On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty, and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exists where the more mind changes the learner is willing to accept, the lesser the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

Acoustic keyword spotting in speech with applications to data mining

Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.

Cluster-based network model for time-course gene expression data

We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.

Tactical driver lane change model using forward search

This paper describes the development and evaluation of a tactical lane change model using the forward search algorithm, for use in a traffic simulator. The tactical lane change model constructs a set of possible choices of near-term maneuver sequences available to the driver and selects the lane change action at the present time to realize the best maneuver plan. Including near term maneuver planning in the driver behavior model can allow a better representation of the complex interactions in situations such as a weaving section and high-occupancy vehicle (HOV) lane systems where drivers must weave across several lanes in order to access the HOV lanes. To support the investigation, a longitudinal control model and a basic lane change model were also analyzed. The basic lane change model is similar to those used by today's commonly-used traffic simulators. Parameters in all models were best-fit estimated for selected vehicles from a real-world freeway vehicle trajectory data set. The best-fit estimation procedure minimizes the discrepancy between the model vehicle and real vehicle's trajectories. With the best fit parameters, the proposed tactical lane change model gave a better overall performance for a greater number of cases than the basic lane change model.

Encryption safe harbours and data breach notification laws

Data breach notification laws require organisations to notify affected persons or regulatory authorities when an unauthorised acquisition of personal data occurs. Most laws provide a safe harbour to this obligation if acquired data has been encrypted. There are three types of safe harbour: an exemption; a rebuttable presumption and factor-based analysis. We demonstrate, using three condition-based scenarios, that the broad formulation of most encryption safe harbours is based on the flawed assumption that encryption is the silver bullet for personal information protection. We then contend that reliance upon an encryption safe harbour should be dependent upon a rigorous and competent risk-based review that is required on a case-by-case basis. Finally, we recommend the use of both an encryption safe harbour and a notification trigger as our preferred choice for a data breach notification regulatory framework.

Stakeholder perspectives regarding the mandatory notification of Australian data breaches

The advent of data breach notification laws in the United States (US) has unearthed a significant problem involving the mismanagement of personal information by a range of public and private sector organisations. At present, there is currently no statutory obligation under Australian law requiring public or private sector organisations to report a data breach of personal information to law enforcement agencies or affected persons. However, following a comprehensive review of Australian privacy law, the Australian Law Reform Commission (ALRC) has recommended the introduction of a mandatory data breach notification scheme. The issue of data breach notification has ignited fierce debate amongst stakeholders, especially larger private sector entities. The purpose of this article is to document the perspectives of key industry and government representatives to identify their standpoints regarding an appropriate regulatory approach to data breach notification in Australia.

The mandatory notification of data breaches : issues arising for Australian and EU legal developments

Public and private sector organisations are now able to capture and utilise data on a vast scale, thus heightening the importance of adequate measures for protecting unauthorised disclosure of personal information. In this respect, data breach notification has emerged as an issue of increasing importance throughout the world. It has been the subject of law reform in the United States and in other jurisdictions. This article reviews US, Australian and EU legal developments regarding the mandatory notification of data breaches. The authors highlight areas of concern based on the extant US experience that require further consideration in Australia and in the EU.

Concurrent and prospective associations between physical activity, walking and mental health in older women

Background Leisure-time physical activity (LTPA) shows promise for reducing the risk of poor mental health in later life, although gender- and age-specific research is required to clarify this association. This study examined the concurrent and prospective relationships between both LTPA and walking with mental health in older women. Methods Community-dwelling women aged 73–78 years completed mailed surveys in 1999, 2002 and 2005 for the Australian Longitudinal Study on Women's Health. Respondents reported their weekly minutes of walking, moderate LTPA and vigorous LTPA. Mental health was defined as the number of depression and anxiety symptoms, as assessed with the Goldberg Anxiety and Depression Scale (GADS). Multivariable linear mixed models, adjusted for socio-demographic and health-related variables, were used to examine associations between five levels of LTPA (none, very low, low, intermediate and high) and GADS scores. For women who reported walking as their only LTPA, associations between walking and GADS scores were also examined. Women who reported depression or anxiety in 1999 were excluded, resulting in data from 6653 women being included in these analyses. Results Inverse dose–response associations were observed between both LTPA and walking with GADS scores in concurrent and prospective models (p<0.001). Even low levels of LTPA and walking were associated with lowered scores. The lowest scores were observed in women reporting high levels of LTPA or walking. Conclusion The results support an inverse dose–response association between both LTPA and walking with mental health, over 3 years in older women without depression or anxiety.

Grassland management activity data : current sources and future needs

Estimates of potential and actual C sequestration require areal information about various types of management activities. Forest surveys, land use data, and agricultural statistics contribute information enabling calculation of the impacts of current and historical land management on C sequestration in biomass (in forests) or in soil (in agricultural systems). Unfortunately little information exists on the distribution of various management activities that can impact soil C content in grassland systems. Limited information of this type restricts our ability to carry out bottom-up estimates of the current C balance of grasslands or to assess the potential for grasslands to act as C sinks with changes in management. Here we review currently available information about grassland management, how that information could be related to information about the impacts of management on soil C stocks, information that may be available in the future, and needs that remain to be filled before in-depth assessments may be carried out. We also evaluate constraints induced by variability in information sources within and between countries. It is readily apparent that activity data for grassland management is collected less frequently and on a coarser scale than data for forest or agricultural inventories and that grassland activity data cannot be directly translated into IPCC-type factors as is done for IPCC inventories of agricultural soils. However, those management data that are available can serve to delineate broad-scale differences in management activities within regions in which soil C is likely to change in response to changes in management. This, coupled with the distinct possibility of more intensive surveys planned in the future, may enable more accurate assessments of grassland C dynamics with higher resolution both spatially and in the number management activities.

Using digital rights management for securing data in a medical research environment

We propose a digital rights management approach for sharing electronic health records in a health research facility and argue advantages of the approach. We also give an outline of the system under development and our implementation of the security features and discuss challenges that we faced and future directions.

Assessment of the suitability of public mobile data networks for aircraft telemetry and control purposes

This paper provides a review of the state of the art relevant work on the use of public mobile data networks for aircraft telemetry and control proposes. Moreover, it describes the characterisation for airborne uses of the public mobile data communication systems known broadly as 3G. The motivation for this study was the explore how this mature public communication systems could be used for aviation purposes. An experimental system was fitted to a light aircraft to record communication latency, line speed, RF level, packet loss and cell tower identifier. Communications was established using internet protocols and connection was made to a local server. The aircraft was flown in both remote and populous areas at altitudes up to 8500 ft in a region located in South East Queensland, Australia. Results show that the average airborne RF levels are better than those on the ground by 21% and in the order of - 77dbm. Latencies were in the order of 500ms (1/2 the latency of Iridium), an average download speed of 0.48Mb/s, average uplink speed of 0.85Mb/s, a packet of information loss of 6.5%. The maximum communication range was also observed to be 70km from a single cell station. The paper also describes possible limitations and utility of using such communications architecture for both manned and unmanned aircraft systems.