Near-miss event detection at railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand the causal factors of these accidents, a video analytics application is being developed to automatically detect near-miss incidents using forward facing videos from trains. As near-miss events occur more frequently than collisions, by detecting these occurrences there will be more safety data available for analysis. The application that is being developed will improve the objectivity of near-miss reporting by providing quantitative data about the position of vehicles at level crossings through the automatic analysis of video footage. In this paper we present a novel method for detecting near-miss occurrences at railway level crossings from video data of trains. Our system detects and localizes vehicles at railway level crossings. It also detects the position of railways to calculate the distance of the detected vehicles to the railway centerline. The system logs the information about the position of the vehicles and railway centerline into a database for further analysis by the safety data recording and analysis system, to determine whether or not the event is a near-miss. We present preliminary results of our system on a dataset of videos taken from a train that passed through 14 railway level crossings. We demonstrate the robustness of our system by showing the results of our system on day and night videos.

Stop-signal response inhibition in schizophrenia : Behavioural, event-related potential and functional neuroimaging data

Inhibitory control deficits are well documented in schizophrenia, supported by impairment in an established measure of response inhibition, the stop-signal reaction time (SSRT). We investigated the neural basis of this impairment by comparing schizophrenia patients and controls matched for age, sex and education on behavioural, functional magnetic resonance imaging (fMRI) and event-related potential (ERP) indices of stop-signal task performance. Compared to controls, patients exhibited slower SSRT and reduced right inferior frontal gyrus (rIFG) activation, but rIFG activation correlated with SSRT in both groups. Go stimulus and stop-signal ERP components (N1/P3) were smaller in patients, but the peak latencies of stop-signal N1 and P3 were also delayed in patients, indicating impairment early in stop-signal processing. Additionally, response-locked lateralised readiness potentials indicated response preparation was prolonged in patients. An inability to engage rIFG may predicate slowed inhibition in patients, however multiple spatiotemporal irregularities in the networks underpinning stop-signal task performance may contribute to this deficit.

Designing for grassroots food production : an event-based urban agriculture community

As urbanisation of the global population has increased above 50%, growing food in urban spaces increases in importance, as it can contribute to food security, reduce food miles, and improve people’s physical and mental health. Approaching the task of growing food in urban environments is a mixture of residential growers and groups. Permablitz Brisbane is an event-centric grassroots community that organises daylong ‘working bee’ events, drawing on permaculture design principles in the planning and design process. Permablitz Brisbane provides a useful contrast from other location-centric forms of urban agriculture communities (such as city farms or community gardens), as their aim is to help encourage urban residents to grow their own food. We present findings and design implications from a qualitative study with members of this group, using ethnographic methods to engage with and understand how this group operates. Our findings describe four themes that include opportunities, difficulties, and considerations for the creation of interventions by Human-Computer Interaction (HCI) designers.

An efficient and robust system for multi-person event detection in real world indoor surveillance scenes

Due to the popularity of security cameras in public places, it is of interest to design an intelligent system that can efficiently detect events automatically. This paper proposes a novel algorithm for multi-person event detection. To ensure greater than real-time performance, features are extracted directly from compressed MPEG video. A novel histogram-based feature descriptor that captures the angles between extracted particle trajectories is proposed, which allows us to capture motion patterns of multi-person events in the video. To alleviate the need for fine-grained annotation, we propose the use of Labelled Latent Dirichlet Allocation, a “weakly supervised” method that allows the use of coarse temporal annotations which are much simpler to obtain. This novel system is able to run at approximately ten times real-time, while preserving state-of-theart detection performance for multi-person events on a 100-hour real-world surveillance dataset (TRECVid SED).

Adverse weather effects on bus ridership

This study focuses on weather effects on daily bus ridership in Brisbane, given bus’ dominance in this city. The weather pattern of Brisbane varies by season according to its sub-tropical climate characteristics. Bus is prone to inclement weather condition as it shares the road system with general traffic. Moreover, bus stops generally offer less or sometimes no protection from adverse weather. Hence, adverse weather conditions such as rain are conjectured to directly impact on daily travel behaviour patterns. There has been limited Australian research on the impact of weather on daily transit ridership. This study investigates the relationship between rainy day and daily bus ridership for the period of 2010 to 2012. Overall, rainfall affects negatively with varying impacts on different transit groups. However, this analysis confirmed a positive relationship between consecutive rainy days (rain continuing for 3 or more days). A possible explanation could be that people may switch their transport mode to bus to avoid high traffic congestion and higher accident potentiality on rainy days. Also, Brisbane’s segregated busway (BRT) corridor works favourably towards this mode choice. Our study findings enhance the fundamental understanding of traveller behaviour, particularly mode choice behaviour under adverse weather conditions.

The MyD88+ phenotype is an adverse prognostic factor in epithelial ovarian cancer

The prognosis of epithelial ovarian cancer is poor in part due to the high frequency of chemoresistance. Recent evidence points to the Toll-like receptor-4 (TLR4), and particularly its adaptor protein MyD88, as one potential mediator of this resistance. This study aims to provide further evidence that MyD88 positive cancer cells are clinically significant, stem-like and reproducibly detectable for the purposes of prognostic stratification. Expression of TLR4 and MyD88 was assessed immunohistochemically in 198 paraffin-embedded ovarian tissues and in an embryonal carcinoma model of cancer stemness. In parallel, expression of TLR4 and MyD88 mRNA and regulatory microRNAs (miR-21 and miR-146a) was assessed, as well as in a series of chemosensitive and resistant cancer cells lines. Functional analysis of the pathway was assessed in chemoresistant SKOV-3 ovarian cancer cells. TLR4 and MyD88 expression can be reproducibly assessed via immunohistochemistry using a semi-quantitative scoring system. TLR4 expression was present in all ovarian epithelium (normal and neoplastic), whereas MyD88 was restricted to neoplastic cells, independent of tumour grade and associated with reduced progression-free and overall survival, in an immunohistological specific subset of serous carcinomas, p<0.05. MiR-21 and miR-146a expression was significantly increased in MyD88 negative cancers (p<0.05), indicating their participation in regulation. Significant alterations in MyD88 mRNA expression were observed between chemosensitive and chemoresistant cells and tissue. Knockdown of TLR4 in SKOV-3 ovarian cells recovered chemosensitivity. Knockdown of MyD88 alone did not. MyD88 expression was down-regulated in differentiated embryonal carcinoma (NTera2) cells, supporting the MyD88+ cancer stem cell hypothesis. Our findings demonstrate that expression of MyD88 is associated with significantly reduced patient survival and altered microRNA levels and suggest an intact/functioning TLR4/MyD88 pathway is required for acquisition of the chemoresistant phenotype. Ex vivo manipulation of ovarian cancer stem cell (CSC) differentiation can decrease MyD88 expression, providing a potentially valuable CSC model for ovarian cancer.

The N170 observed ‘in the wild’: robust event-related potentials to faces in cluttered dynamic visual scenes

As a social species in a constantly changing environment, humans rely heavily on the informational richness and communicative capacity of the face. Thus, understanding how the brain processes information about faces in real-time is of paramount importance. The N170 is a high temporal resolution electrophysiological index of the brain's early response to visual stimuli that is reliably elicited in carefully controlled laboratory-based studies. Although the N170 has often been reported to be of greatest amplitude to faces, there has been debate regarding whether this effect might be an artifact of certain aspects of the controlled experimental stimulation schedules and materials. To investigate whether the N170 can be identified in more realistic conditions with highly variable and cluttered visual images and accompanying auditory stimuli we recorded EEG 'in the wild', while participants watched pop videos. Scene-cuts to faces generated a clear N170 response, and this was larger than the N170 to transitions where the videos cut to non-face stimuli. Within participants, wild-type face N170 amplitudes were moderately correlated to those observed in a typical laboratory experiment. Thus, we demonstrate that the face N170 is a robust and ecologically valid phenomenon and not an artifact arising as an unintended consequence of some property of the more typical laboratory paradigm.

An MRF based abnormal event detection approach using motion and appearance features

Abnormal event detection has attracted a lot of attention in the computer vision research community during recent years due to the increased focus on automated surveillance systems to improve security in public places. Due to the scarcity of training data and the definition of an abnormality being dependent on context, abnormal event detection is generally formulated as a data-driven approach where activities are modeled in an unsupervised fashion during the training phase. In this work, we use a Gaussian mixture model (GMM) to cluster the activities during the training phase, and propose a Gaussian mixture model based Markov random field (GMM-MRF) to estimate the likelihood scores of new videos in the testing phase. Further-more, we propose two new features: optical acceleration, and the histogram of optical flow gradients; to detect the presence of any abnormal objects and speed violations in the scene. We show that our proposed method outperforms other state of the art abnormal event detection algorithms on publicly available UCSD dataset.

Ranking based clustering for social event detection

The problem of clustering a large document collection is not only challenged by the number of documents and the number of dimensions, but it is also affected by the number and sizes of the clusters. Traditional clustering methods fail to scale when they need to generate a large number of clusters. Furthermore, when the clusters size in the solution is heterogeneous, i.e. some of the clusters are large in size, the similarity measures tend to degrade. A ranking based clustering method is proposed to deal with these issues in the context of the Social Event Detection task. Ranking scores are used to select a small number of most relevant clusters in order to compare and place a document. Additionally,instead of conventional cluster centroids, cluster patches are proposed to represent clusters, that are hubs-like set of documents. Text, temporal, spatial and visual content information collected from the social event images is utilized in calculating similarity. Results show that these strategies allow us to have a balance between performance and accuracy of the clustering solution gained by the clustering method.

Turning event logs into process movies: animating what has really happened

Today’s information systems log vast amounts of data. These collections of data (implicitly) describe events (e.g. placing an order or taking a blood test) and, hence, provide information on the actual execution of business processes. The analysis of such data provides an excellent starting point for business process improvement. This is the realm of process mining, an area which has provided a repertoire of many analysis techniques. Despite the impressive capabilities of existing process mining algorithms, dealing with the abundance of data recorded by contemporary systems and devices remains a challenge. Of particular importance is the capability to guide the meaningful interpretation of “oceans of data” by process analysts. To this end, insights from the field of visual analytics can be leveraged. This article proposes an approach where process states are reconstructed from event logs and visualised in succession, leading to an animated history of a process. This approach is customisable in how a process state, partially defined through a collection of activity instances, is visualised: one can select a map and specify a projection of events on this map based on the properties of the events. This paper describes a comprehensive implementation of the proposal. It was realised using the open-source process mining framework ProM. Moreover, this paper also reports on an evaluation of the approach conducted with Suncorp, one of Australia’s largest insurance companies.

SAIVT-ADMRG @ MediaEval 2014 social event detection

This paper outlines the approach taken by the Speech, Audio, Image and Video Technologies laboratory, and the Applied Data Mining Research Group (SAIVT-ADMRG) in the 2014 MediaEval Social Event Detection (SED) task. We participated in the event based clustering subtask (subtask 1), and focused on investigating the incorporation of image features as another source of data to aid clustering. In particular, we developed a descriptor based around the use of super-pixel segmentation, that allows a low dimensional feature that incorporates both colour and texture information to be extracted and used within the popular bag-of-visual-words (BoVW) approach.

Adverse breast cancer treatment effects: The economic case for making rehabilitative programs standard of care

Purpose: The purpose of this work was to evaluate the patient-borne financial cost of common, adverse breast cancer treatment-associated effects, comparing cost across women with or without these side-effects. Methods: 287 Australian women diagnosed with early-stage breast cancer were prospectively followed starting at six months post-surgery for 12 months, with three-monthly assessment of detailed treatment-related side effects and their direct and indirect patient costs attributable to breast cancer. Bootstrapping statistics were used to analyze cost data and adjusted logistic regression was used to evaluate the association between costs and adverse events from breast cancer. Costs were inflated and converted from 2002 Australian to 2014 US dollars. Results: More than 90% of women experienced at least one adverse effect (i.e. post-surgical issue, reaction to radiotherapy, upper-body symptoms or reduced function, lymphedema, fatigue or weight gain). On average, women paid $5,636 (95%CI: $4,694, $6,577) in total costs. Women with any one of the following symptoms (fatigue, reduced upper-body function, upper-body symptoms) or women who report ≥4 adverse treatment-related effects, have 1.5 to nearly 4 times the odds of having higher healthcare costs than women who do not report these complaints (p<0.05). Conclusions: Women face substantial economic burden due to a range of treatment-related health problems, which may persist beyond the treatment period. Improving breast cancer care by incorporating prospective surveillance of treatment-related side effects, and strategies for prevention and treatment of concerns (e.g., exercise) has real potential for reducing patient-borne costs.

A systematic review of patient-reported outcome instruments of dermatologic adverse events associated with targeted cancer therapies

Purpose Dermatologic adverse events (dAEs) in cancer treatment are frequent with the use of targeted therapies. These dAEs have been shown to have significant impact on health-related quality of life (HRQoL). While standardized assessment tools have been developed for physicians to assess severity of dAEs, there is a discord between objective and subjective measures. The identification of patient-reported outcome (PRO) instruments useful in the context of targeted cancer therapies is therefore important in both the clinical and research settings for the overall evaluation of dAEs and their impact on HRQoL. Methods A comprehensive, systematic literature search of published articles was conducted by two independent reviewers in order to identify PRO instruments previously utilized in patient populations with dAEs from targeted cancer therapies. The identified PRO instruments were studied to determine which HRQoL issues relevant to dAEs were addressed, as well as the process of development and validation of these instruments. Results Thirteen articles identifying six PRO instruments met the inclusion criteria. Four instruments were general dermatology (Skindex-16©, Skindex-29©, Dermatology Life Quality Index (DLQI), and DIELH-24) and two were symptom-specific (functional assessment of cancer therapy-epidermal growth factor receptor inhibitor-18 (FACT-EGFRI-18) and hand-foot syndrome-14 (HFS-14)). Conclusions While there are several PRO instruments that have been tested in the context of targeted cancer therapy, additional work is needed to develop new instruments and to further validate the instruments identified in this study in patients receiving targeted therapies.

Mining resource profiles from event logs

Human resources are often responsible for the execution of business processes. In order to evaluate resource performance and identify best practices as well as opportunities for improvement, managers need objective information about resource behaviours. Companies often use information systems to support their processes and these systems record information about process execution in event logs. We present a framework for analysing and evaluating resource behaviour through mining such event logs. The framework provides a method for extracting descriptive information about resource skills, utilisation, preferences, productivity and collaboration patterns; a method for analysing relationships between different resource behaviours and outcomes; and a method for evaluating the overall resource productivity, tracking its changes over time and comparing it with the productivity of other resources. To demonstrate the applicability of our framework we apply it to analyse behaviours of employees in an Australian company and evaluate its usefulness by a survey among managers in industry.