@misc{KuhnWolffMaier, author = {Kuhn, Johannes and Wolff, Matthias and Maier, Isidor Konrad}, title = {Wortgenerator f{\"u}r Minimalistische Grammatiken}, series = {Elektronische Sprachsignalverarbeitung, Tagungsband der 36. Konferenz, 05.-07.03.2025, Halle/Saale}, journal = {Elektronische Sprachsignalverarbeitung, Tagungsband der 36. Konferenz, 05.-07.03.2025, Halle/Saale}, editor = {Grawunder, Sven}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-95908-803-9}, issn = {0940-6832}, pages = {27 -- 34}, abstract = {Um eine bidirektionale Verarbeitung von Sprache zu realisieren, ist es von Vorteil den gleichen Sprachformalismus f{\"u}r das Parsen und die Generierung zu verwenden. F{\"u}r Minimalistische Grammatiken gibt es eine ausgepr{\"a}gte Literatur zu Parsern, aber noch keine Ver{\"o}ffentlichung zu Generatoren. Diese Arbeit stellt einen ersten Generator vor, der mittels Minimalistischer Grammatiken und λ-Ausdr{\"u}cken, S{\"a}tze erzeugt. Der hier vorgestellte Generator kann entgegen manchen anderen Generatoren, von anderen Grammatikformalismen, mit ε-Regeln arbeiten.}, language = {de} } @misc{HuberLieblRosenowRoemeretal., author = {Huber-Liebl, Markus and Rosenow, Tillmann and R{\"o}mer, Ronald and Wirsching, G{\"u}nther and Wolff, Matthias}, title = {It all starts with a little difference : tensors as data and code.}, series = {Elektronische Sprachsignalverarbeitung 2025 : Tagungsband der 36. Konferenz Halle/Saale, 5.-7. M{\"a}rz 2025}, journal = {Elektronische Sprachsignalverarbeitung 2025 : Tagungsband der 36. Konferenz Halle/Saale, 5.-7. M{\"a}rz 2025}, editor = {Grawunder, Sven}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-95908-803-9}, issn = {0940-6832}, pages = {170 -- 179}, abstract = {We further promote the idea of quantum inspiration and propose to equip cognitive systems not only with tensors for data representation but also for operation representation. We argue that these are two sides of the same coin. For experimental symbolic algorithms we introduce a suitable testbed and give its proper specification. We formalize our method of behavioural control with tensor algebra and discuss its implementation for our physical testbed realization.}, language = {en} } @misc{MaierRosenowTuurietal., author = {Maier, Isidor Konrad and Rosenow, Tillmann and Tuuri, Okko and Wolff, Matthias}, title = {Frequency-magnitude relation of numeral words based on search-engine results}, series = {Elektronische Sprachsignalverarbeitung, Tagungsband der 36. Konferenz, 05.-07.03.2025, Halle/Saale}, journal = {Elektronische Sprachsignalverarbeitung, Tagungsband der 36. Konferenz, 05.-07.03.2025, Halle/Saale}, editor = {Grawunder, Sven}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-95908-803-9}, issn = {0940-6832}, pages = {51 -- 60}, abstract = {We googled various numeral words from 28 languages. Different approaches for the description of the data were investigated. In all of the 28 languages, the found frequency-magnitude dependence fits better to a power law than to an exponential law. The result can be used to distinguish grammatically correct from incorrect numerals based on the prediction of search results.}, language = {en} } @misc{KraljevskiDuckhornSobeetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Sobe, Daniel and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Speech-to-text in upper sorbian : current state}, series = {Elektronische Sprachsignalverarbeitung 2025 : Tagungsband der 36. Konferenz Halle/Saale, 5.-7. M{\"a}rz 2025}, journal = {Elektronische Sprachsignalverarbeitung 2025 : Tagungsband der 36. Konferenz Halle/Saale, 5.-7. M{\"a}rz 2025}, editor = {Grawunder, Sven}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-95908-803-9}, issn = {0940-6832}, pages = {109 -- 116}, abstract = {This study presents recent advancements in Upper Sorbian Speech-to-Text (STT) technology. We provide an overview of the Sorbian languages, the available speech and language resources, and the development of an STT system based on a traditional approach, which includes acoustic, pronunciation, and language modeling. Due to the scarcity of resources for Sorbian languages, our approach leverages sub-word and word-class modeling techniques. The word-class modeling is based on Finite-State Transducer definitions, which are applicable to both offline text parsing and integration into the decoding graph of the STT system. Word-class parsing is performed on the speech corpus and utilized for language modeling with complete words, sub-word units, or both. Additionally, the same definitions can be applied to Named Entity Recognition during the post-processing of recognized transcriptions. This approach significantly reduces out-of-vocabulary words and enables greater customization of the recognizer for domain-specific applications. The system was implemented for the real-time transcription of church sermon broadcasts in Upper Sorbian. The domain-specific system achieved performance comparable to fine-tuned OpenAI Whisper models developed also by other initiatives while also providing a resource-efficient solution with semantically tagged recognition results.}, language = {en} } @book{WirschingSchmittWolff, author = {Wirsching, G{\"u}nther and Schmitt, Ingo and Wolff, Matthias}, title = {Quantenlogik, Band 1 : eine Einf{\"u}hrung f{\"u}r Ingenieure und Informatiker}, edition = {2. Auflage}, publisher = {Springer Vieweg}, address = {Berlin ; Heidelberg}, isbn = {978-3-662-71335-8}, doi = {10.1007/978-3-662-71335-8}, pages = {xiii, 495}, abstract = {Messungen an Quantenobjekten haben eine logische Struktur. Diese schließt die klassische Logik ein, geht jedoch weit dar{\"u}ber hinaus. Das wesentliche Ziel dieses Lehrbuchs ist es, die mathematischen Werkzeuge der Quantenlogik zu vermitteln und technische Anwendungsm{\"o}glichkeiten aufzuzeigen. Die daf{\"u}r erforderlichen mathematischen Sachverhalte werden anhand von Beispielen so erl{\"a}utert und motiviert, dass sie f{\"u}r angehende Ingenieure und Informatiker verst{\"a}ndlich sind. Die vorliegende Auflage wurde korrigiert und um zahlreiche Anwendungen, zus{\"a}tzliche Beispiele und Beweise erweitert sowie um ein neues Beispiel zur Mustererkennung erg{\"a}nzt.}, language = {de} } @misc{RoemerHuberLieblWolff, author = {R{\"o}mer, Ronald and Huber-Liebl, Markus and Wolff, Matthias}, title = {Quantum-inspired representation and querying of semiotic object properties by cognitive agents}, series = {2025 IEEE 16th International Conference on Cognitive Infocommunications (CogInfoCom)}, journal = {2025 IEEE 16th International Conference on Cognitive Infocommunications (CogInfoCom)}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {979-8-3503-5691-5}, doi = {10.1109/CogInfoCom66819.2025.11200567}, pages = {000129 -- 000134}, abstract = {Advanced intelligence often relies on the ability to build and use models. They allow the representation of knowledge, enable predictions, and serve to plan goal-directed actions. However, the successfull use of models requires a certain level of competence in interacting with models. This competence can be acquired by exploiting the separation of the cognitive agent from the objects represented in the model; because it allows the differentiation of individual semiotic levels. This includes the separation of self-sufficient and non self-sufficient object attributes. Non self-sufficient attributes that the agent assigned to potential target objects during the exploration phase form the starting point for the information requirement regarding the self-sufficient attributes of these objects. These attributes are required for target selection and the subsequent alignment of the model-based planning. This process is characterized by a series of queries to the environment model and thus shows a certain similarity to information retrieval (IR). Therefore, in this work we particularly investigate quantum-inspired IR-methods for representation and querying. We demonstrate that they are suitable for modelling cognitive effort and behavior, as they represent a realistic and universally applicable descriptive tool.}, language = {en} } @misc{HuberLieblRosenowRoemeretal., author = {Huber-Liebl, Markus and Rosenow, Tillmann and R{\"o}mer, Ronald and Wirsching, G{\"u}nther and Wolff, Matthias}, title = {Knowledge representation and querying with tensors for a class of practical test problems}, series = {Refereed Paper Proceedings - KM Conference 2025 - Siena, Italy}, journal = {Refereed Paper Proceedings - KM Conference 2025 - Siena, Italy}, publisher = {International Institute for Applied Knowledge Management}, pages = {111 -- 123}, abstract = {We further promote the idea of quantum inspiration and propose to equip cognitive systems not only with tensors for data representation but also for operation representation and querying. We discuss ideas from information retrieval and translate them into our method of behavioral control. We formalize the method with tensor algebra and discuss its extensions to learning. At the example of the problem of 𝑁-dimensional aiming, we visualize the transfer of our stated theoretical foundation to a class of practical test problems.}, language = {en} } @misc{SchmittSowoidnichGosswamietal., author = {Schmitt, Ingo and Sowoidnich, Kay and Gosswami, Tapashi and Sumpf, Bernd and Maiwald, Martin and Wolff, Matthias}, title = {PCA-based peak feature selection for classification of spectroscopic datasets}, series = {Journal of chemometrics}, volume = {39}, journal = {Journal of chemometrics}, number = {11}, publisher = {John Wiley \& Sons Ltd.}, address = {New York, NY}, issn = {0886-9383}, doi = {10.1002/cem.70074}, pages = {1 -- 14}, abstract = {Reducing feature dimensionality in spectroscopic data is crucial for efficient analysis and classification. Using all available features for classification typically results in an unacceptably high runtime and poor accuracy. Popular feature extraction methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), and autoencoders, reduce feature dimensionality by extracting latent features that can be challenging to interpret. To enable better human interpretation of the classification model, we avoid extraction methods and instead propose applying feature selection methods. In this work, we develop an innovative PCA-based feature selection method for spectroscopic data, providing an essential subset of the original features. As an important advantage, no prior knowledge about the characteristic signals of the respective target substance is required. In this proof-of-concept study, the proposed method is initially characterized using simulated Raman and infrared absorption datasets. From the top five PCA eigenvectors of spectroscopic data, we identify a set of three top peaks each at specific wavenumbers (features). The compact set of selected features is then used for classification tasks applying a decision tree. Based on two well-defined spectroscopic datasets, our study demonstrates that our new method of PCA-based peak finding outperforms selected other approaches with regard to interpretability and accuracy. For both investigated datasets, accuracies greater than 97\% are achieved. Our approach shows large potential for accurate classification combined with interpretability in further scenarios involving spectroscopic datasets.}, language = {en} }