Hier finden Sie die Top 5 Paper des SCCH
B. A. Moser, Similarity Recovery from Threshold-Based Sampling under General Conditions, IEEE Transactions on Signal Processing, Vol. 65, issue 17, pp. 4645 – 4654, 2017.
This paper provides a quasi-isometry based mathematical framework for threshold-based sampling as encountered in neuronal computational models and neuro-morphic engineering. The theory provides novel powerful signal reconstruction validation techniques.
W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, S. Saminger-Platz. Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning. International Conference on Learning Representations (ICLR), Toulon, France, April 24-26, 2017.
The conference ICLR is listed as top-tier conference on deep learning; this paper proposes a metric-based approach for deep transfer learning. The approach outperforms state-of-the-art domain adaptation algorithms on standard sentiment analysis and object recognition benchmark datasets.
H. Prähofer, F. Angerer, R. Ramler, F. Grillenberger. Static code analysis of IEC 61131-3 Programs: Comprehensive tool support and experiences from large-scale industrial application. IEEE Transactions on Industrial Informatics, pages online first, DOI: 10.1109/TII.2016.2604760, August, 2016.
This paper introduces a static code analysis approach for detecting a range of issues commonly occurring in programmable logic controller (PLC) programming. The paper presents results from large-scale industrial applications.
E. Börger, K.-D. Schewe, Concurrent Abstract State Machines, Acta Informatica, 53: 469, Springer, 2016.
This paper states and proves an extension of the sequential abstract state machines (ASM) thesis to a concurrent ASM thesis. The paper is listed in the 21st Annual Best Computing papers by ACM Computing Reviews in the category “Theory of Computation”.
G. Chasparis, M. Maggio, E. Bini, K.-E. Årzén. Design and implementation of distributed resource management for time-sensitive applications. Automatica, volume 64, number 2, pages 44-53, DOI 10.1016/j.automatica.2015.09.015, February 2016.
This paper introduces a game-theoretic approach for distributed CPU resource management for time-sensitive applications. The proposed framework is distinguished by its adaptivity and robustness to changes both in the number and nature of applications, while it assumes minimum information available to both applications and the resource manager.