Titlebook: Runtime Verification; 22nd International C Thao Dang,Volker Stolz Conference proceedings 2022 The Editor(s) (if applicable) and The Author(

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Randomized First-Order Monitoring with?Hashing further reduces memory usage. We evaluated our implementation and achieved memory reductions up to . when monitoring traces with large domain values, with error probability less than two in a million.

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Randomized First-Order Monitoring with?Hashingch that compresses the monitor’s state using randomized hash functions. Unlike input sampling, our approach does not require the knowledge of distributions over traces to achieve low error probability. We develop algorithms that insert hash functions into temporal–relational algebra specifications a

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Automated Surgical Procedure Assistance Framework Using Deep Learning and?Formal Runtime Monitoring pain and faster recovery. However, surgeons face a number of obstacles while performing these surgeries, including inadequate depth perception, limited range of motion, and difficulty gauging the force to be delivered in the tissue. As a result, improved support for these surgeries is needed to pro

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Optimizing Prestate Copies in?Runtime Verification of?Function Postconditions, code performing runtime verification has to record prestate values required to evaluate the postconditions, typically by copying part of the memory state, which causes severe verification overhead, both in memory and CPU time..In this paper, we consider the problem of efficiently capturing prestat

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