Association hierarchy mining and its application for network traffic characterisation

This thesis presents an association rule mining approach, association hierarchy mining (AHM). Different to the traditional two-step bottom-up rule mining, AHM adopts one-step top-down rule mining strategy to improve the efficiency and effectiveness of mining association rules from datasets. The thesis also presents a novel approach to evaluate the quality of knowledge discovered by AHM, which focuses on evaluating information difference between the discovered knowledge and the original datasets. Experiments performed on the real application, characterizing network traffic behaviour, have shown that AHM achieves encouraging performance.

The influence of psychosocial factors on expectations and persistent symptom report after mild traumatic brain injury

This thesis examines how psychosocial factors influence the report of persistent symptoms after mild traumatic brain injury. Using quasi-experimental methods, the research program demonstrates how factors unrelated to trauma-induced physiological brain damage can contribute to persistent symptoms after a mild traumatic brain injury. The results of this thesis highlight the possibility that outcome from mild traumatic brain injury could be improved by targeting psychosocial factors.

Co-movements in commodity prices : a note based on network analysis

This article analyses co-movements in a wide group of commodity prices during the time period 1992–2010. Our methodological approach is based on the correlation matrix and the networks inside. Through this approach we are able to summarize global interaction and interdependence, capturing the existing heterogeneity in the degrees of synchronization between commodity prices. Our results produce two main findings: (a) we do not observe a persistent increase in the degree of co-movement of the commodity prices in our time sample, however from mid-2008 to the end of 2009 co-movements almost doubled when compared with the average correlation; (b) we observe three groups of commodities which have exhibited similar price dynamics (metals, oil and grains, and oilseeds) and which have increased their degree of co-movement during the sampled period.

Identifying influential users of micro-blogging services : a dynamic action-based network approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our actionbaseduser influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered.

Towards a spatial language for mobile robots

We present a framework and first set of simulations for evolving a language for communicating about space. The framework comprises two components: (1) An established mobile robot platform, RatSLAM, which has a "brain" architecture based on rodent hippocampus with the ability to integrate visual and odometric cues to create internal maps of its environment. (2) A language learning system based on a neural network architecture that has been designed and implemented with the ability to evolve generalizable languages which can be learned by naive learners. A study using visual scenes and internal maps streamed from the simulated world of the robots to evolve languages is presented. This study investigated the structure of the evolved languages showing that with these inputs, expressive languages can effectively categorize the world. Ongoing studies are extending these investigations to evolve languages that use the full power of the robots representations in populations of agents.

Correlation based method for phase identification in a three phase LV distribution network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques.

Consumer phase identification in a three phase unbalanced LV distribution network

A new technique is presented for automatically identifying the phase connection of domestic customers. Voltage information from a reference three phase house is correlated with voltage information from other customer electricity meters on the same network to determine the highest probability phase connection. The techniques are purely based upon a time series of electrical voltage measurements taken by the household smart meters and no additional equipment is required. The method is demonstrated using real smart meter datasets to correctly identify the phase connections of 75 consumers on a low voltage distribution feeder.

Convolutional Neural Network-based place recognition

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.

The anatomy of the bill tip of kiwi and associated somatosensory regions of the brain : comparisons with shorebirds

Three families of probe-foraging birds, Scolopacidae (sandpipers and snipes), Apterygidae (kiwi), and Threskiornithidae (ibises, including spoonbills) have independently evolved long, narrow bills containing clusters of vibration-sensitive mechanoreceptors (Herbst corpuscles) within pits in the bill-tip. These ‘bill-tip organs’ allow birds to detect buried or submerged prey via substrate-borne vibrations and/or interstitial pressure gradients. Shorebirds, kiwi and ibises are only distantly related, with the phylogenetic divide between kiwi and the other two taxa being particularly deep. We compared the bill-tip structure and associated somatosensory regions in the brains of kiwi and shorebirds to understand the degree of convergence of these systems between the two taxa. For comparison, we also included data from other taxa including waterfowl (Anatidae) and parrots (Psittaculidae and Cacatuidae), non-apterygid ratites, and other probe-foraging and non probe-foraging birds including non-scolopacid shorebirds (Charadriidae, Haematopodidae, Recurvirostridae and Sternidae). We show that the bill-tip organ structure was broadly similar between the Apterygidae and Scolopacidae, however some inter-specific variation was found in the number, shape and orientation of sensory pits between the two groups. Kiwi, scolopacid shorebirds, waterfowl and parrots all shared hypertrophy or near-hypertrophy of the principal sensory trigeminal nucleus. Hypertrophy of the nucleus basorostralis, however, occurred only in waterfowl, kiwi, three of the scolopacid species examined and a species of oystercatcher (Charadriiformes: Haematopodidae). Hypertrophy of the principal sensory trigeminal nucleus in kiwi, Scolopacidae, and other tactile specialists appears to have co-evolved alongside bill-tip specializations, whereas hypertrophy of nucleus basorostralis may be influenced to a greater extent by other sensory inputs. We suggest that similarities between kiwi and scolopacid bill-tip organs and associated somatosensory brain regions are likely a result of similar ecological selective pressures, with inter-specific variations reflecting finer-scale niche differentiation.

Morphometric analysis of telencephalic structure in a variety of neognath and paleognath bird species reveals regional differences associated with specific behavioral traits

Birds exhibit a huge array of behavior, ecology and physiology, and occupy nearly every environment on earth, ranging from the desert outback of Australia to the tropical rain forests of Panama. Some birds have adopted a fully nocturnal lifestyle, such as the barn owl and kiwi, while others, such as the albatross, spend nearly their entire life flying over the ocean. Each species has evolved unique adaptations over millions of years to function in their respective niche. In order to increase processing power or network efficiency, many of these adaptations require enlargements and/or specializations of the brain as a whole or of specific brain regions. In this study, we examine the relative size and morphology of 9 telencephalic regions in a number of Paleognath and Neognath birds and relate the findings to differences in behavior and sensory ecology. We pay particular attention to those species that have undergone a relative enlargement of the telencephalon to determine whether this relative increase in telencephalic size is homogeneous across different brain regions or whether particular regions have become differentially enlarged. The analysis indicates that changes in the relative size of telencephalic regions are not homogeneous, with every species showing hypertrophy or hypotrophy of at least one of them. The three-dimensional structure of these regions in different species was also variable, in particular that of the mesopallium in kiwi. The findings from this study provide further evidence that the changes in relative brain size in birds reflect a process of mosaic evolution.

Evolution of brain size in the palaeognath lineage, with an emphasis on New Zealand ratites

Brain size in vertebrates varies principally with body size. Although many studies have examined the variation of brain size in birds, there is little information on Palaeognaths, which include the ratite lineage of kiwi, emu, ostrich and extinct moa, as well as the tinamous. Therefore, we set out to determine to what extent the evolution of brain size in Palaeognaths parallels that of other birds, i. e., Neognaths, by analyzing the variation in the relative sizes of the brain and cerebral hemispheres of several species of ratites and tinamous. Our results indicate that the Palaeognaths possess relatively smaller brains and cerebral hemispheres than the Neognaths, with the exception of the kiwi radiation (Apteryx spp.). The external morphology and relatively large size of the brain of Apteryx, as well as the relatively large size of its telencephalon, contrast with other Palaeognaths, including two species of historically sympatric moa, suggesting that unique selective pressures towards increasing brain size accompanied the evolution of kiwi. Indeed, the size of the cerebral hemispheres with respect to total brain size of kiwi is rivaled only by a handful of parrots and songbirds, despite a lack of evidence of any advanced behavioral/ cognitive abilities such as those reported for parrots and crows. In addition, the enlargement in brain and telencephalon size of the kiwi occurs despite the fact that this is a precocial bird. These findings form an exception to, and hence challenge, the current rules that govern changes in relative brain size in birds. Copyright (c) 2007 S. Karger AG, Basel.

MRI of postmortem specimens of endangered species for comparative brain anatomy

Many of the 5,500 threatened species of vertebrates found worldwide are highly protected and generally unavailable for scientific investigation. Here we describe a noninvasive protocol to visualize the structure and size of brain in postmortem specimens. We demonstrate its utility by examining four endangered species of kiwi (Apteryx spp.). Frozen specimens are thawed and imaged using MRI, revealing internal details of brain structure. External brain morphology and an estimate of brain volume can be reliably obtained by creating 3D models. This method has facilitated a comparison of brain structure in the different kiwi species, one of which is on the brink of extinction. This new approach has the potential to extend our knowledge of brain structure to species that have until now been outside the reach of anatomical investigation.

A hybrid quality function deployment and cybernetic analytic network process model for project manager selection

Identifying appropriate decision criteria and making optimal decisions in a structured way is a complex process. This paper presents an approach for doing this in the form of a hybrid Quality Function Deployment (QFD) and Cybernetic Analytic Network Process (CANP) model for project manager selection. This involves the use of QFD to translate the owner's project management expectations into selection criteria and the CANP to weight the expectations and selection criteria. The supermatrix approach then prioritises the candidates with respect to the overall decision-making goal. A case study is used to demonstrate the use of the model in selecting a renovation project manager. This involves the development of 18 selection criteria in response to the owner's three main expectations of time, cost and quality.

Facebook for adults ? A longitudinal study of MBA student’s use of an enterprise social network for collaborative learning

The rapid pace of social media means that our understanding of the way in which it facilitates the learning process continues to lag. The findings of a longitudinal study of an executive MBA cohort over the period of eight months in their use of the social media application is presented. Over time the ownership and use of the Yammer site shifted to become student driven and facilitated. The motivations behind the site’s use, perceived advantages and disadvantages and changes in usage patterns are documented. The case provides a useful insight into the way in which students used this technology to facilitate their learning goals and how patterns of behaviour changed in response to the changing needs of the cohort.

A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an R2 goodness of fit of 0.9994 and 0.9982 respectively over a 10 h test period. The utility of the framework is demonstrated on a number of usage scenarios including causal analysis and ‘what-if’ analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.