A randomised controlled trial of an online theory-based intervention to improve adult Australians' sun-protective behaviours

Objective. To evaluate the effectiveness of a single-session online theory of planned behaviour (TPB)-based intervention to improve sun-protective attitudes and behaviour among Australian adults. Methods. Australian adults (N = 534; 38.7% males; Mage = 39.3 years) from major cities (80.9%), regional (17.6%) and remote areas (1.5%)were recruited and randomly allocated to an intervention (N=265) and information only group (N = 267). The online intervention focused on fostering positive attitudes, perceptions of normative support, and control perceptions for sun protection. Participants completed questionnaires assessing standard TPB measures (attitude, subjective norm, perceived behavioural control, intention, behaviour) and extended TPB constructs of group norm (friends, family), personal norm, and image norm, pre-intervention (Time 1) and one week (Time 2) and one month post-intervention (Time 3). Repeated Measures Multivariate Analysis of Variance tested intervention effects across time. Results. Intervention participants reported more positive attitudes towards sun protection and used sunprotective measures more often in the subsequent month than participants receiving information only. The intervention effects on control perceptions and norms were non-significant. Conclusions. A theory-based online intervention fostering more favourable attitudes towards sun safety can increase sun protection attitudes and self-reported behaviour among Australian adults in the short term.

Improving the police response to online fraud

Online fraud poses a significant problem to society in terms of its monetary losses and the devastating impact on victims. It also poses significant challenges to law enforcement agencies, regarding their ability to investigate crimes which are complex, occur in a virtual environment, incorporate multiple (often international) jurisdictions, and have a very low reporting rate. This paper examines the police response to online fraud. It argues that traditionally, fraud has received little attention and priority from police agencies and this is exacerbated in the online context. In contrast to this, the paper presents the example of Project Sunbird, a partnership between the West Australian Police and the West Australian Department of Commerce which has embraced the use of financial intelligence to proactively contact suspected victims of online fraud. This paper argues that a proactive approach to policing online fraud can have substantial positive effects for police and victims alike.

Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

Enhancements to dielectric response measurements of oil-paper insulation for online and offline applications

The research introduces a promising technique for monitoring the degradation status of oil-paper insulation systems of large power transformers in an online mode and innovative enhancements are also made on the existing offline measurements, which afford more direct understanding of the insulation degradation process. Further, these techniques benefit from a quick measurement owing to the chirp waveform signal application. The techniques are improved and developed on the basis of measuring the impedance response of insulation systems. The feasibility and validity of the techniques was supported by the extensive simulation works as well as experimental investigations.

Learning the learning rate for prediction with expert advice

Most standard algorithms for prediction with expert advice depend on a parameter called the learning rate. This learning rate needs to be large enough to fit the data well, but small enough to prevent overfitting. For the exponential weights algorithm, a sequence of prior work has established theoretical guarantees for higher and higher data-dependent tunings of the learning rate, which allow for increasingly aggressive learning. But in practice such theoretical tunings often still perform worse (as measured by their regret) than ad hoc tuning with an even higher learning rate. To close the gap between theory and practice we introduce an approach to learn the learning rate. Up to a factor that is at most (poly)logarithmic in the number of experts and the inverse of the learning rate, our method performs as well as if we would know the empirically best learning rate from a large range that includes both conservative small values and values that are much higher than those for which formal guarantees were previously available. Our method employs a grid of learning rates, yet runs in linear time regardless of the size of the grid.

Second-order quantile methods for experts and combinatorial games

We aim to design strategies for sequential decision making that adjust to the difficulty of the learning problem. We study this question both in the setting of prediction with expert advice, and for more general combinatorial decision tasks. We are not satisfied with just guaranteeing minimax regret rates, but we want our algorithms to perform significantly better on easy data. Two popular ways to formalize such adaptivity are second-order regret bounds and quantile bounds. The underlying notions of 'easy data', which may be paraphrased as "the learning problem has small variance" and "multiple decisions are useful", are synergetic. But even though there are sophisticated algorithms that exploit one of the two, no existing algorithm is able to adapt to both. In this paper we outline a new method for obtaining such adaptive algorithms, based on a potential function that aggregates a range of learning rates (which are essential tuning parameters). By choosing the right prior we construct efficient algorithms and show that they reap both benefits by proving the first bounds that are both second-order and incorporate quantiles.

Superpixel-based appearance change prediction for long-term navigation across seasons

Changing environments pose a serious problem to current robotic systems aiming at long term operation under varying seasons or local weather conditions. This paper is built on our previous work where we propose to learn to predict the changes in an environment. Our key insight is that the occurring scene changes are in part systematic, repeatable and therefore predictable. The goal of our work is to support existing approaches to place recognition by learning how the visual appearance of an environment changes over time and by using this learned knowledge to predict its appearance under different environmental conditions. We describe the general idea of appearance change prediction (ACP) and investigate properties of our novel implementation based on vocabularies of superpixels (SP-ACP). Our previous work showed that the proposed approach significantly improves the performance of SeqSLAM and BRIEF-Gist for place recognition on a subset of the Nordland dataset under extremely different environmental conditions in summer and winter. This paper deepens the understanding of the proposed SP-ACP system and evaluates the influence of its parameters. We present the results of a large-scale experiment on the complete 10 h Nordland dataset and appearance change predictions between different combinations of seasons.

Love hurts: The costly reality of online romance fraud

Online dating and romance scams continue to lure in Australians with figures this week showing people have lost more than A$23 million this year alone, with average individual losses at A$21,000 – three times higher than other types of fraud. The Australian Competition and Consumer Commission (ACCC) set up the Scam Disruption Project in August to help target those it believes have been caught in such scams. Over three months it sent 1,500 letters to potential victims in New South Wales and the Australian Capital Territory. The figures released this week show that 50 people have been scammed, losing a total A$1.7 million – that’s an average of A$34,000 per victim. Almost three quarters of the scams were dating and romance related, which saw it evolve into the number one category of fraud victimisation. Romance scams continue to pose a problem – despite the efforts of the police and ACCC – so why is it that people continue to fall for them?

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Online survey of will drafters - Families and generational asset transfers: Making and challenging wills in contemporary Australia

A national online survey of private and public will drafters distributed through State/public trustee offices in seven states/territories and law societies and community legal centres across all states/territories yielded 257 responses. The survey, using questions, scales and case scenarios sought to canvas perceptions of difficulties facing will drafters and the strategies used to address them.

Towards a theoretical framework for online professional discussions

Online interactions are becoming commonplace for a multitude of educational purposes. Each context presents a unique and dynamic mix of variables that combine to shape the practice and the identities of those involved. In this article, sociocultural theories of learning and sociocultural theories of technology are explored as a way to view and to map the complex interactions that can occur. The case of synchronous online moderation meetings are used as an example of the combination of variables that can impact on the development of shared understandings of a practice. Online moderation can involve teachers from geographically diverse areas discussing and negotiating their judgement decisions. These discussions represent an intersection of a national curriculum, standards-referenced assessment, moderation protocols, site-specific practices and understandings, and individual teachers’ knowledges and histories. It is suggested that the proposed theoretical combination addresses some of the limitations of each of the theories when investigating such a dynamic context. As higher education moves into increasing use of online modes of communication and a higher level of accountability the relevance of this discussion to higher education is evident.