CultureMap

CultureMap is the mobile app for the Cultural Atlas of Australia, a digital atlas that maps the locations of more than 200 films, novels, and plays that feature Australian spaces and places. You can use the CultureMap app to find narrative and filming locations near you, you can search for your favourite Australian stories to find out where they are set, or you can search by location to discover what stories have been set there. The app also features our Mapping Ecological Themes showcase, which focuses on films and novels that foreground ecological themes or are set in environmentally sensitive areas. CultureMap is co-funded through an Australian Research Council Discovery Grant and an Inspiring Australia: Unlocking Australia’s Potential grant.

Industry-school partnerships: An ecological case study to understand operational dynamics

This thesis is an ecological systems case study of an industry-school partnership. It examines a minerals and energy sector partnership with Queensland schools and explains the operational dynamics. In doing so, an original contribution to theory and practice was presented, together with implications for the impact of industry on education.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.

Common psychotic symptoms can be explained by the theory of ecological perception

The symptoms of psychiatric illness are diverse, as are the causes of the illnesses that cause them. Yet, regardless of the heterogeneity of cause and presentation, a great deal of symptoms can be explained by the failure of a single perceptual function – the reprocessing of ecological perception. It is a central tenet of the ecological theory of perception that we perceive opportunities to act. It has also been found that perception automatically causes actions and thoughts to occur unless this primary action pathway is inhibited. Inhibition allows perceptions to be reprocessed into more appropriate alternative actions and thoughts. Reprocessing of this kind takes place over the entire frontal lobe and it renders action optional. Choice about what action to take (if any) is the basis for the feeling of autonomy and ultimately for the sense-of-self. When thoughts and actions occur automatically (without choice) they appear to originate outside of the self, thereby providing prima facie evidence for some of the bizarre delusions that define schizophrenia such as delusional misidentification, delusions of control and Cotard’s delusion. Automatic actions and thoughts are triggered by residual stimulation whenever reprocessing is insufficient to balance automatic excitatory cues (for whatever reason). These may not be noticed if they are neutral and therefore unimportant whereas actions and thoughts with a positive bias are desirable. Responses to negative stimulus, on the other hand, are always unwelcome, because the actions that are triggered will carry the negative bias. Automatic thoughts may include spontaneous positive feelings of love and joy, but automatic negative thoughts and visualisations are experienced as hallucinations. Not only do these feel like they emerge from elsewhere but they carry a negative bias (they are most commonly critical, rude and are irrationally paranoid). Automatic positive actions may include laughter and smiling and these are welcome. Automatic behaviours that carry a negative bias, however, are unwelcome and like hallucinations, occur without a sense of choice. These include crying, stereotypies, perseveration, ataxia, utilization and imitation behaviours and catatonia.

Perfusion fMRI evidence for priming of shared feature-to-lexical connections during cumulative semantic interference in spoken word production

The speed at which target pictures are named increases monotonically as a function of prior retrieval of other exemplars of the same semantic category and is unaffected by the number of intervening items. This cumulative semantic interference effect is generally attributed to three mechanisms: shared feature activation, priming and lexical-level selection. However, at least two additional mechanisms have been proposed: (1) a 'booster' to amplify lexical-level activation and (2) retrieval-induced forgetting (RIF). In a perfusion functional Magnetic Resonance Imaging (fMRI) experiment, we tested hypotheses concerning the involvement of all five mechanisms. Our results demonstrate that the cumulative interference effect is associated with perfusion signal changes in the left perirhinal and middle temporal cortices that increase monotonically according to the ordinal position of exemplars being named. The left inferior frontal gyrus (LIFG) also showed significant perfusion signal changes across ordinal presentations; however, these responses did not conform to a monotonically increasing function. None of the cerebral regions linked with RIF in prior neuroimaging and modelling studies showed significant effects. This might be due to methodological differences between the RIF paradigm and continuous naming as the latter does not involve practicing particular information. We interpret the results as indicating priming of shared features and lexical-level selection mechanisms contribute to the cumulative interference effect, while adding noise to a booster mechanism could account for the pattern of responses observed in the LIFG.

Feature overlap slows lexical selection: Evidence from the picture–word interference paradigm

How does the presence of a categorically related word influence picture naming latencies? In order to test competitive and noncompetitive accounts of lexical selection in spoken word production, we employed the picture–word interference (PWI) paradigm to investigate how conceptual feature overlap influences naming latencies when distractors are category coordinates of the target picture. Mahon et al. (2007. Lexical selection is not by competition: A reinterpretation of semantic interference and facilitation effects in the picture-word interference paradigm. Journal of Experimental Psychology. Learning, Memory, and Cognition, 33(3), 503–535. doi:10.1037/0278-7393.33.3.503) reported that semantically close distractors (e.g., zebra) facilitated target picture naming latencies (e.g., HORSE) compared to far distractors (e.g., whale). We failed to replicate a facilitation effect for within-category close versus far target–distractor pairings using near-identical materials based on feature production norms, instead obtaining reliably larger interference effects (Experiments 1 and 2). The interference effect did not show a monotonic increase across multiple levels of within-category semantic distance, although there was evidence of a linear trend when unrelated distractors were included in analyses (Experiment 2). Our results show that semantic interference in PWI is greater for semantically close than for far category coordinate relations, reflecting the extent of conceptual feature overlap between target and distractor. These findings are consistent with the assumptions of prominent competitive lexical selection models of speech production.

Quality-aware review selection based on product feature taxonomy

As of today, user-generated information such as online reviews has become increasingly significant for customers in decision making process. Meanwhile, as the volume of online reviews proliferates, there is an insistent demand to help the users tackle the information overload problem. In order to extract useful information from overwhelming reviews, considerable work has been proposed such as review summarization and review selection. Particularly, to avoid the redundant information, researchers attempt to select a small set of reviews to represent the entire review corpus by preserving its statistical properties (e.g., opinion distribution). However, one significant drawback of the existing works is that they only measure the utility of the extracted reviews as a whole without considering the quality of each individual review. As a result, the set of chosen reviews may consist of low-quality ones even its statistical property is close to that of the original review corpus, which is not preferred by the users. In this paper, we proposed a review selection method which takes review quality into consideration during the selection process. Specifically, we examine the relationships between product features based upon a domain ontology to capture the review characteristics based on which to select reviews that have good quality and preserve the opinion distribution as well. Our experimental results based on real world review datasets demonstrate that our proposed approach is feasible and able to improve the performance of the review selection effectively.

Improving deep convultional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.

Investigating the effectiveness of an ecological approach to learning design in a first year mathematics for engineering unit

This paper reports on the results of a project aimed at creating a research-informed, pedagogically reliable, technology-enhanced learning and teaching environment that would foster engagement with learning. A first-year mathematics for engineering unit offered at a large, metropolitan Australian university provides the context for this research. As part of the project, the unit was redesigned using a framework that employed flexible, modular, connected e-learning and teaching experiences. The researchers, interested in an ecological perspective on educational processes, grounded the redesign principles in probabilistic learning design (Kirschner et al., 2004). The effectiveness of the redesigned environment was assessed through the lens of the notion of affordance (Gibson, 1977,1979, Greeno, 1994, Good, 2007). A qualitative analysis of the questionnaire distributed to students at the end of the teaching period provided insight into factors impacting on the successful creation of an environment that encourages complex, multidimensional and multilayered interactions conducive to learning.

Image processing and classification procedure for the analysis of Australian frog vocalisations

Frog species have been declining worldwide at unprecedented rates in the past decades. There are many reasons for this decline including pollution, habitat loss, and invasive species [1]. To preserve, protect, and restore frog biodiversity, it is important to monitor and assess frog species. In this paper, a novel method using image processing techniques for analyzing Australian frog vocalisations is proposed. An FFT is applied to audio data to produce a spectrogram. Then, acoustic events are detected and isolated into corresponding segments through image processing techniques applied to the spectrogram. For each segment, spectral peak tracks are extracted with selected seeds and a region growing technique is utilised to obtain the contour of each frog vocalisation. Based on spectral peak tracks and the contour of each frog vocalisation, six feature sets are extracted. Principal component analysis reduces each feature set down to six principal components which are tested for classification performance with a k-nearest neighbor classifier. This experiment tests the proposed method of classification on fourteen frog species which are geographically well distributed throughout Queensland, Australia. The experimental results show that the best average classification accuracy for the fourteen frog species can be up to 87%.

Acoustic feature extraction using perceptual wavelet packet decomposition for frog call classification

Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

SPSA Based Feature Relevance Estimation For Video Retrieval

With the availability of a huge amount of video data on various sources, efficient video retrieval tools are increasingly in demand. Video being a multi-modal data, the perceptions of ``relevance'' between the user provided query video (in case of Query-By-Example type of video search) and retrieved video clips are subjective in nature. We present an efficient video retrieval method that takes user's feedback on the relevance of retrieved videos and iteratively reformulates the input query feature vectors (QFV) for improved video retrieval. The QFV reformulation is done by a simple, but powerful feature weight optimization method based on Simultaneous Perturbation Stochastic Approximation (SPSA) technique. A video retrieval system with video indexing, searching and relevance feedback (RF) phases is built for demonstrating the performance of the proposed method. The query and database videos are indexed using the conventional video features like color, texture, etc. However, we use the comprehensive and novel methods of feature representations, and a spatio-temporal distance measure to retrieve the top M videos that are similar to the query. In feedback phase, the user activated iterative on the previously retrieved videos is used to reformulate the QFV weights (measure of importance) that reflect the user's preference, automatically. It is our observation that a few iterations of such feedback are generally sufficient for retrieving the desired video clips. The novel application of SPSA based RF for user-oriented feature weights optimization makes the proposed method to be distinct from the existing ones. The experimental results show that the proposed RF based video retrieval exhibit good performance.

Comparative leaf anatomy and micromorphology of the Chilean Myrtaceae: Taxonomic and ecological implications

The family Myrtaceae in Chile comprises 26 species in 10 genera. The species occur in a diverse rangeof environments including humid temperate forests, swamps, riparian habitats and coastal xeromorphicshrublands. Most of these species are either endemic to Chile or endemic to the humid temperate forestsof Chile and Argentina. Although many taxa have very restricted distributions and are of conservationconcern, little is known about their biology and vegetative anatomy. In this investigation, we describe andcompare the leaf anatomy and micromorphology of all Chilean Myrtaceae using standard protocols forlight and scanning electron microscopy. Leaf characters described here are related to epidermis, cuticle,papillae, stomata, hairs, mesophyll, crystals, secretory cavities and vascular system. Nearly all the specieshave a typical mesophytic leaf anatomy, but some species possess xerophytic characters such as doubleepidermis, hypodermis, pubescent leaves, thick adaxial epidermis and straight epidermal anticlinal walls,which correlate with the ecological distribution of the species. This is the first report on leaf anatomyand micromorphology in most of these species. We identified several leaf characters with potential tax-onomic and ecological significance. Some combinations of leaf characters can reliably delimitate genera,while others are unique to some species. An identification key using micromorphological and anatomicalcharacters is provided to distinguish genera and species.

Ecological infrastructure vs. techno-fix: A design framework for renewable energy infrastructure in public spaces

Renewable energy is commonly considered a technological addition to urban environments. By contrast, this PhD used a holistic approach to develop a design framework for integrating local electricity production into the ecological function and cultural use of public space. The framework addresses social engagement related to public interaction, and economic engagement related to the estimated quantity of electricity produced, in conjunction with environmental engagement related to the embodied energy required to construct the renewable energy infrastructure. The outcomes will contribute to social and environmental change by engaging society, enriching the local economy and increasing social networks.