TY - JOUR A1 - Schlör, Daniel A1 - Ring, Markus A1 - Hotho, Andreas T1 - iNALU: Improved Neural Arithmetic Logic Unit JF - Frontiers in Artificial Intelligence N2 - Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently proposed Neural Arithmetic Logic Unit (NALU) is a novel neural architecture which is able to explicitly represent the mathematical relationships by the units of the network to learn operations such as summation, subtraction or multiplication. Although NALUs have been shown to perform well on various downstream tasks, an in-depth analysis reveals practical shortcomings by design, such as the inability to multiply or divide negative input values or training stability issues for deeper networks. We address these issues and propose an improved model architecture. We evaluate our model empirically in various settings from learning basic arithmetic operations to more complex functions. Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence. KW - neural networks KW - machine learning KW - arithmetic calculations KW - neural architecture KW - experimental evaluation Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-212301 SN - 2624-8212 VL - 3 ER - TY - JOUR A1 - Li, Ningbo A1 - Guan, Lianwu A1 - Gao, Yanbin A1 - Du, Shitong A1 - Wu, Menghao A1 - Guang, Xingxing A1 - Cong, Xiaodan T1 - Indoor and outdoor low-cost seamless integrated navigation system based on the integration of INS/GNSS/LIDAR system JF - Remote Sensing N2 - Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching. KW - vehicular navigation KW - GNSS/INS integrated navigation KW - INS/LIDAR integrated navigation KW - switching navigation Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-216229 SN - 2072-4292 VL - 12 IS - 19 ER - TY - JOUR A1 - Kraft, Robin A1 - Birk, Ferdinand A1 - Reichert, Manfred A1 - Deshpande, Aniruddha A1 - Schlee, Winfried A1 - Langguth, Berthold A1 - Baumeister, Harald A1 - Probst, Thomas A1 - Spiliopoulou, Myra A1 - Pryss, Rüdiger T1 - Efficient processing of geospatial mHealth data using a scalable crowdsensing platform JF - Sensors N2 - Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case. KW - mHealth KW - crowdsensing KW - tinnitus KW - geospatial data KW - cloud-native KW - stream processing KW - scalability KW - architectural design Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207826 SN - 1424-8220 VL - 20 IS - 12 ER - TY - JOUR A1 - Davidson, Padraig A1 - Düking, Peter A1 - Zinner, Christoph A1 - Sperlich, Billy A1 - Hotho, Andreas T1 - Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study JF - Sensors N2 - The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions. KW - artificial intelligence KW - endurance KW - exercise intensity KW - precision training KW - prediction KW - wearable Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205686 SN - 1424-8220 VL - 20 IS - 9 ER - TY - RPRT ED - Hoßfeld, Tobias ED - Wunderer, Stefan T1 - White Paper on Crowdsourced Network and QoE Measurements – Definitions, Use Cases and Challenges N2 - The goal of the white paper at hand is as follows. The definitions of the terms build a framework for discussions around the hype topic ‘crowdsourcing’. This serves as a basis for differentiation and a consistent view from different perspectives on crowdsourced network measurements, with the goal to provide a commonly accepted definition in the community. The focus is on the context of mobile and fixed network operators, but also on measurements of different layers (network, application, user layer). In addition, the white paper shows the value of crowdsourcing for selected use cases, e.g., to improve QoE or regulatory issues. Finally, the major challenges and issues for researchers and practitioners are highlighted. This white paper is the outcome of the Würzburg seminar on “Crowdsourced Network and QoE Measurements” which took place from 25-26 September 2019 in Würzburg, Germany. International experts were invited from industry and academia. They are well known in their communities, having different backgrounds in crowdsourcing, mobile networks, network measurements, network performance, Quality of Service (QoS), and Quality of Experience (QoE). The discussions in the seminar focused on how crowdsourcing will support vendors, operators, and regulators to determine the Quality of Experience in new 5G networks that enable various new applications and network architectures. As a result of the discussions, the need for a white paper manifested, with the goal of providing a scientific discussion of the terms “crowdsourced network measurements” and “crowdsourced QoE measurements”, describing relevant use cases for such crowdsourced data, and its underlying challenges. During the seminar, those main topics were identified, intensively discussed in break-out groups, and brought back into the plenum several times. The outcome of the seminar is this white paper at hand which is – to our knowledge – the first one covering the topic of crowdsourced network and QoE measurements. KW - Crowdsourcing KW - Network Measurements KW - Quality of Service (QoS) KW - Quality of Experience (QoE) KW - crowdsourced network measurements KW - crowdsourced QoE measurements Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-202327 ER - TY - JOUR A1 - Kammerer, Klaus A1 - Pryss, Rüdiger A1 - Hoppenstedt, Burkhard A1 - Sommer, Kevin A1 - Reichert, Manfred T1 - Process-driven and flow-based processing of industrial sensor data JF - Sensors N2 - For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results. KW - data stream processing KW - cyber-physical systems KW - processing pipeline KW - sensor networks Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-213089 SN - 1424-8220 VL - 20 IS - 18 ER - TY - RPRT A1 - Metzger, Florian T1 - Crowdsensed QoE for the community - a concept to make QoE assessment accessible N2 - In recent years several community testbeds as well as participatory sensing platforms have successfully established themselves to provide open data to everyone interested. Each of them with a specific goal in mind, ranging from collecting radio coverage data up to environmental and radiation data. Such data can be used by the community in their decision making, whether to subscribe to a specific mobile phone service that provides good coverage in an area or in finding a sunny and warm region for the summer holidays. However, the existing platforms are usually limiting themselves to directly measurable network QoS. If such a crowdsourced data set provides more in-depth derived measures, this would enable an even better decision making. A community-driven crowdsensing platform that derives spatial application-layer user experience from resource-friendly bandwidth estimates would be such a case, video streaming services come to mind as a prime example. In this paper we present a concept for such a system based on an initial prototype that eases the collection of data necessary to determine mobile-specific QoE at large scale. In addition we reason why the simple quality metric proposed here can hold its own. KW - Quality of Experience KW - Crowdsourcing KW - Crowdsensing KW - QoE Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203748 N1 - Originally written in 2017, but never published. ER - TY - JOUR A1 - Krupitzer, Christian A1 - Eberhardinger, Benedikt A1 - Gerostathopoulos, Ilias A1 - Raibulet, Claudia T1 - Introduction to the special issue “Applications in Self-Aware Computing Systems and their Evaluation” JF - Computers N2 - The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Umeå, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions. KW - self-aware computing systems KW - quality evaluation KW - measurements KW - quality assurance KW - autonomous KW - self-adaptive KW - self-managing systems Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203439 SN - 2073-431X VL - 9 IS - 1 ER - TY - JOUR A1 - Kaiser, Dennis A1 - Lesch, Veronika A1 - Rothe, Julian A1 - Strohmeier, Michael A1 - Spieß, Florian A1 - Krupitzer, Christian A1 - Montenegro, Sergio A1 - Kounev, Samuel T1 - Towards Self-Aware Multirotor Formations JF - Computers N2 - In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with multirotor formations to address this problem. The self-awareness is envisioned to improve the dynamic behavior of multirotors. The formation scheme that is implemented is called platooning, which arranges vehicles in a string behind the lead vehicle and is proposed to bring order into chaotic air space. Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. A modification for the LRA-M self-awareness loop is proposed and named Platooning Awareness. The implemented framework is able to offer two flight modes that enable waypoint following and the self-awareness module to find a path through scenarios, where obstacles are present on the way, onto a goal position. The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple scenarios. KW - self-aware computing KW - unmanned aerial vehicles KW - multirotors KW - quadcopters KW - intelligent transportation systems Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200572 SN - 2073-431X VL - 9 IS - 1 ER - TY - JOUR A1 - Grohmann, Johannes A1 - Herbst, Nikolas A1 - Chalbani, Avi A1 - Arian, Yair A1 - Peretz, Noam A1 - Kounev, Samuel T1 - A Taxonomy of Techniques for SLO Failure Prediction in Software Systems JF - Computers N2 - Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research. KW - taxonomy KW - survey KW - failure prediction KW - anomaly prediction KW - anomaly detection KW - self-aware computing KW - self-adaptive systems KW - performance prediction Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200594 SN - 2073-431X VL - 9 IS - 1 ER - TY - JOUR A1 - Du, Shitong A1 - Lauterbach, Helge A. A1 - Li, Xuyou A1 - Demisse, Girum G. A1 - Borrmann, Dorit A1 - Nüchter, Andreas T1 - Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration JF - Sensors N2 - Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences. KW - mapping KW - continuous-time SLAM KW - deformation-based method KW - time calibration Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-219988 SN - 1424-8220 VL - 20 IS - 23 ER - TY - INPR A1 - Dandekar, Thomas T1 - Biological heuristics applied to cosmology suggests a condensation nucleus as start of our universe and inflation cosmology replaced by a period of rapid Weiss domain-like crystal growth N2 - Cosmology often uses intricate formulas and mathematics to derive new theories and concepts. We do something different in this paper: We look at biological processes and derive from these heuristics so that the revised cosmology agrees with astronomical observations but does also agree with standard biological observations. We show that we then have to replace any type of singularity at the start of the universe by a condensation nucleus and that the very early period of the universe usually assumed to be inflation has to be replaced by a period of rapid crystal growth as in Weiss magnetization domains. Impressively, these minor modifications agree well with astronomical observations including removing the strong inflation perturbations which were never observed in the recent BICEP2 experiments. Furthermore, looking at biological principles suggests that such a new theory with a condensation nucleus at start and a first rapid phase of magnetization-like growth of the ordered, physical laws obeying lattice we live in is in fact the only convincing theory of the early phases of our universe that also is compatible with current observations. We show in detail in the following that such a process of crystal creation, breaking of new crystal seeds and ultimate evaporation of the present crystal readily leads over several generations to an evolution and selection of better, more stable and more self-organizing crystals. Moreover, this explains the “fine-tuning” question why our universe is fine-tuned to favor life: Our Universe is so self-organizing to have enough offspring and the detailed physics involved is at the same time highly favorable for all self-organizing processes including life. This biological theory contrasts with current standard inflation cosmologies. The latter do not perform well in explaining any phenomena of sophisticated structure creation or self-organization. As proteins can only thermodynamically fold by increasing the entropy in the solution around them we suggest for cosmology a condensation nucleus for a universe can form only in a “chaotic ocean” of string-soup or quantum foam if the entropy outside of the nucleus rapidly increases. We derive an interaction potential for 1 to n-dimensional strings or quantum-foams and show that they allow only 1D, 2D, 4D or octonion interactions. The latter is the richest structure and agrees to the E8 symmetry fundamental to particle physics and also compatible with the ten dimensional string theory E8 which is part of the M-theory. Interestingly, any other interactions of other dimensionality can be ruled out using Hurwitz compositional theorem. Crystallization explains also extremely well why we have only one macroscopic reality and where the worldlines of alternative trajectories exist: They are in other planes of the crystal and for energy reasons they crystallize mostly at the same time, yielding a beautiful and stable crystal. This explains decoherence and allows to determine the size of Planck´s quantum h (very small as separation of crystal layers by energy is extremely strong). Ultimate dissolution of real crystals suggests an explanation for dark energy agreeing with estimates for the “big rip”. The halo distribution of dark matter favoring galaxy formation is readily explained by a crystal seed starting with unit cells made of normal and dark matter. That we have only matter and not antimatter can be explained as there may be right handed mattercrystals and left-handed antimatter crystals. Similarly, real crystals are never perfect and we argue that exactly such irregularities allow formation of galaxies, clusters and superclusters. Finally, heuristics from genetics suggest to look for a systems perspective to derive correct vacuum and Higgs Boson energies. KW - heuristics KW - inflation KW - cosmology KW - crystallization KW - crystal growth KW - E8 symmetry KW - Hurwitz theorem KW - evolution KW - Lee Smolin Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-183945 ER - TY - JOUR A1 - Gehrke, Alexander A1 - Balbach, Nico A1 - Rauch, Yong-Mi A1 - Degkwitz, Andreas A1 - Puppe, Frank T1 - Erkennung von handschriftlichen Unterstreichungen in Alten Drucken JF - Bibliothek Forschung und Praxis N2 - Die Erkennung handschriftlicher Artefakte wie Unterstreichungen in Buchdrucken ermöglicht Rückschlüsse auf das Rezeptionsverhalten und die Provenienzgeschichte und wird auch für eine OCR benötigt. Dabei soll zwischen handschriftlichen Unterstreichungen und waagerechten Linien im Druck (z. B. Trennlinien usw.) unterschieden werden, da letztere nicht ausgezeichnet werden sollen. Im Beitrag wird ein Ansatz basierend auf einem auf Unterstreichungen trainierten Neuronalen Netz gemäß der U-Net Architektur vorgestellt, dessen Ergebnisse in einem zweiten Schritt mit heuristischen Regeln nachbearbeitet werden. Die Evaluationen zeigen, dass Unterstreichungen sehr gut erkannt werden, wenn bei der Binarisierung der Scans nicht zu viele Pixel der Unterstreichung wegen geringem Kontrast verloren gehen. Zukünftig sollen die Worte oberhalb der Unterstreichung mit OCR transkribiert werden und auch andere Artefakte wie handschriftliche Notizen in alten Drucken erkannt werden. N2 - The recognition of handwritten artefacts like underlines in historical printings allows inference on the reception and provenance history and is necessary for OCR (optical character recognition). In this context it is important to differentiate between handwritten and printed lines, since the latter are common in printings, but should be ignored. We present an approach based on neural nets with the U-Net architecture, whose segmentation results are post processed with heuristic rules. The evaluations show that handwritten underlines are very well recognized if the binarisation of the scans is adequate. Future work includes transcription of the underlined words with OCR and recognition of other artefacts like handwritten notes in historical printings. T2 - Recognition of handwritten underlines in historical printings KW - Brüder Grimm Privatbibliothek KW - Erkennung handschriftlicher Artefakte KW - Convolutional Neural Network KW - regelbasierte Nachbearbeitung KW - Grimm brothers personal library KW - handwritten artefact recognition KW - convolutional neural network KW - rule based post processing Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193377 SN - 1865-7648 SN - 0341-4183 N1 - Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich. VL - 43 IS - 3 SP - 447 EP - 452 ER - TY - JOUR A1 - Wick, Christoph A1 - Hartelt, Alexander A1 - Puppe, Frank T1 - Staff, symbol and melody detection of Medieval manuscripts written in square notation using deep Fully Convolutional Networks JF - Applied Sciences N2 - Even today, the automatic digitisation of scanned documents in general, but especially the automatic optical music recognition (OMR) of historical manuscripts, still remains an enormous challenge, since both handwritten musical symbols and text have to be identified. This paper focuses on the Medieval so-called square notation developed in the 11th–12th century, which is already composed of staff lines, staves, clefs, accidentals, and neumes that are roughly spoken connected single notes. The aim is to develop an algorithm that captures both the neumes, and in particular its melody, which can be used to reconstruct the original writing. Our pipeline is similar to the standard OMR approach and comprises a novel staff line and symbol detection algorithm based on deep Fully Convolutional Networks (FCN), which perform pixel-based predictions for either staff lines or symbols and their respective types. Then, the staff line detection combines the extracted lines to staves and yields an F\(_1\) -score of over 99% for both detecting lines and complete staves. For the music symbol detection, we choose a novel approach that skips the step to identify neumes and instead directly predicts note components (NCs) and their respective affiliation to a neume. Furthermore, the algorithm detects clefs and accidentals. Our algorithm predicts the symbol sequence of a staff with a diplomatic symbol accuracy rate (dSAR) of about 87%, which includes symbol type and location. If only the NCs without their respective connection to a neume, all clefs and accidentals are of interest, the algorithm reaches an harmonic symbol accuracy rate (hSAR) of approximately 90%. In general, the algorithm recognises a symbol in the manuscript with an F\(_1\) -score of over 96%. KW - optical music recognition KW - historical document analysis KW - medieval manuscripts KW - neume notation KW - fully convolutional neural networks Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197248 SN - 2076-3417 VL - 9 IS - 13 ER - TY - THES A1 - Peng, Dongliang T1 - An Optimization-Based Approach for Continuous Map Generalization T1 - Optimierung für die kontinuierliche Generalisierung von Landkarten N2 - Maps are the main tool to represent geographical information. Geographical information is usually scale-dependent, so users need to have access to maps at different scales. In our digital age, the access is realized by zooming. As discrete changes during the zooming tend to distract users, smooth changes are preferred. This is why some digital maps are trying to make the zooming as continuous as they can. The process of producing maps at different scales with smooth changes is called continuous map generalization. In order to produce maps of high quality, cartographers often take into account additional requirements. These requirements are transferred to models in map generalization. Optimization for map generalization is important not only because it finds optimal solutions in the sense of the models, but also because it helps us to evaluate the quality of the models. Optimization, however, becomes more delicate when we deal with continuous map generalization. In this area, there are requirements not only for a specific map but also for relations between maps at difference scales. This thesis is about continuous map generalization based on optimization. First, we show the background of our research topics. Second, we find optimal sequences for aggregating land-cover areas. We compare the A$^{\!\star}$\xspace algorithm and integer linear programming in completing this task. Third, we continuously generalize county boundaries to provincial boundaries based on compatible triangulations. We morph between the two sets of boundaries, using dynamic programming to compute the correspondence. Fourth, we continuously generalize buildings to built-up areas by aggregating and growing. In this work, we group buildings with the help of a minimum spanning tree. Fifth, we define vertex trajectories that allow us to morph between polylines. We require that both the angles and the edge lengths change linearly over time. As it is impossible to fulfill all of these requirements simultaneously, we mediate between them using least-squares adjustment. Sixth, we discuss the performance of some commonly used data structures for a specific spatial problem. Seventh, we conclude this thesis and present open problems. N2 - Maps are the main tool to represent geographical information. Users often zoom in and out to access maps at different scales. Continuous map generalization tries to make the changes between different scales smooth, which is essential to provide users with comfortable zooming experience. In order to achieve continuous map generalization with high quality, we optimize some important aspects of maps. In this book, we have used optimization in the generalization of land-cover areas, administrative boundaries, buildings, and coastlines. According to our experiments, continuous map generalization indeed benefits from optimization. N2 - Landkarten sind das wichtigste Werkzeug zur Repräsentation geografischer Information. Unter der Generalisierung von Landkarten versteht man die Aufbereitung von geografischen Informationen aus detaillierten Daten zur Generierung von kleinmaßstäbigen Karten. Nutzer von Online-Karten zoomen oft in eine Karte hinein oder aus einer Karte heraus, um mehr Details bzw. mehr Überblick zu bekommen. Die kontinuierliche Generalisierung von Landkarten versucht die Änderungen zwischen verschiedenen Maßstäben stetig zu machen. Dies ist wichtig, um Nutzern eine angenehme Zoom-Erfahrung zu bieten. Um eine qualitativ hochwertige kontinuierliche Generalisierung zu erreichen, kann man wichtige Aspekte bei der Generierung von Online-Karten optimieren. In diesem Buch haben wir Optimierung bei der Generalisierung von Landnutzungskarten, von administrativen Grenzen, Gebäuden und Küstenlinien eingesetzt. Unsere Experimente zeigen, dass die kontinuierliche Generalisierung von Landkarten in der Tat von Optimierung profitiert. KW - land-cover area KW - administrative boundary KW - building KW - morphing KW - data structure KW - zooming KW - Generalisierung KW - Landnutzungskartierung KW - Optimierung Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-174427 SN - 978-3-95826-104-4 SN - 978-3-95826-105-1 N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, 978-3-95826-104-4, 24,90 EUR. PB - Würzburg University Press CY - Würzburg ET - 1. Auflage ER - TY - JOUR A1 - Oberdörfer, Sebastian A1 - Latoschik, Marc Erich T1 - Knowledge encoding in game mechanics: transfer-oriented knowledge learning in desktop-3D and VR JF - International Journal of Computer Games Technology N2 - Affine Transformations (ATs) are a complex and abstract learning content. Encoding the AT knowledge in Game Mechanics (GMs) achieves a repetitive knowledge application and audiovisual demonstration. Playing a serious game providing these GMs leads to motivating and effective knowledge learning. Using immersive Virtual Reality (VR) has the potential to even further increase the serious game’s learning outcome and learning quality. This paper compares the effectiveness and efficiency of desktop-3D and VR in respect to the achieved learning outcome. Also, the present study analyzes the effectiveness of an enhanced audiovisual knowledge encoding and the provision of a debriefing system. The results validate the effectiveness of the knowledge encoding in GMs to achieve knowledge learning. The study also indicates that VR is beneficial for the overall learning quality and that an enhanced audiovisual encoding has only a limited effect on the learning outcome. KW - games Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-201159 VL - 2019 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 - 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 - 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 -