Ryan Green

Johannes Sedlmeir, Tim Wagner, Emil Djerekarov et al.

Enterprises have been attracted by the capability of blockchains to provide a single source of truth for workloads that span companies, geographies, and clouds while retaining the independence of each party's IT operations. However, so far production applications have remained rare, stymied by technical limitations of existing blockchain technologies and challenges with their integration into enterprises' IT systems. In this paper, we collect enterprises' requirements on distributed ledgers for data sharing and integration from a technical perspective, argue that they are not sufficiently addressed by available blockchain frameworks, and propose a novel distributed ledger design that is "serverless", i.e., built on cloud-native resources. We evaluate its qualitative and quantitative properties and give evidence that enterprises already heavily reliant on cloud service providers would consider such an approach acceptable, particularly if it offers ease of deployment, low transactional cost structure, and a combination of latency and scalability aligned with real-time IT application needs.

Hadrien Bride, Jin Song Dong, Ryan Green et al.

While AI techniques have found many successful applications in autonomous systems, many of them permit behaviours that are difficult to interpret and may lead to uncertain results. We follow the "verification as planning" paradigm and propose to use model checking techniques to solve planning and goal reasoning problems for autonomous systems. We give a new formulation of Goal Task Network (GTN) that is tailored for our model checking based framework. We then provide a systematic method that models GTNs in the model checker Process Analysis Toolkit (PAT). We present our planning and goal reasoning system as a framework called Goal Reasoning And Verification for Independent Trusted Autonomous Systems (GRAVITAS) and discuss how it helps provide trustworthy plans in an uncertain environment. Finally, we demonstrate the proposed ideas in an experiment that simulates a survey mission performed by the REMUS-100 autonomous underwater vehicle.

Jiuyong Li, Lin Liu, Jixue Liu et al.

It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper demonstrates that an ensemble classifier, Diversified Multiple Tree (DMT), is more robust in classifying noisy data than other widely used ensemble methods. DMT is tested on three real world biomedical data sets from different laboratories in comparison with four benchmark ensemble classifiers. Experimental results show that DMT is significantly more accurate than other benchmark ensemble classifiers on noisy test data. We also discuss a limitation of DMT and its possible variations.