Analysis of Business Process Variants in Apromore

In this paper we illustrate a set of features of the Apromore process model repository for analyzing business process variants. Two types of analysis are provided: one is static and based on differences on the process control flow, the other is dynamic and based on differences in the process behavior between the variants. These features combine techniques for the management of large process model collections with those for mining process knowledge from process execution logs. The tool demonstration will be useful for researchers and practitioners working on large process model collections and process execution logs, and specifically for those with an interest in understanding, managing and consolidating business process variants both within and across organizational boundaries.

Translating road safety research on night-time visibility to the context of mining

In recent years a significant amount of research has been undertaken in collision avoidance and personnel location technology in order to reduce the number of incidents involving pedestrians and mobile plant equipment which are a high risk in underground coal mines. Improving the visibility of pedestrians to drivers would potentially reduce the likelihood of these incidents. In the road safety context, a variety of approaches have been used to make pedestrians more conspicuous to drivers at night (including vehicle and roadway lighting technologies and night vision enhancement systems). However, emerging research from our group and others has demonstrated that clothing incorporating retroreflective markers on the movable joints as well as the torso can provide highly significant improvements in pedestrian visibility in reduced illumination. Importantly, retroreflective markers are most effective when positioned on the moveable joints creating a sensation of “biological motion”. Based only on the motion of points on the moveable joints of an otherwise invisible body, observers can quickly recognize a walking human form, and even correctly judge characteristics such as gender and weight. An important and as yet unexplored question is whether the benefits of these retroreflective clothing configurations translate to the context of mining where workers are operating under low light conditions. Given that the benefits of biomotion clothing are effective for both young and older drivers, as well as those with various eye conditions common in those >50 years reinforces their potential application in the mining industry which employs many workers in this age bracket. This paper will summarise the visibility benefits of retroreflective markers in a biomotion configuration for the mining industry, highlighting that this form of clothing has the potential to be an affordable and convenient way to provide a sizeable safety benefit. It does not involve modifications to vehicles, drivers, or infrastructure. Instead, adding biomotion markings to standard retroreflective vests can enhance the night-time conspicuity of mining workers by capitalising on perceptual capabilities that have already been well documented.

New graph-based algorithms to efficiently solve large scale open pit mining optimisation problems

In the mining optimisation literature, most researchers focused on two strategic-level and tactical-level open-pit mine optimisation problems, which are respectively termed ultimate pit limit (UPIT) or constrained pit limit (CPIT). However, many researchers indicate that the substantial numbers of variables and constraints in real-world instances (e.g., with 50-1000 thousand blocks) make the CPIT’s mixed integer programming (MIP) model intractable for use. Thus, it becomes a considerable challenge to solve the large scale CPIT instances without relying on exact MIP optimiser as well as the complicated MIP relaxation/decomposition methods. To take this challenge, two new graph-based algorithms based on network flow graph and conjunctive graph theory are developed by taking advantage of problem properties. The performance of our proposed algorithms is validated by testing recent large scale benchmark UPIT and CPIT instances’ datasets of MineLib in 2013. In comparison to best known results from MineLib, it is shown that the proposed algorithms outperform other CPIT solution approaches existing in the literature. The proposed graph-based algorithms leads to a more competent mine scheduling optimisation expert system because the third-party MIP optimiser is no longer indispensable and random neighbourhood search is not necessary.

Improving Near-Miss Event Detection Rate at Railway Level Crossings

Even though crashes between trains and road users are rare events at railway level crossings, they are one of the major safety concerns for the Australian railway industry. Nearmiss events at level crossings occur more frequently, and can provide more information about factors leading to level crossing incidents. In this paper we introduce a video analytic approach for automatically detecting and localizing vehicles from cameras mounted on trains for detecting near-miss events. To detect and localize vehicles at level crossings we extract patches from an image and classify each patch for detecting vehicles. We developed a region proposals algorithm for generating patches, and we use a Convolutional Neural Network (CNN) for classifying each patch. To localize vehicles in images we combine the patches that are classified as vehicles according to their CNN scores and positions. We compared our system with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. Experimental results on a railway dataset show that the recall rate of our proposed system is 29% higher than what can be achieved with DPM or R-CNN detectors.

The hidden injuries of mining: Frontier cultural conflict

This chapter addresses a topic of growing significance to green criminology - the harmful effects of mining on local communities and the environment (Ruggiero and South 2013; White 2013a). While mining has long been recognised as an agent of environmental harm (White 2013a), less recognised is that its global expansion also has harmful effects on localised patterns of violence, work and community life in mining towns. Australia provides an excellent case study for exploring some of these mining impacts.

A data mining approach for fault diagnosis: An application of anomaly detection algorithm

Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.

Permissions in Deontic Event-Calculus

Permissions are special case of deontic effects and play important role compliance. Essentially they are used to determine the obligations or prohibitions to contrary. A formal language e.g., temporal logic, event-calculus et., not able to represent permissions is doomed to be unable to represent most of the real-life legal norms. In this paper we address this issue and extend deontic-event-calculus (DEC) with new predicates for modelling permissions enabling it to elegantly capture the intuition of real-life cases of permissions.

Application of image processing techniques for frog call classification

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector

Big Data and predictive analytics have received significant attention from the media and academic literature throughout the past few years, and it is likely that these emerging technologies will materially impact the mining sector. This short communication argues, however, that these technological forces will probably unfold differently in the mining industry than they have in many other sectors because of significant differences in the marginal cost of data capture and storage. To this end, we offer a brief overview of what Big Data and predictive analytics are, and explain how they are bringing about changes in a broad range of sectors. We discuss the “N=all” approach to data collection being promoted by many consultants and technology vendors in the marketplace but, by considering the economic and technical realities of data acquisition and storage, we then explain why a “n « all” data collection strategy probably makes more sense for the mining sector. Finally, towards shaping the industry’s policies with regards to technology-related investments in this area, we conclude by putting forward a conceptual model for leveraging Big Data tools and analytical techniques that is a more appropriate fit for the mining sector.

Enhancement of relevant features for text mining

With the explosion of information resources, there is an imminent need to understand interesting text features or topics in massive text information. This thesis proposes a theoretical model to accurately weight specific text features, such as patterns and n-grams. The proposed model achieves impressive performance in two data collections, Reuters Corpus Volume 1 (RCV1) and Reuters 21578.

Compact features for birdcall retrieval from environmental acoustic recordings

Bioacoustic data can be used for monitoring animal species diversity. The deployment of acoustic sensors enables acoustic monitoring at large temporal and spatial scales. We describe a content-based birdcall retrieval algorithm for the exploration of large data bases of acoustic recordings. In the algorithm, an event-based searching scheme and compact features are developed. In detail, ridge events are detected from audio files using event detection on spectral ridges. Then event alignment is used to search through audio files to locate candidate instances. A similarity measure is then applied to dimension-reduced spectral ridge feature vectors. The event-based searching method processes a smaller list of instances for faster retrieval. The experimental results demonstrate that our features achieve better success rate than existing methods and the feature dimension is greatly reduced.

#auspol: The hashtag as community, event, and material object for engaging with Australian politics

Critical, loud, highly discursive and polarised; the #auspol hashtag represents a space, an event and a network for politically involved individuals to engage in and with Australian politics and speak to, at and about a variety of involved stakeholders. Contributors declare, debate and often berate each other’s opinions about current Australian politics. The hashtag itself is an important material object and engagement event involved within this performance of political participation. As a long-standing institution in the Twittersphere, and one studied by the authors and their colleagues since its early beginnings (Bruns and Burgess, 2011; Bruns and Stieglitz, 2012; 2013), the #auspol hashtag provides a potent case study through which to explore the discursive and affective dimensions of a hashtag public. This chapter that engages both empirically and theoretically with the use of this particular hashtag on Twitter to provide a qualitatively illustrated case in point for thinking about the long-term use of political hashtags as engagement events.

Using global formulary apportionment for international profit allocation: The case of Indonesia's mining industry

My thesis examined an alternative approach, referred to as the unitary taxation approach to the allocation of profit, which arises from the notion that as a multinational group exists as a single economic entity, it should be taxed as one taxable unit. The plausibility of a unitary taxation regime achieving international acceptance and agreement is highly contestable due to its implementation issues, and economic and political feasibility. Using a case-study approach focusing on Freeport-McMoRan and Rio Tinto's mining operations in Indonesia, this thesis compares both tax regimes against the criteria for a good tax system - equity, efficiency, neutrality and simplicity. This thesis evaluates key issues that arise when implementing a unitary taxation approach with formulary apportionment based on the context of mining multinational firms in Indonesia.

Detecting rare events using Kullback-Leibler divergence: A weakly supervised approach

Video surveillance infrastructure has been widely installed in public places for security purposes. However, live video feeds are typically monitored by human staff, making the detection of important events as they occur difficult. As such, an expert system that can automatically detect events of interest in surveillance footage is highly desirable. Although a number of approaches have been proposed, they have significant limitations: supervised approaches, which can detect a specific event, ideally require a large number of samples with the event spatially and temporally localised; while unsupervised approaches, which do not require this demanding annotation, can only detect whether an event is abnormal and not specific event types. To overcome these problems, we formulate a weakly-supervised approach using Kullback-Leibler (KL) divergence to detect rare events. The proposed approach leverages the sparse nature of the target events to its advantage, and we show that this data imbalance guarantees the existence of a decision boundary to separate samples that contain the target event from those that do not. This trait, combined with the coarse annotation used by weakly supervised learning (that only indicates approximately when an event occurs), greatly reduces the annotation burden while retaining the ability to detect specific events. Furthermore, the proposed classifier requires only a decision threshold, simplifying its use compared to other weakly supervised approaches. We show that the proposed approach outperforms state-of-the-art methods on a popular real-world traffic surveillance dataset, while preserving real time performance.

Survival analysis of time-to-event data in respiratory health research studies

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan–Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.