Copyright & Risk: Scoping the Wellcome Digital Library Project (peer reviewed)

Copyright & Risk: Scoping the Wellcome Digital Library is a comprehensive case study which assesses the merits of the risk-managed approach to copyright clearance adopted by the Wellcome Library in the course of their pilot digitisation project Codebreakers: Makers of Modern Genetics (http://wellcomelibrary.org/collections/digital-collections/makers-of-modern-genetics/#).

The After Slavery Website: A New Online Resource for Teaching U. S. Slave Emancipation

Historiographical essay and evaluation of textbooks and web-based resource for teaching slave emancipation. Published to coincide with re-launch of After Slavery website (www.afterslavery.com) in partnership with Lowcountry Digital Library, College of Charleston, SC.

An Architecture of a Multi-Agent System for SCADA - Dealing With Uncertainty, Plans and Actions

This paper presents a multi-agent system approach to address the difficulties encountered in traditional SCADA systems deployed in critical environments such as electrical power generation, transmission and distribution. The approach models uncertainty and combines multiple sources of uncertain information to deliver robust plan selection. We examine the approach in the context of a simplified power supply/demand scenario using a residential grid connected solar system and consider the challenges of modelling and reasoning with
uncertain sensor information in this environment. We discuss examples of plans and actions required for sensing, establish and discuss the effect of uncertainty on such systems and investigate different uncertainty theories and how they can fuse uncertain information from multiple sources for effective decision making in
such a complex system.

A Lightweight Tool for Anomaly Detection in Cloud Data Centres

Cloud data centres are critical business infrastructures and the fastest growing service providers. Detecting anomalies in Cloud data centre operation is vital. Given the vast complexity of the data centre system software stack, applications and workloads, anomaly detection is a challenging endeavour. Current tools for detecting anomalies often use machine learning techniques, application instance behaviours or system metrics distribu- tion, which are complex to implement in Cloud computing environments as they require training, access to application-level data and complex processing. This paper presents LADT, a lightweight anomaly detection tool for Cloud data centres that uses rigorous correlation of system metrics, implemented by an efficient corre- lation algorithm without need for training or complex infrastructure set up. LADT is based on the hypothesis that, in an anomaly-free system, metrics from data centre host nodes and virtual machines (VMs) are strongly correlated. An anomaly is detected whenever correlation drops below a threshold value. We demonstrate and evaluate LADT using a Cloud environment, where it shows that the hosting node I/O operations per second (IOPS) are strongly correlated with the aggregated virtual machine IOPS, but this correlation vanishes when an application stresses the disk, indicating a node-level anomaly.