Misericordiae Antemortem - The 1955 Mount Superbus Crash

On the 9th April 1955, RAAF Lincoln Bomber A73-64, on a mercy flight to transfer a critically ill infant from Townsville to Brisbane, crashed at Mount Superbus killing the four crew and two civilians on board. The immediate search and rescue was organised by a group of Brisbane bushwalkers who were camping in the area. Police and RAAF personnel subsequently joined the civilians at the crash site to recover the victims. During their initial search of the crash they located what were believed to be the remains of five adults. The arrival of the RAAF Senior Medical Officer (SMO) the following day revealed that only four adult bodies had been found and the bodies of both civilians, an adult and infant, were missing. Later that day the remains of six victims were recovered from the crash site and conveyed to the Warwick Police Station for identification. The RAAF SMO was responsible for the identifications of the aircrew while the Government Medical Officer, police and coroner were responsible for the identifications of the civilians. Eight days later, further remains of the infant were found by a civilian looking through the wreckage. This paper uses archival records not previously researched from a Disaster Victim Identification (DVI) perspective to stimulate interest among forensic practitioners, criminologists and other interested parties in the history of DVI and how practices in Australia have evolved.

Automatic particle picking of biological molecules imaged by electron microscopy

One of the next great challenges of cell biology is the determination of the enormous number of protein structures encoded in genomes. In recent years, advances in electron cryo-microscopy and high-resolution single particle analysis have developed to the point where they now provide a methodology for high resolution structure determination. Using this approach, images of randomly oriented single particles are aligned computationally to reconstruct 3-D structures of proteins and even whole viruses. One of the limiting factors in obtaining high-resolution reconstructions is obtaining a large enough representative dataset ($>100,000$ particles). Traditionally particles have been manually picked which is an extremely labour intensive process. The problem is made especially difficult by the low signal-to-noise ratio of the images. This paper describes the development of automatic particle picking software, which has been tested with both negatively stained and cryo-electron micrographs. This algorithm has been shown to be capable of selecting most of the particles, with few false positives. Further work will involve extending the software to detect differently shaped and oriented particles.

The Growing Pains of Data Breach Notification Law

Mandatory data breach notification laws are a novel statutory solution in relation to organizational protections of personal information. They require organizations which have suffered a breach of security involving personal information to notif'y those persons whose information may have been affected. These laws originated in the state based legislatures of the United States during the last decade and have subsequently garnered worldwide legislative interest. Despite their perceived utility, mandatory data breach notification laws have several conceptual and practical concems that limit the scope of their applicability, particularly in relation to existing information privacy law regimes. We outline these concerns, and in doing so, we contend that while mandatory data breach notification laws have many useful facets, their utility as an 'add-on' to enhance the failings of current information privacy law frameworks should not necessarily be taken for granted.

Timed and Probabilistic Automata for Automatic Animal Call Recognition

Automatic Call Recognition is vital for environmental monitoring. Patten recognition has been applied in automatic species recognition for years. However, few studies have applied formal syntactic methods to species call structure analysis. This paper introduces a novel method to adopt timed and probabilistic automata in automatic species recognition based upon acoustic components as the primitives. We demonstrate this through one kind of birds in Australia: Eastern Yellow Robin.

The impact of penalty increases on speeding behaviour in Queensland and a characterisation of speeding offenders

This project examined the effects of speeding penalty changes that occurred in Queensland in 2003 on the behaviour of speeding offenders. These penalty changes included increasing the number of offence categories, and in turn narrowing the range of speeds associated with the offence categories; increasing the monetary fines for all offences, with the largest increases observed for high-range offences; and introducing automatic licence suspension and an eight demerit point penalty for the highest offence category. To explore the impact of the penalty changes, offence data collected for two cohorts of motorists in Queensland who were caught speeding prior to and subsequent to the penalty changes (N = 84,456) were compared. The first cohort consisted of individuals (operators of all vehicles including motorcycles) who committed a speeding offence in May 2001 (two years prior to the speeding penalty change); and individuals who committed a speeding offence in May 2003 (one month after the introduction of the penalty change). Four measures of recidivism were devised and used to assess the effects of the new penalties with regard to deterring the speeding behaviour of offenders. Additionally, the project investigated the relationship between speeding offences, other risky driving behaviours, crash involvement, and criminal behaviour.

Automatic Audio Segmentation Using the Generalized Likelihood Ratio

This paper presents a novel technique for segmenting an audio stream into homogeneous regions according to speaker identities, background noise, music, environmental and channel conditions. Audio segmentation is useful in audio diarization systems, which aim to annotate an input audio stream with information that attributes temporal regions of the audio into their specific sources. The segmentation method introduced in this paper is performed using the Generalized Likelihood Ratio (GLR), computed between two adjacent sliding windows over preprocessed speech. This approach is inspired by the popular segmentation method proposed by the pioneering work of Chen and Gopalakrishnan, using the Bayesian Information Criterion (BIC) with an expanding search window. This paper will aim to identify and address the shortcomings associated with such an approach. The result obtained by the proposed segmentation strategy is evaluated on the 2002 Rich Transcription (RT-02) Evaluation dataset, and a miss rate of 19.47% and a false alarm rate of 16.94% is achieved at the optimal threshold.

On the commonly accepted assumptions regarding observed motor vehicle crash counts at transport system locations

Readily accepted knowledge regarding crash causation is consistently omitted from efforts to model and subsequently understand motor vehicle crash occurrence and their contributing factors. For instance, distracted and impaired driving accounts for a significant proportion of crash occurrence, yet is rarely modeled explicitly. In addition, spatially allocated influences such as local law enforcement efforts, proximity to bars and schools, and roadside chronic distractions (advertising, pedestrians, etc.) play a role in contributing to crash occurrence and yet are routinely absent from crash models. By and large, these well-established omitted effects are simply assumed to contribute to model error, with predominant focus on modeling the engineering and operational effects of transportation facilities (e.g. AADT, number of lanes, speed limits, width of lanes, etc.) The typical analytical approach—with a variety of statistical enhancements—has been to model crashes that occur at system locations as negative binomial (NB) distributed events that arise from a singular, underlying crash generating process. These models and their statistical kin dominate the literature; however, it is argued in this paper that these models fail to capture the underlying complexity of motor vehicle crash causes, and thus thwart deeper insights regarding crash causation and prevention. This paper first describes hypothetical scenarios that collectively illustrate why current models mislead highway safety researchers and engineers. It is argued that current model shortcomings are significant, and will lead to poor decision-making. Exploiting our current state of knowledge of crash causation, crash counts are postulated to arise from three processes: observed network features, unobserved spatial effects, and ‘apparent’ random influences that reflect largely behavioral influences of drivers. It is argued; furthermore, that these three processes in theory can be modeled separately to gain deeper insight into crash causes, and that the model represents a more realistic depiction of reality than the state of practice NB regression. An admittedly imperfect empirical model that mixes three independent crash occurrence processes is shown to outperform the classical NB model. The questioning of current modeling assumptions and implications of the latent mixture model to current practice are the most important contributions of this paper, with an initial but rather vulnerable attempt to model the latent mixtures as a secondary contribution.

A comparison of perceived crash risk among cyclists and drivers in Brisbane

The Queensland Government has implemented strategies promoting a shift from individual car use to active transport, a transition which requires drivers to adapt to sharing the road with increased numbers of people cycling through transport network. For this to occur safely, changes in both road infrastructure and road user expectations and behaviors will be needed. Creating separate cycle infrastructure does not remove the need for cyclists to commence, cross or finish travel on shared roads. Currently intersections are one of the predominant shared road spaces where crashes result in cyclists being injured or killed. This research investigates how Brisbane cyclists and drivers perceive risk when interacting with other road users at intersections. The current study replicates a French study conducted by co-authors Chaurand and Delhomme in 2011 and extends it to assess gender effects which have been reported in other Australian cycling research. An online survey was administered to experienced cyclists and drivers. Participants rated the level of risk they felt when imagining a number of different road situations. Based on the earlier French study it is expected that perceived crash risk will be influenced both by the participant’s mode of travel and the type of interacting vehicle and perceived risk will be greater when the interaction is with a car than a bicycle. It is predicted that risk perception will decrease as the level of experience increases and that male participants will have a higher perception of skill and lower perception of risk than females. The findings of this Queensland study will provide a valuable insight into perceived risk and the traffic behaviours of drivers and cyclists when interacting with other road users and results will be available for presentation at the Congress.

Using static analysis for automatic assessment and mitigation of unwanted and malicious activities within Android applications

In the last decade, smartphones have gained widespread usage. Since the advent of online application stores, hundreds of thousands of applications have become instantly available to millions of smart-phone users. Within the Android ecosystem, application security is governed by digital signatures and a list of coarse-grained permissions. However, this mechanism is not fine-grained enough to provide the user with a sufficient means of control of the applications' activities. Abuse of highly sensible private information such as phone numbers without users' notice is the result. We show that there is a high frequency of privacy leaks even among widely popular applications. Together with the fact that the majority of the users are not proficient in computer security, this presents a challenge to the engineers developing security solutions for the platform. Our contribution is twofold: first, we propose a service which is able to assess Android Market applications via static analysis and provide detailed, but readable reports to the user. Second, we describe a means to mitigate security and privacy threats by automated reverse-engineering and refactoring binary application packages according to the users' security preferences.

Effects of eco-driving instructions using a vehicle with automatic transmission on urban roads

Eco-driving instructions could reduce fuel consumption to up to 20% (EcoMove, 2010). Participants (N=13) drove an instrumented vehicle (i.e. Toyota Camry 2007) with an automatic transmission. Fuel consumption of the participants were compared before and after they received eco-driving instructions. Participants drove the same vehicle on the same urban route under similar traffic conditions. Results show that, on free flow sections of the track, all participants drove slightly faster (on average, 0.7 Km/h faster), during the lap for which they were instructed to drive in an eco-friendly manner as compared to when they were not given the eco-driving instruction. Suprisingly, eco-driving instructions increased the RPM significantly in most cases. Fuel consumption slightly decreased (6%) after the eco-driving instructions. We have found strong evidence showing that the fuel saving observed in our experiment (urban environment, automatic transmission) fall short of the 20% reduction claimed in other international trials.

Comparing cooperative and non-cooperative crash risk assessment

Cooperative Systems provide, through the multiplication of information sources over the road, a lot of potential to improve the assessment of the road risk describing a particular driving situation. In this paper, we compare the performance of a cooperative risk assessment approach against a non-cooperative approach; we used an advanced simulation framework, allowing for accurate and detailed, close-to-reality simulations. Risk is estimated, in both cases, with combinations of indicators based on the TTC. For the non-cooperative approach, vehicles are equipped only with an AAC-like forward-facing ranging sensor. On the other hand, for the cooperative approach, vehicles share information through 802.11p IVC and create an augmented map representing their environment; risk indicators are then extracted from this map. Our system shows that the cooperative risk assessment provides a systematic increase of forward warning to most of the vehicles involved in a freeway emergency braking scenario, compared to a non-cooperative system.

Characteristics of Chinese drivers attending a mandatory training course following license suspension

Penalties and sanctions to deter risky/illegal behaviours are important components of traffic law enforcement. Sanctions can be applied to the vehicle (e.g., impoundment), the person (e.g., remedial programs or jail), or the licence (e.g., disqualification). For licence sanctions, some offences attract automatic suspension while others attract demerit points which can indirectly lead to licence loss. In China, a licence is suspended when a driver accrues twelve demerit points within one year. When this occurs, the person must undertake a one-week retraining course at their own expense and successfully pass an examination to become relicensed. Little is known about the effectiveness of this program. A pilot study was conducted in Zhejiang Province to examine basic information about participants of a retraining course. The aim was to gather baseline data for future comparison. Participants were recruited at a driver retraining centre in a large city in Zhejiang Province. In total, 239 suspended drivers completed an anonymous questionnaire which included demographic information, driving history, and crash involvement. Overall, 87% were male with an overall mean age of 35.02 years (SD=8.77; range 21-60 years). A large proportion (83.3%) of participants owned a vehicle. Commuting to work was reported by 64% as their main reason for driving, while 16.3% reported driving for work. Only 6.4% reported holding a licence for 1 year or less (M=8.14 years, SD=6.5, range 1-31 years) and people reported driving an average of 18.06 hours/week (SD=14.4, range 1-86 hours). This represents a relatively experienced group, especially given the increase in new drivers in China. The number of infringements reportedly received in the previous year ranged from 2 to 18 (M=4.6, SD=3.18); one third of participants reported having received 5 or more infringements. Approximately one third also reported having received infringements in the previous year but not paid them. Various strategies for avoiding penalties were reported. The most commonly reported traffic violations were: drink driving (DUI; 0.02-0.08 mg/100ml) with 61.5% reporting 1 such violation; and speeding (47.7% reported 1-10 violations). Only 2.2% of participants reported the more serious drunk driving violation (DWI; above 0.08mg/100ml). Other violations included disobeying traffic rules, using inappropriate licence, and licence plate destroyed/not displayed. Two-thirds of participants reported no crash involvement in the previous year while 14.2% reported involvement in 2-5 crashes. The relationship between infringements and crashes was limited, however there was a small, positive significant correlation between crashes and speeding infringements (r=.2, p=.004). Overall, these results indicate the need for improved compliance with the law among this sample of traffic offenders. For example, lower level drink driving (DUI) and speeding were the most commonly reported violations with some drivers having committed a large number in the previous year. It is encouraging that the more serious offence of drunk driving (DWI) was rarely reported. The effectiveness of this driver retraining program and the demerit point penalty system in China is currently unclear. Future research including driver follow up via longitudinal study is recommended to determine program effectiveness to enhance road safety in China.

Comparison of moped, scooter and motorcycle crash risk and crash severity

The increased popularity of mopeds and motor scooters in Australia and elsewhere in the last decade has contributed substantially to the greater use of powered two-wheelers (PTWs) as a whole. As the exposure of mopeds and scooters has increased, so too has the number of reported crashes involving those PTW types, but there is currently little research comparing the safety of mopeds and, particularly, larger scooters with motorcycles. This study compared the crash risk and crash severity of motorcycles, mopeds and larger scooters in Queensland, Australia. Comprehensive data cleansing was undertaken to separate motorcycles, mopeds and larger scooters in police-reported crash data covering the five years to 30 June 2008. The crash rates of motorcycles (including larger scooters) and mopeds in terms of registered vehicles were similar over this period, although the moped crash rate showed a stronger downward trend. However, the crash rates in terms of distance travelled were nearly four times higher for mopeds than for motorcycles (including larger scooters). More comprehensive distance travelled data is needed to confirm these findings. The overall severity of moped and scooter crashes was significantly lower than motorcycle crashes but an ordered probit regression model showed that crash severity outcomes related to differences in crash characteristics and circumstances, rather than differences between PTW types per se. Greater motorcycle crash severity was associated with higher (>80 km/h) speed zones, horizontal curves, weekend, single vehicle and nighttime crashes. Moped crashes were more severe at night and in speed zones of 90 km/h or more. Larger scooter crashes were more severe in 70 km/h zones (than 60 km/h zones) but not in higher speed zones, and less severe on weekends than on weekdays. The findings can be used to inform potential crash and injury countermeasures tailored to users of different PTW types.

Robust automatic speaker linking and attribution

This research makes a major contribution which enables efficient searching and indexing of large archives of spoken audio based on speaker identity. It introduces a novel technique dubbed as “speaker attribution” which is the task of automatically determining ‘who spoke when?’ in recordings and then automatically linking the unique speaker identities within each recording across multiple recordings. The outcome of the research will also have significant impact in improving the performance of automatic speech recognition systems through the extracted speaker identities.