rdf2rml

convert RDF data to either R2RML script or PlantUML diagram

The rdf2rml project provides two command-line tools: * rdf2rml - convert RDF example to R2RML script * rdfpuml - convert RDF to PlantUML diagram

libxnnpack0.20241108

High-efficiency floating-point neural network inference operators (libs)

XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 platforms. XNNPACK is not intended for direct use by deep learning practitioners and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as TensorFlow Lite, TensorFlow.js, PyTorch, and MediaPipe.

xfconf-gsettings-backend

utilities for managing settings in Xfce - gsettings backend

xfconf contains xfconfd and xfconf-query. - xfconfd handles the Xfce settings storage - xfconf-query enables users to tune settings from command line
Automatic build accelerator cache

firebuild

Automatic build accelerator cache

It works by caching the outputs of executed commands and replaying the results when the same commands are executed with the same parameters within the same environment.

libfst26

weighted finite-state transducers library (runtime)

OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). Weighted finite-state transducers are automata where each transition has an input label, an output label, and a weight. The more familiar finite-state acceptor is represented as a transducer with each transition's input and output label equal. Finite-state acceptors are used to represent sets of strings (specifically, regular or rational sets); finite-state transducers are used to represent binary relations between pairs of strings (specifically, rational transductions). The weights can be used to represent the cost of taking a particular transition.

libjavascriptcoregtk-5.0-0

JavaScript engine library from WebKitGTK

JavaScriptCore is the JavaScript engine used in WebKit. It consists of the following building blocks: lexer, parser, start-up interpreter (LLInt), baseline JIT, a low-latency optimizing JIT (DFG), and a high-throughput optimizing JIT (FTL).