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We assume so-called run-to-completion modules that exhibit start and done signals indicating the start and end of execution, respectively. We present a formal verification approach that exploits the concept of proof-carrying hardware. The approach tasks the creator of a hardware module with constructing a proof of the worst-case completion time, which can then easily be checked by the user of the module, just prior to reconfiguration. After explaining the verification approach and a corresponding tool flow, we present results from two case studies, a short term synthesis filter and a multihead weigher. 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Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems, imposing high demands on its integrity.Wikidata can be edited by anyone and, unfortunately, it frequently gets vandalized, exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper, we present a new machine learning-based approach to detect vandalism in Wikidata.We propose a set of 47 features that exploit both content and context information, and we report on 4 classifiers of increasing effectiveness tailored to this learning task. Our approach is evaluated on the recently published Wikidata Vandalism Corpus WDVC-2015 and it achieves an area under curve value of the receiver operating characteristic, ROC-AUC, of 0.991. 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