Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario

Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a user will like a manga that received few ratings from the community? Content-based techniques can alleviate this issue but require extra information, that is usually expensive to gather. In this paper, we use a deep learning technique, Illustration2Vec, to easily extract tag information from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE (Blended Alternate Least Squares with Explanation), a new model for collaborative filtering, that benefits from this extra information to recommend mangas. We show, using real data from an online manga recommender system called Mangaki, that our model improves substantially the quality of recommendations, especially for less-known manga, and is able to provide an interpretation of the taste of the users.Comment: 6 pages, 3 figures, 1 table, accepted at the MANPU 2017 workshop, co-located with ICDAR 2017 in Kyoto on November 10, 201

scp client in OpenSSH 8.2 incorrectly sends duplicate responses upon a utimes system call failure: CVE-2020-12062

The mission of the CVE Program is to identify, define, and catalog publicly disclosed cybersecurity vulnerabilities.** DISPUTED ** The scp client in OpenSSH 8.2 incorrectly sends duplicate responses to the server upon a utimes system call failure, which allows a malicious unprivileged user on the remote server to overwrite arbitrary files in the client's download directory by creating a crafted subdirectory anywhere on the remote server. The victim must use the command scp -rp to download a file hierarchy containing, anywhere inside, this crafted subdirectory. NOTE: the vendor points out that "this attack can achieve no more than a hostile peer is already able to achieve within the scp protocol" and "utimes does not fail under normal circumstances.