Risk perception and the public acceptance of drones

Unmanned aircraft, or drones, are a rapidly emerging sector of the aviation industry. There has been limited substantive research, however, into the public perception and acceptance of drones. This paper presents the results from two surveys of the Australian public designed to investigate (a) whether the public perceive drones to be riskier than existing manned aviation, (b) whether the terminology used to describe the technology influences public perception, and (c) what the broader concerns are that may influence public acceptance of the technology. We find that the Australian public currently hold a relatively neutral attitude towards drones. Respondents did not consider the technology to be overly unsafe, risky, beneficial, or threatening. Drones are largely viewed as being of comparable risk to that of existing manned aviation. Further, terminology had a minimal effect on the perception of the risks or acceptability of the technology. The neutral response is likely due to a lack of knowledge about the technology, which was also identified as the most prevalent public concern as opposed to the risks associated with its use. Privacy, military use and misuse (e.g., terrorism) were also significant public concerns. The results suggest that society is yet to form an opinion of drones. As public knowledge increases, the current position is likely to change. Industry communication and media coverage will likely influence the ultimate position adopted by the public, which can be difficult to change once established.

Creating a legal framework for copyright management of open access within the Australian academic and research sector

This chapter analyses the copyright law framework needed to ensure open access to outputs of the Australian academic and research sector such as journal articles and theses. It overviews the new knowledge landscape, the principles of copyright law, the concept of open access to knowledge, the recently developed open content models of copyright licensing and the challenges faced in providing greater access to knowledge and research outputs.

Antidepressant direct-to-consumer prescription drug advertising and public stigma of depression : the mediating role of perceived prevalence of depression

This study examines whether memory of antidepressant direct-to-consumer (DTC) prescription drug advertising is associated with the public stigma attached to depression. Results indicate that those who better remember antidepressant DTC ads tend to have a higher perceived prevalence of depression (i.e., more people suffer from depression). And, the perceived prevalence of depression is inversely associated with the public stigma toward depression. That is, those who have a higher perceived prevalence of depression report that they are more supportive of and comfortable with people who have depression. The results suggest that the perceived prevalence of depression is a mediating variable that accounts for the relationship between memory of antidepressant DTC ads and the public stigma toward depression. The implications and limitations of the study, as an exploratory investigation, are discussed.

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?

No laughing matter: Blaming the victim of online fraud

There is a strong sense of negativity associated with online fraud victimization. Despite an increasing awareness, understanding about the reality of victimization experiences is not apparent. Rather, victims of online fraud are constructed as greedy and gullible and there is an overwhelming sense of blame and responsibility levelled at them for the actions that led to their losses. This belief transcends both non-victims and victims. The existence of this victim-blaming discourse is significant. Based on interviews with 85 seniors across Queensland, Australia, who received fraudulent emails, this article establishes the victim-blaming discourse as an overwhelmingly powerful and controlling discourse about online fraud victimization. However, the article also examines how humour acts as a tool to reinforce this discourse by isolating victims and impacting on their ability to disclose to those around them. Identifying and challenging this victim-blaming discourse, as well as the role of humour and its social acceptance, is a first step in the facilitation of victim recovery and future well-being.

Rediscovering law students as citizens: Critical thinking and the public value of legal education

In a series of publications over the last decade, Australian National University Professor Margaret Thornton has documented a disturbing change in the nature of legal education. This body of work culminates in a recently published book based on interviews with 145 legal academics in Australia, the United Kingdom, New Zealand and Canada. In it, Thornton describes a feeling of widespread unease among legal academics that society, government, university administrators and students themselves are moving away from viewing legal education as a public good which benefits both students and society. Instead, legal education is increasingly being viewed as a purely private good, for consumption by the student in the quest for individual career enhancement.

Public disclosure of biological sequences in global patent practice

Biological sequences are an important part of global patenting, with unique challenges for their effective and equitable use in practice and in policy. Because their function can only be determined with computer-aided technology, the form in which sequences are disclosed matters greatly. Similarly, the scope of patent rights sought and granted requires computer readable data and tools for comparison. Critically, the primary data provided to the national patent offices and thence to the public, must be comprehensive, standardized, timely and meaningful. It is not yet. The proposed global Patent Sequence (PatSeq) Data platform can enable national and regional jurisdictions meet the desired standards.

Dynamic Space Warping Of Strokes For Recognition Of Online Handwritten Characters

This paper suggests a scheme for classifying online handwritten characters, based on dynamic space warping of strokes within the characters. A method for segmenting components into strokes using velocity profiles is proposed. Each stroke is a simple arbitrary shape and is encoded using three attributes. Correspondence between various strokes is established using Dynamic Space Warping. A distance measure which reliably differentiates between two corresponding simple shapes (strokes) has been formulated thus obtaining a perceptual distance measure between any two characters. Tests indicate an accuracy of over 85% on two different datasets of characters.

Clustering Users of Online Content Service

Online content services can greatly benefit from personalisation features that enable delivery of content that is suited to each user's specific interests. This thesis presents a system that applies text analysis and user modeling techniques in an online news service for the purpose of personalisation and user interest analysis. The system creates a detailed thematic profile for each content item and observes user's actions towards content items to learn user's preferences. A handcrafted taxonomy of concepts, or ontology, is used in profile formation to extract relevant concepts from the text. User preference learning is automatic and there is no need for explicit preference settings or ratings from the user. Learned user profiles are segmented into interest groups using clustering techniques with the objective of providing a source of information for the service provider. Some theoretical background for chosen techniques is presented while the main focus is in finding practical solutions to some of the current information needs, which are not optimally served with traditional techniques.

Determinants of online sperm donor success: How women choose

Because the worldwide demand for sperm donors is much higher than the actual supply available through fertility clinics, an informal online market has emerged for sperm donation. Very little empirical evidence exists, however, on this newly formed market and even less on the characteristics that lead to donor success. This article therefore explores the determinants of online sperm donors’ selection success, which leads to the production of offspring via informal donation. We find that donor age and income play a significant role in donor success as measured by the number of times selected, even though there is no requirement for ongoing paternal investment. Donors with less extroverted and lively personality traits who are more intellectual, shy and systematic are more successful in realizing offspring via informal donation. These results contribute to both the economic literature on human behaviour and on large-scale decision-making.

Machine recognition of online handwritten Devanagari characters

In this paper, we describe a system for the automatic recognition of isolated handwritten Devanagari characters obtained by linearizing consonant conjuncts. Owing to the large number of characters and resulting demands on data acquisition, we use structural recognition techniques to reduce some characters to others. The residual characters are then classified using the subspace method. Finally the results of structural recognition and feature-based matching are mapped to give final output. The proposed system Ifs evaluated for the writer dependent scenario.

Choice of Classifiers in Hierarchical Recognition of Online Handwritten Kannada and Tamil Aksharas

In this paper, we propose a novel dexterous technique for fast and accurate recognition of online handwritten Kannada and Tamil characters. Based on the primary classifier output and prior knowledge, the best classifier is chosen from set of three classifiers for second stage classification. Prior knowledge is obtained through analysis of the confusion matrix of primary classifier which helped in identifying the multiple sets of confused characters. Further, studies were carried out to check the performance of secondary classifiers in disambiguating among the confusion sets. Using this technique we have achieved an average accuracy of 92.6% for Kannada characters on the MILE lab dataset and 90.2% for Tamil characters on the HP Labs dataset.

Lexicon-free, novel segmentation of online handwritten indic words

Research in the field of recognizing unlimited vocabulary, online handwritten Indic words is still in its infancy. Most of the focus so far has been in the area of isolated character recognition. In the context of lexicon-free recognition of words, one of the primary issues to be addressed is that of segmentation. As a preliminary attempt, this paper proposes a novel script-independent, lexicon-free method for segmenting online handwritten words to their constituent symbols. Feedback strategies, inspired from neuroscience studies, are proposed for improving the segmentation. The segmentation strategy has been tested on an exhaustive set of 10000 Tamil words collected from a large number of writers. The results show that better segmentation improves the overall recognition performance of the handwriting system.

Performance enhancement of online handwritten Tamil symbol recognition with reevaluation techniques

In this article, we aim at reducing the error rate of the online Tamil symbol recognition system by employing multiple experts to reevaluate certain decisions of the primary support vector machine classifier. Motivated by the relatively high percentage of occurrence of base consonants in the script, a reevaluation technique has been proposed to correct any ambiguities arising in the base consonants. Secondly, a dynamic time-warping method is proposed to automatically extract the discriminative regions for each set of confused characters. Class-specific features derived from these regions aid in reducing the degree of confusion. Thirdly, statistics of specific features are proposed for resolving any confusions in vowel modifiers. The reevaluation approaches are tested on two databases (a) the isolated Tamil symbols in the IWFHR test set, and (b) the symbols segmented from a set of 10,000 Tamil words. The recognition rate of the isolated test symbols of the IWFHR database improves by 1.9 %. For the word database, the incorporation of the reevaluation step improves the symbol recognition rate by 3.5 % (from 88.4 to 91.9 %). This, in turn, boosts the word recognition rate by 11.9 % (from 65.0 to 76.9 %). The reduction in the word error rate has been achieved using a generic approach, without the incorporation of language models.