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 two-stage approach for generating topic models

Topic modeling has been widely utilized in the fields of information retrieval, text mining, text classification etc. Most existing statistical topic modeling methods such as LDA and pLSA generate a term based representation to represent a topic by selecting single words from multinomial word distribution over this topic. There are two main shortcomings: firstly, popular or common words occur very often across different topics that bring ambiguity to understand topics; secondly, single words lack coherent semantic meaning to accurately represent topics. In order to overcome these problems, in this paper, we propose a two-stage model that combines text mining and pattern mining with statistical modeling to generate more discriminative and semantic rich topic representations. Experiments show that the optimized topic representations generated by the proposed methods outperform the typical statistical topic modeling method LDA in terms of accuracy and certainty.

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.

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.

A data mining driven crash risk profiling method for road asset management

This thesis takes a new data mining approach for analyzing road/crash data by developing models for the whole road network and generating a crash risk profile. Roads with an elevated crash risk due to road surface friction deficit are identified. The regression tree model, predicting road segment crash rate, is applied in a novel deployment coined regression tree extrapolation that produces a skid resistance/crash rate curve. Using extrapolation allows the method to be applied across the network and cope with the high proportion of missing road surface friction values. This risk profiling method can be applied in other domains.

NTCIR-10 crosslink-2 task : a link mining strategy

At NTCIR-10 we participated in the cross-lingual link discovery (CrossLink-2) task. In this paper we describe our systems for discovering cross-lingual links between the Chinese, Japanese, and Korean (CJK) Wikipedia and the English Wikipedia. The evaluation results show that our implementation of the cross-lingual linking method achieved promising results.

A data mining driven risk profiling method for road asset management

Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.

A data mining framework for relevance feature discovery

This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.

Making sense of purpose, direction and innovation : an embedded design led innovation case study in the Australian mining industry

The mining equipment technology services sector is driven by a reactive and user-centered design approach, with a technological focus on incremental new product development. As Australia moves out of its sustained mining boom, companies need to rethink their strategic position, to become agile to stay relevant in an enigmatic market. This paper reports on the first five months on an embedded case study within an Australian, family-owned mining manufacturer. The first author is currently engaged in a longitudinal design led innovation project, as a catalyst to guide the company’s journey to design integration. The results find that design led innovation could act as a channel for highlighting and exploring company disconnections with the marketplace and offer a customer-centric catalyst for internal change. Data collected for this study is from 12 analysed semistructured interviews, a focus group and a reflective journal, over a five-month period. This paper explores limitations to design integration, and highlights opportunities to explore and leverage entrepreneurial characteristics to stay agile, broaden innovation and future-proof through the next commodity cycle in the mining industry.

Personalized ontology learning for enhancing text mining effectiveness

Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.

A people-to-people matching system using graph mining techniques

A people-to-people matching system (or a match-making system) refers to a system in which users join with the objective of meeting other users with the common need. Some real-world examples of these systems are employer-employee (in job search networks), mentor-student (in university social networks), consume-to-consumer (in marketplaces) and male-female (in an online dating network). The network underlying in these systems consists of two groups of users, and the relationships between users need to be captured for developing an efficient match-making system. Most of the existing studies utilize information either about each of the users in isolation or their interaction separately, and develop recommender systems using the one form of information only. It is imperative to understand the linkages among the users in the network and use them in developing a match-making system. This study utilizes several social network analysis methods such as graph theory, small world phenomenon, centrality analysis, density analysis to gain insight into the entities and their relationships present in this network. This paper also proposes a new type of graph called “attributed bipartite graph”. By using these analyses and the proposed type of graph, an efficient hybrid recommender system is developed which generates recommendation for new users as well as shows improvement in accuracy over the baseline methods.

An examination of the legislative framework governing commuter safety in the Queensland Mining Industry

Commuting in the mining industry -Background -The problem -Journey management -The structure of the legislative framework Legislation and Regulation -Workplace safety in Queensland mining -Risk management -Mining legislation and journey management -Commuting and employee responsibilities -Queensland Workers’ Compensation Scheme Industry standards -Industry standards and journey management Regulated and organisational policy documents -Policy documents and journey management Observations & Conclusions

The impact of mining and smelting activities on property values : a study of Mount Isa city, Queensland, Australia

Much publicity has been given to the problem of high levels of environmental contaminants, most notably high blood lead concentration levels among children in the city of Mount Isa because of mining and smelting activities. The health impacts from mining-related pollutants are now well documented. This includes published research being discussed in an editorial of the Medical Journal of Australia (see Munksgaard et al. 2010). On the other hand, negative impacts on property prices, although mentioned, have not been examined to date. This study rectifies this research gap. This study uses a hedonic property price approach to examine the impact of mining- and smelting-related pollution on nearby property prices. The hypothesis is that those properties closer to the lead and copper smelters have lower property (house) prices than those farther away. The results of the study show that the marginal willingness to pay to be farther from the pollution source is AUS $13 947 per kilometre within the 4 km radius selected. The study has several policy implications, which are discussed briefly. We used ordinary least squares, geographically weighted regression, spatial error and spatial autoregressive or spatial lag models for this analysis.

A data mining approach to improve the automated quality of data

This thesis describes the development of a robust and novel prototype to address the data quality problems that relate to the dimension of outlier data. It thoroughly investigates the associated problems with regards to detecting, assessing and determining the severity of the problem of outlier data; and proposes granule-mining based alternative techniques to significantly improve the effectiveness of mining and assessing outlier data.

Understanding the fabric of social interactions for ridesharing through mining social networking sites

The design of applications for dynamic ridesharing or carpooling is often formulated as a matching problem of connecting people with an aligned set of transport needs within a reasonable interval of time and space. This problem formulation relegates social connections to being secondary factors. Technology assisted ridesharing applications that put the matching problem first have revealed that they suffer from being unable to address the factor of social comfort, even after adding friend features or piggybacking on social networking sites. This research aims to understand the fabric of social interactions through which ridesharing happens. We take an online observation approach in order to understand the fabric of social interactions for ridesharing that is happening in highly subscribed online groups of local residents. This understanding will help researchers to identify design challenges and opportunities to support ridesharing in local communities. This paper contributes a fundamental understanding of how social interactions and social comfort precede rideshare requests in local communities.