Awesome List Updates on Apr 12, 2025

8 awesome lists updated today.

1. Awesome Embedded Rust

arbitrary-int: This crate implements arbitrary numbers for Rust. Once included, you can use types like u5 or u120

bitbybit: macros that create bit fields and bit enums, which are useful in bit packing code (e.g. in drivers or networking code)

bitfield-struct: Procedural macro for bitfields that allows specifying bitfields as structs

yvann-ba/ft_transcendence (⭐6) - Minimalist Project banner, clear GIF gallery in table layout. Colorful architecture diagram. Clear tech stack description. Team section with contributor avatars.

@elastic/apm-rum-angular - Elastic APM Real User Monitoring for Angular applications.

groupix-spinner-library (⭐2) - A lightweight Angular spinner library for seamless loading animations!

ngx-core-business (⭐1) - An Angular library in active development, built on top of @angular/material. It aims to provide reusable, enterprise-grade UI components and utilities to streamline the development of scalable Angular applications.

Abductive Plan Recognition by Extending Bayesian Logic Programs - ECML'11, 2011. [All Versions]. Plan recognition is the task of predicting an agent’s top-level plans based on its observed actions. It is an abductive reasoning task that involves inferring cause from effect. Most existing approaches to plan recognition use either first-order logic or probabilistic graphical models. While the former cannot handle uncertainty, the latter cannot handle structured representations. In order to overcome these limitations, this work develops an approach to plan recognition using Bayesian Logic Programs (BLPs), which combine first-order logic and Bayesian networks. Since BLPs employ logical deduction to construct the networks, they cannot be used effectively for plan recognition. Therefore, the authors extend BLPs to use logical abduction to construct Bayesian networks and call the resulting model Bayesian Abductive Logic Programs (BALPs). The authors learn the parameters in BALPs using the Expectation Maximization algorithm adapted for BLPs. Finally, the authors present an experimental evaluation of BALPs on three benchmark data sets and compare its performance with the state-of-the-art for plan recognition.