Collaborative adaptive filtering for machine learning

Quantitative performance criteria for the analysis of machine learning architectures and algorithms have long been established. However, qualitative performance criteria, which identify fundamental signal properties and ensure any processing preserves the desired properties, are still emerging. In many cases, whilst offline statistical tests exist such as assessment of nonlinearity or stochasticity, online tests which not only characterise but also track changes in the nature of the signal are lacking. To that end, by employing recent developments in signal characterisation, criteria are derived for the assessment of the changes in the nature of the processed signal. Through the fusion of the outputs of adaptive filters a single collaborative hybrid filter is produced. By tracking the dynamics of the mixing parameter of this filter, rather than the actual filter performance, a clear indication as to the current nature of the signal is given. Implementations of the proposed method show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. This is then extended (in the real domain) from dealing with nonlinearity in general, to a more specific example, namely sparsity. Extensions of adaptive filters from the real to the complex domain are non-trivial and the differences between the statistics in the real and complex domains need to be taken into account. In terms of signal characteristics, nonlinearity can be both split- and fully-complex and complex-valued data can be considered circular or noncircular. Furthermore, by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion. To produce online tests for sparsity, adaptive filters for sparse environments are investigated and a unifying framework for the derivation of proportionate normalised least mean square (PNLMS) algorithms is presented. This is then extended to derive variants with an adaptive step-size. In order to create an online test for noncircularity, a study of widely linear autoregressive modelling is presented, from which a proof of the convergence of the test for noncircularity can be given. Applications of this method are illustrated on examples such as biomedical signals, speech and wind data

Changing the role of tutors in distance education with information and communication technologies

The Open University plans to make more extensive use of information and communications technologies (ICTs) for distance teaching and learning and for administrative contacts between students, tutors and the University's headquarters. This paper reports on a survey of the Tuition and Counselling (TAC) staff, most of whom work only part‐time for the OU. It established the extent to which TAC staff currently have access to and familiarity with ICTs and their perceived needs for training and other forms of support for its effective use. The paper discusses the possible impact on TAC staff of the greater use of new technologies in their OU work, and the organisational and pedagogic changes that may ensue

Embedding accessibility and usability: considerations for e-learning research and development projects

This paper makes the case that if e‐learning research and development projects are to be successfully adopted in real‐world teaching and learning contexts, then they must effectively address accessibility and usability issues; and that these need to be integrated throughout the project. As such, accessibility and usability issues need to be made explicit in project documentation, along with allocation of appropriate resources and time. We argue that accessibility and usability are intrinsically inter‐linked. An integrated accessibility and usability evaluation methodology that we have developed is presented and discussed. The paper draws on a series of mini‐case studies from e‐learning projects undertaken over the past 10 years at the Open University

Stakeholders' conceptions of quality in distance higher education

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Use of the R-group descriptor for alignment-free QSAR

An R-group descriptor characterises the distribution of some atom-based property, such as elemental type or partial atomic charge, at increasing numbers of bonds distant from the point of substitution on a parent ring system. Application of Partial Least Squares (PLS) to datasets for which bioactivity data and R-group descriptor information are available is shown to provide an effective way of generating QSAR models with a high level of predictive ability. The resulting models are competitive with the models produced by established QSAR approaches, are readily interpretable in structural terms, and are shown to be of value in the optimisation of a lead series