Titlebook: Runtime Verification; 21st International C Lu Feng,Dana Fisman Conference proceedings 2021 Springer Nature Switzerland AG 2021 Theory of co

Lucubrate

Robust

Neural Predictive Monitoring Under Partial Observability the most realistic settings where only partial and noisy observations of the state are available at runtime. Such settings directly affect the accuracy and reliability of the reachability predictions, jeopardizing the safety of the system. In this work, we present a learning-based method for PM tha

门窗的侧柱

A Compositional Framework for Quantitative Online Monitoring over Continuous-Time Signalsider an abstract algebraic semantics based on complete lattices, which subsumes the Boolean (qualitative) semantics and the real-valued robustness (quantitative) semantics. Our semantics also extends to truth values that are partially ordered and allows the modeling of uncertainty in satisfaction. W

灾难

Nested Monitors: Monitors as Expressions to Build Monitors(data verdicts). In SRV, the actual data operations are separated from the temporal dependencies, therefore generalizing monitoring algorithms for temporal logics into the computation of richer verdicts. In this paper we study a new and powerful feature, which consists of lifting the execution of mo

有机体

Diamont: Dynamic Monitoring of Uncertainty for Distributed Asynchronous Programsroduce approximate results. These applications can distribute computation across multiple (often resource-constrained) processing units. Analyzing the reliability and accuracy of such applications is challenging, since most existing techniques operate on specific fixed error models, check for indivi

GEON

Assumption-Based Runtime Verification of Infinite-State Systemspecification. In Assumption-Based Runtime Verification (ABRV), runtime monitors may be synthesized from not only the specification but also a system model (either full or partial), which represents the assumptions on which the input traces are expected to follow. With assumptions the monitor can add

Veneer

Differential Monitoringond program implementing the same informal specification. Similar ideas have been proposed before, and are currently implemented in restricted form for testing and specialized run-time analyses, aspects of which we combine. We discuss the challenges of implementing differential monitoring as a gener

Iatrogenic

带子

Gaussian-Based Runtime Detection of Out-of-distribution Inputs for Neural Networksction. The approach is based on inferring Gaussian models of some of the neurons and layers. Despite its simplicity, it performs better than recently introduced approaches based on interval abstractions which are traditionally used in verification.

Employee

Parallel and Multi-objective Falsification with , and scenario specification language and . toolkit that improve the scalability of sampling-based falsification methods by using parallelism and extend falsification to multi-objective specifications. We first present a parallelized framework that is interfaced with both the simulation and sampling capab