Vote with your feet : hyperlocal public polling for urban screens

Technological advances have led to an ongoing spread of public displays in urban areas. However, they still mostly show passive content such as commercials and digital signage. Researchers took notice of their potential to spark situated civic discourse in public space and have begun working on interactive public display applications. Attracting people’s attention and providing a low barrier for user participation have been identified as major challenges in their design. This thesis presents Vote With Your Feet, a hyperlocal public polling tool for urban screens allowing users to express their opinions. Similar to vox populi interviews on TV or polls on news websites, the tool is meant to reflect the mindset of the community on topics such as current affairs, cultural identity and local matters. It shows one Yes/No question at a time and enables users to vote by stepping on one of two tangible buttons on the ground. This user interface was introduced to attract people’s attention and to lower participation barriers. Vote With Your Feet was informed by a user-centred design approach that included a focus group, expert interviews and extensive preliminary user studies in the wild. Deployed at a bus stop, Vote With Your Feet was evaluated in a field study over the course of several days. Observations of people and interviews with 30 participants revealed that the novel interaction technology was perceived as inviting and that Vote With Your Feet can spark discussions among co-located people.

A demand-responsive decision support system for coal transportation

In this paper, a demand-responsive decision support system is proposed by integrating the operations of coal shipment, coal stockpiles and coal railing within a whole system. A generic and flexible scheduling optimisation methodology is developed to identify, represent, model, solve and analyse the coal transport problem in a standard and convenient way. As a result, the integrated train-stockpile-ship timetable is created and optimised for improving overall efficiency of coal transport system. A comprehensive sensitivity analysis based on extensive computational experiments is conducted to validate the proposed methodology. The mathematical proposition and proof are concluded as technical and insightful advices for industry practice. The proposed methodology provides better decision making on how to assign rail rolling-stocks and upgrade infrastructure in order to significantly improve capacity utilisation with the best resource-effectiveness ratio. The proposed decision support system with train-stockpile-ship scheduling optimisation techniques is promising to be applied in railway or mining industry, especially as a useful quantitative decision making tool on how to use more current rolling-stocks or whether to buy additional rolling-stocks for mining transportation.

Point-to-point speed enforcement : recommendations for better practice

Point-to-point speed cameras are a relatively new and innovative technological approach to speed enforcement that is increasingly been used in a number of highly motorised countries. Previous research has provided evidence of the positive impact of this approach on vehicle speeds and crash rates, as well as additional traffic related outcomes such as vehicle emissions and traffic flow. This paper reports on the conclusions and recommendations of a large-scale project involving extensive consultation with international and domestic (Australian) stakeholders to explore the technological, operational, and legislative characteristics associated with the technology. More specifically, this paper provides a number of recommendations for better practice regarding the implementation of point-to-point speed enforcement in the Australian and New Zealand context. The broader implications of the research, as well as directions for future research, are also discussed.

Campaigning for street trees, Sydney Botanic Gardens, 1890s to 1920s

Between the 1890s and 1920s street trees became a more prominent feature in streetscapes across New South Wales, Australia. The Sydney Botanic Gardens, with their extensive nursery system, were responsible for supplying seedlings to councils and municipalities for use as street trees. As such, this institution was a primary mover of what a street tree should be, how they should be used and what plants were best suited to this particular use in urban environments. This paper analyses the nurturing of this use of street trees by the Sydney Botanic Gardens and the Director Joseph Maiden. This institution was a place that moved not just stock and seedlings, but ideas about how nature's inclusion into urban environments had the capacity to influence and enhance the cultivation of civilised citizens. This was affected through access to transnational resources available to the Sydney Botanic Gardens.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.

Fetuin-A : a major fetal serum protein that promotes "wound closure" and scarless healing [Letter to the Editor]

Burn-wound healing is a dynamic, interactive process involving a number of cellular and molecular events and is characterized by inflammation, granulation tissue formation, re-epithelialization, and tissue remodeling (Greenhalgh, 2002; Linares, 2002). Unlike incisional-wound healing, it also requires extensive re-epithelialization due to a predominant horizontal loss of tissue and often heals with abnormal scarring when burns involve deep dermis. The early mammalian fetus has the remarkable ability to regenerate normal epidermis and dermis and to heal dermal incisional wounds with no signs of scarring. Extensive research has indicated that scarless healing appears to be intrinsic to fetal skin (McCallion and Ferguson, 1996; Ferguson and O’Kane, 2004). Previously, we reported a fetal burn model, in which 80-day-old ovine fetuses (gestation¼ 145–153 days) healed deep dermal partial thickness burns without scars, whereas postnatal lambs healed equal depth burns with significant scarring (Cuttle et al., 2005; Fraser et al., 2005). This burn model provided early evidence that fetal skin has the capacity to repair and restore dermal horizontal loss, not just vertical injuries.

A topic based document relevance ranking model

Topic modelling has been widely used in the fields of information retrieval, text mining, machine learning, etc. In this paper, we propose a novel model, Pattern Enhanced Topic Model (PETM), which makes improvements to topic modelling by semantically representing topics with discriminative patterns, and also makes innovative contributions to information filtering by utilising the proposed PETM to determine document relevance based on topics distribution and maximum matched patterns proposed in this paper. Extensive experiments are conducted to evaluate the effectiveness of PETM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models.

On Chinese Modal Particle A (啊) : A Pragmatic and Semantic Study

Chinese modal particles feature prominently in Chinese people’s daily use of the language, but their pragmatic and semantic functions are elusive as commonly recognised by Chinese linguists and teachers of Chinese as a foreign language. This book originates from an extensive and intensive empirical study of the Chinese modal particle a (啊), one of the most frequently used modal particles in Mandarin Chinese. In order to capture all the uses and the underlying meanings of the particle, the author transcribed the first 20 episodes, about 20 hours in length, of the popular Chinese TV drama series Kewang ‘Expectations’, which yielded a corpus data of more than 142’000 Chinese characters with a total of 1829 instances of the particle all used in meaningful communicative situations. Within its context of use, every single occurrence of the particle was analysed in terms of its pragmatic and semantic contributions to the hosting utterance. Upon this basis the core meanings were identified which were seen as constituting the modal nature of the particle.

Differential expression of VEGF ligands and receptors in prostate cancer

BACKGROUND Prostate cancer disseminates to regional lymph nodes, however the molecular mechanisms responsible for lymph node metastasis are poorly understood. The vascular endothelial growth factor (VEGF) ligand and receptor family have been implicated in the growth and spread of prostate cancer via activation of the blood vasculature and lymphatic systems. The purpose of this study was to comprehensively examine the expression pattern of VEGF ligands and receptors in the glandular epithelium, stroma, lymphatic vasculature and blood vessels in prostate cancer. METHODS The localization of VEGF-A, VEGF-C, VEGF-D, VEGF receptor (VEGFR)-1, VEGFR-2, and VEGFR-3 was examined in cancerous and adjacent benign prostate tissue from 52 subjects representing various grades of prostate cancer. RESULTS Except for VEGFR-2, extensive staining was observed for all ligands and receptors in the prostate specimens. In epithelial cells, VEGF-A and VEGFR-1 expression was higher in tumor tissue compared to benign tissue. VEGF-D and VEGFR-3 expression was significantly higher in benign tissue compared to tumor in the stroma and the endothelium of lymphatic and blood vessels. In addition, the frequency of lymphatic vessels, but not blood vessels, was lower in tumor tissue compared with benign tissue. CONCLUSIONS These results suggest that activation of VEGFR-1 by VEGF-A within the carcinoma, and activation of lymphatic endothelial cell VEGFR-3 by VEGF-D within the adjacent benign stroma may be important signaling mechanisms involved in the progression and subsequent metastatic spread of prostate cancer. Thus inhibition of these pathways may contribute to therapeutic strategies for the management of prostate cancer.

Comprehensive Addictive Behaviors and Disorders, Volume 3 : Interventions for Addiction

"Interventions for Addiction examines a wide range of responses to addictive behaviors, including psychosocial treatments, pharmacological treatments, provision of health care to addicted individuals, prevention, and public policy issues. Its focus is on the practical application of information covered in the two previous volumes of the series, Comprehensive Addictive Behaviors and Disorders. Readers will find information on treatments beyond commonly used methods, including Internet-based and faith-based therapies, and criminal justice interventions. The volume features extensive coverage of pharmacotherapies for each of the major drugs of abuse-including disulfiram, buprenorphine, naltrexone, and others-as well as for behavioral addictions. In considering public policy, the book examines legislative efforts, price controls, and limits on advertising, as well as World Health Organization (WHO) efforts. Interventions for Addiction is one of three volumes comprising the 2,500-page series, Comprehensive Addictive Behaviors and Disorders. This series provides the most complete collection of current knowledge on addictive behaviors and disorders to date. In short, it is the definitive reference work on addictions."--publisher website

Pattern-based topics for document modelling in information filtering

Many mature term-based or pattern-based approaches have been used in the field of information filtering to generate users’ information needs from a collection of documents. A fundamental assumption for these approaches is that the documents in the collection are all about one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, and this has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering has not been so well explored. Patterns are always thought to be more discriminative than single terms for describing documents. However, the enormous amount of discovered patterns hinder them from being effectively and efficiently used in real applications, therefore, selection of the most discriminative and representative patterns from the huge amount of discovered patterns becomes crucial. To deal with the above mentioned limitations and problems, in this paper, a novel information filtering model, Maximum matched Pattern-based Topic Model (MPBTM), is proposed. The main distinctive features of the proposed model include: (1) user information needs are generated in terms of multiple topics; (2) each topic is represented by patterns; (3) patterns are generated from topic models and are organized in terms of their statistical and taxonomic features, and; (4) the most discriminative and representative patterns, called Maximum Matched Patterns, are proposed to estimate the document relevance to the user’s information needs in order to filter out irrelevant documents. Extensive experiments are conducted to evaluate the effectiveness of the proposed model by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models

Selective combination of visual and thermal imaging for resilient localization in adverse conditions : day and night, smoke and fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions.

Reliable automatic camera-laser calibration

Camera-laser calibration is necessary for many robotics and computer vision applications. However, existing calibration toolboxes still require laborious effort from the operator in order to achieve reliable and accurate results. This paper proposes algorithms that augment two existing trustful calibration methods with an automatic extraction of the calibration object from the sensor data. The result is a complete procedure that allows for automatic camera-laser calibration. The first stage of the procedure is automatic camera calibration which is useful in its own right for many applications. The chessboard extraction algorithm it provides is shown to outperform openly available techniques. The second stage completes the procedure by providing automatic camera-laser calibration. The procedure has been verified by extensive experimental tests with the proposed algorithms providing a major reduction in time required from an operator in comparison to manual methods.

Forming a joint venture project team for multi-discipline engineering services in Australia

Joint venture design teams are formed to combine resources and expertise in order to secure multi-discipline engineering design services on major projects. Bringing together resources from two ordinarily competing companies to form one joint team is however challenging as each parent company brings to the project its own organisational culture, processes and team attitudes. This study examined the factors that impact on forming a successful joint venture project team. Three critical areas were identified from an extensive literature review; Joint Venture Arrangements, Parent Companies and Forming the Team; and a survey was conducted with professionals who have worked in joint venture project teams in the Australian building industry in order to identify factors that affected successful joint venture team formation, and the common lessons learnt. This study reinforced the importance of three key criteria - trust, commitment and compatibility - for partner alignment. The results also identified four key lessons learnt which included; selecting the right resources, enabling a collaborative working environment by way of project office, implementing an independent Joint Venture Manager, and allocating work which is best for project with fees reflecting risk where risk is disproportionate.

Traversability estimation for a planetary rover via experimental kernel learning in a Gaussian process framework

A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.