End-user action-sound mapping design for mid-air music performance

How to design the relationship between a performer’s actions and an instrument’s sound response has been a consistent theme in Digital Musical Instrument (DMI) research. Previously, mapping was seen purely as an activity for DMI creators, but more recent work has exposed mapping design to DMI musicians, with many in the field introducing soware to facilitate end-user mapping, democratising this aspect of the DMI design process. This end-user mapping process provides musicians with a novel avenue for creative expression, and offers a unique opportunity to examine how practising musicians approach mapping design.Most DMIs suffer from a lack of practitioners beyond their initial designer, and there are few that are used by professional musicians over extended periods. The Mi.Mu Gloves are one of the few examples of a DMI that is used by a dedicated group of practising musicians, many of whom use the instrument in their professional practice, with a significant aspect of creative practice with the gloves being end-user mapping design. The research presented in this dissertation investigates end-user mapping practice with the Mi.Mu Gloves, and what influences glove musicians’ design decisions based on the context of their music performance practice, examining the question: How do end-users of a glove-based mid-air DMI design action–sound mapping strategies for musical performance?In the first study, the mapping practice of existing members of the Mi.Mu Glove community is examined. Glove musicians performed a mapping design task, which revealed marked differences in the mapping designs of expert and novice glove musicians, with novices designing mappings that evoked conceptual metaphors of spatial relationships between movement and music, while more experienced musicians focused on designing ergonomic mappings that minimised performer error.The second study examined the initial development period of glove mapping practice. A group of novice glove musicians were tracked in a longitudinal study. The findings supported the previous observation that novices designed mappings using established conceptual metaphors, and revealed that transparency and the audience’s ability to perceive their mappings was important to novice glove musicians. However, creative mapping was hindered by system reliability and the novices’ poorly trained posture recognition.The third study examined the mapping practice of expert glove musicians, who took part in a series of interviews. Findings from this study supported earlier observations that expert glove musicians focus on error minimisation and ergonomic, simple controls, but also revealed that the expert musicians embellished these simple controls with performative ancillary gestures to communicate aesthetic meaning. The expert musicians also suffered from system reliability, and had developed a series of gestural techniques to mitigate accidental triggering.The fourth study examined the effects of system-related error in depth. A laboratory study was used to investigate how system-related errors impacted a musician’s ability to acquire skill with the gloves, finding that a 5% rate of system error had a significant effect on skill acquisition.Learning from these findings, a series of design heuristics are presented, applicable for use in the fields of DMI design, mid-air interaction design and end-user mapping design

Is Yorkshire and the Humber Suffering from Widening Health Inequalities?

The government has made tackling health inequalities one of its top priorities. This article investigates spatial variations in mortality in the region and demonstrates that whilst life expectancy is rising, mortality disparities are widening

Investigating Temporary Mobility in Australia: Contemporary Measures Using Data from the 2001 Census

Although temporary mobility in Australia, like many western countries, has been increasing in significance, this has not been associated with a corresponding increase in systematic research. We address this issue and explore the structure of temporary mobility, in comparison to permanent migration, by using a set of contemporary systematic, quantitative measures to analyse the comprehensive data from the 2001 Census. This foundation is provided by reference to four key dimensions namely intensity, distance, connectivity and impact. The results show that temporary mobility clearly differs from permanent migration in all four of these dimensions: not only do temporary movers display different age-sex profiles, but temporary movements occur over longer distances, have greater levels of connectivity and have a greater impact on settlement patterns. We seek explanations for these differences and to conclude, highlight worthwhile avenues for further research in this field

Are socioeconomic inequalities in mortality decreasing or increasing within some British regions? An observational study, 1990-98

Background: This paper evaluates claims in a recent study that inequalities in small area mortality rates have lessened. We examine the effect of differently estimated populations on time trends in age-specific mortality rates for Yorkshire and the Humber and East of England. Methods: Populations were estimated for wards using four methods that introduce increasing amounts of information. Age-specific mortality rates for age-groups 45-54, 55-64, 65-74 and 75-84 for both sexes were calculated for population-weighted deprivation quintiles. Inequality was tracked using ratios of rates in the most deprived quintile divided by those in the least. Results: When constant 1991 populations are used, rate ratios decrease for all age-sex groups, indicating shrinking inequality. When a method adjusting small area populations to official district estimates is used, both decreases and increases are observed in the mortality rate ratios. These results differ from Trent region findings of decreases in inequality. When small area populations are cohort-survived and adjusted to district populations, most differences in rate ratios indicate increasing inequality. When a method is used that includes information on migration and special populations, then seven out of eight age-sex groups exhibit increasing inequality. Conclusions: A judgement about trends in mortality inequality is highly dependent upon the denominator population used. Simpler estimation methods result in convergence of rate ratios, whereas more sophisticated methods result in increasing inequalities in most age-sex groups

Performance of a second order electrostatic particle-in-cell algorithm on modern many-core architectures

In this paper we present the outline of a novel electrostatic, second order Particle-in-Cell (PIC) algorithm, that makes use of 'ghost particles' located around true particle positions in order to represent a charge distribution. We implement our algorithm within EMPIRE-PIC, a PIC code developed at Sandia National Laboratories. We test the performance of our algorithm on a variety of many-core architectures including NVIDIA GPUs, conventional CPUs, and Intel's Knights Landing. Our preliminary results show the viability of second order methods for PIC applications on these architectures when compared to previous generations of many-core hardware. Specifically, we see an order of magnitude improvement in performance for second order methods between the Tesla K20 and Tesla P100 GPU devices, despite only a 4× improvement in the theoretical peak performance between the devices. Although these initial results show a large increase in runtime over first order methods, we hope to be able to show improved scaling behaviour and increased simulation accuracy in the future

An optimised competency framework to prepare students for employment

The language of competency is heavily utilised by employers when considering staff selection, appraisal, continued professional development, technical training and personal development. However, students and new graduates are not proficient in this language and therefore face challenges when entering the employment market. Competency frameworks exist in virtually all professional and employment sectors, but are particularly prolific in science, medicine, engineering, computing and IT, where they are often aligned to continuing professional development and certification. In this paper, we present a competency framework developed by adapting a number of existing professional competency frameworks used within the IT industry. Our competency framework is designed to be used by and for students on a degree programme with an embedded work-related learning course. The framework has two specific aims: firstly, that it must be usable by students for self-evaluation and self-regulation purposes, and secondly, that it must allow for the support and dispensing of developmental feedback. We also present the results of a study conducted to test the competency framework with 125 students on a Computing-related degree. Understanding, through cluster and correlation analysis, the way in which students perceive their own competencies has led us to optimise our framework to include the twelve most significant competencies within the Academic, Workplace and Personal Effectiveness categories. In our study, it is the Personal Effectiveness competencies such as ‘self-management’ ‘adaptability’ and ‘integrity’ that feature prominently and it is this category of competencies that students find the most challenging to refine

Classifying and evaluating assessment feedback practices

The provision of assessment feedback to students is an area which has received much interest in modern education, particularly in the Higher Education context. As current pedagogic practices strongly encourage the provision of feedback and given also the advances in digital technology, feedback mechanisms are becoming ever more sophisticated. However, considering that a great deal of effort is expended on timely, actionable and constructive feedback by tutors, the student perception of the value of the feedback given to them is not as positive as it could be. Currently a multitude of feedback practices have been developed and utilised, though with varying degrees of productiveness. Research in this area is understandably extremely broad as subject disciplines, use of technology, assessment types, methods and tools, educator preferences, student audience and peer and self-assessment capability all have a significant part to play. Given that the approaches to providing feedback are myriad, it is desirable to advance a systematic method of understanding the most constructive feedback types. This paper describes the development of a taxonomical classification which provides structure, order and frame to current popular practices that have evolved during the last decade. The taxonomy is then evaluated with the use of dimensions such as effectiveness/impact, satisfaction, adoption/engagement and quantity of feedback. The main finding of the taxonomical evaluation is the significance of developmental feed-forward guidance with which students are able to self-regulate and evaluate themselves. The paper concludes that this powerful combination should underpin further investigations into how assessment and feedback provision can be optimised for the experiential learning domain in general and to the work-based learning area in particular

Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks

A new continuous learning method is used to optimise the selection of services in response to user requests in an active computer network simulation environment. The learning is an enhanced version of the ‘snap-drift’ algorithm, which employs the complementary concepts of fast, minimalist (snap) learning and slower drift (towards the input patterns) learning, in a non-stationary environment where new patterns arrive continually. Snap is based on Adaptive Resonance Theory, and drift on Learning Vector Quantisation. The new algorithm swaps its learning style between these two self-organisational modes when declining performance is detected, but maintains the same learning mode during episodes of improved performance. Performance updates occur at the end of each epoch. Reinforcement is implemented by enabling learning on any given pattern with a probability that increases linearly with declining performance. This method, which is capable of rapid re-learning, is used in the design of a modular neural network system: Performance-guided Adaptive Resonance Theory (P-ART). Simulations involving a requirement to continuously adapt to make appropirate decisions within a BT active computer network environment, demonstrate the learning is stable, and able to discover alternative solutions in rapid response to new performance requirements or significant changes in the stream of input patterns

Deep Learning in an Adaptive Function Neural Network

Artificial neural network learning is typically accomplished via adaptation between neurons. This paper describes adaptation that is simultaneously between and within neurons. The conventional neurocomputing wisdom is that by adapting the pattern of connections between neurons the network can learn to respond differentially to classes of incoming patterns. The success of this approach in an age of massively increasing computing power that has made high speed neurocomputing feasible on the desktop and more recently in the palm of the hand, has resulted in little attention being paid to the implications of adaptation within the individual neurons. The computational assumption has tended to be that the internal neural mechanism is fixed. However, there are good computational and biological reasons for examining the internal neural mechanisms of learning. Recent neuroscience suggests that neuromodulators play a role in learning by modifying the neuron’s activation function [Scheler] and with an adaptive function approach it is possible to learn linearly inseparable problems fast, even without hidden nodes. The ADaptive FUction Neural Network (ADFUNN) presented in this paper is based on a linear piecewise neuron activation function that is modified by a novel gradient descent supervised learning algorithm [Palmer-Brown;Kang]. It has been applied to the Iris dataset, and a natural language phrase recognition problem, exhibiting impressive generalisation classification ability with no hidden neurons