reCAPTCHA is a great success story of security through obscurity because probably less than 100 people have reverse engineered it and much less than that have produced a working solver for it that doesn't require a headless browser. Snapchat would be another good example - almost no one is going to put in the work to understand this [0]. Most companies just half ass it though and accordingly achieve nothing with the obscurity at all besides worse performance.
I wish we lived in a world where price wasn't the only way to filter out annoying passengers... if this experiment works maybe it will prove you can offer amenities more widely if you set high standards. I want an airline where at the end of the flight you rate the people next to you thumbs up or thumbs down and if someone gets >= 3 thumbs downs they are banned from the airline for 5 years. Eventually ordinary people would be able have the serene experience that only those in first class can presently enjoy as the people who get up thirty times to retrieve items from carry on or eat obnoxiously are removed.
This wouldn't have helped here but there is a related field of research called hotel recognition. Many of these videos are filmed in hotels so being able to recognize if it was a Mariott or even better a non chain local hotel can be very helpful to investigators. They basically train CNNs that learn to pick up on the bathroom fixtures or kind of bedding used by different hotels. One researcher in particular has done a tone of work on this: https://scholar.google.com/citations?user=mNoB9SgAAAAJ&hl=en
I wonder if you could get the interior of every house from Zillow/realtor websites and then do something like this for every house in the country... Clearview for bedrooms?
The malleability of the ciphertext matters because it enables certain circuit tagging attacks as the article explains. It means that the exit relay could confirm you are using a guard relay also controlled by them and thus discover your origin IP address.
Nice article and especially so for including the parsing that most people just outsource. What's great about using an emulator is that you can also do fun things with the syscalls like implementing your own "virtual filesystem" instead of just translating directly to the x86_64 equivalent syscall: https://github.com/gamozolabs/fuzz_with_emus/blob/master/src... (not my code but basically something like this)
OK, I think the problem was that you are only supposed to input the user ID number. I just limited the form to numbers only and updated the description to make this more clear.
Hi, I just realized that the confusion here was that you are only supposed to input the numeric user ID. I just limited the form to numbers only and updated the description to make this more clear.
I am not familiar with Cinematch, is there a writeup about it? When training I used every input book and did not include ratings as a feature. In the future I want to experiment with treating 1 or 2 star ratings as negative feedback.
Can you access your profile page in incognito (ie is your account public)? Alternatively, if you have more than 5000 books in your shelf, that might break it. I just tried a number of users and I was able to import them all.
I did not add what you requested exactly because I think in many cases authors have written less popular books that people may not be aware of but if you try again you should see less highly repetitive things like 5 of the same series in a row in the results.
What do you think the probability that someone else read 15 books you also read is? It’s very unlikely unless they are all staples of a genre, part of the same series, or just extremely popular in general. 3-5 books is how much I would use on that page. I have found interesting accounts of medievalists, people who work at think tanks, etc with it.
Fake users I would agree should be filtered, but I don’t think filtering out users who gave it a bad review is necessarily the intended behavior. If I put in 3 semi obscure Russian history books, I am presumably looking for someone who is an expert in Russian history to see what else they read. In that case I don’t care if they didn’t like one of the books or not. Approximate matches would require something like LSH or cosine similarity of average input book embedding against average embedding of read books of every user which I think wouldn’t work well anyone for retrieving anyone with a moderately long interaction history.
https://gamozolabs.github.io/metrology/2019/08/19/sushi_roll...
https://gamozolabs.github.io/metrology/2019/12/30/load-port-...