TY - JOUR AB - ZusammenfassungLehrkräftekooperation wird generell eine positive Bedeutung in Bezug auf Schul- und Unterrichtsentwicklung zugeschrieben. Dabei sind empirische Belege für eine positive Wirksamkeit nach wie vor kaum vorhanden, es gibt sogar Befunde zu ‚negativen‘ Konsequenzen von Lehrkräftekooperation. Um diese Widersprüchlichkeit zu klären, wurde in der vorliegenden Arbeit Kooperation nicht als Instrument bzw. als Technik betrachtet, sondern als soziale Praxis verstanden, in der eigenlogisches, kollektiv-implizites Wissen (re)produziert wird (Community of Practice). Parallel dazu wurde ein praxeologisches Kompetenzverständnis (Praxiskompetenz) eingeführt, das wesentlich auf die Praxeologie Pierre Bourdieus zurückgeht und den Zusammenhang zwischen Lehrkräftekooperation als Community of Practice und kollektiv strukturierter, individueller Kompetenz theoretisch verdeutlicht. Die empirischen Befunde, welche mittels der Dokumentarischen Methode generiert wurden, verweisen auf die Bedeutung unterschiedlicher Relationslogiken (Nicht-Passung, Entfaltung, Herausforderung) für das ‚Lernen‘ von oder innerhalb von Praxiskompetenz(en) und damit auch auf die Wichtigkeit einer grundlegend kollektiv gerahmten Perspektive auf Lehrkräftekooperation. Vor diesem Hintergrund ist ein allzu positiver Blick auf Lehrkräftekooperationsprozesse kritisch zu betrachten. AU - Bloh, Thiemo ID - 27881 JF - Zeitschrift für Bildungsforschung SN - 2190-6890 TI - Entwicklung von Praxiskompetenz durch Kooperationsprozesse von Lehrkräften ER - TY - JOUR AB - Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers. AU - Wever, Marcel Dominik AU - Tornede, Alexander AU - Mohr, Felix AU - Hüllermeier, Eyke ID - 21004 JF - IEEE Transactions on Pattern Analysis and Machine Intelligence KW - Automated Machine Learning KW - Multi Label Classification KW - Hierarchical Planning KW - Bayesian Optimization SN - 0162-8828 TI - AutoML for Multi-Label Classification: Overview and Empirical Evaluation ER - TY - CONF AB - Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering. LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines. AU - Hasnain, Asif AU - Karl, Holger ID - 21005 KW - Coflow scheduling KW - Reinforcement learning KW - Deadlines T2 - IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) TI - Learning Coflow Admissions ER - TY - JOUR AB - The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase of attention in science and industry, which caused or was caused by an equally significant improvement in recognition accuracy. Meanwhile it has entered the consumer market with digital home assistants with a spoken language interface being its most prominent application. Speech recorded at a distance is affected by various acoustic distortions and, consequently, quite different processing pipelines have emerged compared to ASR for close-talk speech. A signal enhancement front-end for dereverberation, source separation and acoustic beamforming is employed to clean up the speech, and the back-end ASR engine is robustified by multi-condition training and adaptation. We will also describe the so-called end-to-end approach to ASR, which is a new promising architecture that has recently been extended to the far-field scenario. This tutorial article gives an account of the algorithms used to enable accurate speech recognition from a distance, and it will be seen that, although deep learning has a significant share in the technological breakthroughs, a clever combination with traditional signal processing can lead to surprisingly effective solutions. AU - Haeb-Umbach, Reinhold AU - Heymann, Jahn AU - Drude, Lukas AU - Watanabe, Shinji AU - Delcroix, Marc AU - Nakatani, Tomohiro ID - 21065 IS - 2 JF - Proceedings of the IEEE TI - Far-Field Automatic Speech Recognition VL - 109 ER - TY - JOUR AB - Acoustic waves in plates have proven a viable tool for testing and material characterisation purposes. There are a multitude of options for excitation and detection of theses waves, such as optical and piezoelectric systems. While optical systems, with thermoelastic excitation and interferometric detection, have the benefit of being contactless, they usually require rather complex and expensive experimental setups. Piezoelectric systems are more easily realised but require direct contact with the specimen and usually have a limited bandwidth, especially in case of piezoelectric excitation. In this work, the authors compare the properties of piezoelectric and optical detection methods for broad-band acoustic signals. The shape (e. g. the displacement) of a propagating plate wave is given by its frequency and wave number, allowing to investigate correlations between mode shapes and received signal strengths. This is aided by evaluations in normalised frequency and wavenumber space, facilitating comparisons of different specimens. Further, the authors explore possibilities to utilise the specific properties of the detection methods to determine acoustic material parameters. AU - Claes, Leander AU - Schmiegel, Hanna AU - Grünsteidl, Clemens AU - Johannesmann, Sarah AU - Webersen, Manuel AU - Henning, Bernd ID - 21067 IS - 3 JF - tm - Technisches Messen SN - 2196-7113 TI - Investigating peculiarities of piezoelectric detection methods for acoustic plate waves in material characterisation applications VL - 88 ER - TY - JOUR AU - Itner, Dominik AU - Gravenkamp, Hauke AU - Dreiling, Dmitrij AU - Feldmann, Nadine AU - Henning, Bernd ID - 21082 JF - PAMM SN - 1617-7061 TI - Simulation of guided waves in cylinders subject to arbitrary boundary conditions for applications in material characterization ER - TY - GEN AU - Werthmann, Julian ID - 21084 TI - Derandomization and Local Graph Problems in the Node-Capacitated Clique ER - TY - JOUR AB - Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which are costly but often ineffective because they are canceled due to a timeout. In this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions. AU - Mohr, Felix AU - Wever, Marcel Dominik AU - Tornede, Alexander AU - Hüllermeier, Eyke ID - 21092 JF - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning ER - TY - JOUR AU - Aldahhak, Hazem AU - Hogan, Conor AU - Lindner, Susi AU - Appelfeller, Stephan AU - Eisele, Holger AU - Schmidt, Wolf Gero AU - Dähne, Mario AU - Gerstmann, Uwe AU - Franz, Martin ID - 21094 JF - Physical Review B SN - 2469-9950 TI - Electronic structure of the Si(111)3×3R30°−B surface from theory and photoemission spectroscopy ER - TY - JOUR AU - Kismann, Michael AU - Riedl, Dr. Thomas AU - Lindner, Prof. Dr. Jörg KN ID - 21125 JF - Materials Science in Semiconductor Processing TI - Ordered arrays of Si nanopillars with alternating diameters fabricated by nanosphere lithography and metal-assisted chemical etching ER - TY - THES AB - Die präzise Kenntnis der Eigenschaften verwendeter Materialien hat große Bedeutung für den Entwurf technischer Systeme aller Art, aber auch für die Überwachung solcher Systeme im Betrieb. Für verschiedene physikalische Eigenschaften, Betriebsbedingungen und Materialklassen werden daher geeignete messtechnische Verfahren zur Materialcharakterisierung benötigt. In der vorliegenden Arbeit wird ein Verfahren zur ultraschallbasierten Charakterisierung der mechanischen Eigenschaften von homogenen und faserverstärkten thermoplastischen Polymeren unter Berücksichtigung der Richtungsabhängigkeit vorgestellt. Plattenförmige Probekörper werden dazu mittels Laser-Pulsen hoher Energie breitbandig angeregt und die resultierenden akustischen Lamb-Wellen aufgezeichnet. Auf Basis der dispersiven Eigenschaften der detektierten Wellenleitermoden werden in einem inversen Verfahren die Parameter eines linear-elastischen Materialmodells identifiziert. Darüber hinaus wird ein Verfahren zur vollständigen Charakterisierung der Richtungsabhängigkeit in orthotropen Materialien wie Faserverbundwerkstoffen unter Verwendung eines zweidimensionalen Simulationsmodells vorgestellt. Das Messverfahren wird anhand einer Untersuchungsreihe an künstlich gealterten Polymer- und Faserverbundwerkstoffen verifiziert und die Übertragbarkeit der Ergebnisse auf den quasistatischen Fall betrachtet. Im Vergleich mit den Ergebnissen mechanischer Zugversuche werden die Voraussetzungen und Einschränkungen, insbesondere durch die Annahme eines ideal-elastischen Materialmodells, diskutiert. AU - Webersen, Manuel ID - 21183 TI - Zerstörungsfreie Charakterisierung der elastischen Materialeigenschaften thermoplastischer Polymerwerkstoffe mittels Ultraschall ER - TY - JOUR AU - Goelz, Christian AU - Mora, Karin AU - Stroehlein, Julia Kristin AU - Haase, Franziska Katharina AU - Dellnitz, Michael AU - Reinsberger, Claus AU - Vieluf, Solveig ID - 21195 JF - Cognitive Neurodynamics TI - Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults ER - TY - GEN AU - Mengshi, Ma ID - 21197 TI - Self-stabilizing Arrow Protocol on Spanning Trees with a Low Diameter ER - TY - CONF AU - Kucklick, Jan-Peter AU - Müller, Oliver ID - 21204 T2 - The AAAI-21 Workshop on Knowledge Discovery from Unstructured Data in Financial Services TI - A Comparison of Multi-View Learning Strategies for Satellite Image-based Real Estate Appraisal ER - TY - JOUR AB - Simple thermal treatment of guanine at temperatures ranging from 600 to 700 °C leads to C1N1 condensates with unprecedented CO2/N2 selectivity when compared to other carbonaceous solid sorbents. Increasing the surface area of the CN condensates in the presence of ZnCl2 salt melts enhances the amount of CO2 adsorbed while preserving the high selectivity values and C1N1 structure. Results indicate that these new materials show a sorption mechanism a step closer to that of natural CO2 caption proteins and based on metal free structural cryptopores. AU - Kossmann, Janina AU - Piankova, Diana AU - V. Tarakina, Nadezda AU - Heske, Julian Joachim AU - Kühne, Thomas AU - Schmidt, Johannes AU - Antonietti, Markus AU - López-Salas, Nieves ID - 21207 JF - Carbon KW - CN KW - Cryptopores KW - Carbon dioxide capture SN - 0008-6223 TI - Guanine condensates as covalent materials and the concept of cryptopores VL - 172 ER - TY - JOUR AB - Heterogeneous platforms with FPGAs have started to be employed in the High-Performance Computing (HPC) field to improve performance and overall efficiency. These platforms allow the use of specialized hardware to accelerate software applications, but require the software to be adapted in what can be a prolonged and complex process. The main goal of this work is to describe and evaluate mechanisms that can transparently transfer the control flow between CPU and FPGA within the scope of HPC. Combining such a mechanism with transparent software profiling and accelerator configuration could lead to an automatic way of accelerating regular applications. In this work, a mechanism based on the ptrace system call is proposed, and its performance on the Intel Xeon+FPGA platform is evaluated. The feasibility of the proposed approach is demonstrated by a working prototype that performs the transparent control flow transfer of any function call to a matching hardware accelerator. This approach is more general than shared library interposition at the cost of a small time overhead in each accelerator use (about 1.3ms in the prototype implementation). AU - Granhão, Daniel AU - Canas Ferreira, João Canas ID - 21208 JF - Electronics KW - pc2-harp-ressources SN - 2079-9292 TI - Transparent Control Flow Transfer between CPU and Accelerators for HPC ER - TY - JOUR AU - Lüttenberg, Hedda AU - Beverungen, Daniel AU - Poniatowski, Martin AU - Kundisch, Dennis AU - Wünderlich, Nancy ID - 21242 IS - 2 JF - Wirtschaftsinformatik & Management TI - Drei Strategien zur Etablierung digitaler Plattformen in der Industrie VL - 13 ER - TY - CONF AU - Franke, Mario AU - Klingler, Florian ID - 21258 T2 - 40th IEEE International Conference on Computer Communications (INFOCOM 2021), Poster Session TI - HiL meets Commodity Hardware -- SimbaR for coupling IEEE 802.11 Radio Channels ER - TY - CONF AU - Klingler, Florian AU - Schettler, Max AU - Dimce, Sigrid AU - Amjad, Muhammad Sohaib AU - Dressler, Falko ID - 21259 T2 - 40th IEEE International Conference on Computer Communications (INFOCOM 2021), Poster Session TI - mmWave on the Road -- Field Testing IEEE 802.11ad WLAN at 60 GHz ER - TY - CONF AU - zur Heiden, Philipp AU - Winter, Daniel ID - 21263 T2 - Proceedings of the 16th International Conference on Wirtschaftsinformatik TI - Discovering Geographical Patterns of Retailers' Locations for Successful Retail in City Centers ER -