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Learning from Crowds (LFC) seeks to induce a high-quality classifier from training instances, which are linked to a range of possible noisy annotations from crowdsourcing workers under their various levels of skills and their own preconditions. Recent studies on LFC focus on designing new methods to improve the performance of the classifier trained from crowdsourced labeled data. To this day, however, there remain under-explored security aspects of LFC systems. In this work, we seek to bridge this gap. We first show that LFC models are vulnerable to adversarial examples---small changes to input data can cause classifiers to make prediction mistakes. Second, we propose an approach, A-LFC for training a robust classifier from crowdsourced labeled data. Our empirical results on three real-world datasets show that the proposed approach can substantially improve the performance of the trained classifier even with the existence of adversarial examples. On average, A-LFC has 10.05% and 11.34% higher test robustness than the state-of-the-art in the white-box and black-box attack settings, respectively.

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In this paper, an in-plane Z-shaped structure with multi-span elastic supports is proposed to investigate the natural frequency and transmission response through employing in-plane governing equation and transfer matrix method. Based on the solutions of transverse vibration and torsional vibration, the total transfer matrix for the Z-shaped structure with multi-span elastic supports is derived. Furthermore, natural frequencies and mode shapes of the Z-shaped structure are calculated. Finite element simulation method (FEM) is conducted here to verify the theoretical results. In order to effectively evaluate the vibration reduction performance, transmission response of the Z-shaped structure under different boundary supports and multi-span elastic supports is analyzed. This work is significant for the vibration reduction of in-plane Z-shaped structure, especially the multi-span elastic support case in engineering applications.