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

Transforming mobile personal life log into autobiographical multimedia eChronicles

Mobile devices are becoming indispensable personal assistants in people's daily life as these devices support work, study, play and socializing activities. The multi-modal sensors and rich features of smartphones can capture abundant information about users' life experience, such as taking photos or videos on what they see and hear, and organizing their tasks and activities using calendar, to-do lists, and notes. Such vast information can become useful to help users recalling episodic memories and reminisce about meaningful experiences. In this paper, we propose to apply autobiographical memory framework to provide an effective mechanism to structure mobile life-log data. The proposed model is an attempt towards a more complete personal life-log indexing model, which will support long term capture, organization, and retrieval. To demonstrate the benefits of the proposed model, we propose some design solutions for enabling users-driven capture, annotation, and retrieval of autobiographical multimedia chronicles tools.

Anomalous event detection using a semi-two dimensional hidden Markov model

The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

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.

How activation, entanglement, and searching a semantic network contribute to event memory

Free association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist cuing, primed free association, intralist cuing, and single-item recognition tasks. The findings also show that when a related word is presented to cue the recall of a studied word, the cue activates it in an array of related words that distract and reduce the probability of its selection. The activation of the semantic network produces priming benefits during encoding and search costs during retrieval. In extralist cuing recall is a negative function of cue-to-distracter strength and a positive function of neighborhood density, cue-to-target strength, and target-to cue strength. We show how four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks indicating that the contribution of the semantic network varies with the context provided by the task. We evaluate spreading activation and quantum-like entanglement explanations for the priming effect produced by neighborhood density.

Hazard based models for freeway traffic incident duration

Assessing and prioritising cost-effective strategies to mitigate the impacts of traffic incidents and accidents on non-recurrent congestion on major roads represents a significant challenge for road network managers. This research examines the influence of numerous factors associated with incidents of various types on their duration. It presents a comprehensive traffic incident data mining and analysis by developing an incident duration model based on twelve months of incident data obtained from the Australian freeway network. Parametric accelerated failure time (AFT) survival models of incident duration were developed, including log-logistic, lognormal, and Weibul-considering both fixed and random parameters, as well as a Weibull model with gamma heterogeneity. The Weibull AFT models with random parameters were appropriate for modelling incident duration arising from crashes and hazards. A Weibull model with gamma heterogeneity was most suitable for modelling incident duration of stationary vehicles. Significant variables affecting incident duration include characteristics of the incidents (severity, type, towing requirements, etc.), and location, time of day, and traffic characteristics of the incident. Moreover, the findings reveal no significant effects of infrastructure and weather on incident duration. A significant and unique contribution of this paper is that the durations of each type of incident are uniquely different and respond to different factors. The results of this study are useful for traffic incident management agencies to implement strategies to reduce incident duration, leading to reduced congestion, secondary incidents, and the associated human and economic losses.

SAIVT-QUT@TRECVid 2012 : interactive surveillance event detection

In this paper, we propose an approach which attempts to solve the problem of surveillance event detection, assuming that we know the definition of the events. To facilitate the discussion, we first define two concepts. The event of interest refers to the event that the user requests the system to detect; and the background activities are any other events in the video corpus. This is an unsolved problem due to many factors as listed below: 1) Occlusions and clustering: The surveillance scenes which are of significant interest at locations such as airports, railway stations, shopping centers are often crowded, where occlusions and clustering of people are frequently encountered. This significantly affects the feature extraction step, and for instance, trajectories generated by object tracking algorithms are usually not robust under such a situation. 2) The requirement for real time detection: The system should process the video fast enough in both of the feature extraction and the detection step to facilitate real time operation. 3) Massive size of the training data set: Suppose there is an event that lasts for 1 minute in a video with a frame rate of 25fps, the number of frames for this events is 60X25 = 1500. If we want to have a training data set with many positive instances of the event, the video is likely to be very large in size (i.e. hundreds of thousands of frames or more). How to handle such a large data set is a problem frequently encountered in this application. 4) Difficulty in separating the event of interest from background activities: The events of interest often co-exist with a set of background activities. Temporal groundtruth typically very ambiguous, as it does not distinguish the event of interest from a wide range of co-existing background activities. However, it is not practical to annotate the locations of the events in large amounts of video data. This problem becomes more serious in the detection of multi-agent interactions, since the location of these events can often not be constrained to within a bounding box. 5) Challenges in determining the temporal boundaries of the events: An event can occur at any arbitrary time with an arbitrary duration. The temporal segmentation of events is difficult and ambiguous, and also affected by other factors such as occlusions.

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.

Hot topic detection in news blogs from the perspective of W2T

News blog hot topics are important for the information recommendation service and marketing. However, information overload and personalized management make the information arrangement more difficult. Moreover, what influences the formation and development of blog hot topics is seldom paid attention to. In order to correctly detect news blog hot topics, the paper first analyzes the development of topics in a new perspective based on W2T (Wisdom Web of Things) methodology. Namely, the characteristics of blog users, context of topic propagation and information granularity are unified to analyze the related problems. Some factors such as the user behavior pattern, network opinion and opinion leader are subsequently identified to be important for the development of topics. Then the topic model based on the view of event reports is constructed. At last, hot topics are identified by the duration, topic novelty, degree of topic growth and degree of user attention. The experimental results show that the proposed method is feasible and effective.