Adult Learning and Self Work

The purpose of this paper is to theorize adult education as a vehicle for self change and to explore how such theorizing has consequences for practice as an adult educator

Understanding customers' holistic perception of switches in automotive human–machine interfaces

For successful new product development, it is necessary to understand the customers' holistic experience of the product beyond traditional task completion, and acceptance measures. This paper describes research in which ninety-eight UK owners of luxury saloons assessed the feel of push-switches in five luxury saloon cars both in context (in-car) and out of context (on a bench). A combination of hedonic data (i.e. a measure of ‘liking’), qualitative data and semantic differential data was collected. It was found that customers are clearly able to differentiate between switches based on the degree of liking for the samples' perceived haptic qualities, and that the assessment environment had a statistically significant effect, but that it was not universal. A factor analysis has shown that perceived characteristics of switch haptics can be explained by three independent factors defined as ‘Image’, ‘Build Quality’, and ‘Clickiness’. Preliminary steps have also been taken towards identifying whether existing theoretical frameworks for user experience may be applicable to automotive human–machine interfaces

Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed

Towards online concept drift detection with feature selection for data stream classification

Data Streams are unbounded, sequential data instances that are generated very rapidly. The storage, querying and mining of such rapid flows of data is computationally very challenging. Data Stream Mining (DSM) is concerned with the mining of such data streams in real-time using techniques that require only one pass through the data. DSM techniques need to be adaptive to reflect changes of the pattern encoded in the stream (concept drift). The relevance of features for a DSM classification task may change due to concept drifts and this paper describes the first step towards a concept drift detection method with online feature tracking capabilities

Low Power Adaptive Equaliser Architectures for Wireless LMMSE Receivers

Power consumption requires critical consideration during system design for portable wireless communication devices as it has a direct influence on the battery weight and volume required for operation. Wideband Code Division Multiple Access (W-CDMA) techniques are favoured for use in future generation mobile communication systems. This thesis investigates novel low power techniques for use in system blocks within a W-CDMA adaptive linear minimum mean squared error (LMMSE) receiver architecture. Two low power techniques are presented for reducing power dissipation in the LMS adaptive filter, this being the main power consuming block within this receiver. These low power techniques are namely the decorrelating transform, this is a differential coefficient technique, and the variable length update algorithm which is a dynamic tap-length optimisation technique. The decorrelating transform is based on the principle of reducing the wordlength of filter coefficients by using the computed difference between adjacent coefficients in calculation of the filter output. The effect of reducing the wordlength of filter coefficients being presented to multipliers in the filter is a reduction in switching activity within the multiplier thus reducing power consumed. In the case of the LMS adaptive filter, with coefficients being continuously updated, the decorrelating transform is applied to these calculated coefficients with minimal hardware or computational overhead. The correlation between filter coefficients is exploited to achieve a wordlength reduction from 16 bits down to 10 bits in the FIR filter block. The variable length update algorithm is based on the principle of optimising the number of operational filter taps in the LMS adaptive filter according to operating conditions. The number of taps in operation can be increased or decreased dynamically according to the mean squared error at the output of the filter. This algorithm is used to exploit the fact that when the SNR in the channel is low the minimum mean squared error of the short equaliser is almost the same as that of the longer equaliser. Therefore, minimising the length of the equaliser will not result in poorer MSE performance and there is no disadvantage in having fewer taps in operation. If fewer taps are in operation then switching will not only be reduced in the arithmetic blocks but also in the memory blocks required by the LMS algorithm and FIR filter process. This reduces the power consumed by both these computation intensive functional blocks. Power results are obtained for equaliser lengths from 73 to 16 taps and for operation with varying input SNR. This thesis then proposes that the variable length LMS adaptive filter is applied in the adaptive LMMSE receiver to create a low power implementation. Power consumption in the receiver is reduced by the dynamic optimisation of the LMS receiver coefficient calculation. A considerable power saving is seen to be achieved when moving from a fixed length LMS implementation to the variable length design. All design architectures are coded in Verilog hardware description language at register transfer level (RTL). Once functional specification of the design is verified, synthesis is carried out using either Synopsys DesignCompiler or Cadence BuildGates to create a gate level netlist. Power consumption results are determined at the gate level and estimated using the Synopsys DesignPower tool

Towards real-time feature tracking technique using adaptive micro-clusters

Data streams are unbounded, sequential data instances that are generated with high velocity. Classifying sequential data instances is a very challenging problem in machine learning with applications in network intrusion detection, financial markets and sensor networks. Data stream classification is concerned with the automatic labelling of unseen instances from the stream in real-time. For this the classifier needs to adapt to concept drifts and can only have a single pass through the data if the stream is fast. This research paper presents our work on a real-time pre-processing technique, in particular a feature tracking technique that takes concept drift into consideration. The feature tracking technique is designed to improve Data Stream Mining (DSM) classification algorithms by enabling real-time feature selection. The technique is based on adaptive summaries of the data and class distributions, known as Micro-Clusters. Currently the technique is able to detect concept drift and identifies which features have been involved

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks

A Hybrid Sequencing Approach Completes the Genome Sequence of Thermoanaerobacter ethanolicus JW 200

Thermoanaerobacter ethanolicus JW 200 has been identified as a potential sustainable biofuel producer due to its ability to readily ferment carbohydrates to ethanol. A hybrid sequencing approach, combining Oxford Nanopore and Illumina DNA sequence reads, was applied to produce a single contiguous genome sequence of 2,911,280 bp

Scalable real-time classification of data streams with concept drift

Inducing adaptive predictive models in real-time from high throughput data streams is one of the most challenging areas of Big Data Analytics. The fact that data streams may contain concept drifts (changes of the pattern encoded in the stream over time) and are unbounded, imposes unique challenges in comparison with predictive data mining from batch data. Several real-time predictive data stream algorithms exist, however, most approaches are not naturally parallel and thus limited in their scalability. This paper highlights the Micro-Cluster Nearest Neighbour (MC-NN) data stream classifier. MC-NN is based on statistical summaries of the data stream and a nearest neighbour approach, which makes MC-NN naturally parallel. In its serial version MC-NN is able to handle data streams, the data does not need to reside in memory and is processed incrementally. MC-NN is also able to adapt to concept drifts. This paper provides an empirical study on the serial algorithm’s speed, adaptivity and accuracy. Furthermore, this paper discusses the new parallel implementation of MC-NN, its parallel properties and provides an empirical scalability study

On the nature of the ultraluminous X-ray transient in Cen~A (NGC 5128)

We combine 9 ROSAT, 9 Chandra, and 2 XMM-Newton observations of the Cen~A galaxy to obtain the X-ray light curve of 1RXH J132519.8-430312 (=CXOU J132519.9$-$430317) spanning 1990 to 2003. The source reached a peak 0.1-2.4 keV flux F_X>10^{-12} ergs cm^{-2} s^{-1} during a 10~day span in 1995 July. The inferred peak isotropic luminosity of the source therefore exceeded 3 10^{39} ergs s^{-1}, which places the source in the class of ultra-luminous X-ray sources. Coherent pulsations at 13.264 Hz are detected during a second bright episode (F_X >3 times 10^{-13} ergs cm^{-2} s^{-1}) in 1999 December. The source is detected and varies significantly within three additional observations but is below the detection threshold in 7 observations. The X-ray spectrum in 1999 December is best described as a cut-off power law or a disk-blackbody (multi-colored disk). We also detect an optical source, m_F555W ~ 24.1 mag, within the Chandra error circle of 1RXH J132519.8-430312 in HST images taken 195~days before the nearest X-ray observation. The optical brightness of this source is consistent with a late O or early B star at the distance of Cen A. If the optical source is the counterpart, then the X-ray and optical behavior of 1RXH J132519.8-430312 are similar to the transient Be/X-ray pulsar A 0538-66.Comment: 7 pages, 8 figures. ApJ (accepted