An Effective Strategy for the Flexible Provisioning of Service Workflows

Recent advances in service-oriented frameworks and semantic Web technologies have enabled software agents to discover and invoke resources over large distributed systems, in order to meet their high-level objectives. However, most work has failed to acknowledge that such systems are complex and dynamic multi-agent systems, where service providers act autonomously and follow their own decision-making procedures. Hence, the behaviour of these providers is inherently uncertain - services may fail or take uncertain amounts of time to complete. In this work, we address this uncertainty and take an agent-oriented approach to the problem of provisioning service providers for the constituent tasks of abstract workflows. Specifically, we describe an algorithm that uses redundancy to deal with unreliable providers, and we demonstrate that it achieves an 8-14% improvement in average utility over previous work, while performing up to 6 times as well as approaches that do not consider service uncertainty. We also show that our algorithm performs well in the presence of inaccurate service performance information

Sensitivity Analysis of Flexible Provisioning

This technical report contains a sensitivity analysis to extend our previous work. We show that our flexible service provisioning strategy is robust to inaccurate performance information (when the available information is within 10% of the true value), and that it degrades gracefully as the information becomes less accurate. We also identify and discuss one particular case where inaccurate information may lead to undesirable losses in highly unreliable environments

A spoonful of sugar: the application of glycopolymers in therapeutics

Glycopolymers, synthetic polymers displaying carbohydrate moieties, have been linked to many potential applications at the biology–chemistry interface. One area that holds particular promise is the employment of glycopolymers as vehicles for therapeutics or as therapeutics themselves. This review summarises some of the more prominent examples as well as those in the early stages of development

Sequential Attention: A Context-Aware Alignment Function for Machine Reading

In this paper we propose a neural network model with a novel Sequential Attention layer that extends soft attention by assigning weights to words in an input sequence in a way that takes into account not just how well that word matches a query, but how well surrounding words match. We evaluate this approach on the task of reading comprehension (on the Who did What and CNN datasets) and show that it dramatically improves a strong baseline--the Stanford Reader--and is competitive with the state of the art.Comment: To appear in ACL 2017 2nd Workshop on Representation Learning for NLP. Contains additional experiments in section 4 and a revised Figure