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 -