Curious Resources: Volume 2

E-Commerce Inside of Games, Synthetic Biology and Simulations, & Deep Learning for Weapon Detection

Photo by Marat Gilyadzinov on Unsplash

Welcome back!

Each edition we try to bring you a set of interesting finds in interactive technology. If you missed the previous edition, there’s another collection of great links waiting for you when you’re done here.

This week we’re covering:

  • E-Commerce Inside of Games
  • Synthetic Biology and Simulation
  • Deep Learning for Weapon Detection

Follow me on Github or get in touch with me on LinkedIn!

E-Commerce Inside of Games: Buy the Story

If you can sell your branded products to players at the correct time in the story, you’ll provide delight on demand and build a meaningful bridge between the physical and digital worlds. You’ll make people happy and make a ton of money! But the integrations should be more than trying to sell all of your swag at the end of every level. They need to mean something.

I’d love to do an integration that unlocks a T-shirt for purchase only after you’ve beaten a particularly difficult boss— if you saw someone else wearing the T-shirt in public, you’d know they were legit. Or in a cooking game, order a package of ingredients to match a meal you just made in the game kitchen!

Shopify has made it easy to connect with Unity using their new SDK.

If there’s anything missing from the new unity-buy-sdk, you might be able to patch it with functionality from this more mature C# library.

nozzlegear/ShopifySharp

Or setup a lambda function on AWS and connect to Shopify in any language you want by invoking the lambda function inside of Unity with a network request.

Synthetic Biology & Simulations: It’s Alive!

The application of technology to the creation of life is today’s computing revoluion. It comes down to cost and time-to-iteration — and both are dropping precipitously. If there is no penalty to experimentation, great strides forward will occur with regularity. When it comes to synthetic biology, we’re almost there. In the meantime, simulations can help save time and money.

If you know Verilog from programming FPGAs or embedded systems — well, you’re already awesome — but did you know how awesome? You can make living circuits!

CIDARLAB/cello

Or for those with web development background, write standards-compliant creatures with JavaScript

Model some bacteria populations to learn what you can

CellSimulationLabs/bsim

Or simulate a whole worm inside of your computer with OpenWorm

openworm/OpenWorm

This is the engine behind the body physics in OpenWorm

openworm/sibernetic

Once you’ve reached the limits of simulation, order parts from an online database of plasmids and do it “for real”

Addgene: Homepage

Deep Learning for Weapon Detection

While technological change is no substitute for actual policy, computer vision systems can augment policy and provide more options to policymakers. Computerized threat or weapon detection is clearly in the public interest, but I couldn’t actually find very much code out there.

That’s probably why Homeland Security recently teamed up with data-science contest host Kaggle on a $1.5 million challenge for passenger screening at airports.

Passenger Screening Algorithm Challenge

Since there isn’t much code, I’ve found a few recent papers instead:

  • An Automatic Hybrid Approach to Detect Concealed Weapons Using Deep Learning
  • Developing a Real-Time Gun Detection Classifier
  • Automatic Handgun Detection Alarm in Videos Using Deep Learning

The first of these papers addresses detecting concealed weapons. Here, detection of weapons in high-security areas might be improved with deep learning through image fusion and hyperspectral imaging. Varied types of input data can be put together to form the tensors to train the network on, which should make the network more robust

However, this benefit may be limited to expensive installations or specialized equipment. The hope would be to find some kind of ubiquitous field or sensor (wifi?) where the presence of a concealed weapon might be detected in a broader scope. Weapons made with ceramics and 3D-printed materials will continue to make this task difficult, even for specialized imaging and algorithms.

The latter papers deal with detecting weapons in plain sight, a task that sounds easier than it actually is, considering the millions of hours of video uploaded to the internet or fed through security systems every day. A classifier can recognize weapons in security footage and social networks alike that might have been overlooked by human eyes. The appropriate civil liberties need to be exercised when deciding how to act upon this information, but these kinds of safeguards might constitute an early-warning system that we can build upon for a more peaceful future.

Okay, so I did manage to find a few image fusion and hyperspectral imaging tools. The basic idea is to collect as much data from as many sensors as you can so that you can combine them into a single data-rich information vector.

A lot of these types of projects are trained on images of weapons from the Internet Movie Firearms Database. Which is a really cool resource that I’m sure provides some interesting training data. Real life, however, is often more subtle than a movie, so I’m not sure I’d trust these projects in the wild without some retraining. Hasta la vista!

Internet Movie Firearms Database – Guns in Movies, TV and Video Games


Curious Resources: Volume 2 was originally published in Curious Resources on Medium, where people are continuing the conversation by highlighting and responding to this story.

Source: Deep Learning on Medium