Probabilistic Semantics for RoboChart A Weakest Completion Approach

We outline a probabilistic denotational semantics for the RoboChart language, a diagrammatic, domain-specific notation for de- scribing robotic controllers with their hardware platforms and operating environments. We do this using a powerful (but perhaps not so well known) semantic technique: He, Morgan, and McIver’s weakest completion semantics, which is based on Hoare and He’s Unifying Theories of Programming. In this approach, we do the following: (1) start with the standard semantics for a nondeterministic programming language; (2) propose a new probabilistic semantic domain; (3) propose a forgetful function from the probabilistic semantic domain to the standard semantic domain; (4) use the converse of the forgetful function to embed the standard semantic domain in the probabilistic semantic domain; (5) demonstrate that this embedding preserves program structure; (6) define the probabilistic choice operator. Weakest completion semantics guides the semantic definition of new languages by building on existing semantics and, in this case, tackling a notoriously thorny issue: the relationship between demonic and probabilistic choice. Consistency ensures that programming intuitions, development techniques, and proof methods can be carried over from the standard language to the probabilistic one. We largely follow He et al., our contribution being an explication of the technique with meticulous proofs suitable for mechanisation in Isabelle/UTP

Quantifying Eventual Consistency with PBS

Data replication results in a fundamental trade-off between operation latency and consistency. At the weak end of the spectrum of possible consistency models is eventual consistency, which provides no limit to the staleness of data returned. However, anecdotally, eventual consistency is often “good enough ” for practitioners given its latency and availability benefits. In this work, we explain this phenomenon and demonstrate that, despite their weak guarantees, eventually consistent systems regularly return consistent data while providing lower latency than their strongly consistent counterparts. To quantify the behavior of eventually consistent stores, we introduce Probabilistically Bounded Staleness (PBS), a consistency model that provides expected bounds on data staleness with respect to both versions and wall clock time. We derive a closed-form solution for version-based staleness and model real-time staleness for a large class of quorum replicated, Dynamo-style stores. Using PBS, we measure the trade-off between latency and consistency for partial, non-overlapping quorum systems under Internet production workloads. We quantitatively demonstrate how and why eventually consistent systems frequently return consistent data within tens of milliseconds while offering large latency benefits. 1

Weakly Supervised Learning of Objects, Attributes and Their Associations

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10605-2_31]”

Assumption Generation for the Verification of Learning-Enabled Autonomous Systems

Providing safety guarantees for autonomous systems is difficult as these systems operate in complex environments that require the use of learning-enabled components, such as deep neural networks (DNNs) for visual perception. DNNs are hard to analyze due to their size (they can have thousands or millions of parameters), lack of formal specifications (DNNs are typically learnt from labeled data, in the absence of any formal requirements), and sensitivity to small changes in the environment. We present an assume-guarantee style compositional approach for the formal verification of system-level safety properties of such autonomous systems. Our insight is that we can analyze the system in the absence of the DNN perception components by automatically synthesizing assumptions on the DNN behaviour that guarantee the satisfaction of the required safety properties. The synthesized assumptions are the weakest in the sense that they characterize the output sequences of all the possible DNNs that, plugged into the autonomous system, guarantee the required safety properties. The assumptions can be leveraged as run-time monitors over a deployed DNN to guarantee the safety of the overall system; they can also be mined to extract local specifications for use during training and testing of DNNs. We illustrate our approach on a case study taken from the autonomous airplanes domain that uses a complex DNN for perception

Formal methods and tools for the development of distributed and real time systems : Esprit Project 3096 (SPEC)

The Basic Research Action No. 3096, Formal Methods snd Tools for the Development of Distributed and Real Time Systems, is funded in the Area of Computer Science, under the ESPRIT Programme of the European Community. The coordinating institution is the Department of Computing Science, Eindhoven University of Technology, and the participating Institutions are the Institute of Computer Science of Crete. the Swedish Institute of Computer Science, the Programmimg Research Group of the University of Oxford, and the Computer Science Departments of the University of Manchester, Imperial College. Weizmann Institute of Science, Eindhoven University of Technology, IMAG Grenoble. Catholic University of Nijmegen, and the University of Liege. This document contains the synopsis. and part of the sections on objectives and area of advance, on baseline and rationale, on research goals, and on organisation of the action, as contained in the original proposal, submitted June, 198S. The section on the state of the art (18 pages) and the full list of references (21 pages) of the original proposal have been deleted because of limitation of available space