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Investigating value propositions in social media: studies of brand and customer exchanges on Twitter
Social media presents one of the richest forums to investigate publicly explicit brand value propositions and its corresponding customer engagement. Seldom have researchers investigated the nature of value propositions ...
Removing spatial boundaries in immersive mobile communications
Despite a worldwide trend towards mobile computing, current telepresence experiences focus on stationary desktop computers, limiting how, when, and where researched solutions can be used. In this thesis I demonstrate that ...
Optimal coalition structure generation on large-scale renewable energy smart grids
Most renewable energy sources are dependent on unpredictable weather conditions, which have considerable variation over space and time. The intermittent nature of this production means that any renewable energy prosumer ...
Ensemble learning through cooperative evolutionary computation
Building ensembles of classifiers is an active area of research for machine learning, with the fundamental goal of combining the predictions of multiple classifiers to improve prediction accuracy over an individual classifier. ...
Prioritisation of requests, bugs and enhancements pertaining to apps for remedial actions. Towards solving the problem of which app concerns to address initially for app developers
Useful app reviews contain information related to the bugs reported by the app’s end-users along with the requests or enhancements (i.e., suggestions for improvement) pertaining to the app. App developers expend exhaustive ...
Investigating Cultural Dimensions via Developers Artefacts: The Utility of Repository Mining
A growing body of research is using artefacts from online development communities to explore the impact of developers’ behaviours on the software development process. Although this research has produced many insights, ...
Deep generative models for transductive transfer learning
To achieve satisfactory generalization abilities, machine learning models usually require large amounts of labelled data. However, data labelling is very costly, even in the era of big data. Transductive transfer learning ...
Agent-based models of long-distance trading societies
Studying historical trading societies helps us to identify the institutions (e.g. rules) and characteristics that lead to their success or failure. Historically, long-distance trading societies, as a more particular example ...
Error decomposition of evolutionary machine learning
Algorithms or models are often measured using a fitness function that calculates total prediction error. While reducing total error is typically the overall objective, examining error as an aggregate value does not provide ...
Data transformation and knowledge retrieval for humanitarian crisis response
Humanitarian crises are unpredictable and complex environments, in which access to basic services and infrastructures is not adequately available. Computing in a humanitarian crisis environment is different from any other ...