Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

Design and Development of an In-Vehicle Human Machine Interface to Improve Fuel Efficiency & Safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behaviour change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, this research seeks to use human factors related theories and practices to inform the design and evaluation of an in-vehicle Human Machine Interface (HMI) providing real-time driver feedback with the aim of improving both fuel efficiency and safety.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Shrinking Violets

Description of the work Shrinking Violets is comprised of two half scale garments in laser cut silk organza, developed with a knotting device to allow for disassembly and reassembly. The first is a jacket in layered red organza including black storm flap details. The second is a vest in jade organza with circles of pink organza attached through a pattern of knots. Research Background This practice-led fashion design research sits within the field of Design for Sustainability (DfS) in fashion that seeks to mitigate the environmental and ethical impacts of fashion consumption and production. The research explores new systems of garment construction for DfS, and examines how these systems may involve ‘designing’ new user interactions with the garments. The garments’ construction system allows them to be disassembled and recycled or reassembled by users to form a new garment. Conventional garment design follows a set process of cutting and construction, with pattern pieces permanently machine-stitched together. Garments typically contain multiple fibre types; for example a jacket may be constructed from a shell of wool/polyester, an acetate lining, fusible interlinings, and plastic buttons. These complex inputs mean that textile recycling is highly labour intensive, first to separate the garment pieces and second to sort the multiple fibre types. This difficulty results in poor quality ‘shoddy’ comprised of many fibre types and unsuitable for new apparel, or in large quantities of recyclable textile waste sent to landfill (Hawley 2011). Design-led approaches that consider the garment’s end of life in the design process are a way of addressing this problem. In Gulich’s (2006) analysis, use of single materials is the most effective way to ensure ease of recycling, with multiple materials that can be detached next in effectiveness. Given the low rate of technological innovation in most apparel manufacturing (Ruiz 2011), a challenge for effective recycling is how to develop new manufacturing methods that allow for garments to be more easily disassembled at end-of-life. Research Contribution This project addresses the research question: How can design for disassembly be considered within the fashion design process? I have employed a practice-led methodology in which my design process leads the research, making use of methods of fashion design practice including garment and construction research, fabric and colour research, textile experimentation, drape, patternmaking, and illustration as well as more recent methods such as laser cutting. Interrogating the traditional approaches to garment construction is necessarily a technical process; however fashion design is as much about the aesthetic and desirability of a garment as it is about the garment’s pragmatics or utility. This requires a balance between the technical demands of designing for disassembly with the aesthetic demands of fashion. This led to the selection of luxurious, semi-transparent fabrics in bold floral colours that could be layered to create multiple visual effects, as well as the experimentation with laser cutting for new forms of finishing and fastening the fabrics together. Shrinking Violets makes two contributions to new knowledge in the area of design for sustainability within fashion. The first is in the technical development of apparel modularity through the system of laser cut holes and knots that also become a patterning device. The second contribution lies in the design of a system for users to engage with the garment through its ability to be easily reconstructed into a new form. Research Significance Shrinking Violets was exhibited at the State Library of Queensland’s Asia Pacific Design Library, 1-5 November 2015, as part of The International Association of Societies of Design Research’s (IASDR) biannual design conference. The work was chosen for display by a panel of experts, based on the criteria of design innovation and contribution to new knowledge in design. References Gulich, B. (2006). Designing textile products that are easy to recycle. In Y. Wang (Ed.), Recycling in Textiles (pp. 25-37). London: Woodhead. Hawley, J. M. (2011). Textile recycling options: exploring what could be. In A. Gwilt & T. Rissanen (Eds.), Shaping Sustainable Fashion: Changing the way we make and use clothes (pp. 143 - 155). London: Earthscan. Ruiz, B. (2014). Global Apparel Manufacturing. Retrieved 10 August 2014, from http://clients1.ibisworld.com/reports/gl/industry/default.aspx?entid=470

Cost-efficient virtual machine management in data centers

Virtual Machine (VM) management is an obvious need in today's data centers for various management activities and is accomplished in two phases— finding an optimal VM placement plan and implementing that placement through live VM migrations. These phases result in two research problems— VM placement problem (VMPP) and VM migration scheduling problem (VMMSP). This research proposes and develops several evolutionary algorithms and heuristic algorithms to address the VMPP and VMMSP. Experimental results show the effectiveness and scalability of the proposed algorithms. Finally, a VM management framework has been proposed and developed to automate the VM management activity in cost-efficient way.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.