TY - GEN A1 - Maier, Isidor Konrad A1 - Kuhn, Johannes Ferdinand Joachim A1 - Duckhorn, Frank A1 - Kraljevski, Ivan A1 - Sobe, Daniel A1 - Wolff, Matthias A1 - Tschöpe, Constanze T1 - Word Class Based Language Modeling: A Case of Upper Sorbian T2 - Proceedings of The Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference, Marseille, France N2 - In this paper we show how word class based language modeling can support the integration of a small language in modern applications of speech technology. The methods described in this paper can be applied for any language. We demonstrate the methods on Upper Sorbian. The word classes model the semantic expressions of numerals, date and time of day. The implementation of the created grammars was realized in the form of finite-state-transducers (FSTs) and minimalists grammars (MGs). We practically demonstrate the usage of the FSTs in a simple smart-home speech application, that is able to set wake-up alarms and appointments expressed in a variety of spontaneous and natural sentences. While the created MGs are not integrated in an application for practical use yet, they provide evidence that MGs could potentially work more efficient than FSTs in built-on applications. In particular, MGs can work with a significantly smaller lexicon size, since their more complex structure lets them generate more expressions with less items, while still avoiding wrong expressions. KW - word classes, minimalist grammar, language modeling, speech recognition, Upper Sorbian Y1 - 2022 UR - http://www.lrec-conf.org/proceedings/lrec2022/workshops/EURALI/pdf/2022.eurali-1.5.pdf SN - 978-2-493814-07-4 SP - 28 EP - 35 PB - European Language Resources Association ER - TY - GEN A1 - Kraljevski, Ivan A1 - Ju, Yong Chul A1 - Ivanov, Dmitrij A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - How to Do Machine Learning with Small Data? - A Review from an Industrial Perspective T2 - arXiv N2 - Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of "small data" and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data. Y1 - 2023 UR - https://arxiv.org/abs/2311.07126 ER - TY - GEN A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Tschöpe, Constanze A1 - Schubert, Frank A1 - Wolff, Matthias T1 - Paper Tissue Softness Rating by Acoustic Emission Analysis T2 - Applied Sciences N2 - 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. KW - acoustic emission KW - machine learning KW - tissue softness analysis Y1 - 2022 U6 - https://doi.org/10.3390/app13031670 SN - 2076-3417 VL - 13 IS - 3 ER - TY - GEN A1 - Ju, Yong Chul A1 - Kraljevski, Ivan A1 - Neunübel, Heiko A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - Acoustic Resonance Testing of Small Data on Sintered Cogwheels T2 - Sensors N2 - Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset. KW - acoustic resonance testing (ART) non-destructive testing (NDT) KW - deep learning KW - machine learning KW - small-data KW - non-destructive testing (NDT) Y1 - 2022 U6 - https://doi.org/10.3390/s22155814 SN - 1424-8220 VL - 22 IS - 15 ER - TY - GEN A1 - Maier, Isidor Konrad A1 - Kuhn, Johannes A1 - Duckhorn, Frank A1 - Kraljevski, Ivan A1 - Sobe, Daniel A1 - Wolff, Matthias A1 - Tschöpe, Constanze T1 - Word Class Based Language Modeling: A Case of Upper Sorbian, LREC 2022, Marseille T2 - Language Resources and Evaluation Conference 2022 (LREC 2022), Marseille, 2022-06-13 Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7501145 ER -