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Merge branch 'main' into feature/clean-up-newlines
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maxkratz committed Oct 8, 2024
2 parents bfc7984 + fda6a3c commit a75c6a9
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3 changes: 3 additions & 0 deletions .gitmodules
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[submodule "themes/hugo-cloak-email"]
path = themes/hugo-cloak-email
url = https://github.com/martignoni/hugo-cloak-email.git
[submodule "themes/hugo-notice"]
path = themes/hugo-notice
url = https://github.com/martignoni/hugo-notice.git
2 changes: 1 addition & 1 deletion config.yaml
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languageCode: en-us
defaultContentLanguage: en
title: eMoflon Project Site
theme: ["hugo-cloak-email", "cayman-hugo-theme"]
theme: ["hugo-cloak-email", "hugo-notice", "cayman-hugo-theme"]

# Syntax highlighting
pygmentsCodefences: true
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6 changes: 5 additions & 1 deletion content/ibex/index.md
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---
title: "eMoflon::IBeX"
date: 2023-01-30
date: 2024-10-08
draft: false
---

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___
## eMoflon::IBeX-TGG

{{< notice warning >}}
The development of eMoflon::IBeX-TGG was discontinued in 2024.
{{< /notice >}}

**Triple Graph Grammars (TGGs)** are a declarative and rule-based technique to specify bidirectional model transformations.
Basically, this means that **TGGs** are used to express a consistency relationship between two models.
This relationship in the form of a set of grammar rules is then automatically transformed to obtain different consistency restoring operations such as translators or synchronizers.
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4 changes: 3 additions & 1 deletion content/publications/index.md
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### 2024
<!--https://doi.org/10.1007/978-3-031-71874-8_9-->
- [**"Generating Training Data Sets for Machine Learning Approaches with GIPS"**](https://link.springer.com/chapter/10.1007/978-3-031-71874-8_9).\
<!--https://link.springer.com/chapter/10.1007/978-3-031-71874-8_9-->
<!--https://www.springerprofessional.de/en/generating-training-data-sets-for-machine-learning-approaches-wi/50002264-->
- [**"Generating Training Data Sets for Machine Learning Approaches with GIPS"**](https://doi.org/10.1007/978-3-031-71874-8).\
Maximilian Kratz, Sebastian Ehmes, Marco Volle, Andy Schürr.
- [**"Using Application Conditions to Rank Graph Transformations for Graph Repair"**](https://doi.org/10.1007/978-3-031-64285-2_8).\
Lars Fritsche, Alexander Lauer, Andy Schürr, Gabriele Taentzer.
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1 change: 1 addition & 0 deletions themes/hugo-notice
Submodule hugo-notice added at 40327a

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