TY - JOUR A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - A deep reinforcement learning based hyper-heuristic for modular production control JF - International journal of production research N2 - In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios. KW - production control KW - modular production KW - multi-agent system KW - deep reinforcement learning KW - deep learning KW - multi-objective optimisation Y1 - 2023 U6 - https://doi.org/10.1080/00207543.2023.2233641 SN - 0020-7543 SN - 1366-588X SN - 0278-6125 SP - 1 EP - 22 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Wilksch, Moritz A1 - Abramova, Olga T1 - PyFin-sentiment BT - towards a machine-learning-based model for deriving sentiment from financial tweets JF - International journal of information management data insights N2 - Responding to the poor performance of generic automated sentiment analysis solutions on domain-specific texts, we collect a dataset of 10,000 tweets discussing the topics of finance and investing. We manually assign each tweet its market sentiment, i.e., the investor’s anticipation of a stock’s future return. Using this data, we show that all existing sentiment models trained on adjacent domains struggle with accurate market sentiment analysis due to the task’s specialized vocabulary. Consequently, we design, train, and deploy our own sentiment model. It outperforms all previous models (VADER, NTUSD-Fin, FinBERT, TwitterRoBERTa) when evaluated on Twitter posts. On posts from a different platform, our model performs on par with BERT-based large language models. We achieve this result at a fraction of the training and inference costs due to the model’s simple design. We publish the artifact as a python library to facilitate its use by future researchers and practitioners. KW - sentiment analysis KW - financial market sentiment KW - opinion mining KW - machine learning KW - deep learning Y1 - 2023 U6 - https://doi.org/10.1016/j.jjimei.2023.100171 SN - 2667-0968 VL - 3 IS - 1 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Krestel, Ralf A1 - Chikkamath, Renukswamy A1 - Hewel, Christoph A1 - Risch, Julian T1 - A survey on deep learning for patent analysis JF - World patent information N2 - Patent document collections are an immense source of knowledge for research and innovation communities worldwide. The rapid growth of the number of patent documents poses an enormous challenge for retrieving and analyzing information from this source in an effective manner. Based on deep learning methods for natural language processing, novel approaches have been developed in the field of patent analysis. The goal of these approaches is to reduce costs by automating tasks that previously only domain experts could solve. In this article, we provide a comprehensive survey of the application of deep learning for patent analysis. We summarize the state-of-the-art techniques and describe how they are applied to various tasks in the patent domain. In a detailed discussion, we categorize 40 papers based on the dataset, the representation, and the deep learning architecture that were used, as well as the patent analysis task that was targeted. With our survey, we aim to foster future research at the intersection of patent analysis and deep learning and we conclude by listing promising paths for future work. KW - deep learning KW - patent analysis KW - text mining KW - natural language processing Y1 - 2021 U6 - https://doi.org/10.1016/j.wpi.2021.102035 SN - 0172-2190 SN - 1874-690X VL - 65 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schirrmann, Michael A1 - Landwehr, Niels A1 - Giebel, Antje A1 - Garz, Andreas A1 - Dammer, Karl-Heinz T1 - Early detection of stripe rust in winter wheat using deep residual neural networks JF - Frontiers in plant science : FPLS N2 - Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 x 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks. KW - yellow rust KW - monitoring KW - deep learning KW - wheat crops KW - image recognition KW - camera sensor KW - ResNet KW - smart farming Y1 - 2021 U6 - https://doi.org/10.3389/fpls.2021.469689 SN - 1664-462X VL - 12 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Evsevleev, Sergei A1 - Paciornik, Sidnei A1 - Bruno, Giovanni T1 - Advanced deep learning-based 3D microstructural characterization of multiphase metal matrix composites JF - Advanced engineering materials N2 - The quantitative analysis of microstructural features is a key to understanding the micromechanical behavior of metal matrix composites (MMCs), which is a premise for their use in practice. Herein, a 3D microstructural characterization of a five-phase MMC is performed by synchrotron X-ray computed tomography (SXCT). A workflow for advanced deep learning-based segmentation of all individual phases in SXCT data is shown using a fully convolutional neural network with U-net architecture. High segmentation accuracy is achieved with a small amount of training data. This enables extracting unprecedently precise microstructural parameters (e.g., volume fractions and particle shapes) to be input, e.g., in micromechanical models. KW - computed tomography KW - convolutional neural networks KW - deep learning KW - metal KW - matrix composites KW - segmentations Y1 - 2020 U6 - https://doi.org/10.1002/adem.201901197 SN - 1438-1656 SN - 1527-2648 VL - 22 IS - 4 PB - Wiley-VCH CY - Weinheim ER - TY - JOUR A1 - Ayzel, Georgy T1 - Deep neural networks in hydrology BT - the new generation of universal and efficient models BT - новое поколение универсальных и эффективных моделей JF - Vestnik of Saint Petersburg University. Earth Sciences N2 - For around a decade, deep learning - the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers - modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources. identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of "Gartner Hype Curve", which in the general details describes a life cycle of modern technologies. N2 - В течение последнего десятилетия глубокое обучение - область машинного обучения, относящаяся к искусственным нейронным сетям, состоящим из множества вычислительных слоев, - изменяет ландшафт развития статистических моделей во многих областях исследований, таких как классификация изображений, машинный перевод, распознавание речи. Географические науки, а также входящая в их состав область исследования гидрологии суши, не стоят в стороне от этого движения. В последнее время применение современных технологий и методов глубокого обучения активно набирает популярность для решения широкого спектра гидрологических задач: моделирования и прогнозирования речного стока, районирования модельных параметров, оценки располагаемых водных ресурсов, идентификации факторов, влияющих на современные изменения водного режима. Такой рост популярности глубоких нейронных сетей продиктован прежде всего их высокой универсальностью и эффективностью. Представленные качества в совокупности с быстрорастущим количеством накопленной информации о состоянии окружающей среды, а также ростом доступности вычислительных средств и ресурсов, позволяют говорить о глубоких нейронных сетях как о новом поколении математических моделей, призванных если не заменить существующие решения, то значительно обогатить область моделирования геофизических процессов. В данной работе представлен краткий обзор текущего состояния области разработки и применения глубоких нейронных сетей в гидрологии. Также в работе предложен качественный долгосрочный прогноз развития технологии глубокого обучения для решения задач гидрологического моделирования на основе использования «кривой ажиотажа Гартнера», в общих чертах описывающей жизненный цикл современных технологий. T2 - Глубокие нейронные сети в гидрологии KW - deep neural networks KW - deep learning KW - machine learning KW - hydrology KW - modeling KW - глубокие нейронные сети KW - глубокое обучение KW - машинное обучение KW - гидрология KW - моделирование Y1 - 2021 U6 - https://doi.org/10.21638/spbu07.2021.101 SN - 2541-9668 SN - 2587-585X VL - 66 IS - 1 SP - 5 EP - 18 PB - Univ. Press CY - St. Petersburg ER - TY - JOUR A1 - Döllner, Jürgen Roland Friedrich T1 - Geospatial artificial intelligence BT - potentials of machine learning for 3D point clouds and geospatial digital twins JF - Journal of photogrammetry, remote sensing and geoinformation science : PFG : Photogrammetrie, Fernerkundung, Geoinformation N2 - Artificial intelligence (AI) is changing fundamentally the way how IT solutions are implemented and operated across all application domains, including the geospatial domain. This contribution outlines AI-based techniques for 3D point clouds and geospatial digital twins as generic components of geospatial AI. First, we briefly reflect on the term "AI" and outline technology developments needed to apply AI to IT solutions, seen from a software engineering perspective. Next, we characterize 3D point clouds as key category of geodata and their role for creating the basis for geospatial digital twins; we explain the feasibility of machine learning (ML) and deep learning (DL) approaches for 3D point clouds. In particular, we argue that 3D point clouds can be seen as a corpus with similar properties as natural language corpora and formulate a "Naturalness Hypothesis" for 3D point clouds. In the main part, we introduce a workflow for interpreting 3D point clouds based on ML/DL approaches that derive domain-specific and application-specific semantics for 3D point clouds without having to create explicit spatial 3D models or explicit rule sets. Finally, examples are shown how ML/DL enables us to efficiently build and maintain base data for geospatial digital twins such as virtual 3D city models, indoor models, or building information models. N2 - Georäumliche Künstliche Intelligenz: Potentiale des Maschinellen Lernens für 3D-Punktwolken und georäumliche digitale Zwillinge. Künstliche Intelligenz (KI) verändert grundlegend die Art und Weise, wie IT-Lösungen in allen Anwendungsbereichen, einschließlich dem Geoinformationsbereich, implementiert und betrieben werden. In diesem Beitrag stellen wir KI-basierte Techniken für 3D-Punktwolken als einen Baustein der georäumlichen KI vor. Zunächst werden kurz der Begriff "KI” und die technologischen Entwicklungen skizziert, die für die Anwendung von KI auf IT-Lösungen aus der Sicht der Softwaretechnik erforderlich sind. Als nächstes charakterisieren wir 3D-Punktwolken als Schlüsselkategorie von Geodaten und ihre Rolle für den Aufbau von räumlichen digitalen Zwillingen; wir erläutern die Machbarkeit der Ansätze für Maschinelles Lernen (ML) und Deep Learning (DL) in Bezug auf 3D-Punktwolken. Insbesondere argumentieren wir, dass 3D-Punktwolken als Korpus mit ähnlichen Eigenschaften wie natürlichsprachliche Korpusse gesehen werden können und formulieren eine "Natürlichkeitshypothese” für 3D-Punktwolken. Im Hauptteil stellen wir einen Workflow zur Interpretation von 3D-Punktwolken auf der Grundlage von ML/DL-Ansätzen vor, die eine domänenspezifische und anwendungsspezifische Semantik für 3D-Punktwolken ableiten, ohne explizite räumliche 3D-Modelle oder explizite Regelsätze erstellen zu müssen. Abschließend wird an Beispielen gezeigt, wie ML/DL es ermöglichen, Basisdaten für räumliche digitale Zwillinge, wie z.B. für virtuelle 3D-Stadtmodelle, Innenraummodelle oder Gebäudeinformationsmodelle, effizient aufzubauen und zu pflegen. KW - geospatial artificial intelligence KW - machine learning KW - deep learning KW - 3D KW - point clouds KW - geospatial digital twins KW - 3D city models Y1 - 2020 U6 - https://doi.org/10.1007/s41064-020-00102-3 SN - 2512-2789 SN - 2512-2819 VL - 88 IS - 1 SP - 15 EP - 24 PB - Springer International Publishing CY - Cham ER - TY - JOUR A1 - Risch, Julian A1 - Krestel, Ralf ED - Agarwal, Basant ED - Nayak, Richi ED - Mittal, Namita ED - Patnaik, Srikanta T1 - Toxic comment detection in online discussions JF - Deep learning-based approaches for sentiment analysis N2 - Comment sections of online news platforms are an essential space to express opinions and discuss political topics. In contrast to other online posts, news discussions are related to particular news articles, comments refer to each other, and individual conversations emerge. However, the misuse by spammers, haters, and trolls makes costly content moderation necessary. Sentiment analysis can not only support moderation but also help to understand the dynamics of online discussions. A subtask of content moderation is the identification of toxic comments. To this end, we describe the concept of toxicity and characterize its subclasses. Further, we present various deep learning approaches, including datasets and architectures, tailored to sentiment analysis in online discussions. One way to make these approaches more comprehensible and trustworthy is fine-grained instead of binary comment classification. On the downside, more classes require more training data. Therefore, we propose to augment training data by using transfer learning. We discuss real-world applications, such as semi-automated comment moderation and troll detection. Finally, we outline future challenges and current limitations in light of most recent research publications. KW - deep learning KW - natural language processing KW - user-generated content KW - toxic comment classification KW - hate speech detection Y1 - 2020 SN - 978-981-15-1216-2 SN - 978-981-15-1215-5 U6 - https://doi.org/10.1007/978-981-15-1216-2_4 SN - 2524-7565 SN - 2524-7573 SP - 85 EP - 109 PB - Springer CY - Singapore ER - TY - JOUR A1 - Stober, Sebastian T1 - Toward Studying Music Cognition with Information Retrieval Techniques: Lessons Learned from the OpenMIIR Initiative JF - Frontiers in psychology N2 - As an emerging sub-field of music information retrieval (MIR), music imagery information retrieval (MIIR) aims to retrieve information from brain activity recorded during music cognition-such as listening to or imagining music pieces. This is a highly interdisciplinary endeavor that requires expertise in MIR as well as cognitive neuroscience and psychology. The OpenMIIR initiative strives to foster collaborations between these fields to advance the state of the art in MIIR. As a first step, electroencephalography (EEG) recordings ofmusic perception and imagination have beenmade publicly available, enabling MIR researchers to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking or tempo estimation on this new kind of data. This paper reports on first results of MIIR experiments using these OpenMIIR datasets and points out how these findings could drive new research in cognitive neuroscience. KW - music cognition KW - music perception KW - music information retrieval KW - deep learning KW - representation learning Y1 - 2017 U6 - https://doi.org/10.3389/fpsyg.2017.01255 SN - 1664-1078 VL - 8 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Stober, Sebastian T1 - Toward Studying Music Cognition with Information Retrieval Techniques BT - Lessons Learned from the OpenMIIR Initiative JF - Frontiers in psychology N2 - As an emerging sub-field of music information retrieval (MIR), music imagery information retrieval (MIIR) aims to retrieve information from brain activity recorded during music cognition–such as listening to or imagining music pieces. This is a highly inter-disciplinary endeavor that requires expertise in MIR as well as cognitive neuroscience and psychology. The OpenMIIR initiative strives to foster collaborations between these fields to advance the state of the art in MIIR. As a first step, electroencephalography (EEG) recordings of music perception and imagination have been made publicly available, enabling MIR researchers to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking or tempo estimation on this new kind of data. This paper reports on first results of MIIR experiments using these OpenMIIR datasets and points out how these findings could drive new research in cognitive neuroscience. KW - music cognition KW - music perception KW - music information retrieval KW - deep learning KW - representation learning Y1 - 2017 U6 - https://doi.org/10.3389/fpsyg.2017.01255 SN - 1664-1078 VL - 8 PB - Frontiers Research Foundation CY - Lausanne ER -