User Beware: The Fine Line Between Content And Code

Everyone loves themes. Doesn’t matter if it’s a text editor or a smart display in the kitchen, we want to be able to easily customize its look and feel to our liking. When setting up a new device or piece of software, playing around with the available themes may be one of the first things you do without giving it much thought. After all, it’s not like picking the wrong one is going to do something crazy like silently delete all the files on your computer, right?

Unfortunately, that’s exactly what happened a few days ago to [JeansenVaars] while trying out a Plasma Global Theme from the KDE Store. According to their Reddit post, shortly after installing the “Gray Layout” theme for the popular Linux graphical environment, the system started behaving oddly and then prompted for a root password. Realizing something didn’t seem right they declined, but at that point, it was already too late for all of the personal files in their home directory.

Continue reading “User Beware: The Fine Line Between Content And Code”

It’s About Time

I’m pretty good with time zones. After all, I live in Germany, Hackaday’s server is in Los Angeles, and our writers are scattered all over the globe. I’m always translating one time into another, and practice makes (nearly) perfect. But still, it got me.

I was in the states visiting my parents, when Daylight Saving Time struck, but only in the USA. Now all my time conversions were off by an hour, and once I’d worked through the way the sun travels around the globe, I thought I had it made. And then my cell phone started reporting a time that was neither CEST nor EDT, but a third time zone that was an hour off. Apparently some cell towers don’t transmit time zone information, and my phone defaults to UTC. Who knew? For a short while, my phone lied to me, the microwave oven clock in the hotel lied to me, and I felt like I was going nuts.

But this all got me thinking about clocks and human time, and possibly the best advice I’ve ever heard for handling it in your own programs. Always keep time in something sensible like UNIX time – seconds elapsed since an epoch – because you don’t have to worry about anything more than adding one to a counter every second. When and if you need to convert to or from human times, you can write the function to do that simply enough, if you don’t already have a library function to do so.

Want to set an alarm for 2 hours from now? That’s easy, because you only need to add 7,200 seconds, and you don’t need to worry about 59 wrapping around to 0 or 23:59 to 0:00. Time math is easy in seconds. February 29th? That’s just another 86,400 seconds. It’s only us humans who make it complicated.

AI Image Generation Meets Virtual Dress Up

Image generators have really taken off thanks to machine learning, and all kinds of new ideas have been turned on in people’s heads as a result. OOTDiffusion is one such project, its job being to allow virtual try-ons of clothing by combining a picture of a person and an item of clothing, and doing so in a coherent way.

A model sporting a 2021 Remoticon shirt.

When it comes to AI image generators, maintaining consistency of a particular subject in a picture while changing or combining other parts of the image isn’t a trivial task. (If you’re unfamiliar with the basics of how diffusion-type AI image generators work, we have you covered.)

Virtual try-on of clothing is not a new idea, but it’s also far from being a completely solved problem. It’s easy to feed a system high-quality images of people and clothing and ask it to combine them, but the outputs rarely emerge with all their limbs intact, figuratively speaking.

OOTDiffusion addresses the two big challenges in this area: making sure the outputs look natural and realistic, and preserving as much of the garment’s appearance and qualities as possible in the process.

It seems to to a very good job, and you can try it for yourself in the online demo. Check out the research paper for more details, and the GitHub repository provides all the code if you’d like to get a little more hands-on.

Your Text Needs More JPEG

We’ve all been victims of bad memes on the Internet, but they’re not all just bad jokes gone wrong. Some are simply bad as a result of being copies-of-copies, as each reposter adds another layer of compression to an already lossy image format like JPEG. Compression can certainly be a benefit in areas like images and videos, but [Michal] had a bit of a fever dream imagining this process applied to text. Rather than let the idea escape, he built the Lossifizer to add JPEG-like compression to text.

JPEG compression uses a system similar to the fast Fourier transform (FFT) called the discrete cosine transform (DCT) to reduce the amount of data in an image by essentially removing some frequency information. The data lost is often not noticeable to the human eye, at least until it gets out of hand. [Michal]’s system performs the same transform on text instead, with a slider to control the “amount of JPEG” in the output text. The code for this script uses a “perceptual” character map, clustering similarly-looking and similarly-sounding characters next to each other, resembling “leet speak” from days of yore, although at high enough compression this quickly gets out of hand.

One of the quirks that [Michal] discovered is that certain AI chat bots have a much less difficult time interpreting this JPEG-ified text than a human probably would have, which provides a bit of insight into how some of these algorithms might be functioning under the hood. For some more insight into how JPEG actually works on images, we posted about a deep dive into the image format a while back.

Making Floating Point Calculations Less Cursed When Accuracy Matters

Inverting the earlier exponentiation to reduce floating point arithmetic error. (Credit: exozy)
Inverting the earlier exponentiation to reduce floating point arithmetic error. (Credit: exozy)

An unfortunate reality of trying to represent continuous real numbers in a fixed space (e.g. with a limited number of bits) is that this comes with an inevitable loss of both precision and accuracy. Although floating point arithmetic standards – like the commonly used IEEE 754 – seek to minimize this error, it’s inevitable that across the range of a floating point variable loss of precision occurs. This is what [exozy] demonstrates, by showing just how big the error can get when performing a simple division of the exponential of an input value by the original value. This results in an amazing error of over 10%, which leads to the question of how to best fix this.

Obviously, if you have the option, you can simply increase the precision of the floating point variable, from 32-bit to 64- or even 256-bit, but this only gets you so far. The solution which [exozy] shows here involves using redundant computation by inverting the result of ex. In a demonstration using Python code (which uses IEEE 754 double precision internally), this almost eradicates the error. Other than proving that floating point arithmetic is cursed, this also raises the question of why this works.

Continue reading “Making Floating Point Calculations Less Cursed When Accuracy Matters”

This Piano Does Not Exist

A couple of decades ago one of *the* smartphone accessories to have was a Bluetooth keyboard which projected the keymap onto a table surface where letters could be typed in a virtual space. If we’re honest, we remember them as not being very good. But that hasn’t stopped the idea from resurfacing from time to time.

We’re reminded of it by [Mayuresh1611]’s paper piano, in which a virtual piano keyboard is watched over by a webcam to detect the player’s fingers such that the correct note from a range of MP3 files is delivered.

The README is frustratingly light on details other than setup, but a dive into the requirements reveals OpenCV as expected, and TensorFlow. It seems there’s a training step before a would-be virtual virtuoso can tinkle on the non-existent ivories, but the demo shows that there’s something playable in there. We like the idea, and wonder whether it could also be applied to other instruments such as percussion. A table as a drum kit would surely be just as much fun.

This certainly isn’t the first touch piano we’ve featured, but we think it may be the only one using OpenCV. A previous one used more conventional capacitive sensors.