TY - GEN ED - Wirsching, Günther ED - Wolff, Matthias ED - Schmitt, Ingo T1 - Quantenlogik : eine Einführung für Ingenieure und Informatiker N2 - Das wesentliche Ziel dieses Lehrbuchs ist es, die mathematischen Werkzeuge zur Modellierung kognitiver Strukturen und Prozesse auf der Grundlage der klassischen Logik und der Quantenlogik zu entwickeln. Die dafür erforderlichen mathematischen Sachverhalte werden so dargestellt und anhand von Beispielen motiviert, dass sie für angehende Ingenieure und Informatiker verständlich sind. - Kompakte logische Darstellung der mathematischen Werkzeuge zur Modellierung kognitiver Strukturen und Prozesse - Beispiele erläutern die Anwendung im Engineering Zielgruppen sind insbesondere Studierende der Ingenieurwissenschaften und der Informatik, aber auch Studierende der Mathematik oder der Physik können durch den anwendungsbezogenen Blick ihren Horizont erweitern. KW - Projektive Geometrie KW - Lineare Algebra KW - Verbandstheorie KW - Information Retrieval KW - Kognitive Systeme KW - Künstliche Intelligenz KW - vektor-symbolische Architekturen KW - Quantenmessung KW - Wahrscheinlichkeiten KW - Anfragesysteme KW - Informationskodierung Y1 - 2023 UR - https://link.springer.com/book/10.1007/978-3-662-66780-4 SN - 978-3-662-66779-8 SN - 978-3-662-66780-4 U6 - https://doi.org/10.1007/978-3-662-66780-4 PB - Springer Vieweg CY - Berlin ; Heidelberg ET - 1. Auflage ER - TY - GEN A1 - Beim Graben, Peter A1 - Huber-Liebl, Markus A1 - Klimczak, Peter A1 - Wirsching, Günther T1 - Machine Semiotics T2 - arXiv N2 - Recognizing a basic difference between the semiotics of humans and machines presents a possibility to overcome the shortcomings of current speech assistive devices. For the machine, the meaning of a (human) utterance is defined by its own scope of actions. Machines, thus, do not need to understand the conventional meaning of an utterance. Rather, they draw conversational implicatures in the sense of (neo-)Gricean pragmatics. For speech assistive devices, the learning of machine-specific meanings of human utterances, i.e. the fossilization of conversational implicatures into conventionalized ones by trial and error through lexicalization appears to be sufficient. Using the quite trivial example of a cognitive heating | device, we show that — based on dynamic semantics — this process can be formalized as the reinforcement learning of utterance-meaning pairs (UMP). KW - Semiotics KW - machine learning KW - dynamic semantics KW - pragmatic implicatures KW - fossilization Y1 - 2023 UR - https://arxiv.org/abs/2008.10522v2 SP - 1 EP - 48 ER - TY - CHAP A1 - Kraljevski, Ivan A1 - Tschöpe, Constanze A1 - Wolff, Matthias ED - Klimczak, Peter ED - Petersen, Christer T1 - Limits and Prospects of Big Data and Small Data Approaches in AI Applications T2 - AI - Limits and Prospects of Artificial Intelligence N2 - The renaissance of artificial intelligence (AI) in the last decade can be credited to several factors, but chief among these is the ever-increasing availability and miniaturization of computational resources. This process has contributed to the rise of ubiquitous computing via popularizing smart devices and the Internet of Things in everyday life. In turn, this has resulted in the generation of increasingly enormous amounts of data. The tech giants are harvesting and storing data on their clients’ behavior and, at the same time, introducing concerns about data privacy and protection. Suddenly, such an abundance of data and computing power, which was unimaginable a few decades ago, has caused a revival of old and the invention of new machine learning paradigms, like Deep Learning. Artificial intelligence has undergone a technological breakthrough in various fields, achieving better than human performance in many areas (such as vision, board games etc.). More complex tasks require more sophisticated algorithms that need more and more data. It has often been said that data is becoming a resource that is "more valuable than oil"; however, not all data is equally available and obtainable. Big data can be described by using the "four Vs"; data with immense velocity, volume, variety, and low veracity. In contrast, small data do not possess any of those qualities; they are limited in size and nature and are observed or produced in a controlled manner. Big data, along with powerful computing and storage resources, allow “black box” AI algorithms for various problems previously deemed unsolvable. One could create AI applications even without the underlying expert knowledge, assuming there are enough data and the right tools available (e.g. end-to-end speech recognition and generation, image and object recognition). There are numerous fields in science, industry and everyday life where AI has vast potential. However, due to the lack of big data, application is not straightforward or even possible. A good example is AI in medicine, where an AI system is intended to assist physicians in diagnosing and treating rare or previously never observed conditions, and there is no or an insufficient amount of data for reliable AI deployment. Both big and small data concepts have limitations and prospects for different fields of application. This paper will try to identify and present them by giving real-world examples in various AI fields. Y1 - 2023 UR - https://www.transcript-verlag.de/chunk_detail_seite.php?doi=10.14361%2F9783839457320-006 SN - 978-3-8376-5732-6 U6 - https://doi.org/10.14361/9783839457320-006 SP - 115 EP - 142 PB - transcript Verlag CY - Bielefeld ER - TY - GEN A1 - Maier, Isidor Konrad A1 - Kuhn, Johannes A1 - Beisegel, Jesse A1 - Huber-Liebl, Markus A1 - Wolff, Matthias T1 - Minimalist Grammar: Construction without Overgeneration T2 - arXiv N2 - 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}. Y1 - 2023 UR - https://arxiv.org/abs/2311.01820 U6 - https://doi.org/10.48550/arXiv.2311.01820 ER - TY - GEN A1 - Römer, Ronald A1 - beim Graben, Peter A1 - Huber-Liebl, Markus A1 - Wolff, Matthias T1 - (Pre-)linguistic Problem Solving based on Dynamic Semantics T2 - 14th IEEE International Conference on Cognitive Infocommunications – CogInfoCom 2023, September 22-23, Budapest, Hungary Y1 - 2023 SN - 979-8-3503-2565-2 SN - 979-8-3503-2566-9 U6 - https://doi.org/10.1109/CogInfoCom59411.2023.10397487 SN - 2473-5671 SN - 2380-7350 SP - 147 EP - 152 ER - TY - GEN A1 - Uhlig, Sebastian A1 - Alkhasli, Ilkin A1 - Schubert, Frank A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - A Review of Synthetic and Augmented Training Data for Machine Learning in Ultrasonic Non-Destructive Evaluation T2 - Ultrasonics N2 - Ultrasonic Testing (UT) has seen increasing application of machine learning (ML) in recent years, promoting higher-level automation and decision-making in flaw detection and classification. Building a generalized training dataset to apply ML in non-destructive evaluation (NDE), and thus UT, is exceptionally difficult since data on pristine and representative flawed specimens are needed. Yet, in most UT test cases flawed specimen data is inherently rare making data coverage the leading problem when applying ML. Common data augmentation (DA) strategies offer limited solutions as they don’t increase the dataset variance, which can lead to overfitting of the training data. The virtual defect method and the recent application of generative adversarial neural networks (GANs) in UT are sophisticated DA methods targeting to solve this problem. On the other hand, well-established research in modeling ultrasonic wave propagations allows for the generation of synthetic UT training data. In this context, we present a first thematic review to summarize the progress of the last decades on synthetic and augmented UT training data in NDE. Additionally, an overview of methods for synthetic UT data generation and augmentation is presented. Among numerical methods such as finite element, finite difference, and elastodynamic finite integration methods, semi-analytical methods such as general point source synthesis, superposition of Gaussian beams, and the pencil method as well as other UT modeling software are presented and discussed. Likewise, existing DA methods for one- and multidimensional UT data, feature space augmentation, and GANs for augmentation are presented and discussed. The paper closes with an in-detail discussion of the advantages and limitations of existing methods for both synthetic UT training data generation and DA of UT data to aid the decision-making of the reader for the application to specific test cases. KW - Non-destructive testing KW - NDT KW - Non-destructive evaluation KW - NDE KW - Ultrasonic testing KW - Ultrasonics KW - Flaw detection KW - Machine learning KW - Artificial intelligence KW - Deep learning KW - Synthetic training data KW - Data augmentation Y1 - 2023 UR - https://www.sciencedirect.com/science/article/pii/S0041624X23001178 U6 - https://doi.org/10.1016/j.ultras.2023.107041 SN - 1874-9968 IS - 134 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 - Römer, Ronald A1 - beim Graben, Peter A1 - Huber-Liebl, Markus A1 - Wolff, Matthias T1 - Unifying Physical Interaction, Linguistic Communication, and Language Acquisition of Cognitive Agents by Minimalist Grammars T2 - Frontiers in Computer Science N2 - Cognitive agents that act independently and solve problems in their environment on behalf of a user are referred to as autonomous. In order to increase the degree of autonomy, advanced cognitive architectures also contain higher-level psychological modules with which needs and motives of the agent are also taken into account and with which the behavior of the agent can be controlled. Regardless of the level of autonomy, successful behavior is based on interacting with the environment and being able to communicate with other agents or users. The agent can use these skills to learn a truthful knowledge model of the environment and thus predict the consequences of its own actions. For this purpose, the symbolic information received during the interaction and communication must be converted into representational data structures so that they can be stored in the knowledge model, processed logically and retrieved from there. Here, we firstly outline a grammar-based transformation mechanism that unifies the description of physical interaction and linguistic communication and on which the language acquisition is based. Specifically, we use minimalist grammar (MG) for this aim, which is a recent computational implementation of generative linguistics. In order to develop proper cognitive information and communication technologies, we are using utterance meaning transducers (UMT) that are based on semantic parsers and a mental lexicon, comprising syntactic and semantic features of the language under consideration. This lexicon must be acquired by a cognitive agent during interaction with its users. To this aim we outline a reinforcement learning algorithm for the acquisition of syntax and semantics of English utterances. English declarative sentences are presented to the agent by a teacher in form of utterance meaning pairs (UMP) where the meanings are encoded as formulas of predicate logic. Since MG codifies universal linguistic competence through inference rules, thereby separating innate linguistic knowledge from the contingently acquired lexicon, our approach unifies generative grammar and reinforcement learning, hence potentially resolving the still pending Chomsky-Skinner controversy. btuktuminglear, btuktsptech, btuktcogsys Y1 - 2022 UR - https://www.frontiersin.org/article/10.3389/fcomp.2022.733596 U6 - https://doi.org/10.3389/fcomp.2022.733596 SN - 2624-9898 IS - 4 ER - TY - GEN A1 - Gergs, Tobias A1 - Borislavov, Borislav A1 - Trieschmann, Jan T1 - Efficient plasma-surface interaction surrogate model for sputtering processes based on autoencoder neural networks T2 - Journal of Vacuum Science & Technology. B N2 - Simulations of thin film sputter deposition require the separation of the plasma and material transport in the gas phase from the growth/sputtering processes at the bounding surfaces (e.g., substrate and target). Interface models based on analytic expressions or look-up tables inherently restrict this complex interaction to a bare minimum. A machine learning model has recently been shown to overcome this remedy for Ar ions bombarding a Ti-Al composite target. However, the chosen network structure (i.e., a multilayer perceptron, MLP) provides approximately 4×106 degrees of freedom, which bears the risk of overfitting the relevant dynamics and complicating the model to an unreliable extent. This work proposes a conceptually more sophisticated but parameterwise simplified regression artificial neural network for an extended scenario, considering a variable instead of a single fixed Ti-Al stoichiometry. A convolutional 𝛽-variational autoencoder is trained to reduce the high-dimensional energy-angular distribution of sputtered particles to a low-dimensional latent representation with only two components. In addition to a primary decoder that is trained to reconstruct the input energy-angular distribution, a secondary decoder is employed to reconstruct the mean energy of incident Ar ions as well as the present Ti-Al composition. The mutual latent space is hence conditioned on these quantities. The trained primary decoder of the variational autoencoder network is subsequently transferred to a regression network, for which only the mapping to the particular low-dimensional space has to be learned. While obtaining a competitive performance, the number of degrees of freedom is drastically reduced to 15 111 (0.378% of the MLP) and 486 (0.012% of the MLP) parameters for the primary decoder and the remaining regression network, respectively. The underlying methodology is very general and can easily be extended to more complex physical descriptions (e.g., taking into account dynamical surface properties) with a minimal amount of data required. Y1 - 2022 U6 - https://doi.org/10.1116/6.0001485 SN - 2166-2754 VL - 40 IS - 1 SP - 012802-1 EP - 012802-15 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 -