Titlebook: Dependable Software Engineering. Theories, Tools, and Applications; 9th International Sy Holger Hermanns,Jun Sun,Lei Bu Conference proceedi

滋養(yǎng)

Yu. Z. Miropol’sky,O. D. Shishkinaalence definition without substitution or name swapping. Furthermore, we provide structural and rule induction principles that encapsulate the Barendregt Variable Convention, enabling formal proofs mirroring informally-styled ones. These principles are leveraged to establish foundational results suc

HARP

符合規(guī)定

https://doi.org/10.1007/978-94-017-1325-2 effective in the domain of spacecraft rendezvous, owing to its high performance in complex continuous control tasks and low online storage and computation cost. However, the lack of safety guarantees during the learning process restricts the application of RL to safety-critical control systems with

者變

Akira Takeishi,Tsuyoshi Numagamie at the core of the abstract interpretation framework, and the numerical abstract domains aiming at analyzing numerical properties have received extensive attention. The template constraint matrix domain (also called the template polyhedra domain) is widely used due to its configurable constraint m

Anecdote

Allodynia

,String Constraints with?Regex-Counting and?String-Length Solved More Efficiently,e in regexes that counts the number of matchings of sub-patterns. The state-of-the-art string solvers are incapable of solving string constraints with regex-counting and string-length efficiently, especially when the counting and length bounds are large. In this work, we propose an automata-theoreti

沙草紙

,Reachability Based Uniform Controllability to?Target Set with?Evolution Function,ving from the initial set can enter into the target set. For this purpose, we first introduce the evolution function (EF) for exactly describing the reachable set and give an over-approximation of the reachable set with high precision using the series representation of the evolution function. Subseq

BRINK

羅盤

,Graph-Based Log Anomaly Detection via?Adversarial Training,addition, existing methods often overlook log event structural relationships, causing instability. In this work, we propose AdvGraLog, a Generative Adversarial Network (GAN) model based on log graph representation, to detect anomalies when the reconstruction error of discriminator is terrible. We co

patriarch