thifhi

A shell's stdin and stdout can be redirected to a tcp socket which connects to the attacker. Here are some examples: https://www.invicti.com/learn/reverse-shell/

> Performance and compensation were completely separate, which was also nice.

Huh? How is that nice? Does performance and compensation not correlate in your ideal world, or am I misunderstanding it?

Surely there are diminishing returns for the AI computing though? I mean, is a model with 10x the parameter count 10x better? I think it is still possible that the training costs will be irrelevant for all players at some point with this non-linear scale. Access to data is another story

This reminded me of my implementation for a university course where you played by tilting your head to the left and right with the help of an accelerometer embedded earbud. Everyone got nauseous after a couple runs but it was very fun indeed :)

I remember that one of the technical employees of the said exchange claimed cyber attacks to their servers were so rampant and many succesful that they were simply not able to continue business with all the stolen funds. I honestly believe that incompetentness might have played a huge role in this "fraud" story.

I just checked the official annual budget for all of the penal institutions in Turkey. The 2022 budget is 3.2 Billion Liras[1] ($180M) for about 300.000 inmates in total. Dividing, you get about $600 per inmate per year! (and honestly it doesn't even sound that absurd considering the low cost of living here)

[1]: https://www.sbb.gov.tr/wp-content/uploads/2021/10/2022_Yili_...

So each interceptor missle costs upwards of 70k$ and they typically intercept rockets that cost around 800$. How is that remotely sustainable?

Edit: I guess we should also take into account what a missle that hits its target costs to Israel, so I am not that disappointed in the efficacy now.

I was hesitant to share because it's quite new and still in development but I'm glad someone from outside actually tried it! It has some issues as you have pointed out and I really appreciate the detailed report, I've noted them.

There are no currently no explanation of the ML parameters since I still use the website mostly for manual testing. Sadly, some of them are crucial for model accuracy and they are not easy to manually configure for the user, so the plan is to integrate meta-learning that finds the best possible parameters on its own. I will take a look into the model now :)

Edit: After a quick inspection I see that the problem is actually sample size. Your samples are about 3 seconds long and for a consistent model I need at least 30 seconds of sample for each label.

Maybe some background: You can simply set the recording duration to 30 seconds and keep tapping for 30 seconds, you don't need 10 samples each 3 seconds long. The server splits the recordings into 0.3 seconds long windows for training and the model tries to interpolate the function between these windows and their labels.

In your case, only 13 windows could be extracted from your recordings so the model has only 13 data points for learning the mapping and tries to guess the function based on them. The beginning and the end of a recording is also cut because the sensors are not very reliable during these periods, so a 3 seconds long recording becomes about 2 seconds long after that. This is why tiny recordings are generally not very useful for the model training (and I should definitely change the default recording duration which is 3 seconds!). From a 30 seconds recording, we can extract ~100 data points and that is usually enough for a somewhat reliable model.

But of course none of these are obvious (not even close..) and I should do a much better job explaining the process to the user. I learnt a lot from your experience, so thanks again :)

I want to share a small project, closely related to the paper. You can try it out if you have free 5-10 minutes and an Android device: https://edge-ml.dmz.teco.edu/

You login; create a so-called workspace; add some labels like walking, jumping, tapping on the device etc.. (simple actions for testing purposes); collect data for your labels with your mobile device and create a machine learning model with a single click. Then scan the QR-code of the model and your actions are predicted by the model in real-time. It's really interesting how accurate the predictions are even with a small sample size.