Fast and robust stereo matching algorithms for mining automation

The mining environment, being complex, irregular and time varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper evaluates a number of matching techniques for possible use in a stereo vision sensor for mining automation applications. Area-based techniques have been investigated because they have the potential to yield dense maps, are amenable to fast hardware implementation, and are suited to textured scenes. In addition, two non-parametric transforms, namely, the rank and census, have been investigated. Matching algorithms using these transforms were found to have a number of clear advantages, including reliability in the presence of radiometric distortion, low computational complexity, and amenability to hardware implementation.

Challenging Mining Workforce Practices : Implications for Frontline Rural Communities

Global demand for minerals and energy products has fuelled Australia’s recent ‘resources boom’ and led to the rapid expansion of mining projects not solely in remote regions but increasingly in long-settled traditionally agriculture-dependent rural areas. Not only has this activity radically changed the economic geography of the nation but a fundamental shift has also occurred to accommodate the acceleration in industry labour demands. In particular, the rush to mine has seen the entrenchment of workforce arrangements largely dependent on fly-in, fly-out (FIFO) and drive–in, drive–out (DIDO) workers. This form of employment has been highly contentious in rural communities at the frontline of resource sector activities. In the context of structural sweeping changes, the selection of study locations informed by a range of indices of violence. Serendipitously we carried out fieldwork in communities undergoing rapid change as a result of expanding resource sector activities. The presence of large numbers of non-resident FIFO and DIDO workers was transforming these frontline communities. This chapter highlights some implications of these changes, drawing upon one particular location, which historically depended on agriculture but has undergone redefinition through mining.

Diversification for sustainable development in rural and regional Australia : how local community leaders conceptualise the impacts and opportunities from agriculture, tourism and mining

Although the multiple economic, environmental and social challenges threatening the viability of rural and regional communities in Australia are well-known, little research has explored how community leaders conceptualise the impact and opportunities associated with economic diversification from agriculture into alternative industries, such as tourism and mining. This qualitative research, utilising the Darling Downs in Queensland as a case study, documents how 28 local community leaders have experienced this economic diversification process. The findings reveal that local community leaders have a deep understanding about the opportunities and challenges presented by diversification, articulating a clear vision about how to achieve the best possible future for their region. Despite excitement about growth, there were concerns about preserving heritage, the increased pressure on local infrastructure and an ageing population. By documenting local leader’s insights, these findings may help inform planning for rural and regional communities and facilitate management of the exciting yet challenging process of growth and diversification

Retrieving information from microblog using pattern mining and relevance feedback

Retrieving information from Twitter is always challenging due to its large volume, inconsistent writing and noise. Most existing information retrieval (IR) and text mining methods focus on term-based approach, but suffers from the problems of terms variation such as polysemy and synonymy. This problem deteriorates when such methods are applied on Twitter due to the length limit. Over the years, people have held the hypothesis that pattern-based methods should perform better than term-based methods as it provides more context, but limited studies have been conducted to support such hypothesis especially in Twitter. This paper presents an innovative framework to address the issue of performing IR in microblog. The proposed framework discover patterns in tweets as higher level feature to assign weight for low-level features (i.e. terms) based on their distributions in higher level features. We present the experiment results based on TREC11 microblog dataset and shows that our proposed approach significantly outperforms term-based methods Okapi BM25, TF-IDF and pattern based methods, using precision, recall and F measures.

Product reputation model : an opinion mining based approach

Product rating systems are very popular on the web, and users are increasingly depending on the overall product ratings provided by websites to make purchase decisions or to compare various products. Currently most of these systems directly depend on users’ ratings and aggregate the ratings using simple aggregating methods such as mean or median [1]. In fact, many websites also allow users to express their opinions in the form of textual product reviews. In this paper, we propose a new product reputation model that uses opinion mining techniques in order to extract sentiments about product’s features, and then provide a method to generate a more realistic reputation value for every feature of the product and the product itself. We considered the strength of the opinion rather than its orientation only. We do not treat all product features equally when we calculate the overall product reputation, as some features are more important to customers than others, and consequently have more impact on customers buying decisions. Our method provides helpful details about the product features for customers rather than only representing reputation as a number only.

Compensation covenants for mining activities run with the land : a win for successive landowners

The Queensland Supreme Court case of Cape Flattery Silica Mines Pty Ltd v Hope Vale Aboriginal Shire Council [2012] QSC 381 provides guidance on the long-term ramifications of compensation agreements for mining activities. The central issue considered by the Court was whether compensation payments relate to land and run with the land pursuant to s 53(1) of the Property Law Act.

Using patterns co-occurrence matrix for cleaning closed sequential patterns for text mining

With the overwhelming increase in the amount of texts on the web, it is almost impossible for people to keep abreast of up-to-date information. Text mining is a process by which interesting information is derived from text through the discovery of patterns and trends. Text mining algorithms are used to guarantee the quality of extracted knowledge. However, the extracted patterns using text or data mining algorithms or methods leads to noisy patterns and inconsistency. Thus, different challenges arise, such as the question of how to understand these patterns, whether the model that has been used is suitable, and if all the patterns that have been extracted are relevant. Furthermore, the research raises the question of how to give a correct weight to the extracted knowledge. To address these issues, this paper presents a text post-processing method, which uses a pattern co-occurrence matrix to find the relation between extracted patterns in order to reduce noisy patterns. The main objective of this paper is not only reducing the number of closed sequential patterns, but also improving the performance of pattern mining as well. The experimental results on Reuters Corpus Volume 1 data collection and TREC filtering topics show that the proposed method is promising.

Integration of opinion into customer analysis model

As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. In order to enhance customer satisfaction and their shopping experiences, it has become important to analysis customers reviews to extract opinions on the products that they buy. Thus, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers’ behavior for businesses purpose. The right decision in producing new products or services based on data about customers’ characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers’ characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers’ orientation for all possible products’ attributes.

Environmental incidents in a developing country and corporate environmental disclosures : a study of a multinational gas company

Purpose – The purpose of this paper is to examine the environmental disclosure initiatives of Niko Resources Ltd – a Canada-based multinational oil and gas company – following the two major environmental blowouts at a gas field in Bangladesh in 2005. As part of the examination, the authors particularly focus on whether Niko's disclosure strategy was associated with public concern pertaining to the blowouts. Design/methodology/approach – The authors reviewed news articles about Niko's environmental incidents in Bangladesh and Niko's communication media, including annual reports, press releases and stand-alone social responsibility report over the period 2004-2007, to understand whether news media attention as proxy for public concern has an impact on Niko's disclosure practices in relation to the affected local community in Bangladesh. Findings – The findings show that Niko did not provide any non-financial environmental information within its annual reports and press releases as a part of its responsibility to the local community which was affected by the blowouts, but it did produce a stand-alone report to address the issue. However, financial environmental disclosures, such as the environmental contingent liability disclosure, were adequately provided through annual reports to meet the regulatory requirements concerning environmental persecutions. The findings also suggest that Niko's non-financial disclosure within a stand-alone report was associated with the public pressures as measured by negative media coverage towards the Niko blowouts. Research limitations/implications – This paper concludes that the motive for Niko's non-financial environmental disclosure, via a stand-alone report, reflected survival considerations: the company's reaction did not suggest any real attempt to hold broader accountability for its activities in a developing country.

Summarization of association rules in multi-tier granule mining

It is a big challenge to find useful associations in databases for user specific needs. The essential issue is how to provide efficient methods for describing meaningful associations and pruning false discoveries or meaningless ones. One major obstacle is the overwhelmingly large volume of discovered patterns. This paper discusses an alternative approach called multi-tier granule mining to improve frequent association mining. Rather than using patterns, it uses granules to represent knowledge implicitly contained in databases. It also uses multi-tier structures and association mappings to represent association rules in terms of granules. Consequently, association rules can be quickly accessed and meaningless association rules can be justified according to the association mappings. Moreover, the proposed structure is also an precise compression of patterns which can restore the original supports. The experimental results shows that the proposed approach is promising.

Reconnection : a contemporary development in cultural landscape theory contributing to rehabilitation strategies for Australian open-cut coal mining landscapes

A value-shift began to influence global political thinking in the late 20th century, characterised by recognition of the need for environmentally, socially and culturally sustainable resource development. This shift entailed a move away from thinking of ‘nature’ and ‘culture’ as separate entities – the former existing to serve the latter – toward the possibility of embracing the intrinsic worth of the nonhuman world. Cultural landscape theory recognises ‘nature’ as at once both ‘natural’, and a ‘cultural’ construct. As such, it may offer a framework through which to progress in the quest for ‘sustainable development’. This study makes a contribution to this quest by asking whether contemporary developments in cultural landscape theory can contribute to rehabilitation strategies for Australian open-cut coal mining landscapes. The answer is ‘yes’. To answer the research question, a flexible, ‘emergent’ methodological approach has been used, resulting in the following outcomes. A thematic historical overview of landscape values and resource development in Australia post-1788, and a review of cultural landscape theory literature, contribute to the formation of a new theoretical framework: Reconnecting the Interrupted Landscape. This framework establishes a positive answer to the research question. It also suggests a method of application within the Australian open-cut coal mining landscape, a highly visible exemplar of the resource development landscape. This method is speculatively tested against the rehabilitation strategy of an operating open-cut coal mine, concluding with positive recommendations to the industry, and to government.

A hybrid association rule mining approach for characterizing network traffic behaviour

Understanding network traffic behaviour is crucial for managing and securing computer networks. One important technique is to mine frequent patterns or association rules from analysed traffic data. On the one hand, association rule mining usually generates a huge number of patterns and rules, many of them meaningless or user-unwanted; on the other hand, association rule mining can miss some necessary knowledge if it does not consider the hierarchy relationships in the network traffic data. Aiming to address such issues, this paper proposes a hybrid association rule mining method for characterizing network traffic behaviour. Rather than frequent patterns, the proposed method generates non-similar closed frequent patterns from network traffic data, which can significantly reduce the number of patterns. This method also proposes to derive new attributes from the original data to discover novel knowledge according to hierarchy relationships in network traffic data and user interests. Experiments performed on real network traffic data show that the proposed method is promising and can be used in real applications. Copyright2013 John Wiley & Sons, Ltd.

Australia's mining interests within Nigeria and Libya : policies, corruption and conflict

Crude petroleum remains the single most imported commodity into Australia and is sourced from a number of countries around the world (Department of Foreign Affairs and Trade (DFAT), 2011a). While interest in crude petroleum is widespread, in recent years Australia's focus has been drawn to the continent of Africa, where increased political stability, economic recovery and an improved investment climate has made one of the largest oil reserves in the world increasingly more attractive. Despite improvement across the continent, there remain a number of risks which have the potential to significantly damage Australia's economic interests in the petroleum sector,including government policies and legislation, corruption and conflict. The longest exporters of crude petroleum products to Australia – Nigeria and Libya – have been subject to these factors in recent years and, accordingly, are the focus of this paper. Once identified, the impact of political instability, conflict, government corruption and other risk factors to Australia's mining interests within these countries is examined, and efforts to manage such risks are discussed.

Profiling event logs to configure risk indicators for process delays

Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators(PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.

Recommending people in social networks using data mining

This thesis improves the process of recommending people to people in social networks using new clustering algorithms and ranking methods. The proposed system and methods are evaluated on the data collected from a real life social network. The empirical analysis of this research confirms that the proposed system and methods achieved improvements in the accuracy and efficiency of matching and recommending people, and overcome some of the problems that social matching systems usually suffer.