Changing musical emotion : a computational rule system for modifying score and performance

When communicating emotion in music, composers and performers encode their expressive intentions through the control of basic musical features such as: pitch, loudness, timbre, mode, and articulation. The extent to which emotion can be controlled through the systematic manipulation of these features has not been fully examined. In this paper we present CMERS, a Computational Music Emotion Rule System for the control of perceived musical emotion that modifies features at the levels of score and performance in real-time. CMERS performance was evaluated in two rounds of perceptual testing. In experiment I, 20 participants continuously rated the perceived emotion of 15 music samples generated by CMERS. Three music works, each with five emotional variations were used (normal, happy, sad, angry, and tender). The intended emotion by CMERS was correctly identified 78% of the time, with significant shifts in valence and arousal also recorded, regardless of the works’ original emotion.

The 10% rule. Maximising learning through collaborative game design

This paper presents a retrospective view of a game design practice that recently switched from the development of complex learning games to the development of simple authoring tools for students to design their own learning games for each other. We introduce how our ‘10% Rule’, a premise that only 10% of what is learnt during a game design process is ultimately appreciated by the player, became a major contributor to the evolving practice. We use this rule primarily as an analytical and illustrative tool to discuss the learning involved in designing and playing learning games rather than as a scientifically and empirically proven rule. The 10% rule was promoted by our experience as designers and allows us to explore the often overlooked and valuable learning processes involved in designing learning games and mobile games in particular. This discussion highlights that in designing mobile learning games, students are not only reflecting on their own learning processes through setting up structures for others to enquire and investigate, they are also engaging in high-levels of independent inquiry and critical analysis in authentic learning settings. We conclude the paper with a discussion of the importance of these types of learning processes and skills of enquiry in 21st Century learning.

Trace, a novel and exegesis

Trace concerns writing-walking and walking-writing. The multiple voices of both novel and exegesis assemble a rhizomic map of a walk and create a never-entirely-certain wandering look upon a woman walking, rather than a single cocksure gaze. Trace explores the aesthetics of Western walking literature and the various nostalgias inherent in that tradition. Trace wonders how lost a character can become on a walk and whether a walk is itself a kind of becoming. In the undefined liminal space where the urban bleeds into the rural, Trace challenges the singular perspective of the dominating gaze with a wandering look, which aims to make an original contribution to both the walk in literature and to exegetical form.

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.

Digital constitutionalism and the role of the rule of law in the governance of virtual communities

This project proposes a new conceptual framework for the regulation of social networks and virtual communities. By applying a model based upon the rule of law, this thesis addresses the growing tensions that revolve around the public use of private networks. This research examines the shortcomings of traditional contractual governance models and cyberlaw theory and provides a reconstituted approach that will allow public constitutional-type interests to be recognised in the interpretation and enforcement of contractual doctrine.

The role of the rule of law in virtual communities

There is a severe tendency in cyberlaw theory to delegitimize state intervention in the governance of virtual communities. Much of the existing theory makes one of two fundamental flawed assumptions: that communities will always be best governed without the intervention of the state; or that the territorial state can best encourage the development of communities by creating enforceable property rights and allowing the market to resolve any disputes. These assumptions do not ascribe sufficient weight to the value-laden support that the territorial state always provides to private governance regimes, the inefficiencies that will tend to limit the development utopian communities, and the continued role of the territorial state in limiting autonomy in accordance with communal values. In order to overcome these deterministic assumptions, this article provides a framework based upon the values of the rule of law through which to conceptualise the legitimacy of the private exercise of power in virtual communities. The rule of law provides a constitutional discourse that assists in considering appropriate limits on the exercise of private power. I argue that the private contractual framework that is used to govern relations in virtual communities ought to be informed by the values of the rule of law in order to more appropriately address the governance tensions that permeate these spaces. These values suggest three main limits to the exercise of private power: that governance is limited by community rules and that the scope of autonomy is limited by the substantive values of the territorial state; that private contractual rules should be general, equal, and certain; and that, most importantly, internal norms be predicated upon the consent of participants.

Influence of historic land use change on the biosphere-atmosphere-exchange of C and N trace gases in the humid, subtropical region of Queensland

Increases in atmospheric concentrations of the greenhouse gases (GHGs) carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) due to human activities have been linked to climate change. GHG emissions from land use change and agriculture have been identified as significant contributors to both Australia’s and the global GHG budget. This is expected to increase over the coming decades as rates of agriculture intensification and land use change accelerate to support population growth and food production. Limited data exists on CO2, CH4 and N2O trace gas fluxes from subtropical or tropical soils and land uses. To develop effective mitigation strategies a full global warming potential (GWP) accounting methodology is required that includes emissions of the three primary greenhouse gases. Mitigation strategies that focus on one gas only can inadvertently increase emissions of another. For this reason, detailed inventories of GHGs from soils and vegetation under individual land uses are urgently required for subtropical Australia. This study aimed to quantify GHG emissions over two consecutive years from three major land uses; a well-established, unfertilized subtropical grass-legume pasture, a 30 year (lychee) orchard and a remnant subtropical Gallery rainforest, all located near Mooloolah, Queensland. GHG fluxes were measured using a combination of high resolution automated sampling, coarser spatial manual sampling and laboratory incubations. Comparison between the land uses revealed that land use change can have a substantial impact on the GWP on a landscape long after the deforestation event. The conversion of rainforest to agricultural land resulted in as much as a 17 fold increase in GWP, from 251 kg CO2 eq. ha-1 yr-1 in the rainforest to 889 kg CO2 eq. ha-1 yr-1 in the pasture to 2538 kg CO2 eq. ha-1 yr-1 in the lychee plantation. This increase resulted from altered N cycling and a reduction in the aerobic capacity of the soil in the pasture and lychee systems, enhancing denitrification and nitrification events, and reducing atmospheric CH4 uptake in the soil. High infiltration, drainage and subsequent soil aeration under the rainforest limited N2O loss, as well as promoting CH4 uptake of 11.2 g CH4-C ha-1 day-1. This was among the highest reported for rainforest systems, indicating that aerated subtropical rainforests can act as substantial sink of CH4. Interannual climatic variation resulted in significantly higher N2O emission from the pasture during 2008 (5.7 g N2O-N ha day) compared to 2007 (3.9 g N2O-N ha day), despite receiving nearly 500 mm less rainfall. Nitrous oxide emissions from the pasture were highest during the summer months and were highly episodic, related more to the magnitude and distribution of rain events rather than soil moisture alone. Mean N2O emissions from the lychee plantation increased from an average of 4.0 g N2O-N ha-1 day-1, to 19.8 g N2O-N ha-1 day-1 following a split application of N fertilizer (560 kg N ha-1, equivalent to 1 kg N tree-1). The timing of the split application was found to be critical to N2O emissions, with over twice as much lost following an application in spring (emission factor (EF): 1.79%) compared to autumn (EF: 0.91%). This was attributed to the hot and moist climatic conditions and a reduction in plant N uptake during the spring creating conditions conducive to N2O loss. These findings demonstrate that land use change in subtropical Australia can be a significant source of GHGs. Moreover, the study shows that modifying the timing of fertilizer application can be an efficient way of reducing GHG emissions from subtropical horticulture.

A Two-stage information filtering based on rough decision rule and pattern mining

Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.