https://web4.cs.ucl.ac.uk/staff/j.gow/papers/clase05.pdf

Constructing Induction Rules for Deductive Synthesis Proofs

This is a tutorial paper on the tool Uppaal. Its goal is to be a short introduction on the flavor of timed automata implemented in the tool, to present its interface, and to explain how to use the tool. The contribution of the paper is to provide reference examples and modeling patterns.

A Tutorial on UPPAAL

This article proposes a taxonomy of model transformation, based on the discussions of a working group on model transformation of the Dagstuhl seminar on Language Engineering for Model-Driven Software Development. This taxonomy can be used, among others, to help developers in deciding which model transformation language or tool is best suited to carry out a particular model transformation activity.

/pdf/a-taxonomy-of-model-transformation-3cyk0bxs24.pdf

A Taxonomy of Model Transformation

Minimal recursion semantics (MRS) is a framework for computational semantics that is suitable for parsing and generation and that can be implemented in typed feature structure formalisms. We discuss why, in general, a semantic representation with minimal structure is desirable and illustrate how a descriptively adequate representation with a nonrecursive structure may be achieved. MRS enables a simple formulation of the grammatical constraints on lexical and phrasal semantics, including the principles of semantic composition. We have integrated MRS with a broad-coverage HPSG grammar.

/pdf/minimal-recursion-semantics-an-introduction-4hdjxn0v8q.pdf

Minimal Recursion Semantics An Introduction

Pex automatically produces a small test suite with high code coverage for a .NET program. To this end, Pex performs a systematic program analysis (using dynamic symbolic execution, similar to path-bounded model-checking) to determine test inputs for Parameterized Unit Tests. Pex learns the program behavior by monitoring execution traces. Pex uses a constraint solver to produce new test inputs which exercise different program behavior. The result is an automatically generated small test suite which often achieves high code coverage. In one case study, we applied Pex to a core component of the .NET runtime which had already been extensively tested over several years. Pex found errors, including a serious issue.

/pdf/pex-white-box-test-generation-for-net-1kpnw99ssd.pdf

Pex: white box test generation for .NET

We present a novel application of business process modelling and simulation of manufacturing workflows. Using formal methods, we produce correct-by-construction executable models that can be simulated in an interleaved way. The simulation draws advanced analytics from live IoT monitoring as well as an ERP system to provide predictive business intelligence. We describe our process and resource modelling efforts in the context of a collaborative project with two manufacturing partners. We evaluate our results based on the improvement of the scheduling accuracy for real production flows.

/pdf/a-real-world-case-study-of-process-and-data-driven-2do5ut0tyy.pdf

A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows

We introduce a theorem proving approach to the specification and generation of temporal logical constraints for training neural networks. We formalise a deep embedding of linear temporal logic over finite traces (LTL$_f$) and an associated evaluation function characterising its semantics within the higher-order logic of the Isabelle theorem prover. We then proceed to formalise a loss function $\mathcal{L}$ that we formally prove to be sound, and differentiable to a function $d\mathcal{L}$. We subsequently use Isabelle's automatic code generation mechanism to produce OCaml versions of LTL$_f$, $\mathcal{L}$ and $d\mathcal{L}$ that we integrate with PyTorch via OCaml bindings for Python. We show that, when used for training in an existing deep learning framework for dynamic movement, our approach produces expected results for common movement specification patterns such as obstacle avoidance and patrolling. The distinctive benefit of our approach is the fully rigorous method for constrained training, eliminating many of the risks inherent to ad-hoc implementations of logical aspects directly in an"unsafe"programming language such as Python.

Constrained Training of Neural Networks via Theorem Proving

Comparing the Interpretability of Machine Learning Classifiers for Brain Tumour Survival Prediction

Collaboration across institutional boundaries is widespread and increasing today. It depends on federations sharing data that often have governance rules or external regulations restricting their use. However, the handling of data governance rules (aka. data-use policies) remains manual, time-consuming and error-prone, limiting the rate at which collaborations can form and respond to challenges and opportunities, inhibiting citizen science and reducing data providers' trust in compliance. Using an automated system to facilitate compliance handling reduces substantially the time needed for such non-mission work, thereby accelerating collaboration and improving productivity. We present a framework, Dr.Aid, that helps individuals, organisations and federations comply with data rules, using automation to track which rules are applicable as data is passed between processes and as derived data is generated. It encodes data-governance rules using a formal language and performs reasoning on multi-input-multi-output data-flow graphs in decentralised contexts. We test its power and utility by working with users performing cyclone tracking and earthquake modelling to support mitigation and emergency response. We query standard provenance traces to detach Dr.Aid from details of the tools and systems they are using, as these inevitably vary across members of a federation and through time. We evaluate the model in three aspects by encoding real-life data-use policies from diverse fields, showing its capability for real-world usage and its advantages compared with traditional frameworks. We argue that this approach will lead to more agile, more productive and more trustworthy collaborations and show that the approach can be adopted incrementally. This, in-turn, will allow more appropriate data policies to emerge opening up new forms of collaboration.

Dr.Aid: Supporting Data-governance Rule Compliance for Decentralized Collaboration in an Automated Way

The Archive of Formal Proofs (AFP) is an online repository of formal proofs for the Isabelle proof assistant It serves as a central location for publishing, discovering, and viewing libraries of proofs We conducted an online survey in November 2020 to assess the suitability of the website In this report, we present and discuss the results, which showed that long-term users of the website are generally satisfied with the AFP but that there are a number of areas, such as navigation, search and script browsing, that need improvement

Jacques Fleuriot

Papers

A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows

Constrained Training of Neural Networks via Theorem Proving

Comparing the Interpretability of Machine Learning Classifiers for Brain Tumour Survival Prediction

Dr.Aid: Supporting Data-governance Rule Compliance for Decentralized Collaboration in an Automated Way

An Evaluation of the Archive of Formal Proofs.