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.

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.

Controlling buoyancy-driven profiling floats for applications in ocean observation

Establishing a persistent presence in the ocean with an autonomous underwater vehicle (AUV) capable of observing temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we examine the utility of vehicles that can only control their depth in the water column for such extended deployments. We present a strategy that utilizes ocean model predictions to facilitate a basic level of autonomy and enables general control for these profiling floats. The proposed method is based on experimentally validated techniques for utilizing ocean current models to control autonomous gliders. With the appropriate vertical actuation, and utilizing spatio–temporal variations in water speed and direction, we show that general controllability results can be met. First, we apply an A* planner to a local controllability map generated from predictions of ocean currents. This computes a path between start and goal waypoints that has the highest likelihood of successful execution. A computed depth plan is generated with a model-predictive controller (MPC), and selects the depths for the vehicle so that ambient currents guide it toward the goal. Mission constraints are included to simulate and motivate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA, USA, that show encouraging results in the ability of a drifting vehicle to reach a desired location.

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.

Mining temporal and spatial travel regularities for transit planning

Smart Card data from Automated Fare Collection system has been considered as a promising source of information for transit planning. However, literature has been limited to mining travel patterns from transit users and suggesting the potential of using this information. This paper proposes a method for mining spatial regular origins-destinations and temporal habitual travelling time from transit users. These travel regularity are discussed as being useful for transit planning. After reconstructing the travel itineraries, three levels of Density-Based Spatial Clustering of Application with Noise (DBSCAN) have been utilised to retrieve travel regularity of each of each frequent transit users. Analyses of passenger classifications and personal travel time variability estimation are performed as the examples of using travel regularity in transit planning. The methodology introduced in this paper is of interest for transit authorities in planning and managements

A marine reservoir correction for the Houtman-Abrolhos Archipelago, East Indian Ocean, Western Australia

High-precision analysis using accelerator mass spectrometry (AMS) was performed upon known-age Holocene and modern, pre-bomb coral samples to generate a marine reservoir age correction value (ΔR) for the Houtman-Abrolhos Archipelago (28.7°S, 113.8°E) off the Western Australian coast. The mean ΔR value calculated for the Abrolhos Islands, 54 ± 30 yr (1σ) agrees well with regional ΔR values for Leeuwin Current source waters (N-NW Australia-Java) of 60 ± 38. The Abrolhos Islands show little variation with ΔR values of the northwestern and north Australian coast, underlining the dominance of the more equilibrated western Pacific-derived waters of the Leeuwin Current over local upwelling. The Abrolhos Islands ΔR values have remained stable over the last 2896 yr cal BP, being also attributed to the Leeuwin Current and the El Niño Southern Oscillation (ENSO) signal during this period. Expected future trends will be a strengthening of the teleconnection of the Abrolhos Islands to the climatic patterns of the equatorial Pacific via enhanced ENSO and global warming activity strengthening the Leeuwin Current. The possible effect upon the trend of future ΔR values may be to maintain similar values and an increase in stability. However, warming trends of global climate change may cause increasing dissimilarity of ΔR values due to the effects of increasing heat stress upon lower-latitude coral communities.

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