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ZENODO
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License: CC BY
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License: CC BY
Data sources: Datacite
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Compositional Learning for Interleaving Parallel Automata (CL-Star)

Authors: Labbaf, Faezeh; Groot, Jan Friso; Hojjat, Hossein; Mousavi, Mohammad Reza;

Compositional Learning for Interleaving Parallel Automata (CL-Star)

Abstract

This repository contains the source codes and results of "Compositional Learning for Interleaving Parallel Automata". This tool is implemented in Java, on top of the LaernLib framework, a state-of-the-art library for automata learning. Refer to the "READMe.md" file for more information, and for instructions on running the experiments and reproducing the results, read the "documentation.txt" file.

{"references": ["Raffelt, H., Steffen, B.: Learnlib: A library for automata learning and experimentation. In: Baresi, L., Heckel, R. (eds.) Fundamental Approaches to Software Engineering. pp. 377\u2013380. Springer Berlin Heidelberg, Berlin, Heidelberg (2006)"]}

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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