Nelson Bore

Blair Edwards, Paolo Fraccaro, Nikola Stoyanov et al.

The application of models to assess the risk of the physical impacts of weather and climate and their subsequent consequences for society and business is of the utmost importance in our changing climate. The operation of such models is historically bespoke and constrained to specific compute infrastructure, driving datasets and predefined configurations. These constraints introduce challenges with scaling model runs and putting the models in the hands of interested users. Here we present a cloud-based modular framework for the deployment and operation of geospatial models, initially applied to climate impacts. The Climate Impact Modelling Frameworks (CIMF) enables the deployment of modular workflows in a dynamic and flexible manner. Users can specify workflow components in a streamlined manner, these components can then be easily organised into different configurations to assess risk in different ways and at different scales. This also enables different models (physical simulation or machine learning models) and workflows to be connected to produce combined risk assessment. Flood modelling is used as an end-to-end example to demonstrate the operation of CIMF.

Sekou L Remy, Aisha Walcott-Bryant, Nelson K Bore et al.

In popular usage, Data Gravity refers to the ability of a body of data to attract applications, services and other data. In this work we introduce a broader concept, "Digital Gravity" which includes not just data, but other elements of the AI/ML workflow. This concept is born out of our recent experiences in developing and deploying an AI-based decision support platform intended for use in a public health context. In addition to data, examples of additional considerations are compute (infrastructure and software), DevSecOps (personnel and practices), algorithms/programs, control planes, middleware (considered separately from programs), and even companies/service providers. We discuss the impact of Digital Gravity on the pathway to adoption and suggest preliminary approaches to conceptualize and mitigate the friction caused by it.

Andrew Kinai, Fred Otieno, Nelson Bore et al.

Attacks targeting several millions of non-internet based application users are on the rise. These applications such as SMS and USSD typically do not benefit from existing multi-factor authentication methods due to the nature of their interaction interfaces and mode of operations. To address this problem, we propose an approach that augments blockchain with multi-factor authentication based on evidence from blockchain transactions combined with risk analysis. A profile of how a user performs transactions is built overtime and is used to analyse the risk level of each new transaction. If a transaction is flagged as high risk, we generate n-factor layers of authentication using past endorsed blockchain transactions. A demonstration of how we used the proposed approach to authenticate critical financial transactions in a blockchain-based asset financing platform is also discussed.

Ravi Kiran Raman, Roman Vaculin, Michael Hind et al.

Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, and healthcare to understand phenomena, and design high-stakes policies affecting everyday health and economy. For instance, the OpenMalaria framework is a computationally-intensive simulation used by various non-governmental and governmental agencies to understand malarial disease spread and effectiveness of intervention strategies, and subsequently design healthcare policies. Given that such shared results form the basis of inferences drawn, technological solutions designed, and day-to-day policies drafted, it is essential that the computations are validated and trusted. In particular, in a multi-agent environment involving several independent computing agents, a notion of trust in results generated by peers is critical in facilitating transparency, accountability, and collaboration. Using a novel combination of distributed validation of atomic computation blocks and a blockchain-based immutable audits mechanism, this work proposes a universal framework for distributed trust in computations. In particular we address the scalaibility problem by reducing the storage and communication costs using a lossy compression scheme. This framework guarantees not only verifiability of final results, but also the validity of local computations, and its cost-benefit tradeoffs are studied using a synthetic example of training a neural network.