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  • Theoretical Computer Science 11/2014; 425:2–3. DOI:10.1016/j.tcs.2012.02.014
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    ABSTRACT: The classical machinery of supervised learning machines relies on a correct set of training labels. Unfortunately, there is no guarantee that all of the labels are correct. Labelling errors are increasingly noticeable in today׳s classification tasks, as the scale and difficulty of these tasks increases so much that perfect label assignment becomes nearly impossible. Several algorithms have been proposed to alleviate the problem of which a robust Kernel Fisher Discriminant is a successful example. However, for classification, discriminative models are of primary interest, and rather curiously, the very few existing label-robust discriminative classifiers are limited to linear problems. In this paper, we build on the widely used and successful kernelising technique to introduce a label-noise robust Kernel Logistic Regression classifier. The main difficulty that we need to bypass is how to determine the model complexity parameters when no trusted validation set is available. We propose to adapt the Multiple Kernel Learning approach for this new purpose, together with a Bayesian regularisation scheme. Empirical results on 13 benchmark data sets and two real-world applications demonstrate the success of our approach.
    Pattern Recognition 11/2014; 47(11):3641–3655. DOI:10.1016/j.patcog.2014.05.007
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    ABSTRACT: http://etheses.bham.ac.uk/4911/ Process mining algorithms use event logs to learn and reason about business processes. Although process mining is essentially a machine learning task, little work has been done on systematically analysing algorithms to understand their fundamental properties, such as how much data is needed for confidence in mining. Nor does any rigorous basis exist on which to choose between algorithms and representations, or compare results. We propose a framework for analysing process mining algorithms. Processes are viewed as distributions over traces of activities and mining algorithms as learning these distributions. We use probabilistic automata as a unifying representation to which other representation languages can be converted. To validate the theory we present analyses of the Alpha and Heuristics Miner algorithms under the framework, and two practical applications. We propose a model of noise in process mining and extend the framework to mining from ?noisy? event logs. From the probabilities and sub-structures in a model, bounds can be given for the amount of data needed for mining. We also consider mining in non-stationary environments, and a method for recovery of the sequence of changed models over time. We conclude by critically evaluating this framework and suggesting directions for future research.
    School of Computer Science, University of Birmingham, 07/2014, Degree: PhD, Supervisor: Behzad Bordbar, Peter Tino
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    ABSTRACT: The matrix method, due to Bibel and Andrews, is a proof procedure designed for automated theorem-proving. We show that un-derlying this method is a fully structured combinatorial model of con-ventional classical proof theory.
    Journal of Logic and Computation 02/2014; 24(1). DOI:10.1093/logcom/exs045
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    ABSTRACT: Direct touch manipulation interactions with technology are now commonplace and significant interest is building around their use in the culture and heritage domain. Such interactions can give people the opportunity to explore materials and artefacts in ways that would otherwise be unavailable. These are often heavily annotated and can be linked to a large array of related digital content, thus enriching the experience for the user. Research has addressed issues of how to present digital documents and their related annotations but at present it is unclear what the optimal interaction approach to navigating these annotations in a touch display context might be. In this paper we investigate the role of two alternative approaches to support the navigation of annotations in digitised documents in the context of a touch interface. Through a control study we demonstrate that, whilst the navigation paradigm displays a significant interaction with the type of annotations task performed, there is no discernible advantage of using a natural visual metaphor for annotation in this context. This suggests that design of digital document annotation navigation tools should account for the context and navigation tasks being considered.
    International Journal of Human-Computer Studies 12/2013; 71(12):1103–1111. DOI:10.1016/j.ijhcs.2013.08.017
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    ABSTRACT: The announcement of the discovery of a Higgs boson-like particle at CERN will be remembered as one of the milestones of the scientific endeavor of the 21(st) century. In this paper we present a study of information spreading processes on Twitter before, during and after the announcement of the discovery of a new particle with the features of the elusive Higgs boson on 4(th) July 2012. We report evidence for non-trivial spatio-temporal patterns in user activities at individual and global level, such as tweeting, re-tweeting and replying to existing tweets. We provide a possible explanation for the observed time-varying dynamics of user activities during the spreading of this scientific "rumor". We model the information spreading in the corresponding network of individuals who posted a tweet related to the Higgs boson discovery. Finally, we show that we are able to reproduce the global behavior of about 500,000 individuals with remarkable accuracy.
    Scientific Reports 10/2013; 3:2980. DOI:10.1038/srep02980
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    ABSTRACT: ContextEnsembles of learning machines and locality are considered two important topics for the next research frontier on Software Effort Estimation (SEE).Objectives We aim at (1) evaluating whether existing automated ensembles of learning machines generally improve SEEs given by single learning machines and which of them would be more useful; (2) analysing the adequacy of different locality approaches; and getting insight on (3) how to improve SEE and (4) how to evaluate/choose machine learning (ML) models for SEE.MethodA principled experimental framework is used for the analysis and to provide insights that are not based simply on intuition or speculation. A comprehensive experimental study of several automated ensembles, single learning machines and locality approaches, which present features potentially beneficial for SEE, is performed. Additionally, an analysis of feature selection and regression trees (RTs), and an investigation of two tailored forms of combining ensembles and locality are performed to provide further insight on improving SEE.ResultsBagging ensembles of RTs show to perform well, being highly ranked in terms of performance across different data sets, being frequently among the best approaches for each data set and rarely performing considerably worse than the best approach for any data set. They are recommended over other learning machines should an organisation have no resources to perform experiments to chose a model. Even though RTs have been shown to be more reliable locality approaches, other approaches such as k-Means and k-Nearest Neighbours can also perform well, in particular for more heterogeneous data sets.Conclusion Combining the power of automated ensembles and locality can lead to competitive results in SEE. By analysing such approaches, we provide several insights that can be used by future research in the area.
    Information and Software Technology 08/2013; 55(8):1512–1528. DOI:10.1016/j.infsof.2012.09.012
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    ABSTRACT: A preference function is a function which selects a subset of objects based on (partial) information. As information increases, different objects may be selected. We examine conditions under which the selection of objects converges to the choice that would be made if full information were available, making use of tools from domain theory. The work is motivated by previous research on co-evolutionary algorithms in which an evolving population of agents interact with each other and, it is hoped, produce better and better quality behaviour. The formalisation of how quality can be measured in this context has introduced the concept of a convex preference function (or “solution concept”). We simplify and extend the scope of this previous work, examining the relationship between convexity and convergence properties.
    Theoretical Computer Science 06/2013; 488:66–77. DOI:10.1016/j.tcs.2013.03.023
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    ABSTRACT: The approach Clark labels "action-oriented predictive processing" treats all cognition as part of a system of on-line control. This ignores other important aspects of animal, human, and robot intelligence. He contrasts it with an alleged "mainstream" approach that also ignores the depth and variety of AI/Robotic research. I don't think the theory presented is worth taking seriously as a complete model, even if there is much that it explains.
    Behavioral and Brain Sciences 05/2013; 36(3):50-51. DOI:10.1017/S0140525X12002439
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    ABSTRACT: A large scale High Level Architecture (HLA)-based simulation can be constructed using a network of simulation federations to form a “federation community”. This effort is often for the sake of enhancing scalability, interoperability, composability and enabling information security. Synchronization mechanisms are essential to coordinate the execution of federates and event transmissions across the boundaries of interlinked federations. We have developed a generic synchronization mechanism for federation community networks with its correctness mathematically proved. The synchronization mechanism suits various types of federation community network and supports the reusability of legacy federates. It is platform-neutral and independent of federate modeling approaches. The synchronization mechanism has been evaluated in the context of the Grid-enabled federation community approach, which allows simulation users to benefit from both Grid computing technologies and the federation community approach. A series of experiments has been carried out to validate and benchmark the synchronization mechanism. The experimental results indicate that the proposed mechanism provides correct time management services to federation communities. The results also show that the mechanism exhibits encouraging performance in terms of synchronization efficiency and scalability.
    Journal of Parallel and Distributed Computing 04/2013; 70(2):144-159. DOI:10.1016/j.jpdc.2009.10.006
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