Rethinking regulatory design : The Australian National Classification Scheme review

This paper considers issues of methodological innovation in communication, media and cultural studies, that arise out of the extent to which we now live in a media environment characterised by an digital media abundance, the convergence of media platforms, content and services, and the globalisation of media content through ubiquitous computing and high-speed broadband networks. These developments have also entailed a shift in the producer-consumer relationships that characterised the 20th century mass communications paradigm, with the rapid proliferation of user-created content, accelerated innovation, the growing empowerment of media users themselves, and the blurring of distinctions between public and private, as well as age-based distinctions in terms of what media can be accessed by whom and for what purpose. It considers these issues through a case study of the Australian Law Reform Commission's National Classification Scheme Review.

Using Web Search Logs to Identify Query Classification Terms

Purpose – The work presented in this paper aims to provide an approach to classifying web logs by personal properties of users. Design/methodology/approach – The authors describe an iterative system that begins with a small set of manually labeled terms, which are used to label queries from the log. A set of background knowledge related to these labeled queries is acquired by combining web search results on these queries. This background set is used to obtain many terms that are related to the classification task. The system then ranks each of the related terms, choosing those that most fit the personal properties of the users. These terms are then used to begin the next iteration. Findings – The authors identify the difficulties of classifying web logs, by approaching this problem from a machine learning perspective. By applying the approach developed, the authors are able to show that many queries in a large query log can be classified. Research limitations/implications – Testing results in this type of classification work is difficult, as the true personal properties of web users are unknown. Evaluation of the classification results in terms of the comparison of classified queries to well known age-related sites is a direction that is currently being exploring. Practical implications – This research is background work that can be incorporated in search engines or other web-based applications, to help marketing companies and advertisers. Originality/value – This research enhances the current state of knowledge in short-text classification and query log learning. Classification schemes, Computer networks, Information retrieval, Man-machine systems, User interfaces

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.

Strategy taxonomy and classification system development : study of two state governments

There is limited understanding about business strategies related to parliamentary government's departments. This study focuses on the strategies of departments of two state governments in Australia. The strategies are derived from department strategic plans available in public domain and collected from respective websites. The results of this research indicate that strategies fall into seven categories: internal, development, political, partnership, environment, reorientation and status quo. The strategies of the departments are mainly internal or development where development strategy is mainly the focus of departments such as transport, and infrastructure. Political strategy is prevalent for departments related to communities, and education and training. Further three layers of strategies are identified as kernel, cluster and individual, which are mapped to the developed taxonomy.

Distribution feeder loads classification and decomposition

Load in distribution networks is normally measured at the 11kV supply points; little or no information is known about the type of customers and their contributions to the load. This paper proposes statistical methods to decompose an unknown distribution feeder load to its customer load sector/subsector profiles. The approach used in this paper should assist electricity suppliers in economic load management, strategic planning and future network reinforcements.