Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

A comparison of classification algorithms within the Classifynder pollen imaging system

We describe an investigation into how Massey University's Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University's pollen reference collection (2890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. In addition to the Classifynder's native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples. © 2013 AIP Publishing LLC.

Classification of inventory data suit for managing Australian railway bridges

With a focus to optimising the life cycle performance of Australian Railway bridges, new bridge classification and environmental classification systems are proposed. The new bridge classification system is mainly to facilitate the implementation of novel Bridge Management System (BMS) which optimise the life cycle cost both at project level and network level while environment classification is mainly to improve accuracy of Remaining Service Potential (RSP) module of the proposed BMS. In fact, limited capacity of the existing BMS to trigger the maintenance intervention point is an indirect result of inadequacies of the existing bridge and environmental classification systems. The proposed bridge classification system permits to identify the intervention points based on percentage deterioration of individual elements and maintenance cost, while allowing performance based rating technique to implement for maintenance optimisation and prioritisation. Simultaneously, the proposed environment classification system will enhance the accuracy of prediction of deterioration of steel components.

Fine-grained plant classification using convolutional neural networks for feature extraction

We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0:249 on the test set of LifeCLEF 2014.

Powering innovation through tax concessions: The changing research & development tax incentives

The changes to the R&D tax concession in 2011 were touted as the biggest reform to business innovation policy in over a decade. Three years later, as part of the 2014 Federal Budget, a reduction in the concession rates was announced. While the most recent of the pro-posed changes are designed to align with the reduction in company tax rate, the Australian Federal Government also indicated that the gain to revenue from the reduction in the incentive scheme will be redirected by the Government to repair the Budget and fund policy priori-ties. The consequence is that the R&D concessions, while designed to encourage innovation, are clearly linked with the tax system. As such, the first part of this article considers whether the R&D concession is a changing tax for changing times. Leading on from part one, this article also addresses a second question of ‘what’s tax got to do with it’? To answer this question, the article argues that, rather than ever being substantive tax reform, the constantly changing measures simply alter the criteria and means by which companies become eligible for a Federal Government subsidy for qualifying R&D activity, whatever that amount is. It further argues that when considered as part of the broader innovation agenda, all R&D tax concessions should be evaluated as a government spending program in the same way as any direct spending on innovation. When this is done, the tax regime is arguably merely the administrative policy instrument by which the subsidy is delivered. However, this may not be best practice to distribute those funds fairly, efficiently, and without distortion, while at the same time maintaining adequate government control and accountability. Finally, in answering the question of ‘what’s tax got to do with it?’ the article concludes that the answer is: very little.

The small business CGT concessions: Evidence from the perspective of the tax practitioner

On 21 September 1999 Division 152 was inserted into the Income Tax Assessment Act (1997) (ITAA 1997). Division 152 contains the small business CGT concessions, which enables eligible small business taxpayers to reduce the amount of tax payable on capital gains arising from certain CGT events that occur after 11:45 am on 21 September 1999. One of the principal objectives of the legislation is to provide a concessionary regime for small business owners who do not have the same ability to access the concessionary superannuation regime generally available to employees. When announcing the introduction of the concessions the then Federal Treasurer, Mr Peter Costello, specifically stated that the objective of Division 152 was to provide ‘small business people with access to funds for retirement or expansion’. The purpose of this article is to: one, assess the extent to which small business taxpayers understand the CGT small business concessions, particularly when considering the sale of their business; two, determine which of the four small business CGT concessions are most commonly adopted and/or recommended by tax practitioners to clients; and three, to determine whether the superannuation changes in relation to the capping of the concessional superannuation thresholds have had an impact on the use of the small business retirement concession.

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.

Emerging from the shadows: Tax as a research discipline (Editorial)

More than ever, research is playing an important part in supporting proposed tax reforms and finding solutions to Australia’s tax system. Also, for tax academics the importance of quality research is critical in an increasingly competitive tertiary environment. However, life for an academic can be an isolating experience at time, especially if one’s expertise is in an area that many of their immediate colleagues do not share an interest in. Collegiately and the ability to be able to discuss research is seen as critical in fostering the next generation of academics. It is with this in mind that on the 5th of July 2010 the Inaugural Queensland Tax Teachers’ Symposium was hosted by Griffith University at its Southbank campus. The aim was to bring together for one day tax academics in Queensland, and further afield, to present their current research projects and encourage independent tax research. If was for this reason that the symposium was later re-named the Queensland Tax Researchers’ Symposium (QTRS) to reflect its emphasis. The Symposium has been held annually mid-year on four occasions with in excess of 120 attendees over this period. The fifth QTRS is planned for June 2014 to be hosted by James Cook University.

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.

Why a movie tax will make even more of us pirates

In a nation of rampant illegal downloaders, a tax on movies and television downloads is the last thing we need. Australian consumers and content producers are among those likely to be worse off should Joe Hockey succeed in his efforts to extend GST to online video-on-demand services like Netflix. It is easy to see why Mr Hockey and his state treasurer counterparts have reportedly agreed to this move. That doesn’t mean it’s a good idea.

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.

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.