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.
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 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.
Netflix used to have a great recommendation engine based on what you liked/disliked. It included all of their members ratings. They had a contest in which they offered $1M to anyone who could improve their algorithm by 10%. The winning team used some kind of customized version of Singular Value Decomposition. The algorithm is public.
I think it is essential to use the negative ratings.
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.
I wanted to find users that loved the same kinds of classical novels. The core of my list was each famous work of famous classical writers like Dostoievsky, Tolstoi, Huxley and Borges. I added a few excellent authors, still famous but to a lesser degree, like Italo Calvino or Marguerite Yourcenar. I know there are many readers of the whole list I wrote, I could name a few among my friends and family.
So I think the problem was not in the existence of similar readers, but in the way to reach them. Few people that read classical books log in Goodreads (I don't) and even fewer input what they've read over the past decades.
