A key requirement for supervised machine learning is labeled training data, which is created by annotating unlabeled data with the appropriate class. Because this process can in many cases not be done by machines, labeling needs to be performed by human domain experts. This process tends to be expensive both in time and money, and is prone to errors. Additionally, reviewing an entire labeled dataset manually is often prohibitively costly, so many real world datasets contain mislabeled instances.To address this issue, we present in this paper a nonparametric end-to-end pipeline to find mislabeled instances in numerical, image and natural language datasets. We evaluate our system quantitatively by adding a small number of label noise to 29 datasets, and show that we find mislabeled instances with an average precision of more than 0.84 when reviewing our system's top 1% recommendation. We then apply our system to publicly available datasets and find mislabeled instances in CIFAR-100, Fashion-MNIST, and others. Finally, we publish the code and an applicable implementation of our approach.
Figure 1: Offset-resistant audio adversarial example. Original phrase: "they're calling to us not to give up and to keep on fighting". Target phrase: "even coming down on the train together she wrote me".
The recent emergence of deepfakes, computerized realistic multimedia fakes, brought the detection of manipulated and generated content to the forefront. While many machine learning models for deepfakes detection have been proposed, the human detection capabilities have remained far less explored. This is of special importance as human perception differs from machine perception and deepfakes are generally designed to fool the human. So far, this issue has only been addressed in the area of images and video.To compare the ability of humans and machines in detecting audio deepfakes, we conducted an online gamified experiment in which we asked users to discern bonda-fide audio samples from spoofed audio, generated with a variety of algorithms. 200 users competed for 8967 game rounds with an artificial intelligence (AI) algorithm trained for audio deepfake detection. With the collected data we found that the machine generally outperforms the humans in detecting audio deepfakes, but that the converse holds for a certain attack type, for which humans are still more accurate. Furthermore, we found that younger participants are on average better at detecting audio deepfakes than older participants, while IT-professionals hold no advantage over laymen. We conclude that it is important to combine human and machine knowledge in order to improve audio deepfake detection.
An account is given of one expanded suicide and two cases of selfkilling by a livestock narcotic device. These cases are remarkable in respect to the maintained capability for action, and the combination of hanging with the shot-bolt remaining in the head. The reported cases are discussed in connection with the referred literature. Zusammenfassung. Es wird fiber einen erweitertenSuicid und zwei Selbstt6tungen mit Viehbet/iubungsger~ten berichtet, die hinsichtlich der erhaltenen Handlungsf~ihigkeit und der Kombination mit Erh~ingen bzw. des im Kopf verbliebenen Schul3bolzens bemerkenswert sind. Die mitgeteilten F~lle werden im Zusammenhang mit der referierten Literatur diskutiert. * Herrn Prof. Dr. Franz Schleyer zum 70. Geburtstag gewidmet Sonderdruckanfragen an: Dr. I. Wirth (Adresse siehe oben)
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