint, producing vivid depictions of animals and nature.

THE PROBLEM

Carle’s work is as complex as it is beautiful. How can we make it easier for children to produce their own homages to his creations?

THE SOLUTION

Neural style transfer is a technique that allows you to compose images in another’s style using deep learning. That is, you teach a computer to identify key elements of an image’s style and redraw that image in that style it has just learned.

I found this excellent Google Colab notebook which taught me all about how to do this with tf.Keras!

Taking the code from the tutorial I built a website that lets you upload images, have the style of Eric Carle’s The Very Hungry Caterpillar transferred to it, and display it for the world to see and for you to download! At any given time the latest 10 images will be displayed for any visitors to see. The website is built in one of my favorite frameworks, Flask.

You can access the website at ericcarletransfer.ml. Be warned, the transfer time can be in excess of 10 minutes- it is very computationally intensive.

The results have been encouraging though! Take a look:

The neural network is picking up on the look of the tissue paper and paint. In the future I want to work on reducing the amount of noise seen in the backgrounds.

SHARING THE SOLUTION

The URL again is http://ericcarlearttransfer.ml/

As always, the entire project is opens source and can be found here on GitHub!

A novel method for preventing “Zoom Bombing”

THE PROBLEM

Zoom Bombing is exposing children learning remotely to inappropriate content and disrupting meetings so a few pranksters can have a laugh. The biggest unsolved issue with Zoom Bombing is that people are sharing links and passwords on social media in order to egg trolls and classmates on to bomb these classes and meetings. How can we share a meeting without disclosing the meeting ID and password?

THE SOLUTION

BombSquad(4)

BombSquad is a solution I built on Amazon Web Services to help mitigate the worst of Zoom Bombing. Here’s how it works:

  1. Get a Zoom meeting invitation link like normal (and make sure the password feature is turned on!)
  2. Go to www.BombSquad.us
  3. Select your meeting options- you can permanently turn off the participant microphone and camera so that nobody can reenable it by clicking the checkboxes.
  4. Paste your invitation link
  5. Get a sharable cloaked URL that goes right to your meeting!
  6. Continue orchestrating your meeting from the Zoom client like normal.

The technical details are as follows: BombSquad takes your URL, transforms it to force the user to use the Zoom web client, stores the original URL securely, and only redirects the browser to the real meeting URL if the user clicks through the sharable link you receive. The invitation link inside the window is disabled. Thus, all a user can see are BombSquad URLs! This is performed using a combination of AWS S3 and Lambda instances as shown above, making this a neat example of a serverless application– the first I am distributing publicly!

SHARING THE SOLUTION

Head on over to www.bombsquad.us and give it a try!

interface
BombSquad interface

FreeDisplay – Share your screen with everyone on your local network for free!

COVID-19 is taxing our internet infrastructure, and many stuck at home are struggling with tasks where it would be useful to share one’s screen with others, such as teaching from home, sharing content with someone without handing them your device and getting it contaminated, or monitoring what is happening on a home computer in real time.

FreeDisplay is a free open-source program written in Python that allows you to share your screen with anyone on your local network, such as your home Wi-Fi network. It creates a QR code other can scan for easy sharing and serves a simple webpage with a mirror of your screen so that any device with a web browser can easily view your screen! Use it for home teaching, sharing content without handing someone your device, presentations, monitoring activity on your home computer and more. Download for free here: https://kevinl95.github.io/freedisplay/

As always, the code is open-source and can be viewed here: https://github.com/kevinl95/freedisplay

Making your first Capsule for Samsung Bixby – An Exercise in Teaching your Phone to Listen

 

Today Samsung made the Bixby Developer Studio available for download and use so that developers can start building capsules to publish on their marketplace starting in 2019. I am an early adopter of the new Bixby and wanted to share how to build a simple capsule using the new developer kit as well as share my experience with using the new platform. Readers of this blog know that I have made and published Skills for Amazon’s Alexa and published tutorials on how you can develop for that platform. Similarly this writeup will focus on a minimal application that will help you get started with the features of Bixby’s impressive development tools.

Bixby is a wonderful platform to develop for and it has top-notch development tools. Building software for Bixby is a lot like teaching someone a new skill. It uses natural language model training so you can show Bixby what parts of user phrases are important and it uses the idea of concepts to define Bixby’s understanding of what capability you are giving it. These concepts will be discussed in detail below.

Today we will be making a capsule to generate passwords made up of a random string of words, inspired by XKCD’s Password Strength comic. We will be taking advantage of Bixby’s visual interface to make a password users can easily remember as well as easily copy and use for their accounts. The XKCD algorithm sticks random, memorable words together so that passwords are complex but can also be easily recalled by the user. They also are higher entropy than a random string of characters and numbers, making them harder to crack my many brute-force methods. After giving the comic a quick look, read on.

As always, you can download and view the code on my GitHub!

THE PROBLEM

We want to build a Bixby capsule that can generate memorable passwords for users. These passwords should be of a user-specified length.

The overall requirements are:

  • Generates a password using regular English words
  • Takes in a user’s specified length
  • Displays the password graphically for copying
  • Displays a calculation of the entropy of the password so the user knows how good the password is.

THE SOLUTION

You should now have Bixby Studio installed on your system. As of writing it is available for Windows and macOS.

Create a new project by clicking File>New Capsule.

The first bit of code we will focus on is the generator.js file. This is where we define our entry point and what we are going to return.

// Correct Horse Battery Staple
// An implementation of the XKCD password algorithm https://xkcd.com/936/
// The idea behind this generator is that randomized passwords are easy to guess
// and difficult to remember, while a password composed of four english words is
// both easy to remember and difficult to guess. This capsule adds the ability
// to generate such passwords to Bixby.
// Main entry point
function generate(numWords) {
var Password = "";
// Get our list of words
var wordfile = require("./lib/wordlist");
var wordList = [];
wordfile.forEach(function(newWords){
wordList = newWords.commonWords;
})
// Loop through the number of words the user asked for
for (var i = 0; i < numWords; i++) {
var rand = wordList[Math.floor(Math.random() * wordList.length)];
Password = Password + rand;
}
// Capture the length in words of the current password
var length = numWords;
// Calculate the approximate entropy of the password
var entropy = length * 11;
// Calculate the approximate number of years to guess the password
// at 1000 guesses/second
var years = Math.floor(Math.pow(2, entropy) / 31536000000);
// Password Result
return {
Password: Password, // required password
length: length, // Number of words used
entropy: entropy, // Approximate bits of entropy of the password
years: years // Approximate number of years to guess password
}
}
// Exports
module.exports = {
function: generate
}

view raw
generator.js
hosted with ❤ by GitHub

Notice how we export the function generate- this is the function where I generate everything we need for the response you see in the screenshot above. We take our wordlist dictionary file (how to get that will be discussed shortly), we build our password using a user-specified length called numWords, and we calculate the entropy of the password. We then return a result we can parse into a nice, visual response like in the screenshot.

Whew, there’s a lot going on in here though! Let’s start with the wordlist. This is a JSON-formatted list of common English words I found searching for open-source corpora. Why JSON? As you might have surmised from the above code snippet, Bixby capsules are written in JavaScript! Importing this data as JSON makes it very easy to loop through and use, as you can see in my generate function. I stored this in a directory called lib but you can call it whatever you please. Just be sure to update the path in the generator!

Next, we need to discuss how we get numWords. This is the user-input. We want the user to say ‘Make me a password with three words’ and Bixby needs to know how to do that.

In the resources directory you will find endpoints.bxb. The actions your capsule can take are called endpoints. Let’s define one for generating a password:

endpoints {
authorization {
none
}
action-endpoints {
action-endpoint (generate) {
accepted-inputs (numWords)
local-endpoint ("generator.js")
}
}
}

view raw
bixbyendpoints.bxb
hosted with ❤ by GitHub

Let’s look at what we have here: We have authorization set to none because this endpoint is public and available to any user without authorization. We have specified an action endpoint for our generate function as defined in the generator.js snippet above and we have told Bixby that the input for this endpoint is numWords. We also tell it what file it will find the definition for this endpoint in- generator.js.

Now that Bixby has an available action in the form of the endpoint, we get to the really interesting stuff- teaching Bixby what everything in our capsule means. The way we do this is via a model. In the model directory we have actions and concepts. These make up Bixby’s understanding of what your capsule can do, and we just need to write some high-level markup to make this work. Let’s start with the action our capsule is going to have- generating passwords. This will inform what concepts Bixby needs to have definitions for so that we can move on to training our natural language model.

action (generate) {
collect{
input (numWords) {
type (NumWords)
min (Required)
max (One)
// add a default value of four words, like the comic
default-init {
intent {
goal {NumWords}
value {
NumWords (4)
}
}
}
// add a validation dialog prompt when the user indicates less
// than 2 sides.
validate {
if (numWords == 0) {
prompt {
dialog {
template ("You need to have at least one word in your password.")
}
}
}
if (numWords < 0) {
prompt {
dialog {
template ("You need to have at least one word in your password.")
}
}
}
}
}
}
output (PasswordResult)
type (Calculation)
}

view raw
bixbyaction.bxb
hosted with ❤ by GitHub

Above is the generator.model.bxb action file. You will find it in my action directory. What does this do? Read through the comments carefully. It defines the actions Bixby will take when running this capsule, and it covers all our bases regarding various user inputs! We tell it our action is to run our generate function. We tell it to collect numWords, and we tell it that numWords is of the numWords concept type which we will define shortly. We tell Bixby that there can be at most one numWords (so that we ignore other numbers in the user’s invocation) and we tell Bixby that this value is required. If Bixby cannot find a number in the invocation to use, we define a default initialization with four words- the same as our XKCD comic! We then do some validation in the event we find a number in the user’s invocation. If numWords is 0 or less, we want to display some text telling the user that you cannot have a password that is negative in length (duh, but the bulk of software development is anticipating stupid). Finally we tell Bixby what our result is going to be- an instance of our PasswordResult concept, which will be of the type Calculation. This is a type Bixby provides for a result that it needs to compute or otherwise derive. Let’s get started defining what these concepts are.

If you are following along in the repo, look at the numWords concept.

integer (NumWords) {
description (The number of number of words to use in the password.)
}

view raw
numWords.bxb
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This is a good minimal example of a Bixby concept. These are the variables that are key to our capsule working. You can think of them as teaching Bixby a new idea, slowly building for it the picture of what you are trying to achieve. We tell Bixby that NumWords is an integer (we don’t want fractional words). We also give a brief description of what this has to do with our capsule. For NumWords this is obvious- it is the number of words in the password.

Password is almost the same except this concept is given the ‘name’ type since we need an output string. We describe it as the output password. Entropy is similar- we describe it as the approximate bits of ‘randomness’ in our password and give it the integer type since it will be a number we calculate. Length, predictably, is an integer that represents the length in words for our password. This is utilized in the entropy calculation, which taking the formula from the comic is taking two to the power of the number of words and then dividing for the number of attempts to brute force the password you could make if your computer could make 1000 attempts per second for a year. This yields an estimation of the number of years the password would take to crack in these circumstances. Finally Years is given the integer type and described as the number of years simple brute forcing would take to crack this password- it is also part of the entropy calculation we display at the bottom of our result as you can see in the above screenshot.

The most complicate concept is our PasswordResult:

structure (PasswordResult) {
description (The resulting password from the generate action.)
property (Password) {
description (The resulting password string from the generator.)
type (Password)
min (Required)
max (One)
}
property (length) {
description (The number of words used.)
type (length)
min (Required)
max (One)
}
property (entropy) {
description (Approximate bits of entropy for the password.)
type (entropy)
min (Required)
max (One)
}
property (years) {
description (Approximate number of years to guess the password at 1000 guesses per second.)
type (years)
min (Required)
max (One)
}
}

It has the type Structure because it contains multiple properties- namely every concept we have just defined. We give these properties types- I just made these the same as the property name for simplicity but they can be used in more complicated capsules to link properties together with a descriptive type. We again describe each property and what it does, tell Bixby if the property is required, and for each tell Bixby that there can be at most one value for each. This result, as you may recall from the generate method, is what we will use to generate our visual response on the screen of the device. We have now explicitly told Bixby everything there is to know about how our capsule is going to work! It knows every concept and every result we are going to want. We now can teach Bixby how to handle speech.

Click training in the resources/en directory.

Screen Shot 2018-11-07 at 7.11.44 PM

You will see a list of training examples I have provided the natural language model. We are effectively training Bixby to understand how to parse user phrases and turn them into useful input for our capsule. This is an application of machine learning! Notice the examples I have provided. I have made one: ‘generate a password for me’ with no numbers in it- this is to provide an example where Bixby should use our default input of four words from above, like the XKCD comic. I also provide numerous examples with varying numbers of words asking Bixby to generate a password in various ways. Notice how I have clicked on and highlighted the number in each training phrase and I have labeled this value as numWords! You will do this for each input your capsule needs- the more examples the better. Bixby will use the labels and examples you provide to teach itself that when something sounds similar to your examples Bixby is being asked to open your capsule and feed the data that is similar to the labeled phrases you gave it to the capsule as input. Bixby is learning, so make sure to spend plenty of time here to make sure Bixby really gets it! Compiling the model will make Bixby learn each of your examples and you can view what Bixby’s output for your examples would be so you can be sure that Bixby has not mis-learned how to handle your examples. A well-trained model will make your users happier and your capsule easier to use. This is my favorite part of the Bixby developer tools- it is very intuitive and fun to use, and it offers a look for machine learning enthusiasts into the underlying technologies behind Bixby. This is a defining attribute of the platform for me- it feels much more flexible than Alexa, which as a developer seems to encourage a more robotic and specific interface for its skills than the more flexible Bixby interfaces for capsules.

With your model trained and your concepts laid out, the last thing to do is to specify how Bixby should display our output. This is done with dialogs and layouts.

Dialogs define for Bixby’s interface the concepts (inputs) and the results. Therefore for each input you need there will be a dialog and for each result there will be a dialog.

NumWords therefore gets a dialog like so:

dialog (Concept) {
match {
// Look for this type
NumWords
}
// Use the following template text with this type
template("Number of Words")
}

view raw
NumWords.dialog.bxb
hosted with ❤ by GitHub

This is pretty bare-bones: We define a concept dialog (input dialog), tell it to look for NumWords (like in our training!) and we provide some template text for this type if we wanted to display something related to this input (in my project I ended up not using it).

The Password Result Dialog defines the dialog for our result. This one is more important for this project as it will populate our layout.

dialog (Result) {
match {
PasswordResult(this) {
from-output: generate(numWords)
}
}
template ("I generated a memorable, secure password for you using common english words:")
}

We define an output (result) dialog, have it match this time for our PasswordResult concept (passing in the output from calling generate with our numWords result) and then we tell Bixby what to write on the screen with the template text: Notice that this is the first bit of text in the above screenshot that appears when Bixby is displaying a result telling the user what it did for them!

<layout mode="Details">
<match>
PasswordResult (r)
</match>
<content>
<layout-macro id="common:card">
<title>Your New Password</title>
<titleSize>large</titleSize>
<bodyText>{{r.Password}}</bodyText>
</layout-macro>
<div>This password has a length of {{r.length}} words. This password has ~{{r.entropy}} bits of entropy and would therefore take a computer about {{r.years}} years to guess at 1000 guesses per second.</div>
<br>
<div>Inspired by <a href="https://xkcd.com/936/">"Password Strength"</a> by Randall Munroe</div>
</content>
</layout>

The layouts for the visual part of the display (like this one, PasswordResult.layout.bml) look a lot like HTML! There are many documented UI widgets you can use such as pictures, hyperlinks, cards, and more. Here you can see we use a card to display the actual password, making it wrap onto the next line for long passwords and making them easy to copy. Down below in a div tag we display the password entropy. This is calculated using the formula from the XKCD comic, as described above. Finally we hyperlink to the comic that inspired this project as a way of giving credit.

A few more example passwords are shown below:

You can try it out for yourself in Bixby Studio! Simply click the icon that looks like a phone on the left hand side of the screen to open the Simulator, giving you an idea of what your capsule will look like on an actual Samsung device when the marketplace opens in a few months.

SHARING THE SOLUTION

This project can be found in its entirety on my GitHub! I hope this very early tutorial can help developers make their first steps into developing for Bixby, which I think has some very compelling development tools and technology behind it.

How to download a YouTube video for Seesaw

Have you ever wanted to download a YouTube video to share elsewhere but found that every ‘YouTube Downloader’ on the internet is an adware-filled, slow-to-load piece of junk? This is especially problematic when you are trying to get video content to your students- most of the time you cannot directly link to YouTube on a school network connection as it will be blocked, leaving your only option to download the video and re-share it on a platform like Seesaw, a popular platform for student engagement. So how can you get the raw video file to share without getting your computer infected? I have designed a simple desktop application that downloads YouTube videos and I am offering it for free to download! It is available for MacOS and Windows. Best of all it’s a portable app- meaning no installer and therefore no need for an administrator password to use on your school computer!

If you are interested in how I developed this program, read on.

THE PROBLEM

YouTube download websites are filled with adware, malware, deceiving links, and often inappropriate ads. Educators need to be able to download YouTube videos to get around school content filters. They need to be able to use a program that can also evade roadblocks like not being able to install software on a school-issued computer.

THE SOLUTION

screenshot
The Windows version of the application

The solution was to develop a desktop application (ensuring the longevity of the project as there would be no need to pay for hosting a website or risking my web application getting blocked by a content filter). I also wanted to write this application once and deploy to my two target platforms – Mac and Windows. There are various libraries for downloading YouTube videos simply – I figure a lot of the scammy websites are making use of the open-source ytdl-core or similar JavaScript libraries to download their videos and quickly deploy new websites. I wanted to use something similar as these libraries are easy to use, well-supported, and cross-platform. The solution for all of my problems was to build my app using Electron, which lets you build cross-platform apps using HTML, CSS, and JavaScript. I could build a website using ytdl-core to download videos using the same technology as the sites people are being driven to by Google and then make it a desktop application. Then, using the exact same code for both platforms, I could then deploy to Windows and Mac! And that’s exactly what I did- what you see above in these screenshots is literally a simple website using jQuery, Bootstrap, and ytdl-core that takes in the YouTube link the user pastes in the box, uses regex to check that the link is a properly formatted YouTube link, and then opens a save prompt to save the video to the user’s computer! The save prompt is one of the neat elements of the Electron API that you can just use and have it work one each platform, pulling up a native save dialog and letting me configure it to ensure the user can only save MP4 files, for example, as that is the file format Seesaw accepts. The open-source Electron Packager then lets me bundle all of my dependencies together and create a Windows and Mac application bundle. These bundles include everything needed to run the application in exactly the spot the application needs it, meaning there is no installer to run and therefore it can run without administrator access on your computer! The whole ecosystem let me hack this together very quickly with almost no friction between any of the tools.

ytdl-core lets me ensure that we automatically grab the highest-quality version of the video and download it in MP4 format, making sure everything is as simple as possible to go from video to Seesaw fast, saving teachers time. This all happens in the background – the goal was to make a tool that takes minimal thought to use. Just grab the video your students need, and paste the link!

SHARING THE SOLUTION

This application is completely open-source. You can view it on GitHub here! This also serves a project homepage you can share with your friends and colleagues as it features download buttons for both Mac and Windows. It is also where you should go to get the most up-to-date versions of the software- as I improve it and make bug fixes the links below will not be updated, but they will always get you the first release.

V1.0 for Windows can be downloaded here directly.

V1.0 for MacOS can be downloaded here directly.