**BIMAN**, or **Bot Identification by commit Message, commit Association, and author Name**, is a technique to detect bots that commit code. It is comprised of three methods that consider independent aspects of the commits made by a particular author: 1) Commit Message: Identify if commit messages are being generated from templates; 2) Commit Association: Predict if an author is a bot using a random forest model, with features related to files and projects associated with the commits as predictors; and 3) Author Name: Match author’s name and email to common bot patterns.

Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next.

Internet Explorer

Internet Explorer: Targeted Representation Learning on the Open Web

BIMAN

Detecting and Characterizing Bots that Commit Code

**nnFormer**, or **not-another transFormer**, is a semantic segmentation model with an interleaved architecture based on empirical combination of self-attention and [convolution](https://paperswithcode.com/method/convolution). Firstly, a light-weight convolutional embedding layer ahead is used ahead of [transformer](https://paperswithcode.com/method/transformer) blocks. In comparison to directly flattening raw pixels and applying 1D pre-processing, the convolutional embedding layer encodes precise (i.e., pixel-level) spatial information and provide low-level yet high-resolution 3D features. After the embedding block, transformer and convolutional down-sampling blocks are interleaved to fully entangle long-term dependencies with high-level and hierarchical object concepts at various scales, which helps improve the generalization ability and robustness of learned representations.

Source	Detecting and Characterizing Bots that Commit Code
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com