TY - RPRT A1 - Rossi, Angelo Pio A1 - Maurelli, Francesco A1 - Unnithan, Vikram A1 - Dreger, Hendrik A1 - Mathewos, Kedus A1 - Pradhan, Nayan A1 - Corbeanu, Dan-Andrei A1 - Pozzobon, Riccardo A1 - Massironi, Matteo A1 - Ferrari, Sabrina A1 - Pernechele, Claudia A1 - Paoletti, Lorenzo A1 - Simioni, Emanuele A1 - Maurizio, Pajola A1 - Santagata, Tommaso A1 - Borrmann, Dorit A1 - Nüchter, Andreas A1 - Bredenbeck, Anton A1 - Zevering, Jasper A1 - Arzberger, Fabian A1 - Reyes Mantilla, Camilo Andrés T1 - DAEDALUS - Descent And Exploration in Deep Autonomy of Lava Underground Structures BT - Open Space Innovation Platform (OSIP) Lunar Caves-System Study N2 - The DAEDALUS mission concept aims at exploring and characterising the entrance and initial part of Lunar lava tubes within a compact, tightly integrated spherical robotic device, with a complementary payload set and autonomous capabilities. The mission concept addresses specifically the identification and characterisation of potential resources for future ESA exploration, the local environment of the subsurface and its geologic and compositional structure. A sphere is ideally suited to protect sensors and scientific equipment in rough, uneven environments. It will house laser scanners, cameras and ancillary payloads. The sphere will be lowered into the skylight and will explore the entrance shaft, associated caverns and conduits. Lidar (light detection and ranging) systems produce 3D models with high spatial accuracy independent of lighting conditions and visible features. Hence this will be the primary exploration toolset within the sphere. The additional payload that can be accommodated in the robotic sphere consists of camera systems with panoramic lenses and scanners such as multi-wavelength or single-photon scanners. A moving mass will trigger movements. The tether for lowering the sphere will be used for data communication and powering the equipment during the descending phase. Furthermore, the connector tether-sphere will host a WIFI access point, such that data of the conduit can be transferred to the surface relay station. During the exploration phase, the robot will be disconnected from the cable, and will use wireless communication. Emergency autonomy software will ensure that in case of loss of communication, the robot will continue the nominal mission. T3 - Forschungsberichte in der Robotik = Research Notes in Robotics - 21 KW - Lunar Caves KW - Spherical Robot KW - Lunar Exploration KW - Mapping KW - 3D Laser Scanning KW - Mond KW - Daedalus-Projekt KW - Lava Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-227911 SN - 978-3-945459-33-1 SN - 1868-7466 ER - TY - THES A1 - Niebler, Thomas T1 - Extracting and Learning Semantics from Social Web Data T1 - Extraktion und Lernen von Semantik aus Social Web-Daten N2 - Making machines understand natural language is a dream of mankind that existed since a very long time. Early attempts at programming machines to converse with humans in a supposedly intelligent way with humans relied on phrase lists and simple keyword matching. However, such approaches cannot provide semantically adequate answers, as they do not consider the specific meaning of the conversation. Thus, if we want to enable machines to actually understand language, we need to be able to access semantically relevant background knowledge. For this, it is possible to query so-called ontologies, which are large networks containing knowledge about real-world entities and their semantic relations. However, creating such ontologies is a tedious task, as often extensive expert knowledge is required. Thus, we need to find ways to automatically construct and update ontologies that fit human intuition of semantics and semantic relations. More specifically, we need to determine semantic entities and find relations between them. While this is usually done on large corpora of unstructured text, previous work has shown that we can at least facilitate the first issue of extracting entities by considering special data such as tagging data or human navigational paths. Here, we do not need to detect the actual semantic entities, as they are already provided because of the way those data are collected. Thus we can mainly focus on the problem of assessing the degree of semantic relatedness between tags or web pages. However, there exist several issues which need to be overcome, if we want to approximate human intuition of semantic relatedness. For this, it is necessary to represent words and concepts in a way that allows easy and highly precise semantic characterization. This also largely depends on the quality of data from which these representations are constructed. In this thesis, we extract semantic information from both tagging data created by users of social tagging systems and human navigation data in different semantic-driven social web systems. Our main goal is to construct high quality and robust vector representations of words which can the be used to measure the relatedness of semantic concepts. First, we show that navigation in the social media systems Wikipedia and BibSonomy is driven by a semantic component. After this, we discuss and extend methods to model the semantic information in tagging data as low-dimensional vectors. Furthermore, we show that tagging pragmatics influences different facets of tagging semantics. We then investigate the usefulness of human navigational paths in several different settings on Wikipedia and BibSonomy for measuring semantic relatedness. Finally, we propose a metric-learning based algorithm in adapt pre-trained word embeddings to datasets containing human judgment of semantic relatedness. This work contributes to the field of studying semantic relatedness between words by proposing methods to extract semantic relatedness from web navigation, learn highquality and low-dimensional word representations from tagging data, and to learn semantic relatedness from any kind of vector representation by exploiting human feedback. Applications first and foremest lie in ontology learning for the Semantic Web, but also semantic search or query expansion. N2 - Einer der großen Träume der Menschheit ist es, Maschinen dazu zu bringen, natürliche Sprache zu verstehen. Frühe Versuche, Computer dahingehend zu programmieren, dass sie mit Menschen vermeintlich intelligente Konversationen führen können, basierten hauptsächlich auf Phrasensammlungen und einfachen Stichwortabgleichen. Solche Ansätze sind allerdings nicht in der Lage, inhaltlich adäquate Antworten zu liefern, da der tatsächliche Inhalt der Konversation nicht erfasst werden kann. Folgerichtig ist es notwendig, dass Maschinen auf semantisch relevantes Hintergrundwissen zugreifen können, um diesen Inhalt zu verstehen. Solches Wissen ist beispielsweise in Ontologien vorhanden. Ontologien sind große Datenbanken von vernetztem Wissen über Objekte und Gegenstände der echten Welt sowie über deren semantische Beziehungen. Das Erstellen solcher Ontologien ist eine sehr kostspielige und aufwändige Aufgabe, da oft tiefgreifendes Expertenwissen benötigt wird. Wir müssen also Wege finden, um Ontologien automatisch zu erstellen und aktuell zu halten, und zwar in einer Art und Weise, dass dies auch menschlichem Empfinden von Semantik und semantischer Ähnlichkeit entspricht. Genauer gesagt ist es notwendig, semantische Entitäten und deren Beziehungen zu bestimmen. Während solches Wissen üblicherweise aus Textkorpora extrahiert wird, ist es möglich, zumindest das erste Problem - semantische Entitäten zu bestimmen - durch Benutzung spezieller Datensätze zu umgehen, wie zum Beispiel Tagging- oder Navigationsdaten. In diesen Arten von Datensätzen ist es nicht notwendig, Entitäten zu extrahieren, da sie bereits aufgrund inhärenter Eigenschaften bei der Datenakquise vorhanden sind. Wir können uns also hauptsächlich auf die Bestimmung von semantischen Relationen und deren Intensität fokussieren. Trotzdem müssen hier noch einige Hindernisse überwunden werden. Beispielsweise ist es notwendig, Repräsentationen für semantische Entitäten zu finden, so dass es möglich ist, sie einfach und semantisch hochpräzise zu charakterisieren. Dies hängt allerdings auch erheblich von der Qualität der Daten ab, aus denen diese Repräsentationen konstruiert werden. In der vorliegenden Arbeit extrahieren wir semantische Informationen sowohl aus Taggingdaten, von Benutzern sozialer Taggingsysteme erzeugt, als auch aus Navigationsdaten von Benutzern semantikgetriebener Social Media-Systeme. Das Hauptziel dieser Arbeit ist es, hochqualitative und robuste Vektordarstellungen von Worten zu konstruieren, die dann dazu benutzt werden können, die semantische Ähnlichkeit von Konzepten zu bestimmen. Als erstes zeigen wir, dass Navigation in Social Media Systemen unter anderem durch eine semantische Komponente getrieben wird. Danach diskutieren und erweitern wir Methoden, um die semantische Information in Taggingdaten als niedrigdimensionale sogenannte “Embeddings” darzustellen. Darüberhinaus demonstrieren wir, dass die Taggingpragmatik verschiedene Facetten der Taggingsemantik beeinflusst. Anschließend untersuchen wir, inwieweit wir menschliche Navigationspfade zur Bestimmung semantischer Ähnlichkeit benutzen können. Hierzu betrachten wir mehrere Datensätze, die Navigationsdaten in verschiedenen Rahmenbedingungen beinhalten. Als letztes stellen wir einen neuartigen Algorithmus vor, um bereits trainierte Word Embeddings im Nachhinein an menschliche Intuition von Semantik anzupassen. Diese Arbeit steuert wertvolle Beiträge zum Gebiet der Bestimmung von semantischer Ähnlichkeit bei: Es werden Methoden vorgestellt werden, um hochqualitative semantische Information aus Web-Navigation und Taggingdaten zu extrahieren, diese mittels niedrigdimensionaler Vektordarstellungen zu modellieren und selbige schließlich besser an menschliches Empfinden von semantischer Ähnlichkeit anzupassen, indem aus genau diesem Empfinden gelernt wird. Anwendungen liegen in erster Linie darin, Ontologien für das Semantic Web zu lernen, allerdings auch in allen Bereichen, die Vektordarstellungen von semantischen Entitäten benutzen. KW - Semantik KW - Maschinelles Lernen KW - Soziale Software KW - Semantics KW - User Behavior KW - Social Web KW - Machine Learning Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-178666 ER - TY - JOUR A1 - Hein, Rebecca M. A1 - Latoschik, Marc Erich A1 - Wienrich, Carolin T1 - Inter- and transcultural learning in cocial virtual reality: a proposal for an inter- and transcultural virtual object database to be used in the implementation, reflection, and evaluation of virtual encounters JF - Multimodal Technologies and Interaction N2 - Visual stimuli are frequently used to improve memory, language learning or perception, and understanding of metacognitive processes. However, in virtual reality (VR), there are few systematically and empirically derived databases. This paper proposes the first collection of virtual objects based on empirical evaluation for inter-and transcultural encounters between English- and German-speaking learners. We used explicit and implicit measurement methods to identify cultural associations and the degree of stereotypical perception for each virtual stimuli (n = 293) through two online studies, including native German and English-speaking participants. The analysis resulted in a final well-describable database of 128 objects (called InteractionSuitcase). In future applications, the objects can be used as a great interaction or conversation asset and behavioral measurement tool in social VR applications, especially in the field of foreign language education. For example, encounters can use the objects to describe their culture, or teachers can intuitively assess stereotyped attitudes of the encounters. KW - virtual stimuli KW - implicit association test KW - virtual reality KW - social VR KW - InteractionSuitcase Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-278974 SN - 2414-4088 VL - 6 IS - 7 ER - TY - JOUR A1 - Djebko, Kirill A1 - Puppe, Frank A1 - Kayal, Hakan T1 - Model-based fault detection and diagnosis for spacecraft with an application for the SONATE triple cube nano-satellite JF - Aerospace N2 - The correct behavior of spacecraft components is the foundation of unhindered mission operation. However, no technical system is free of wear and degradation. A malfunction of one single component might significantly alter the behavior of the whole spacecraft and may even lead to a complete mission failure. Therefore, abnormal component behavior must be detected early in order to be able to perform counter measures. A dedicated fault detection system can be employed, as opposed to classical health monitoring, performed by human operators, to decrease the response time to a malfunction. In this paper, we present a generic model-based diagnosis system, which detects faults by analyzing the spacecraft’s housekeeping data. The observed behavior of the spacecraft components, given by the housekeeping data is compared to their expected behavior, obtained through simulation. Each discrepancy between the observed and the expected behavior of a component generates a so-called symptom. Given the symptoms, the diagnoses are derived by computing sets of components whose malfunction might cause the observed discrepancies. We demonstrate the applicability of the diagnosis system by using modified housekeeping data of the qualification model of an actual spacecraft and outline the advantages and drawbacks of our approach. KW - fault detection KW - model-based diagnosis KW - nano-satellite Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-198836 SN - 2226-4310 VL - 6 IS - 10 ER - TY - JOUR A1 - Zimmerer, Chris A1 - Fischbach, Martin A1 - Latoschik, Marc Erich T1 - Semantic Fusion for Natural Multimodal Interfaces using Concurrent Augmented Transition Networks JF - Multimodal Technologies and Interaction N2 - Semantic fusion is a central requirement of many multimodal interfaces. Procedural methods like finite-state transducers and augmented transition networks have proven to be beneficial to implement semantic fusion. They are compliant with rapid development cycles that are common for the development of user interfaces, in contrast to machine-learning approaches that require time-costly training and optimization. We identify seven fundamental requirements for the implementation of semantic fusion: Action derivation, continuous feedback, context-sensitivity, temporal relation support, access to the interaction context, as well as the support of chronologically unsorted and probabilistic input. A subsequent analysis reveals, however, that there is currently no solution for fulfilling the latter two requirements. As the main contribution of this article, we thus present the Concurrent Cursor concept to compensate these shortcomings. In addition, we showcase a reference implementation, the Concurrent Augmented Transition Network (cATN), that validates the concept’s feasibility in a series of proof of concept demonstrations as well as through a comparative benchmark. The cATN fulfills all identified requirements and fills the lack amongst previous solutions. It supports the rapid prototyping of multimodal interfaces by means of five concrete traits: Its declarative nature, the recursiveness of the underlying transition network, the network abstraction constructs of its description language, the utilized semantic queries, and an abstraction layer for lexical information. Our reference implementation was and is used in various student projects, theses, as well as master-level courses. It is openly available and showcases that non-experts can effectively implement multimodal interfaces, even for non-trivial applications in mixed and virtual reality. KW - multimodal fusion KW - multimodal interface KW - semantic fusion KW - procedural fusion methods KW - natural interfaces KW - human-computer interaction Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197573 SN - 2414-4088 VL - 2 IS - 4 ER - TY - JOUR A1 - Loda, Sophia A1 - Krebs, Jonathan A1 - Danhof, Sophia A1 - Schreder, Martin A1 - Solimando, Antonio G. A1 - Strifler, Susanne A1 - Rasche, Leo A1 - Kortüm, Martin A1 - Kerscher, Alexander A1 - Knop, Stefan A1 - Puppe, Frank A1 - Einsele, Hermann A1 - Bittrich, Max T1 - Exploration of artificial intelligence use with ARIES in multiple myeloma research JF - Journal of Clinical Medicine N2 - Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice. KW - natural language processing KW - ontology KW - artificial intelligence KW - multiple myeloma KW - real world evidence Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197231 SN - 2077-0383 VL - 8 IS - 7 ER - TY - JOUR A1 - Lopez-Arreguin, A. J. R. A1 - Montenegro, S. T1 - Improving engineering models of terramechanics for planetary exploration JF - Results in Engineering N2 - This short letter proposes more consolidated explicit solutions for the forces and torques acting on typical rover wheels, that can be used as a method to determine their average mobility characteristics in planetary soils. The closed loop solutions stand in one of the verified methods, but at difference of the previous, observables are decoupled requiring a less amount of physical parameters to measure. As a result, we show that with knowledge of terrain properties, wheel driving performance rely in a single observable only. Because of their generality, the formulated equations established here can have further implications in autonomy and control of rovers or planetary soil characterization. KW - Wheel KW - Terramechanics KW - Forces KW - Torque KW - Robotics Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-202490 VL - 3 ER - TY - THES A1 - Budig, Benedikt T1 - Extracting Spatial Information from Historical Maps: Algorithms and Interaction T1 - Extraktion räumlicher Informationen aus historischen Landkarten: Algorithmen und Interaktion N2 - Historical maps are fascinating documents and a valuable source of information for scientists of various disciplines. Many of these maps are available as scanned bitmap images, but in order to make them searchable in useful ways, a structured representation of the contained information is desirable. This book deals with the extraction of spatial information from historical maps. This cannot be expected to be solved fully automatically (since it involves difficult semantics), but is also too tedious to be done manually at scale. The methodology used in this book combines the strengths of both computers and humans: it describes efficient algorithms to largely automate information extraction tasks and pairs these algorithms with smart user interactions to handle what is not understood by the algorithm. The effectiveness of this approach is shown for various kinds of spatial documents from the 16th to the early 20th century. N2 - Historische Landkarten sind faszinierende Dokumente und eine wertvolle Informationsquelle für Wissenschaftler verschiedener Fächer. Viele dieser Karten liegen als gescannte Bitmap-Bilder vor, aber um sie auf nützliche Art durchsuchbar zu machen ist eine strukturierte Repräsentation der enthaltenen Informationen wünschenswert. Dieses Buch beschäftigt sich mit der Extraktion räumlicher Informationen aus historischen Landkarten. Man kann nicht erwarten, dass dies vollautomatisch geschieht (da komplizierte Semantik involviert ist), aber es ist auch zu aufwändig, um im großen Stil manuell durchgeführt zu werden. Die Methodik, die in diesem Buch verwendet wird, kombiniert die Stärken von Computern und Menschen: Es werden effiziente Algorithmen beschrieben, die Extraktionsaufgaben weitgehend automatisieren, und dazu passende Nutzerinteraktionen entworfen, mit denen Fälle gelöst werden, die die Algorithmen nicht vestehen. Die Effekitivität dieses Ansatzes wird anhand verschiedener räumlicher Dokumente aus dem 16. bis frühen 20. Jahrhundert gezeigt. KW - Karte KW - Effizienter Algorithmus KW - Interaktion KW - Information Extraction KW - Smart User Interaction KW - Historical Maps KW - Itineraries KW - Deep Georeferencing KW - Benutzerinteraktion KW - Historische Landkarten KW - Itinerare KW - Georeferenzierung KW - Historische Karte KW - Raumdaten Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-160955 SN - 978-3-95826-092-4 SN - 978-3-95826-093-1 N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, ISBN 978-3-95826-092-4, 32,90 Euro. PB - Würzburg University Press CY - Würzburg ET - 1. Auflage ER - TY - JOUR A1 - Rodrigues, Johannes A1 - Weiß, Martin A1 - Hewig, Johannes A1 - Allen, John J. B. T1 - EPOS: EEG Processing Open-Source Scripts JF - Frontiers in Neuroscience N2 - Background: Since the replication crisis, standardization has become even more important in psychological science and neuroscience. As a result, many methods are being reconsidered, and researchers’ degrees of freedom in these methods are being discussed as a potential source of inconsistencies across studies. New Method: With the aim of addressing these subjectivity issues, we have been working on a tutorial-like EEG (pre-)processing pipeline to achieve an automated method based on the semi-automated analysis proposed by Delorme and Makeig. Results: Two scripts are presented and explained step-by-step to perform basic, informed ERP and frequency-domain analyses, including data export to statistical programs and visual representations of the data. The open-source software EEGlab in MATLAB is used as the data handling platform, but scripts based on code provided by Mike Cohen (2014) are also included. Comparison with existing methods: This accompanying tutorial-like article explains and shows how the processing of our automated pipeline affects the data and addresses, especially beginners in EEG-analysis, as other (pre)-processing chains are mostly targeting rather informed users in specialized areas or only parts of a complete procedure. In this context, we compared our pipeline with a selection of existing approaches. Conclusion: The need for standardization and replication is evident, yet it is equally important to control the plausibility of the suggested solution by data exploration. Here, we provide the community with a tool to enhance the understanding and capability of EEG-analysis. We aim to contribute to comprehensive and reliable analyses for neuro-scientific research. KW - EEG KW - electroencephalography KW - event-related potentials-ERP KW - EEG processing KW - EEG preprocessing KW - EEG frequency band analysis Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-240221 SN - 1662-453X VL - 15 ER - TY - JOUR A1 - Hirth, Matthias A1 - Seufert, Michael A1 - Lange, Stanislav A1 - Meixner, Markus A1 - Tran-Gia, Phuoc T1 - Performance evaluation of hybrid crowdsensing and fixed sensor systems for event detection in urban environments JF - Sensors N2 - Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario. KW - crowdsensing KW - event detection KW - detection time simulation KW - performance analysis Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-245245 SN - 1424-8220 VL - 21 IS - 17 ER -