Stepping stones : teacher lesson plans : year 2

"ORIGO Stepping Stones gives mathematics teachers the best of both worlds by delivering lessons and teacher guides on a digital platform blended with the more traditional printed student journals." -- Publisher website

Stepping stones : teacher lesson plans : year K

"ORIGO Stepping Stones gives mathematics teachers the best of both worlds by delivering lessons and teacher guides on a digital platform blended with the more traditional printed student journals." -- Publisher website

Operations research for mining : a classification and literature review

In this paper, we describe the main processes and operations in mining industries and present a comprehensive survey of operations research methodologies that have been applied over the last several decades. The literature review is classified into four main categories: mine design; mine production; mine transportation; and mine evaluation. Mining design models are further separated according to two main mining methods: open-pit and underground. Moreover, mine production models are subcategorised into two groups: ore mining and coal mining. Mine transportation models are further partitioned in accordance with fleet management, truck haulage and train scheduling. Mine evaluation models are further subdivided into four clusters in terms of mining method selection, quality control, financial risks and environmental protection. The main characteristics of four Australian commercial mining software are addressed and compared. This paper bridges the gaps in the literature and motivates researchers to develop more applicable, realistic and comprehensive operations research models and solution techniques that are directly linked with mining industries.

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.

Solder joint defects classification using the Log-Gabor Filter, the Discrete Wavelet Transform and the Discrete Cosine Transform

Inspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure project quality and reliability. This paper proposes the use of the Log-Gabor filter bank, Discrete Wavelet Transform and Discrete Cosine Transform for feature extraction of solder joint images on Printed Circuit Boards (PCBs). A distance based on the Mahalanobis Cosine metric is also presented for classification of five different types of solder joints. From the experimental results, this methodology achieved high accuracy and a well generalised performance. This can be an effective method to reduce cost and improve quality in the production of PCBs in the manufacturing industry.

Improved facial expression recognition via uni-hyperplane classification

Large margin learning approaches, such as support vector machines (SVM), have been successfully applied to numerous classification tasks, especially for automatic facial expression recognition. The risk of such approaches however, is their sensitivity to large margin losses due to the influence from noisy training examples and outliers which is a common problem in the area of affective computing (i.e., manual coding at the frame level is tedious so coarse labels are normally assigned). In this paper, we leverage the relaxation of the parallel-hyperplanes constraint and propose the use of modified correlation filters (MCF). The MCF is similar in spirit to SVMs and correlation filters, but with the key difference of optimizing only a single hyperplane. We demonstrate the superiority of MCF over current techniques on a battery of experiments.

ACE research vignette 003 : different strokes for different folks - classification of entrepreneurial musicians and their use of online social networks

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international entrepreneurship researchers. In this vignette Dr Maria Kaya and Associate Professor Paul Steffens consider both the classification of musicians and their use of online social networks.

A classification framework for design-build variants from an operational perspective

Design-build (DB) is a generic form of construction procurement, and, rather than simply representing a single system, it has evolved in practice into a variety of forms, each of which is similar to, and yet different from each other. Although the importance of selecting an appropriate DB variant has been widely accepted, difficulties occur in practice due to the multiplicity of terms and concepts used. What is needed is some kind of taxonomy or framework within which the individual variants can be placed and their relative attributes identified and understood. Through a comprehensive literature review and content analysis, this paper establishes a systematic classification framework for DB variants based on their operational attributes. In addition to providing much needed support for decision-making, this classification framework provides client/owners with perspectives to understand and examine different categories of DB variants from an operational perspective.

Globalization, media policy and regulatory design : rethinking the Australian media classification scheme

This paper considers the debate about the relationship between globalization and media policy from the perspective provided by a current review of the Australian media classification scheme. Drawing upon the author’s recent experience in being ‘inside’ the policy process, as Lead Commissioner on the Australian National Classification Scheme Review, it is argued that theories of globalization – including theories of neoliberal globalization – fail to adequately capture the complexities of the reform process, particularly around the relationship between regulation and markets. The paper considers the pressure points for media content policies arising from media globalization, and the wider questions surrounding media content policies in an age of media convergence.

Unsupervised multi-label text classification using a world knowledge ontology

The development of text classification techniques has been largely promoted in the past decade due to the increasing availability and widespread use of digital documents. Usually, the performance of text classification relies on the quality of categories and the accuracy of classifiers learned from samples. When training samples are unavailable or categories are unqualified, text classification performance would be degraded. In this paper, we propose an unsupervised multi-label text classification method to classify documents using a large set of categories stored in a world ontology. The approach has been promisingly evaluated by compared with typical text classification methods, using a real-world document collection and based on the ground truth encoded by human experts.

Building prosperous knowledge cities : policies, plans and metrics

This unique book reveals the procedural aspects of knowledge-based urban planning, development and assessment. Concentrating on major knowledge city building processes, and providing state-of-the-art experiences and perspectives, this important compendium explores innovative models, approaches and lessons learnt from a number of key case studies across the world. Many cities worldwide, in order to brand themselves as knowledge cities, have undergone major transformations in the 21st century. This book provides a thorough understanding of these transformations and the key issues in building prosperous knowledge cities by focusing particularly on the policy-making, planning process and performance assessment aspects. The contributors reveal theoretical and conceptual foundations of knowledge cities and their development approach of knowledge-based urban development. They present best-practice examples from a number of key case studies across the globe. This important book provides readers with a thorough understanding of the key issues in planning and developing prosperous knowledge cities of the knowledge economy era, which will prove invaluable to national, state/regional and city governments’ planning and development departments. Academics, postgraduate and undergraduate students of regional and urban studies will also find this path-breaking book an intriguing read.

Monte Carlo evaluation of collapsed-cone convolution calculations in head and neck radiotherapy treatment plans

The presence of air and bone interfaces makes the dose distribution for head and neck cancer treatments difficult to accurately predict. This study compared planning system dose calculations using the collapsed-cone convolution algorithm with EGSnrcMonte Carlo simulation results obtained using the Monte Carlo DICOMToolKit software, for one oropharynx, two paranasal sinus and three nodal treatment plans. The difference between median doses obtained from the treatment planning and Monte Carlo calculations was found to be greatest in two bilateral treatments: 4.8%for a retropharyngeal node irradiation and 6.7% for an ethmoid paranasal sinus treatment. These deviations in median dose were smaller for two unilateral treatments: 0.8% for an infraclavicular node irradiation and 2.8% for a cervical node treatment. Examination of isodose distributions indicated that the largest deviations between Monte Carlo simulation and collapsed-cone convolution calculations were seen in the bilateral treatments, where the increase in calculated dose beyond air cavities was most significant.