@misc{MaierKuhnBeisegeletal., author = {Maier, Isidor Konrad and Kuhn, Johannes and Beisegel, Jesse and Huber-Liebl, Markus and Wolff, Matthias}, title = {Minimalist Grammar: Construction without Overgeneration}, series = {arXiv}, journal = {arXiv}, doi = {10.48550/arXiv.2311.01820}, pages = {51}, abstract = {In this paper we give instructions on how to write a minimalist grammar (MG). In order to present the instructions as an algorithm, we use a variant of context free grammars (CFG) as an input format. We can exclude overgeneration, if the CFG has no recursion, i.e. no non-terminal can (indirectly) derive to a right-hand side containing itself. The constructed MGs utilize licensors/-ees as a special way of exception handling. A CFG format for a derivation A_eats_B↦∗peter_eats_apples, where A and B generate noun phrases, normally leads to overgeneration, e.\,g., i_eats_apples. In order to avoid overgeneration, a CFG would need many non-terminal symbols and rules, that mainly produce the same word, just to handle exceptions. In our MGs however, we can summarize CFG rules that produce the same word in one item and handle exceptions by a proper distribution of licensees/-ors. The difficulty with this technique is that in most generations the majority of licensees/-ors is not needed, but still has to be triggered somehow. We solve this problem with ϵ-items called \emph{adapters}.}, language = {en} } @misc{RoemerbeimGrabenHuberLiebletal., author = {R{\"o}mer, Ronald and beim Graben, Peter and Huber-Liebl, Markus and Wolff, Matthias}, title = {(Pre-)linguistic Problem Solving based on Dynamic Semantics}, series = {14th IEEE International Conference on Cognitive Infocommunications - CogInfoCom 2023, September 22-23, Budapest, Hungary}, journal = {14th IEEE International Conference on Cognitive Infocommunications - CogInfoCom 2023, September 22-23, Budapest, Hungary}, isbn = {979-8-3503-2565-2}, issn = {2473-5671}, doi = {10.1109/CogInfoCom59411.2023.10397487}, pages = {147 -- 152}, language = {en} } @misc{MaierKuhnDuckhornetal., author = {Maier, Isidor Konrad and Kuhn, Johannes and Duckhorn, Frank and Kraljevski, Ivan and Sobe, Daniel and Wolff, Matthias and Tsch{\"o}pe, Constanze}, title = {Word Class Based Language Modeling: A Case of Upper Sorbian, LREC 2022, Marseille}, series = {Language Resources and Evaluation Conference 2022 (LREC 2022), Marseille, 2022-06-13}, journal = {Language Resources and Evaluation Conference 2022 (LREC 2022), Marseille, 2022-06-13}, doi = {10.5281/zenodo.7501145}, pages = {1}, language = {en} } @misc{MaierWolff, author = {Maier, Isidor Konrad and Wolff, Matthias}, title = {A Decomposition Algorithm for Numerals based on Arithmetics}, doi = {10.5281/zenodo.7501698}, pages = {1}, abstract = {Poster presentation for an idea to decompose numerals}, language = {en} } @misc{TschoepeMuehleJuetal., author = {Tsch{\"o}pe, Constanze and M{\"u}hle, Maximilian and Ju, Yong Chul and Kraljevski, Ivan and Wolff, Matthias}, title = {K{\"u}nstliche Intelligenz in der ZfP - Welchen Beitrag kann KI in der ZfP leisten?}, series = {DGZfP-Jahrestagung 2021, 10.-11. Mai}, journal = {DGZfP-Jahrestagung 2021, 10.-11. Mai}, isbn = {978-3-947971-18-3}, pages = {17}, abstract = {K{\"u}nstliche Intelligenz zieht derzeit in alle Bereiche der Gesellschaft und des Lebens ein. Aber welchen Stellenwert hat sie momentan auf dem Gebiet der zerst{\"o}rungsfreien Pr{\"u}fung? Was kann KI leisten? Welche Herausforderungen m{\"u}ssen erfolgreich bew{\"a}ltigt werden? Gibt es das eine KI-Verfahren, welches prinzipiell f{\"u}r ZfP geeignet ist? Bei der Bauteil- und Materialpr{\"u}fung w{\"a}hrend und unmittelbar nach der Herstellung, der {\"U}berwachung von Verschleißteilen in Maschinen und Anlagen oder der Schadensdetektion an Bauteilen und Komponenten liefern ZfP-Verfahren Daten, die bewertet werden m{\"u}ssen. Obwohl inzwischen sehr leistungsf{\"a}hige Toolkits verf{\"u}gbar sind, erfordert der optimale Einsatz der KI f{\"u}r ein ZfP-Verfahren oftmals mehr. Die meisten Kunden m{\"o}chten nicht nur eine L{\"o}sung ihres Problems; sie wollen verstehen, warum die KI so und nicht anders entschieden hat, warum der Klassifikator das Bauteil einer bestimmten Klasse (z. B. gut/schlecht oder neuwertig/verschlissen/defekt) zugewiesen hat. Abh{\"a}ngig von der Klassifikationsaufgabe sowie der Art und der Anzahl der vorliegenden Daten kann ein geeignetes Verfahren bestimmt werden. Mit Methoden des maschinellen Lernens werden Modelle gebildet, welche die Basis f{\"u}r die KI-Verfahren zur Klassifikation bilden. Der Beitrag liefert einen {\"U}berblick {\"u}ber KI-Verfahren und deren Anwendungen in der zerst{\"o}rungsfreien Pr{\"u}fung. Zahlreiche Beispiele und Ergebnisse werden vorgestellt, um die Mannigfaltigkeit des Einsatzes in der ZfP und der bestehenden M{\"o}glichkeiten zu demonstrieren.}, language = {de} } @misc{TschoepeKraljevskiDuckhornetal., author = {Tsch{\"o}pe, Constanze and Kraljevski, Ivan and Duckhorn, Frank and Wolff, Matthias}, title = {Sprachtechnologie und akustische Mustererkennung in der medizinischen Anwendung}, series = {16. Landeskonferenz „Digitalisierung im Gesundheitswesen" 2021}, journal = {16. Landeskonferenz „Digitalisierung im Gesundheitswesen" 2021}, pages = {9}, language = {de} } @misc{KraljevskiDuckhornTschoepeetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Schubert, Frank and Wolff, Matthias}, title = {Paper Tissue Softness Rating by Acoustic Emission Analysis}, series = {Applied Sciences}, volume = {13}, journal = {Applied Sciences}, number = {3}, issn = {2076-3417}, doi = {10.3390/app13031670}, abstract = {Softness is one of the essential properties of hygiene tissue products. Reliably measuring it is of utmost importance to ensure the balance between customer expectations and cost-effective tissue production. This study presents a method for assessing softness by analyzing acoustic emissions produced while tearing a tissue specimen. The aim was to train neural network models using the corrected results of human panel tests as the ground truth labels and to predict the tissue softness in two- and three-class recognition tasks. We also investigate the possibility of predicting some production parameters related to the softness property. The results proved that tissue softness and production parameters could be reliably estimated only by the tearing noise.}, language = {en} }