Injury narrative text classification using factorization model

Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93.

Decision boundary setting and classifier combination for text classification

This thesis presents a promising boundary setting method for solving challenging issues in text classification to produce an effective text classifier. A classifier must identify boundary between classes optimally. However, after the features are selected, the boundary is still unclear with regard to mixed positive and negative documents. A classifier combination method to boost effectiveness of the classification model is also presented. The experiments carried out in the study demonstrate that the proposed classifier is promising.

Representing places never visited: Landscape and the diasporic imagination

While landscape photography’s complicity with the colonial possession of new territory has been substantially discussed and well understood, this paper considers the role of the European landscape as the focus of diasporic desire. The interdisciplinary project, S2Q/Good Blood began as a social history map of Scandinavian and Nordic migration to Queensland in the nineteenth century, incorporating archival material from local collections with visual field trip data gathered in Denmark, Sweden, Norway and Finland. In 2011, some of this material found its way into the installation work, 'my mother is water, my father is wood'. What emerged from this experiment was an imaginary landscape, melding its loci through original photography and video footage in tandem with stock imagery and historical material. This juxtaposition reinforced the represented landscape as a narrative landscape and evidence of the performativity of belonging. This practitioner reflection utilizes Lynette Russell’s research into landscape archaeology to consider the significance of relationships with landscapes that are “not always empirically demonstrable.”

Classification of activities of daily living of individual with limb loss

The monitoring of the actual activities of daily living of individuals with lower limb amputation is essential for an evidence-based fitting of the prosthesis, more particularly the choice of components (e.g., knees, ankles, feet)[1-4]. The purpose of this presentation was to give an overview of the categorization of the load regime data to assess the functional output and usage of the prosthesis of lower limb amputees has presented in several publications[5, 6]. The objectives were to present a categorization of load regime and to report the results for a case.

Performance dispersion for evidence-based classification of stationary throwers

Background There is a need for better understanding of the dispersion of classification-related variable to develop an evidence-based classification of athletes with a disability participating in stationary throwing events. Objectives The purposes of this study are (A) to describe tools designed to comprehend and represent the dispersion of the performance between successive classes, and (B) to present this dispersion for the elite male and female stationary shot-putters who participated in Beijing 2008 Paralympic Games. Study design Retrospective study Methods This study analysed a total of 479 attempts performed by 114 male and female stationary shot-putters in three F30s (F32-F34) and six F50s (F52-F58) classes during the course of eight events during Beijing 2008 Paralympic Games. Results The average differences of best performance were 1.46±0.46 m for males between F54 and F58 classes as well as 1.06±1.18 m for females between F55 and F58 classes. The results demonstrated a linear relationship between best performance and classification while revealing two male Gold Medallists in F33 and F52 classes were outliers. Conclusions This study confirms the benefits of the comparative matrices, performance continuum and dispersion plots to comprehend classification-related variables. The work presented here represents a stepping stone into biomechanical analyses of stationary throwers, particularly on the eve of the London 2012 Paralympic Games where new evidences could be gathered.

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.

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

Cultural policy and Australia's national cultural heritage: Issues and challenges in the GLAM landscape

In 2012 the Australian Commonwealth government was scheduled to release the first dedicated policy for culture and the arts since the Keating government's Creative Nation (1994). Investing in a Creative Australia was to appear after a lengthy period of consultation between the Commonwealth government and all interested cultural sectors and organisations. When it eventuates, the policy will be of particular interest to those information professionals working in the GLAM (galleries, libraries, archives and museums) environment. GLAM is a cross-institutional field which seeks to find points of commonality among various cultural-heritage institutions, while still recognising their points of difference. Digitisation, collaboration and convergence are key themes and characteristics of the GLAM sector and its associated theoretical discipline. The GLAM movement has seen many institutions seeking to work together to create networks of practice that are beneficial to the cultural-heritage industry and sector. With a new Australian cultural policy imminent, it is timely to reflect on the issues and challenges that GLAM principles present to national cultural-heritage institutions by discussing their current practices. In doing so, it is possible to suggest productive ways forward for these institutions which could then be supported at a policy level by the Commonwealth government. Specifically, this paper examines four institutions: the National Gallery of Australia, the National Library of Australia, the National Archives of Australia and the National Museum of Australia. The paper reflects on their responses to the Commonwealth's 2011 Cultural Policy Discussion Paper. It argues that by encouraging and supporting collecting institutions to participate more fully in GLAM practices the Commonwealth government's cultural policy would enable far greater public access to, and participation in, Australia's cultural heritage. Furthermore, by considering these four institutions, the paper presents a discussion of the challenges and the opportunities that GLAM theoretical and disciplinary principles present to the cultural-heritage sector. Implications for Best Practice * GLAM is a developing field of theory and practice that encompasses many issues and challenges for practitioners in this area. * GLAM principles and practices are increasingly influencing the cultural-heritage sector. * Cultural policy is a key element in shaping the future of Australia's cultural-heritage sector and needs to incorporate GLAM principles.

An application of fuzzy DL-Based semantic perception to soil container classification

Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments.

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.

From ImageNet to mining: Adapting visual object detection with minimal supervision

This paper presents visual detection and classification of light vehicles and personnel on a mine site.We capitalise on the rapid advances of ConvNet based object recognition but highlight that a naive black box approach results in a significant number of false positives. In particular, the lack of domain specific training data and the unique landscape in a mine site causes a high rate of errors. We exploit the abundance of background-only images to train a k-means classifier to complement the ConvNet. Furthermore, localisation of objects of interest and a reduction in computation is enabled through region proposals. Our system is tested on over 10km of real mine site data and we were able to detect both light vehicles and personnel. We show that the introduction of our background model can reduce the false positive rate by an order of magnitude.

Petrology and textural classification of the Jericho kimberlite, northern Slave Province, Nunavut, Canada

The paper presents data on petrology, bulk rock and mineral compositions, and textural classification of the Middle Jurassic Jericho kimberlite (Slave craton, Canada). The kimberlite was emplaced as three steep-sided pipes in granite that was overlain by limestones and minor soft sediments. The pipes are infilled with hypabyssal and pyroclastic kimberlites and connected to a satellite pipe by a dyke. The Jericho kimberlite is classified as a Group Ia, lacking groundmass tetraferriphlogopite and containing monticellite pseudomorphs. The kimberlite formed, during several consecutive emplacement events of compositionally different batches of kimberlite magma. Core-logging and thin-section observations identified at least two phases of hypabyssal kimberlites and three phases of pyroclastic kimberlites. Hypabyssal kimberlites intruded as a main dyke (HK1) and as late small-volume aphanitic and vesicular dykes. Massive pyroclastic kimberlite (MPK1) predominantly filled the northern and southern lobes of the pipe and formed from magma different from the HK1 magma. The MPK1 magma crystallized Ti-, Fe-, and Cr-rich phlogopite without rims of barian phlogopite, and clinopyroxene and spinel without atoll structures. MPK1 textures, superficially reminiscent of tuffisitic kimberlite, are caused by pervasive contamination by granite xenoliths. The next explosive events filled the central lobe with two varieties of pyroclastic kimberlite: (1) massive and (2) weakly bedded, normally graded pyroclastic kimberlite. The geology of the Jericho pipe differs from the geology of South African or the Prairie kimberlites, but may resemble Lac de Gras pipes, in which deeper erosion removed upper fades of resedimented kimberlites.

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.

Designing 'other' citizens into the city: Investigating perceptions of architectural opportunities for wildlife habitat in the Brisbane CBD

Traditional perceptions of the human-animal relationship in the urban context typically see the spatial rejection of wildlife from the built environment and limiting of biodiversity conservation programs to areas of natural reserve. As urban growth places further spatial demands on natural habitat and contributes to continued global biodiversity loss, the recently introduced conservation approach of reconciliation ecology makes a call promoting ecological stewardship through embedding wildlife habitat within human dominated areas. Coinciding with this, the architectural sphere has seen a recent trend of design investigation addressing artificial animal habitat as features of the built environment. Although these precedents are currently a niche and scattered trend they show potential to address the human-animal dualism challenging the framework of reconciliation ecology. This research explores the role design plays in influencing perceptions of urban wildlife habitat, particularly considering the need to create and communicate value around wildlife biodiversity as a component of urban cultural place-making and ecological literacy. The study purpose sets out to establish a set of approaches and cultural preferences with which to direct further classification and development of this architectural trend. Brisbane is utilised as a case study city, as a locale containing proximities of relatively high wildlife and human populations in an urban setting and an established legislative biodiversity heritage and ethic. Through use of a qualitative and quantitative questionnaire targeting Brisbane residents, the research methodology established that although respondents perceptions generally aligned with traditional prejudice against wildlife around human buildings, artificial habitat intervention would be supported within the CBD provided it allowed for adequate distancing of humans from wildlife and conformed with contextual surroundings, or otherwise addressed habitat through redevelopment at an urban scale. As such further research directions for artificial habitat should focus on integration of artificial habitat as a component of façade design or green infrastructure programs.