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The ONNX Runtime Mobile package is a size optimized inference library for executing ONNX (Open Neural Network Exchange) models on Android. This package is built from the open source inference engine but with reduced disk footprint targeting mobile platforms. To minimize binary size this library supports a reduced set of operators and types aligned to typical mobile applications. The ONNX model must be converted to ORT format in order to use it with this package. See https://onnxruntime.ai/docs/reference/ort-format-models.html for more details.

Small Java library for generating dynamic proxies on top of [ByteBuddy](https://github.com/raphw/byte-buddy) or [Javassist](https://github.com/jboss-javassist/javassist). You can generate data transfer objects, rich traits or even whole implicit DAO implementations dynamically at runtime easily. This library solves the complex stuff, so you can focus on application logic. Serializability, cloning are already solved by us. We also aim for transparent and easily debuggable proxies, because as we know proxies is usually part of "magic" for the team. Hence, the name of this library - Proxycian as a magician for the proxies ;)

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "minimum description length" (MDL) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "evidence-driven state merging" (EDSM) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

This artifact provides the implementation of the L* learning algorithm described in the paper "Learning Regular Sets from Queries and Counterexamples" (https://doi.org/10.1016/0890-5401(87)90052-6) by Dana Angluin including variations and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular Sets" (https://dx.doi.org/10.1006/inco.1995.1070) by Oded Maler and Amir Pnueli or "Inference of finite automata using homing sequences" (http://dx.doi.org/10.1006/inco.1993.1021) by Ronald L. Rivest and Robert E. Schapire.