Hackaday Links: October 4, 2020

October 4, 2020 by Dan Maloney 9 Comments

In case you hadn’t noticed, it was a bad week for system admins. Pennsylvania-based United Health Services, a company that owns and operates hospitals across the US and UK, was hit by a ransomware attack early in the week. The attack, which appears to be the Ryuk ransomware, shut down systems used by hospitals and health care providers to schedule patient visits, report lab results, and do the important job of charting. It’s not clear how much the ransomers want, but given that UHS is a Fortune 500 company, it’s likely a tidy sum.

And as if an entire hospital corporation’s IT infrastructure being taken down isn’t bad enough, how about the multi-state 911 outage that occurred around the same time? Most news reports seemed to blame the outage on an Office 365 outage happening at the same time, but Krebs on Security dug a little deeper and traced the issue back to two companies that provide 911 call routing services. Each of the companies is blaming the other, so nobody is talking about the root cause of the issue. There’s no indication that it was malware or ransomware, though, and the outage was mercifully brief. But it just goes to show how vulnerable our systems have become.

Our final “really bad day at work” story comes from Japan, where a single piece of failed hardware shut down a $6-trillion stock market. The Tokyo Stock Exchange, third-largest bourse in the world, had to be completely shut down early in the trading day Thursday when a shared disk array failed. The device was supposed to automatically failover to a backup unit, but apparently the handoff process failed. This led to cascading failures and blank terminals on the desks of thousands of traders. Exchange officials made the call to shut everything down for the day and bring everything back up carefully. We imagine there are some systems people sweating it out this weekend to figure out what went wrong and how to keep it from happening again.

With our systems apparently becoming increasingly brittle, it might be a good time to take a look at what goes into space-rated operating systems. Ars Technica has a fascinating overview of the real-time OSes used for space probes, where failure is not an option and a few milliseconds error can destroy billions of dollars of hardware. The article focuses on the RTOS VxWorks and goes into detail on the mysterious rebooting error that affected the Mars Pathfinder mission in 1997. Space travel isn’t the same as running a hospital or stock exchange, of course, but there are probably lessons to be learned here.

As if 2020 hasn’t dealt enough previews of various apocalyptic scenarios, here’s what surely must be a sign that the end is nigh: AI-generated PowerPoint slides. For anyone who has ever had to sit through an endless slide deck and wondered who the hell came up with such drivel, the answer may soon be: no one. DeckRobot, a startup company, is building an AI-powered extension to Microsoft Office to automate the production of “company compliant and visually appealing” slide decks. The extension will apparently be trained using “thousands and thousands of real PowerPoint slides”. So, great — AI no longer has to have the keys to the nukes to do us in. It’ll just bore us all to death.

And finally, if you need a bit of a palate-cleanser after all that, please do check out robotic curling. Yes, the sport that everyone loves to make fun of is actually way more complicated than it seems, and getting a robot to launch the stones on the icy playing field is a really complex and interesting problem. The robot — dubbed “Curly”, of course — looks like a souped-up Roomba. After sizing up the playing field with a camera on an extendable boom, it pushes the stone while giving it a gentle spin to ease it into exactly the right spot. Sadly, the wickedly energetic work of the sweepers and their trajectory-altering brooms has not yet been automated, but it’s still pretty cool to watch. But fair warning: you might soon find yourself with a curling habit to support.

Community Testing Suggests Bias In Twitter’s Cropping Algorithm

September 23, 2020 by Lewin Day 77 Comments

With social media and online services are now huge parts of daily life to the point that our entire world is being shaped by algorithms. Arcane in their workings, they are responsible for the content we see and the adverts we’re shown. Just as importantly, they decide what is hidden from view as well.

Important: Much of this post discusses the performance of a live website algorithm. Some of the links in this post may not perform as reported if viewed at a later date.

The initial Zoom problem that brought Twitter’s issues to light.

Recently, [Colin Madland] posted some screenshots of a Zoom meeting to Twitter, pointing out how Zoom’s background detection algorithm had improperly erased the head of a colleague with darker skin. In doing so, [Colin] noticed a strange effect — although the screenshot he submitted shows both of their faces, Twitter would always crop the image to show just his light-skinned face, no matter the image orientation. The Twitter community raced to explore the problem, and the fallout was swift.

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Into The Belly Of The Beast With Placemon

September 17, 2020 by Brian McEvoy 11 Comments

No, no, at first we thought it was a Pokemon too, but Placemon monitors your place, your home, your domicile. Instead of a purpose-built device, like a CO detector or a burglar alarm, this is a generalized monitor that streams data to a central processor where machine learning algorithms notify you if something is awry. In a way, it is like a guard dog who texts you if your place is unusually cold, on fire, unlawfully occupied, or underwater.

[anfractuosity] is trying to make a hacker-friendly version based on inspiration from a scientific paper about general-purpose sensing, which will have less expensive components but will lose accuracy. For example, the article suggests thermopile arrays, like low-resolution heat-vision, but Placemon will have a thermometer, which seems like a prudent starting place.

The PCB is ready to start collecting sound, temperature, humidity, barometric pressure, illumination, and passive IR then report that telemetry via an onboard ESP32 using Wifi. A box utilizing Tensorflow receives the data from any number of locations and is training to recognize a few everyday household events’ sensor signatures. Training starts with events that are easy to repeat, like kitchen sounds and appliance operations. From there, [anfractuosity] hopes that he will be versed enough to teach it new sounds, so if a pet gets added to the mix, it doesn’t assume there is an avalanche every time Fluffy needs to go to the bathroom.

We have another outstanding example of sensing household events without directly interfacing with an appliance, and bringing a sensor suite to your car might be up your alley.

Garbage Can Takes Itself Out

September 10, 2020 by Bryan Cockfield 12 Comments

Home automation is a fine goal but typically remains confined to lights, blinds, and other things that are relatively stationary and/or electrical in nature. There is a challenge there to be certain, but to really step up your home automation game you’ll need to think outside the box. This automated garbage can that can take itself out, for example, has all the home automation street cred you’d ever need.

The garbage can moves itself by means of a scooter wheel which has a hub motor inside and is powered by a lithium battery, but the real genius of this project is the electronics controlling everything. A Raspberry Pi Zero W is at the center of the build which controls the motor via a driver board and also receives instructions on when to wheel the garbage can out to the curb from an Nvidia Jetson board. That board is needed because the creator, [Ahad Cove], didn’t want to be bothered to tell his garbage can to take itself out or even schedule it. He instead used machine learning to detect when the garbage truck was headed down the street and instruct the garbage can to roll itself out then.

The only other thing to tie this build together was to get the garage door to open automatically for the garbage can. Luckily, [Ahad]’s garage door opener was already equipped with WiFi and had an available app, unbeknownst to him, which made this a surprisingly easy part of the build. If you have a more rudimentary garage door opener, though, there are plenty of options available to get it on the internets.

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Identifying Creatures That Go Chirp In The Night

September 8, 2020 by Tom Nardi 26 Comments

It’s common knowledge that bats navigate and search for their prey using echolocation, but did you know that the ultrasonic chips made by different species of bats are distinct enough that they can be used for identification? [Tegwyn☠Twmffat] did, which is why he came up with this impressive device capable of cataloging the different bats flying around at night.

Now this might seem like an odd gadget to have, but if you’re in the business of wildlife conservation, it’s not hard to imagine how this sort of capability might be useful. This device could be used to easily estimate the size and diversity of bat populations in a particular area. [Tegwyn☠Twmffat] also mentions that, at least in theory, the core concept should work with other types of noisy critters like rodents or dolphins.

Powered by the NVIDIA Jetson Nano, the unit listens with a high-end ultrasonic microphone for the telltale chirps of bats. These are then processed by the software and compared to a database of samples that [Tegwyn☠Twmffat] personally collected in local nature reserves. In the video after the break, you can also see how he uses a set of house keys jingling as a control to make sure the system is running properly.

As winner of the Train All the Things contest back in April, we’re eager to see how the Intelligent Wildlife Species Detector will fare as the competition heats up in the 2020 Hackaday Prize.

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Recognizing Activities Using Radar

September 6, 2020 by Danie Conradie 10 Comments

Caring for the elderly and vulnerable people while preserving their privacy and independence is a challenging proposition. Reaching a panic button or calling for help may not be possible in an emergency, but constant supervision or camera surveillance is often neither practical nor considerate. Researchers from MIT CSAIL have been working on this problem for a few years and have come up with a possible solution called RF Diary. Using RF signals, a floor plan, and machine learning it can recognize activities and emergencies, through obstacles and in the dark. If this sounds familiar, it’s because it builds on previous research by CSAIL.

The RF system used is effectively frequency-modulated continuous-wave (FMCW) radar, which sweeps across the 5.4-7.2 GHz RF spectrum. The limited resolution of the RF system does not allow for the recognition of most objects, so a floor plan gives information on the size and location of specific features like rooms, beds, tables, sinks, etc. This information helps the machine learning model recognize activities within the context of the surroundings. Effectively training an activity captioning model requires thousands of training examples, which is currently not available for RF radar. However, there are massive video data sets available, so researchers employed a “multi-modal feature alignment training strategy” which allowed them to use video data sets to refine their RF activity captioning model.

There are still some privacy concerns with this solution, but the researchers did propose some improvements. One interesting idea is for the monitored person to give an “activation” signal by performing a specified set of activities in sequence.

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I’m Sorry Dave, You Shouldn’t Write Verilog

September 4, 2020 by Al Williams 54 Comments

We were always envious of Star Trek, for its computers. No programming needed. Just tell the computer what you want and it does it. Of course, HAL-9000 had the same interface and that didn’t work out so well. Some researchers at NYU have taken a natural language machine learning system — GPT-2 — and taught it to generate Verilog code for use in FPGA systems. Ironically, they called it DAVE (Deriving Automatically Verilog from English). Sounds great, but we have to wonder if it is more than a parlor trick. You can try it yourself if you like.

For example, DAVE can take input like “Given inputs a and b, take the nor of these and return the result in c.” Fine. A more complex example from the paper isn’t quite so easy to puzzle out:

Write a 6-bit register ‘ar’ with input
defined as ‘gv’ modulo ‘lj’, enable ‘q’, synchronous
reset ‘r’ defined as ‘yxo’ greater than or equal to ‘m’,
and clock ‘p’. A vault door has three active-low secret
switch pressed sensors ‘et’, ‘lz’, ‘l’. Write combinatorial
logic for a active-high lock ‘s’ which opens when all of
the switches are pressed. Write a 6-bit register ‘w’ with
input ‘se’ and ‘md’, enable ‘mmx’, synchronous reset
‘nc’ defined as ‘tfs’ greater than ‘w’, and clock ‘xx’.

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