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What constitutes good intelligence is best understood by practitioners but has not been explored through empirical analysis and in the context of a digital age. This paper presents the first research inside all the agencies that form the Australian National Intelligence Community exploring how they are impacted by big data. Intelligence is often opaque to outsiders, yet understanding the characteristics of good intelligence is important to societies that rely on intelligence agencies for national security. This paper reflects the previously unheard perspectives of members of the agencies that form the Australian National Intelligence Community – where there is a significant empirical gap. Semi-structured interviews with 47 participants explored the impact of big data on intelligence and decision-making in Australia. This paper finds that intelligence must meet the following characteristics, many established in historical literature, in order to be considered good intelligence; (i) timely, (ii) purposeful, (iii) actionable, (iv) accurate, (v) provides value-add for an intended audience, and, (vi) is unbiased. This article explores and unpacks each of these characteristics of good intelligence and finds they remain critical in a big data era. [ABSTRACT FROM AUTHOR]

.Commercial platforms are being increasingly used in classrooms as teaching and learning tools. Beyond shifts in practice to accommodate this, the teacher becomes a tool of the platform – generating and collecting data for commercial entities. It is time to move from commercial platforms working through teachers to influence education, towards working with teachers in the ethical use of educational data. The purpose of this paper is twofold. Firstly, we draw on semi-structured interviews to present an exploratory typology of teacher influencer behaviour. Secondly, we argue that there is potential for teacher influencers to act as advocates. We introduce the conceptual notion of an 'educational data advocate'. We call for Teacher Influencers to be recognised as a source of expertise to understand the implications of the use of data-driven educational technologies. By doing so, a new pedagogical economy that promotes ethics and rights alongside educational and commercial outcomes may be generated. [ABSTRACT FROM AUTHOR]

In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a 'soft governance' ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. [ABSTRACT FROM AUTHOR]

This Special Issue addresses the use of linked data for research purposes and to carry out government functions such as child protection, allocation of resources, and debt recovery. Government investment in big data has the potential to change citizens' experience of the welfare state in a broad range of areas in both positive and negative ways. It is therefore important that the Australian social policy community understands and engages with the potential benefits and risks involved in the linkage and analysis of government datasets. Papers in this Special Issue discuss the technical challenges and institutional barriers involved in the construction and governance of linked government data assets and showcase the promise of big data for generating policy relevant insights. This Special Issue also features papers critically interrogating the potential for big data to produce social harms. We contextualise this collection of papers with a brief history of recent policy developments in regards to access to government held data. We also discuss ways of improving public trust and social licence for the use of big data and argue that the voices of First Nations and disadvantaged Australians must be given greater weight in discussions of how their data will be used. [ABSTRACT FROM AUTHOR]

As the sophistication of genomic technologies increases and their cost continues to decrease, they are becoming routine tools in livestock breeding programs and production systems. The convergence of electronic measurement systems, cloud-based computing and fast internet enable the use of powerful decision support to help automate and manage livestock production, animal welfare and quality control. The complexity of livestock production systems, combined with the challenges of producing top-quality discretionary products for consumers, means that no single organisation has the range of expertise to support coordinated development of innovation in the relevant supply chains. Collaboration between a broad spectrum of scientists and industry partners is essential to ensure well integrated input to the design and implementation of programs to deliver improvements in efficiency, quality and profit. The need for collaboration among researchers, among research organisations and with end-users has never been more important. Collaboration brings together the skills needed to manage complex problems and enables the sharing of facilities and scarce resources within Australia and internationally. However, the most important component of effective collaboration is the early engagement of end-users to contribute to the design of programs of innovation, to ensure that investment is well targeted and that there is ownership of the problem as well as the solutions delivered through research. Although the potential benefits of effective collaboration are clear, it often takes more than logic to get individuals and organisations to work together. There needs to be a significant financial incentive combined with strong industry leadership and agreed common goals. Allocating resources to establish these foundations for effective collaboration should precede any major research and development funding initiative. The present paper argues that the new face of animal science in Australia should be structured around coordinated programs of research and development, on the basis of effective collaboration. Research leading to significant change in agricultural practice almost always involves collaboration between teams of researchers and multiple end-users as well as sharing resources across multiple organisations. Although benefits and efficiencies are easily recognised, effective multi-institutional teamwork is often difficult to achieve. The present paper draws on experience from the Sheep CRC, over a period of nearly 20 years, where collaboration among an increasing number of participants contributed to many significant changes in the Australian sheep industry. [ABSTRACT FROM AUTHOR]

This paper uses novel electronic tools to identify the degree to which Australia was listening to Indigenous peoples in a 'national conversation' about constitutional recognition between 2015 and late 2017. The results show that while there was a superficial overlap in themes, there were important differences of framing. Recognition remained a largely formal, elite and non-Indigenous concern, with First Nations focusing on treaties, sovereignty, listening and respect. Interaction was noticeably aggressive, but not exclusively so. Non-Indigenous people avoided discussing racism, and talked more frequently about history, framing issues in the past tense; First Nations talked about the here and now. And despite more focus on everyday racism, Indigenous peoples were consistently more positive and proud, rejecting 'plight' constructions. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) suffer from forged messages. Current studies have typically been focused on the detection of forged messages and do not provide the analysis of the behaviors of message publishers and network strategies to suppress forged messages. This paper carries out the analysis by taking a game theoretic approach, where infinitely repeated games are constructed to capture the interactions between a publisher and a network administrator and suppress forged messages in OSNs. Critical conditions, under which the publisher is disincentivized to publish any forged messages, are identified in the absence and presence of misclassification on genuine messages. Closed-form expressions are established for the maximum number of forged messages that a malicious publisher could publish. Confirmed by the numerical results, the proposed infinitely repeated games reveal that forged messages can be suppressed by improving the payoffs for genuine messages, increasing the cost of bots, and/or reducing the payoffs for forged messages. The increasing detection probability of forged messages or decreasing misclassification probability of genuine messages also has a strong impact on the suppression of forged messages. [ABSTRACT FROM AUTHOR]

• We work on multiple wild animals detection and pose estimation. • We constructed a large wild animal surveillance dataset. • We proposed a ratio based data distillation framework. Monitoring wildlife populations and activities have significance for biology and ecology. With the rapid development of computer vision and deep learning techniques, it is possible to employ state-of-the-art convoluntional neural network (CNN) based detectors to process the big data from field surveillance cameras and assist in the following studies. However, data labelling during the training stage is a very time-consuming, labour intensive and expensive task. In this paper, we detect multiple animals (Kangaroo, emu, dingo, bird and wildcat) in the wild in an Omni-supervised learning setting. The unlabeled data from the surveillance cameras will be filtered and used for training via data distillation approach. Moreover, we also perform joint pose estimation and detection for Kangaroo which has the most samples in the dataset. To study the feasibility, we also built a large full high definition (HD) wild animal surveillance image dataset from collected data from several national parks across the State of Queensland in Australia and this dataset will be made publicly available. Extensive experiments show that the detection and pose estimation results can be further improved by using unlabeled data wisely. [ABSTRACT FROM AUTHOR]

The map presented in this paper summarises the combined land- and airside accessibility within Australia. To this end, we calculate a bimodal accessibility index at the scale of statistical units by aggregating the (shortest) travel time for three route segments: (1) road travel from the origin to a departure airport, (2) air travel, and (3) road travel from an arrival airport to the destination. The average travel time from a statistical unit to all other statistical units is calculated for the units' population centroids, after which an accessibility surface is interpolated using kriging. The map shows that southeastern Australia is generally characterised by a high accessibility index with the most populated cities being hotspots of accessibility. Central and northern Australia are – with few exceptions – far less accessible. In addition to this largely-expected pattern, the map also reveals a number of specific and perhaps more surprising geographical patterns. [ABSTRACT FROM AUTHOR]

There is a growing appetite for large complex databases that integrate a range of personal, socio-demographic, health, genetic and financial information on individuals. It has been argued that 'Big Data' will provide the necessary catalyst to advance both biomedical research and health economics and outcomes research. However, it is important that we do not succumb to being data rich but information poor. This paper discusses the benefits and challenges of building Big Data, analysing Big Data and making appropriate inferences in order to advance cancer care, using Cancer 2015 (a prospective, longitudinal, genomic cohort study in Victoria, Australia) as a case study. Cancer 2015 has been linked to State and Commonwealth reimbursement databases that have known limitations. This partly reflects the funding arrangements in Australia, a country with both public and private provision, including public funding of private healthcare, and partly the legislative frameworks that govern data linkage. Additionally, linkage is not without time delays and, as such, achieving a contemporaneous database is challenging. Despite these limitations, there is clear value in using linked data and creating Big Data. This paper describes the linked Cancer 2015 dataset, discusses estimation issues given the nature of the data and presents panel regression results that allow us to make possible inferences regarding which patient, disease, genomic and treatment characteristics explain variation in health expenditure. [ABSTRACT FROM AUTHOR]

Recent research undertaken revealed that a significant positive relationship exists between quality and safety performance. A major limitation of this research, however, was the nature of the sample; it was heterogeneous (i.e., a combination of U.S. and international projects) and the sample was restricted to 18 projects. Building upon initial research, this paper re-examines the association between quality and safety using a homogeneous sample of 569 projects, which were derived from an Australian construction company with an annual turnover in excess of $1 billion Australian dollars (AU$). A total of 19,314 nonconformances and 17,783 injuries were used to determine the validity and reliability of previous research. A weak association between quality and safety performance was found (p < 0.01). The p-values did not indicate any significant association between first aid and quality rates, except for the injury rate and rework frequency per million scope, which yielded an r-value of 0.307 and p-value 0.046 that is significant at 0.01 level. An association, however, between injuries and rework was identified (r² = 0.70). The discrepancy between this research's findings and that of previous work led to an examination of the issues of using ratios in correlation analysis. Thus, the statistical and arithmetic issues associated with the use of ratios are discussed, and it is recommended that estimating the relationships between quality and safety should be examined using regression techniques or analysis of covariance. Linear regression, therefore, was performed with the injury data as the dependent variable, and rework frequency and personnel hours as the independent variables. The regression results demonstrated that there is a significant association between injuries, and rework and personnel hours; it was revealed that both predictors accounted for 68.2% of the explained variability in injury frequency. The replication of the initial research has enabled a significant advancement in knowledge about relationship between quality and safety performance. [ABSTRACT FROM AUTHOR]

Online behavioural advertising (OBA) comes to consumers at a price. Often unknowingly, people deliver up commercially-valuable personal information as a condition of online user experience, functionality and access. Websites are increasingly tracking user behaviours for commercial purposes and social media derives its income largely from data collection and advertising targeted to the personal disclosures and behavioural attributes which are its data-production mainstay. In this context, consumers face a plethora of information collection practices, all designed to generate data analytics including inferential and predictive profiling to create a ‘digital identity’ for OBA purposes. In this subterranean exchange, consumers are economically redefined as data subjects and advertising targets; a reframing which is perhaps why the OBA industry faces a crisis in consumer concern, both as to privacy and trust. This paper proposes that the regulatory control of OBA in Australia is in disarray. Consumer ignorance of online privacy management and OBA practices is demonstrable. Industry transparency, disclosure, consent processes and compliance practices are questionable. Regulator interest is minimal, industry self-regulation is weak and consumer technical ability and personal responsibility is a last fragile line of defence. Data breaches are ubiquitous in a crowded and poorly-audited supply chain, and entail significant adverse consumer consequences. Yet despite these serious concerns, Australian regulators are failing to respond to OBA issues, either through mandating greater industry disclosure or through regulatory action. The author seeks to expose these weaknesses in calling for consumer and privacy regulators to take more meaningful action to better protect consumers' interests online. [ABSTRACT FROM AUTHOR]

Digitalization and the use of Smart Farming Technologies are considered a major opportunity for the future of agriculture. However, realisation of full benefits is constrained by: (1) farmers' interest in and use of big data to improve farm decision making; (2) issues of data sovereignty and trust between providers and users of data and technology; (3) institutional arrangements associated with the governance of data platforms. This paper examines the case of Australia's dairy herd milk recording system, arguably one of agriculture's first cases of 'big data' use, which collects, analyses and uses farm-level data (milk production, lactation and breeding records) to provide individual cow and herd performance information, used by individual farmers for farm management decisions. The aim of this study was to 1) examine the use of big data to add value to farm decision making; and 2) explore factors and processes, including institutional arrangements, which influence farmer engagement with and use of big data. This paper traces the Australian history of the organisation of dairy herd recording (established in 1912 and digitalized in late 1970s) and then uses findings from a longitudinal study of 7 case study dairy farms, which were incentivised to become involved in herd recording in 2015. Applying a conceptual framework linking path dependency in farm decision making and collaborative governance capacity, we find three new important dimensions of the farm user context influencing farmer demand for big data applications: 1) the transition to a new business stage; 2) the additionality farmers seek from data generated in one component of the farm system to other subsystems, and 3) the use of data in long term or strategic decision making. Further, we identified critical attributes of support services in addressing digital literacy, capacity and capability issues at farm level, including diversity in data presentation formats and facilitation of the on-farm transition process through intermediary herd test organisations. The role of farmers as governance actors, or citizens in the decisions of the trajectory of big data applications, adds to understanding of the nature of collaborative governance arrangements that support farm engagement. • Farmer interest in data relates to business stage and the decision's importance. • Cooperative governance structures facilitate farmer trust and use of big data. • Support services reduce the impact of low digital literacy, capacity and capability. • Inter-dependence occurs between existing data processes and optimising data use. [ABSTRACT FROM AUTHOR]

• Developing a novel approach for using trip sequences instead of separate trips. • Developing a unique similarity measure for the trip sequences. • Focusing only on the temporal attributes of the trip sequences. • Running the method on real-world case studies. • Evaluating the performance of the developed method. • Comparing the outcome with the existing literature. • Achieving a considerable improvement in inferring the trip purpose. Smart card transactions are known as a rich and continuous source of public transit data, but they miss some important attributes about trips and passengers. One of these missing attributes is the trip purpose attribute. This paper proposes a novel method to infer the trip purpose attribute from the sequences of trips of passengers instead of separate trips. The proposed method infers the trip purpose attribute (a missing attribute in the smart card data) from the temporal attributes (available attributes in the smart card data). First, the relation between the temporal attributes and the trip purpose attribute is learnt by discovering clusters of passengers in the Household Travel Survey dataset while each passenger is represented by one sequence of trips. Then, the discovered clusters are utilized to infer the trip purpose of smart card transactions by allocating each passenger to the closest clusters. The proposed method is implemented on the smart card and HTS datasets from southeast Queensland, Australia. The evaluation results showed a considerable improvement in inferring the trip purpose compared to the results published in the literature. Notably, the effect of considering the trip sequence was more significant than considering land use variables. [ABSTRACT FROM AUTHOR]