Richard Payne

Jobie Budd, Kieran Baker, Emma Karoune et al.

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

Harry Coppock, George Nicholson, Ivan Kiskin et al.

Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and analyse a dataset of audio recordings from 67,842 individuals with linked metadata, including reverse transcription polymerase chain reaction (PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects were recruited via the UK governments National Health Service Test-and-Trace programme and the REal-time Assessment of Community Transmission (REACT) randomised surveillance survey. In an unadjusted analysis of our dataset AI classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854]) consistent with the findings of previous studies. However, after matching on measured confounders, such as age, gender, and self reported symptoms, our classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon quantifying the utility of audio based classifiers in practical settings, we find them to be outperformed by simple predictive scores based on user reported symptoms.

Davide Pigoli, Kieran Baker, Jobie Budd et al.

Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studies have limitations in terms of data collection and of the assessment of the performances of the proposed predictive models. This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals, using a dataset collected by the UK Health Security Agency. This dataset includes acoustic recordings and extensive study participant meta-data. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features and we discuss how these can be extended more generally to the development and assessment of predictive methods based on public health datasets.

Oldrich Faldik, Richard Payne, John Fitzgerald et al.

A key challenge in System of Systems (SoS) engineering is the analysis and maintenance of global properties under SoS evolution, and the integration of new constituent elements. There is a need to model the constituent systems composing a SoS in order to allow the analysis of emergent behaviours at the SoS boundary. The Contract pattern allows the engineer to specify constrained behaviours to which constituent systems are required to conform in order to be a part of the SoS. However, the Contract pattern faces some limitations in terms of its accessibility and suitability for verifying contract compatibility. To address these deficiencies, we propose the enrichment of the Contract pattern, which hitherto has been defined using SysML and the COMPASS Modelling Language (CML), by utilising SysML and Object Constraint Language (OCL). In addition, we examine the potential of interface automata, a notation for improving loose coupling between interfaces of constituent systems defined according to the contract, as a means of enabling the verification of contract compatibility. The approach is demonstrated using a case study in audio/video content streaming.

Luis Diogo Couto, Simon Foster, Richard Payne

This paper explores verification of constituent systems within the context of the Symphony tool platform for Systems of Systems (SoS). Our SoS modelling language, CML, supports various contractual specification elements, such as state invariants and operation preconditions, which can be used to specify contractual obligations on the constituent systems of a SoS. To support verification of these obligations we have developed a proof obligation generator and theorem prover plugin for Symphony. The latter uses the Isabelle/HOL theorem prover to automatically discharge the proof obligations arising from a CML model. Our hope is that the resulting proofs can then be used to formally verify the conformance of each constituent system, which is turn would result in a dependable SoS.

Zoe Andrews, Jeremy Bryans, Richard Payne et al.

The nature of Systems of Systems (SoSs), large complex systems composed of independent, geographically distributed and continuously evolving constituent systems, means that faults are unavoidable. Previous work on defining contractual specifications of the constituent systems of SoSs does not provide any explicit consideration for faults. In this paper we address that gap by extending an existing pattern for modelling contracts with fault modelling concepts. The proposed extensions are introduced with respect to an Audio Visual SoS case study from Bang and Olufsen, before discussing how they relate to previous work on modelling faults in SoSs.