To boldly go where no advertising academic has gone before

We are the future: A Proposal for a Special Topic Session from the Students of the World" began as an international collaboration between like-minded and technology-fearing advertising academics."Star to the right..and straight on 'til morning." Videoconferencing exponentially grows the possibilities and the boundaries of academic collaboration.

Developing an international assurance standard on greenhouse gas statements

Worldwide public concern over climate change and the need to limit greenhouse gas (hereafter, GHG) emissions has increasingly motivated public officials to consider more stringent environmental regulation and standards. The authors argue that the development of a new international assurance standard on GHG disclosures is an appropriate response by the auditing and assurance profession to meet these challenges. At its December 2007 meeting, the International Auditing and Assurance Standards Board (hereafter, IAASB) approved a project to consider the development of such a standard aimed at promoting trust and confidence in disclosures of GHG emissions, including disclosures required under emissions trading schemes. The authors assess the types of disclosures that can be assured, and outline the issues involved in developing an international assurance standard on GHG emissions disclosures. The discussion synthesizes the insights gained from four international roundtables on the proposed IAASB assurance standard held in Asia-Pacific, North America, and Europe during 2008, and an IAASB meeting addressing this topic in December 2008.

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

Topical pattern based document modelling and relevance ranking

For traditional information filtering (IF) models, it is often assumed that the documents in one collection are only related to one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling was proposed to generate statistical models to represent multiple topics in a collection of documents, but in a topic model, topics are represented by distributions over words which are limited to distinctively represent the semantics of topics. Patterns are always thought to be more discriminative than single terms and are able to reveal the inner relations between words. This paper proposes a novel information filtering model, Significant matched Pattern-based Topic Model (SPBTM). The SPBTM represents user information needs in terms of multiple topics and each topic is represented by patterns. More importantly, the patterns are organized into groups based on their statistical and taxonomic features, from which the more representative patterns, called Significant Matched Patterns, can be identified and used to estimate the document relevance. Experiments on benchmark data sets demonstrate that the SPBTM significantly outperforms the state-of-the-art models.

Investigating death: the emotional and cultural challenges for police

The over-representation of vulnerable populations within the criminal justice system, and the role of police in perpetuating this, has long been a topic of discussion in criminology. What is less discussed is the way in which non-criminal investigations by police, in areas like a death investigation, may similarly disadvantage and discriminate against vulnerable populations. In Australia, as elsewhere, it is police who are responsible for investigating both suspicious and violent deaths like homicide as well as non-suspicious, violent deaths like accidents and suicides. Police are also the agents tasked with investigating deaths which are neither violent nor suspicious but occur outside hospitals and other care facilities. This paper, part of a larger funded Australian research project focusing on the ways in which cultural and religious differences are dealt with during the death investigation process, reports on how police describe – or are described by others – during their role in a non-suspicious death investigation, and the challenges that such investigations raise for police and policing. The employment of police liaison officers is discussed as one response to the difficulty of policing cultural and religious difference with variable results.

Text mining for screening efficiency? Testing within a Cochrane public health review

Background The requirement for dual screening of titles and abstracts to select papers to examine in full text can create a huge workload, not least when the topic is complex and a broad search strategy is required, resulting in a large number of results. An automated system to reduce this burden, while still assuring high accuracy, has the potential to provide huge efficiency savings within the review process. Objectives To undertake a direct comparison of manual screening with a semi‐automated process (priority screening) using a machine classifier. The research is being carried out as part of the current update of a population‐level public health review. Methods Authors have hand selected studies for the review update, in duplicate, using the standard Cochrane Handbook methodology. A retrospective analysis, simulating a quasi‐‘active learning’ process (whereby a classifier is repeatedly trained based on ‘manually’ labelled data) will be completed, using different starting parameters. Tests will be carried out to see how far different training sets, and the size of the training set, affect the classification performance; i.e. what percentage of papers would need to be manually screened to locate 100% of those papers included as a result of the traditional manual method. Results From a search retrieval set of 9555 papers, authors excluded 9494 papers at title/abstract and 52 at full text, leaving 9 papers for inclusion in the review update. The ability of the machine classifier to reduce the percentage of papers that need to be manually screened to identify all the included studies, under different training conditions, will be reported. Conclusions The findings of this study will be presented along with an estimate of any efficiency gains for the author team if the screening process can be semi‐automated using text mining methodology, along with a discussion of the implications for text mining in screening papers within complex health reviews.

Culture in Business Process Management: How cultural values determine BPM success

There is consensus among practitioners and academics that culture is a critical factor that is able to determine success or failure of BPM initiatives. Yet, culture is a topic that seems difficult to grasp and manage. This may be the reason for the overall lack of guidance on how to address this topic in practice. We have conducted in-depth research for more than three years to examine why and how culture is relevant to BPM. In this chapter, we introduce a framework that explains the role of culture in BPM. We also present the relevant cultural values that compose a BPM culture, and we introduce a tool to examine the supportiveness of organizational cultures for BPM. Our research results provide the basis for further empirical analyses on the topic and support practitioners in the management of culture as an important factor in BPM initiatives.

'I wonder what you know...': Teachers designing requests for factual information

The early years are significant in optimising children’s educational, emotional and social outcomes and have become a major international policy priority. Within Australia, policy levers have prioritised early childhood education, with a focus on program quality, as it is associated with lifelong success. Longitudinal studies have found that high quality teacher-child interactions are an essential element of high quality programs, and teacher questioning is one aspect of teacher-child interactions that has been attributed to affecting the quality of education, linking open ended questioning to higher cognitive achievement. Teachers, however, overwhelmingly ask more closed than open questions. In the classroom, like everyday interaction, questions in interaction require answers. They are used to request, offer, repair, challenge, seek agreement (Curl & Drew, 2008; Enfield, Stivers, & Levinson, 2010; Hayano, 2013; Schegloff, 2007). Teachers use questions to set agendas and manage lessons (McHoul, 1978; Mehan, 1979; Sacks, 1995), and to gauge students’ knowledge and understanding (Lerner, 1995; McHoul, 1978; Mehan, 1979). Drawing on data from the Australian Research Council project Interacting with Knowledge: Interacting with people: Web searching in early childhood, this paper focuses on an extended sequence of talk between a teacher with two students aged between 3.5 and 5 years in a preschool classroom. The episode, drawn from a corpus of over 200 hours of video recorded data, captures how the teacher and children undertake an online search for images of lady beetles and hairy caterpillars on the Web. Ethnomethodological and conversation analysis approaches examine how the teacher asks questions, which call on the children to display their factual knowledge about the search topic. The fine grained analysis shows how teachers design their interactions to prompt children’s displays of factual knowledge, and how the design of factual questions affect a student’s response in terms of what and how they respond. In focussing on how the teacher designs factual questions and how children respond to these questions it shows that question design can close down a student’s reply; or elicit a range of answers, from one word to extended more detailed responses. Understanding how the design of teachers’ questions can influence students’ responses has pedagogic implications and may support educators to make intentional decisions regarding their own questioning techniques.

Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis

The development of methods for real-time crash prediction as a function of current or recent traffic and roadway conditions is gaining increasing attention in the literature. Numerous studies have modeled the relationships between traffic characteristics and crash occurrence, and significant progress has been made. Given the accumulated evidence on this topic and the lack of an articulate summary of research status, challenges, and opportunities, there is an urgent need to scientifically review these studies and to synthesize the existing state-of-the-art knowledge. This paper addresses this need by undertaking a systematic literature review to identify current knowledge, challenges, and opportunities, and then conducts a meta-analysis of existing studies to provide a summary impact of traffic characteristics on crash occurrence. Sensitivity analyses were conducted to assess quality, publication bias, and outlier bias of the various studies; and the time intervals used to measure traffic characteristics were also considered. As a result of this comprehensive and systematic review, issues in study designs, traffic and crash data, and model development and validation are discussed. Outcomes of this study are intended to provide researchers focused on real-time crash prediction with greater insight into the modeling of this important but extremely challenging safety issue.

A rare view: Barbara Hatley’s new book documents a career-long study of, and passion for performance in Central Java

Javanese Performances on an Indonesian Stage: Contesting Culture, Embracing Change, is Barbara Hatley’s first book about the performing arts in Indonesia, a topic that piqued her interest while undergoing a masters program at Yale University in the late 1960s. In this sense, it is a landmark study, for Hatley has since become very well known in Indonesianist circles, especially among those with an interest in matters of culture, popular and elite. Until recently, her writings on Indonesian performing arts have only been available in the form of journal articles and book chapters...

Robotics for the mining industry

The mining industry is highly suitable for the application of robotics and automation technology since the work is both arduous and dangerous. However, while the industry makes extensive use of mechanisation it has shown a slow uptake of automation. A major cause of this is the complexity of the task, and the limitations of existing automation technology which is predicated on a structured and time invariant working environment. Here we discuss the topic of mining automation from a robotics and computer vision perspective — as a problem in sensor based robot control, an issue which the robotics community has been studying for nearly two decades. We then describe two of our current mining automation projects to demonstrate what is possible for both open-pit and underground mining operations.

Deriving word association networks from text corpora

This article presents and evaluates a model to automatically derive word association networks from text corpora. Two aspects were evaluated: To what degree can corpus-based word association networks (CANs) approximate human word association networks with respect to (1) their ability to quantitatively predict word associations and (2) their structural network characteristics. Word association networks are the basis of the human mental lexicon. However, extracting such networks from human subjects is laborious, time consuming and thus necessarily limited in relation to the breadth of human vocabulary. Automatic derivation of word associations from text corpora would address these limitations. In both evaluations corpus-based processing provided vector representations for words. These representations were then employed to derive CANs using two measures: (1) the well known cosine metric, which is a symmetric measure, and (2) a new asymmetric measure computed from orthogonal vector projections. For both evaluations, the full set of 4068 free association networks (FANs) from the University of South Florida word association norms were used as baseline human data. Two corpus based models were benchmarked for comparison: a latent topic model and latent semantic analysis (LSA). We observed that CANs constructed using the asymmetric measure were slightly less effective than the topic model in quantitatively predicting free associates, and slightly better than LSA. The structural networks analysis revealed that CANs do approximate the FANs to an encouraging degree.

(Digital library) education or (digital) library education? An Australian perspective

Education for Library and Information professionals in the digital environment has been an important discussion point the world over. However, before designing and implementing a programme for digital library education, it is prudent that the skills and knowledge required to work in this environment are identified to enable informed decisions to be made. Hitherto, there has been very little research which has sought the opinion of both educators and practitioners on this topic, and none with a wide geographical coverage of Australia. This paper presents the key findings of research undertaken at Tallinn University in the first half of 2009.

Coteaching for parent-school-community engagement: Seen through the four resources model

This paper reflects on a 2008 project in which a teacher invited two parents1 of students in his class to coteach with him on the topic of War and Refugees (Willis, 2013). Although the project occurred in a Year eight context, it has utility for all teachers in showing how the four resources model (FRM) (Freebody and Luke, 1990) of language and literacy teaching and learning may provide a viewing platform for seeing the benefits and potential of coteaching for parent-school-community engagement. For decades, governments nationally and internationally have actively supported parentschool- community involvement initiatives. In Australia, these include the establishment in 2008 of The Family-School and Community Partnerships Bureau and its recent publication, Parental engagement in learning and schooling: Lessons from research (Emerson, Fear, Fox, and Sanders, 2012). These initiatives derive from strong, consistent research evidence that parent involvement in schools not only benefits students, teachers, and schools but also has wide-ranging implications for education reform, employers and communities, and ultimately Australia's future economic prosperity. These initiatives also continue to inform the Australian Institute for Teaching and School Leadership (AITSL) in identifying ways teachers and school leaders can generate and sustain professional engagement with colleagues, parents, and the community to meet new national teaching standards.

Special issue on robot vision

The International Journal of Robotics Research (IJRR) has a long history of publishing the state-of-the-art in the field of robotic vision. This is the fourth special issue devoted to the topic. Previous special issues were published in 2012 (Volume 31, No. 4), 2010 (Volume 29, Nos 2–3) and 2007 (Volume 26, No. 7, jointly with the International Journal of Computer Vision). In a closely related field was the special issue on Visual Servoing published in IJRR, 2003 (Volume 22, Nos 10–11). These issues nicely summarize the highlights and progress of the past 12 years of research devoted to the use of visual perception for robotics.